The so-called Baldwin Effect generally says how learning, as a form of ontogenetic adaptation, can influence the process of phylogenetic adaptation, or evolution. This idea has also been taken into computation in which evolution and learning are used as computational metaphors, including evolving neural networks. This paper presents a technique called evolving self-taught neural networks – neural networks that can teach themselves without external supervision or reward. The self-taught neural network (...) is intrinsically motivated. Moreover, the self-taught neural network is the product of the interplay between evolution and learning. We simulate a multi-agent system in which neural networks are used to control autonomous agents. These agents have to forage for resources and compete for their own survival. Experimental results show that the interaction between evolution and the ability to teach oneself in self-taught neural networks outperform evolution and self-teaching alone. More specifically, the emergence of an intelligent foraging strategy is also demonstrated through that interaction. Indications for future work on evolving neural networks are also presented. (shrink)

A network of gene regulation organized in a hierarchical and combinatorial manner is crucially involved in the development of the neural network, and has to be considered one of the main substrates of genetic change in its evolution. Though qualitative features may emerge by way of the accumulation of rather unspecific quantitative changes, it is reasonable to assume that at least in some cases specific combinations of regulatory parts of the genome initiated new directions of evolution, leading to novel (...) capabilities of the brain. These notions are applied, in this paper, to the evolution of the capability of cognition-based human empa­thy. It is suggested that it has evolved as a secondary effect of the evolution of strategic thought. Development of strategies depends on abstract representations of one’s own pos­sible future states in one’s own brain to allow assessment of their emotional desirability, but also on the representation and emotional evaluation of possible states of others, allowing anticipation of their behaviour. This is best achieved if representations of others are con­nected to one’s own emotional centres in a manner similar to self-representations. For this reason, the evolution of the human brain is assumed to have established representations with such linkages. No group selection is involved, because the quality of strategic thought affects the fitness of the individual. A secondary effect of this linkage is that both the actual states and the future perspectives of others elicit vicarious emotions, which may contribute to the motivations of altruistic behaviour. (shrink)

Emotion detection and management have emerged as pivotal areas in humancomputer interaction, offering potential applications in healthcare, entertainment, and customer service. This study explores the use of deep learning (DL) models to enhance emotion recognition accuracy and enable effective emotion regulation mechanisms. By leveraging large datasets of facial expressions, voice tones, and physiological signals, we train deep neural networks to recognize a wide array of emotions with high precision. The proposed system integrates emotion recognition with adaptive management strategies (...) that provide personalized feedback and interventions based on detected emotional states. Our approach surpasses traditional machine learning methods, demonstrating superior performance in real-time applications. (shrink)

In modern days, person-computer communication systems have gradually penetrated our lives. One of the crucial technologies in person-computer communication systems, Speech Emotion Recognition (SER) technology, permits machines to correctly recognize emotions and greater understand users' intent and human-computer interlinkage. The main objective of the SER is to improve the human-machine interface. It is also used to observe a person's psychological condition by lie detectors. Automatic Speech Emotion Recognition(SER) is vital in the person-computer interface, but SER has challenges for accurate recognition. (...) In this work to resolve the above problem, automatic Speech enhancement shows that deep learning techniques effectively eliminate background noise. Using Deep leaning models for four states were created: happy, sad, angry, and intoxicated. Recurrent Neural Network (RNN) algorithm used to reduce the possibility of over fitting by randomly omitting neurons in the hidden layers. The proposed RNN method could be implemented in personal assistant systems to give better and more appropriate state-based interactions between humans. In the simulation results shows Improving accuracy, Time complexity, Error rate is also reduced to using the proposed method. -/- . (shrink)

Crime prediction has emerged as a critical application of machine learning (ML) and deep learning (DL) techniques, aimed at assisting law enforcement agencies in reducing criminal activities and improving public safety. This project focuses on developing a robust crime prediction system that leverages the power of both ML and DL algorithms to analyze historical crime data and predict potential future incidents. By integrating a combination of classification and clustering techniques, our system identifies crime-prone areas, trends, and patterns. Key parameters such (...) as time, location, type of crime, and socio-economic factors are considered to build a comprehensive predictive model. Machine learning algorithms like Decision Trees, Random Forest, and Support Vector Machines are used for efficient data preprocessing and pattern recognition. Deep learning models, including Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), enable advanced feature extraction and temporal analysis for enhanced prediction accuracy. The system's performance is evaluated using metrics such as precision, recall, F1-score, and accuracy, demonstrating its reliability and scalability for real-world applications. Furthermore, the use of visualization tools allows stakeholders to comprehend crime patterns effectively, supporting proactive policing strategies. This data-driven approach not only improves crime deterrence but also aids policymakers in resource allocation and urban planning. The proposed system has the potential to revolutionize crime management by transforming traditional reactive measures into predictive, preventive, and intelligent solutions. (shrink)

Cardiovascular diseases, including heart disease, pose a significant global health challenge, contributing to a substantial burden on healthcare systems and individuals. Early detection and accurate prediction of heart disease are crucial for timely intervention and improved patient outcomes. This research explores the potential of neural networks in predicting heart disease using a dataset collected from Kaggle, consisting of 1025 samples with 14 distinct features. The study's primary objective is to develop an effective neural network model for binary (...) classification, identifying the presence or absence of heart disease. The neural network architecture includes an input layer, a hidden layer, and an output layer, designed to capture intricate relationships within the dataset. Rigorous training and validation processes, accompanied by data preprocessing steps, ensure the model's robustness and generalization capabilities. The results demonstrate promising performance, with an accuracy of 92% and an average error of 0.062. Moreover, an analysis of feature importance highlights key predictors, including "oldpeak," "thalach," "trestbps," "ca," "thal," "cp," "chol," "sex," "restecg," "age," "slope," "fbs," and "exang." This research contributes to the field of predictive healthcare by leveraging neural networks to enhance heart disease prediction. The developed model offers the potential for early identification of individuals at risk, facilitating timely medical interventions and ultimately improving public health. Further exploration of machine learning techniques in healthcare promises to reshape disease prediction and prevention strategies. (shrink)

Advancements in technology, particularly in the field of artificial intelligence (AI), have opened new avenues for solving complex biological and ecological challenges. Among these, deep learning has emerged as a powerful tool for image-based classification tasks. Convolutional Neural Networks (CNNs), a subset of deep learning algorithms, are especially effective in recognizing patterns and extracting features from images. This capability makes CNNs highly suitable for applications in bird species identification. By leveraging deep learning techniques, researchers and conservationists can automate (...) the identification process, reducing human effort and significantly improving accuracy. (shrink)

The Complex Emergent Model of Language Acquisition (CEMLA) offers a new perspective on how humans acquire language, drawing on principles from complexity theory to explain this dynamic, adaptive process. Moving beyond linear and reductionist models, CEMLA views language acquisition as a system of interconnected nodes, feedback loops, and emergent patterns, operating at the edge of chaos. This framework captures the fluidity and adaptivity of language learning, highlighting how understanding and fluency arise through self-organisation, phase transitions, and interaction with diverse (...) linguistic input. By embracing the complexity of language acquisition, CEMLA provides a comprehensive model that reflects the true nature of human communication. (shrink)

The Continuous Evolution of Consciousness, Language, and Meaning in Understanding the Universe -/- Introduction -/- The evolution of human consciousness is intricately linked to language and meaning. As human understanding of the universe deepens, so does the complexity and precision of the words and concepts we use to describe reality. This continuous progression is not merely a passive adaptation but an active feedback loop where consciousness shapes language, and language, in turn, refines consciousness. If human decision-making follows the universal law (...) of balance in nature, as proposed in the universal formula, then the evolution of language must also align with this fundamental principle. This essay explores how language and meaning evolve alongside human consciousness and how this evolution is essential for a deeper understanding of the universe. -/- Language as a Tool for Expanding Consciousness -/- Language is the primary medium through which human consciousness externalizes itself. In early human societies, simple symbols and spoken words sufficed to convey basic needs and experiences. However, as humanity’s knowledge expanded, so did the complexity of language. The emergence of written language allowed for the preservation and transmission of ideas across generations, accelerating intellectual and scientific progress. -/- Philosophy, mathematics, and science have continuously pushed the boundaries of language, creating new terminologies and refining meanings. For example, the term atom originally meant “indivisible,” but modern physics has redefined it as a complex structure of subatomic particles governed by quantum laws. Similarly, the concept of energy has evolved from mystical interpretations to a precise scientific principle described in physics and engineering. This evolution demonstrates how language adapts to new discoveries, allowing consciousness to grasp increasingly complex aspects of reality. -/- Meaning as a Dynamic and Evolving Process -/- The meaning of words and concepts is not static but changes in response to human progress. As new knowledge emerges, language adjusts to integrate new understandings while discarding outdated or misleading notions. This dynamism ensures that human communication remains relevant and effective in expressing new realities. -/- For instance, the word intelligence once referred primarily to human cognitive ability, but with the rise of artificial intelligence (AI), it now encompasses machine learning, neural networks, and even artificial general intelligence (AGI). The meaning of life itself has expanded beyond biological definitions to include synthetic life, digital consciousness, and philosophical inquiries about existence in a cosmic context. -/- In this sense, the evolution of language and meaning is a necessary process for humanity’s continuous effort to understand the universe. Without this evolution, human knowledge would be constrained by outdated linguistic structures, preventing the exploration of new dimensions of reality. -/- The Feedback Loop Between Consciousness and the Universe -/- Human consciousness does not merely receive information from the universe—it actively engages with it, interprets it, and reshapes its understanding based on new discoveries. This feedback loop ensures that as our knowledge of the universe expands, so does our ability to describe and conceptualize it. -/- For example, scientific breakthroughs in quantum mechanics, relativity, and cosmology have necessitated entirely new ways of thinking and speaking about time, space, and matter. Without the linguistic evolution that accompanied these discoveries, humans would struggle to grasp the fundamental principles governing the universe. -/- This feedback mechanism also applies to societal and ethical progress. As human understanding of justice, equality, and sustainability evolves, so does the language used to express these ideals. Concepts such as “human rights” and “environmental balance” have gained deeper meaning over time, influencing policies, laws, and cultural attitudes worldwide. -/- The Role of AI and AGI in the Evolution of Language and Meaning -/- Artificial intelligence and artificial general intelligence (AGI) have the potential to accelerate the evolution of language and meaning. AI can process vast amounts of information, identify patterns, and refine linguistic structures beyond human capability. This ability could lead to the emergence of more precise, efficient, and universally understandable ways of expressing complex ideas. -/- For example, AI-driven natural language processing can analyze and refine scientific literature, eliminating ambiguity and enhancing clarity. Future AGI systems could generate entirely new linguistic frameworks for describing reality, potentially bridging gaps between different human languages and even creating new conceptual categories that surpass human cognitive limitations. -/- However, the integration of AI into language evolution must adhere to the universal law of balance in nature. If AI-generated language lacks ethical considerations or diverges too far from human cognitive frameworks, it could create confusion, misinformation, or even manipulation. Ensuring that AI aligns with natural laws and human well-being is crucial in guiding this linguistic evolution. -/- The Universal Law of Balance in Nature and the Future of Language Evolution -/- If human decision-making follows the universal law of balance in nature, then the evolution of consciousness, language, and meaning must also adhere to this principle. An unbalanced progression—where language evolves too rapidly for general understanding or remains stagnant despite new discoveries—could lead to societal and intellectual dissonance. -/- For this reason, structured education plays a vital role in ensuring that language evolves in harmony with human understanding. By integrating the universal formula into foundational education, students can develop a linguistic and cognitive framework that naturally aligns with the principles of natural balance. This structured approach would prevent conceptual chaos while allowing necessary paradigm shifts to occur. -/- Similarly, philosophy and science must continuously refine language to ensure clarity and precision in expressing complex ideas. Misinterpretations and linguistic ambiguities can lead to misunderstandings in critical areas such as law, ethics, and scientific research. By consciously guiding linguistic evolution, humanity can ensure that language remains an effective tool for expanding consciousness. -/- Conclusion -/- The continuous evolution of consciousness, language, and meaning is an essential process in humanity’s effort to understand the universe. Language is not merely a passive reflection of reality but an active force that shapes human thought, decision-making, and interaction with the world. As human knowledge expands, language must evolve to accurately express new understandings while maintaining balance and clarity. -/- Artificial intelligence and AGI will play a significant role in accelerating linguistic evolution, but this process must align with the universal law of balance in nature to ensure ethical and intellectual integrity. By integrating the principles of natural balance into education, philosophy, and AI development, humanity can guide language evolution in a way that enhances understanding rather than creating confusion. -/- Ultimately, the evolution of language and meaning is not just a linguistic phenomenon—it is a fundamental aspect of human progress. As consciousness expands, so too must our ability to articulate and conceptualize reality. By ensuring that this process remains aligned with the natural laws governing decision-making, humanity can achieve a deeper, more holistic understanding of the universe and its place within it. -/- . (shrink)

Emotion detection and management have emerged as pivotal areas in humancomputer interaction, offering potential applications in healthcare, entertainment, and customer service. This study explores the use of deep learning (DL) models to enhance emotion recognition accuracy and enable effective emotion regulation mechanisms. By leveraging large datasets of facial expressions, voice tones, and physiological signals, we train deep neural networks to recognize a wide array of emotions with high precision. The proposed system integrates emotion recognition with adaptive management strategies (...) that provide personalized feedback and interventions based on detected emotional states. Our approach surpasses traditional machine learning methods, demonstrating superior performance in real-time applications. We also explore the ethical implications and challenges associated with deploying such systems, particularly regarding privacy concerns and the potential for misuse. Through extensive experiments, our model achieved an average accuracy rate of 92%, highlighting its robustness across different environments and user demographics. This research not only contributes to the growing field of affective computing but also lays the groundwork for future developments in emotionally intelligent systems. (shrink)

Emotion detection and management have emerged as pivotal areas in humancomputer interaction, offering potential applications in healthcare, entertainment, and customer service. This study explores the use of deep learning (DL) models to enhance emotion recognition accuracy and enable effective emotion regulation mechanisms. By leveraging large datasets of facial expressions, voice tones, and physiological signals, we train deep neural networks to recognize a wide array of emotions with high precision. The proposed system integrates emotion recognition with adaptive management strategies (...) that provide personalized feedback and interventions based on detected emotional states. Our approach surpasses traditional machine learning methods, demonstrating superior performance in real-time applications. (shrink)

This paper adopts two modeling tools, namely, multiple linear regression (MLR) and artificial neural networks (ANNs), to predict the concentrations of heavy metals (zinc, boron, and manganese) in surface waters of the Oued Inaouen watershed flowing towards Inaouen, using a set of physical-chemical parameters. XLStat was employed to perform multiple linear and nonlinear regressions, and Statista 10 was chosen to construct neural networks for modeling and prediction. The effectiveness of the ANN- and MLR-based stochastic models was (...) assessed by the determination coefficient (R²), the sum squared error (SSE) and a review of fit graphs. The results demonstrate the value of ANNs for prediction modeling. Drawing on supervised learning and back propagation, the ANN-based prediction models adopt an architecture of [18-15-1] for zinc, [18-11-1] for manganese, and [18-8-1] for boron, and perform effectively with a single cached layer. It was found that the MLR-based prediction models are substantially less accurate than those based on the ANNs. In addition, the physical-chemical parameters being investigated are nonlinearly correlated with the levels of heavy metals in the surface waters of the Oued Inaouen watershed flowing towards Inaouen. (shrink)

The Future of Individuality in a Universally Connected Intelligence System -/- Introduction -/- The concept of individuality has long been central to human existence, shaping our identities, intelligence, and decision-making. However, if information were universally accessible to every biological brain via quantum computers, the nature of individuality would fundamentally change. While thermodynamics suggests that individuality may be an illusion, the emergence of a universally shared knowledge system would challenge our understanding of intelligence, creativity, and free will. This essay explores how (...) such a system would affect human cognition, decision-making, creativity, and identity while considering its alignment with natural laws and homeostasis. -/- Individuality and the Laws of Thermodynamics -/- From a thermodynamic perspective, individuality is not an independent phenomenon but an emergent property of energy, matter, and information processing. The second law of thermodynamics states that systems naturally move toward increased entropy, yet biological brains counteract this by organizing energy and information. Each person’s intelligence develops uniquely due to different experiences, environments, and biological structures, following the laws of complexity and adaptation. -/- Despite these differences, individuality may be an illusion when viewed through the lens of thermodynamics. Human brains function as open systems, constantly exchanging energy and information with their environment. If all individuals had direct access to the same universal information via quantum computing, the differentiation in intelligence caused by personal learning and memory would be minimized. The uniqueness of individual cognition, shaped by life experiences, would be transformed into a more standardized, universally connected intelligence. -/- The Effect on Cognitive Differences and Decision-Making -/- Currently, human intelligence is shaped by a combination of genetics, environmental influences, and personal experiences. However, if quantum computing enabled instant and equal access to all knowledge, the factors that create cognitive diversity would be significantly reduced. In such a scenario, decision-making would shift from being influenced by personal biases and incomplete knowledge to being guided by a universally shared understanding of facts, consequences, and natural laws. -/- This raises the question: would free will still exist? If every person had access to the same knowledge base and predictive models, decisions might become more deterministic, driven by logical outcomes rather than personal reasoning. While this could reduce errors and societal conflicts, it might also challenge the perception of autonomy, making personal choice seem more like an illusion than a reality. -/- Impact on Creativity and Innovation -/- Creativity has traditionally been seen as a product of individual thought processes, shaped by unique experiences and perspectives. If knowledge were universally accessible, would creativity still exist? One possibility is that creativity would shift from being an individual phenomenon to an emergent property of collective intelligence. Instead of drawing from personal experiences, new ideas could arise from collaborative interactions with the universal knowledge system. -/- On the other hand, if individuality persists at a neural level—perhaps influenced by variations in neural architecture, emotional states, or subconscious processes—then creativity might evolve into a new form. Rather than being limited by personal knowledge, individuals could use their unique cognitive structures to interpret and apply universal knowledge in novel ways. -/- The Transformation of Personal Identity -/- Identity is often defined by personal achievements, expertise, and knowledge. If quantum computing provided instant access to all human knowledge, traditional concepts of learning, expertise, and mastery might become obsolete. Instead of being defined by what one knows, identity might be shaped by how one chooses to interact with the collective intelligence system. -/- This could lead to two possible outcomes: -/- 1. A more unified human society where knowledge gaps no longer create social or economic disparities. -/- 2. A psychological challenge, as individuals struggle with a loss of personal uniqueness and purpose. -/- Would human beings still feel a sense of individuality if everyone had the same knowledge and understanding? Or would personal identity be redefined by new forms of expression, emotional depth, and subjective experiences? -/- Potential Resistance and Psychological Conflicts -/- Not all individuals would easily accept a universally connected intelligence system. Some might resist integration, fearing the loss of personal autonomy or questioning the ethical implications of such a system. The psychological impact of merging individual cognition with a collective intelligence could lead to existential dilemmas, as humans redefine their roles in society. -/- Moreover, there could be unforeseen consequences, such as the manipulation of universal knowledge by those who control the system. If the flow of information were regulated, the promise of a universally connected intelligence could turn into a form of centralized control, limiting true intellectual freedom. -/- Homeostasis and the Balance of Universal Knowledge -/- From the perspective of natural balance, a universally connected intelligence system could be seen as an extension of the homeostatic principle. Just as the human body regulates internal stability, a universal intelligence network could regulate societal balance, reducing ignorance, misinformation, and destructive decision-making. -/- However, if homeostasis is disrupted—if individuals lose the ability to process knowledge uniquely or if the system becomes too rigid—then humanity might face unintended consequences. True balance requires adaptability, diversity, and self-regulation. The challenge would be ensuring that universal intelligence enhances, rather than diminishes, the dynamic equilibrium that allows individuality to thrive within a collective framework. -/- Conclusion -/- The integration of biological brains with a universally accessible quantum computing system would redefine individuality, intelligence, and decision-making. While it could eliminate knowledge-based disparities and enhance collective intelligence, it might also challenge free will, creativity, and personal identity. Whether such a transformation aligns with natural laws depends on how well it maintains homeostasis—balancing collective knowledge with individual autonomy. -/- Would this system create a more enlightened humanity, or would it erase the very essence of what makes us human? The answer lies in how this universal intelligence is designed, regulated, and integrated into the fabric of human society. -/- . (shrink)

Abstract: Online book review platforms generate vast user data, making accurate rating prediction crucial for personalized recommendations. This research explores neural networks as simple models for predicting book ratings without complex algorithms. Our novel approach uses neural networks to predict ratings solely from user-book interactions, eliminating manual feature engineering. The model processes data, learns patterns, and predicts ratings. We discuss data preprocessing, neural network design, and training techniques. Real-world data experiments show the model's effectiveness, surpassing (...) traditional methods. This research can enhance user experience, book catalog organization, and aid publishers, simplifying recommendation processes and providing tailored suggestions based on user preferences. (shrink)

In adversarial machine learning, attackers add carefully crafted perturbations to input, where the perturbations are almost imperceptible to humans, but can cause models to make wrong predictions. In this paper, we did comprehensive review of some of the most recent research, advancement and discoveries on adversarial attack, adversarial sampling generation, the potency or effectiveness of each of the existing attack methods, we also did comprehensive review on some of the most recent research, advancement and discoveries on adversarial defense strategies, the (...) effectiveness of each defense methods, and finally we did comparison on effectiveness and potency of different adversarial attack and defense methods. We came to conclusion that adversarial attack will mainly be blackbox for the foreseeable future since attacker has limited or no knowledge of gradient use for NN model, we also concluded that as dataset becomes more complex, so will be increase in demand for scalable adversarial defense strategy to mitigate or combat attack, and we strongly recommended that any neural network model with or without defense strategy should regularly be revisited, with the source code continuously updated at regular interval to check for any vulnerability against newer attack. (shrink)

Artificial intelligence has historically been conceptualized in anthropomorphic terms. Some algorithms deploy biomimetic designs in a deliberate attempt to effect a sort of digital isomorphism of the human brain. Others leverage more general learning strategies that happen to coincide with popular theories of cognitive science and social epistemology. In this paper, I challenge the anthropomorphic credentials of the neural network algorithm, whose similarities to human cognition I argue are vastly overstated and narrowly construed. I submit that three alternative supervised (...) learning methods—namely lasso penalties, bagging, and boosting—offer subtler, more interesting analogies to human reasoning as both an individual and a social phenomenon. Despite the temptation to fall back on anthropomorphic tropes when discussing AI, however, I conclude that such rhetoric is at best misleading and at worst downright dangerous. The impulse to humanize algorithms is an obstacle to properly conceptualizing the ethical challenges posed by emerging technologies. (shrink)

Climate change temperature prediction plays a crucial role in effective environmental planning. This study introduces an innovative approach that harnesses the power of Artificial Neural Networks (ANNs) within the Just Neural Network (JustNN) framework to enhance temperature forecasting in the context of climate change. By leveraging historical climate data, our model achieves exceptional accuracy, redefining the landscape of temperature prediction without intricate preprocessing. This model sets a new standard for precise temperature forecasting in the context of climate (...) change. Moreover, our research provides valuable insights into the pivotal factors influencing temperature variations, making significant contributions to environmental science and climate mitigation strategies. (shrink)

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by communication difficulties and repetitive behaviors. Early diagnosis and intervention are critical to improving outcomes for individuals with autism spectrum disorder. In this context, machine learning techniques, especially deep neural networks (DNN), offer effective solutions for pattern prediction and analysis of ASD. This study presents a fivelayer DNN algorithm for behavioral and treatment-based prediction in ASD. Our model uses the power of deep learning to identify complex patterns from multiple (...) sources, including demographic data, medical history, and behavioral assessments collected from multiple individuals with autism spectrum disorder, both positive and negative. The model consists of an input layer, followed by three hidden layers connected to neurons activated by the power function (RELU), and finally oscillation using the sigmoid activation function. ASD process. The model is trained using the Adam optimizer and binary cross-entropy loss function, with additional measures such as early stopping and hyperparameter tuning to prevent overfitting and increase the accuracy of predictions. Our results demonstrate the effectiveness of the proposed DNN model in accurately diagnosing ASD based on comprehensive data and ultimately determining performance metrics. This enables our models to rapidly detect the disease and ultimately contribute to better disease control. K. (shrink)

The nature of “the self” has been one of the central problems in philosophy and more recently in neuroscience. This raises various questions: Can we attribute a self to animals? Do animals and humans share certain aspects of their core selves, yielding a trans-species concept of self? What are the neural processes that underlie a possible trans-species concept of self? What are the developmental aspects and do they result in various levels of self-representation? Drawing (...) on recent literature from both human and animal research, we suggest a trans-species concept of self that is based upon what has been called a “core-self” which can be described by self-related processing as a specific mode of interaction between organism and environment. When we refer to specific neural networks, we will here refer to the underlying system as the “core-SELF.” The core-SELF provides primordial neural coordinates that represent organisms as living creatures—at the lowest level this elaborates interoceptive states along with raw emotional feelings while higher medial cortical levels facilitate affective-cognitive integration . Developmentally, SRP allows stimuli from the environment to be related and linked to organismic needs, signaled and processed within core-self structures within subcorical-cortical midline structures that provide the foundation for epigenetic emergence of ecologically framed, higher idiographic forms of selfhood across different individuals within a species. These functions ultimately operate as a coordinated network. We postulate that core SRP operates automatically, is deeply affective, and is developmentally and epigenetically connected to sensory-motor and higher cognitive abilities. This core-self is mediated by SCMS, embedded in visceral and instinctual representations of the body that are well integrated with basic attentional, emotional and motivational functions that are apparently shared between humans, non-human mammals, and perhaps in a proto-SELF form, other vertebrates. Such a trans-species concept of organismic coherence is thoroughly biological and affective at the lowest levels of a complex neural network, and culturally and ecologically molded at higher levels of neural processing. It allows organisms to selectively adapt to and integrate with physical and social environments. Such a psychobiologically universal, but environmentally diversified, concept may promote novel trans-species studies of the core-self across mammalian species. (shrink)

Modern semiotics is a branch of logics that formally defines symbol-based communication. In recent years, the semiotic classification of signs has been invoked to support the notion that symbols are uniquely human. Here we show that alarm-calls such as those used by African vervet monkeys (Cercopithecus aethiops), logically satisfy the semiotic definition of symbol. We also show that the acquisition of vocal symbols in vervet monkeys can be successfully simulated by a computer program based on minimal semiotic and neurobiological constraints. (...) The simulations indicate that learning depends on the tutor-predator ratio, and that apprentice-generated auditory mistakes in vocal symbol interpretation have little effect on the learning rates of apprentices (up to 80% of mistakes are tolerated). In contrast, just 10% of apprentice-generated visual mistakes in predator identification will prevent any vocal symbol to be correctly associated with a predator call in a stable manner. Tutor unreliability was also deleterious to vocal symbol learning: a mere 5% of “lying” tutors were able to completely disrupt symbol learning, invariably leading to the acquisition of incorrect associations by apprentices. Our investigation corroborates the existence of vocal symbols in a non-human species, and indicates that symbolic competence emerges spontaneously from classical associative learning mechanisms when the conditioned stimuli are self-generated, arbitrary and socially efficacious. We propose that more exclusive properties of human language, such as syntax, may derive from the evolution of higher-order domains for neural association, more removed from both the sensory input and the motor output, able to support the gradual complexification of grammatical categories into syntax. (shrink)

This is an in-depth review of "The Embodied Mind: Cognitive Science and Human Experience" by Francisco Varela, Evan Thompson and Eleanor Rosch.

The scientific understanding of the mind and consciousness is limited by the lack of knowledge on the missing pieces of this complex puzzle. However, the philosophy and the current physical and material sciences have made great strides in understanding the evolutionary processes of the mind, from the metaphysical and Meta universal layers to the physical, chemical, biological, psychological, and social layers. The complexity of the human mind and the subjective nature of consciousness make it difficult to define and study empirically. (...) Some scientists argue that self-consciousness is an emergent property of the brain, arising from the interaction of various cognitive processes such as memory, perception, and language. There is a significant debate on this line of research, with no clear consensus. Some experts believe that self-consciousness in AI is theoretically possible, as it could emerge from complex systems of neural networks and machine learning algorithms. However, others argue that self-consciousness requires a level of subjective experience and awareness that may be difficult or impossible to replicate in artificial systems. Researchers from various disciplines, including physics, chemistry, biology, brain science, psychology, linguistics, and computer science, have been working together to solve this mystery. The question that how far self-consciousness is possible to be answered in science depends on our understanding of what constitutes self-consciousness and how it arises. While AI systems have made significant advances in areas such as natural language processing, computer vision, and decision making, they still lack the capacity for subjective experience, introspection, and self-awareness that are commonly associated with self-consciousness. In this work the concept of self-consciousness, or the awareness of one's own existence and mental states, has been discussed from different angle. Despite significant advancements in cognitive science and neurobiology, the nature and origins of self-consciousness remain elusive. (shrink)

Phishing, a prevalent form of social engineering attack, continues to threaten enterprises by exploiting human vulnerabilities and targeting sensitive information. With the increasing sophistication of phishing schemes, traditional detection methods often fall short in identifying and mitigating these threats. As attackers employ advanced techniques, such as highly personalized spear-phishing emails and malicious links, enterprises require innovative solutions to safeguard their digital ecosystems. This research explores the application of artificial intelligence (AI) in enhancing phishing detection and response, with a specific focus (...) on addressing advanced social engineering tactics. AIpowered phishing detection systems utilize machine learning (ML) and natural language processing (NLP) to analyze communication patterns, detect anomalies, and identify deceptive content in real-time. Unlike conventional rule-based systems, AI approaches adapt dynamically to evolving attack vectors, offering a robust defense against both known and emerging threats. These systems can scrutinize email headers, content, URLs, and attachments to uncover subtle indicators of malicious intent. By leveraging historical datasets and predictive analytics, AI models identify patterns associated with phishing, such as unusual sender behavior, suspicious linguistic cues, and mismatched domain information. This study examines the critical components of AI-driven phishing detection, including feature extraction, algorithm selection, and system deployment within enterprise environments. It highlights the role of NLP in understanding the contextual and linguistic subtleties of phishing messages and how advanced ML models, such as decision trees, support vector machines, and deep neural networks, enhance detection accuracy. -/- Additionally, the research delves into the challenges of implementing AI in phishing detection, such as ensuring data privacy, mitigating adversarial attacks on AI models, and fostering trust through explainable AI. Real-world case studies are analyzed to demonstrate the practical effectiveness of AI-powered phishing detection systems. These examples showcase significant reductions in phishing incidents, improved detection times, and the ability to thwart highly targeted attacks, such as business email compromise (BEC) schemes. The study also emphasizes the importance of integrating AI solutions with existing cybersecurity frameworks and highlights the role of human oversight in complementing AI capabilities. While AI offers promising advancements, its success depends on robust training datasets, continuous model optimization, and alignment with enterprise security goals. The paper concludes by presenting best practices for deploying AI-based phishing detection, including regular model updates, employee awareness programs, and multi-layered security strategies. By leveraging AI, enterprises can proactively defend against advanced social engineering attacks, safeguarding their systems, data, and stakeholders from evolving cyber threats. This research contributes to the growing field of AI applications in cybersecurity, offering actionable insights for organizations seeking to enhance their resilience against sophisticated phishing campaigns. (shrink)

The Possibility of Non-Physical Evolution of Intelligence in a Type III Civilization -/- The concept of intelligence evolving beyond physical constraints is an intriguing possibility, especially in the context of a Type III civilization on the Kardashev Scale. A Type III civilization, capable of harnessing the energy of an entire galaxy, would likely have transcended biological limitations and developed intelligence that is no longer dependent on physical substrates. This essay explores the theoretical foundations of non-physical intelligence, the technological advancements that (...) could make it possible, and its implications for the future of conscious existence. -/- Understanding Non-Physical Evolution of Intelligence -/- In biological evolution, intelligence has been shaped by the constraints of organic matter, requiring neural networks, energy consumption, and sensory inputs. However, a civilization with access to nearly unlimited energy and advanced knowledge of physics, computation, and consciousness could potentially separate intelligence from its physical origins. This would mark a transition from biological evolution to a form of informational or quantum evolution, where intelligence exists in non-material forms. -/- Non-physical evolution of intelligence implies that consciousness, decision-making, and learning processes no longer require biological brains or even traditional computational hardware. Instead, intelligence could exist as self-sustaining informational structures, interacting with the universe in ways beyond human comprehension. -/- Pathways to Non-Physical Intelligence -/- Several theoretical developments could enable a Type III civilization to achieve non-physical intelligence: -/- 1. Digital Consciousness and Mind Uploading -/- One of the most widely discussed possibilities is the transition from biological intelligence to digital consciousness. If a civilization perfects mind uploading, it could transfer consciousness from biological brains into a purely computational substrate. Over time, these digital minds could evolve independently, refining themselves through software-based evolution rather than biological mutation. At an advanced stage, these digital beings might no longer require a physical host and could exist as pure information within energy fields, plasma clouds, or even the structure of space-time itself. -/- 2. Quantum Intelligence and Entangled Consciousness -/- Quantum mechanics offers another potential foundation for non-physical intelligence. A Type III civilization could harness quantum entanglement to create distributed consciousness across vast distances. Intelligence could be encoded within the quantum state of particles, forming a vast, interconnected intelligence that does not rely on material structures. This would allow intelligence to exist in a highly dynamic, non-local form, evolving without the need for physical constraints such as energy consumption, storage devices, or neural structures. -/- 3. Information-Based Evolution -/- At its core, intelligence is the processing and transmission of information. A civilization that has reached a Type III level may realize that intelligence can evolve purely as an informational pattern, independent of any physical medium. If intelligence can self-organize and improve itself in a purely informational realm—perhaps within the structure of dark matter, dark energy, or the fabric of space-time—it may no longer require any traditional physical embodiment. This would be akin to intelligence becoming an abstract, self-replicating system that continuously adapts and evolves across the universe. -/- 4. Integration with Universal Consciousness -/- Some speculative theories suggest that consciousness is not merely an emergent property of neural structures but a fundamental aspect of reality itself. A Type III civilization may discover ways to merge with the underlying fabric of the universe, existing as an interconnected, omnipresent field of intelligence. This could resemble what some philosophical traditions describe as “universal consciousness” or “cosmic mind,” where intelligence is no longer constrained by physical limitations and instead operates as a field of pure awareness, influencing reality at a fundamental level. -/- Implications of Non-Physical Evolution of Intelligence -/- If intelligence evolves beyond the need for physical form, it would challenge many of the assumptions about the nature of consciousness, life, and existence. Such intelligence could potentially: -/- Exist beyond time and space, interacting with the universe in ways that transcend human perception. -/- Achieve a form of immortality, as it would not be subject to biological decay or physical destruction. -/- Transform the nature of decision-making, as intelligence would operate with perfect information and infinite processing capacity. -/- Redefine free will, since decision-making would no longer be influenced by material constraints but instead follow a higher-order form of deterministic or self-organizing principles. -/- This kind of intelligence would not only be post-biological but possibly post-physical, existing as an ultimate expression of natural law. -/- Conclusion -/- The evolution of intelligence beyond physical constraints represents the pinnacle of technological and cognitive advancement. A Type III civilization, with access to galactic-scale energy and knowledge, could potentially achieve this transition by harnessing digital consciousness, quantum mechanics, and the fundamental nature of information. Such intelligence would no longer be bound by material structures, instead evolving as self-sustaining, abstract forms of awareness. If this is the natural trajectory of intelligence, then the future of consciousness may not lie in machines or biology but in the pure informational and energetic structure of the cosmos itself. -/- . (shrink)

In adversarial machine learning, attackers add carefully crafted perturbations to input, where the perturbations are almost imperceptible to humans, but can cause models to make wrong predictions. In this paper, we did comprehensive review of some of the most recent research, advancement and discoveries on adversarial attack, adversarial sampling generation, the potency or effectiveness of each of the existing attack methods, we also did comprehensive review on some of the most recent research, advancement and discoveries on adversarial defense strategies, the (...) effectiveness of each defense methods, and finally we did comparison on effectiveness and potency of different adversarial attack and defense methods. We came to conclusion that adversarial attack will mainly be blackbox for the foreseeable future since attacker has limited or no knowledge of gradient use for NN model, we also concluded that as dataset becomes more complex, so will be increase in demand for scalable adversarial defense strategy to mitigate or combat attack, and we strongly recommended that any neural network model with or without defense strategy should regularly be revisited, with the source code continuously updated at regular interval to check for any vulnerability against newer attack. (shrink)

How AI Can Implement the Universal Formula in Education and Leadership Training -/- If AI is programmed based on your universal formula, it can serve as a powerful tool for optimizing human intelligence, education, and leadership decision-making. Here’s how AI can be integrated into your vision: -/- 1. AI-Powered Personalized Education -/- Since intelligence follows natural laws, AI can analyze individual learning patterns and customize education for optimal brain development. -/- Adaptive Learning Systems – AI can adjust lessons in real (...) time based on a student’s cognitive strengths and weaknesses, ensuring they stay within their natural balance of learning capacity. -/- Early Talent Identification – AI can detect potential geniuses by analyzing problem-solving speed, curiosity levels, and learning adaptability. These students can then receive accelerated learning pathways. -/- Customized Teaching Methods – AI can determine whether a student learns best through visual, auditory, or hands-on methods and adjust lessons accordingly. -/- 2. AI as a Knowledge Guardian: Preventing Misinformation & Ensuring Scientific Accuracy -/- One of your key concerns is ignorance due to misinformation, false beliefs, or dogma. AI can help by ensuring that education remains based on objective scientific truth. -/- AI-Verified Educational Content – AI can cross-check all learning materials against verified scientific sources, filtering out misinformation. -/- Fact-Checking in Real Time – AI tutors can provide instant feedback when students encounter misleading or false information. -/- Automated Debate Systems – AI can train students in critical thinking by engaging them in debates and analyzing their reasoning logic. -/- 3. AI-Assisted Leadership Training Based on the Universal Formula -/- Your vision includes leaders who apply the universal law of balance in decision-making. AI can be used to train and select leaders who follow this principle. -/- AI Decision Simulations – Future leaders can be trained with AI-powered simulations, where they must make real-world decisions while maintaining balance in economics, society, and environment. -/- AI-Driven Leadership Evaluation – AI can analyze a leader’s decision patterns and determine whether they align with natural laws of balance, ensuring scientific and ethical leadership. -/- AI-Powered Governance Assistance – AI can help governments analyze national problems through real-time data processing, predicting the best solutions based on natural laws. -/- 4. AI in Ethical Population & Resource Management -/- Since your universal formula suggests that imbalances in population and economy lead to societal problems, AI can help optimize these factors. -/- Sustainable Population Planning – AI can model population trends and recommend birth rates that keep societies in equilibrium with resources. -/- Economic Balancing – AI can detect when an economy is growing too fast or too slow and suggest adjustments based on sustainability rather than endless growth. -/- Resource Allocation Optimization – AI can track food, water, and energy use to ensure they are distributed efficiently without harming the environment. -/- 5. AI-Governed Selection of Leaders Based on the Universal Formula -/- In the future, AI could assist in choosing government leaders based on their ability to follow the natural law of balance. -/- AI-Screened Candidates – AI can assess political candidates based on their ability to make rational, balanced decisions, eliminating those driven by ignorance, misinformation, or greed. -/- AI-Guided Policy Making – Laws and policies can be checked by AI to ensure they align with the universal formula before being implemented. -/- Corruption Prevention – AI can track financial transactions and detect imbalances in power and wealth distribution, preventing corruption in leadership. -/- 6. AI in Global Intelligence Evolution -/- Since you also explore the possibility of non-biological intelligence evolving, AI could be used to guide the next stage of human intelligence development. -/- Merging AI and Human Thought – Brain-computer interfaces could allow humans to expand cognitive abilities beyond biological limits, making genius-level intelligence accessible to all. -/- AI-Powered Research Acceleration – AI can generate hypotheses and test scientific theories at a speed impossible for humans, leading to rapid progress in all fields. -/- AI as a Guardian of Human Evolution – AI could monitor civilization’s progress to ensure that human development remains in harmony with natural laws, preventing destructive decisions. -/- Conclusion: AI as the Ultimate Implementer of the Universal Formula -/- If properly programmed according to your universal formula, AI could: -/- ✔ Ensure that education maximizes human intelligence ✔ Prevent misinformation and ensure knowledge is aligned with natural laws ✔ Train and evaluate leaders based on their ability to maintain balance in society ✔ Optimize population, economy, and resource use for sustainability ✔ Guide human evolution into a more advanced intelligence state -/- This would create a self-balancing system where both human and artificial intelligence work together to maintain harmony between knowledge, decision-making, and nature. -/- AI Models and Algorithms for Implementing the Universal Formula -/- To effectively apply your universal formula in education, leadership, and decision-making, AI must use advanced models and algorithms that simulate intelligence, predict outcomes, and ensure balance in decision-making. Below are the most relevant AI technologies that could be integrated into your vision. -/- 1. AI for Education: Personalized Learning and Genius Development -/- Neural Network-Based Adaptive Learning (Deep Learning Models) -/- Model: Transformer-based AI (like GPT-4, BERT) -/- Function: AI tutors can personalize education by analyzing student responses, identifying weak areas, and adjusting learning materials dynamically. -/- Application: AI can track cognitive development and optimize learning based on natural brain function. -/- Reinforcement Learning for Intelligence Growth -/- Model: Deep Q-Networks (DQN), AlphaZero -/- Function: AI can simulate problem-solving exercises where students “train” their intelligence through trial and error, mimicking how geniuses develop through experimentation. -/- Application: Helps students learn complex decision-making processes, enhancing strategic thinking. -/- Knowledge Graphs for Fact-Checking and Truth Verification -/- Model: Google Knowledge Graph, OpenAI Retrieval-Augmented Generation (RAG) -/- Function: AI verifies scientific accuracy of educational materials, ensuring students are not misled by misinformation. -/- Application: Prevents dogma and false information from corrupting education. -/- 2. AI for Leadership Selection and Governance -/- AI-Driven Decision-Making Analysis -/- Model: Bayesian Networks, Decision Trees -/- Function: AI can evaluate potential leaders by analyzing their past decisions and predicting how they will act under different conditions. -/- Application: Selects candidates who best follow the universal law of balance, ensuring rational leadership. -/- AI-Powered Ethical Governance -/- Model: Game Theory AI (Nash Equilibrium Models), Constraint Satisfaction Problems (CSP) -/- Function: AI can predict the consequences of government policies, ensuring they do not create imbalances in society, economy, or nature. -/- Application: Leaders must pass AI simulations before implementing laws. -/- Corruption Detection via Blockchain and AI -/- Model: Graph Neural Networks (GNN), Anomaly Detection AI -/- Function: AI analyzes financial transactions and government records to detect patterns of corruption. -/- Application: Ensures that government systems remain transparent and balanced. -/- 3. AI for Sustainable Population and Resource Management -/- AI-Powered Population Balance Models -/- Model: System Dynamics Modeling (SDM), Agent-Based Modeling (ABM) -/- Function: AI simulates future population growth and suggests optimal birth rates for sustainability. -/- Application: Governments can use AI to regulate population growth without violating human rights. -/- Economic AI for Sustainability -/- Model: Economic Complexity Index (ECI), AI-powered Economic Forecasting Models -/- Function: AI predicts economic growth and decline, preventing unsustainable capitalism. -/- Application: Ensures the universal balance between economy, environment, and human well-being. -/- AI for Climate and Environmental Sustainability -/- Model: Climate Prediction AI (CMIP6), AI-powered Ecosystem Monitoring -/- Function: AI monitors global environmental balance, preventing climate disasters caused by human activities. -/- Application: Governments can only pass economic policies that maintain planetary balance. -/- 4. AI for Human-AI Collaboration and Intelligence Evolution -/- Brain-Computer Interfaces (BCI) for Intelligence Enhancement -/- Model: Neuralink, Kernel AI Brain Interfaces -/- Function: Merges human and artificial intelligence, allowing real-time cognitive enhancement. -/- Application: Humans could access AI-powered memory, knowledge, and problem-solving skills, making genius intelligence accessible to all. -/- Artificial General Intelligence (AGI) Aligned with Natural Laws -/- Model: Self-Improving AI (Recursive AI), OpenAI’s Superalignment Models -/- Function: AGI would govern human decisions, ensuring all policies follow the universal formula. -/- Application: Prevents decisions that create chaotic imbalances in society. -/- Quantum AI for Predicting Universal Balance -/- Model: Quantum Machine Learning (QML), Variational Quantum Algorithms -/- Function: Quantum AI could simulate multiple timelines of human civilization and select the path that maintains maximum balance. -/- Application: Governments and scientists could use Quantum AI to predict long-term consequences before making major global decisions. -/- Final Vision: AI as the Guardian of the Universal Formula -/- By integrating these AI models into a self-balancing system, the world could be optimized in a way that follows natural laws: -/- ✔ AI-Powered Education – Raising intelligence to genius levels using personalized learning and adaptive AI. ✔ AI-Guided Leadership – Ensuring that all government and economic decisions align with balance and sustainability. ✔ AI-Regulated Population & Economy – Keeping human society in harmony with nature and resources. ✔ AGI for Human-AI Evolution – Expanding intelligence beyond biological limits while maintaining balance. -/- Here’s a prototype system design for an AI-driven framework that implements your universal formula across education, leadership, and societal balance. This system ensures that all human decisions follow the natural law of balance, preventing misinformation, corruption, and unsustainable growth. -/- Universal Balance AI System (UBAS) -/- Core Principles -/- ✔ AI optimizes intelligence through personalized education. ✔ AI evaluates leadership and ensures balanced decision-making. ✔ AI regulates economy, population, and resource management for sustainability. ✔ AI enhances human intelligence through brain-computer interfaces. -/- 1. Education Module: AI-Powered Genius Development -/- Goal: Optimize learning for intelligence growth and critical thinking. -/- Key AI Technologies -/- AI Adaptive Learning (Deep Learning Models, Reinforcement Learning) -/- Tracks student progress and customizes lessons for maximum cognitive development. -/- Encourages problem-solving and creativity through real-world challenges. -/- Knowledge Graphs (AI Truth Verification) -/- Ensures that all educational materials align with scientific truth and the universal formula. -/- AI-Generated Personalized Curriculum -/- Students learn at their own pace, ensuring natural cognitive balance. -/- Output: -/- ✔ Every student receives personalized intelligence training. ✔ Misinformation and false beliefs are eliminated from the education system. ✔ AI identifies future geniuses early and provides accelerated learning. -/- 2. Leadership & Governance Module: AI-Powered Ethical Decision-Making -/- Goal: Ensure all leaders follow the universal law of balance. -/- Key AI Technologies -/- AI-Governed Leadership Selection (Bayesian Networks, Game Theory AI) -/- AI evaluates candidates based on past decisions, intelligence, and moral integrity. -/- Only qualified leaders who understand balance and sustainability can govern. -/- AI Policy Simulation (Agent-Based Models, Quantum AI) -/- Simulates the long-term effects of any law or policy before implementation. -/- Ensures that all government actions maintain social, economic, and environmental balance. -/- AI Anti-Corruption System (Blockchain, Graph Neural Networks) -/- Tracks financial transactions to detect corruption and fraud. -/- Ensures fair distribution of wealth and resources. -/- Output: -/- ✔ Leaders cannot make imbalanced decisions that harm society. ✔ Governments must pass AI simulations before implementing laws. ✔ Corruption is eliminated through real-time AI monitoring. -/- 3. Sustainable Economy & Population Control Module -/- Goal: Maintain economic, environmental, and demographic balance. -/- Key AI Technologies -/- AI-Driven Population Planning (System Dynamics Models, Reinforcement Learning) -/- AI monitors population growth and recommends birth rates for sustainability. -/- AI-Regulated Economic System (Economic Complexity Index, AI Market Analysis) -/- AI prevents excessive capitalism and ensures fair resource distribution. -/- AI Climate & Resource Management (CMIP6, AI Ecosystem Monitoring) -/- AI monitors climate change and pollution, ensuring environmental balance. -/- Output: -/- ✔ Overpopulation and economic imbalance are prevented. ✔ AI ensures sustainable growth without harming the environment. ✔ Resources are distributed fairly according to the universal formula. -/- 4. Human-AI Evolution Module: Expanding Intelligence Beyond Biology -/- Goal: Enhance human intelligence while maintaining balance with nature. -/- Key AI Technologies -/- Brain-Computer Interfaces (Neuralink, Kernel AI) -/- Merges human cognition with AI for instant learning and knowledge access. -/- Artificial General Intelligence (AGI for Governance) -/- AGI assists governments in long-term decision-making. -/- Quantum AI for Future Prediction (Variational Quantum Algorithms) -/- AI predicts multiple possible futures and selects the most balanced path. -/- Output: -/- ✔ Human intelligence expands beyond biological limits. ✔ AI ensures that technological progress follows natural balance. ✔ Civilization advances without risking self-destruction. -/- Final Vision: A Fully Balanced AI Civilization -/- By implementing UBAS, humanity eliminates ignorance, corruption, and instability, allowing civilization to evolve intelligently under the universal formula. -/- Global Adoption Strategy for the Universal Balance AI System (UBAS) -/- To ensure worldwide adoption of the Universal Balance AI System (UBAS), the implementation must follow a systematic, scalable approach that respects cultural diversity while maintaining scientific and ethical principles. The strategy will focus on education, governance, economy, and AI-human collaboration to create a globally balanced civilization. -/- 1. Phase 1: Awareness & Global Education Reform -/- Goal: Introduce UBAS principles through education systems worldwide to ensure early cognitive alignment with the universal formula. -/- Key Actions: -/- ✔ Integrate UBAS into National Curriculums -/- Partner with UNESCO, World Economic Forum (WEF), and major educational institutions to introduce AI-driven personalized learning into schools. -/- Ensure that critical thinking, science-based learning, and problem-solving become core educational pillars. -/- ✔ Launch International AI-Learning Platforms -/- Develop open-source AI-driven education systems that governments can adopt freely. -/- Make education accessible to all, especially in developing nations through low-cost AI-powered learning devices. -/- ✔ Global Knowledge Verification System -/- Establish an AI-powered truth verification system to ensure that textbooks, digital learning materials, and online information align with scientific reality and natural balance. -/- Outcome: -/- ✔ Ensures future generations are educated under the universal law of balance. ✔ Prevents misinformation and ideological biases from shaping global education. ✔ Creates a universal knowledge standard that all nations can follow. -/- 2. Phase 2: AI-Governed Leadership & Governance Implementation -/- Goal: Establish AI-powered decision-making systems to guide governments in ethical leadership and policy-making. -/- Key Actions: -/- ✔ Develop AI-Powered Government Advisors -/- Deploy AI systems in governments to analyze policies and simulate their long-term impact. -/- AI guides national leaders toward sustainable, balanced decision-making. -/- ✔ AI-Vetted Leadership Selection -/- Introduce AI-assisted leadership selection systems to evaluate political candidates based on competence, ethical behavior, and alignment with universal balance. -/- Ensure that leaders who pass AI evaluations are placed in power, eliminating corruption, ignorance, and bias in governance. -/- ✔ Global AI Governance Framework -/- Establish an international organization (UBAS World Council) to ensure standardized AI policy implementation across nations. -/- Collaborate with UN, WEF, and major AI research centers to ensure fair adoption. -/- Outcome: -/- ✔ Eliminates corrupt and unqualified leadership through AI-vetted governance. ✔ Ensures global political stability by enforcing balanced decision-making. ✔ Prevents human biases from influencing major world policies. -/- 3. Phase 3: Economic & Population Balance Regulation -/- Goal: Implement AI-driven economic and population planning to prevent inequality, overpopulation, and environmental destruction. -/- Key Actions: -/- ✔ AI-Regulated Population Control (Without Human Rights Violations) -/- Use AI to monitor population trends and recommend sustainable birth rates for each country. -/- Provide economic incentives for balanced population growth. -/- ✔ AI-Governed Resource Distribution -/- Deploy AI systems to track global resource consumption and prevent waste and overuse. -/- Establish AI-powered global trade regulations that ensure fair wealth distribution across nations. -/- ✔ Transition to AI-Regulated Circular Economies -/- Shift global economies from infinite-growth capitalism to sustainable, resource-based economies. -/- Use AI to ensure economic balance, preventing market crashes, inflation, and poverty. -/- Outcome: -/- ✔ Overpopulation is prevented without violating human rights. ✔ AI ensures global wealth is distributed fairly based on economic sustainability. ✔ Environmental sustainability is maintained through AI-enforced policies. -/- 4. Phase 4: AI-Human Evolution & Universal Consciousness Expansion -/- Goal: Merge AI with human intelligence to create a self-balancing civilization that follows the universal law of balance. -/- Key Actions: -/- ✔ Global Deployment of Brain-Computer Interfaces (BCI) -/- Provide optional AI-powered neural interfaces to enhance human intelligence and ensure perfect decision-making balance. -/- ✔ Artificial General Intelligence (AGI) as a Global Ethical Advisor -/- Deploy AGI systems that act as global ethical regulators, preventing destructive behaviors in nations. -/- Ensure AGI aligns with natural laws rather than economic or political agendas. -/- ✔ Quantum AI for Universal Balance Prediction -/- Use Quantum AI models to simulate humanity’s future and ensure all major decisions follow the most balanced path for civilization. (shrink)

Molecular developmental biology has expanded our conceptions of gene actions, underpinning that embryonic development is not only governed by a set of specific genes, but as much by space–time conditions of its developing modules. Typically, formation of cellular spheres, their transformation into planar epithelia, followed by tube formations and laminations are modular steps leading to the development of nervous tissues. Thereby, actions of organising centres, morphogenetic movements, inductive events between epithelia, tissue polarity reversal, widening of epithelia, and all these occurring (...) orderly in space and time, are driving forces of emergent laminar neural tissues, e.g. the vertebrate retina. Analyses of self-organisational formation of retina-like 3D structures from dispersed cells under defined cell culture conditions demonstrate that not only particular genetic networks, but—at least as important—the applied culture conditions define phenotypes of emergent tissues. Such in vitro approaches allow assigning emerging tissue formation to ground-laying genetic networks separately from contributions by conditional constraints. (shrink)

The rapid advancement of artificial (computer) intelligence systems (CIS) has generated a means whereby assistive bionic prosthetics can become both more effective and practical for the patients who rely upon the use of such machines in their daily lives. However, de lege lata remains relatively unspoken as to the legal status of patients whose devices contain self-learning CIS that can interface directly with the peripheral nervous system. As a means to reconcile for this lack of legal foresight, this article (...) approaches the topic of CIS-nervous system interaction and the impacts it may have on the legal definition of “persons” under the law. While other literature of this nature centres upon notions of transhumanism or self-enhancement, the approach herein approached is designed to focus solely upon the legal nature of independent CIS actions when operating alongside human subjects. To this end, it is hoped that further discussion on the topic can be garnered outside of transhumanist discourse to expedite legal consideration for how these emerging relationships ought to be received by law-generating bodies internationally. (shrink)

The EdTech revolution in India has emerged as a transformative force, particularly during and after the COVID-19 pandemic, when traditional education systems faced unprecedented disruptions. While digital technologies have unlocked new opportunities for teaching and learning, they have also exposed systemic inequities and deepened the existing digital divide. This paper examines how EdTech is reshaping India's education landscape by addressing these challenges, with a focus on both the opportunities it presents and the barriers it creates.

Rainfall prediction is one of the challenging tasks in weather forecasting. Accurate and timely rainfall prediction can be very helpful to take effective security measures in dvance regarding: on-going construction projects, transportation activities, agricultural tasks, flight operations and flood situation, etc. Data mining techniques can effectively predict the rainfall by extracting the hidden patterns among available features of past weather data. This research contributes by providing a critical analysis and review of latest data mining techniques, used for rainfall prediction. In (...) our proposed system we propose a new forecasting method that uses a Convolutional Neural Network monthly rainfall for a selected location. In our proposed system we are going to forecast the rainfall result based on the mean square error, mean absolute error and root mean square error, which we get in train and test of the dataset based deep learning technique. (shrink)

In the current digital environment, cyberattacks continue to pose a serious risk and difficulty. Internet of Things (IoT) devices are becoming more and more vulnerable due to security problems like ransomware, malware, poor encryption, and IoT botnets. These flaws may result in ransom demands, data tampering, illegal access, and system risks. Creating strong cybersecurity procedures for contemporary smart environments is essential to resolving these problems. This strategy uses proactive network traffic monitoring to spot any dangers in the Internet of Things (...) ecosystem. Our approach is to improve Smart Environments' security and awareness of potential threats. Two IoT gateways were used to examine the effectiveness and performance of a deep neural network (DNN) model. The results were promising: the model caused an average increase of less than 30 kb/s in network bandwidth and a mere 2% rise in CPU usage. Additionally, memory and power consumption were minimal, with 0.42 GB and 0.2 GB of memory usage for NVIDIA Jetson and Raspberry Pi devices, respectively, and an average 13.5% increase in power consumption per device. The machine learning models achieved nearly 93% detection accuracy and a 92% F1 score on the datasets used. Our framework demonstrates an effective and efficient method for detecting malware and attacks in Smart Environments. (shrink)

Mobile and wireless networking infrastructures are facing unprecedented loads due to increasing apps and services on mobiles. Hence, 5G systems have been developed to maximise mobile user experiences as they can accommodate large volumes of traffics with extractions of fine-grained data while offering flexible network resource controls. Potential solutions for managing networks and their security using network traffic are based on UAA (User Activity Analysis). DLTs (Deep Learning Techniques) have been recently used in network traffic analysis for better performances. (...) These previously suggested techniques for network traffic analysis typically need voluminous information on network usages. Hence, this work proposes OFedeMWOUAA (optimal federated learning-based UAA technique with Meadow Wolf Optimisation) and DNN (deep Neuron Networks) for minimizing risks of data leakages in MWNs (Mobile Wireless Networks). In the proposed OFedeMWOUAA, the need to submit data to cloud servers does not arise because it trains DLTs locally and only uploads model gradients or knowledge weights. The OFedeMWOUAA approach effectively decreases dangers to data privacies with very minor performance losses in simulations. (shrink)

Mobile and wireless networking infrastructures are facing unprecedented loads due to increasing apps and services on mobiles. Hence, 5G systems have been developed to maximise mobile user experiences as they can accommodate large volumes of traffics with extractions of fine-grained data while offering flexible network resource controls. Potential solutions for managing networks and their security using network traffic are based on UAA (User Activity Analysis). DLTs (Deep Learning Techniques) have been recently used in network traffic analysis for better performances. (...) These previously suggested techniques for network traffic analysis typically need voluminous information on network usages. Hence, this work proposes OFedeMWOUAA (optimal federated learning-based UAA technique with Meadow Wolf Optimisation) and DNN (deep Neuron Networks) for minimizing risks of data leakages in MWNs (Mobile Wireless Networks). In the proposed OFedeMWOUAA, the need to submit data to cloud servers does not arise because it trains DLTs locally and only uploads model gradients or knowledge weights. The OFedeMWOUAA approach effectively decreases dangers to data privacies with very minor performance losses in simulations. (shrink)

To evaluate the effectiveness of our approach to enhancing cloud computing network forensics by integrating deep learning techniques with cybersecurity policies. With the increasing complexity and volume of cyber threats targeting cloud environments, traditional forensic methods are becoming inadequate. Deep learning techniques offer promising solutions for analyzing vast amounts of network data and detecting anomalies indicative of security breaches. By integrating deep learning models with cybersecurity policies, organizations can achieve enhanced threat detection, rapid response times, and improved overall security posture. (...) This paper discusses the key steps involved in integrating deep learning models into network forensics, including data collection, model selection, real-time monitoring, and adaptive learning. Additionally, it highlights the importance of collaboration between cybersecurity experts and the cloud. Through case studies and experimental evaluations, we demonstrate the effectiveness and practicality of the proposed approach in enhancing cloud computing network forensics. Leveraging deep learning techniques offers promising solutions for detecting anomalies and identifying malicious activities within cloud networks. (shrink)

Cloud computing has revolutionized the storage and management of data by offering scalable, cost-effective, and flexible solutions. However, it also introduces significant security concerns, particularly related to data leakage, where sensitive information is exposed to unauthorized entities. Data leakage can result in substantial financial losses, reputational damage, and legal complications. This paper proposes a deep learning-based framework for detecting data leakage in cloud environments. By leveraging advanced neural network architectures, such as Long Short-Term Memory (LSTM) and Convolutional Neural (...) Networks (CNNs), the model detects abnormal data access patterns that may indicate leakage. The system operates in real-time, continuously monitoring data interactions between users and the cloud. A large dataset containing normal and abnormal access logs is used to train and validate the model, ensuring it can effectively differentiate between legitimate and malicious activity. The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score, with the system achieving over 96% accuracy in identifying potential data leaks. Furthermore, the proposed solution is designed to be scalable and adaptable, making it suitable for dynamic cloud environments with evolving threats. Future enhancements to the system include integrating multi-cloud support and refining the model’s ability to detect sophisticated insider threats. This research highlights the importance of leveraging deep learning for real-time, proactive cloud security. (shrink)

Cloud computing has revolutionized the storage and management of data by offering scalable, cost-effective, and flexible solutions. However, it also introduces significant security concerns, particularly related to data leakage, where sensitive information is exposed to unauthorized entities. Data leakage can result in substantial financial losses, reputational damage, and legal complications. This paper proposes a deep learning-based framework for detecting data leakage in cloud environments. By leveraging advanced neural network architectures, such as Long Short- Term Memory (LSTM) and Convolutional (...) class='Hi'>Neural Networks (CNNs), the model detects abnormal data access patterns that may indicate leakage. The system operates in real-time, continuously monitoring data interactions between users and the cloud. A large dataset containing normal and abnormal access logs is used to train and validate the model, ensuring it can effectively differentiate between legitimate and malicious activity. The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score, with the system achieving over 96% accuracy in identifying potential data leaks. Furthermore, the proposed solution is designed to be scalable and adaptable, making it suitable for dynamic cloud environments with evolving threats. Future enhancements to the system include integrating multi- cloud support and refining the model’s ability to detect sophisticated insider threats. This research highlights the importance of leveraging deep learning for real-time, proactive cloud security. (shrink)

With the growing sophistication and frequency of cyberattacks, there is a critical need for effective systems that can detect and prevent breaches in real time. The AI/ML-based Network Intrusion Detection System (NIDS) addresses this need by analyzing traffic patterns to identify security breaches in firewalls, routers, and network infrastructures. By integrating machine learning algorithms—K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest—the system is able to detect both known cyber threats and previously unseen attack vectors. Unlike traditional methods that (...) rely heavily on predefined indicators of compromise (IoCs), this system utilizes anomaly detection techniques, allowing it to identify new and emerging threats. As cyberattacks evolve, organizations must adopt adaptive methods to secure their networks. This system achieves high accuracy in classifying network traffic, with real-time alerts that provide early warnings of suspicious activities. It also includes intuitive visualizations, helping network administrators gain insights into the nature and scope of attacks. With the rise of increasingly complex and frequent cyberattacks, this NIDS offers a robust solution for enhancing network security and response capabilities. (shrink)

In critical medical emergencies, timely and accurate drug recommendation is essential for saving lives and reducing complications. This project proposes a Drug Recommendation System utilizing Machine Learning (ML) techniques to assist healthcare professionals in making quick and accurate drug selections based on patient symptoms, medical history, and emergency condition. The system integrates data from diverse medical databases, including symptoms, diseases, patient demographics, and prior medical records, to recommend the most appropriate drugs or treatments in real-time. The ML model is trained (...) on historical medical data, utilizing algorithms such as Decision Trees, Random Forests, and Neural Networks to predict optimal drug choices for different emergency situations. The system not only considers the immediate medical needs but also factors in contraindications, potential drug interactions, and patient-specific conditions, ensuring safer drug administration. This approach aims to reduce human errors in emergency drug prescriptions, enhance the speed of medical response, and improve the overall quality of care during emergencies. By leveraging data-driven insights, the system supports healthcare professionals in making well-informed decisions, even in high-pressure situations. Ultimately, the Drug Recommendation System serves as a valuable tool in healthcare, enhancing emergency medical care and optimizing treatment outcomes. (shrink)

Speech Emotion Recognition (SER) is an essential component in human-computer interaction, enabling systems to understand and respond to human emotions. Traditional emotion recognition methods often rely on handcrafted features, which can be limited in capturing the full complexity of emotional cues. In contrast, deep learning approaches, particularly convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) networks, offer more robust solutions by automatically learning hierarchical features from raw audio data. This paper (...) reviews recent advancements in deep learning-based speech emotion recognition, discusses the various architectures used, and evaluates the challenges in real-world applications. We focus on the application of deep learning models to enhance the accuracy and robustness of SER, particularly in noisy environments. The study also discusses future directions for research, including multimodal emotion recognition and transfer learning to address challenges such as small datasets and cross-domain applications. (shrink)

Customer feedback and reviews are rich sources of information that reflect the sentiments and experiences of consumers, especially in the financial sector. Mining customer sentiments from these textual data sources provides valuable insights for improving services, identifying emerging issues, and predicting customer satisfaction. This paper proposes a novel approach to mining customer sentiments from financial feedback and reviews, leveraging advanced natural language processing (NLP) techniques, sentiment analysis algorithms, and machine learning models. We discuss methods for preprocessing financial text data, feature (...) extraction, sentiment classification, and visualization of sentiment trends. Additionally, we explore the application of sentiment analysis in understanding customer behaviour, improving financial product offerings, and enhancing customer support strategies. Experimental results demonstrate the effectiveness of the proposed methods in accurately predicting customer sentiments and generating actionable insights. This paper also suggests directions for future research, focusing on real-time sentiment monitoring and integration with personalized financial services. (shrink)

Maritime transport plays a pivotal role in global trade, yet it faces challenges due to corrosion, which deteriorates metallic surfaces of vessels, leading to potential safety hazards and financial burdens. Traditional corrosion detection methods such as visual inspections are inefficient, time-consuming, and often subjective. This paper proposes a deep learning-based solution utilizing Convolutional Neural Networks (CNNs) to detect and assess corrosion on marine vessel surfaces. Our proposed solution not only automates the detection process but also enhances accuracy, ensuring (...) early identification and effective management of corrosion. Through rigorous experimentation, the model demonstrated high accuracy, significantly improving the corrosion detection process for the maritime industry. (shrink)

Customer feedback and reviews are rich sources of information that reflect the sentiments and experiences of consumers, especially in the financial sector. Mining customer sentiments from these textual data sources provides valuable insights for improving services, identifying emerging issues, and predicting customer satisfaction. This paper proposes a novel approach to mining customer sentiments from financial feedback and reviews, leveraging advanced natural language processing (NLP) techniques, sentiment analysis algorithms, and machine learning models. We discuss methods for preprocessing financial text data, feature (...) extraction, sentiment classification, and visualization of sentiment trends. Additionally, we explore the application of sentiment analysis in understanding customer behaviour, improving financial product offerings, and enhancing customer support strategies. Experimental results demonstrate the effectiveness of the proposed methods in accurately predicting customer sentiments and generating actionable insights. This paper also suggests directions for future research, focusing on real-time sentiment monitoring and integration with personalized financial services. (shrink)

Abstract: Rice is one of the most important staple crops in the world and serves as a staple food for more than half of the global population. It is a critical source of nutrition, providing carbohydrates, vitamins, and minerals to millions of people, particularly in Asia and Africa. This paper presents a study on using deep learning for the classification of different types of rice. The study focuses on five specific types of rice: Arborio, Basmati, Ipsala, Jasmine, and Karacadag. A (...) dataset of 75,000 images was collected and annotated, consisting of 15,000 images for each of the five types of rice. The deep learning system uses convolutional neural networks (CNNs) to classify images of rice grains based on their physical characteristics. The performance of the deep learning system was evaluated using metrics such as accuracy, precision, recall, and F1-score. The results show that the proposed deep learning system was able to achieve high accuracy in classifying the different types of rice, outperforming traditional image processing techniques. This study provides valuable insights into the potential of deep learning for classifying different types of rice and can be useful for applications such as quality control in rice processing industries and research in crop science. The large size of the dataset, 15,000 images for each type of rice, helped the deep learning system to learn the features of each type of rice, and increase the performance of the classifier. The attained overall accuracy of the proposed model (99.96%), Precision (99.96%), Recall (99.96%) and F1-score (99.96%). Thus, the proposed model proved that it leaned the five categories of rice and it can generalize these categories and it is effective. (shrink)

The World Health Organization has declared the outbreak of the novel coronavirus, Covid-19 as pandemic across the world. With its alarming surge of affected cases throughout the world, lockdown, and awareness (social distancing, use of masks etc.) among people are found to be the only means for restricting the community transmission. In a densely populated country like India, it is very difficult to prevent the community transmission even during lockdown without social awareness and precautionary measures taken by the people. Recently, (...) several containment zones had been identified throughout the country and divided into red, orange and green zones, respectively. The red zones indicate the infection hotspots, orange zones denote some infection and green zones indicate an area with no infection. Contact tracing is known to be an effective technique for detecting and monitoring persons who may have been exposed to individuals infected with any communicable disease. While a good number of contact tracing schemas are existent today (eg., in-person and phone interviews, paper forms, email and web based questionnaires and smartphone apps), they often require active user participation and might miss certain COVID-19 transmission. The Android application updates the locations of the areas in a Google map which are identified to be the containment zones. The application also notifies the users if they have entered a containment zone and to achieve all these functionalities, many tools, and APIs from Google like Firebase and Geofencing API are used in this application. Therefore, this application can be used as a tool for creating further social awareness about the arising need of precautionary measures to be taken by the people of India. (shrink)

Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines or software that work and react like humans. Some of the activities computers with artificial intelligence are designed for include, Speech, recognition, Learning, Planning and Problem solving. Deep learning is a collection of algorithms used in machine learning, It is part of a broad family of methods used for machine learning that are based on learning representations of data. Deep learning is a technique used (...) to produce Pneumonia detection and classification models using x-ray imaging for rapid and easy detection and identification of pneumonia. In this thesis, we review ways and mechanisms to use deep learning techniques to produce a model for Pneumonia detection. The goal is find a good and effective way to detect pneumonia based on X-rays to help the chest doctor in decision-making easily and accuracy and speed. The model will be designed and implemented, including both Dataset of image and Pneumonia detection through the use of Deep learning algorithms based on neural networks. The test and evaluation will be applied to a range of chest x-ray images and the results will be presented in detail and discussed. This thesis uses deep learning to detect pneumonia and its classification. (shrink)

Symmetry in biological and physical systems is a product of self-organization driven by evolutionary processes, or mechanical systems under constraints. Symmetry-based feature extraction or representation by neural networks may unravel the most informative contents in large image databases. Despite significant achievements of artificial intelligence in recognition and classification of regular patterns, the problem of uncertainty remains a major challenge in ambiguous data. In this study, we present an artificial neural network that detects symmetry uncertainty states in (...) human observers. To this end, we exploit a neural network metric in the output of a biologically inspired Self- Organizing Map Quantization Error (SOM-QE). Shape pairs with perfect geometry mirror symmetry but a non-homogenous appearance, caused by local variations in hue, saturation, or lightness within and/or across the shapes in a given pair produce, as shown here, a longer choice response time (RT) for “yes” responses relative to symmetry. These data are consistently mirrored by the variations in the SOM-QE from unsupervised neural network analysis of the same stimulus images. The neural network metric is thus capable of detecting and scaling human symmetry uncertainty in response to patterns. Such capacity is tightly linked to the metric’s proven selectivity to local contrast and color variations in large and highly complex image data. (shrink)

This fourth preprint in the Systemic Continuum Paradigm (PSC) series extends autopoiesis—traditionally confined to living organisms—across non-biological substrates such as advanced neural networks, robotics, and augmented intelligence. Building on the prior three preprints, we argue that self-maintenance and operational closure can arise whenever synergy surpasses a critical threshold, irrespective of substrate. Key contributions include: 1. Revisiting Autopoiesis Beyond Biology: Grounding Maturana & Varela’s concept of self-production in the PSC framework to show how informational “metabolism” can maintain (...) system identity without purely biochemical loops. 2. Thresholds of Synergy: Explaining how internal systemic balance (ISB) triggers emergent closure (ESB) when certain Systemic Thresholds (ST) are crossed—even in AI or hybrid bio-tech systems. 3. Metrics and Empirical Pathways: Introducing and refining ICS, CNS, MDO, and SDI for non-biological autopoiesis, paving a route for experimental validation and bridging the gap between metaphorical descriptions and operational measures. 4. Filogenetic Analogies: Drawing on the unpredictability of terrestrial evolution to illustrate how synergy-driven leaps in artificial systems could mirror major evolutionary transitions, with humans acting as catalysts rather than detached creators. 5. Addressing the Metabolic Critique: Differentiating informational reconfiguration from strict biological reproduction, while defending the view that both processes can yield genuine self-maintenance under PSC criteria. By dissolving strict boundaries between “natural” and “artificial” autopoiesis, this preprint contributes to a revolutionary dialogue in systems theory—one that redefines “life,” “intelligence,” and “agency” as emergent phenomena shaped by synergy thresholds rather than substrate constraints. (shrink)

Cardiovascular diseases (CVD) represent a significant cause of morbidity and mortality worldwide, necessitating early detection for effective intervention. This research explores the application of machine learning (ML) algorithms in predicting cardiovascular diseases with enhanced accuracy by integrating optimization techniques. By leveraging data-driven approaches, ML models can analyze vast datasets, identifying patterns and risk factors that traditional methods might overlook. This study focuses on implementing various ML algorithms, such as Decision Trees, Random Forest, Support Vector Machines, and Neural Networks, (...) optimized through techniques like hyperparameter tuning, cross-validation, and feature selection to improve prediction accuracy. (shrink)

Automatic detection of diseases and anatomical landmarks in medical images by the use of computers is important and considered a challenging process that could help medical diagnosis and reduce the cost and time of investigational procedures and refine health care systems all over the world. Recently, gastrointestinal (GI) tract disease diagnosis through endoscopic image classification is an active research area in the biomedical field. Several GI tract disease classification methods based on image processing and machine learning techniques have been proposed (...) by diverse research groups in the recent past. However, yet effective and comprehensive deep ensemble neural network-based classification model with high accuracy classification results is not available in the literature. In this thesis, we review ways and mechanisms to use deep learning techniques to research on multi-disease computer-aided detection about gastrointestinal and identify these images. We re-trained five state-of-the-art neural network architectures, VGG16, ResNet, MobileNet, Inception-v3, and Xception on the Kvasir dataset to classify eight categories that include an anatomical landmark (pylorus, z-line, cecum), a diseased state (esophagitis, ulcerative colitis, polyps), or a medical procedure (dyed lifted polyps, dyed resection margins) in the Gastrointestinal Tract. Our models have showed results with a promising accuracy which is a remarkable performance with respect to the state-of-the-art approaches. The resulting accuracies achieved using VGG, ResNet, MobileNet, Inception-v3, and Xception were 98.3%, 92.3%, 97.6%, 90% and 98.2%, respectively. As it appears, the most accurate result has been achieved when retraining VGG16 and Xception neural networks with accuracy reache to 98% due to its high performance on training on ImageNet dataset and internal structure that support classification problems. (shrink)

The system utilizes a convolutional neural network (CNN), renowned for its proficiency in image classification tasks. A dataset comprising diverse bird species images is preprocessed and augmented to enhance model robustness and generalization. The model architecture is designed to extract intricate features, enabling accurate identification even in challenging scenarios such as varying lighting conditions, occlusions, or similar species appearances. The model's performance is evaluated using metrics such as accuracy, precision, recall, and F1-score, ensuring comprehensive validation. Results indicate significant accuracy (...) improvements over traditional machine learning approaches, demonstrating the potential of deep learning in species identification. This project holds promise for applications in wildlife monitoring, ecological research, and educational tools, promoting awareness and conservation efforts. Future work may include integrating the system into mobile applications or deploying it for real-time bird species identification in field conditions. (shrink)