Computer Science

Every person has his/her own unique signature that is used mainly for the purposes of personal identification and verification of important documents or legal transactions. There are two kinds of signature verification: static and dynamic. Static(off-line) verification is the process of verifying an electronic or document signature after it has been made, while dynamic(on-line) verification takes place as a person creates his/her signature on a digital tablet or a similar device. Offline signature verification is not efficient and slow for a (...) large number of documents. To overcome the drawbacks of offline signature verification, we have seen a growth in online biometric personal verification such as fingerprints, eye scan etc. In this paper we created CNN model using python for offline signature and after training and validating, the accuracy of testing was 99.70%. (shrink)

Cooperation is vital to our survival and progress. Evolutionary game theory offers a lens to understand the structures and incentives that enable cooperation to be a successful strategy. As artificial intelligence agents become integral to human systems, the dynamics of cooperation take on unprecedented significance. The convergence of human-agent teaming, contract theory, and decentralized frameworks like Web3—grounded in transparency, accountability, and trust—offers a foundation for fostering cooperation by establishing enforceable rules and incentives for humans and AI agents. We conceptualize Incentivized (...) Symbiosis as a social contract between humans and AI, inspired by Web3 principles and encoded in blockchain technology, to define and enforce rules, incentives, and consequences for both parties. By exploring this paradigm, we aim to catalyze new research at the intersection of systems thinking in AI, Web3, and society, fostering innovative pathways for cooperative human-agent coevolution. (shrink)

This article explores the profound impact of Artificial Intelligence (AI) on the realm of religion, exploring the potential for AI to catalyze the birth of new world religions and break the "Solomon's Cycle." Drawing inspiration from King Solomon's timeless declaration, "There is nothing new under the sun," the article examines the challenges faced by new religions in a world dominated by established faiths and traditions. By leveraging the transformative capabilities of AI to inspire creativity, foster cross-cultural dialogue, provide ethical guidance, (...) build virtual communities, and personalize spiritual experiences, individuals are empowered to transcend historical barriers and forge new paths of spiritual exploration. The fusion of AI and human ingenuity holds the promise of birthing diverse, inclusive, and dynamic belief systems that resonate with the complexities of our contemporary world. (shrink)

In our research about Fuzzy Time and modeling time, "Unexpected Hanging Paradox" plays a major role. Here, we compare this paradox to the Zeno Paradox and the relations of them with our standard models of continuum and Fuzzy numbers. To do this, we review the project "Fuzzy Time and Possible Impacts of It on Science" and introduce a new way in order to approach the solutions for these paradoxes. Additionally, we have a more general discussion about paradoxes, as Philosophical back (...) ground of the subject and in this way, we introduce the concepts of General View, the first picture, BBF-General View. (shrink)

CAT4 is proposed as a general method for representing information, enabling a powerful programming method for large-scale information systems. It enables generalised machine learning, software automation and novel AI capabilities. It is based on a special type of relation called CAT4, which is interpreted to provide a semantic representation. This is Part 2 of a five-part introduction. The focus here is on defining key mathematical properties of CAT2, identifying the topology and defining essential functions over a coordinate system. The analysis (...) is from first principles. This develops on from the axioms introduced in Part 1. The interpretation of fact networks is introduced in Part 3, and the full application to semantic theory comes in Part 4, where we introduce general functions, including the language interpretation or linguistic functions. In Part 5, we turn to software design considerations, to show how files, indexes, functions and screens can be defined to implement a CAT4 system efficiently. (shrink)

(This is one of the essays to be included in a book examining the causes of day-to-day strife in the populations of modern democracies vying to live and assert the freedoms promised to them by systems of governance supposed and expected to represent them.) "The artisan of old, the artist, the researcher, the developer, and the scientist today have this in common, that in refining, perfecting and pushing the boundaries of their respective crafts, they cannot achieve satisfaction or adequately perform (...) without simultaneously and progressively adapting, re-configuring, developing, and continuously changing the techniques and approaches they employ together with the tools and parameters for gauging actual success within the realm that only rigorous acquaintance, imaginative freedom, cross-functional, multifaceted learning, and distributive interaction may allow.". (shrink)

This book serves as an introduction to mathematical logic for STEM (Science, Technology, Engineering, and Mathematics) students, written for undergraduates (in particular, 1st and 2nd year undergraduates). A focus on this book is on logical thinking, not simply rote memorization, with a focus on examples and analogies relevant to students aimed at becoming technical leaders and problem solvers. This book includes propositional logic, set theory, functions and relations, and more, with coding examples provided in the Python programming language.

This paper argues that Maximum Likelihood Estimation (MLE) is the wrong approach for parameter estimation, both conceptually and in its results. We propose a new estimation method, called PLE, that offers four benefits over MLE: 1) PLE produces less biased estimates of the true parameters 2) PLE reduces overfitting 3) PLE more fairly represents minority (low-frequency) data and 4) PLE is more resilient to MAD collapse. We show how these benefits result from PLE's incorporation of counterfactual samples.

Dữ liệu trên RDocumentation (CRAN) cho thấy phần mềm máy tính bayesvl có lượng download trong tháng 1/2024 cao vượt bậc so với tháng 12/2023, tăng 164%. Sự hào hứng này đã cho tôi động lực tiếp tục tìm hiểu mức độ lan tỏa của bayesvl. Nhờ thế nên tôi mới phát hiện ra 2 thông tin thú vị.

Long Range and short range, guns and violence, everyday life in cities and streets, between social and group identity and faith and religious belief, the vision to the "things of the world that cannot be seen" (Heróis do Mar).

In a first investigation, a Lacan-motivated template of the Poe story is fitted to the data. A segmentation of the storyline is used in order to map out the diachrony. Based on this, it will be shown how synchronous aspects, potentially related to Lacanian registers, can be sought. This demonstrates the effectiveness of an approach based on a model template of the storyline narrative. In a second and more Comprehensive investigation, we develop an approach for revealing, that is, uncovering, Lacanian (...) register relationships. Objectives of this work include the wide and general application of our methodology. This methodology is strongly based on the “letting the data speak” Correspondence Analysis analytics Platform of Jean-Paul Benzécri, that is also the geometric data analysis, both qualitative and quantitative analytics, developed by Pierre Bourdieu. (shrink)

Data on RDocumentation (CRAN) shows that the bayesvl R package had an exceptionally high number of downloads in January 2024 compared to December 2023, with an increase of 164%. This excitement motivated me to investigate the extent of bayesvl’s spread further, leading to the discovery of two interesting pieces of information.

We review the progress of the Mindsponge concept and the bayesvl R package in scientific research from 2018 to 2021.

The rapid advancement of Information Technology (IT) platforms and programming languages has transformed the dynamics and development of human society. The cyberspace and associated utilities are expanding, leading to a gradual shift from real-world living to virtual life (also known as cyberspace or digital space). The expansion and development of Natural Language Processing (NLP) models and Large Language Models (LLMs) demonstrate human-like characteristics in reasoning, perception, attention, and creativity, helping humans overcome operational barriers. Alongside the immense potential of artificial intelligence (...) (AI) are new security loopholes and more complex information security risks. As society is still trying to transition to a new phase to adapt to technological changes, the AI revolution continues to unfold, necessitating a reconsideration of the trajectory of societal transformation as it could exacerbate the aforementioned information security risks. Specifically, how should society evolve to keep pace with the transformative impact of the current AI technology wave? How can we manage and harness their power while ensuring information security as our presence in the virtual world increases? This article aims to shed light on and address these questions. (shrink)

Conversion of mind, due to some experience and knowledge, plus a lot of patience.

Current work in language models (LMs) helps us speed up or even skip thinking by accelerating and automating cognitive work. But can LMs help us with critical thinking -- thinking in deeper, more reflective ways which challenge assumptions, clarify ideas, and engineer new concepts? We treat philosophy as a case study in critical thinking, and interview 21 professional philosophers about how they engage in critical thinking and on their experiences with LMs. We find that philosophers do not find LMs to (...) be useful because they lack a sense of selfhood (memory, beliefs, consistency) and initiative (curiosity, proactivity). We propose the selfhood-initiative model for critical thinking tools to characterize this gap. Using the model, we formulate three roles LMs could play as critical thinking tools: the Interlocutor, the Monitor, and the Respondent. We hope that our work inspires LM researchers to further develop LMs as critical thinking tools and philosophers and other 'critical thinkers' to imagine intellectually substantive uses of LMs. (shrink)

Abstract: As artificial intelligence (AI) technologies rapidly evolve and permeate various aspects of society, the need for effective regulation and governance has become increasingly critical. This paper explores the current landscape of AI regulation, examining existing frameworks and their efficacy in addressing the unique challenges posed by AI. Key issues such as ensuring compliance, mitigating biases, and maintaining transparency are analyzed. The paper also delves into ethical considerations surrounding AI governance, emphasizing the importance of fairness and accountability. Through case studies (...) of different regulatory approaches and their outcomes, the paper highlights both successes and areas for improvement. Finally, it offers insights into future directions for AI regulation, advocating for innovative policies and enhanced international cooperation to ensure that AI technologies are developed and deployed responsibly. This comprehensive analysis aims to contribute to the ongoing discourse on creating robust regulatory mechanisms that can keep pace with the rapid advancement of AI. (shrink)

It is argued that logic, and in particular mathematical logic, should play a key role in the undergraduate curriculum for students in the computing fields, which include electrical engineering (EE), computer engineering (CE), and computer science (CS). This is based on 1) the history of the field of computing and its close ties with logic, 2) empirical results showing that students with better logical thinking skills perform better in tasks such as programming and mathematics, and 3) the skills students are (...) expected to have in the job market. Further, the authors believe teaching logic to students explicitly will improve student retention, especially involving underrepresented minorities in STEM, whose rate of attrition is higher than for non-minority students. Though this work focuses specifically on the computing fields, these results demonstrate the importance of logic education to STEM (science, technology, engineering, and mathematics) as a whole. (shrink)

Emotions are a central aspect of communication. Consequently, emotion analysis (EA) is a rapidly growing field in natural language processing (NLP). However, there is no consensus on scope, direction, or methods. In this paper, we conduct a thorough review of 154 relevant NLP publications from the last decade. Based on this review, we address four different questions: (1) How are EA tasks defined in NLP? (2) What are the most prominent emotion frameworks and which emotions are modeled? (3) Is the (...) subjectivity of emotions considered in terms of demographics and cultural factors? and (4) What are the primary NLP applications for EA? We take stock of trends in EA and tasks, emotion frameworks used, existing datasets, methods, and applications. We then discuss four lacunae: (1) the absence of demographic and cultural aspects does not account for the variation in how emotions are perceived, but instead assumes they are universally experienced in the same manner; (2) the poor fit of emotion categories from the two main emotion theories to the task; (3) the lack of standardized EA terminology hinders gap identification, comparison, and future goals; and (4) the absence of interdisciplinary research isolates EA from insights in other fields. Our work will enable more focused research into EA and a more holistic approach to modeling emotions in NLP. (shrink)

Abstract: Vegetables that are popular and versatile over the world are peppers. Precise categorisation of pepper cultivars is vital for multiple uses, such as assessing market trends, regulating quality, and conducting genetic research. Classifying peppers using traditional methods can be subjective and time-consuming. This research proposes an automated pepper variety classification method based on deep learning. A deep convolutional neural network (CNN) model was trained on a dataset of 2,368 photos of peppers. With the purpose of accurately classifying the pepper (...) photos, the CNN was built to extract essential features from them. The trained model demonstrated its efficacy in differentiating between pepper varieties with an astounding accuracy of 100% on a held-out test set. Deep learning has the capacity to classify peppers accurately and efficiently, as this study shows. The suggested strategy can help the food processing, quality control, and agriculture sectors advance. (shrink)

Abstract: As artificial intelligence (AI) systems become increasingly complex and pervasive, the need for transparency and interpretability has never been more critical. Explainable AI (XAI) addresses this need by providing methods and techniques to make AI decisions more understandable to humans. This paper explores the core principles of XAI, highlighting its importance for trust, accountability, and ethical AI deployment. We examine various XAI techniques, including interpretable models and post-hoc explanation methods, and discuss their strengths and limitations. Additionally, we present case (...) studies demonstrating the practical applications of XAI across diverse domains such as healthcare, finance, and autonomous systems. The paper also addresses the ongoing challenges and outlines future research directions aimed at enhancing the effectiveness and applicability of XAI. By bridging the gap between complex AI systems and human understanding, XAI plays a pivotal role in fostering more reliable and responsible AI technologies. (shrink)

Abstract. This study investigates the application of convolutional neural networks (CNNs) to the task of classifying male and female eyes. Using a dataset of eye images, the research explores the potential of deep learning to accurately distinguish between the genders based solely on eye features. The proposed CNN model achieved 94% accuracy on the training set and 91% on the validation set. The study addresses the challenges and limitations in feature extraction from eye images and compares the proposed model with (...) traditional machine learning approaches. The results demonstrate the model's robustness, providing significant insights into gender recognition through partial facial analysis. (shrink)

Abstract. This study explores the use of convolutional neural networks (CNNs) for classifying different pineapple varieties, specifically pineapples and mini pineapples. By using a dataset of pineapple images, the research demonstrates the effectiveness of a pre-trained VGG16-based CNN model in accurately classifying these fruit categories. The model achieved over 99% accuracy on both the training and validation sets. The performance of the CNN was compared to traditional machine learning algorithms to highlight the advantages of deep learning in image classification tasks. (...) The results underscore the model’s ability to generalize well to the classification task, offering insights into feature extraction from complex image datasets. (shrink)

Abstract: Abstract: The classification of nuts is crucial for food security; nevertheless, accurate and swift identification continues to be a challenge in numerous areas due to insufficient infrastructure. The rise in smartphone utilization, along with advancements in computer vision driven by deep learning, has facilitated smartphone-assisted nut classification. We trained a deep convolutional neural network to categorize five distinct nut types (Chestnut, Hazelnut, Nut Forest, Nut Pecan, and Walnut) using a public dataset of 2,850 photos gathered under controlled conditions. The (...) model attained an accuracy of 98.37% on a reserved test set, illustrating the viability of this method. This approach offers a viable avenue for large-scale smartphone-assisted nut categorization. (shrink)

Abstract: In modern agriculture, ensuring the quality of crops plays a vital role in enhancing production and minimizing waste. This research focuses on the classification of lemon plants into two categories: good quality and bad quality, using deep learning techniques. We employ convolutional neural networks (CNN) to develop a classification model that can accurately predict plant quality based on images. Through a structured pipeline involving data collection, preprocessing, model design, and evaluation, we demonstrate the effectiveness of CNNs in performing quality (...) assessments. This paper discusses the experimental results in applying deep learning to agricultural tasks. (shrink)

Abstract: This research focuses on the classification of nine varieties of dates using deep learning techniques. The study aims to develop an accurate and efficient model capable of identifying different types of dates based on images. A Convolutional Neural Network (CNN) was employed, trained on a dataset comprising thousands of date images, processed to enhance classification performance. The model was evaluated on multiple metrics, achieving high accuracy rates, demonstrating the feasibility of using deep learning in date classification. This approach can (...) significantly aid in automating the identification process, which is crucial for the agricultural industry. The results indicate that deep learning techniques offer a robust solution for the classification of date varieties, with potential applications in quality control and market sorting. (shrink)

The “sloppy models” program originated in systems biology, but has seen applications across a range of fields. Sloppy models are dependent on a large number of parameters, but highly insensitive to the vast majority of parameter combinations. Sloppy models proponents claim that the program may explain the success of science. I argue that the sloppy models program can at best provide a very partial explanation. Drawing a parallel with renormalization group realism, I argue that it would only give us grounds (...) for a minimal kind of scientific realism. Nonetheless, the program can offer certain epistemic virtues. (shrink)

Manifold learning and effective model building are generally viewed as fundamentally different types of procedure. After all, in one we build a simplified model of the data, in the other, we construct a simplified model of the another model. Nonetheless, I argue that certain kinds of high-dimensional effective model building, and effective field theory construction in quantum field theory, can be viewed as special cases of manifold learning. I argue that this helps to shed light on all of these techniques. (...) First, it suggests that the effective model building procedure depends upon a certain kind of algorithmic compressibility requirement. All three approaches assume that real-world systems exhibit certain redundancies, due to regularities. The use of these regularities to build simplified models is essential for scientific progress in many different domains. (shrink)

Abstract: Accurate identification of marine species is critical for effective fishery management, biodiversity conservation, and the aquaculture industry. Traditional methods of fish identification rely on expert knowledge and manual labor, making them time- consuming, expensive, and error-prone. In this research, we explore a machine learning-based approach to automate the classification of nine fish species using image recognition techniques. The fish species under study include Black Sea Sprat, Gilt- Head Bream, Horse Mackerel, Red Sea Bream, Shrimp, Trout, Striped Red Mullet, Sea (...) Bass, and Red Mullet. We collected 1000 images per species, yielding a total of 9000 images, with 750 used for training and 250 reserved for testing. We utilized convolutional neural networks (CNNs) pre-trained on the ImageNet dataset and fine-tuned them to perform fish species classification. Preliminary experiments showed that CNNs, particularly transfer learning models like VGG16 and ResNet50, achieved accuracy rates as high as 98.6% on the test set. This research highlights the potential of deep learning in automating marine species identification, enabling faster, more reliable monitoring. (shrink)

This paper explores whether extraterrestrial or other forms of cognition and morality that are unobservable and incomprehensible to humans constitute an existential risk (X-risk) or an existential opportunity (X-opportunity) for humanity. It is being argued that the human epistemological apparatus is fundamentally limited, rendering certain forms of life—both extraterrestrial and potentially terrestrial—imperceptible and incomprehensible (which is also a novel solution to the Fermi paradox that we propose). By integrating philosophical reasoning with empirical insights from (astro)biology, the paper examines the potential (...) implications of interacting with such entities. It will be concluded that extra-terrestrial or other sentient forms of cognition and morality could represent both a risk and an opportunity, with the likelihood of a favorable outcome increasing in correlation with scientific progress and cognitive and moral enhancement of humanity. (shrink)

The concept of Generative Artificial Intelligence (GenAI) is ubiquitous in the public and semi-technical domain, yet rarely defined precisely. We clarify main concepts that are usually discussed in connection to GenAI and argue that one ought to distinguish between the technical and the public discourse. In order to show its complex development and associated conceptual ambiguities, we offer a historical-systematic reconstruction of GenAI and explicitly discuss two exemplary cases: the generative status of the Large Language Model BERT and the differences (...) between protein structure predictions from AlphaFold 2 and 3. Our analysis shows that there is no unique and unambiguous definition of GenAI based on a purely technical account of the term. Following this conclusion, we argue that the public discourse is not simply a less complex way of speaking, but instead transcends its technical basis. As a means to structure this newly emerging discussion landscape we introduce a non-exhaustive list of four central aspects of GenAI: (multi-)modality, interaction, flexibility, and productivity. These dimensions constitute a first step towards defining GenAI beyond its technical basis. (shrink)

D'Agostino et al. recently launched book in the College Publication series on Logic and Bounded Rationality is reviewed. Applications to human-oriented AI are emphasized.

No natural language has contrafactive attitude verbs. Because factives are universal across natural languages, this means that there is a major asymmetry between contrafactives and factives. We previously hypothesised that this asymmetry arises partly because the meaning of contrafactives is significantly harder to learn than that of factives. Here we test this hypothesis by using a production-oriented computational experiment that overcomes two limitations of our previous experiments. We find that our results do not support our previous hypothesis.

This mixed methods study investigates whether technology mediated collaborative practices during a professional development (PD) session led to growth in the collective efficacy of 21 special education teachers at an independent 1-12 school in Southeastern Virginia. This school specializes in individualized instruction for students with learning differences not limited to Autism Spectrum Disorder, Attention Deficit Hyperactivity Disorder, Specific Learning Disability, and their comorbidities. Teacher collective efficacy, which subsumes cohesive perceptions of classroom learning and behavior management, has been shown as strongly (...) related to student achievement and healthy school culture. Mastery experiences with specific tasks related to teaching and learning can spur collective efficacy. Cutting-edge technologies (Vibe and Newline collaborative all-in-one hardware and software interfaces) were used to engage teachers in an interdisciplinary vision boarding activity to outline academic, social, and technological goals within and across subject areas. Teachers also engaged in a behavior management reflection exercise. Pre- and post-survey analysis was conducted using a paired samples t-test, showing overall growth in collective efficacy after the brief PD session. Regression analyses revealed that technology savvy teachers grew most in collective efficacy. Network analysis of vision board output and a descriptive analysis of the behavior management surveys showed that teachers became united in interdisciplinary goals for learning, and in strategies for behavior management through conversational practices. (shrink)

Abstract: The integration of artificial intelligence (AI) into drug discovery is revolutionizing the pharmaceutical industry by accelerating the development of novel therapeutics. AI-powered tools enable researchers to process vast datasets, identify drug candidates, and predict their efficacy and safety with unprecedented speed and accuracy. This paper explores the transformative impact of AI on drug discovery, highlighting key advancements in machine learning algorithms, deep learning, and predictive modeling. Additionally, it addresses the challenges associated with AI implementation, including data quality, regulatory hurdles, (...) and ethical considerations. By analyzing case studies of AI-driven pharmaceutical breakthroughs, this paper underscores AI's potential to streamline drug development, reduce costs, and address unmet medical needs. The future of drug discovery is poised to shift dramatically as AI continues to advance, offering promising solutions for improving human health. (shrink)

Abstract: Artificial Intelligence (AI) has rapidly advanced, offering significant potential to transform business decision-making. This paper delves into how AI can be harnessed to enhance strategic decision-making within business contexts. It investigates the integration of AI-driven analytics, predictive modeling, and automation, emphasizing their role in improving decision accuracy and operational efficiency. By examining current applications and case studies, the paper underscores the opportunities AI offers, including improved data insights, risk management, and personalized customer experiences. It also addresses the challenges businesses (...) face when adopting AI, such as data privacy concerns, integration difficulties, and the need for skilled professionals. The paper concludes by offering recommendations for successfully implementing AI solutions to maximize benefits while mitigating potential challenges. (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)

Abstract: As artificial intelligence (AI) technologies become increasingly integrated into various sectors, ethical considerations surrounding their development and deployment have become paramount. This paper explores the multifaceted ethical landscape of AI, focusing on key challenges such as bias, transparency, privacy, and accountability. It examines how these issues manifest in AI systems and their impact on society. The paper also evaluates current approaches and solutions aimed at mitigating these ethical concerns, including regulatory frameworks, ethical guidelines, and best practices for AI design. (...) By providing a comprehensive analysis of these challenges and solutions, this paper aims to contribute to the ongoing discourse on responsible AI development and implementation, advocating for a balance between technological innovation and ethical integrity. (shrink)

Abstract: In recent years, the outbreak of various poultry diseases has posed a significant threat to the global poultry industry. Therefore, the accurate and timely detection of chicken diseases is critical to reduce economic losses and prevent the spread of diseases. In this study, we propose a method for classifying chicken diseases using a convolutional neural network (CNN). The proposed method involves preprocessing the chicken images, building and training a CNN model, and evaluating the performance of the model. The dataset (...) used in this study comprises chicken images of three different diseases: Newcastle disease, avian influenza, and infectious bursal disease. The proposed method achieved an overall F1-score of 99.04% in classifying the chicken diseases. The results demonstrate the effectiveness of using CNN in classifying chicken diseases and provide a promising approach for early detection and diagnosis of poultry diseases. (shrink)

Abstract: The integration of Artificial Intelligence (AI) into leadership practices is rapidly transforming organizational dynamics and decision-making processes. This paper explores the ways in which AI enhances leadership effectiveness by providing data- driven insights, optimizing decision-making, and automating routine tasks. Additionally, it examines the challenges leaders face when adopting AI, including ethical considerations, potential biases in AI systems, and the need for upskilling. By analyzing current applications of AI in leadership and discussing future trends, this study aims to offer a (...) comprehensive overview of the opportunities AI presents for effective leadership, as well as the strategies required to address its associated challenges. (shrink)

Abstract: A corn crop typically refers to a large-scale cultivation of corn (also known as maize) for commercial purposes such as food production, animal feed, and industrial uses. Corn is one of the most widely grown crops in the world, and it is a major staple food for many cultures. Corn crops are grown in various regions of the world with different climates, soil types, and farming practices. In the United States, for example, the Midwest is known as the "Corn (...) Belt" due to its extensive corn production. Corn crops can be grown using a variety of methods, including conventional tillage, no-till farming, and the use of genetically modified crops. Due to its importance, there is a need for discovering the corn diseases and treating them. The aim of this study is to propose a deep learning model for the classification of corn leaf diseases based on Convolutional Neural Network. A model for classifying maize diseases was developed using a dataset for classifying Corn diseases that contains 4 classes of disease. The dataset was collected from Keggel website. The proposed model was trained, validated, and tested. The F1-score (99.83%), Recall (99.83%), precision (99.83%), Accuracy (99.83%). (shrink)

Abstract: The incorporation of Artificial Intelligence (AI) into educational technology has dramatically transformed learning through Intelligent Tutoring Systems (ITS). These systems utilize AI to offer personalized, adaptive instruction tailored to each student's needs, thereby improving learning outcomes and engagement. This paper examines the development and impact of ITS, focusing on AI technologies such as machine learning, natural language processing, and adaptive algorithms that drive their functionality. Through various case studies and applications, it illustrates how ITS have revolutionized traditional educational methods (...) and addresses the challenges and limitations, including data privacy and system biases. The discussion also covers future directions in ITS development, highlighting emerging trends and potential advancements. The goal is to provide a thorough understanding of how ITS use AI to enhance educational experiences and suggest avenues for future research in this evolving field. (shrink)

Abstract: The integration of Artificial Intelligence (AI) into medical imaging represents a transformative shift in healthcare, offering significant improvements in diagnostic accuracy, efficiency, and patient outcomes. This paper explores the application of AI technologies in the analysis of medical images, focusing on techniques such as convolutional neural networks (CNNs) and deep learning models. We discuss how these technologies are applied to various imaging modalities, including X-rays, MRIs, and CT scans, to enhance disease detection, image segmentation, and diagnostic support. Additionally, the (...) paper addresses the challenges faced in AI-driven medical imaging, including data quality, model interpretability, and ethical considerations. By examining recent advancements and real-world case studies, this paper provides insights into the current state of AI in medical imaging and its potential future directions. The findings highlight the ongoing evolution of AI technologies and their crucial role in advancing medical diagnostics and treatment strategies. (shrink)

Abstract: Context: grapevine leaves are an important agricultural product that is used in many Middle Eastern dishes. The species from which the grapevine leaf originates can differ in terms of both taste and price. Method: In this study, we build a deep learning model to tackle the problem of grape leaf classification. 500 images were used (100 for each species) that were then increased to 10,000 using data augmentation methods. Convolutional Neural Network (CNN) algorithms were applied to build this model (...) specifically using the pre-trained model on top of the VGG16 architecture. Then, dense layers were added to classify the output of the Convolutional layers and classify outputs to the five classes (species) the leaf belonged to. Results: It was found that feature extraction without fine-tuning the convolutional layers yielded poor results, about 86% accuracy, while training the whole network along with some data preprocessing gave the best results, about 99.45% accuracy on the testing dataset. Conclusions: The proposed CNN model is an effective one for the problem of classification of grape leaf species. (shrink)

Abstract: The integration of Artificial Intelligence (AI) into Human Resource Management (HRM) is fundamentally transforming how organizations approach recruitment, performance management, and employee engagement. This paper explores the multifaceted impact of AI on HR practices, highlighting its role in enhancing efficiency, reducing bias, and driving strategic decision-making. Through an in-depth analysis of AI-driven recruitment tools, performance management systems, and personalized employee engagement strategies, this study examines both the opportunities and challenges associated with AI in HRM. Ethical considerations, including data privacy, (...) algorithmic bias, and the potential displacement of human jobs, are critically discussed to provide a balanced perspective on the adoption of AI technologies in the HR domain. By presenting case studies of organizations successfully leveraging AI, this paper offers insights into the future trajectory of HRM in an increasingly AI-driven world. Ultimately, the findings underscore the need for HR professionals to adapt and innovate in response to the growing influence of AI, ensuring a harmonious balance between technological advancement and the human element in the workplace. (shrink)

Abstract: Early detection of breast cancer plays a pivotal role in improving patient prognosis and reducing mortality rates. Recent technological advancements have significantly enhanced the accuracy and effectiveness of breast cancer screening methods. This paper explores the latest innovations in early detection, including the evolution of digital mammography, the impact of 3D mammography (tomosynthesis), and the use of advanced imaging techniques such as molecular imaging and MRI. Furthermore, the integration of artificial intelligence (AI) in diagnostic tools is discussed, highlighting how (...) machine learning algorithms are refining imaging analyses and reducing diagnostic errors. Advances in genetic screening, liquid biopsies, and biomarkers are also examined, showcasing their potential in identifying high-risk individuals and enabling personalized treatment plans. This paper provides insights into the future of breast cancer detection, outlining both the opportunities and challenges that lie ahead in adopting these innovative technologies to improve patient outcomes. (shrink)

Abstract: Artificial Intelligence (AI) is transforming the agricultural sector, enhancing both productivity and sustainability. This paper delves into the impact of AI technologies on agriculture, emphasizing their application in precision farming, predictive analytics, and automation. AI-driven tools facilitate more efficient crop and resource management, leading to higher yields and a reduced environmental footprint. The paper explores key AI technologies, such as machine learning algorithms for crop monitoring, robotics for automated planting and harvesting, and data analytics for optimizing resource use. Additionally, (...) it discusses challenges like data privacy, barriers to technology adoption, and the ethical implications of AI in farming. Integrating AI into agricultural practices holds the promise of greater efficiency and sustainability, paving the way for future innovations. (shrink)

Abstract: Artificial Intelligence (AI) is rapidly becoming a cornerstone of modern healthcare, offering unprecedented capabilities in diagnostics, treatment planning, patient care, and healthcare management. This paper explores the transformative impact of AI on the healthcare sector, examining how it enhances patient outcomes, improves the efficiency of medical practices, and introduces new ethical and operational challenges. By analyzing current applications such as AI-driven diagnostic tools, personalized medicine, and hospital management systems, this paper highlights the significant advancements AI has brought to the (...) field. However, the integration of AI also raises concerns related to data privacy, algorithmic bias, and the displacement of healthcare professionals. This study provides a comprehensive overview of these developments, offering insights into the future potential of AI in revolutionizing healthcare while addressing the ethical considerations that must accompany its adoption. The findings suggest that while AI holds the promise of transforming healthcare delivery, careful consideration of its implications is essential to ensure equitable and effective implementation. (shrink)

Much ado – and increased complexity – is generally the case when it comes to checking formally the (intended) meaning of programs, as formal semantics for programs are typically extrinsic to both them and the formal grammars that generate the programming languages in which they are written. The van Wijngaarden grammars, on the contrary, have an intrinsic semantics in the sense that their rules contain or express the (intended) meaning of the terminal strings generated by them. This intrinsicness allows for (...) preservation of meaning both in syntactic change and context-sensitive conditions. In order better to show the intrinsicness of the semantics of the van Wijngaarden grammars I contrast them with the attribute grammars, a frequent choice when the semantic analysis of programming languages is called for (e.g., in compilation). (shrink)

Abstract: Fish are important for both nutritional and economic reasons. They are a good source of protein, vitamins, and minerals and play a significant role in human diets, especially in coastal and island communities. In addition, fishing and fish farming are major industries that provide employment and income for millions of people worldwide. Moreover, fish play a critical role in marine ecosystems, serving as prey for larger predators and helping to maintain the balance of aquatic food chains. Overall, fish play (...) a vital role in supporting human well-being and the health of our planet. . Fishes have a lots of types such that: Red Mullet, Sea Bass, Striped Red Mullet and Shrimp. Each of them has its own shape and characteristics that differ from other types. We proposed a system that recognize nine types of fishes using deep learning. We trained the model with a dataset that contain 9000 images that were slit into 6300 images for training, 1350 for validation and 1350 for testing. The proposed model achieved accuracy (99.68%), precision (99.69%), recall (99.68%), and f1-score (99.68%). This indicates that our proposed model can effectively predicate and classify different types of fish with very high accuracy. (shrink)