If this can be seen as a long way from the beginning of the ancient history, where humans have envisioned different new things and then invented them to make their life’s working smoother and easier, then it can be found that they have attributed their discoveries to various aspects and names of Gods and tried to signify their belief in the form of portraying the God’s powers through the nature of their discovery. Rather, in much modern times, when humans have (...) developed the natural insights and prorated those in the form of a much more logically biased discovery, then, the atheists have started to dominate the scientific arena for their foreplay in the work of their discoveries. However, as time passes by, five special types of research communities have gathered to allocate their discovered data’s in the respective norms as such, i) Atheists to whom, there is in essence no God and nothing in this universe can be attributed to them. ii) Believer of only one God as a whole, where the principle of religious biasing is effluent and supreme in their regards of the physical laws associated with the life and stimuli, iii) Believer of multiple God’s as a whole which poses no such scientific evaluation but can be thought of as a conjugate mechanism for the creator of the universe, iv) A divine supreme, where there is exactly no God, but the whole universe is a manifestation of the nature, where nature indirectly focuses the presence of God’s or the supreme creator that maintains symmetry and working of the universe, v) The not-so-sure about mechanism of the God’s presence, where the researcher sometimes tends to belief in God when something magical happens and attribute all to their divine grace, or in times of failures where they accused them for not being philosophically intrinsic as to their own needs, and in times with the believe that, ‘we won’t believe what we can’t see!’, either in the middle of the good or bad moments or in general in a casual and carefree mode. But, this should be kept in mind, that, the Eastern mysticism has always signifies God’s action in terms of every scientific actions with a proper mythological essence which when accompanied by knowledge produces something so scientific in the ancient of the days, that modern day researchers also have had no options left than to attribute affirmation to the fantastic achievements in the ‘so’ ancient times discovery with a strict modern effluence. Therefore, to model this paper, I have gathered data’s and ideas from researchers of various fields mostly (PhDs) in their respective discipline, so as to emulate their minds in the norms of God and its associated beliefs. (shrink)

This article describes various ways actors in Kashmiri carpet weaving practice deploy a range of artifacts, from symbolic, to material, to hybrid, in order to achieve diverse cognitive accomplishments in their particular task domains: information representation, inter and intra-domain communication, distribution of cognitive labor across people and time, coordination of team activities, and carrying of cultural heritage. In this repertoire, some artifacts position themselves as naïve tools in the actors’ environment to the point of being ignored; however, their usage-in-context (...) unfolds their cognitive involvement in the tasks. These usages-in-context are shown through artifact analysis of their routine, improvised, and opportunistic uses, where cognitive artifacts like talim—the central artifact of this practice—are shown to play not only multifunctional roles beyond representation, but are also complemented by trade-specific skills bearing strong cognitive implications in a task. (shrink)

Abstract: Lotteries have long been a source of fascination and intrigue, offering the tantalizing prospect of unexpected fortunes. In this research paper, we delve into the world of lottery predictions, employing cutting-edge AI techniques to unlock the secrets of lottery outcomes. Our dataset, obtained from Kaggle, comprises historical lottery draws, and our goal is to develop predictive models that can anticipate future winning numbers. This study explores the use of deep learning and time series analysis to achieve this (...) elusive feat. Through rigorous experimentation and data-driven approaches, we seek to determine the viability of AI in the realm of lottery predictions. Our findings reveal both the promise and limitations of AI in this context, shedding light on the complexities of lottery data and the potential need for quantum computing as a last resort. (shrink)

The health of plants plays a crucial role in ensuring agricultural productivity and food security. Early detection of plant diseases can significantly reduce crop losses, leading to improved yields. This paper presents a novel approach for plant disease recognition using deep learning techniques. The proposed system automates the process of disease detection by analyzing leaf images, which are widely recognized as reliable indicators of plant health. By leveraging convolutional neural networks (CNNs), the model identifies various plant diseases with high (...) accuracy. The experimental setup includes a dataset consisting of healthy and diseased leaf images of different plant species. The dataset is preprocessed to remove noise and augmented to address the issue of class imbalance. The CNN model is then trained, validated, and tested on this dataset. (shrink)

Near the end of December 2019, the globe was hit with a major crisis, which is nothing but the coronavirusbased pandemic. The authorities at the train station should also keep in mind the need to limit the spread of the covid virus in the event of a global pandemic. When it comes to controlling the COVID-19 epidemic, public transportation facilities like train stations play a pivotal role because of the proximity of so many people who may be exposed to the (...) virus. Using common place CCTV cameras and deep learning with simple online and real-time (DeepSORT) methods, this study develops social distance monitoring using a YOLOv4 identification of a Surveillance Object Model. Based on experiments conducted with a minicomputer equipped with an Intel 11th Gen Intel(R) Core(TM) i3-1115G4 at 3.00GHz, 2995 Mhz, two Core(s), four Logical processor, four gigabytes of random-access memory (RAM), this paper makes use of CCTV surveillance, which was put into practice at the Guindy railway station, Chennai, Tamilnadu in India in order to detect the violation of social distancing. (shrink)

Using hokku poet Basho’s aesthetics of wandering, as defined by Thomas Heyd, I argue that, by detailing the excruciating pointlessness of work undertaken according to commands that take little or no account of their feasibility, Richard Flanagan’s novel, The Narrow Road to the Deep North (which takes its title from Basho's work) transforms the features of this aesthetics into the lived experience of prisoners of war on the ‘line’. In doing so, Flanagan transfers Basho’s aesthetics into a represented actuality (...) through the privileging of subjectivity over identity and the dissolution of the body on the line. The three prongs to Basho’s aesthetics are found in Flanagan’s novel. In this, Flanagan is identifying the complexity of meanings evident in the terminology of such aesthetics, rendering what appears positive in the context of Basho’s poetry negative in its practical application as this is articulated through the prisoners’ wartime experiences. Rather than being formative, Flanagan’s novel suggests wartime experience has a complexly ‘opposite’ effect. This is apparent in the complications of identity represented in post-war terms as a disunity (rather than a coherent unity), as articulated through the use of spatial metaphors that reverse the formative intensities of subjectivity and body through symbolic acts of dispersal and dissolution. (shrink)

The rise in cyber threats targeting financial institutions has led to the adoption of artificial intelligence (AI) and deep learning models to mitigate risks and detect fraud in real-time. This paper explores AI-powered cybersecurity frameworks that enhance fraud detection, risk analytics, and compliance automation in financial transactions.

Abstract: People all over the world have been drinking tea for thousands of centuries, and for good reason. Many types of teas can help you stay healthy by boosting your immune system, reducing inflammation, and even preventing cancer and heart disease. There is sufficient material to show that regularly consuming tea can improve your health over the long term. A deep learning model that categorizes tea disorders has been completed. When focusing on the tea, we must also focus on (...) and take into account the diseases that harm it. The aim of this paper is to diagnose tea diseases based on a dataset containing 8000 picture and 8 classes .The model gave the final accuracy test with an F1-score of 99.41% in an estimated time 3 second, as with these results it proved its effectiveness and good ability in the classification test. (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)

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)

Richard Watson maintains that deep ecology suffers from an internal contradiction and should therefore be rejected. Watson contends that deep ecology claims to be non-anthropocentric while at the same time is committed to setting humans apart from nature, which is inherently anthropocentric. I argue that Watson’s objection arises out of a fundamental misunderstanding of how deep ecologist’s conceive of the ‘Self.’ Drawing on resources from Buddhism, I offer an understanding of the ‘Self’ that is fully consistent (...) with deep ecology, and does not lead to the anthropocentric contradiction that Watson identifies. The paper will proceed as follows: First, I articulate Watson’s objection, and briefly discuss the traditional deep ecology position. Next, I turn to a discussion of the ‘Self’ and show that there are conceptions of human nature that are not separate from ‘Nature.’ It will thus be shown that deep ecology is not inconsistent and need not be rejected. (shrink)

Time and modality show remarkable similarities. Each of the most discussed theories in philosophy of time finds an analogous counterpart in modal metaphysics, suggesting that the parallel between the two notions is metaphysically deep. This chapter offers a brief overview of their analogies. Section 1 addresses the analogy between presentism and actualism. Section 2 explores the analogy between non-presentist theories and possibilism. Section 3 discusses the analogy between temporal and modal persistence.

A pineapple is a tropical plant with eatable leafy foods most monetarily critical plant in the family Bromeliaceous. The pineapple is native to South America, where it has been developed for a long time. The acquaintance of the pineapple with Europe in the seventeenth century made it a critical social symbol of extravagance. Since the 1820s, pineapple has been industrially filled in nurseries and numerous tropical manors. Further, it is the third most significant tropical natural product in world creation. (...) In the twentieth century, Hawaii was a prevailing maker of pineapples, particularly for the US, be that as it may, by 2016, Costa Rica, Brazil, and the Philippines represented almost 33% of the world's creation of pineapples. In this paper, machine learning based approach is presented for identifying type pineapple with a dataset that contains 1,311images use 946 images for training, 197 images for validation and 168 images for testing. A deep learning technique that extensively applied to image recognition was used. use 70% from image for training and 30% from image for validation. Our trained model achieved an accuracy of 100% on a heldout test set. (shrink)

Papaya is a tropical fruit with a green cover, yellow pulp, and a taste between mango and cantaloupe, having commercial importance because of its high nutritive and medicinal value. The process of sorting papaya fruit based on maturely is one of the processes that greatly determine the mature of papaya fruit that will be sold to consumers. The manual grading of papaya fruit based on human visual perception is time-consuming and destructive. The objective of this paper is to the (...) status classification of papaya fruits if it's mature or partially matured or unmatured. A deep learning technique that was extensively applied to image recognition was used. The trained model achieved an accuracy of 100% on a held-out test set, demonstrating the feasibility of this approach. Classification model of VGG16 achieved a 100% accuracy and 112 seconds of training time. (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)

In Deep Medicine, Eric Topol argues that the development of artificial intelligence (AI) for healthcare will lead to a dramatic shift in the culture and practice of medicine. Topol claims that, rather than replacing physicians, AI could function alongside of them in order to allow them to devote more of their time to face-to-face patient care. Unfortunately, these high hopes for AI-enhanced medicine fail to appreciate a number of factors that, we believe, suggest a radically different picture for (...) the future of healthcare. Far from facilitating a return to “the golden age of doctoring”, the role of economic and institutional considerations in determining how medical AI will be used mean that it is likely to further erode therapeutic relationships and threaten professional and patient satisfaction. (shrink)

Seabed mining, the extraction of minerals from the deep-sea floor, is hotly contested. Policymakers have agreed on the need for a regulatory framework. However, traditional ethical theories and principles are not well equipped for the ethics of the alien deep sea. Engaging with the sea means engaging with something abstract that we can only access indirectly. We argue that this invisibility and alienness of the sea and its inhabitants can give new insights into how ethics are done. Rather (...) than getting even more grip on what is already directly known, considering what is outside our usual moral view may be just as valuable. To do so, art can help us think with these unknown and invisible parts of the ecosystem. For that, we describe three cases. The scientist and artist Eugen Ransonnet engaged with the underwater world in the late 19th century. The Victorian flower painter Marianne North's unruly approach greatly influenced the botanic discourse until today. And finally, Christina Stadlbauer's Institute for the Relocation of Biodiversity and her work with the mollusk Pinna Nobilis in Mar Menor, Spain. We describe the Pinna Nobilis video project and the ethical and political opportunities it opens. We propose that engaging with the arts can pave the way towards including the unknown in ethical reflection and, at the same time, how it can rephrase questions that can help us reconsider what is outside of our moral view. (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)

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)

Time as the key to a theory of everything became recently a renewed topic in scientific literature. Social constructivism applied to physics abandons the inevitable essentials of nature. It adopts uncertainty in the scope of the existential activity of scientific research. We have enlightened the deep role of social constructivism of the predetermined Newtonian time and space notions in natural sciences. Despite its incompatibility with determinism governing the Newtonian mechanics, randomness and entropy are inevitable when negative localized (...) energy is transformed into spatially dissipative heat. In sharp contrast to the Newtonian notion, social constructivism makes room for the temporal twin of cyclic conservation and pseudo-linearly structural evolution both reconciling mechanical order and thermodynamic chaos in Leibnizian space-time. In the broad scope of natural sciences this triad nature of time produces a multiple reality and gives appropriate answers on virtual physical paradoxes. (shrink)

Abstract: Vegetables are an essential component of a healthy diet and play a critical role in promoting overall health and well- being. Vegetables are rich in important vitamins and minerals, including vitamin C, folate, potassium, and iron. They also provide fiber, which helps maintain digestive health and prevent chronic diseases. We are proposing a deep learning model for the classification of vegetables. A dataset was collected from Kaggle depository for Vegetable with 15000 images for 15 different classes. The data (...) was preprocessed, normalized and split into three sets (train, valid, test). The proposed model was trained and validated using the train and valid sets and accuracy of both training and validation was very high. For the evaluation of the proposed model we utilized these metrics: accuracy, F1-score, precision, Recall and time required for testing. We then tested the proposed model using the test set. The result of the testing was accuracy (99.95%), F1-score (99.95%), precision (99.95%), Recall (99.95%) and time required for testing the test set was 1.38 seconds. (shrink)

Deep Brain Stimulation is currently being investigated as an experimental treatment for patients suffering from treatment-refractory AN, with an increasing number of case reports and small-scale trials published. Although still at an exploratory and experimental stage, initial results have been promising. Despite the risks associated with an invasive neurosurgical procedure and the long-term implantation of a foreign body, DBS has a number of advantageous features for patients with SE-AN. Stimulation can be fine-tuned to the specific needs of the particular (...) patient, is relatively reversible, and the technique also allows for the crucial issue of investigating and comparing the effects of different neural targets. However, at a time when DBS is emerging as a promising investigational treatment modality for AN, lesioning procedures in psychiatry are having a renaissance. Of concern it has been argued that the two kinds of interventions should instead be understood as rivaling, yet “mutually enriching paradigms” despite the fact that lesioning the brain is irreversible and there is no evidence base for an effective target in AN. We argue that lesioning procedures in AN are unethical at this stage of knowledge and seriously problematic for this patient group, for whom self-control is particularly central to wellbeing. They pose a greater risk of major harms that cannot justify ethical equipoise, despite the apparent superiority in reduced short term surgical harms and lower cost. (shrink)

In this paper, I discuss several temporal aspects of paleontology from a philosophical perspective. I begin by presenting the general problem of “taming” deep time to make it comprehensible at a human scale, starting with the traditional geologic time scale: an event-based, relative time scale consisting of a hierarchy of chronological units. Not only does the relative timescale provide a basis for reconstructing many of the general features of the history of life, but it is also (...) consonant with the cognitive processes humans use to think about time. Absolute dating of rocks, fossils, and evolutionary events (such as branching events on the tree of life) can be accomplished through the use of radiometric dating, chronological signals extractable from fossil growth patterns, and the “molecular clock.” Sometimes these different methods of absolute dating, which start from largely independent assumptions and evidentiary bases, converge in their temporal estimates, resulting in a consilience of inductions. At other times they fail to agree, either because fossils and molecules are giving temporal information about different aspects of nature and should not be expected to agree, or because of flawed assumptions that give rise to an inaccurate estimate. I argue that in general, despite the fact that it can be difficult to integrate disparate kinds of evidence, the principle of total evidence should be applied to the dating of evolutionary events. As a historical science, paleontology studies past events we cannot observe directly. This raises questions of epistemic access, meaning that due to the fragmentary nature of the fossil record we may find ourselves without access to the relevant traces to adjudicate between rival hypotheses about the past. The problems and prospects of epistemic access are explored through a case study of the reconstruction of the colors of dinosaurs. The paper closes with a reflection on the Darwin- Lyell metaphor of the fossil record as a highly fragmentary history book, and a call for a reconsideration of the book metaphor in favor of a systems view of the geologic and fossil records. (shrink)

Traffic sign detection and recognition (TSDR) is a critical aspect of autonomous driving and intelligent transportation systems. Traditional methods of traffic sign detection rely on handcrafted features and classical machine learning algorithms, which often struggle to achieve high accuracy in complex real-world environments. In contrast, deep learning techniques, particularly Convolutional Neural Networks (CNNs), have shown remarkable performance in both detecting and recognizing traffic signs in diverse conditions. This paper reviews the application of deep learning methods for TSDR, focusing (...) on recent advancements in the use of CNNs, Region-based CNNs (R-CNNs), and YOLO (You Only Look Once) for real-time detection and recognition of traffic signs. We discuss the challenges, including the variability of traffic signs, environmental factors, and computational requirements, and present solutions proposed in the literature. The paper also highlights future directions for improving the robustness, accuracy, and speed of traffic sign detection and recognition systems, with an emphasis on real-time applications. (shrink)

Abstract: Sarcasm is commonly used in news and detecting sarcasm in headline news is challenging for humans and thus for computers. The media regularly seem to engage sarcasm in their news headline to get the attention of people. However, people find it tough to detect the sarcasm in the headline news, hence receiving a mistaken idea about that specific news and additionally spreading it to their friends, colleagues, etc. Consequently, an intelligent system that is able to distinguish between can sarcasm (...) none sarcasm automatically is very important. The aim of the study is to build a sarcasm model that detect headline news using machine and deep learning and attempt to understand how a computer learns the patterns of sarcasm. The dataset used in this study was collected from Kaggle depository. We examined 21 algorithms of machine learning and one deep learning algorithm for detecting sarcasm in headline news. The evaluation metric used in this study are Accuracy, F1-measure, Recall, Precision, and Time needed for training and evaluation. The deep learning model achieved accuracy (95.27%), recall (96.62%), precision (94.15%), F1-score (95.37%) and time needed to train the mode (400 seconds), with loss of around 0.3398. However, the algorithm of machine learning that achieved the highest F1-Score is Passive Aggressive Classifier. It was the top classier for sarcasm detection among the machine learning algorithms with accuracy (95.50%), recall (96.09 %), precision (94.30%), F1-score (95.19%) and time needed to train the mode (0.31 seconds). (shrink)

Traffic sign detection and recognition (TSDR) is a critical aspect of autonomous driving and intelligent transportation systems. Traditional methods of traffic sign detection rely on handcrafted features and classical machine learning algorithms, which often struggle to achieve high accuracy in complex real-world environments. In contrast, deep learning techniques, particularly Convolutional Neural Networks (CNNs), have shown remarkable performance in both detecting and recognizing traffic signs in diverse conditions. This paper reviews the application of deep learning methods for TSDR, focusing (...) on recent advancements in the use of CNNs, Region-based CNNs (R-CNNs), and YOLO (You Only Look Once) for real-time detection and recognition of traffic signs. We discuss the challenges, including the variability of traffic signs, environmental factors, and computational requirements, and present solutions proposed in the literature. The paper also highlights future directions for improving the robustness, accuracy, and speed of traffic sign detection and recognition systems, with an emphasis on real-time applications. (shrink)

Indeterminacy in its various forms has been the focus of a great deal of philosophical attention in recent years. Much of this discussion has focused on the status of vague predicates such as ‘tall’, ‘bald’, and ‘heap’. It is determinately the case that a seven-foot person is tall and that a five-foot person is not tall. However, it seems difficult to pick out any determinate height at which someone becomes tall. How best to account for this phenomenon is, of course, (...) a controversial matter. For example, some (such as Sorensen (2001) and Williamson (2002)) maintain that there is a precise height at which someone becomes tall and such apparent cases of indeterminacy merely reflects our ignorance of this fact. Others maintain that there is some genuine – and not merely epistemic – indeterminacy present is such cases and offer various accounts of how best to account for it. Supervaluationists (such as Keefe (2008)), for example, claim that the indeterminacy with respect to vague terms lies in their not having a single definite extension. Rather, each term is associated with a range of possible precise extensions or precisifications such that it is semantically unsettled which is the correct extension. One precisification of ‘tall’ might allow that anyone over five feet ten inches is tall, whereas another would only allow those over six foot to qualify; but no precisification will take someone who is five foot to be tall, and someone who is seven foot will count as tall on all precisifications. Thus – while someone who is seven foot will be determinately tall and someone who is five foot determinately not so – it will be indeterminate whether someone who stands at five foot eleven inches is tall. -/- Yet, it is important to stress that putative cases of indeterminacy are not limited to vague predicates of this kind. Philosophers have invoked indeterminacy in discussions of topics as diverse as moral responsibility (Bernstein (forthcoming)), identity over time (Williams (2014)), and the status of the future (Barnes and Cameron (2009)). In this paper, we focus on two areas where discussion of various kinds of indeterminacy has been commonplace: physics and fiction. We propose a new model for understanding indeterminacy across these domains and argue that it has some notable advantages when compared to earlier accounts. Treating physics and fiction cases univocally also indicates an interesting connection between indeterminacy in these two areas. (shrink)

Bird species identification plays a vital role in biodiversity conservation and ecological studies, offering insights into habitat health and species distribution. Traditional methods for identifying bird species are time-intensive and prone to human error, necessitating automated solutions. This project, Bird Species Identification Using Deep Learning, proposes an advanced system leveraging the power of deep learning to accurately identify bird species from images. 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)

Bird species identification plays a vital role in biodiversity conservation and ecological studies, offering insights into habitat health and species distribution. Traditional methods for identifying bird species are time-intensive and prone to human error, necessitating automated solutions. This project, Bird Species Identification Using Deep Learning, proposes an advanced system leveraging the power of deep learning to accurately identify bird species from images. 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. (shrink)

Abstract: Dates are the fruit of date palm trees, and it is one of the fruits famous for its high nutritional value. It is a summer fruit spread in the Arab world. In the past, the Arabs relied on it in their daily lives. Dates take an oval shape and vary in size from 20 to 60 mm in length and 8 to 30 mm in diameter. The ripe fruit consists of a hard core surrounded by a papery cover called (...) the tartar that separates the core from the fleshy part that is eaten. Historians disagreed about the place of its origin, so some historians expressed their belief that it had originated around the Arabian Gulf, and some of them say that the oldest known information about palm trees was in Babylon 4 thousand years ago BC. It is known about the ancient Egyptians using dates in wine. Dates contain a high nutritional value and are considered a basic food for man since ancient times. The fruits of dates are considered the highest fruits in containing sugars. These components vary according to the nature of the fruit, whether it is wet, semi-dry or dry, as well as according to the environmental conditions surrounding the trees. The components of the fruits also differ in different varieties and increase the percentage of sugars in dates is 70-78% of the fruit’s components. These sugars are characterized by their rapid absorption and transfer into the blood directly, digestion and burning. 10 dates (about 100 grams) per day provide a person with all his daily needs of magnesium, manganese, copper, sulfur and half of his needs of calcium and potassium. In this paper we presented a system that recognize the nine types of dates based on deep learning using python on Colab, and classifying using a dataset contain 1350 images. Our trained model achieved an accuracy of 99.44% on a proven test set. (shrink)

The estimated calorie value is then displayed to the user in real-time. This project leverages key technologies, including image recognition, deep learning, and nutrition analysis. It is designed to be integrated into mobile applications or web platforms, allowing users to track their daily caloric intake efficiently. The system's accuracy is continuously improved through training on a diverse dataset, ensuring reliable calorie estimation across different food items. This tool has the potential to revolutionize personal health management by promoting healthier (...) eating habits. (shrink)

Current and recent philosophy of history contemplates a deep change in fundamental notions of the presence of the past. This is called breaking up time. The chief value for this change is enhancing the moral reach of historical research and writing. However, the materialist view of reality that most historians hold cannot support this approach. The origin of the notion in the thought of Walter Benjamin is suggested. I propose a neo-idealist approach called perennialism, centered on recurrent moral (...) dilemmas and choices. This suggests a view of the relations of moral thought and ontology placed in the diachronic context that historians study. (shrink)

Computational complexity is a discipline of computer science and mathematics which classifies computational problems depending on their inherent difficulty, i.e. categorizes algorithms according to their performance, and relates these classes to each other. P problems are a class of computational problems that can be solved in polynomial time using a deterministic Turing machine while solutions to NP problems can be verified in polynomial time, but we still do not know whether they can be solved in polynomial time (...) as well. A solution for the so-called NP-complete problems will also be a solution for any other such problems. Its artificial-intelligence analogue is the class of AI-complete problems, for which a complete mathematical formalization still does not exist. In this chapter we will focus on analysing computational classes to better understand possible formalizations of AI-complete problems, and to see whether a universal algorithm, such as a Turing test, could exist for all AI-complete problems. In order to better observe how modern computer science tries to deal with computational complexity issues, we present several different deep-learning strategies involving optimization methods to see that the inability to exactly solve a problem from a higher order computational class does not mean there is not a satisfactory solution using state-of-the-art machine-learning techniques. Such methods are compared to philosophical issues and psychological research regarding human abilities of solving analogous NP-complete problems, to fortify the claim that we do not need to have an exact and correct way of solving AI-complete problems to nevertheless possibly achieve the notion of strong AI. (shrink)

By leveraging convolutional neural networks (CNNs), the model identifies various plant diseases with high accuracy. The experimental setup includes a dataset consisting of healthy and diseased leaf images of different plant species. The dataset is preprocessed to remove noise and augmented to address the issue of class imbalance. The CNN model is then trained, validated, and tested on this dataset. The results indicate that the deep learning model achieves a classification accuracy of over 95% for most plant diseases. Additionally, (...) the system is designed to provide real-time feedback to farmers, helping them take immediate corrective action. This automated approach eliminates the need for expert human intervention and can be deployed on mobile devices for ease of use in rural areas. The potential future applications of this system include integration with precision agriculture techniques and expansion to a wider range of crops. (shrink)

Readers familiar with the workhorse of cosmology, the hot big bang model, may think that cosmology raises little of interest about time. As cosmological models are just relativistic spacetimes, time is understood just as it is in relativity theory, and all cosmology adds is a few bells and whistles such as inflation and the big bang and no more. The aim of this chapter is to show that this opinion is not completely right...and may well be dead wrong. (...) In our survey, we show how the hot big bang model invites deep questions about the nature of time, how inflationary cosmology has led to interesting new perspectives on time, and how cosmological speculation continues to entertain dramatically different models of time altogether. Together these issues indicate that the philosopher interested in the nature of time would do well to know a little about modern cosmology. (shrink)

Identification and mapping of natural vegetation are major issues for biodiversity management and conservation. Remote sensing monitoring has two important sub fields viz, classification and change detection. Change detection from remotely sensed images is a process that utilizes the images acquired over the same geographical area at different times to identify the changes that may have occurred between the considered acquisition dates. Remotely sensed data with very high spatial resolution are currently used to study vegetation, but most satellite sensors are (...) limited to four spectral bands, which is insufficient to identify some natural vegetation formations. To address this issue, this work intends to use multispectral and high spatial resolution data (Sentinel-2) to do the task of natural vegetation analysis using a popular CNN architecture. This proposed project concept is completely based on Deep Learning architecture using Remote Sensing technology for vegetation of lands in Hassan city of Karnataka India. The results are discussed within the scope of recent studies involving machine learning and Sentinel-2 data and key knowledge gaps identified. The changes of various vegetations are compared and their changes difference is displayed. (shrink)

: One of the most active study fields in natural language processing, web mining, and text mining is sentiment analysis. Big data is an important research component in education that is used to advance the value of education by watching students' performance and understanding their learning habits. Real-time student feedback will enable teachers and students to understand teaching and learning challenges in the most user-friendly manner for students. By linking learning analytics to grounded theory, the proposed Deep Convolutional (...) Neural Network (DCNN) analyses students' sentiments and emotions through feedback using a Lexicon-based emotional analysis approach. The sentiment analysis approach is a computer procedure that identifies and classifies subjective information from the source material as good, negative, or neutral. (shrink)

Pneumonia is a serious respiratory infection that poses significant health risks, particularly if not diagnosed and treated promptly. Traditional methods of pneumonia diagnosis rely on the manual interpretation of chest X-ray images by radiologists, a process that can be time-consuming, subjective, and error-prone, especially in regions with limited access to experienced medical professionals. To address these challenges, this study explores the development of an automated deep learning-based system for pneumonia detection using chest X-ray images. The results demonstrate that (...) the deep learning model can achieve high levels of accuracy, sensitivity, and specificity, making it a valuable tool for assisting radiologists in diagnosing pneumonia more quickly and reliably. Moreover, the system's scalability and ease of deployment make it particularly beneficial in resource-limited settings, where timely and accurate diagnosis is crucial. This research highlights the potential of deep learning to revolutionize medical diagnostics, improving patient outcomes through enhanced diagnostic accuracy and efficiency. (shrink)

Abstract: Tomatoes are among the most popular vegetables in the world due to their frequent use in many dishes, which fall into many varieties in common and traditional foods, and due to their rich ingredients such as vitamins and minerals, so they are frequently used on a daily basis, When we focus our attention on this vegetable, we must also focus and take into consideration the diseases that affect this vegetable, a deep learning model that classifies tomato diseases has (...) been proposed. The aim of this paper is to diagnose tomato diseases based on a dataset containing 11000 picture and 11 classes. The model gave the final accuracy test with Accuracy (99.87%), F1-score (99.87%), Recall (99.87%), and Precision (99.88%) in an estimated time 1.36 second, as with these results it proved its effectiveness and good ability in the classification of the testing data. (shrink)

La psychologie a toujours traité le comportement et l’expérience comme étant enchâssés dans un flux temporel unidimensionnel, « le courant du comportement » dans lequel les événements et les actions occupent des intervalles de temps qui ne se chevauchent pas. Pourtant, une analyse phénoménologique révèle que la structure de nos vies est bien plus riche et intéressante. En utilisant la notion de « quasidécomposabilité » de Herbert Simon, je décris cette structure comme un assemblage d’épisodes quasi-indépendants se réalisant de façon (...) concurrente, et de nature asynchrone. Il s’agit d’une « structure profonde» des vies contrairement à la conception courante qui conçoit les vies comme « plates ». (shrink)

Are the distinctions between past, present and future, and the apparent ‘passage’ of time, features of the world in itself, or manifestations of the human perspective? Questions of this kind have been at the heart of metaphysics of time since antiquity. The latter view has much in common with pragmatism, though few in these debates are aware of that connection, and few of the view’s proponents think of themselves as pragmatists. For their part, pragmatists are often unaware of (...) this congenial application of their methodology; some associate pragmatism with the other side of the old debate in the metaphysics of time. In my view, this link between time and pragmatism only scratches the surface of the deep two-way dependencies between these two topics. The human temporal perspective turns out to be deeply implicated not merely in our temporal notions themselves, but in many other conceptual categories – arguably, in fact, in all of them, and in the nature of language and thought. In this way, reflection on our own temporal character vindicates James’ famous slogan for global pragmatism: ‘The trail of the human serpent is thus over everything.’. (shrink)

By leveraging convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, this research presents an intelligent malware detection framework capable of identifying both known and zero-day threats. The methodology involves feature extraction from static, dynamic, and hybrid malware datasets, followed by training DL models to classify malicious and benign software with high precision. A robust experimental setup evaluates the framework using benchmark malware datasets, yielding a 96% detection accuracy and demonstrating resilience against adversarial attacks. Real-time analysis capabilities further (...) improve response times, reducing the risk of potential damage. The study also incorporates visualization tools to provide interpretable insights into model decisions, enhancing transparency for cybersecurity practitioners. Concluding with a discussion on the challenges and future prospects, this research paves the way for scalable, AI-driven solutions to combat evolving cyber threats. (shrink)

Chronic Kidney Disease (CKD) is a significant global health issue, often leading to kidney failure and requiring costly medical treatments such as dialysis or transplants. Early detection of CKD is essential for timely intervention and improved patient outcomes. This project aims to develop a machine learning-based predictive model for diagnosing CKD at an early stage. By utilizing a range of clinical features such as age, blood pressure, blood sugar, and other relevant biomarkers, we employ machine learning algorithms, including Decision Trees, (...) Random Forests, and Support Vector Machines (SVM), to predict the likelihood of a patient developing CKD. The dataset used in this study includes medical records of patients with various kidney conditions, and preprocessing techniques such as normalization and missing data handling are applied to ensure the model’s robustness. The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score to ensure reliable predictions. This approach not only aims to improve diagnostic accuracy but also provides a data-driven solution to assist healthcare professionals in making informed decisions. The outcome of this project can contribute to better management of CKD, ultimately helping to reduce the burden on healthcare systems and improving patient care. (shrink)

This dissertation is a collection of standalone papers about a novel version of the deep self view of moral responsibility. Taken on its own, each chapter deals with a different thesis. But as the title of my dissertation reveals, taken together, the three chapters in it constitute the groundwork for my deep self view of moral responsibility. In Chapter 1, I develop and defend the thesis of responsibility for the deep self. In Chapter 2, I argue for (...) a sufficient condition for responsibility for one’s self that centers on the idea of aspiration. Drawing upon resources form the first two chapters, in Chapter 3, I further develop and defend a thesis of responsibility for what one does and its downstream consequences. -/- Here is a summary of my view. I argue that an agent acts freely and is morally responsible for what she does in the accountability sense only if she has a deep self for which she is responsible. How is one responsible for one’s deep self? To be responsible for the deep self, one must have a history where one was afforded the unimpeded opportunity to develop and exercise the ability to shape one’s own self. Exercise that ability in what way? I suggest that a critical way in which an agent shapes her own self is when she engages with various activities that I call aspirational self-shaping. Nevertheless, an agent needs not exercise the ability to shape her self and thus engage with aspirational self-shaping every time she acts freely. Indeed, being responsible for what she does is consistent with her failing to exercise that ability when she acts freely and responsibly. -/- Standard deep self views in the literature say something much stronger. They contend that an agent acts freely and responsibly for what she does if and only if her actions or omissions issue from, and so express, her deep self. Counterexamples proliferate. By offering a necessary condition for accountability drawing upon resources from responsibility for the deep self, my view escapes counterexamples that standard views face, while retains the core of the deep self view. Indeed, an agent may be blameworthy for her wrongdoing without it issuing from, and so expressing, her deep self. And yet, she must have a deep self for which she is responsible to be blameworthy for her wrongdoing. All of this is ultimately achieved by paying closer attention to the historical dimension of the deep self than other deep self proponents have. (shrink)

A face reveals a lot of information about a person's identity, age, sex, race, and emotional as well as psychological state. Facial expressions are often used in the behavioral interpretation of emotions and play a key role in social interactions. Due to its potential applications such as HCI, behavioral science, automatic facial emotion detection is one of the most intriguing and challenging areas in computer vision. Our Facial Emotion Recognition system performs detection and location of faces in a cluttered scene, (...) facial feature extraction, and facial expression classification. Numerous models were experimented such as decision trees and SVM’s before arriving at a Convolutional Neural Networks (CNN) model. Due to large number of filters, CNNs are efficient for image recognition tasks since they can capture spatial features of the inputs. After observing unbelievable performance with deep learning models, we used deep convolution features to better represent the given image instead of using the traditional handcrafted features. We have proposed and developed a Deep Convolutional Neural Network for classifying human emotions from dynamic facial expressions in real-time. Kaggle’s FER-2013 dataset with seven facial emotions labels as happy, sad, surprise, fear, anger, disgust, and neutral is used to train the model. An overall training accuracy of 92.86% and test accuracy of 64.42% are achieved. Finally, a live video stream connected to a face detector feeds images to the neural network. The network subsequently classifies an arbitrary number of faces per image in real-time simutaneously, wherein appropriate emotions are displayed over the subject’s faces and their probabilities are displayed over a real-time bar graph on a separate window. (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)

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)

This project aims to provide an automated system for accurately estimating the calorie content of food and beverages using advanced deep learning algorithms. With the increasing demand for health-conscious individuals, there is a need for a reliable, efficient, and easy-to-use tool that can help users make informed dietary choices. The project utilizes image processing techniques and deep learning models, such as Convolutional Neural Networks (CNN), to analyze food images and predict the corresponding calorie content. The system works by (...) first capturing an image of the food or beverage, which is then processed and passed through a pre-trained deep learning model. The model is trained on a large dataset containing images of various food items along with their nutritional information. After preprocessing the input image, the model classifies the food and estimates the calorie count by leveraging its learned features. The estimated calorie value is then displayed to the user in real-time. This project leverages key technologies, including image recognition, deep learning, and nutrition analysis. It is designed to be integrated into mobile applications or web platforms, allowing users to track their daily caloric intake efficiently. The system's accuracy is continuously improved through training on a diverse dataset, ensuring reliable calorie estimation across different food items. This tool has the potential to revolutionize personal health management by promoting healthier eating habits. (shrink)

The chapters of the book are seemingly short, but deep explorations on the various fields and possibilities of human being and existence. Such explorations of course reorder and reformulate the timely and essential possibilities of philosophy and philosophizing. These together convey the true weight and stakes of things. For it is indeed so that: „Philosophy is destined to deal with the Deepest and most disturbing questions. It would hardly survive, if they were definitively solved.”.

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)