The primary objective of this research is to enhance the precision and speed of stock handling while minimizing human intervention and error. Our design incorporates state-of-the-art sensors, real-time tracking systems, and autonomous robots programmed with advanced algorithms for object identification, gripping, and movement. We propose a systematic workflow for automating the storage and retrieval process, starting from the identification of the stock to its precise placement and retrieval within the storage facility. The design also addresses potential challenges such as robot (...) mobility, collision avoidance, and space optimization. Performance metrics, including accuracy, time efficiency, and system scalability, are measured using simulation-based experiments in a controlled environment. The results show significant improvements in operational efficiency compared to traditional stock management approaches. This integration paves the way for future advancements in fully automated warehouses, reducing the need for human labor and increasing reliability. Finally, we discuss potential enhancements, including AI-based decision-making algorithms, multi-robot collaboration, and integration with Internet of Things (IoT) for real-time data analysis and continuous system improvement. (shrink)

The classification system was evaluated using benchmark phishing datasets, and the results demonstrated a significant improvement in detection accuracy and reduced false positives. The proposed model outperformed traditional machine learning algorithms, showing promise for real-world deployment in phishing detection systems. We conclude with suggestions for future improvements, such as incorporating more behavioral data and deploying the system in realtime monitoring applications.

Accelerated computing leverages specialized hardware and software techniques to optimize the performance of computationally intensive tasks, offering significant speed-ups in scientific, engineering, and data-driven fields. This paper presents a comprehensive study examining the role of accelerated computing in enhancing processing capabilities and reducing execution times in diverse applications. Using a custom-designed experimental framework, we evaluated different methodologies for parallelization, GPU acceleration, and CPU-GPU coordination. The aim was to assess how various factors, such as data size, computational complexity, and task concurrency, (...) impact processing efficiency. (shrink)

Results indicate substantial improvements in efficiency, accuracy, and personalized care, but also highlight the challenges of data privacy, ethical considerations, and system scalability. By providing a structured analysis, this research contributes insights into optimizing generative AI deployments for both banking and healthcare, ensuring a balance between innovation and risk management. The study concludes with recommendations for future research directions, including advanced model training, ethical guidelines, and enhanced privacy measures. These insights aim to inform practitioners on the benefits of generative AI, (...) ensuring sustainable integration into banking and healthcare ecosystems. (shrink)

he 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)

The continuous growth of healthcare data has made it essential to develop efficient systems that not only alert healthcare providers but also visualize patient data in a comprehensible way. This study introduces a Health Alert System integrated with Report Visualization powered by Data Analytics to improve patient monitoring and alerting mechanisms. By leveraging real-time data from wearable sensors and hospital records, the system generates health alerts based on deviations from normal parameters. The proposed system combines predictive analytics and historical data (...) to flag potential emergencies before they occur. The visual analytics platform provides comprehensive reports to healthcare providers, enabling them to monitor trends, identify risk factors, and make informed decisions. (shrink)

This paper provides a comprehensive workflow for organizations considering a move from legacy database systems to Snowflake, demonstrating how a structured migration process can result in enhanced data accessibility, operational flexibility, and long-term scalability. Future directions for research are also discussed, including integration with advanced analytics and machine learning applications, which could further augment Snowflake’s utility in big data ecosystems.

This paper explores the application of AI-driven HR analytics in shaping workforce agility, focusing on how real-time data collection, analysis, and modeling foster an adaptable workforce. It highlights the role of predictive analytics in forecasting workforce needs, identifying skill gaps, and optimizing talent deployment. Additionally, the paper discusses how AI enhances strategic decision-making by providing precise metrics and insights into employee behavior, productivity, and satisfaction. The integration of AI into HR systems ultimately shifts HR from a traditionally reactive to a (...) highly proactive, data-driven function. (shrink)

The exponential growth of data storage requirements has become a pressing challenge in hybrid cloud environments, necessitating efficient data deduplication methods. This research proposes a novel Smart Deduplication Framework (SDF) designed to identify and eliminate redundant data, thus optimizing storage usage and improving data retrieval speeds. The framework leverages a hybrid cloud architecture, combining the scalability of public clouds with the security of private clouds. By employing a combination of client-side hashing, metadata indexing, and machine learning-based duplicate detection, the framework (...) achieves significant storage savings without compromising data integrity. Real-time testing on a hybrid cloud setup demonstrated a 65% reduction in storage needs and a 40% improvement in data retrieval times. Additionally, the system employs blockchain for immutable logging of deduplication activities, enhancing transparency and traceability. This study concludes with an evaluation of the deduplication framework's impact on cost efficiency, system performance, and potential scalability. Future enhancements aim to integrate multi-cloud interoperability and advanced compression algorithms to further refine storage management. (shrink)

This paper provides a comprehensive workflow for organizations considering a move from legacy database systems to Snowflake, demonstrating how a structured migration process can result in enhanced data accessibility, operational flexibility, and long-term scalability. Future directions for research are also discussed, including integration with advanced analytics and machine learning applications, which could further augment Snowflake’s utility in big data ecosystems.

This article endeavors to recognize the consumer perception towards the Apple products and what is Apple’s ideology. In this article the researcher has listed down 12 variables which affect the consumer behavior most while purchasing Apple products. The researcher took 50 sample sizes to make this paper and this study is being done in Tiruchirappalli District. To prepare the factors, I have used Factor analysis test.

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)

The EdTech revolution in India has emerged as a transformative force, particularly during and after the COVID-19 pandemic, when traditional education systems faced unprecedented disruptions. While digital technologies have unlocked new opportunities for teaching and learning, they have also exposed systemic inequities and deepened the existing digital divide. This paper examines how EdTech is reshaping India's education landscape by addressing these challenges, with a focus on both the opportunities it presents and the barriers it creates. The shift to digital learning (...) platforms has empowered students with access to a broader array of learning resources, interactive content, and personalized learning paths. However, millions of students, particularly those from rural areas or lower-income families, struggle to access these resources due to a lack of digital infrastructure, reliable internet connectivity, and affordability of devices. Furthermore, the role of teachers has evolved, requiring new pedagogical approaches and technical skills to effectively harness these digital tools. (shrink)

The study highlights the advantages telemedicine offers, such as increased flexibility, reduced commuting time, and better time management. It also examines the challenges doctors face, including the demands of always being digitally accessible, the learning curve of new technologies, and the difficulty in maintaining boundaries between personal and professional life. Using both quantitative and qualitative data, this research compares the telemedicine adaptation experiences of doctors in public versus private hospitals, considering factors such as hospital infrastructure, support systems, and patient load.

The rapid advancement of artificial intelligence (AI) technologies has revolutionized various industries, including the realm of education and career guidance. This project endeavors to harness the power of AI to develop a sophisticated career guidance application that offers personalized and effective recommendations to students and job seekers. The primary objective of this project is to address the limitations of traditional career guidance methods, which often lack customization and fail to adapt to individual preferences, skills, and aspirations. Through the integration of (...) AI algorithms, NLP techniques, and ML models, our application aims to provide accurate interest assessments, skill matching, and tailored educational pathways. The scope of the project encompasses the design, development, and implementation of AI-driven functionalities such as interest assessment, skill analysis, resume building, and personalized recommendations. The methodology involves data collection through user inputs, preprocessing of data for analysis, and the creation of a robust system architecture comprising frontend interfaces, backend servers, and database management. (shrink)

With the proliferation of sophisticated cyber threats, traditional malware detection techniques are becoming inadequate to ensure robust cybersecurity. This study explores the integration of deep learning (DL) techniques into malware detection systems to enhance their accuracy, scalability, and adaptability. 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)

We investigate the application of Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and a hybrid CNN-LSTM model for forecasting air pollution levels based on historical data. Our experimental setup uses real-world air quality datasets from multiple regions, containing measurements of pollutants like PM2.5, PM10, CO, NO2, and SO2, alongside meteorological data such as temperature, humidity, and wind speed. The models are trained, validated, and tested using a split dataset, and their accuracy is evaluated using performance metrics like Mean (...) Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared. (shrink)

. The accuracy levels of VGG19 and 3D CNN are compared using the performance metrics. This comparison helps in identifying which model performs better in the task of facial reconstruction from distorted images. Visualizing the results in the form of a graph provides a clear and concise way to understand the comparative performance of the algorithms. The ultimate goal of this project is to develop a system that can accurately reconstruct distorted faces, which can be invaluable in identifying accident victims (...) or assisting in medical treatments. By providing a reliable method for facial reconstruction, this technology can potentially save lives and improve outcomes for individuals involved in accidents. (shrink)

Underwater Wireless Sensor Networks (UWSNs) have emerged as a critical tool for various marine applications, such as underwater surveillance, oceanographic data collection, pollution monitoring, and resource exploration. These networks consist of a group of sensor nodes deployed underwater to monitor and collect data, which are then transmitted to a surface station for analysis.

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)

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.

Birds are an integral part of our ecosystem, playing diverse roles in pollination, seed dispersal, pest control, and ecological balance. Monitoring bird populations and identifying species are crucial for understanding biodiversity, assessing ecosystem health, and implementing conservation strategies. Traditionally, bird species identification has relied on manual observation, which requires significant expertise and time. However, this process is often prone to human error and inefficiency, especially when distinguishing between visually similar species. As global biodiversity faces increasing threats, there is a pressing (...) need for innovative and automated solutions that can accurately identify bird species. (shrink)

Is capitalism inherently predatory? Must there be winners and losers? Is public interest outdated and free-riding rational? Is consumer choice the same as self-determination? Must bargainers abandon the no-harm principle? Prisoners of Reason recalls that classical liberal capitalism exalted the no-harm principle. Although imperfect and exclusionary, modern liberalism recognized individual human dignity alongside individuals' responsibility to respect others. Neoliberalism, by contrast, views life as ceaseless struggle. Agents vie for scarce resources in antagonistic competition in which every individual seeks dominance. This (...) political theory is codified in non-cooperative game theory; the neoliberal citizen and consumer is the strategic rational actor. Rational choice justifies ends irrespective of means. Money becomes the medium of all value. Solidarity and good will are invalidated. Relationships are conducted on a quid pro quo basis. However, agents can freely opt out of this cynical race to the bottom by embracing a more expansive range of coherent action. (shrink)

As these opening quotes acknowledge, the Prisoner’s Dilemma (PD) represents a core puzzle within the formal mathematics of game theory.3 Its rise in conspicuity is evident figure 2.1 above demonstrating a relatively steady rise in incidences of the phrase’s usage between 1960 to 1995, with a stable presence persisting into the twenty first century. This famous two-person “game,” with a stock narrative cast in terms of two prisoners who each independently must choose whether to remain silent or speak, each advancing (...) self-interest at the expense of the other and thereby achieving a mutually suboptimal outcome, mires any social interaction it is applied to into perplexity. The logic of this game proves the inverse of Adam Smith’s invisible hand: individuals acting on self interest will achieve a mutually suboptimal outcome. However, as this chapter illuminates, the assumptions underlying game theory drive this conclusion. (shrink)

Along with inefficiencies of mainstream sciences to find solutions for world problems, and besides the unpleasant difficulties in human lives due to such matters as poverty and economic gap, environmental pollution and climate change, the question raised is whether alternative sciences are contingent, which could preserve mainstream sciences’ potencies and avoid inefficiencies. Along this, religious incentives also seek ways to compromise sciences with divine learnings. To answer this question and benefit from alternative sciences, the contingency of heterodox sciences has to (...) be discussed. The article Starts by an introduction to efforts made towards Islamization of science, then contingency of heterodox sciences is analyzed from the metaphysics of science point of view. The pioneer inquiry in this regard is a less-known essay of Immanuel Kant titled Metaphysical Foundations of Natural Science. This article touches topics such as the relation of metaphysics to science, the contingency of metaphysics of science, the contingency of different metaphysics, and metaphysical foundations of social sciences. And later explains the contingency conditions of heterodox sciences and some advantages of metaphysical approach to these sciences. (shrink)

This chapter uses Uskali Mäki’s (2009) concepts of “good” and “bad” imperialism to investigate the “economics imperialism” thesis. If science expands by offering (a) consilience, and (b) epistemological and ontological unity – that is, it explains more phenomena with greater parsimony – then this is good scientific expansion. Economics imperialism is only bad if the methodology of economics expands outside its domain without increasing understanding in the above manners.

This chapter discusses cryptocurrency, distributed ledger technology and blockchain tokens within the context of technological innovation, the history of money and accounting practices, and their multiple functionalities beyond those of standard currencies. This discussion is motivated by the design of cryptocurrencies for specific community needs, and to reflect anti-rival, positive sum value.

The goal of this essay is to explore "the highly contested nature of [decision-making through adopting] a historically comparative and interdisciplinary approach." Internalist history of game theory treats decision theory as a science of making choices to maximize expected gain. Game theory is applied to nuclear deterrence and military strategy, building markets and designing institutions, analyzing collective action, developing jurisprudence, and addressing crime and punishment. This essay draws on recent historiography of Cold War decision-making to draw into focus the constructive (...) aspects of decision theory to argue that the perceived need to avoid worst-case scenarios has instead contributed to a world in which the worst outcome is probabilistically assured to occur over the long duree timeframe. (shrink)

This chapter uncovers the complex negotiations for authority in various representations about futures of life which have been advanced by different branches of the sciences, and have culminated in the emerging concept of life as algorithm. It charts the historical shifts in expertise and representations of life, from naturalists, to mathematical modellers, and specialists in computation, and argues that physicists, game theorists, and economists now take a leading role in explaining and projecting futures of life. The chapter identifies Richard Dawkins (...) and Max Tegmark at the forefront of the concept of life as algorithm: the first inspired by game theory as a means to study evolution; the second proposing that a materialist basis of life could place humans and artificial intelligence on an equal footing. Through close reading of their respective texts, the chapter demonstrates that both thinkers consider life as an algorithm programmed to achieve success in survival and reproduction. (shrink)

This paper reconsiders whether rational choice and game theory represent cases of economics imperialism. It follows the work of Uskali Maki who analyzes the significance and characteristics of disciplinary imperialism in natural science and social science. "Economics Imperialism" is a term often used to describe the increasing impact and reach of economics with respect to its encroachment on other disciplines including political science and psychology. Maki provides a framework for assessing whether the influence of one discipline on another could be (...) a case of "good imperialism" consistent with unification and greater explanatory power. The paper examines whether in the case of economics, "rational choice imperialism" is a better understanding of the question of contention. Rational choice has been used to model addiction, sex, marriage, sleep, and suicide. Applying tools offered by Maki, this paper argues that although the imperialism of rational choice over multiple disciplines provides a unified means of modeling human action, it does not demonstrably achieve deeper understanding of either individual or collective action. Furthermore, rational choice modeling may alter the human practices it analyzes if it is used in public policy. Thus although rational choice offers a means to check for deductive consistency within and among its models, it has elements suggestive of the "bad disciplinary imperialism" that Uskali provides a means to critique. (shrink)

This paper examines how the concepts of utility, impartiality, and universality worked together to form the foundation of Adam Smith's jurisprudence. It argues that the theory of utility consistent with contemporary rational choice theory is insufficient to account for Smith's use of utility. Smith's jurisprudence relies on the impartial spectator's sympathetic judgment over whether third parties are injured, and not individuals' expected utility associated with individuals' expected gains from rendering judgments over innocence or guilt.

This chapter discusses cryptocurrency, distributed ledger technology and blockchain tokens within the context of technological innovation, the history of money and accounting practices, and their multiple functionalities beyond those of standard currencies. This discussion is motivated by the design of cryptocurrencies for specific community needs, and to reflect anti-rival, positive sum value.

In this paper an Artificial Neural Network (ANN) model, for predicting the category of a tumor was developed and tested. Taking patients’ tests, a number of information gained that influence the classification of the tumor. Such information as age, sex, histologic-type, degree-of-diffe, status of bone, bone-marrow, lung, pleura, peritoneum, liver, brain, skin, neck, supraclavicular, axillar, mediastinum, and abdominal. They were used as input variables for the ANN model. A model based on the Multilayer Perceptron Topology was established and trained using (...) data set which its title is “primary tumor” and was obtained from the University Medical Centre, Institute of Oncology, Ljubljana, Yugoslavia Test data evaluation shows that the ANN model is able to correctly predict the tumor category with 76.67 % accuracy. (shrink)

Multidimensional concepts are everywhere, and they are important. Examples include moral value, welfare, scientific confirmation, democracy, and biodiversity. How, if at all, can we aggregate the underlying dimensions of a multidimensional concept F to yield verdicts about which things are Fer than which overall? Social choice theory can be used to model and investigate this aggregation problem. Here, we focus on a particularly thorny problem made salient by this social choice-theoretic framework: the underlying dimensions of a given concept might be (...) measurable on different types of scales—e.g., some ordinal and some cardinal. An underappreciated impossibility theorem due to Anna Khmelnitskaya shows that seemingly plausible constraints on aggregation across scale types are inconsistent. This impossibility threatens to render the notion of overall Fness incoherent. We attempt to defuse this threat, arguing that the impossibility depends on an overly restrictive conception of measurement and of how measurement constrains aggregation. Adopting a more flexible—and, we think, more perspicuous—conception of measurement opens an array of possibilities for aggregation across disparate scale types. (shrink)

It is crucial to actively detect the risks of transactions in a financial company to improve customer experience and minimize financial loss. In this study, we compare different machine learning algorithms to effectively and efficiently predict the legitimacy of financial transactions. The algorithms used in this study were: MLP Repressor, Random Forest Classifier, Complement NB, MLP Classifier, Gaussian NB, Bernoulli NB, LGBM Classifier, Ada Boost Classifier, K Neighbors Classifier, Logistic Regression, Bagging Classifier, Decision Tree Classifier and Deep Learning. The dataset (...) was collected from Kaggle depository. It consists of 6362620 rows and 10 columns. The best classifier with unbalanced dataset was the Random Forest Classifier. The Accuracy 99.97%, precession 99.96%, Recall 99.97% and the F1-score 99.96%. However, the best classifier with balanced dataset was the Bagging Classifier. The Accuracy 99.96%, precession 99.95%, Recall 99.98% and the F1-score 99.96%. (shrink)

As inductive decision-making procedures, the inferences made by machine learning programs are subject to underdetermination by evidence and bear inductive risk. One strategy for overcoming these challenges is guided by a presumption in philosophy of science that inductive inferences can and should be value-free. Applied to machine learning programs, the strategy assumes that the influence of values is restricted to data and decision outcomes, thereby omitting internal value-laden design choice points. In this paper, I apply arguments from feminist philosophy of (...) science to machine learning programs to make the case that the resources required to respond to these inductive challenges render critical aspects of their design constitutively value-laden. I demonstrate these points specifically in the case of recidivism algorithms, arguing that contemporary debates concerning fairness in criminal justice risk-assessment programs are best understood as iterations of traditional arguments from inductive risk and demarcation, and thereby establish the value-laden nature of automated decision-making programs. Finally, in light of these points, I address opportunities for relocating the value-free ideal in machine learning and the limitations that accompany them. (shrink)

It has been a common assumption that words are substances that instantiate or have properties. In this paper, I question the assumption that our ontology of words requires posting substances by outlining a bundle theory of words, wherein words are bundles of various sorts of properties (such as semantic, phonetic, orthographic, and grammatical properties). I argue that this view can better account for certain phenomena than substance theories, is ontologically more parsimonious, and coheres with claims in linguistics.

Recently, bioethicists and the UNCRPD have advocated for supported medical decision-making on behalf of patients with intellectual disabilities. But what does supported decision-making really entail? One compelling framework is Anita Silvers and Leslie Francis’ mental prosthesis account, which envisions supported decision-making as a process in which trustees act as mere appendages for the patient’s will; the trustee provides the cognitive tools the patient requires to realize her conception of her own good. We argue that supported decision-making would be better understood (...) as a collaborative process, giving patients with intellectual disabilities the opportunity to make decisions in a respectful relationship with trusted others. We offer an alternative account of supported decision-making where the primary constraint is to protect the patient from domination by the trustee. This is advantageous in its preservation of the prospects for genuine collaboration, for the mental prosthesis approach ultimately reinforces a problematic ideal of isolated patient self-determination. (shrink)

I argue for an account of grasping, or understanding that, on which we grasp via a higher‐order mental act of Husserlian fulfillment. Fulfillment is the act of matching up the objects of our phenomenally presentational experiences with those of our phenomenally representational thought. Grasping‐by‐fulfilling is importantly different from standard epistemic aims, in part because it is phenomenal rather than inferential. (I endorse Bourget's (2017) arguments to that effect.) I show that grasping‐by‐fulfilling cannot be a species of propositional knowledge or belief, (...) and that it is not essentially connected to justification. I motivate a revisionary epistemology on which achieving propositional knowledge and coming to grasp are dual epistemic aims. My account makes sense of a common occurrence—that we are often unmoved to act on our beliefs until we come to phenomenally experience them in some way. It also explains puzzling features of human inquiry. (shrink)

Some philosophers argue that martial arts training is maladaptive, contributes to psychological illness, and provides a social harm, whereas others argue that martial arts training is adaptive, contributes to psychological wellness, and provides a social benefit. This debate is important to scholars and the general public since beliefs about martial arts training can have a real impact on how we evaluate martial artists for job opportunities and career advancement, and in general, how we treat martial artists from different cultures in (...) our communities. This debate is also important for children and adults that have considered enrolling in martial arts training programs but remain uncertain about potential outcomes of training due to the lack of research in this area. This article therefore contributes to the literature on martial arts by (1) outlining a framework that characterizes psychological well-being in terms of five elements, (2) discussing how results from empirical research support the hypothesis that Muay Thai training can contribute to psychological well-being by contributing to all five component elements, (3) discussing the psychological benefits of martial arts training from the perspective of an Everlast Master Instructor, and (4) discussing how martial arts training involves the cultivation of combat-relevant affordances. (shrink)

The problem of evil is broadly considered to be one of the greatest intellectual threats to traditional brands of theism. And William Rowe’s 1979 formulation of the problem in “The Problem of Evil and Some Varieties of Atheism” is the most cited formulation in the contemporary philosophical literature. In this paper, we explore how the tools and resources of experimental philosophy might be brought to bear on Rowe’s seminal formulation, arguing that our empirical findings raise significant questions regarding the ultimate (...) success of Rowe’s argument. Such a result would be quite notable within philosophy of religion, since this is considered one of the most formidable arguments against theism. However, further testing is needed before any firm conclusions can be drawn. -/- In section 1, we elucidate Rowe’s formulation of the problem of evil and the intuitions that seem to underwrite it. In section 2, we explore how the tools and resources of experimental philosophy might be brought to bear on Rowe’s formulation, outlining our hypotheses and our methods for testing them before showcasing our results. In section 3, we discuss the philosophical import of our results–arguing that our results, when taken together, pose an initial challenge to Rowe’s seminal argument. (shrink)

The genetic modification of pigs as a source of transplantable organs is one of several possible solutions to the chronic organ shortage. This paper describes existing ethical tensions in xenotransplantation (XTx) that argue against pursuing it. Recommendations for lifelong infectious disease surveillance and notification of close contacts of recipients are in tension with the rights of human research subjects. Parental/guardian consent for pediatric xenograft recipients is in tension with a child’s right to an open future. Individual consent to transplant is (...) in tension with public health threats that include zoonotic diseases. XTx amplifies concerns about justice in organ transplantation and could exacerbate existing inequities. The prevention of infectious disease in source animals is in tension with the best practices of animal care and animal welfare, requiring isolation, ethologically inappropriate housing, and invasive reproductive procedures that would severely impact the well-being of intelligent social creatures like pigs. (shrink)

Waiting time is widely used in health and social policy to make resource allocation decisions, yet no general account of the moral significance of waiting time exists. We provide such an account. We argue that waiting time is not intrinsically morally significant, and that the first person in a queue for a resource does not ipso facto have a right to receive that resource first. However, waiting time can and sometimes should play a role in justifying allocation decisions. First, there (...) is a duty of fairness prohibiting line-cutting where a sufficiently just queue exists. Second, waiting time has several morally attractive features that can justify its incorporation into allocation schemes. Where candidates are in relevantly similar circumstances, allocating by waiting time is relatively efficient, maximizes distribution equality relative to other Pareto efficient distributions, and treats candidates fairly. The claim that allocation using waiting time is fair is controversial. Some have claimed that formal lotteries are a fairer way to select among equal beneficiaries. We argue that lotteries are no fairer than allocation based on waiting time when it is equiprobable how a prospective queue will be ordered. In practice, lotteries share many of the disadvantages of queues; which is fairer will depend on contingent features of the allocation scenario. The upshot is that first-come, first-served is in fact a just way to allocate resources in many of the cases where it seems pre-theoretically compelling, and waiting time has unique normative properties which frequently justify its incorporation into resource allocation schemes. (shrink)

Abstract: Ensuring the proactive detection of transaction risks is paramount for financial institutions, particularly in the context of managing credit scores. In this study, we compare different machine learning algorithms to effectively and efficiently. The algorithms used in this study were: MLogisticRegressionCV, ExtraTreeClassifier,LGBMClassifier,AdaBoostClassifier, GradientBoostingClassifier,Perceptron,RandomForestClassifier,KNeighborsClassifier,BaggingClassifier, DecisionTreeClassifier, CalibratedClassifierCV, LabelPropagation, Deep Learning. The dataset was collected from Kaggle depository. It consists of 164 rows and 8 columns. The best classifier with unbalanced dataset was the LogisticRegressionCV. The Accuracy 100.0%, precession 100.0%,Recall100.0% and the F1-score (...) 100.0%. However, the best classifier with balanced dataset was the LogisticRegressionCV. The Accuracy 100.0%, precession 100.0%, Recall 100.0% and the F1-score 100.0%. (shrink)

Biological evolution is a complex blend of ever changing structural stability, variability and emergence of new phe- notypes, niches, ecosystems. We wish to argue that the evo- lution of life marks the end of a physics world view of law entailed dynamics. Our considerations depend upon dis- cussing the variability of the very ”contexts of life”: the in- teractions between organisms, biological niches and ecosys- tems. These are ever changing, intrinsically indeterminate and even unprestatable: we do not know ahead of (...) time the ”niches” which constitute the boundary conditions on selec- tion. More generally, by the mathematical unprestatability of the ”phase space”, no laws of mo- tion can be formulated for evolution. We call this radical emergence, from life to life. The purpose of this paper is the integration of variation and diversity in a sound concep- tual frame and situate unpredictability at a novel theoretical level, that of the very phase space. Our argument will be carried on in close comparisons with physics and the mathematical constructions of phase spaces in that discipline. The role of symmetries as invariant preserving transformations will allow us to under- stand the nature of physical phase spaces and to stress the differences required for a sound biological theoretizing. In this frame, we discuss the novel notion of ”enablement”. Life lives in a web of enablement and radical emergence. This will restrict causal analyses to differential cases. Mutations or other causal differ- ences will allow us to stress that ”non conservation princi- ples” are at the core of evolution, in contrast to physical dynamics, largely based on conservation principles as sym- metries. Critical transitions, the main locus of symmetry changes in physics, will be discussed, and lead to ”extended criticality” as a conceptual frame for a better understanding of the living state of matter. (shrink)

Empirical philosophers of science aim to base their philosophical theories on observations of scientific practice. But since there is far too much science to observe it all, how can we form and test hypotheses about science that are sufficiently rigorous and broad in scope, while avoiding the pitfalls of bias and subjectivity in our methods? Part of the answer, we claim, lies in the computational tools of the digital humanities, which allow us to analyze large volumes of scientific literature. Here (...) we advocate for the use of these methods by addressing a number of large-scale, justificatory concerns—specifically, about the epistemic value of journal articles as evidence for what happens elsewhere in science, and about the ability of DH tools to extract this evidence. Far from ignoring the gap between scientific literature and the rest of scientific practice, effective use of DH tools requires critical reflection about these relationships. (shrink)

This paper aims to analyze the relationship between applied social sciences and practical wisdom. Utilizing conceptual analysis methodology, it begins by defining application, action, and practice, then delves into the conceptual analysis of applied social sciences and practical wisdom. The concept of phronesis in Aristotle's philosophy and practical wisdom in Muslim philosophers are studied and analyzed. By examining different definitions of practical wisdom among Muslim scholars and comparing their views with those of Aristotle, the paper evaluates their perspectives. Subsequently, it (...) focuses on exploring the relationship between applied social sciences and practical wisdom, analyzing their points of divergence and convergence. Considering the inherent nature of applied social sciences as technological and practical, contrasted with the virtue-based nature of practical wisdom, the paper raises the question of whether phronesis can logically benefit from pure applied social sciences as theoretical wisdom to guide individuals towards virtuous action. It also questions whether this scenario could be considered a form of applied social sciences. (shrink)

Background: Mint is a grassy, perennial plant, belonging to the oral platoon, fast growing and spreading, its leaves are green in color, fragrant, tart, refreshing, square-shaped leg, bifurcated, erect, ranging in height from (10 - 201 cm). Home to Europe and Asia. The mint plant has many benefits, the most important of which are pain relief, treatment of gallbladder disorders, the expulsion of gases, anti-inflammatory, and relaxing nerves. While the mint plant is the ideal option for the start of gardens, (...) it is prone to some common diseases that affect the plant's growth. Objectives: The main goal of this expert system is to get the appropriate diagnosis of disease and the correct treatment. Methods: In this paper, the design of the proposed Expert System was produced to help Farmers and those interested in agriculture in diagnosing many of the Mint diseases such as Mint rust, Verticillium wilt, Anthracnose, Powdery mildew, Black Stem Rot, Stem and stolon canker, Septoria leaf spot. The proposed expert system presents an overview of mint diseases are given, the cause of diseases outlined and the treatment of disease whenever possible is given out. CLIPS Expert System language was used for designing and implementing the proposed expert system. Results: The proposed Mint diseases diagnosis expert system was evaluated by Agricultural Students at AL Azhar University and some friends interested in agriculture and they were satisfied with its performance. Conclusions: The proposed expert system is very useful for Farmers and those interested in agriculture. (shrink)

Abstract: As artificial intelligence (AI) technologies become more integrated across various sectors, ethical considerations in their development and application have gained critical importance. This paper delves into the complex ethical landscape of AI, addressing significant challenges such as bias, transparency, privacy, and accountability. It explores how these issues manifest in AI systems and their societal impact, while also evaluating current strategies aimed at mitigating these ethical concerns, including regulatory frameworks, ethical guidelines, and best practices in AI design. Through a comprehensive (...) analysis of these challenges and proposed solutions, this paper seeks to contribute to the ongoing discourse on responsible AI development, emphasizing the need for a balance between technological advancement and ethical integrity. (shrink)

Binding specificity is a centrally important concept in molecular biology, yet it has received little philosophical attention. Here I aim to remedy this by analyzing binding specificity as a causal property. I focus on the concept’s role in drug design, where it is highly prized and hence directly studied. From a causal perspective, understanding why binding specificity is a valuable property of drugs contributes to an understanding of causal selection—of how and why scientists distinguish between causes, not just causes from (...) noncauses. In particular, the specificity of drugs is precisely what underwrites their value as experimental interventions on biological processes. (shrink)