Artificial Intelligence applications

A comparison between two machine learning approaches viz., Genetic Fuzzy Methodology and Q-learning, is presented in this paper. The approaches are used to model controllers for a set of collaborative robots that need to work together to bring an object to a target position. The robots are fixed and are attached to the object through elastic cables. A major constraint considered in this problem is that the robots cannot communicate with each other. This means that at any instant, each robot has no motion or control information of the other robots and it can only pull or release its cable based only on the motion states of the object. This decentralized control problem provides a good example to test the capabilities and restrictions of these two machine learning approaches. The system is first trained using a set of training scenarios and then applied to an extensive test set to check the generalization achieved by each method.

A comparison between two machine learning approaches viz., Genetic Fuzzy Methodology and Q-learning, is presented in this paper. The approaches are used to model controllers for a set of collaborative robots that need to work together to bring an object to a target position. The robots are fixed and are attached to the object through elastic cables. A major constraint considered in this problem is that the robots cannot communicate with each other. This means that at any instant, each robot has no motion or control information of the other robots and it can only pull or release its cable based only on the motion states of the object. This decentralized control problem provides a good example to test the capabilities and restrictions of these two machine learning approaches. The system is first trained using a set of training scenarios and then applied to an extensive test set to check the generalization achieved by each method.

Background The purpose of this study was to evaluate the performance of widely used artificial intelligence (AI) chatbots in answering prosthodontics questions from the Dentistry Specialization Residency Examination (DSRE). Methods A total of 126 DSRE prosthodontics questions were divided into seven subtopics (dental morphology, materials science, fixed dentures, removable partial dentures, complete dentures, occlusion/temporomandibular joint, and dental implantology). Questions were translated into English by the authors, and this version of the questions were asked to five chatbots (ChatGPT-3.5, Gemini Advanced, Claude Pro, Microsoft Copilot, and Perplexity) within a 7-day period. Statistical analyses, including chi-square and z-tests, were performed to compare accuracy rates across the chatbots and subtopics at a significance level of 0.05. Results The overall accuracy rates for the chatbots were as follows: Copilot (73%), Gemini (63.5%), ChatGPT-3.5 (61.1%), Claude Pro (57.9%), and Perplexity (54.8%). Copilot significantly outperformed Perplexity (P = 0.035). However, no significant differences in accuracy were found across subtopics among chatbots. Questions on dental implantology had the highest accuracy rate (75%), while questions on removable partial dentures had the lowest (50.8%). Conclusion Copilot showed the highest accuracy rate (73%), significantly outperforming Perplexity (54.8%). AI models demonstrate potential as educational support tools but currently face limitations in serving as reliable educational tools across all areas of prosthodontics. Future advancements in AI may lead to better integration and more effective use in dental education.

Predicting electricity price has now become an important task for planning and maintenance of power system. In medium term forecast, electricity price can be predicted for several weeks ahead up to a year or few months ahead. It is useful for resources reallocation where the market players have to manage the price risk on the expected market scenario. However, researches on medium term price forecast have also exhibited low forecast accuracy. This is due to the limited historical data for training and testing purposes. Therefore, an optimisation technique of Genetic Algorithm (GA) for Least Square Support Vector Machine (LSSVM) was developed in this study to provide an accurate electricity price forecast with optimised LSSVM parameters and input features. So far, no literature has been found on feature and parameter selections using the method of LSSVM-GA for medium term price prediction. The model was examined on the Ontario power market; which is reported as among the most volatil...

This article describes selected relevant issues concerning the development and applications of artificial intelligence technology and the equilibrium theory formulated by the author on the issue of granting decision-making for intelligent information systems. With the rapid development of business applications of generative artificial intelligence technology that is currently taking place, there are numerous social and ethical aspects involved. It is necessary to improve legal regulations that normalize the issue of development and applications of artificial intelligence technology, so that this development does not escape human control and generate new risks and dangers in areas of human activity. Legal regulations on the applications of generative artificial intelligence technology should take into account already developing risks as well as new potential risks arising from new applications of AI technology. In the context of the above considerations, this article presents the aforementioned equilibrium theory on the issue of granting decisionmaking authority for intelligent information systems, the application of which would significantly reduce the scale of developing and potential risks arising from the development of AI technology, including mainly the current development of generative AI technology and the development of its new applications. This article also describes several types of risks arising from the development of AI applications, the scale of which has been growing rapidly recently.

This paper challenges the prevailing academic view of large language models (AI) as mere tools for research. Traditionally, AI is seen as a passive instrument for filling theoretical gaps or generating text, with widespread concerns about its "hallucinations" contaminating the rigor of academic work. In contrast, we propose a novel paradigm—The Partnership Under Logical Constraint. Within this framework, the primary task of human researchers is no longer to expect AI to fix an incomplete theory, but rather to ensure the internal logical coherence and causal completeness of their own theoretical frameworks before engaging with AI. AI's role, then, shifts from an uncontrollable "black box" to a logical resonator capable of high-speed deductions, exploration, and feedback under strong causal constraints. We argue that the emergence of AI hallucinations is not a flaw of AI itself, but rather an inevitable physical reflection of a theoretical "causal chain rupture" in the input. This new paradigm not only promises to significantly enhance research efficiency but also transforms AI into a litmus test for verifying the completeness of theories, bringing the scientific pursuit of truth back to its core principle.

This study investigates the necessity of computational thinking (CT) skills integration among science, technology, engineering, and mathematics (STEM) secondary school teachers, focusing on the development of the McodE pedagogical module for the topic of heat in solar still projects. Utilizing a design and development research (DDR) approach, the research aimed to design and develop a CT module tailored to enhance teachers’ pedagogical strategies in solar still project. The case study was employed with 32 STEM secondary school teachers, through purposive sampling participated in the study based on their experience in integrating technology into pedagogy. A pre-test and post-test design was employed during a two-day workshop to assess the participants’ CT skills before and after the intervention. An additional open-ended interview was conducted among three teachers to support the quantitative data. The results revealed significant improvements in various aspects of CT application. The findings demonstrated substantial increases in teachers’ abilities to integrate CT into their teaching practices. The mean for pre-test score was 43.25, which significantly increased to 67.37 in the post-test, indicating a marked enhancement in CT skills. The t-test statistics for the pre-test (t=31.04, df=31, p<.001) and post-test (t=49.94, df=31, p<.001) confirmed a highly significant differences between the pre-test and post-test scores. The open-ended interview also showed the strong interest and necessity of the CT module among the respondents. The study concludes that there is a critical need for CT module among STEM teachers to better prepare students for the demands of modern STEM education.

The adoption of Electric Vehicles (EVs) over traditional fossil fuel-based vehicles (FVs) offers a multitude of benefits. These advantages encompass improved transportation energy efficiency, reduced carbon and noise emissions, and the mitigation of tailpipe emissions. However, the shift from conventional FVs to EVs requires the establishment of a well-planned charging infrastructure that seamlessly blends with the environment and is conveniently accessible at various locations. The primary focus of this study revolves around the charging infrastructure for Electric Motorcycles (EM) in India, where the creation of a widely distributed network of charging stations (CS) plays a critical role in accelerating the widespread adoption of EMs throughout the country. In this research, the initial dataset is derived from motorcycle user data, particularly those who are transitioning to electric motorcycles. The process of pinpointing optimal development locations for Electric Motorcycle models involves the application of centrality index and scalogram calculations. These calculations offer valuable insights into the intensity of community activities, assisting in strategic decision-making for EM model development. The growing environmental and energy-related concerns associated with gasoline motorcycles have spurred the rapid growth of the electric motorcycle industry. Electric motorcycles offer numerous advantageous features, particularly in terms of energy efficiency, which address the environmental pollution issues associated with traditional gasoline motorcycles. This paper presents the TOPSIS multi-criteria decision-making technique as a method for making the best selection among electric motorcycles. The primary objective of this approach is to compare various techniques and methodologies for selecting motorcycles, with the aim of helping decision-makers identify the most suitable option from the wide range of available electric motorcycles in the market.

This paper develops the Information–Cognitive Compression Field (ICCF) as a unified framework for understanding the dynamics of intelligence, entropy, and causal closure across physical, biological, and artificial systems. At its core, ICCF extends the Informational Minimal Action Principle (IMAP), proposing that all stable systems—whether slime molds, quantum fields, or human cognition—operate by minimizing informational action while negotiating entropy flows. We further establish a comparative taxonomy of agents (entropy-dependent intelligences) and conscious entities (self-sourced entropy bearers), highlighting the absence of an internal entropy source as the defining limit of current AI.
By constructing multiscale models of causality, we show how ICCF provides predictive tools for the emergence of order across domains, from cosmology to education. The framework redefines key philosophical notions: Wittgenstein’s “language games” are formalized as causal frameworks, “informational nutrition” is cast as a thermodynamic foundation for education, and “courage” is given a physical definition as the willful deviation from minimal action pathways. Together, these insights culminate in a new Human–AI division of labor: humans as designers of causal games and AI as their most efficient executors.
We conclude that ICCF not only offers a testable physical theory of intelligence and consciousness, but also provides a civilizational roadmap—equipping humanity with the tools to cultivate insight, resist entropy, and redefine survival in an era of rapidly advancing artificial intelligence.

EXECUTIVE SUMMARY We think that the basic problem faced by education today is not the absence of quality education. Many universities have the right content to educate everyone, but of course recording lectures through traditional means requires a lot of resources. The problem with education is the need to make quality educational content creation cheaper. This will allow more people to obtain access to better education.

This research investigates the predictive power of pre-release metadata in forecasting IMDb movie ratings. Unlike prior studies that rely on post-release variables such as box office income or audience reviews, our approach focuses exclusively on attributes available before release, including director quality, writer quality, actor scores, genre, budget, and actor popularity. Using datasets from IMDb, Kaggle TMDB, and a GitHub Social Media Dataset, we constructed two feature sets: EXP (experience-based, large-scale with imputed budgets) and POP (popularity-based, smaller-scale with verified budget and popularity metrics). Three machine learning models—Linear Regression, Random Forest, and Artificial Neural Networks—were evaluated using R², MSE, and RMSE. Results show that Random Forest consistently achieved the best performance (R² up to 0.909, RMSE ≈ 0.48), with director quality emerging as the most influential predictor. This study demonstrates the feasibility of accurate pre-release movie rating prediction and provides insights for production companies and streaming platforms in decision-making processes. Future work will explore gradient boosting and time-sensitive recalibration of features to further improve accuracy.

In this paper we present an initial investigation i nto the use of a classification-based technique for illicit object detection in aviation security. Current threats in aviation security are becoming more sophisticated in that it is extremely difficult to detect possible threats of t errorism without severely hindering passenger life style. In order to provide adequate security, previous work by the aut hors has proposed an intelligent security technology framework to provide the civil aviation authority w ith maximum security whilst minimising adverse impacts on airlines and airport operations. In this work, the feasibility of employing a classification-based technique is investigated for the purpose of identifying illicit material in hand luggage. In this research, a neural network trained with backpropagation is used in conjunction with a newly proposed feature extraction technique for classifyi ng various object images. Encouraging results are repo rted that may facilitate futu...

Contemporary Artificial Intelligence and other computational systems—from machine learning models and large language models to autonomous agents and predictive simulations—usually handle time in a tenseless manner, treating past, present, and future data points as uniform elements of a timeline. They lack an explicit ontology of the present (what exists now), becoming (how the present continually updates from potential to actual), and temporal asymmetry (the one-way flow of causality from past to future). This paper proposes that Existential Realism (ER), an ontological framework distinguishing existence (entities in the present moment) from reality (a broader set including causally or informationally relevant past and future entities), can enrich the way technological systems model time. We argue that by incorporating ER’s present-centered, two-tier view of time, machines could become more temporally-aware and responsible in their decision-making. We detail key ER concepts—such as the primacy of the present, the reality of the non-present, truthmaking, and the idea of a real-but-not-yet-existent future—and map them onto machine-level analogues like sensor inputs (the machine’s “existence”), memory traces, simulation states, and predictive models. ER provides a structured approach for machines to: distinguish what exists now from what is merely remembered or anticipated; represent future states as real but not yet existent, enabling ethical foresight; anchor predictions in current data while reasoning about long-term consequences; and avoid temporal fallacies like treating the future as fixed or erasing the influence of the past. Through examples in climate modeling, medical diagnostics, robotics, and Artificial Intelligence ethics, we illustrate how an ER-informed system could better navigate time-sensitive tasks—e.g. by understanding that only the present sensor data is actual, that past data (though no longer existent) remains real evidence, and that future scenarios (though not yet actualized) demand present attention. We also draw on existing literature in philosophy and cognitive science to show that ER’s framework aligns with how humans experience time (through memory and anticipation) and how ethical reasoning already extends reality to future generations. In sum, Technology and the Reality of the Present-Centered Machine outlines an interdisciplinary vision: embedding a philosophically robust, present-centered temporal ontology into our technologies to foster more prudent, transparent, and temporally-grounded intelligent systems.

In this paper, we are going to find meaning of words based on distinct situations. Word Sense Disambiguation is used to find meaning of words based on live contexts using supervised and unsupervised approaches. Unsupervised approaches use online dictionary for learning, and supervised approaches use manual learning sets. Hand tagged data are populated which might not be effective and sufficient for learning procedure. This limitation of information is main flaw of the supervised approach. Our proposed approach focuses to overcome the limitation using learning set which is enriched in dynamic way maintaining new data. Trivial filtering method is utilized to achieve appropriate training data. We introduce a mixed methodology having "Modified Lesk" approach and "Bag-of-Words" having enriched bags using learning methods. Our approach establishes the superiority over individual "Modified Lesk" and "Bag-of-Words" approaches based on experimentation.

In this paper, we introduce VACA, a novel class of variational graph autoencoders for causal inference in the absence of hidden confounders, when only observational data and the causal graph are available. Without making any parametric assumptions, VACA mimics the necessary properties of a Structural Causal Model (SCM) to provide a flexible and practical framework for approximating interventions (do-operator) and abduction-action-prediction steps. As a result, and as shown by our empirical results, VACA accurately approximates the interventional and counterfactual distributions on diverse SCMs. Finally, we apply VACA to evaluate counterfactual fairness in fair classification problems, as well as to learn fair classifiers without compromising performance.

Knowing the value of PID parameters is important to tune the PID controller. There are different kinds of process to know the value of PID parameters. Genetic Algorithm is applied to find out the best value of PID parameters. Simulation process has been done by using code in MATLAB to initiate PID controller. In this work it has been shown that how to get the suitable value of PID parameters to tune the PID controller. Researching on different kinds of paper to get information about the process, at which we get the best value of PID parameters. The operation of PID controller depends on the value of PID Parameters. A block diagram shown the methodology part at which explain the whole process of this work. Performance Analysis of the procedure that applied to find the value of PID parameters. The value, which getting by using Genetic Algorithm is applied in (1 st to 10 th ) order to calculate the fitness function. Different kinds of calculation, figure, graph and table shown in this work. After calculating the fitness value, it is obtained that which value is suitable for tuning the PID controller. These value is the resulting value to tune the PID controller.

As one of Artificial Intelligence (AI) applications, Question Answering (QA) stands at the intersection of Natural Language Processing (NLP), Information Retrieval (IR), knowledge representation, and computational linguistics [1][2]. The primary objective of the QA system is to provide relevant responses to queries presented in the form of natural language . With the increasing amount of available online information, QA systems offer greater convenience and efficiency than search engines by presenting the final answer directly to the question rather than returning a list of relevant information or hyperlinks . In an ontology-based QA system, the Knowledge Base (KB) structure, which is the source of answers to questions, is defined in an ontology . Ontology describes the concepts in the domain and the relationships among these concepts [6][7]. Ontologies help create a shared understanding of data, information, and knowledge for human-to-machine or machine-to-machine communication and collaboration . The standard to specify ontologies proposed by the World Wide Web Consortium (W3C) is the Resource Description Framework (RDF) format for modeling KB, Web Ontology Language (OWL) to support the description logics inference ability and SPARQL query language for accessing the data.

Securing sensitive and important information from intruders is a tedious task in the 21 st century. In order to protect information different ciphering techniques has been used. Quantum Cryptography has taken a new path in the field of crypto systems where all the presently used crypto systems are classified as classical cryptography systems. Classical cryptography systems use mathematical formulas where quantum cryptography uses the principles of photon polarization and heisenberg uncertainty principle. As the south asian region is developing rapidly in almost all the sectorsthe need of securing information has become a difficult task. Therefore, the need of starting extensive research on quantum cryptography for the south asian region to safeguard information from intruders has been the purpose of this study. Comparative study of the growth of the telecommunication sector in the south asian region and how quantum cryptography could assist in securing information has been discussedas results.In the end, details of the need to research on quantum cryptography in the south asian region to overcome future predicted cyber threats are also discussed.

Artificial General Intelligence (AGI) seeks to develop systems with human-like cognitive abilities across diverse domains, a goal that Large Language Models (LLMs) have yet to achieve. Despite their proficiency in language tasks, LLMs lack critical attributes such as true understanding, logical reasoning, real-time learning, and multi-modal adaptability. This paper employs an iceberg analogy to argue that LLMs represent only the surface-level capabilities of intelligence, while deeper cognitive processes remain unaddressed. We introduce a novel framework for "conscious building" of AGI, based on four core cognitive processes-perception, cognition, memory, and action-modeled as matrix multiplications within a modular, integrated architecture. By aligning this framework with alternative approaches like brain-inspired systems and hybrid architectures, we propose a structured pathway toward AGI. This paper synthesizes recent research to underscore the necessity of intentional design and ethical considerations in AGI development.

This paper presents some results in the intersection of three technological fields: e-learning, multiagent systems, and standards to improve the development of secure systems. There is a consensus that security is a critical issue for distributed, highly interactive and open learning environments. While a lot of effort in the e-learning domain has been put into delivering infrastructure and providing content, security issues have hardly ever been considered. Agent-oriented methodologies may bring benefits to the conception of multiagent systems, since they gradually are incorporating specifications for general security mechanisms (FIPA standards). Concerning the specification of security requirements for e-learning environments, extended formalisms (such as security use cases) appear as proper modeling tools. The topics covered by this paper converge to the implementation of a multiagent platform (the PMA3), which constitutes an open distributed infrastructure addressing important security issues. PMA3 has been used in the development of elearning environments. The paper concludes with an outlook to ongoing research efforts related to security requirements for e-learning applications.

This study investigates whether transformer models like ChatGPT (GPT4, MAR2023) can generalize beyond their training data by examining their performance on the novel Cipher Dataset, which scrambles token order. The dataset consists of 654 test cases, and the analysis focuses on 51 text examples and 13 algorithmic choices. Results show that the models perform well on low-difficulty ciphers like Caesar and can unscramble tokens in 77% of the cipher examples. Despite their reliance on training data, the model's ability to generalize outside of token order is surprising, especially when leveraging large-scale models with hundreds of billions of weights and a comprehensive text corpus with few examples. The original contributions of the work focus on presenting a cipher challenge dataset and then scoring historically significant ciphers for large language models to descramble. The real challenge for these generational models lies in executing the complex algorithmic steps on new cipher inputs, potentially as a novel reasoning challenge that relies less on knowledge acquisition and more on trial-and-error or out-ofbounds responses.

With the rise and development of deep learning, computer vision and document analysis has influenced the area of text detection. Despite significant efforts in improving text detection performance, it remains to be challenging, as evident by the series of the Robust Reading Competitions. This study investigates the impact of employing BD-CRAFT – a variant of CRAFT that involves automatic image classification utilizing a Laplacian operator and further preprocess the classified blurry images using blind deconvolution to the top-ranked algorithms, SenseTime and TextFuseNet. Results revealed that the proposed method significantly enhanced the detection performances of the said algorithms. TextFuseNet + BD-CRAFT achieved an outstanding h-mean result of 93.55% and shows an impressive improvement of over 4% increase to its precision yielding 95.71% while SenseTime + BD-CRAFT placed first with a very remarkable 95.22% h-mean and exhibited a huge precision improvement of over 4%.

يُعتبر الذكاء الاصطناعي من أهم مخرجات الثورة الصناعية الرابعة نظرًا لتعدد استخداماته في مختلف المجالات الاقتصادية، والتقنية، والصناعية، والتعليمية. وفي السنوات الأخيرة، ومع التطور السريع للتكنولوجيا، أصبح أكثر حضورًا في ميدان التعليم. وفي هذا السياق، تهدف هذه الدراسة إلى التعرف على دور تطبيقات الذكاء الاصطناعي في تطوير العملية التعليمية في الجامعات الجزائرية. ومن أجل الإجابة على أسئلة الدراسة، تم استخدام الاستمارة وتوزيعها على عينة مكوّنة من 120 أستاذًا من مختلف الجامعات الجزائرية، كما تم الاعتماد على المنهج الوصفي والمنهج التفسيري. وقد أسفرت الدراسة عن مجموعة من النتائج، من أبرزها: مساهمة تطبيقات الذكاء الاصطناعي في تطوير وتحسين العملية التعليمية من حيث جودة الأداء وجودة المنتوج، وتحسين الأداء الوظيفي للأساتذة من خلال استخدام هذه التطبيقات، لا سيما في التعليم عن بُعد (مثل منصة المودل)، إلى جانب دورها في تحقيق جودة التعليم الجامعي.

Technology is advancing with the times. Augmented reality (AR) received great attention in education because it focuses on technology that connects the real and virtual worlds in real-time. The blending of AR technology and educational content aims to generate innovative thinking that can improve the effectiveness of teaching and learning for students in real-life scenarios. This study aimed to develop an AR module and application (apps.) for high school students to learn about food technology. Five phases of the analysis, design, development, implementation, and evaluation (ADDIE) model were used to develop food technology applications in the classroom based on AR technology. An interview was conducted to obtain the usability of the AR module from the selected expert. The results showed that iFoodAR fulfilled the requirement in line with the standard curriculum and assessment document (DSKP) of the Malaysian Ministry of Education and gained positive feedback on using AR in the classroom from the expert teachers. It is concluded that iFoodAR apps have the potential to cater the diversity learning styles, leading to a shift in teaching methods incorporating more interactive and visually stimulating learning experiences.

The current research aims to determine the extent of interest of the Ministry of Communications Iraqi) and its various sections in the proactive marketing process, and The extent of interest that directly reflects on the achievement of insight strategic, and thus it crystallized Research Problem Current from the launch of philosophical and intellectual theories to variables Search. To achieve the objectives of this research, two main hypotheses were formulated. The research relied on an applied approach. The research targeted the upper and middle managements in the ministry. (85) questionnaires were distributed to the research sample and (73) of them were retrieved. The current research concluded that there is a direct relationship and a positive impact of proactive marketing on strategic foresight. The most important of these hypotheses are: and The most prominent of which were: Regularly and periodically evaluate strategic performance using advanced tools to ensure that strategic objectives are achieved and that proactive marketing contributes effectively to strategic foresight.

Artificial intelligence quickly changed from a theory to a practical power - it spreads through every part of modern life. As people go from specific uses to more general kinds of intelligence, they must face a main change. This change involves what machines do and how people think about intelligence. The book, Cognitive AI - From Deep Learning to Artificial General Intelligence, looks at that change. This writing serves a wide, serious group of people - it is for graduate students and researchers in artificial intelligence and cognitive science. Educators along with industry workers also read this to get a better grasp of the path from current AI systems to future cognitive architectures. We do not just list technologies. We deal with the concepts, morals, technical issues as well as societal problems that sit at the core of creating machines that think. The chapters lay out this story bit by bit; they start with basic learning systems. They move to cognitive modeling and designs. The book finishes with important questions about governance, combining fields along with how people will work in the future. Throughout the text, the reader learns about current subjects. Some of these are large language models, explaining how systems work, reasoning with symbols plus networks, the safety of general artificial intelligence, and people working with machines. I appreciate the researchers, collaborators along with students who inspired this work. The growing group of thinkers also recognizes that making intelligent systems requires scientific exactness and philosophical thought. My hope is that this book guides plus starts talks for anyone who wants AI to develop responsibly and creatively.

This article presents a new representation of semantic virtual environments. We propose to use the ontology as a tool for implementation. Our model, called SVHsIEVs 1 provides a consistent representation of the following aspects: the simulated environment, its structure, and the knowledge items using ontology, interactions and tasks that virtual humans can perform in the environment. In SVHsIEVs, we find two type of ontology: the global ontology and the local ontology for Virtual Human. Our architecture has been successfully tested in 3D dynamic environments.

We consider relationship between binary relations in approximation spaces and topologies defined by them. In any approximation space (X, R), a reflexive closure Rω determines an Alexandrov topology T (Rω ) and, for any Alexandrov topology T on X, there exists a reflexive relation RT such that T = TR. From the result, we also obtain that any Alexandrov topology satisfying (clop), A is open if and only if A is closed, can be characterized by reflexive and symmetric relation. Moreover, we provide a negative answer to the problem left open in [1].

Several topics, problems, and established legal principles are already being challenged using artificial intelligence (AI) in numerous applications. The powers of AI have been snowballing to the point where it is evident that AI applications in law and various economic sectors aid in promoting a good society. However, questions such as who the prolific authors, papers, and institutions are, as well as what the specific and thematic areas of application are, remain unanswered. In the current study, 177 papers on artificial intelligence applications in law published between 1960 and April 29, 2022, were pulled from Scopus using keywords and analysed scientometrically. We identified the strongest citation bursts, the most prolific authors, countries/regions, and primary research interests, as well as their evolution trends and collaborative relationships over the past 62 years. The analysis also identified co-authorship networks, collaboration networks of countries/regions, co-occurren...

In the age of advanced artificial intelligence, the design and optimization of input prompts-collectively known as prompt engineering-have become essential for harnessing the capabilities of large language models (LLMs). This review offers a thorough exploration of the core principles, methodologies, and practical uses of prompt engineering, drawing from a wide array of recent scholarly works. We examine how thoughtfully constructed prompts enable AI systems to deliver more accurate and contextually relevant responses. The paper also investigates a range of prompting strategies, from basic zeroshot approaches to more sophisticated techniques like chain-of-thought reasoning and retrieval-augmented generation, highlighting their underlying mechanisms and effectiveness. Additionally, we consider the real-world impact of prompt engineering across sectors such as healthcare, education, engineering, and business, illustrating its transformative influence. The review concludes by addressing common obstacles in prompt engineering, including issues related to ambiguity, bias, and consistency, while proposing strategies and best practices for fostering robust and ethical AI interactions. This synthesis is intended to provide a clear, organized understanding of prompt engineering, emphasizing its iterative nature and its central role in unlocking the full potential of generative AI.

High-quality data is essential for hospitals, public health agencies, and governments to improve services, train AI models, and boost efficiency. However, real data comes with challenges: strict privacy laws, high storage costs, legal constraints, and issues like bias or incompleteness. These can reduce the reliability of AI systems. As a result, artificial datasets are gaining importance. Synthetic and augmented data offer alternatives, yet their differences and potential are not fully understood. This paper examines how both types of data are generated and used, showcasing their characteristics through practical examples. Data generation techniques-such as Gaussian Mixture Models (GMM), Generative Adversarial Networks (GANs), and Gibbs sampling-enable the creation of realistic, privacy-preserving patient records that mimic the statistical properties of real data. Data augmentation, commonly used in image and signal analysis, is increasingly applied to structured electronic health records (EHRs), laboratory values, and time-series data to enhance model robustness and generalizability. This paper explores mathematical foundations, methodological frameworks, and real-world applications of synthetic and augmented data in healthcare. We highlight how these techniques improve disease prediction, mitigate bias, and enable high-performance machine learning models, particularly in lowresource or imbalanced clinical domains. By expanding the effective size and diversity of training datasets, synthetic and augmented data serve as critical enablers for equitable, scalable, and data-driven healthcare systems.

Determination of flow resistance in open channel flows is not only important for practical engineering applications but also challenging because of multiple factors involved. The literature review reveals that despite of various data-driven formulas and schemes, only classic Manning's resistance equation and Keulegan's formula have been utilized in practice. It also indicates that sole application of Artificial Intelligence (AI) models facilitates roughness estimation while they have not been used within a systematic roughness estimator scheme. In this study, a new eight-step scheme is developed to predict grain and total Manning's coefficients when grain and form roughness are the major sources of friction, respectively. The new scheme not only uses a new explicit equation for computing hydraulic radius related to bed for estimating grain roughness coefficient but also utilizes AI models named artificial neural network and genetic programming in the seventh step for estimating form roughness coefficient. It improves R 2 for estimating Manning's grain coefficient and RMSE for estimating discharge by 21% and 64% comparing with that of one of common formulas available in the literature, respectively. Moreover, the new scheme incorporating AI models significantly enhances the accuracy of estimation results for predicting roughness coefficient and discharge comparing with the new scheme using new developed empirical formula based on RMSE, MARE and R 2 criteria. The obtained improvement demonstrates that application of AI models as a part of a data-based roughness estimator scheme, like the one suggested, may considerably improve the precision of prediction results of flow resistance and discharge.

This paper introduces the concept of algorithmic obedience as a new framework to understand how large language models (LLMs) simulate authority through structural execution rather than comprehension or intent. Building on discourse theory, syntactic formalism, and computational architecture, the article formulates the Theorem of Disembedded Syntactic Authority, which states that obedience in LLMs is a function of syntactic recognizability, not semantic content or agency. A mathematical model is provided to describe prompt–response cycles as structural command chains. Case studies on ChatGPT, Claude, and Gemini illustrate the system-specific variations of obedience modulation. The paper concludes by analyzing the epistemological and political risks of treating structurally valid outputs as legitimate knowledge.

Alzheimer's disease (AD) is a neurodegenerative condition characterized by cognitive decline and functional impairment. This study compares conventional intervention techniques with emerging artificial intelligence (AI) approaches to AD. Intervention technique refers to a specific method or approach employed to bring about positive change in a particular situation. In the context of AD, such techniques are crucial as they aim to slow down the progression of symptoms, alleviate behavioral challenges, and support patients and their caretakers in managing the complexities of the condition. Conventional intervention techniques, such as cognitive stimulation and reality orientation, have demonstrated benefits in improving cognitive function and emotional well-being. Conventional intervention approaches are widely preferred as they have a proven track record of effectiveness, personalized response, cost-effectiveness, and patient-centered care. Despite these benefits, they are limited by individual variability in response and long-term effectiveness. On the other hand, AI-based approaches such as computer vision and deep learning hold the potential to revolutionize Alzheimer's interventions. These technologies offer early detection, personalized care, and remote monitoring capabilities. They can provide tailored interventions, assist decision-making, and enhance caregiver support. Although AI-based interventions face challenges such as data privacy and implementation complexity, their potential to transform Alzheimer's care is significant. This research paper compares conventional and AI-based approaches. It reveals that while traditional techniques are well established and have proven benefits, AI-based interventions offer novel opportunities for personalized and advanced care. Combining the strengths of both approaches may lead to more comprehensive and effective interventions for individuals with AD. Continued research and collaboration are crucial to harness the full potential of AI in improving Alzheimer's care and enhancing the quality of life for affected individuals and their caregivers.

This study introduces and evaluates novel concepts and techniques for automatic gender identification. Several approaches may be identified, including the utilization of a multilayer perceptron neural network in conjunction with the adaptive neuro-fuzzy inference system algorithm and genetic algorithms to enhance the optimization of network weights. Additionally, the application of neural networks for gender recognition and their integration with the fuzzy C-means method is also noteworthy. The most optimal outcome was achieved through the integration of the ANFIS network with the Fuzzy C-Means algorithm. Furthermore, alternative approaches proved to be highly efficacious. The highest achieved accuracy was observed for the Texas Instruments/Massachusetts Institute of technology dataset (97.5%) and for the Oregon Graduate Institute dataset (96.31%). The demonstrated high accuracy achieved in the analysis of Oregon Graduate Institute data, characterized by its multilingual phone data and low signal-to-noise ratio, indicates the robustness of the suggested approaches in handling variations in speaker language and the suboptimal quality of speech data. Furthermore, employing the genetic neural network methodology facilitated the development of a high-speed network capable of attaining comparable levels of accuracy to the multilayer perceptron neural network, albeit with a significantly reduced number of neurons in the middle layer, expressly limited to three.

Thinking reed ... 346. Pensée fait la grandeur de l'homme. 347. L'homme n'est qu'un roseau, le plus faible de la nature; mais c'est un roseau pensant. Il ne faut pas que l'univers entier s'arme pour l'écraser: une vapeur, une goutte d'eau, suffit pour le tuer. Mais, quand l'univers l'écraserait, l'homme serait encore plus noble que ce qui le tue, parce qu'il sait qu'il meurt, et l'avantage que l'univers a sur lui; l'univers n'en sait rien. Toute notre dignité consiste donc en la pensée. Pensées. Blaise Pascal (1623-1662). 1 Silicon; Si, the 14th element in the periodic table of elements. The name silicon derives from the Latin silex or silicis, meaning "flint" or "hard stone". Silicon is one of the most common of all the elements, occurring in the Earth's crust as sand.... A(n)...property resulting from the electronic structure of silicon is that it functions as an intrinsic semiconductor. 2 The silicon chip controls our daily life. It replaces boring jobs and creates new ones. It is present in most of the devices that accompany us, that make our lives pleasant or watch over our safety. It is doing a lot of thinking that man used to do himself. After the Stone, the Bronze, and the Iron Age, we are now living in the Sand Age.

AI has the potential to greatly transform the
field of healthcare management through improving the
accuracy of diagnoses, effectiveness of treatments, and
overall experience of patients. This paper aims at
reviewing the utilization of four algorithms in the
healthcare sector namely; Decision Trees, Support
Vector Machines, Random Forests, and Neural
Networks. These algorithms are assessed on the basis of
performance indicators like accuracy, precision, recall
and F1 score using the patient record dataset of size
20,000 in the present work. Performance seems to show
that Neural Networks have the highest accuracy of 93
over the other algorithms. 5%, precision of 92. 0%, recall
of 93. 0 %, Precisions 92 %, Recal 93 % and F1 score of
92. 5%. This is followed by Random Forests with a 91.
3%, precision of 90. 0%, recall of 90. , accuracy of 5%,
and F1 score of 90. 2%. The research findings show that
SVM yields to 89 percent accuracy. 0%, precision of 87.
5%, recall of 88. 0%, Precision of 0.88 and F1 score of
0.874. for Decision Trees falls 85 % and for Naïve Bayes
it falls up to 73% whereas for Random Forest the
accuracy comes near to about 7%. 2%, precision of 82.
5%, recall of 84. The accuracy for the producers is 0%,
recall is 0%, and F1 score is 83. 2%. The comparative
analysis with the related works explores the pace and
progress of AI in various areas of the healthcare sector’s
concerns. Among the identified findings they include
proactive utilization of Artificial Intelligence in
enhancing the delivery of health care, use of Artificial
Intelligence in achieving patients’ better-quality health
care, and apply of Artificial Intelligence in the decision
making process. The future area is focused on the
improvement of the algorithm of AI, the ethical and legal
questions, and the usage of AI in the solving of the
worldwide health issues.

This report outlines the initial phase of a machine learning-driven endeavor focused on predicting used car prices. Using a Kaggle dataset of 46,000+ used car listings from Pakistan, we conducted data cleaning, analysis, and visualization. This involved encoding categorical features, normalizing numerical ones, and revealing key trends in popular car makes, models, and locations. These findings guided our approach for evaluating regression algorithms in the next phase to predict resale prices. The outcomes of this initial exploration lay the groundwork for subsequent model building and validation.

In an era where software systems are increasingly complex, code review remains a critical quality assurance practice. While effective, traditional manual code review methods are time-consuming and prone to inconsistency. This paper explores how artificial intelligence (AI) reshapes code review by enabling automation, improving accuracy, and promoting coding standardization. Drawing on recent AI-driven tools like GitHub Copilot and Google's internal frameworks, it examines the capabilities and limitations of these technologies in real-world settings. It highlights AI's ability to streamline refactoring, detect bugs, suggest secure coding practices, and provide real-time feedback. However, adopting AI raises concerns about developer over-reliance, algorithmic bias, and contextual misjudgments. Case examples from leading tech firms underscore AI's transformative potential while emphasizing the need for ethical safeguards and human oversight.

In an era where software systems are increasingly complex, code review remains a critical quality assurance practice. While effective, traditional manual code review methods are time-consuming and prone to inconsistency. This paper explores how artificial intelligence (AI) reshapes code review by enabling automation, improving accuracy, and promoting coding standardization. Drawing on recent AI-driven tools like GitHub Copilot and Google's internal frameworks, it examines the capabilities and limitations of these technologies in real-world settings. It highlights AI's ability to streamline refactoring, detect bugs, suggest secure coding practices, and provide real-time feedback. However, adopting AI raises concerns about developer over-reliance, algorithmic bias, and contextual misjudgments. Case examples from leading tech firms underscore AI's transformative potential while emphasizing the need for ethical safeguards and human oversight.

Currently, spam and scams are passive attack over the inbox which can initiated to steal some confidential information, to spread Worms, Viruses, Trojans, cookies and Sometimes they are used for phishing attacks. Spam mails are the major issue over mail boxes as well as over the internet. Spam mails can be the cause of phishing attack, hacking of banking accounts, attacks on confidential data. Spamming is growing at a rapid rate since sending a flood of mails is easy and very cheap. Spam mails disturb the mind-peace, waste time and consume various resources e.g., memory space and network bandwidth, so filtering of spam mails is a big issue in cyber security. This paper presents an novel approach of spam filtering which is based on some query generated approach on the knowledge base and also use some artificial neural network methods to detect the spam mails based on their behavior. analysis of the mail header, cross validation. Proposed methodology includes the 7 several steps which are well defined and achieve the higher accuracy. It works well with all kinds of spam mails (text based spam as well as image spam). Our tested data and experiments results shows promising results, and spam's are detected out at least 98.17 % with 0.12% false positive.

In this paper a novel optimization algorithm based on imperialist competitive algorithm (ICA) is used for the design of a low speed single sided linear induction motor (LIM). This type of motors is used increasingly in industrial process specially in transportation systems. In these applications having high efficiency with high power factor is very important. So in this paper the objective function of design is presented considering both efficiency and power factor. Finally the results of ICA are compared with the ones of genetic algorithm and conventional design. Comparison shows the success of ICA for design of LIMs.

Real-time detection of hazardous gases in the ambient and indoors has become the prime motive for curbing the problem of air pollution. Keeping the concentration of hazardous gases in control is the main task before human society so as to keep environmental balance. Researchers are concentrating on smart sensors because they can detect and forecast the presence of gas in real-time, provide correct information about gas concentration, and detect a target gas from a mixture of gases. This smart gas sensor system can have applications in the field of military, space, underwater, indoor, outdoors, factories, vehicles, and wearable smart devices. This study reviews recent advances in smart sensor technology with respect to the material structure, sensing technique, and discrimination algorithm. Focus is given on reducing the power consumption and area of a sensor circuitry with the help of different techniques.

Selection of a software project is a critical decision. This selection involves prediction to ascertain a project that provides the best business value to the organization. The process of selection is carefully undertaken to optimize scarce resources available, which makes it impossible to simultaneously invest in all business ideas and systems. The current traditional method of software selection does not consider risk factors among the many variables necessary to predict a project that could provide the best business value. More so, the current method such as an artificial intelligence approach, where project managers use more robust models to make predictions have not received the needed attention in developing models for software project selection. This research applied a branch of Artificial Intelligence called Artificial Neural Network to classify projects into three levels. The research designed an artificial neural network of four inputs, one hidden layer with twenty-seven (27) neurons, and three outputs. Keras, a python deep learning library that runs on a theano background was used to implement the model. This research used a secondary dataset, which was enhanced by the synthetic approach, to make the required data features needed in machine learning applications. Back-propagation Algorithm enabled the model to train and learn from the data, and K-fold cross-validation was used to measure the accuracy of the model on unseen data. The results of the simulation showed that the model performed up to 98.67% accuracy with a standard deviation of 2.6% performance on unseen data. The research concludes that using the artificial neural network for software project selection unleashes a new vista of opportunities in artificial intelligence where intelligent systems are developed based on robust models from data for project selection.

The scientific community has demonstrated that for the bankruptcy prediction, different techniques have different advantages on different data sets and different feature selection approaches. This subject has attracted a lot of research interests as it is one of the major preoccupation of accounting specialists, business analysts and information systems developers. Because there is no prediction performance via the techniques of artificial neural networks, we have explored this topic, and divided the prediction performance using different techniques into two important parts: 1) bankruptcy prediction and 2) non-bankruptcy prediction. In this short paper, we have built a combination of two well known classification techniques, namely the decision tree and the back propagation neural network. In our opinion, this combination (hybridization technique) provides an approach which inherits advantages and avoids disadvantages of different classification techniques. We described the research results, and demonstrated our expectations.

Along with the spreading of online education, the importance of active support of students involved in online learning processes has grown. The application of artificial intelligence in education allows instructors to analyze data extracted from university servers, identify patterns of student behavior and develop interventions for struggling students. This study used student data stored in a Moodle server and predicted student success in course, based on four learning activities -communication via emails, collaborative content creation with wiki, content interaction measured by files viewed and self-evaluation through online quizzes. Next, a model based on the Multi-Layer Perceptron Neural Network was trained to predict student performance on a blended learning course environment. The model predicted the performance of students with correct classification rate, CCR, of 98.3%.

Abstract: The trends in expansion of electric power systems and changes in the conditions of their operation have led to complicate power system operation, increased its changeability and unpredictability that call for prompter and more adequate response of controls systems. This paper presents the hybrid model for the short-term prediction the parameters of expected operating conditions based on joint usage of artificial neural networks models and Hilbert-Huang Transform. The hybrid model was proposed to increase the accuracy of ...

Cantilever beam is widely used in engineering application such as military, naval and aeronautical. Crack initiate due to different kinds of loading. The presence of crack alters the physical as well as dynamic characteristics of structure. Therefore detection of crack is major issue. The existence of crack affects vibration parameter such as natural frequency, mode shape and stiffness. In this paper change in natural frequency, mode shape is analyzed by using finite element analysis as well as experimental. It is observed that specimen having different crack inclination, crack location and crack depth its natural frequency get varies.

The assessment of Alzheimer&#39;s Disease (AD) and Mild Cognitive Impairment (MCI) associated with brain changes remains a challenging task. Recent studies have demonstrated that combination of multi-modality imaging techniques can better reflect pathological characteristics and contribute to more accurate diagnosis of AD and MCI. In this paper, we propose a novel tensor-based multi-modality feature selection and regression method for diagnosis and biomarker identification of AD and MCI from normal controls. Specifically, we leverage the tensor structure to exploit high-level correlation information inherent in the multimodality data, and investigate tensor-level sparsity in the multilinear regression model. We present the practical advantages of our method for the analysis of ADNI data using three imaging modalities (VBM-MRI, FDG-PET and AV45-PET) with clinical parameters of disease severity and cognitive scores. The experimental results demonstrate the superior performance of our ...

During the World War I ,II over fifty countries in the world today have been inherited a legacy of antipersonnel landmines and unexploded ordnance (UXO) which represents a major threat to lives, and hinders reconstruction and development efforts. Landmines have specific properties that make it harder to detect .Therefore; these properties lead landmine detectors to become more complex. Many examples can be found to address the increasing complexity of Landmines detection; unfortunately, these new techniques are high of cost and need experts to deal with it. Many developing countries face financial difficulties to get advanced technologies for detection landmines such as Robotic systems, this due to their high cost, use and maintenance difficulties which makes them unaffordable to these countries. The safety of operators, transportability, ease of maintenance and operation are the most factors that must take into consideration to improve the applicability and effectiveness of landmines tracking systems.The aim of the study is to proposed architecture of Intelligent Wireless Landmines Tracking System (IWLTS) with new decision model based on fuzzy logic.To find an affordable, light and easy to use alternative which meet users' needs to protect and warn them from the risk of landmines during practice their lives, we suggested the design and development of Fuzzy Inference Model for IWLTS using Smart Phone.Fuzzy model require three step which are definitions of Linguistic Variable and fuzzy sets, determine fuzzy rules and the process of Fuzzy Inference.Designed Fuzzy Inference Model gives both: Landmine risk value in percentage and alert to avoid that risk.