Intelligent JAVA Applications for the Internet and Intranets

This position paper presents a synopsis of the imperative function artificial intelligence (AI) has partaken in software engineering (SE) as well as in software testing. In addition, the paper discusses how graphical user interface (GUI), and event driven software testing can derive benefits from the use of AI techniques. Artificial intelligence has significantly aided the process of the automation of different software process. The employment of AI in software testing is not novel, having played a crucial role in the automation of software testing since its innovation. The usage of AI techniques not only reduces the cost but it also guarantees better quality as well as thorough testing. GUI Testing can be considered as the most challenging area of software testing. Although the results are quite preliminary, but the application of different AI techniques for GUI testing has proven to produce ideal results. Nevertheless, the application of AI techniques in GUI testing, in comparison with software testing, which has procured much assistance by venturing with AI, leaves much to be desired.

Aim/Purpose: This study carried out a systematic review of the literature on artificial intelligence (AI) in English language teaching (ELT). The objective was to delineate the current research progress in the field and to further understand the challenges. Background: The study analyzed articles published between 2015 and 2021. Methodology: The qualitative research method was employed. Five steps were taken to steer the review. 200 articles were scrutinized; 64 were retained. Contribution: Prior research on AI in ELT has not investigated how the literature is progressing or what areas of AI are being covered. Without a holistic picture, some important research findings could be missed. Understanding how studies on AI in ELT are designed and implemented will contribute to a greater understanding of the existing state of research. Findings: Findings show that there is a promising future for AI in ELT. AI in ELT yielded positive results in terms of optimizing the English language skills, translation, assessment, recognition, attitude, satisfaction, etc. It was also found out that more and more articles on the topic are being published; the mixed research method is the most commonly used, higher education level is the most sampled, students as participants are the most sampled, and most studies developed novel AI-based systems. Various AI approaches have been identified in the reviewed studies, including machine learning, neural network, support vector machine, genetic algorithms, deep learning, decision tree, expert system, natural language processing, data mining, cloud computing, and edge computing. However, AI in ELT is still in its infancy, where little research has been conducted and gaps in the literature are still present, especially in terms of inherent issues related to body language, gestures, expressions, emotions, translation, lack of elaborate description of teaching material used for learning driven by AI, uncertainties and vagueness with regards to what can be considered under the realm of AI, and most authors being outside of the ELT discipline. Recommendations for Practitioners: This literature review is likely to provide practitioners with an overview of the current adopted technology, research method, instruments and/or tools, educational level, language skill, and the effects reported by the AI-based studies for designing effective systems for the use of AI in their ELT classrooms. Recommendation for Researchers: Researchers need to conduct research on AI in ELT along with a detailed in-depth description of the methodology, research design, and the proposed systems used to achieve AI in ELT. Furthermore, it is recommended that researchers explore the efficiency of AI-based systems used in previous research and ensure their relevance and functionality. They are also required to provide in-depth analysis of the challenges inherent to systems that have been highlighted in the literature, which will maximize the potentials of these AI-based technologies. Impact on Society: The findings of this paper can provide visualization of research findings that could particularly benefit researchers, educators, and AI specialists who are involved in the study of the applications of AI in ELT. Future Research: Future AI research needs to seriously include more detailed descriptions of the method in further research.

Artificial Intelligence in the American Healthcare Industry: Looking Forward to 2030

Side weir is one of the structures which are widely used in water engineering projects So study on the flow characteristics especially discharge coefficient ( ) of this type of weir is important. Several Empirical formulas proposed to calculate the that they usually associated with significant errors. Thus, using mathematical methods based on artificial intelligence is inevitable. Artificial neural network (ANN) is a very useful data-modeling tool that is able to capture and represent complex input and outputs relationships. In this study, accuracy the some famous empirical formula was assessed and Borghei and Parvaneh was most accuracy formula with ( ). To increase the accuracy of production the multi-layer neural network (MLP) has developed. This model, in the first, trained by a back propagation method such as levenberg_marquardt and its performance was determined. the error indexes in the train and testing process are equal to ( and ). To increase the accuracy of MLP model, instead of increasing the size of the network (increase in the number of neurons and layers), Genetic Algorithm was used to train this model (GANN)and obtained an optimized value for Bias and weights. The final results show that using the GA to training the MLP model cased to increase in accuracy .The performance of the optimized neural network (GANN) in training and testing is equal to ( and ). Normal 0 false false false EN-US X-NONE AR-SA

Background. Modern society is developing in a world of information overload and digitalization, which has led to a need for artificial intelligence (AI) to help analyze and process information more efficiently. Most AI models use natural language processing, and while they can imitate human speech, they generate texts based on templates set by humans. This creates an online language space that is unique to AI, with its own characteristics due to its isolation from human interaction. Purpose – analysis of lexical and grammatical features of artificial intelligence language and comparison with human language. Materials and methods. To solve the tasks set in the research, we used various methods of analysis: observation, description, and the comparative method. The article is based on an analysis of texts generated by neural networks such as ChatGPT, Rytr, Smodin, and ChatSonic, which were created at the request of the authors. Results. The functioning of artificial intelligence (AI) is based on simulating the thought processes that are inherent in the human mind. This imitation is made possible by the development of language modules by AI specialists for use by neural networks in natural language processing (NLP). The language space of AI, limited by the input data from these modules, can generate texts that are practically indistinguishable from natural language, although they have features that are determined by the conditions under which they are created. These features include attachment to keywords in the task, potential loss of the main idea, inconsistency with required style, repetition of similar ideas, lack of specific facts or personal experiences, and limitations in vocabulary, linguistic patterns, and grammatical structures. The current inability of artificial intelligence (AI) to go beyond its current linguistic capabilities is due to a lack of a concept such as consciousness in neural networks. This allows for the accumulation of experience and the ability to think with insights, which are essential for AI to progress beyond its current limitations.

Implementation of a Chatbot System using AI and NLP

تناولت الدراسة الحالية واحدة من أهم المشاكل التي تعاني منها المنظمات الرياضية عالية الاداء وهي عدم الاستفادة القصوى من تطبيقات تقنيات الذكاء الاصطناعي، فلقد تمثلت مشكلة البحث الأساسية في معرفة أثر تطبيق تقنيات الذكاء الاصطناعي في ادارة المنظمات الرياضية عالية الأداء وما مدى مساهمة تطبيقات تقنيات الذكاء الاصطناعي في تشخيص ومعالجة المعوقات الادارية والتقنية في اقسام النشاط الرياضي والمدرسي في المديريات العامة للتربية في محافظة بغداد للتوجه نحو الاداء العالي وفي هذا السياق، فلقد تمثل هدف الدراسة الرئيس في بناء مقياس للاطار المفاهيمي لتطبيقات تقنيات الذكاء الاصطناعي في المنظمات الرياضية عالية الاداء -اقسام النشاط الرياضي والمدرسي في المديريات العامة للتربية في محافظة بغداد أنموذجاً. ولغرض تحقيق أهداف الدراسة اتبعت الباحثتان المنهج الوصفي بالأسلوب التحليلي لملامته لطبيعة المشكلة كذلك القيام بدراسة استطلاعيه غير مقننه في اقسام النشاط الرياضي والمدرسي وكانت الاستبانة هي الأداة المستخدمة في ذلك، وتوصلت الدراسة إلى بناء مقياس رصين لتطبيقات تقنيات الذكاء الاصطناعي في المنظمات الرياضية عالية الاداء ومكون من خمس محاور وهي النظم الخبيرة والواقع الافتراضي والوكلاء الاذكياء وتقنيات المعلومات واتمتة العمليات وقد جاء اختيار حجم العينة مساويا للمجتمع لتلام طبيعة البحث، وقد تم اثبات صدق وموثوقية هذا المقياس من خلال الوسائل الإحصائية كمعامل الفا كرو نباخ بمقدار 89,7، لقد احتوت محاور المقياس الخمسة على خمسة وعشرون عبارة، ولقد تراوحت الأهمية النسبية لهذه العبارات ما بين (63-84)%، وكانت اعلى قيمه للأهمية النسبية تبلغ 84% لعباره الثانية من محور النظم الخبيرة وهي (تعمل النظم الخبيرة كخبير استشاري للإدارة العليا في اقسام النشاط الرياضي والمدرسي لتسهم في اتخاذ القرارات الصحيحة)، بينما كانت اقل قيمة للأهمية النسبية لعبارة الأولى من محور الواقع الافتراضي وهي (استخدام الواقع الافتراضي في اقسام النشاط الرياضي والمدرسي يسهم في اكتساب مهارات متنوعة للموظفين ويعمل على تطوير الاداء الاداري والتقني)

Artificial intelligence (AI) is the science of designing systems that exhibit similar characteristics associated with the intelligence found in human behavior. It is a significant aspect of computer science that deals with system recognition such as learning, reasoning, understanding language, and taking actions to solve problems. As a computational system, AI can, for a given set of human-defined goals, make predictions and recommendations or make influencing decisions in real or virtual environments. The major branches of AI are expert systems, robotics, machine learning, neural network, fuzzy logic, natural language processing. The development of AI technologies is having a profound impact on the way governments work and design policies. The applications touch areas such as health services, transportation, and security. Public servants are using AI to help them make welfare payments and immigration decisions, answer citizen inquiries, and classify health care cases, among many other activities.

The article is devoted to the issues of forming the conceptual bases for the development and application of artificial intelligence technologies in the forecasting of economic processes. The relevance of developing a conceptual approach to the application of artificial intelligence technologies to predicting economic processes is substantiated. An overview analysis of relevant related scientific research works was conducted and the state of problem development was studied. Artificial intelligence includes machine and deep learning, natural language processing, computer vision, expert systems, fuzzy logic, genetic algorithms, etc. such methods and means were explained. Artificial intelligence methods based on evolution-algorithm, Data-mining technology, machine learning technology, and computer vision were analyzed in forecasting economic processes. The basis of the application of the neoclassical production function in predicting the impact of artificial intelligence on economic growth has been investigated. Neoclassical models were used to investigate the impact of artificial intelligence technology on production. Based on data mining technology, the economic development potential forecasting model and its forecasting adequacy were explained. The structural scheme of the main components of the application of artificial intelligence technology in the forecasting of economic processes has been developed. In order to increase the stability of economic development and predict economic processes by means of artificial intelligence technologies, relevant proposals, and recommendations were made, taking into account the technological requirements and priorities of the elements of the Industry 4.0 platform.

In the era of digitalization, electronic gadgets such as Google Translate, Siri, and Alexa have at least one characteristic: They are all the products of natural language processing (NLP). “Natural Language” refers to a human language used for daily communication, such as English, Hindi, Bengali, etc. Natural languages, as opposed to artificial languages such as computer languages and mathematical nomenclature, have evolved as they have been transmitted from generation to generation and are challenging to explain with clear limits in the first instance. In natural language processing, artificial intelligence (Singh et al., 2021), linguistics, information processing, and cognitive science are all related fields (NLP). NLP aims to use intelligent computer techniques to process human language. However, NLP technologies such as voice recognition, language comprehension, and machine translation exist. With such limited obvious exclusions, machine learning algorithms in NLP sometimes lacked sufficient capacity to consume massive amounts of training data. In addition, the algorithms, techniques, and infrastructural facilities lack enough strength. Humans design features in traditional machine learning, and feature engineering is a limitation that requires significant human expertise. Simultaneously, the accompanying superficial algorithms lack depiction capability and, as a result, the ability to generate layers of duplicatable concepts that would naturally separate intricate aspects in forming visible linguistic data. Deep learning overcomes the challenges mentioned earlier by using deep, layered modelling architectures, often using neural networks and the corresponding full-stack learning methods. Deep learning has recently enhanced natural language processing by using artificial neural networks based on biological brain systems and Backpropagation. Deep learning approaches that use several processing layers to develop hierarchy data representations have produced cutting-edge results in various areas. This chapter introduces natural language processing (NLP) as an AI component. The history of NLP is next. Distributed language representations are the core of NLP's profound learning revolution. After the survey, the boundaries of deep learning for NLP are investigated. The paper proposes five NLP scientific fields.

The control and monitoring of power generation plants is being complicated day by day, with the increase size and capacity of equipments involved in power generation process. This calls for the presence of experienced and well trained operators for decision making and management of various plant related activities. Scarcity of well trained and experienced plant operators is one of the major problems faced by modern power industry. Application of artificial intelligence techniques, especially expert systems whose main characteristics is to simulate expert plant operator’s actions is one of the actively researched areas in the field of plant automation. This paper presents an overview of various expert system applications in power generation plants. It points out technological advancement of expert system technology and its integration with various types of modern techniques such as fuzzy, neural network, machine vision and data acquisition systems. Expert system can significantly reduce the work load on plant operators and experts, and act as an expert for plant fault diagnosis and maintenance. Various other applications include data processing, alarm reduction, schedule optimisation, operator training and evaluation. The review point out that integration of modern techniques such as neural network, fuzzy, machine vision, data base, simulators etc. with conventional rule based methodologies have added greater dimensions to problem solving capabilities of an expert system. DOI: http://dx.doi.org/10.11591/ijece.v4i1.5025

Artificial Intelligence (AI) has significantly transformed multiple industries, enabling automation, data-driven decision-making, and advanced problem-solving capabilities. This research paper explores the evolution and advanced applications of AI, focusing on Prolog, a logic-based AI programming language used in expert systems, natural language processing (NLP), and automated reasoning. The study presents a comparative analysis of Prolog versus other AI programming languages, including Python and Lisp, and highlights its continued relevance in knowledge-based AI. Additionally, this paper discusses challenges and future research directions for Prolog-based AI models. Despite the rise of machine learning and deep learning, symbolic AI—represented by Prolog—remains crucial for explainable AI (XAI), rule-based automation, and high-level reasoning. Keywords: Artificial Intelligence, Prolog, AI Programming, Machine Learning, Expert Systems, Symbolic AI

In the present era, of globalization and automation, artificial intelligence (AI) has come out as a better implement to solve many problems that require decision making. In this work, it has been tried to amalgamate the research work done related to application of soft computing techniques particularly in the area of mechanical engineering. A lot of researches have being carried out in this domain because it has been a pool of infinite scope for innovative works. This paper gives a quick view of the different applications of AI techniques, such as neural networks, fuzzy logic, neurofuzzy (NF), simulated annealing (SA), genetic algorithm (GA), genetic programming (GP), and data mining (DM), and provides a conscious view in the different paradigms of mechanical engineering where the soft computing techniques are separately used and even in combination with one another to perform certain tasks which if performed by human becomes a dreary task. Several applications of soft computing have been proposed in the literature to solve the problems related with complicated mechanical systems. It is felt that a review of the application in various mechanical areas would help to compare their main features and their relative advantages or limitations to allow choose the most suitable technique for a particular application and also throw light on aspects that needs further attention.

The study comprehensively reviews artificial intelligence (AI) techniques for addressing algorithmic bias in job hiring. More businesses are using AI in curriculum vitae (CV) screening. While the move improves efficiency in the recruitment process, it is vulnerable to biases, which have adverse effects on organizations and the broader society. This research aims to analyze case studies on AI hiring to demonstrate both successful implementations and instances of bias. It also seeks to evaluate the impact of algorithmic bias and the strategies to mitigate it. The basic design of the study entails undertaking a systematic review of existing literature and research studies that focus on artificial intelligence techniques employed to mitigate bias in hiring. The results demonstrate that the correction of the vector space and data augmentation are effective natural language processing (NLP) and deep learning techniques for mitigating algorithmic bias in hiring. The findings underscore the potential of artificial intelligence techniques in promoting fairness and diversity in the hiring process with the application of artificial intelligence techniques. The study contributes to human resource practice by enhancing hiring algorithms’ fairness. It recommends the need for collaboration between machines and humans to enhance the fairness of the hiring process. The results can help AI developers make algorithmic changes needed to enhance fairness in AI-driven tools. This will enable the development of ethical hiring tools, contributing to fairness in society.

Introduction: In the last decades, artificial intelligence (AI) established itself as a transformative tool in accounting and auditing. Its implementation allowed automating repetitive tasks, increasing accuracy in data analysis and freeing professionals to focus on strategic functions. Since its beginnings in the 1950s, AI evolved from theoretical ideas to practical applications with great impact in financial and administrative sectors.Development: The development of AI began with the creation of the Logic Theory Machine and advanced with paradigms such as symbolic and connectionist AI. Over the years, technologies such as neural networks, virtual assistants and machine learning systems were integrated into various fields, including finance. In accounting, AI facilitated reporting and decision making by reducing the operational burden. In auditing, it made it possible to detect fraud and errors more quickly and reliably than traditional methods. Tools such as expert systems, neural networks and intelligent agents were incorporated to improve accounting review and ensure compliance with international standards. However, concerns were raised about job losses, technological dependence and ethical dilemmas.Conclusion: AI represented a substantial advance in the efficiency and quality of accounting and auditing processes. Although it generated job uncertainty and ethical challenges, it was viewed as a strategic ally when applied in a conscious and regulated manner. Its responsible integration strengthened business decision-making and sustainable economic development.