AI-based solution

The integration of conversational artificial intelligence (AI) chatbots into English as a Foreign Language (EFL) education represents a transformative technological advancement with significant potential for enhancing both student learning outcomes and teacher professional development. This comprehensive review examines how AI chatbots can be effectively utilized to develop linguistic competencies across speaking, listening, reading, and writing skills while supporting EFL teachers' professional growth. Drawing from recent empirical research and theoretical frameworks, this article provides evidence-based guidance for educators, administrators, and researchers seeking to implement chatbot technology in language education contexts. The analysis reveals that AI chatbots, particularly advanced systems like ChatGPT, can provide personalised learning experiences, immediate feedback, and scalable educational support that addresses traditional challenges in EFL instruction. Key findings indicate significant improvements in learners' speaking fluency, writing proficiency, and willingness to communicate when chatbots are integrated thoughtfully into pedagogical practices. For teachers, AI chatbots serve as valuable tools for content creation, assessment development, and instructional strategy enhancement, while also supporting their own language proficiency development. However, Prof. Mahmoud Abdallah-14 August 2025 2 successful implementation requires careful attention to pedagogical integration, ethical considerations, and maintaining balanced human-AI interaction. This article presents a comprehensive framework for chatbot integration that encompasses theoretical foundations, practical implementation strategies, specific functional applications, and professional development approaches. The evidence suggests that when properly implemented with appropriate teacher training and pedagogical support, Conversational AI chatbots can significantly enhance EFL education quality and accessibility while preparing both teachers and students for increasingly AI-integrated educational futures.

Aim:The present study focuses to investigate the effect of AI-based marketing forecasting and employee retention strategies on financial growth at organizations with employee satisfaction as a moderator and Sustainable Development Goal (SDG) orientation as a mediator in the NCR region of India. Research Methodology:A cross-sectional, quantitative research design was also used to gather data by using a structured Likert-scale questionnaire, from 435 mid to senior-level professionals, in different industries in Delhi-NCR. The constructs used in the study involve the constructs of technology adoption, human resource practices, and sustainable development, which are integrated into the single analytical model.

The proposed work describes the design and implementation of ShopAssist, an offline-capable customer support
chatbot system designed for e-commerce applications. ShopAssist offers a chatbot that supports customer queries
by combining semantic and contextual query understanding with user authentication and personalized customer
support. By combining TF-IDF vectorization, fuzzy string matching, and transformer-based embeddings, the
model can provide the most accurate and relevant answers in direct response to queries. With a graphical user
interface, the chatbot supports login-session-based control as well as contextual support. By combining user
authentication, contextual query understanding, and methods such as the Sentence-BERT method, Random Forest
classifiers, and Tkinter GUI elements for user interaction, ShopAssist provides offline usability in resolving
queries. In summary, the proposed human-centred solution has immediate implications for deriving significant
value by automating customer interactions, such as FAQs or customized responses, thereby enhancing customer
engagement in a seamless digital marketplace.

Artificial Intelligence (AI) is transforming the cybersecurity landscape, providing unprecedented capabilities in threat detection, incident response, and adaptive defense. This paper explores the intersection of AI and cybersecurity by assessing the dual role AI plays as both a defensive asset and a potential vulnerability. The study outlines how machine learning models enhance detection accuracy, automate responses, and identify sophisticated attack patterns in real time. At the same tiame, it acknowledges the dangers posed by adversarial AI, automation errors, and the lack of interpretability in current AI systems. The paper also evaluates leading AI-driven security platforms, compares their implementations, and discusses future developments in context-aware defense, federated learning, and explainable AI. The goal is to provide security professionals, researchers, and policymakers with a comprehensive overview of opportunities and challenges in AI-powered cybersecurity.

The rapid growth of mobile technology has fundamentally changed how people access information, communicate, and learn. The increasing ubiquity of mobile devices presents unique opportunities to expand elearning for adult learners, who often face specific challenges in accessing traditional learning environments. This study examines the role of mobile technology in facilitating e-learning access for adult learners. As mobile devices become ubiquitous, they offer unique opportunities to bridge gaps in adult education, especially for individuals with limited time and flexibility. Through a qualitative research approach, data was gathered from interviews with adult learners and educators to identify the benefits, challenges, and potential of mobile-assisted learning (m-learning) in adult education. Key findings highlight mobile technology's capacity to offer flexible, personalized, and socially connected learning experiences, making it an ideal medium for adult learners. However, challenges such as screen limitations, connectivity issues, and the risk of distractions were also observed. This paper discusses implications for educators and policymakers and suggests practical strategies for optimizing mobile learning experiences for adults.

Loneliness is a serious and growing public health concern in America, affecting millions of people and causing mental and physical distress. Studies have shown that prolonged social isolation increases the risk of depression, anxiety, cognitive decline, cardiovascular problems, and premature death. The traditional interventions have had some effects, but their scope is limited by obstacles such as cost, accessibility, and social stigma-therapy groups, community programs, and social support networks. Artificial intelligence (AI) offers a new and different avenue for combating loneliness in this age. With the advent of NLP, machine learning, and HCI, AI systems can now provide empathetic communication and emotional support and be companions. This paper analyzes the AI-enabled solutions of conversational chatbots, entertainment robots, virtual assistants, and AI-powered social networking tools to address loneliness in American adults. A mixed-methods strategy of qualitative interviews, quantitative surveys, and case studies was used. A nationwide survey of 500 participants explored respondents' perceptions of AI tools' effectiveness in providing emotional support and fostering social connection. Sentiment analysis was also performed on human-AI interactions to assess emotional impacts. Findings suggest that AI technologies are promising in alleviating loneliness. With 68% of respondents saying they saw some improvement in their emotional well-being due to AI companionship, applications using AI for conversational systems showed great promise for delivering emotionally responsive personalized interactions such as the Replika chatbot. Additionally, social networking systems with AI-enhanced recommendations led to the formation of online communities and meaningful digital interactions. The study raises further ethical concerns, including passivity toward AI, possible erosion of genuine human relationships, and risks posed to data privacy; all these topics need follow-up research and regulation.

The exponential growth of digital reading materials has created a pressing demand for intelligent tools that help users efficiently comprehend and navigate large volumes of text. BookSurfer is an open-source, web-based application designed to meet this need by leveraging artificial intelligence for automated book summarization and contextual question-answering. By integrating freely accessible books from public repositories, BookSurfer aims to democratize knowledge and support academic and independent learning. This paper details the design, development, and evaluation of BookSurfer, focusing on its use of advanced natural language processing (NLP) models to generate concise summaries and accurate answers to user queries. Empirical testing demonstrates that BookSurfer is effective and user-friendly, making it a valuable resource for students, educators, and lifelong learners.

The growing demand for efficient educational platforms has revealed challenges for engineering students in accessing diverse learning resources. Current platforms often provide fragmented solutions, hindering student's academic and career progression. This study introduces an integrated platform designed to Centralize essential resources like lecture notes (with TTS functionality), video tutorials, programming tools, DSA materials, technology roadmaps, and aptitude preparation. Using Al-driven Personalized learning paths, and smart chatbots, the offers an adaptive and interactive learning environment. With Al-based Summarization, mock test proctoring, and semantic search, the platform reduces resource search time by 70% and boosts academic efficiency by 90%. It also supports both regular and remote learners with features like cloud-based sync, offline access, and Personalized accounts. Prioritizing security with encryption, two-factor authentication, and file validation, it ensures ethical learning practices. By unifying resources, offering structured roadmaps in emerging fields like AI, ML, and IoT, and promoting skill development, the platform enhances learning outcomes for engineering students.

Large Language Models (LLMs) have revolutionized natural language processing and AI-driven applications. This paper explores the design and implementation of LLM orchestration tools tailored for enterprise needs, focusing on scalability, cost-efficiency, and adaptability. The proposed framework integrates advanced APIs with enterprise workflows to automate processes, enhance customer interactions, and derive actionable insights. Real-world case studies highlight significant improvements in decision-making, operational efficiency, and customer satisfaction. The study emphasizes the transformative potential of LLMs when strategically applied in enterprise ecosystems.

The Personalization in online shopping with user behavior data: a prospective study of transformative potential for consumer insights through e-commerce environments E-commerce platform can tap huge user interaction data like click streams, search queries and purchase history and dwell time once stored together it becomes of prime importance for customer's preference and intent as platforms collect loads of data. The platforms use advanced analytics, machine learning and recommendation systems to offer content, product recommendations and the user experience based on individual behavior. Such personalized engagement not only boosts user engagement but also increases conversion rates with uplifted customer loyalty. Linked to drawing data-driven techniques, businesses can then produce increased and accelerated service. The work looks at well-used techniques such as collaborative filtering, process mining and recommendation engines in real-time. It also notes what kind of effect personalization has on metrics like retention, average order value and browsing time. This manuscript examines the plausibility of data privacy, ethical personalization and trade-off between customization and user control. Using real-world case studies, it provides examples of personalization strategies and competitive advantage in practice. In addition, it touches on integration problems between data on devices and platforms. Ultimately, the study postulates a behavior-driven personalization-oriented dynamic framework E-commerce. User Behavior Analytics is pretty much to identify those based enablers that bring about optimization in the data economic ecosystem.

The accessible visualizations need to be designed to advance inclusivity and equity, especially among individuals with visual and cognitive impairments. The research combines rich literature that refers to inclusive design, user modeling, and accessibility heuristics in the aim of enhancing the understanding of data by various stakeholders. The key challenges in accessibility are visual complexity, inadequate color contrast, and assistive technology support. The colorblind-friendly color palettes that accommodate various types of color vision deficiency and screen reader-friendly graphics that present graphical information in aural or tactile format. Cognitive load is countered through simplification, clear distinction, and constant pattern structure. Accessible interfaces are facilitated through user-centered design and simulation methodologies that simulate disability to ensure usability prior to deployment. Techniques like hyper modal dissemination, tactile visualizations, and adaptive visual feedback enhance accessibility to charts and scientific data. Learning analytics, uncertainty visualization, and user gaze tracking are also included to enhance personalized visualization. These accessible techniques allow all users irrespective of their capability to access complex data meaningfully. The future should involve further exploring the use of AI to enable visualizations to be real-time adaptable as a function of user needs. This data-driven, inclusive approach has the potential to turn digital interfaces into more egalitarian channels of communication and decision-making.

This paper introduces "FindATutor," a web application built using the MERN stack (MongoDB, Express.js, React.js, and Node.js) that brings teachers and students directly together for offline and online learning. Conventional education systems have a tendency to compel tutors to use go-betweens-commissions of as much as 30% paying brokers-thereby cutting tutor profits and adding extra expenses to students. FindATutor does away with these middlemen by providing an open, efficient, and user-friendly solution for user registration, searching for tutors, scheduling, secure payment, and reviews. The design of the platform, with a focus on personalized interaction and direct communication

This research addresses the impact of online learning on student engagement using a qualitative content analysis method. As educational institutions shift to online platforms at an unprecedented rate, understanding the determinants of student engagement becomes imperative in improving the effectiveness and quality of online education. The objective of this research is to assess the impact of different variables such as instructor engagement, course structure, and learner-tolearner engagement on student engagement within the provided online learning contexts. To do this, the study examines a variety of sources including scholarly articles, course evaluations, and case reports of students taking online courses. The methodology involved in examining this content yielded balanced results in terms of prominent themes and patterns in engagement. The results reveal that the presence of an instructor is crucial to continuously engaging learners. Instructors' active participation, especially during feedback sessions, discussion moderation, and interactive learning environments, contributed towards students' participation and motivation. Moreover, students' interest and involvement in learning are enhanced by course designs which allow inclusion of multimedia presentations, group projects, and well-organized courses. Student interaction was also a key driver of engagement. Students who interacted with other learners through group discussions reported greater engagement in and satisfaction with the course. Nonetheless, this research revealed that platform user friendliness and poor internet connection, among other technical issues, greatly reduce engagement. Likewise, reduced interaction in online environments leading to feelings of solitude was also viewed to impede engagement. To enhance student participation and achievement in digital learning environments, this study concludes with suggestions concerning the design of online courses and support services aimed at integrating students more fully and academically into the online learning environment.

Customer Relationship Management (CRM) recruitment tools powered by Machine Learning (ML) are transforming talent acquisition by automating and optimizing the hiring process. This system leverages ML algorithms for resume parsing, candidate screening, and job matching, ensuring efficient and data-driven decision-making. By analysing historical hiring patterns, candidate behaviour, and job descriptions, the tool enhances recruitment accuracy and reduces time-to-hire. Natural Language Processing (NLP) enables sentiment analysis of candidate interactions, improving engagement and retention. In addition to, predictive analytics help forecast hiring needs, minimizing workforce gaps. This ML-driven CRM recruitment tool enhances efficiency, diversity, and overall hiring quality, making the recruitment process more intelligent and scalable.

Vāṇītyā-An Indic Language Learning Platform presents an integrated approach to language education that leverages advanced machine learning techniques and modern full-stack development to create an adaptive, personalized learning experience. In addressing real-world challenges, Vāṇītyā specifically targets common difficulties faced by learners of Indic languages, such as mastering the nuances of phonetics and script variations. For instance, learners often struggle with distinguishing subtle differences in vowel sounds-a fundamental yet challenging aspect of language acquisition. By dynamically adjusting the frequency and difficulty of exercises based on user performance, the platform not only reinforces core language skills but also builds confidence and competence over time. This adaptive, data-driven approach demonstrates a significant advancement in educational technology, offering a scalable solution to overcome the barriers encountered in traditional language learning environments. The platform synergizes a fine-tuned large language model, trained on over 1.5 million tokens sourced from IndicTrans, with a custom-built reinforcement learning model to generate, evaluate, and tailor language exercises in real time. This dual-model architecture supports diverse language exercises-including transliteration, translation, and interactive quizzes-and seamlessly adapts to individual user performance.

In later a long time, progressions in counterfeit insights and computer vision have empowered computerized arrangements for wellbeing observing and dietary evaluation. In this article, a deep learning-based system for estimating food calories is presented with the help of object recognition and image processing. The proposed system uses the Yolov5 model for food detection, OpenCV for the preprocessing of the interactive user interface, and power supplies. By using pre-formed models and pre-defined calorie data sets, the system recognizes food and provides approximate calorie counts based on volume estimation techniques. This paper presents a profound learning-based framework for evaluating nourishment calories utilizing question location and picture handling. The proposed framework utilizes the YOLOv5 demonstrate for nourishment acknowledgment, OpenCV for picture preprocessing, and Stream lit for an intelligently client interface. The comes about demonstrate that this approach is productive and adaptable for real-time applications in individual wellbeing tracking.

EduBot is an AI-powered chatbot developed using Dialogflow to streamline student queries and administrative processes at JBIET. It provides instant, 24/7 assistance on topics such as admissions, available courses, rankings and accreditations, transportation, fees, and campus facilities. By leveraging intent recognition, webhook integrations, and structured responses, EduBot enhances user experience by delivering quick and accurate information. This chatbot reduces the workload on administrative staff, improves accessibility, and ensures a seamless, interactive communication platform for students, faculty, and prospective applicants. Future enhancements will focus on voice support, multilingual capabilities, personalized responses, and mobile app integration to further improve its functionality.

The convergence of behavioral economics and data science has revolutionized how businesses personalize consumer experiences and optimize revenue generation. Traditional economic models assume rational decisionmaking; however, behavioral economics reveals that consumers often make irrational choices influenced by cognitive biases, heuristics, and emotional factors. By integrating big data analytics, machine learning, and artificial intelligence (AI), organizations can decode consumer behavior patterns, predict purchasing decisions, and tailor marketing strategies to maximize engagement and profitability. Data-driven behavioral insights enable companies to refine pricing models, customer segmentation, and recommendation systems, aligning products and services with individual preferences and psychological triggers. Predictive analytics, coupled with A/B testing and reinforcement learning, enhances businesses' ability to determine the optimal pricing strategies, discount structures, and promotional offers that drive conversions. Moreover, real-time behavioral tracking through AIpowered recommendation engines allows businesses to deliver hyper-personalized experiences, increasing customer satisfaction, loyalty, and lifetime value. Revenue optimization strategies leverage data science models such as dynamic pricing algorithms, sentiment analysis, and purchase propensity modelling to adjust pricing in response to consumer demand, competitor actions, and economic conditions. Companies implementing these strategies have reported higher conversion rates, reduced churn, and increased profit margins. However, challenges such as ethical concerns, data privacy regulations, and algorithmic bias necessitate responsible data governance and transparency in AI-driven decision-making. This paper explores the synergistic role of behavioral economics and data science in business strategy, offering insights into how organizations can harness cognitive biases and predictive analytics to enhance consumer engagement, increase revenue, and maintain competitive advantage.

This study presents a comparative analysis of the impact of team-based activities on leadership skills in physical
education in Lucknow region. The aim of the study is to understand the impact of team-based physical activities on the
development of leadership skills and its various dimensions. The study involved students from various schools and
colleges, and motivated them to participate in team-based activities.
The data was collected through questionnaires, interviews, and behavioral observation. The study used parameters such
as self-confidence, decision-making ability, cooperation, and communication skills to measure leadership skills. The
analysis of the data concluded that team-based activities play a vital role in the development of leadership skills. It was
found that participants who regularly engage in team-based activities have greater improvement in self-confidence and
decision-making ability. Additionally, skills such as cooperation and effective communication also improved. The study
also suggested that giving priority to team-based activities in physical education programs can help in the overall
personality development of students.

Dietary management plays a crucial role in preventing and managing chronic health conditions such as diabetes,
cardiovascular diseases, and obesity. However, many individuals struggle to adhere to optimal meal plans due to a lack
of personalized guidance tailored to their unique dietary needs and medical conditions. This challenge is particularly
pronounced in regions with limited access to professional nutritionists. To address this issue, we developed an AI-driven
Personalized Meal Planning Application that provides customized meal recommendations based on user-specific medical
data and dietary preferences. Our approach integrates machine learning algorithms with a PostgreSQL database to analyze
nutritional requirements and generate optimized meal plans. The system leverages TensorFlow-based models for dietary
pattern recognition and React.js for an intuitive user interface, ensuring seamless interaction. By incorporating role-based
access control and secure authentication, the platform ensures data security while enhancing user experience. The
proposed solution offers a scalable, efficient, and user-friendly approach to personalized nutrition, ultimately improving
adherence to healthy eating habits and supporting better health outcomes.

The quick-paced development of Generative Artificial Intelligence (Gen AI) has created new possibilities for software
engineering, which mainly involves automated code analysis. The generation of data dependence graphs (DDGs) from
source code needs attention because manual work or rule-based systems usually perform this task. Data dependence
graphs are vital because they demonstrate the data movement patterns in programs that facilitate debugging and,
optimization and security assessment procedures. The article describes how Gen AI transforms Data Dependence
Graphs by applying sophisticated machine learning systems to decode and evaluate code relationships at maximal
speed and precision. The automation of this task under Gen AI simultaneously decreases human work while building
more scalable and precise software analysis tools.
Gen AI enables DDG production by allowing models to process big code repository databases to learn dependency
connections and data movement patterns. The models use transformer architectures to interpret complex code
structures across various programming languages and frameworks. Next-generation artificial intelligence excels over
classic systems since it detects hidden dependencies and addresses unusual circumstances within dynamic
programming code. Delivery of this function proves essential when developers work with modern codebases that
consist of large heterogeneous evolving systems. The article details how Gen AI platforms create DDGs that are
interactive and understandable to developers so they can discover system vulnerabilities and optimize runtime
performance.
DDG adoption through Gen AI faces obstacles due to requirements for premium training resources, processing power,
and user-friendly readabilities from AI output generation. The article lists fine-tuning pre-trained models and
implementing knowledge related to particular domains as new solutions to tackle identified problems. Gen AIpowered DDG generation systems promise to disappear the gap between AI and software engineering applications,
creating revolutionary changes in developer code interaction methods and software development effectiveness.

Abstract

The abstract explores the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) on modern library systems. It traces the evolution of digital libraries and highlights how AI enhances information retrieval, cataloging, and user interaction. The study reviews key AI technologies such as Natural Language Processing (NLP) for automated cataloging, Machine Learning algorithms for predictive analytics, and chatbots for personalized support. Through comparative analyses, the article shows how AI improves search accuracy, resource management, and user experiences over traditional methods. Case studies of AI in academic and special collections libraries provide real-world insights, while the article emphasizes the need for ethical AI guidelines and best practices for adoption, including staff training and collaboration with technology providers. The future of AI in libraries envisions them as dynamic information hubs, fostering more efficient and intuitive resource discovery, and maintaining their role as reliable knowledge stewards in an AI-driven landscape.

The rapid growth of mobile technology has fundamentally changed how people access information, communicate, and learn. The increasing ubiquity of mobile devices presents unique opportunities to expand elearning for adult learners, who often face specific challenges in accessing traditional learning environments. This study examines the role of mobile technology in facilitating e-learning access for adult learners. As mobile devices become ubiquitous, they offer unique opportunities to bridge gaps in adult education, especially for individuals with limited time and flexibility. Through a qualitative research approach, data was gathered from interviews with adult learners and educators to identify the benefits, challenges, and potential of mobile-assisted learning (m-learning) in adult education. Key findings highlight mobile technology's capacity to offer flexible, personalized, and socially connected learning experiences, making it an ideal medium for adult learners. However, challenges such as screen limitations, connectivity issues, and the risk of distractions were also observed. This paper discusses implications for educators and policymakers and suggests practical strategies for optimizing mobile learning experiences for adults.

Abstract
In the era of rapid technological advancements, resource centers like libraries face the challenge
of adapting to emerging knowledge systems while aligning with sustainable development goals.
This article explores the integration of hybrid green technology systems in libraries to enhance
knowledge mining, resource sharing, and sustainable community development. Key areas
include the role of artificial intelligence, renewable energy solutions, and innovative tools to
create eco-friendly, efficient, and adaptive information systems. The paper aims to provide a
roadmap for transforming libraries into green knowledge hubs catering to the needs of the future
knowledge community.

In the current complex world of IT specialization, organizations are moving towards cloud infrastructure to improve scalability, cost optimization, and flexibility. Microsoft Azure is one of the available platforms that provides a rich, flexible, and robust environment designed to face every migration, including SAP systems or other critical databases for the business. However, for migration to be smooth, it must be done systematically and effectively through a very well-secured, well-designed architecture for the cloud and a plan that will enable minimal disruption of the business in case of disasters. In this article, the author focuses on the most critical strategies and considerations that must be considered for a well-thought-out architecture that can support the migration of SAP workloads and other databases to the Azure Cloud. Organizations can now leverage Azure native tools, inclusive of Azure Migrate, SAP on Azure services, and database migration solutions, which shall enable organizations to experience a smooth journey to the Azure environment while adhering to compliance standards, achieving maximum performance efficiency and minimizing costs in the process. Migration is a complex process containing certain phases involving the assessment, planning, process of performing, and optimization. Discovery is the first step in migration, where the organization looks at the current on-premise environment to see what is there, where the problems are, what the costs are, and how to prioritize. This was succeeded by implementing a high-availability design on Azure that would bring scalability, disaster recovery, and multi-region support to enterprise applications. This article discusses approaches to avoiding interruption of SAP systems and database workloads like SQL Server, Oracle, and PostgreSQL during migration. This is to maintain data integrity during replication, utilize cloud-native technology for always-on synchronization, and use a step-by-step migration approach to avoid risks relating to migration. Moreover, the article also focuses on the integral aspect of the requitable architecture regarding the ISO, GDPR policies, and other requirements for safe data processing and compliance with the legislation. Optimization after migration is highly recommended to enhance the return on investment aspect of Azure Cloud applications. Azure Monitor, Azure Cost Management, and several other in-built tools for performance tuning help organizations better control their resources and applications. Moreover, it discusses using Azure pricing models like RI and Autoscale to control such costs. These are some of the ways that, if implemented, would help lock the business's infrastructure for the future and also help increase performance and stability. This article is a

The increasing adoption of artificial intelligence (AI) in higher education has revolutionized language learning, academic writing, and personalized instruction. AI-powered tools such as ChatGPT, Grammarly, Quillionz, and DeepL offer students and faculty enhanced learning opportunities, allowing for more efficient writing processes, automated feedback, and real-time language support. This study investigates the role of AI in university-level English language instruction by analyzing the experiences of 15 instructors and 15 students from various disciplines. Using surveys, interviews, and document analysis, the research highlights AI's benefits, including increased efficiency and accuracy in academic writing, while also addressing concerns such as overreliance, lack of critical thinking, and ethical implications. The findings emphasize that while AI can significantly support language learning, it should complement, rather than replace, human instruction.

The development of the Clinical Group Decision Support System (CGDSS), so far, generally combines traditional and modern methods, such as Waterfall, Agile, and Prototyping. Challenges in developing CGDSS are data integration issues, user complexity, compliance with clinical standards, data security, and complexity in designing system interfaces and functionality. In addition, the algorithm's accuracy in providing data-based recommendations is also vital because errors can directly impact patient care. This study developed CGDSS for Dengue management with the System Engineering Process Methodology (SEPM) approach as a solution to combining the advantages of various methods by emphasizing structured, flexible, and adaptive system engineering. It can overcome various problems related to data integration, algorithm accuracy, and user acceptance.

In India, the principles of law and justice are fundamental to societal harmony and progress. However, access to legal information and awareness remains a significant challenge, particularly for those in rural areas and with low literacy rates. This paper proposes the development of a digital assistant integrated with a comprehensive Know Your Rights (KYR) framework tailored to the needs of Indian citizens. By harnessing technology, this assistant aims to democratize legal awareness, empower individuals to assert their rights and foster a culture of accountability within India's legal ecosystem.

This research aims to implement a website to increase sales and make it easier for consumers of “Ratu Hijab” business in Takalar
Regency. The research method used is Research and Development with a qualitative descriptive approach. The research process
for designing this business model adopts a 4D development model consisting of Define, Design, Develop, and Disseminate. Data
collection techniques are carried out through observation, interviews, and documentation. The results of the study show that a
website designed with design and features such as easy navigation and attractive content is able to expand marketing reach and
increase sales conversion. The results of the design that have been prepared in such a way are tested by media experts to assess
the design of the business model that has been designed by the researcher. After expert testing, the results of the business model
design can be recommended for “Ratu Hijab” business to develop her business.

The study determined the dimensions of the satisfaction for building permit applicants. The country already has
a governing decree that ensures standards and guidelines for buildings and structures. However, citizens
illegally construct due to difficulties in efficient office procedures and dissatisfaction with the services offered
(Omorog et al., 2018; Nurrahmah & Tuti, 2021). This study aims to determine the dimensions influencing the
satisfaction of building permit applicants through non-experimental quantitative Exploratory Factor Analysis
(EFA). A research-designed survey was given to 150 applicants.
The researchers determined four dimensions, namely efficient processing, responsive staff and ease of access,
fairness and transparency, and courteous and straightforward, that influence applicants' satisfaction with the
building permit process. The results of this study can improve the service delivery of permit offices, providing
satisfactory performance to applicants.

This research paper investigates how entertainment media, especially movies and television show,
affects college students pursuing fashion-related degrees in terms of their style preferences and fashion
choices. Given that movies and television are visual forms of media, they have long influenced fashion
trends, and students frequently mimic the fashions, characters, and costumes portrayed. The present
study is going to search into the ways in which media influences the creativity, choices, and career
inspiration of fashion students. It emphasizes the significance of media consumption in molding their
understanding of fashion trends, identity, and self-expression. It is going to highlight how much the
media influences culture and shapes the fashion sense of aspiring experts in the field.

As of 2024, WhatsApp has over 2 billion monthly active users, making it one of the most widely used
messaging platforms globally. In addition, more than 100 billion messages are sent daily on the app,
highlighting its integral role in daily communication (K. Kaushik and Y. Katara, 2022 [4]) This project aims on
innovating an approach for analyzing WhatsApp chat data, providing users with a powerful tool to upload their
chat histories in text format and receive detailed analytical reports through interactive visualizations. The
application serves as an engine for comprehensive data analysis, examining critical aspects of WhatsApp
conversations. Users can access insights related to the total number of messages exchanged, the overall word
count, and the number of media files and links shared within the chats. Moreover, the tool features timeline
analyses that display chat activity on a daily, weekly, and monthly basis, allowing contributors to understand
patterns in their communications over time. It identifies the busiest days and months, helping users pinpoint
when their conversations are most active, as well as highlighting the most engaged users within their groups.
Additional features include an exploration of common and top words used in conversations and detailed emoji
analysis, which offers a fun yet insightful view of emotional expressions in chats. The project is to be built using
Python for robust data analysis and MERN stack or creating a user-friendly, interactive web application. This
platform is particularly advantageous for institutions and businesses that leverage WhatsApp as a
communication channel, as it offers a clear and concise view of customer interactions, marketing efforts, and
group dynamics. By providing valuable insights into chat patterns and user engagement, the project empowers
users to make informed, data-driven decisions, for improving communication effectiveness with scope for
enhancing their overall business strategies. Ultimately, this project not only addresses the need for analytical
tools in WhatsApp but also opens up avenues for optimizing user interactions and driving better outcomes in a
fast-paced digital environment across multiple conversational avenues.

This research paper explores the role of centralized educational support platforms in reducing dropout rates,
with a focus on Shiksha-Setu, a platform designed to assist dropout students in India. By consolidating
resources such as government schemes, NGO support, and sponsorship opportunities, Shiksha-Setu creates a
streamlined process for dropouts to access educational and career-related resources, helping them overcome
barriers to re-engagement. Through a detailed analysis of user engagement, platform interaction data, and
feedback from stakeholders, this study investigates how centralized platforms foster re-enrollment, bridge
resource gaps, and improve educational outcomes. The insights from this research underscore the platform's role
in reducing dropout rates and present a model for future educational support initiatives aimed at mitigating the
dropout crisis and addressing the socioeconomic disadvantages faced by students who leave the education
system prematurely

In recent years, the demand for online examinations has surged, driven by the growth of e-learning and remote
assessments. Ensuring examination integrity has become a primary concern, especially when face-to-face proctoring is
not feasible. AI-based proctoring systems present a solution by using advanced image processing and machine learning
to monitor candidates in real-time. This research paper explores a proctoring system incorporating face detection, eye
tracking, head-pose estimation, and mouth opening detection, all of which are essential for detecting suspicious
behaviors. Leveraging tools such as OpenCV, TensorFlow, and Python, this system provides a comprehensive solution
for academic integrity in remote examinations. The proposed system continuously monitors students' behavior, ensuring
transparency and fairness by identifying and flagging any potentially unauthorized actions.

Dermatology, due to its complexity, remains one of the most challenging fields for accurate diagnosis. Often,
diagnosing skin conditions requires multiple tests and relies on practitioner expertise, making it time-consuming
and inaccessible for many. This paper proposes an automated image-based system leveraging machine learning
to classify and diagnose skin diseases, aiming to reduce dependency on traditional methods. This system
enhances the quality of patient care by providing accessible, accurate, and timely skin disease assessments.

This paper introduces InvestWise, a web-based personal finance advisory platform aimed at empowering users
with personalized investment recommendations and financial insights. By leveraging advanced machine learning
algorithms such as Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM) networks, and
Reinforcement Learning, InvestWise provides users with predictive analyses of potential returns on diverse
investment options, including stocks, real estate and mutual funds.. Additionally, InvestWise includes educational
resources and interactive visualizations to simplify complex financial concepts, enhancing financial literacy. With
real-time data integration and a userfriendly interface, InvestWise offers a holistic approach to financial planning,
making financial advisory services more accessible and personalized.

The AI-Powered Learning Management Systems (LMS) project aims to develop an intelligent, web-based
application designed to enhance the student experience in education. This application features a custom AIpowered chatbot capable of summarizing educational videos and providing real-time doubt resolution,
facilitating a more interactive and efficient learning process. Leveraging tools like ffmpeg-python, Cloudinary
and transformers, the system processes video transcripts to create concise summaries, supporting students in
quick content revision, especially during exams. The process involves transcript retrieval, summarization
through pipelining and Whisper, and displaying the reduced content, achieving over 70% reduction in transcript
length. By addressing key technological, institutional, and user-related factors, this project seeks to provide
valuable insights for integrating AI-driven solutions within web-based learning platforms, ultimately aiming to
improve learning outcomes, increase student engagement, and transform the Indian higher education landscape

This toolkit has been designed to provide employers and careers and employment advisers with crucial information on how to support young people in their transitions to the labour market in the context of new technologies used in recruitment and selection processes. The toolkit is a chance to consider how Asynchronous Video Interviews (AVIs) are being used in recruitment, and the implications for job-seekers, platforms and employers. It is underpinned by in-depth qualitative interviews with young job-seekers and an evidence review Our research sets out to demystify some of these technologies and to highlight the lived experience of young people undergoing recruitment in this format, and to provide guidance to employers and careers and employment services on the implications for their practice. It builds on detailed research conducted by Dr Zahira Jaser and team, at the University of Sussex Business School, who used a mix of interviews of young job seekers, analysis of material publis...

With the increased adaptability of remote work patterns, secure and efficient identity verification has also become a paramount concern. The traditional model of authentication involving the use of passwords or security tokens fails to provide any continuous verification of the user and is also prone to hacking attempts. Behavioral biometrics, especially keystroke dynamics and mouse movement patterns, offer an effective alternative as they allow for unobtrusive user authentication that is based on the individual user's behavior and is therefore continuous. This paper explores the application of Long Short-Term Memory (LSTM) networks, a sequence-based AI model, for studying and differentiating behavioral biometrics. We are using freely available data sets of keystroke and mouse dynamics to design and test an LSTM based system which is capable of making a distinction between users and imposters.
We have shown that LSTM networks are significantly better in handling time series data such as state transition sequences than other statistical machinery traditional machine learning methods, such as Random Forests and Support Vector Machines, where they scored 89% accuracy between normal user operations and abuse activity. This aspect of the research addresses the reason why LSTM networks are appropriate for live remote identity verification systems, which is their ability to learn long strips of sequences and the behavioral flow. The aim of the study is to expand the systems secure authentication solutions based on artificial intelligence systems that can be integrated to facilitate remote working in any sector for enhanced security.

Artificial Intelligence tools, such as ChatGPT, have significantly transformed patterns of learning and language acquisition patterns, rendering them more efficient and allowing for information retrieval in reduced timeframes. ChatGPT is a practical resource for learners to develop reading, listening, speaking, and writing skills. Consequently, ChatGPT possesses the potential to revolutionise traditional language learning methodologies within English as a Second Language (ESL) education by providing learners with a distinctive, interactive platform that enhances their linguistic abilities (Javaid, 2023). The application of artificial intelligence in language learning has progressed significantly in recent years, characterized by the emergence of various intelligence-based software tools designed for language acquisition. These tools provide a broad spectrum of language learning opportunities, encompassing practices in speaking and have demonstrated the capacity to facilitate second language acquisition with both efficiency and effectiveness. This article aims to analyze the efficacy of cognitive-focused language learning software in second language development through a comprehensive review of existing research on spoken vocabulary. In this article, I will examine different language learning software, their learning activities, and the research backing their effectiveness in promoting second language development. Additionally, I will discuss the pros and cons of using AIassisted language learning software and consider potential future research directions in this field. This article will delve into different language learning software, the learning activities they offer, and the research backing their effectiveness in enhancing second language acquisition. Additionally, we will discuss the pros and cons of using AI-assisted language learning software and consider potential paths for future research in this field.Chun et al. (2016) argue that technology has become a crucial component of the educational process in the changing landscape of language instruction, providing innovative tools that enhance language acquisition.

AI-based L2 learning tools, while promising personalized and engaging language learning, harbor potential for bias. This abstract explores the types of bias (data, algorithmic, feedback), their consequences (unequitable learning, reinforced stereotypes, ethical concerns), and mitigation strategies (data diversification, algorithmic auditing, inclusive feedback, user education). Contextual awareness and human oversight are crucial for ethical and equitable use. Addressing bias is an ongoing challenge in AI-based L2 learning, but by being aware and proactive, we can ensure these tools contribute to a more inclusive and equitable language learning experience for all.

Abstract: This article provides an overview of the current application of artificial intelligence (AI) in European Union (EU) securities markets and assesses the degree of adoption of AI-based tools. It highlights that while an increasing number of asset managers leverage AI in investment strategies, risk management, and compliance, only a few of them have developed fully AI-based investment processes and publicly promote the use of AI. Similarly, in other parts of the market, such as credit rating agencies, proxy advisory firms, and financial market participants, AI is used to enhance information sourcing and data analysis. However, the overall adoption of AI is not leading to a fast and disruptive overhaul of business processes. The report concludes that while AI is increasingly being adopted to support and optimize certain activities, there are risks associated with its widespread use, such as the concentration of systems and models among a few "big players." Therefore, further attention and monitoring are required to ensure that AI developments and the related potential risks are well understood and taken into account. The report is based on an analysis of information collected by the European Securities and Markets Authority (ESMA) through several channels between April and November 2022, including interviews with securities market participants in the EU and written questionnaires sent to entities directly supervised by ESMA.

Keywords: Artificial Intelligence, Exchange-traded financial assets, Non-bank financial institutions

JEL classification: C45, D83, G17, G17, G23

Links to the article:
1.) https://cyberleninka.ru/article/n/ai-based-tools-in-european-securities-markets-an-overview-of-adoption-use-cases-findings-and-implications/viewer (EN)
2.) https://cyberleninka.ru/article/n/ai-based-tools-in-european-securities-markets-an-overview-of-adoption-use-cases-findings-and-implications (EN)
3.) https://cyberleninka.ru/journal/n/endless-light-in-science?i=1119229
4.) https://www.researchgate.net/publication/370510294_Mezdunarodnaa_naucno-prakticeskaa_konferencia_Science_and_Technologies_Aprel_2023 (EN)

ORCID: https://orcid.org/0009-0006-8726-6291

This toolkit has been designed to provide employers and careers and employment advisers with crucial information on how to support young people in their transitions to the labour market in the context of new technologies used in recruitment and selection processes. The toolkit is a chance to consider how Asynchronous Video Interviews (AVIs) are being used in recruitment, and the implications for job-seekers, platforms and employers. It is underpinned by in-depth qualitative interviews with young job-seekers and an evidence review Our research sets out to demystify some of these technologies and to highlight the lived experience of young people undergoing recruitment in this format, and to provide guidance to employers and careers and employment services on the implications for their practice. It builds on detailed research conducted by Dr Zahira Jaser and team, at the University of Sussex Business School, who used a mix of interviews of young job seekers, analysis of material publis...

Recently applicant information services and interview-assistance services based on big data and AI technology are distributed rapidly worldwide to introduce an interview system that secures efficiency and fairness in the job interview market. Accordingly, this study presents an AI-based interview system developed based on deep-learning technology in which more than 100,000 evaluation data sets were derived from 400,000 interview image data sets. The resulting AI interview system has been applied to enterprises with a reliability of 0.88 Pearson score. Particularly, applying this system to 5 major public enterprises in Korea is presented in this paper. It turned out that the level of satisfaction with fairness and efficiency was as high as 85% in such aspects as evaluation processes, job fitness, and organization fitness. As the applicable range of AI-based solutions is expanding to the general area of personnel management with its time and cost efficiency, as well as reliability and fairness recognized, the deep learning-based job interview solution proposed by the present study needs to be applied widely to written examinations and personality and aptitude tests.

One of the most important and recognized activities of this process is the job interview, despite this, there are few investigations that help the candidate to have an adequate preparation to face this process and little data to carry out a system capable of helping to improve the process. Being able to know the preferences of the user or the ideas they have about an interviewer, would facilitate the improvement of the current process in the interviews and would be very important for carrying out implementations in order to improve the processes. For this reason, in the present study, an analysis of the person's reactions to an interviewer character is carried out through a mobile system, and their preferences are known through a survey to complement the desired information. The results obtained from the analysis are intended to help this research area and to facilitate the creation of a system that is considered as future work.