Next-Generation Question Creation: A Machine Learning Perspective on Document-Driven Automation

Abstract

Designing questions and answers is a necessary task when working for some organisations in the education and training sectors and even in some other fields with user interactions. However, the standard ways of doing it can be heavy on budget and resource. This work provides a brief description of a request online application built around AI to generate question and answer from text documents using recent NLP and machine learning techniques. Users that find the wizard interface of the web application (without having to register) dialogs very simple, will simply upload their text documents and get Questions and Answers in various formats including PDF, CSV and XLS. This paper presents a study on generating multiple-choice questions (MCQs) automatically quite distinctive from the fact that computer science education is a very evolving subject with multiple sub-domains.

Design and Implementation of an Arduino-Based Home Security System with Fire, Gas and Intrusion Detection

Abstract

Home security has become a major concern due to increasing incidents of fire hazards, gas leakage, and unauthorized intrusions. Traditional security systems are often expensive and limited to a single function. This paper presents the design and implementation of a low-cost Arduino-based home security system capable of detecting fire, gas leakage, and human intrusion using a flame sensor, MQ-2 gas sensor, and PIR motion sensor. The system provides real-time alerts through a buzzer and a 16×2 LCD display. Experimental results show fast response time, reliable performance, and effective hazard detection. The proposed system is economical, easy to implement, and suitable for homes, offices, and small commercial environments. Index Terms—Arduino Uno, Home Security System, Flame Sensor, MQ-2 Gas Sensor, PIR Sensor, Embedded Systems

Epistaxis : Diverse Causes – Hematologist has a significant role in optimal care

Abstract

Abstract - Background: Epistaxis is among the most common otorhinolaryngological emergencies, affecting up to 60% of the population. While most cases are benign and managed with local measures, unusual hematological causes may underlie recurrent or severe presentations. Objective: To present a case series highlighting diverse hematological etiologies of epistaxis encountered during the initial practice period of a budding hematologist in a remote region of Karnataka, India. Methods: Six consecutive patients presenting with epistaxis were evaluated comprehensively. Detailed hematological workup was performed, and management was tailored to the underlying diagnosis. Results: The series included: • Drug-induced thrombocytopenia managed successfully with TPO agonists. • Paroxysmal nocturnal hemoglobinuria diagnosed during pancytopenia evaluation and referred for anti-complement therapy trials. • Fanconi anemia confirmed by cytogenetics and mutation analysis, managed with transfusion support and antifibrinolytics, with transplant planning underway. • Immune thrombocytopenic purpura treated with steroids and hematinics. • Eisenmenger syndrome with erythrocytosis managed by phlebotomy. • Erythrocytosis with suspected acquired von Willebrand disease controlled with local measures, hydration, and lifestyle modification. Conclusion: This case series underscores the importance of hematologist involvement in epistaxis evaluation, especially in uncovering rare and systemic causes beyond routine ENT practice. Awareness of conditions such as PNH, Fanconi anemia, acquired vWD, and drug-induced thrombocytopenia is crucial for timely diagnosis and appropriate management. Presentation: This work was presented at the Association for Haemophilia and Allied Disorders – Asia Pacific (AHAD AP) 2024, Bengaluru, India.

EARLY DETECTION & ANALYSIS OF AUTISM SPECTRUM DISORDER USING MACHINE LEARNING

Abstract

Autism Spectrum Disorder (ASD) is a neuro-disorder in which a person has a lifelong effect on interaction and communication with others. Autism can be diagnosed at any stage in once life and is said to be a "behavioral disease" because in the first two years of life symptoms usually appear. According to the ASD problem starts with childhood and continues to keep going on into adolescence and adulthood. Propelled with the rise in use of machine learning techniques in the research dimensions of medical diagnosis, in this there is an attempt to explore the possibility to use Naïve Bayes, Support Vector Machine, Logistic Regression, KNN, Neural Network and Convolutional Neural Network for predicting and analysis of ASD problems in a child, adolescents, and adults. The proposed techniques are evaluated on publicly available three different non-clinically ASD datasets. First dataset related to ASD screening in children has 292 instances and 21 attributes. Second dataset related to ASD screening Adult subjects contains a total of 704 instances and 21 attributes. Third dataset related to ASD screening in Adolescent subjects comprises of 104 instances and 21 attributes. After applying various machine learning techniques and handling missing values, results strongly suggest that CNN based prediction models work better on all these datasets with higher accuracy of 99.53%, 98.30%, 96.88% for Autistic Spectrum Disorder Screening in Data for Adult, Children, and Adolescents respectively.

The Effect Of Digital Innovation On Tax Filing And Refund Administration In India

Abstract

Digital innovation has fundamentally transformed the structure and functioning of tax administration systems across the globe, and India has emerged as one of the leading developing economies adopting large-scale digital reforms in taxation. Over the past decade, the Indian government has implemented a wide range of digital initiatives aimed at simplifying tax filing procedures, improving refund administration, enhancing transparency, and strengthening taxpayer confidence. Innovations such as e-filing portals, centralized processing centers, pre-filled income tax returns, faceless assessments, automated refund mechanisms, artificial intelligence-driven data analytics, and real-time information integration have significantly altered the traditional tax administration framework. This study examines the effect of digital innovation on tax filing and refund administration in India using secondary data sources. The research draws upon official publications from the Central Board of Direct Taxes (CBDT), Income Tax Department reports, Ministry of Finance documents, Economic Surveys, academic research papers, and policy reports issued by international organizations. The study analyzes how digital transformation has reduced compliance costs, minimized manual intervention, improved processing speed, enhanced accuracy, and increased overall taxpayer satisfaction. At the same time, it explores persistent challenges such as technological glitches, cybersecurity concerns, digital literacy gaps, accessibility issues for rural and elderly taxpayers, and system adaptability during peak filing periods. The findings of the study indicate that digital innovation has played a pivotal role in modernizing India’s tax administration by fostering efficiency, accountability, and transparency. However, to ensure inclusive and sustainable digital tax governance, continued investments in infrastructure, user-centric design, taxpayer education, and grievance redressal mechanisms are essential. The study contributes to the growing body of literature on digital public finance management and provides insights for policymakers, tax administrators, and researchers. Keywords: Digital Innovation, Tax Filing, Refund Administration, E-Governance, Income Tax, CBDT, India, Digital Transformation.

IoT -Based Wild Animals Monitoring and Alert System Using ESP-32 CAM

Abstract

Crop damage caused by wild animals is a serious problem for farmers, especially in rural and forest-adjacent areas. Traditional protection methods such as manual guarding, scarecrows, and fencing are often ineffective, unsafe, or costly. This paper presents an IoT-based Wild Animal Monitoring and Alert System that uses an ESP32-CAM module, PIR motion sensor, buzzer, and Telegram-based alert mechanism to provide real-time detection, visual confirmation, and immediate deterrence. The system captures images when motion is detected, alerts farmers instantly through a mobile application, and activates a buzzer to scare animals away. The proposed system is low-cost, easy to deploy, and suitable for small and medium farms.

Study of FOMO Behaviour Among Equity Investors in Kerala (A Secondary Data Analysis)

Abstract

The Fear of Missing Out (FOMO) has become a defining psychological trait in the digital investment era. With social media, online trading platforms, and financial influencers shaping investor sentiment, the emotion of “not wanting to be left behind” has become a powerful force in equity markets. This paper explores FOMO behaviour among equity investors in Kerala, a state known for its high literacy rate, strong remittance inflows, and emerging financial awareness. Relying exclusively on secondary data sources—ranging from reports by SEBI, NSE, and RBI to academic literature and media analyses—this study investigates how FOMO influences investment patterns, trading frequency, and risk-taking tendencies. The findings reveal that while financial literacy is increasing, emotional impulses continue to dominate trading decisions. The research concludes that behavioral biases such as FOMO, amplified by digital exposure, can significantly distort rational investment choices.

Design & Development of Double Acting Cylinder to Prepare Hydraulic Test Rig

Abstract

Fluid power systems, particularly hydraulics, play a critical role in industrial applications, yet there is often a disconnect between theoretical education and practical skills. This paper presents the design, fabrication, and validation of a modular Hydraulic Trainer Kit focused on demonstrating the operation of a double-acting hydraulic cylinder (DAC) through basic control circuits. The kit integrates a hydraulic power pack, directional control valves, pressure relief valves, and quick-release couplings (QRCs) mounted on an aluminium T-slot profile plate for ease of reconfiguration. Key calculations for cylinder sizing, force, speed, and flow rates were performed at a design pressure of 50 bar, yielding an extension force of 6.28 kN. and retraction force of 4.71 kN. The system emphasizes safety, modularity, and educational value, bridging the gap between classroom theory and industry practice. Validation confirms reliable bidirectional motion control, making it suitable for engineering laboratories.

Design and Implementation of an Accelerometer Based Gesture Controlled Robotic Car Using Arduino Uno

Abstract

Gesture based control has emerged as an intuitive alternative to conventional robotic control interfaces. This paper presents the design and implementation of a gesture controlled robotic car using Arduino Uno and an accelerometer sensor. Hand gestures are captured through accelerometer-based tilt detection and translated into directional motion commands. Wireless communication is used to transmit control signals from the transmitter module to the robotic vehicle in real time.

Automatic Dam control system

Abstract

An Automated Dam Control System is designed to enhance the safety, efficiency, and reliability of dam operations by minimizing human intervention through intelligent monitoring and control mechanisms. Traditional dam management relies heavily on manual observation and decision-making, which can lead to delayed responses during critical situations such as floods or sudden increases in water levels. The proposed system addresses these limitations by integrating sensors, microcontrollers, and automated control algorithms to continuously monitor water levels and regulate gate operations in real time. The system utilizes water level sensors to detect changes in reservoir levels and processes this data using a microcontroller. Based on predefined threshold values, the controller automatically opens or closes dam gates to maintain safe water levels and prevent overflow or structural damage. The automated approach ensures timely decision-making, reduces the risk of flooding in downstream areas, and improves operational accuracy. Additionally, the system can be enhanced with alert mechanisms and remote monitoring features for better supervision. Overall, the Automated Dam Control System provides a reliable, cost-effective, and efficient solution for modern water resource management and disaster prevention.

Temperature based fan controller

Abstract

Temperature control is an important requirement in electronic systems to ensure stable operation and to prevent damage caused by excessive heat. This work presents the design and development of a temperature-based fan controller using an IC 741 operational amplifier and basic analog components. An NTC thermistor is employed to sense ambient temperature variations and convert them into corresponding electrical changes. These changes are processed using a comparator circuit, where the sensed signal is evaluated against a predefined reference level adjusted through a potentiometer. When the temperature exceeds the set limit, a transistor switching stage activates the cooling fan automatically. The proposed system operates entirely in the analog domain and does not require any programmable devices, making it simple, economical, and suitable for educational applications. Experimental observations indicate reliable switching behavior and effective thermal response, demonstrating the practicality of the design for basic temperature control applications.

INHALABLE INSULIN: A REVOLUTION IN DIABETES MANAGEMENT

Abstract

Considerable time and financial resources have been dedicated to the development of new medications that target various essential enzymes and signalling pathways, which have temporarily aided in mitigating this growing pandemic. Insulin continues to be regarded as the gold standard for treatment; however, it is frequently rejected by both patients and healthcare professionals (clinical inertia) due to the method of administration of this medication. Although ultra-short-acting insulin analogues assist in managing prandial glucose spikes, they necessitate 2-3 doses depending on meal intake. Furthermore, long-acting basal insulin is often needed to replicate normal physiological insulin baseline levels. This results in an average of 2-4 insulin injections per day, which many individuals find quite distressing. Patients frequently feel overwhelmed by the necessity of finger pricks for regular blood glucose monitoring, and the prospect of tracking blood glucose levels has often deterred a significant number of patients who guess their sugar levels before and after meals. Insulin therapy requires more stringent blood glucose monitoring, and in cases of hypoglycaemic episodes or uncontrolled hyperglycaemias, multiple finger pricks may be necessary. The discrepancies in blood glucose readings across various Point of Care (POC) glucometer devices do not alleviate the situation and only contribute to the existing frustration. Emphasizing alternative and innovative drug delivery methods for existing molecules can help shift the therapeutic paradigm towards more favourable outcomes. This article will explore one such transformative change in the drug delivery of insulin.

Integrating Green Finance and Digital Payment Systems for Sustainable Consumption: A Systematic Review in Emerging Economies

Abstract

In the era of digital intelligence, organizations and policymakers are increasingly challenged to navigate complexity while balancing economic growth with environmental sustainability. Green finance has emerged as a strategic mechanism to channel financial resources toward environmentally responsible activities, while digital payment systems enable transparency, efficiency, and data-driven decision-making across financial ecosystems. This paper presents a systematic and critical review of peer-reviewed literature examining the integration of green finance mechanisms and digital payment systems as an adaptive and sustainable management practice, with a particular focus on emerging economies.

A Study on Impact of Influencer Marketing on the Adoption of the Digital Wallets in Bangalore (A Secondary Data Analysis)

Abstract

In recent years, the adoption of digital wallets has increased rapidly in urban India due to the growth of smartphones, internet access, and digital payment systems, and Bangalore, being a major technology hub, has seen wide usage of digital wallets for daily transactions. Along with this growth, influencer marketing has emerged as an important promotional tool through social media platforms such as YouTube, Instagram, and Twitter, where influencers share reviews, experiences, and recommendations related to digital wallet applications. This study examines the impact of influencer marketing on the adoption of digital wallets in Bangalore using secondary data. The study is descriptive and analytical in nature and is based on data collected from regulatory reports, fintech industry studies, academic journals, and published articles. The analysis focuses on factors such as influencer credibility, social media exposure, trust, and peer influence in shaping consumer behaviour. The findings reveal that influencer marketing plays a significant role in increasing awareness and adoption of digital wallets, especially among young users and working professionals. Although digital awareness is high in Bangalore, adoption decisions are often influenced by social and emotional factors rather than purely rational evaluation. The study concludes that influencer marketing acts as an important behavioural factor in accelerating digital wallet adoption, while also highlighting the need for responsible influencer practices and improved financial awareness. Keywords: Influencer marketing, digital wallets, digital payments, consumer behaviour, Bangalore, secondary data

AI-Powered Forensic Accounting: The Future of Fraud Detection and Financial Transparency

Abstract

ABSTRACT In an age where financial ecosystems are more interconnected than ever, fraud and misrepresentation continue to evolve with the same sophistication as the systems meant to detect them. The emergence of artificial intelligence (AI) in forensic accounting has ushered in a paradigm shift, transforming the traditional methods of fraud detection into highly predictive, real-time, and intelligent mechanisms. This paper explores the evolving landscape of AI-powered forensic accounting through secondary data analysis—drawing from existing literature, case studies, financial reports, and regulatory findings. It examines the fusion of machine learning, natural language processing, and predictive analytics within forensic accounting and how these technologies enhance transparency, efficiency, and accuracy. The study highlights the implications for auditors, regulators, and financial institutions, while addressing ethical concerns and the need for accountability frameworks. Ultimately, the paper underscores how AI-driven forensic accounting represents not just a technological revolution but a structural realignment of financial truth-seeking in the digital era

Information Retrieval Systems for Efficient Multimedia Information Access

Abstract

An Information Retrieval System (IRS) is designed to store, organize, retrieve, and maintain information in response to user queries. Unlike traditional database systems that rely on structured data and exact matching, an IRS focuses on retrieving relevant information from large collections of unstructured or semi-structured data such as text, images, audio, video, and other multimedia content. With the rapid growth of the Internet and advances in low-cost computing and storage technologies, information retrieval systems have become essential tools for managing vast digital repositories and enabling efficient access to knowledge. The primary objective of an IRS is to reduce the user’s effort in locating needed information. This effort, known as information retrieval overhead, includes query formulation, execution, examination of retrieved results, and reading non-relevant items. To evaluate system effectiveness, two key performance measures are used: precision, which reflects the accuracy of retrieved results, and recall, which measures the completeness of retrieval. A balance between these measures is crucial for effective information access. Modern information retrieval systems support natural language queries, allowing users to express their information needs in everyday language. Internally, an IRS operates through several functional processes, including item normalization, selective dissemination of information, document database search, and index database search. Item normalization converts diverse data formats into standardized, searchable representations through processes such as zoning, token identification, and stop-word removal. Indexing and automatic file-building techniques further enhance retrieval efficiency.

Optimizing Cloud Access Control with the Principle of Least Privilege

Abstract

As cloud setups keep exploding across the board, businesses are doubling down on spread-out networks to stash away, crunch through, and swap out all sorts of confidential info. The upside? Massive room to grow and bend without snapping. The downside? A tangled web of security headaches, especially around figuring out who's allowed in and what they can touch. Right in the mix of solid security basics is the Principle of Least Privilege (PoLP)—that straightforward idea of handing out just the bare-bones access needed to get the job done, slashing the chances of a hack turning into a catastrophe if someone's login goes south.

A Study on Rupee Cost Averaging Through Flexible SIPs

Abstract

This research provides an expert-level evaluation of Rupee Cost Averaging (RCA) and its evolution into flexible and value-based Systematic Investment Plan (SIP) models within the Indian equity market from 2000 to 2025. While traditional SIPs have democratized equity participation through a fixed-amount approach, this study analyses the quantitative efficiency of adaptive models that leverage valuation triggers like Price-to-Earnings (P/E) ratios and the Yield Gap. Through an empirical lens, the study identifies a significant "behaviour gap" of 1.5% to 2% annually, wherein retail investors often sabotage mathematical cost-averaging benefits by pausing SIPs during market downturns. Analysis of a 22-year dataset (2003–2024) reveals that intra-month timing specifically aligning SIPs with Futures and Options (F&O) expiry volatility yields a tactical advantage of 0.5% to 2.5% annually. Furthermore, while Value Averaging (VA) generated higher returns in 352 out of 359 analysed Indian companies, the study highlights the practical liquidity constraints that make it more suitable for high-net-worth individuals than salaried retail investors. The findings challenge the industry narrative of consistent 12–15% returns, revealing an empirical 20-year pre-tax CAGR of 6.7% for Nifty 50 SIPs, and provide strategic recommendations for navigating volatility through systematic automation and regime-aware asset allocation.

AI in medicine

Abstract

Artificial Intelligence (AI) has made significant strides in the field of medicine and healthcare, offering transformative potential to improve patient outcomes, streamline processes, and enhance clinical decision-making. From diagnostic tools to treatment planning, AI is reshaping the healthcare landscape by offering innovative solutions. This review presents an overview of both current and near-future applications of AI in medicine, categorizing them into diagnostic, therapeutic, administrative, and personalized healthcare domains. Additionally, the article explores the ethical challenges and social implications that accompany the widespread integration of AI technologies in healthcare, focusing on issues such as data privacy, equity, accountability, and the potential impact on healthcare professionals and patients alike. The article concludes by discussing the evolving role of AI in shaping the future of healthcare, emphasizing the need for responsible implementation to maximize benefits while mitigating risks.

Performance study on two wheeler vehicle to four wheeler vehicle

Abstract

This study compares the performance of two-wheeler and four-wheeler vehicles based on fuel efficiency, speed, load capacity, stability, and environmental impact. Two-wheelers are found to be more fuel-efficient, cost-effective, and suitable for short-distance urban travel, while four-wheelers provide better stability, comfort, safety, and higher load-carrying capacity for longer journeys. The analysis shows that each vehicle type has advantages depending on usage conditions such as traffic, distance, and passenger requirements. This performance study helps in understanding the practical benefits and limitations of both vehicle categories for everyday transportation.

Fortifying Cloud Ecosystems: A Comparative Evaluation of Access Control Models through the Principle of Least Privilege

Abstract

The proliferation of cloud computing infrastructures has compelled organizations to adopt distributed architectures for the storage, processing, and dissemination of sensitive information. Although these platforms deliver unparalleled scalability and adaptability, they concurrently engender intricate security vulnerabilities, most notably in the domains of authorization and access governance. Central to mitigating these risks is the Principle of Least Privilege (PoLP), a cornerstone security doctrine that prescribes granting entities solely the essential permissions requisite for their designated functions, thereby curtailing the expanse of potential breaches stemming from credential compromise. This investigation undertakes a rigorous examination of prevailing cloud authorization paradigms, appraised through the prism of parsimonious security imperatives. It scrutinizes the efficacy with which extant models uphold PoLP stipulations within multifaceted, multi-tenant cloud ecosystems. Conventional methodologies—encompassing Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC), and Policy-Based Access Control (PBAC)— undergo methodical appraisal, juxtaposed against nascent frameworks such as relational, risk-responsive, credence- oriented, and situational authorization constructs.

AI-Mediated Phonetic Automatization Theory (AIPAT): A Conceptual Model for Accelerated L2 Pronunciation Development in Adult Learners

Abstract

Artificial intelligence (AI) has transformed second language (L2) pronunciation training by delivering real-time, segment-specific acoustic feedback yet theoretical models in L2 phonetics have not kept pace. This paper proposes the AI-Mediated Phonetic Automatization Theory (AIPAT), a domain-specific conceptual framework explaining how AI-driven feedback accelerates the shift from effortful to automatic speech production in adult EFL learners. Developed through empirical work with 50 Arabic-speaking English teachers from diverse Iraqi institutions, AIPAT defines true phonetic automatization as the convergence of two independent markers: (1) reduced speech onset latency (a cognitive indicator of processing efficiency) and (2) stabilized acoustic parameters, such as consistent frication duration for /θ/. The model rests on four core assumptions, including the Temporal Precedence Principle, which holds that reaction time improvements precede acoustic stabilization a reversal of traditional pedagogical sequencing. AIPAT outlines a three-stage developmental trajectory and generates falsifiable predictions about phoneme markedness, adult plasticity, and non-linear learning curves. While deliberately narrow in scope, this micro-theory offers a testable scaffold for experimental phonetics, intelligent tutoring systems, and L2 teacher education.

AI-POWERED EAELY DETECTION OF MELANOMA

Abstract

Early and accurate identification of malignant melanoma continues to be a major challenge for clinicians in the field. Traditional diagnostic approaches, including physical examination, histology, imaging, and nodal assessments, are frequently costly, require significant expertise, and can display large variations among clinicians. These factors may result in missed or misdiagnosis, which often significantly affects a patient’s prognosis. We examine in detail how the application of AI methods such as machine learning and deep learning can be used to advance early detection and identification of melanoma. We review various AI algorithms, including standard classifiers, ensemble techniques, and complex deep learning models. Hybrid models that combine convolutional neural networks (CNNs) and support vector machines (SVMs) are emphasized in this review, as they show enhanced performance and improved resistance to variations in the diagnostician’s input. Better utility of transfer learning and data augmentation approaches is discussed to overcome the challenges posed by small and unbalanced medical datasets. The authors consider the combination of various types of medical information for more effective cancer diagnosis. However, significant obstacles, including model explain ability, privacy safeguarding, and clinical evaluation, still need to be addressed. Extensive efforts are needed to overcome these barriers if AI systems are to be effectively adopted within healthcare environments. We suggest that AI offers the opportunity to revolutionize melanoma care by enabling rapid decision support and individualized treatment plans. Realizing this opportunity will depend on effective partnerships between researchers, clinicians, and industry to bring together advances in technology and their effective implementation in the healthcare system.

A COMPARATIVE STUDY ON CUSTOMER EXPERIENCE AND SERVICE QUALITY IN SMALL BANKS AND NEW GENERATION BANKS

Abstract

Two decades of financial liberalization, rapid technological innovation, and changing customer expectations have transformed the Indian banking sector beyond recognition. A uniquely heterogeneous banking ecosystem has evolved where legacy small banks coexist with technology-driven new generation banks, and customer experience has become a critical determinant of competitiveness and sustainability. The present study reports a primary data–based empirical examination of customer experience and service quality in small banks and new generation banks. The main purpose of this study is to compare customer perceptions in both the banking segments regarding service efficiency, responsiveness of the staff, digital accessibility, trust, speed of transaction, and grievance redressal mechanisms. Primary data were collected through a structured questionnaire administered on 50 bank customers comprising users of small banks and new generation banks. A descriptive and comparative research design was adopted for the study, wherein the data were analysed using percentage analysis, comparative tabulation, and interpretative techniques. These findings undoubtedly create a distinction between the two banking models in terms of customer expectation and satisfaction. Small banks have still retained the loyalty of customers through their personalized services, emotional trust, and close banker–customer relationships, especially among rural and elderly consumers. New generation banks, with their digital convenience, real-time transaction capability, efficiency of mobile and internet banking, and structured grievance handling mechanisms, are more appealing to the young and urban consumer segment. However, new generation bank customers also showed apprehensions over hidden charges, less human interaction, and dependency on technology. The findings of the study indicate that, in the contemporary banking ecosystem, customer experience is increasingly shaped by the dual demand for relational trust and digital efficiency. The paper contributes to the existing literature by providing a primary data–driven comparative perspective and provides practical recommendations for banks on how to combine technological advancement with human-centered service delivery to enhance customer satisfaction and long-term loyalty. Keywords: Customer Experience, Small Banks, New Generation Banks, Service Quality, Digital Banking, Customer Satisfaction, Primary Data

Training and Development as a Strategic HR Tool in the Hospitality Industry: Evidence from Qatar

Abstract

Employee competence, behaviour, and service delivery are vital determinants of the organisational success of the hospitality industry, as it is highly service-based. In this regard, training and development are primarily viewed as imperative human resource factors in improving employee performance, motivation, and the quality of the service offered. This paper is a research on the role and efficiency of training and development practices in the hotel business in Qatar, with special focus on employee development, satisfaction, and perceived performance. The study selects secondary data as a quantitative method of research, critically examining empirical evidence achieved by using a structured questionnaire to respondents who are a sample of employees and managers employed in four and five-star international and local hotels based in Doha. The results show that training is a compulsory and institutionalized process in the sampled hotels, particularly in the initial phases of employment. Training is usually perceived by the employees as relevant, especially in enhancing motivation, job performance, and self-confidence. There is, however, an observed difference in training exposure among job positions whereby more development opportunity is given to senior employees compared to the frontline and middle-line employees. Although the general response about the training programs is quite satisfactory, the respondents point to the repetitive qualification and the excessive emphasis on theory and time constraints that disrupt the normal course of business. Limitations in the evaluation practice of training also emerge in the study, which are mostly based on employee satisfaction instead of quantifiable performance results. These results imply that even though training is considered to be worthwhile, the strategic potential is not met. It concludes that training and development ought to be viewed as a long-term investment in human capital, not a cost, and suggests an increased number of more role-specific, practical, and systematically tested training programs to improve the quality of service and organizational competitiveness in the hospitality industry of Qatar. Keywords: Training and Development, Hospitality, Employee Performance, HRM

LEADERSHIP AND MANAGEMENT STRATEGIES DURING AND AFTER CRISIS”: Leading with Empathy through the COVID-19 Pandemic

Abstract

The COVID-19 crisis caused tremendous discontinuity to organizations and exceptional demands on leaders and employees. This study examines the role of empathetic leadership in managing crises and enabling organizations to stay stable throughout these difficult times. The study has utilized a secondary quantitative methodology where the researcher relies on the previous theses and evaluates the current statistics. The analysis involved revising the descriptive trends, relationships, dependability indices, and regression findings to comprehend the influence of empathy on employee experiences and organizational performances. The findings indicate that empathetic leadership has a potent influence on enhancing trust, morale, psychological safety, and stability within organizations. The analysis also shows that the quality of communication serves as the mediating variable between empathy and resilience, i.e., empathetic leaders perform better when communication is consistent and open. The findings of this study influence the recommendations that organizations need to consider empathy as a long-term leadership skill instead of a short-term response that is applied only during a crisis. The findings indicate that empathetic leadership not only enhances crisis reactions but also benefits the well-being of employees and helps them recover faster and healthier. Keywords: Empathy, Crisis Leadership, Organizational Resilience, Communication, Psychological Safety, COVID-19

Advancements in User Experience and Emotion Integration in Digital Design

Abstract

Abstract—Traditionally, the process of User Experience (UX are mostly disorganized, resulting in a split in the field of. On the one hand, static text mining of net reviews offers extensive, semantically rich, post-hoc analyses on user sentiment and product feature opinions.[1, 1] However, this approach lacks real- time applicability for interface adaptation. On the other hand, dynamic “affective computing models” capture real-time, high-resolution user emotion through modalities such as facial recognition, speech, and “physiological biometrics. [1, 1, 1] These immediate, and are ”context-blind,” in the sense that they lack an understanding specific product-related *cause* of the user’s affective state. We propose a novel **Hierarchical Affective Fusion (HAF) Model** in order to bridge this critical gap. HAF is a novel neural architecture that, for the first time, combines these disparate and trans- temporal data streams. It uses a Static UX Profile Encoder, these models have been trained on large review corpora, in order to generate a product- specific semantic knowledge base. The static profile then gets utilized in contextualize the real-time output of a Dynamic Affect Encoder, which combines video, audio, and physiologic signals. The HAF The model employs a mixture of BERT-based topic models, Vision ViT - transformers, Temporal-CNNs - bi and an innovative fusion layer called Gated Multimodal Units (GMU) and cross-modal attention. We describe an extensive experimental designed in order compare the HAF-powered adaptive interface with both static and unimodal adaptive baselines. We predict that the The HAF-adaptive interface will produce statistically significant increases in task success rates, measurable decreases in user frustration, and increased scores on measures of self- reported engagement (e.g., SAM/PANAS).[1] The present paper shows the application of the art in making responsive, emotion- aware systems. [2] The the primary contribution offered in the proposed work using HAF model for computational UX, bridging the gap between long-term, retrospective sentiment and in-the- moment, immediate affect. Index Terms—Affective Computing, User Experience (UX), Multimodal Fusion, Hierarchical Atten- tion, Adaptive Interfaces, Biometrics, Sentiment Analysis, Human Computer Interaction Index Terms—Affective Computing, User Experience (UX), Multimodal Fusion, Hierarchical Attention, Adaptive Interfaces, Biometrics, Sentiment Analysis, Human-Computer Interaction

Exploring Teacher Perceptions of Artificial Intelligence in Education: A Study of Pedagogical Beliefs, Technology Use, and the Impact of Experience on AI Adoption

Abstract

Advancements in artificial intelligence (AI) have stimulated the development of AI tools for education, offering potential to transform teaching and learning processes. However, teachers' acceptance remains a critical barrier to successful integration, and little is known about how their underlying pedagogical beliefs shape perceptions of different AI implementations. This study examines how secondary school teachers' pedagogical orientations influence their acceptance of AI, specifically comparing collaborative "co-pilot" tools that augment teacher decision-making with autonomous systems that operate independently. A quantitative, cross-sectional survey design was employed with 500 teachers in Hyderabad, Pakistan. The questionnaire measured constructivist and instructivist pedagogical beliefs using adapted scales and assessed AI acceptance through an extended Technology Acceptance Model applied to two distinct operational scenarios. Results indicate that teachers demonstrate a clear and significant preference for collaborative AI over autonomous systems, with higher perceived usefulness and behavioral intention for co-pilot implementations. Constructivist pedagogical beliefs strongly and positively predict acceptance of collaborative AI tools, while instructivist beliefs positively predict acceptance of autonomous systems. Technology use frequently emerges as a consistent positive predictor across all acceptance models, and perceived ease of use significantly influences behavioral intentions. The regression models explain 8-15% of variance in AI acceptance, indicating meaningful but partial explanatory power of pedagogical beliefs. These findings highlight that teachers' educational philosophies serve as crucial filters through which they evaluate technological innovations, with constructivist-oriented teachers favoring augmentation tools and instructivist-oriented teachers showing greater openness to automation. The study contributes to extending technology acceptance frameworks by demonstrating pedagogical beliefs as significant external variables and provides practical guidance for developing human-centered AI designs that align with teachers' diverse educational approaches and values

DECTECTING WEED PLANTS IN FIELDS USING AI TECHNIQUES

Abstract

Weed infestation is a major challenge in agriculture, significantly affecting crop yield, quality, and overall farm productivity. Conventional weed detection methods, such as manual inspection and uniform herbicide application, are labor-intensive, time-consuming, and environmentally harmful. With recent advancements in Artificial Intelligence (AI) and computer vision, automated and precise weed detection has become feasible. This paper presents an AI-based approach for detecting weed plants in agricultural fields using deep learning techniques. Field images captured through cameras or unmanned aerial vehicles are processed using convolutional neural networks to accurately differentiate weeds from crops. The proposed system focuses on efficient feature extraction, robust classification, and reliable weed localization under varying field conditions. The AI-based approach supports precision agriculture by enabling targeted weed control, reducing chemical usage, minimizing labor costs, and promoting sustainable farming practices. The results demonstrate the potential of AI techniques to improve weed management efficiency and enhance agricultural productivity.