Journal of Information Technology, Cybersecurity, and Artificial Intelligence
Journal of Information Technology, Cybersecurity, and Artificial Intelligence (JITCAI) The Journal of Information Technology, Cybersecurity, and Artificial Intelligence (JITCAI) is an international, peer-reviewed, open-access journal dedicated to advancing knowledge in the interconnected fields of Information Technology, Cybersecurity, and Artificial Intelligence. JITCAI provides a platform for academics, researchers, and industry professionals to publish cutting-edge research, comprehensive reviews, case studies, and insightful short communications that address contemporary challenges and emerging trends in these dynamic disciplines. The journal aims to foster innovation and knowledge sharing by bridging the gap between theory and practical application while promoting interdisciplinary collaboration. JITCAI welcomes high-quality contributions that explore technological advancements, security solutions, artificial intelligence innovations, and ethical considerations shaping the future of digital systems. As a Gold Open Access journal, JITCAI makes all published articles freely accessible worldwide, supporting the global dissemination of impactful research. The journal operates with an affordable Article Processing Charge (APC) of USD $50, payable only after manuscript acceptance, while maintaining rigorous editorial and double-blind peer review standards. A waiver and discount option is available for authors facing financial constraints, ensuring accessibility and inclusivity for contributors worldwide. JITCAI publishes twelve (12) issues per year on a monthly publication schedule and accepts manuscripts ranging from 1,000 to 5,000 words, providing flexibility to accommodate diverse scholarly contributions.
FNDMFF: Enhancing Fake News Detection via Multi-Source Feature Fusion Framework
Section: Articles Categories: Artificial Intelligence & Intelligent Systems Keywords: Fake news detection, Hypergraph neural network, Multi-source feature fusion, Propagation graph, Attention mechanism Subjects: AI, DL, computation Disciplines: text classification Current approaches to identifying fake news frequently overlook the comprehensive utilization of layered characteristics or fail to capture advanced relational patterns during information dissemination. In response, we introduce FNDMFF, a framework designed to improve fake news identification by integrating structural, temporal, and textual features through a specialized gating mechanism for multi-source integration. The process begins by extracting dissemination structures via an improved hypergraph neural network, followed by analyzing temporal patterns with a variable-scale timing component, and concludes by extracting textual details using a multi-head attention approach. In particular, FNDMFF includes a gating unit for feature fusion that adaptively modifies the importance of various feature aspects, allowing for the seamless integration of diverse data types. Tests on the Politifact and Twitter16 datasets reveal that FNDMFF surpasses established models such as UPFD, HGNN, and the state-of-the-art RTRUST method. Our system delivers a 3.63% boost in precision and a 3.40% gain in F1-score for Politifact, alongside 0.54% and 0.55% enhancements for Twitter16. This highlights the value of fusing multiple data sources for detecting misinformation.
Artificial Intelligence & Intelligent Systems
Copyright (c) 2026 Dr. Hani Iwidat (Author) https://creativecommons.org/licenses/by/4.0
Arabic Dialect NLP: A Unified Taxonomic, Methodological, and Trend‑Driven Survey
Section: Articles Categories: Artificial Intelligence & Intelligent Systems Keywords: Arabic Dialect, Dialect applications, Nature Language Processing (NLP), Taxonomy Subjects: natural language processing Disciplines: Information Technology, Information systems, Natural Language Processing Background: Natural language processing of Arabic dialects has faced significant challenges due to the language's complex nature, while Modern Standard Arabic enjoys widespread support in this field. The rapid spread of social media has also shifted research focus towards Arabic dialects, thus creating a critical need to criticize this massive body of research. Objective: This study aims to provide a survey and application-oriented review of the Arabic Dialectal NLP landscape. The primary goal is to map the relationship between foundational tasks, while benchmarking the resources and methodologies that have defined the field. Participants and Setting: The study analyzes a comprehensive dataset of 400 research articles published between 2020 and 2025. Methods: A survey was conducted, utilizing a multi-taxonomic clustering approach. Research papers were categorized into eight functional clusters. Trends were analyzed by year, geographic focus, and algorithm type (Traditional Machine Learning vs. Deep Learning vs. Transformers and LLMs). Results: The study analysis reveals that Sentiment Analysis category is the dominant application about 32% of the literature, followed by 21% for resource building group. Identification and Code-Switching is 10%. Research output peaked in 2022-2025, marking a definitive shift from traditional machines learning model to Transformer-based architectures like AraBERT and MARBERT. Regional coverage is broad, with a notable trend toward the identification and handling of code-switched text, which has emerged as the current state-of-the-art. Conclusions: The survey demonstrates that dialect identification is no longer a standalone goal but a prerequisite for sentiment and translation systems. The field has progressed notably in many areas, such as SA, but future work must prioritize under-resourced dialects, reproducible benchmarks, and cross-dialect transfer learning, and bond these specific dialectal models with the zero-shot capabilities of generative LLMs.
Copyright (c) 2026 Dr. Hani Iwidat, Dr. Mamoun Abu Helou (Author) https://creativecommons.org/licenses/by/4.0
ML TO MEASURE EFFECTIVENESS OF DATABASE SECURITY
Section: Articles Categories: Artificial Intelligence & Intelligent Systems Keywords: Artificial Intelligence, Machine Learning Enforcing database security to protect data is crucial in today’s digital world. As modern organizations increasingly rely on cloud infrastructure to run their business operations, database security becomes essential to protect the data of critical assets against malicious actors, while also maintaining compliance and regulatory requirements. This paper explores a machine learning approach to measuring the effectiveness of Database security. It utilizes logs from the AWS RDS service to construct a representative dataset, with key features including query latency ms, user role, access hour, query type, security alert, and top threat type. These features are used to train a model to predict the effectiveness of database security using AWS RDS log patterns. This enables organizations to prevent data breaches, protect sensitive information, and ensure ongoing compliance with relevant regulations.
Artificial Intelligence, Machine Learning
Copyright (c) 2026 Upakar Bhatta (Author) https://creativecommons.org/licenses/by/4.0
Cybersecurity Education in K–12 Schools: A Thematic Analysis of Pandemic-Era Literature
Section: Articles Keywords: COVID-19, cybersecurity, digital citizenship, qualitative study, curriculum development Subjects: Cybersecurity, Cybersecurity Awareness, Artificial intelligence; Higher education; Marketing Disciplines: Computer Science; Software Engineering; Distributed Systems; Cloud Computing; Service Reliability Engineering; Data Analytics; Intelligent Systems. The COVID-19 pandemic (2020–2025) accelerated digitalization in K–12 education, exposing students, educators, and families to heightened cybersecurity risks. This systematic review synthesizes more than 60 peer-reviewed studies, reports, and policy documents to examine how cybersecurity education initiatives evolved during and after the pandemic and what evidence exists regarding their effectiveness. Using Braun and Clarke’s (2006) thematic analysis, seven themes emerged: evolving cyber risks; cyberbullying and digital safety; teacher preparation; equity and access; curricular innovation; governance and policy; and socio-emotional wellbeing. Interventions included digital citizenship curricula, gamified learning tools, AI-supported safety monitoring, teacher professional development, and national policy frameworks. Findings indicate that initiatives are most effective when embedded within socio-technical systems aligning pedagogy, technology, and governance. However, uneven implementation, equity gaps, and limited teacher capacity constrained impact. The review highlights the need for sustained professional development, equity-focused policy, holistic digital wellbeing, and longitudinal, cross-cultural research emphasizing student agency.
Artificial intelligence; Higher education; Marketing
Computer Science; Software Engineering; Distributed Systems; Cloud Computing; Service Reliability Engineering; Data Analytics; Intelligent Systems.
Copyright (c) 2026 Ian Herzing (Author) https://creativecommons.org/licenses/by/4.0
The Inclusion–Risk Paradox in FinTech and InsurTech: Effects of Algorithmic Access Expansion, Opacity, and Regulatory Safeguards
Section: Articles Keywords: FinTech; InsurTech; algorithmic access expansion; algorithmic opacity; regulatory safeguards; financial inclusion; insurance inclusion; institutional vulnerability; inclusion–risk paradox; algorithmic governance Subjects: FinTech; InsurTech; algorithmic access expansion; algorithmic opacity; regulatory safeguards; financial inclusion; insurance inclusion; institutional vulnerability; inclusion–risk paradox; algorithmic governance Disciplines: Business, Data Science The rapid diffusion of FinTech and InsurTech has fundamentally reconfigured access to financial and insurance services, particularly in emerging economies. While algorithmic systems are widely promoted as mechanisms for expanding inclusion, growing evidence suggests that these same systems may intensify institutional risk, thereby producing an inclusion–risk paradox. This study investigates the effects of algorithmic access expansion, algorithmic opacity, and regulatory safeguards on financial and insurance inclusion and institutional vulnerability within FinTech and InsurTech ecosystems. Drawing on institutional theory and algorithmic governance perspectives, the study employs a quantitative research design using survey data collected from senior banking executives, FinTech leaders, and InsurTech decision-makers across regulated financial institutions in the Philippines. Structural equation modeling is used to examine the direct and moderating relationships among the study variables, with rigorous validity and reliability procedures applied to ensure measurement robustness. The findings demonstrate that algorithmic access expansion significantly enhances financial and insurance inclusion, yet simultaneously exacerbates institutional vulnerability when algorithmic opacity remains unmitigated. Regulatory safeguards are found to play a critical moderating role, dampening risk amplification while stabilizing inclusion outcomes. By empirically articulating the coexistence of inclusion gains and risk escalation, this study advances the theoretical understanding of algorithmic paradoxes in financial systems and extends institutional governance literature within the FinTech and InsurTech domains. The results offer important implications for policymakers, regulators, and industry leaders seeking to balance innovation-driven inclusion with institutional resilience in digitally mediated financial environments.
FinTech; InsurTech; algorithmic access expansion; algorithmic opacity; regulatory safeguards; financial inclusion; insurance inclusion; institutional vulnerability; inclusion–risk paradox; algorithmic governance
Copyright (c) 2026 Dr. Christian Anthony R. Flores (Author) https://creativecommons.org/licenses/by/4.0
The admissibility of electronic fingerprints as evidence in criminal proceedings
Section: Articles Keywords: electronic fingerprints; deepfakes; biometric data; chain of possession. Subjects: electronic fingerprints; deepfakes; biometric data; chain of possession. Disciplines: Digital Forensics, cybersecurity, Cybercrimes This study aimed to analyze the admissibility of electronic fingerprints in criminal evidence from a legal and technical perspective, highlighting the challenges and opportunities associated with their use as a modern evidentiary tool. The study employed a descriptive-analytical approach, supported by consultations with legal professionals and digital evidence specialists. The study reached several key findings, including: that electronic fingerprints have become legally accepted at the textual level, but their practical application in evidentiary procedures varies depending on the quality of the procedures and implementing legislation; that there has been tangible progress in adopting electronic fingerprint technologies in Palestinian governmental, banking, and security institutions, despite limited adoption due to available resources; that the chain of possession and technical standards are crucial factors in the admissibility of evidence, while the shortage of qualified personnel and accredited laboratories represents a major obstacle to accurate analysis; that the increasing threat of modern technologies, such as deepfakes, and the lack of international consensus on standards and jurisdictional restrictions hinder access to digital data stored outside the country; and that the need to protect privacy and individual rights when dealing with biometric data has become apparent. Finally, there is a growing trend toward hybrid biometric technologies to enhance accuracy and resist forgery. The study recommended establishing a comprehensive legislative and regulatory framework to govern the collection, storage, and use of biometric data, along with safeguards to protect rights; standardizing technical and procedural criteria in accordance with international standards to ensure the validity of evidence and the integrity of the chain of possession; establishing a national digital forensics laboratory and training judicial and security personnel in its use; and investing in digital forgery detection technologies and adopting hybrid biometric systems to enhance the reliability of electronic fingerprints as a means of criminal proof.
electronic fingerprints; deepfakes; biometric data; chain of possession.
Copyright (c) 2026 Abdullah Mahmmoud, Ribhi wajeeh mousa Dola, Kamal Alieyan, Waheeb Abu-ulbeh (Author) https://creativecommons.org/licenses/by/4.0
Measuring Cybersecurity Awareness of Students: a Study of State College Students
Section: Articles Keywords: Cybersecurity Awareness (CSA), Cybersecurity, Awareness, behavior Subjects: Cybersecurity Awareness, Awareness behavior, Cybersecurity Disciplines: Cybersecurity Awareness (CSA) This study investigates the level of cybersecurity awareness and online security behavior among State College students, utilizing a 54-item structured questionnaire. The survey was administered online via Microsoft Forms over a period of 204 days, yielding 135 responses, of which 128 were retained after data cleaning and processing. The instrument captures four dimensions: demographic characteristics, conceptual knowledge of cybersecurity, self-reported security practices, and behavioral responses to realistic digital scenarios. A descriptive analysis of the responses reveals that students possess a relatively strong conceptual understanding of the selected security principles. For example, more than 95% correctly identified the characteristics of a strong password, and 83.7% reported maintaining an active and regularly updated antivirus program. However, behavioral data reveal inconsistent application of this knowledge in practice. Many students continue to reuse passwords, connect to unsecured Wi-Fi networks, or rely on public devices and unverified software sources when under pressure for convenience or time. An overall awareness score computed from the behavioral items averaged 77.28% on the adopted awareness scale, indicating that a substantial proportion of students exhibit risk-prone habits despite their baseline knowledge of cybersecurity. These findings highlight a persistent knowledge–behavior gap and underscore the need for targeted educational interventions. The study recommends scenario-based awareness training, integration of cybersecurity content across curricula, and institutional policies that reinforce secure digital habits among students.
Copyright (c) 2025 Ahmed Al Zaidy (Author) https://creativecommons.org/licenses/by/4.0
Improving Public Service Grievance Analysis: A Comparative Study of Topic Modelling Techniques with a Multi-Metric Data Cleaning Framework
Section: Articles Keywords: E-governance, Natural Language Processing, Artificial Intelligence, Machine learning, Topic Modelling, LDA, BERTopic, NMF, LSA Subjects: E-Governance, Artificial intelligence, machine learning, Topic Modelling, Topic Classification, Natural Language Processing Disciplines: Computer Science, Artificial Intelligence, Natural Language Processing, Topic Modelling The SewaSetu portal, a single window system for government services in Assam, India, processes thousands of applications and hundreds of grievances daily. And many such government grievance portals routinely receive a substantial volume of public complaints, each containing valuable information but often embedded in unstructured text. Extracting patterns from such data can enable public agencies to respond more efficiently and allocate resources more strategically. Manual classification of these grievances is a time-consuming bottleneck. This paper describes a methodology for reliable topic discovery in this noisy domain. Unlike standard studies that rely solely on stopword removal, this paper introduces a robust ”Multi-Metric Gibberish Filtering Pipeline to ensure that the subsequent dataset was free of incoherent noise. We then proceeded to perform a comprehensive coherence benchmarking of four primary topic modeling algorithms, Latent Semantic Analysis(LSA), Non-Negative Matrix Factorization (NMF), Latent Dirichlet Allocation(LDA), and BERTopic across varied topic counts (K=5 to 50) on the cleaned grievance data. Analysis through the Cv (coherence score) showed that NMF considerably outperformed the alternatives, reaching the highest overall score of 0.7898 at K=35. This work sets a benchmark for preparing data to handle noisy government feedback and asserts the NMF with 35-topic configuration as the most effective and coherent model in extracting interpretable themes from public service delivery grievances.
Copyright (c) 2025 Rahul Deka, Reetesh Kumar Srivastava (Author) https://creativecommons.org/licenses/by/4.0
Artificial Intelligence as a Formative Tool in Undergraduate Marketing: Integrative Review and Inductive Study
Section: Articles Keywords: Artificial intelligence; Higher education; Marketing; Critical thinking; Authentic assessment. Subjects: Artificial intelligence; Higher education; Marketing Disciplines: Marketing, strategy, creativity, and data analysis Artificial intelligence (AI) is revolutionizing university education and directly changing the training of students. This article integrates a narrative review with an inductive study conducted in Marketing courses and classes at the Fundación Universitaria del Área Andina (Areandina) and the Universidad Nacional Abierta y a Distancia (UNAD) in Colombia. The central hypothesis states that AI should be integrated as a pedagogical tool to enhance creativity and critical thinking without replacing the learning process. Observations in classes and analysis of student work were performed, technological literacy needs and gaps in understanding AI approaches were identified. Descriptive statistics were collected to examine performance evolution (originality, argumentation, traceability), distinguishing between an “executive” use (shortcuts and direct generation of responses) and an “instrumental” use (verification, ideation and defense of strategies). The hypothesis suggests that AI add value when guiding ideation and analysis so that students design and defend their own Marketing strategies, rather than generating them automatically. Collected evidence confirms benefits (efficiency, personalized learning, automated feedback) and risks (technological dependence, academic integrity), requiring authentic process-centered assessment and clear ethical policies. Recent sources indicate high student use of AI for content creation and problem-solving, alongside gaps in specific AI training; institutional practices to reinforce originality and responsible use were documented. AI should not be excessively restricted (or prohibited); instead, ethical and critical use should be promoted within clear institutional guidelines and evaluation designs that prioritize process and defense of strategics and decisions.
Artificial intelligence; Higher education; Marketing; Critical thinking; Authentic assessment.
Marketing, strategy, creativity, and data analysis
Copyright (c) 2025 Msc. Fernando Ivan Jaimes Rada, Msc. Julia Enith Herrera Mendoza (Author) https://creativecommons.org/licenses/by/4.0
The Impact of Artificial Intelligence on Business Strategy: Redefining Competitive Advantage in the Digital Era
Section: Articles Keywords: Artificial Intelligence, Business Strategy, Competitive Advantage, Organizational Transformation, Digital Innovation, Strategic Decision-Making Artificial Intelligence has become a disruptive force that essentially reinvents business strategy in all industries across the world. This paper will examine how AI technologies can be used to formulate corporate strategy, defining six new sources of competitive advantage and will critically examine the transformation that is necessary in the organization, the mechanisms of accelerating innovation, and how to improve customer experience. The study utilizes both qualitative and quantitative methods, which included bibliometric review of 1,039 articles and systematic review of 180 articles, financial performance data of Fortune 500 corporations, and 28 case studies in the industry. Based on the framework of analysis, the synthesis of the Resource-Based View, Dynamic Capabilities Framework, and Technology Acceptance Model are used to evaluate the strategic implications of AI. Findings indicate that three-quarters of organizations use AI in one or more business operations with a potential economic impact of US $2.6 to US $4.4 trillion a year. The overall shareholder return premium of 10.7 percentage points was realized by firms that achieved competitive advantages in all six of the areas of AI capabilities identified. However, the percentage of firms that reached a mature AI capability was only 1 percent, and 42 percent give up because of difficulties in data preparedness, skills shortage, and integration issues. Organizational redesign is required to achieve successful AI adoption, and workflow reconfiguration is the best indicator of business impact. Although AI adoption is accelerating at a rapid pace, the realization of strategic value demands fundamental organizational transformation rather than superficial technological overlays.
Copyright (c) 2025 Muhammad Mustafa Shakil, Md. Halimuzzaman, Arif Uz Zaman Khan, Jannatul Fardous (Author) https://creativecommons.org/licenses/by/4.0
Enhancing Temperature and Rainfall Prediction Accuracy Through Deep Learning Frameworks
Section: Articles Keywords: Artificial Intelligence, Deep Learning, Temperature, Rainfall, Climate Forecasting The information of accurate forecasting on temperature, rainfall is also very crucial for disaster preparedness, as well for climate management. Typical statistical and machine learning approaches have limited ability to capture nonlinear and spatiotemporally varying structure of climate fields. This research utilized recent state-of-the-art deep learning models to improve the prediction models for both temperature and rainfall. The hybrid Convolutional Neural Networks and Long Short-Term Memory (CNN–LSTM) method achieved the best results (R² = 0.98 for temperature, 0.91 for rainfall), outperforming those of Multiple Linear Regression (MLR) and Random Forest (RF) as a traditional model. The Physics-Informed Neural Network (PINN) model delivered physically consistent and stable predictions, especially under extreme weather such as heavy rainfall or heatwaves. Relative humidity, atmospheric pressure and sea surface temperature were found as most important predictors-base on feature importance analysis. The regional analysis remained that the coastal region performed best, whereas the hilly region with the high topographical complexity presented a relatively lower accuracy. In general, embedding deep learning into physical constraints ended up improving a lot both correctness and robustness of predictions. Further work should be carried out to improve interpretability, inclusiveness of data and transferability in space of such models with the ambition to build a more sustainable real-time weather forecasting system.
Copyright (c) 2025 Mohammad Sohel Kabir, Abdullah Al Naseeh Chowdhury, Mozibur Rahman, Md. Rasel, Md Muhasin Ali (Author) https://creativecommons.org/licenses/by/4.0
Hybrid Approach with a focus on Preprocessing Techniques for Detecting Phishing Websites
Section: Articles Keywords: Cybersecurity, phishing detection, hybrid, approach, URL, analysis, Deep, Learning Subjects: URL Analysis, Hybrid CNN-LSTM-Attention Model, Cybersecurity, Phishing Detection, Deep Learning, Machine Learning Phishing is regarded as a significant cybersecurity problem in the digital era, utilizing the fabrication of fraudulent websites to deceive users and expropriate their sensitive information, including passwords and financial data. The growing dependence on the internet has led to a marked increase in the frequency of these attacks, resulting in considerable financial losses for individuals and businesses alike. This underscores the pressing necessity for efficient strategies to counteract such assaults. This research aims to create a hybrid model for identifying phishing websites via URL analysis. The suggested model combines Convolutional Neural Networks (CNN) with Long Short-Term Memory networks (LSTM) and an Attention Mechanism to make predictions more accurate and uncover hidden patterns in the data. The model was trained on the "Url_Detection_Dataset" from the Kaggle platform, and its performance was assessed using precision, recall, and F1-score measures. The results showed that the hybrid model is better than traditional methods at telling apart real and harmful URLs, making it a useful tool in cybersecurity. The results provide a framework for subsequent research and promote the creation of more resilient, flexible, and effective solutions.
Copyright (c) 2025 Abeer Yahya Salawi (Author) https://creativecommons.org/licenses/by/4.0
MACHINE LEARNING (ML) TO EVALUATE GOVERNANCE, RISK, AND COMPLIANCE (GRC) RISKS ASSOCIATED WITH LARGE LANGUAGE MODELS (LLMs)
Section: Articles Keywords: Artificial Intelligence, Machine learning, Large Language Model, Governance, Risk, Compliance Subjects: Artificial Intelligence, Machine Learning, Large Language Model, Governance Risk Compliance In today’s AI-driven digital world, Governance, Risk, and Compliance (GRC) has become vital for organizations as they leverage AI technologies to drive business success and resilience. GRC represents a strategic approach that helps organization using Large Language Models (LLMs) automation tasks and enhances customer service, while maintaining the regulatory complexity across various industries and regions. This paper explores a machine learning approach to evaluate Governance, Risk, and Compliance (GRC) risks associated with Large Language Models (LLMs). It utilizes Azure OpenAI Service logs to construct a representative dataset, with key features including response_time_ms, model_type, temperature, tokens_used, is_logged, data_sensitivity, compliance_flag, bias_score, and toxicity_score. These features are used to train a model that predicts GRC risk levels in LLM interactions, enabling organizations to improve efficiency, foster innovation, and deliver customer value, while maintaining compliance and regulatory requirements.
Artificial Intelligence, Machine Learning, Large Language Model, Governance Risk Compliance
Copyright (c) 2025 Upakar Bhatta (Author) https://creativecommons.org/licenses/by/4.0
Named Entity Recognition for Hindi Current Landscape and Emerging Trends
Section: Articles Keywords: NLP, NAMED ENTITY RECOGNITION, Artificial Intelligence, Machine learning Subjects: machine learning, natural language processing, named entity recognition Disciplines: machine learning, linguistics, Artificial Intelligence Named Entity Recognition (NER) plays a crucial role in Natural Language Processing (NLP) by automatically identifying and classifying entities such as names of people, places, organizations, dates, and numerical values within unstructured text. While NER has seen major advancements in resource-rich languages like English, building robust NER systems for Indian languages—particularly Hindi—remains a significant challenge. Hindi presents unique linguistic complexities such as rich morphology, free word order, the absence of capitalization cues, and widespread use of code-mixed text, which complicate the task further. Over the years, researchers have explored a wide range of approaches to address these challenges, starting with rule-based and statistical models and progressing to sophisticated deep learning and transformer-based techniques. Multilingual models like mBERT, IndicBERT, and MuRIL have shown promise in improving accuracy and generalizability for Hindi NER. This review offers an in-depth look at the current state of Hindi NER, including the available annotated datasets, computational models, and performance benchmarks. It highlights the gaps that persist, such as the scarcity of high-quality annotated data, difficulties in handling informal and domain-specific language, and limited adaptability across different text types. The paper also outlines future directions for research, emphasizing the need for low-resource learning strategies, domain adaptation, and better handling of noisy and code-mixed data. As Hindi continues to dominate communication in various digital spaces, advancing NER systems for this language is more relevant than ever.
Copyright (c) 2025 SHALINI SHARMA, Dr. Piyush Pratap Singh (Author) https://creativecommons.org/licenses/by/4.0
Digital Payment Solutions Drive Efficiency in Supply Chains of Bangladesh
Section: Articles Keywords: Digital Payment Solutions, Supply Chain Efficiency, Transaction Speed, Cost Reduction, Transparency, Efficiency Performance This paper discusses how digital payment systems can enhance efficiency of the supply chain in Bangladesh based on the quantitative research method where its data was collected through 400 respondents, who included suppliers, distributors, wholesalers, and retailers. This paper applied standardized questionnaire to assess effects of digital payment in the speed of transaction, reduction of costs, transparency and trust, efficiency in general and perception by stakeholders. Descriptive analytics revealed that a strong agreement had been reached on the claim that digital payments accelerate transactions, improve transparency, and increase performance of the supply chain, though a moderate level of variability was noted between perceptions with regard to cost-cutting and adoption. Correlation and regression analysis of the hypothesis proved the statistically significant positive correlation between the use of digital payments and the principal efficiency indicators. These findings indicate that it is a strategic means of modernizing the supply chain in Bangladesh using digital payments, which is consistent with global trends of digital transformation. The research contributes to the theoretical bases and practical implementation as it provides empirical data about the benefits of implementing digital payments. It further highlights the need to have supportive policies, strong digital infrastructure, and advocacy campaigns to deal with the resistance to change, limited access, and unequal digital literacy levels. These are insights that can be used in the firms, politicians, and banks that desire to establish sustainable and competitive supply chain ecosystems.
Copyright (c) 2025 Sanjana Ahmed Chaity, Md. Ahsan Shoishob (Author) https://creativecommons.org/licenses/by/4.0
Section: Articles Keywords: Industrial robots, Artificial Intelligence, Self-driving cars, Chatbots, Production-line robots. Subjects: Industrial robots, Artificial Intelligence, Self-driving cars, Chatbots, Production-line robots. Disciplines: cybercrimes and AI regulations This study examines the legal nature of industrial robots in light of the rapid progress in artificial intelligence technologies and the growing reliance on such systems in various aspects of life. It raises new legal and ethical questions about the extent to which industrial robots can be granted legal personality, and the forms and nature of such personality. The study discusses this concept as a legal framework that permits attributing certain rights or obligations to robots, depending on their use and degree of functional independence from humans. The importance of this discussion becomes evident with the emergence of advanced applications of industrial robots, such as self-driving cars, production-line robots, chatbots, and algorithms capable of making impactful decisions that may cause material or moral harm without direct human intervention. The study further investigates whether the legal system needs to adopt a special model of legal personality for these entities to enable accountability for their actions or the outcomes of their decisions. Using the descriptive-analytical approach, the study centered on the following question: What is the legal nature of industrial robots? The study concludes that granting robots a special or conditional legal personality may help fill the legislative gap concerning liability, particularly in cases where tracing the direct human actor proves difficult. The findings emphasize the necessity of establishing gradual and balanced legal frameworks through which industrial robots can be granted limited and well-regulated legal personality. Such frameworks must both account for technological advancement and ensure effective human oversight. The study also calls for international cooperation to formulate unified legal standards consistent with legal and human values.
Industrial robots, Artificial Intelligence, Self-driving cars, Chatbots, Production-line robots.
Copyright (c) 2025 Abdullah Theeb Mahmmoud, Huda hassan baraghethi, Hasan Ahmed Breijieh, Waheeb Abu-Ulbeh (Author) https://creativecommons.org/licenses/by/4.0
Machine Learning in Assessing the Impact of Climate Change on Water Resources
Section: Articles Keywords: Climate Change, Groundwater, Machine Learning for IoT, River Flow, Water Resources The water resources of Bangladesh are highly susceptible to climate change which is reflected in patterns of river flows, groundwater and water quality. This work uses a machine learning (ML) approach to quantify the changes induced in representative river basins under historical and future SSP2-4.5 and SSP5-8.5 scenarios. Historical climate monitoring also indicated significant regional warming, especially in the coastal region (+0.25 °C/decade) and growing spatial heterogeneities of rainfall. Future projections under SSP5-8.5 suggest dramatic changes, such as a +4.1 °C temperature increase and a 13.5% change in rainfall by the end of the century. Comparing different ML models for predicting streamflow, Long Short-Term Memory (LSTM) model proved to be the best, with an accuracy and the fewest errors. The increase in mean annual streamflow in the Ganges, Brahmaputra, and Meghna basins is substantial under the LSTM projections and the Meghna basin could witness as high as 21% increase under SSP5-8.5. These transformations align with a significant increase in the frequency of extreme floods, that might extend to yearly events before the century is over. Groundwater is estimated to decrease drastically, and water quality is likely to deteriorate, with coastal salinity increasing by almost 100%. Spatial vulnerability diagnosis indicates that the South-West and coastal zones are severely vulnerable due to both water scarcity and salinity encroachment. A region-specific adaptation plan for sustainable groundwater management needs to be developed, with an advanced plan for enhancing the resilience of Bangladesh’s groundwater resources under future climate scenarios.
Copyright (c) 2025 Kazi Rumanuzzaman, Mst Sanjida Alam, Akram Hossain, Sheikh Urvana Akter Mim, Md Maheadi Hasan (Author) https://creativecommons.org/licenses/by/4.0
Use Artificial Intelligence in Autism Spectrum Disorder: a comprehensive review in assessment, intervention, and data collection
Section: Articles Keywords: Autism Spectrum Disorder (ASD), Artificial Intelligence, Applied Behaviour Analysis (ABA), Assessment, Intervention, Data Collection, Ethics Autism Spectrum Disorder (ASD) presents varied challenges across assessment, intervention, and behavioural monitoring. With recent advancements in Artificial Intelligence (AI), introduced tools have emerged that support clinicians, educators, and families in understanding and managing ASD more effectively. Significant innovations include explainable AI, mobile applications, computer vision, and wearable devices, which establish promising potential to increase accuracy, accessibility, and consistency of care. This review produces findings from recent literature to examine how AI contributes to the ASD care process. It highlights the benefits of AI while noting ethical and practical considerations. Also addresses challenges in data security, privacy, and ethical AI deployment within ASD care settings.
Copyright (c) 2025 Muna Abualhoul (Author) https://creativecommons.org/licenses/by/4.0
Responsible AI for AI Sustainable Future: Governance, Ethics, and The Reality Behind the Promise
Section: Articles Keywords: Ethics, Governance, Responsible AI, Sustainability Subjects: Responsible Artificial Intelligence, AI Ethics, AI Governance, Sustainable Development, Algorithmic Accountability, Ethical AI Frameworks, Environmental Sustainability and AI Disciplines: Artificial Intelligence, Information Technology, Science, Technology, and Society (STS), Ethics and Philosophy of Technology Artificial intelligence has emerged as a powerful force shaping global development, offering promising solutions across health, education, climate change, and governance. However, its rapid integration into critical sectors raises urgent questions about ethics, governance, and sustainability. This systematic review explores the promise and practice of responsible AI through the lens of three core objectives: the governance mechanisms guiding AI implementation, the ethical frameworks shaping its design, and the practical realities influencing its deployment across contexts. Drawing from sixty peer-reviewed articles published between 2017 and 2024, the review identifies strong global consensus on foundational principles such as fairness, accountability, and transparency. Nonetheless, a significant implementation gap persists, particularly in low-resource settings, where enforcement mechanisms and institutional readiness are often lacking. The findings also reveal that ethical commitments are frequently undermined by organizational constraints and commercial interests, leading to surface-level adherence without substantive change. Environmental sustainability, a critical dimension of responsible AI, remains underrepresented in current governance discussions despite mounting evidence of AI’s carbon footprint. This review contributes to the growing body of scholarship advocating for inclusive, enforceable, and context-sensitive approaches to responsible AI. It underscores the need for deeper engagement with the political, social, and environmental realities that shape AI’s impact on sustainable development. Ultimately, bridging the gap between AI’s ethical aspirations and real-world outcomes requires not only technical innovation but also strong institutional leadership, interdisciplinary collaboration, and meaningful stakeholder participation.
Copyright (c) 2025 Miracle Atianashie, Mark K. Kuffour, Bernard Kyiewu , Philipa Serwaa (Author) https://creativecommons.org/licenses/by/4.0
The Impact of Artificial Intelligence on Enhancing Guest Experience of Hotel Industry in Kuala Lumpur, Malaysia
Section: Articles Keywords: Artificial Intelligence, Perceived Usefulness, Perceived Ease of Use, Attitude Towards Using Artificial Intelligence, Guest Satisfaction, Hospitality Industry Utilizing the Technology Acceptance Model (TAM) as its theoretical backbone, this research examines AI integration into hotel guest experiences of Kuala Lumpur. The study explores relationships between PU, PEOU, Attitude Toward Using AI, and Guest Satisfaction (GUSA), investigating AUAI as a mediator and examining the moderating role of guest age, previous AI experience and trip purpose. Through the empirical survey of 630 hotel customers and analyzing the data with structure equation model (SEM), this study finds that PUSE and PEUS have significant impacts on AUAI and thus indirectly affect GUSA; meanwhile, PUSE and PEUS are directly influencing customer satisfaction simultaneously so that there are parallel direct and indirect effects. Adding to the TAM-strain in hospitality research, this result places post-adoption satisfaction as a primary outcome. Disparities between demographic and experience segments imply specificity in implementing AI. The findings have broader implications in both theoretical and practical ways: theoretically, it furthers the understanding of AI adoption within services context by incorporating technology acceptance with satisfaction outcomes; practically, it provides hoteliers and developers of AI technologies with practical steps toward creating solutions that are useful, easy to use, and constructs favorable guest attitudes insights valuable for strategically fast growing urban destinations such as Kuala Lumpur.
Attitude Towards Using Artificial Intelligence
Copyright (c) 2025 Shah Bin Taufiqur Rahman, Dr. Darian Low Eng Swee (Author) https://creativecommons.org/licenses/by/4.0
Implementation of Blockchain for Secure, Transparent, and Trustworthy Cross-Border Payments in Global Banking
Section: Articles Keywords: Blockchain, Security, Transparency, Efficiency, Cross Border Payments, Global Banking This paper investigated the application of the blockchain as an innovative solution for secure, transparent and confident cross border payments in global banking. This research was prompted by legacy systems' high costs, slow processing, security concerns, and lack of transparency, which reduced efficiency and user trust. The purposes were to investigate if the blockchain could improve security, transparency, trust, efficiency, adoption intention and use satisfaction of international transactions. The study design was quantitative in nature, and a structured questionnaire was used to collect data via a sampling of banking professionals, fintech gurus, corporate clients and cross-border payers. Data were analyzed through descriptive statistics and frequency distribution to find out the perception of the respondents. The findings showed that the majority of respondents have experienced inefficiencies in the current systems and were confident blockchain will provide a remedy by offering tamper-proof records, real-time tracking of transactions, reduced cost, and faster processing. In addition, the results also showed high adoption intention and favorable satisfaction expectations, which means that blockchain has potential to reshape global banking by decreasing dependence on intermediaries and increasing confidence in institutions. The research showed that clarity of regulation, organizational readiness, and user awareness are key for effective implementation. In summary, we certify that blockchain technology has potential to revolutionize cross-border payments and improve the financial global integrations.
Copyright (c) 2025 Tanveer Ahmed Siddquee (Author) https://creativecommons.org/licenses/by/4.0
A Gray Image Quantum Encryption using GNEQR Representation
Section: Articles Keywords: Quantum image encryption, GNEQR representation, quantum image processing, quantum gate , quantum encryption circuit Subjects: Quantum Image Cryptography Disciplines: Quantum computing In the digital era, characterized by extensive online data exchange, information security has become a priority. While traditional encryption methods have proven effective in protecting data transfers, the advent of advanced quantum computing has increased susceptibility to security breaches. Quantum encryption provides a revolutionary solution to this problem by using quantum mechanics principles to establish algorithms that are impermeable to decryption. Using these quantum properties, cryptographic protocols are developed to provide superior security, unlike traditional encryption methods. The image plays an important role in transmitting information in all areas. Therefore, quantum image encryption methods are specifically designed to counter the potential risks posed by quantum computers, which can compromise conventional encryption protocols. This ensures the preservation of data security despite advances in quantum computing technology. In addition, quantum image encryption improves data transmission efficiency by establishing secure communication channels using quantum stats, thereby reducing the need for bandwidth and improving transmission speed. This paper proposes a new method of quantum encryption based on GNEQR representation and the modification of pixel values and positions in an image. After converting the image into a quantum form, we applied an algorithm to modify the values and positions of the pixels using a succession of quantum gates. We concluded this study with a statistical analysis showing the robustness of our quantum image encryption method.
Copyright (c) 2025 Achraf ZEMATE, Moulay Brahim Sedra (Author) https://creativecommons.org/licenses/by/4.0
Comprehensive Analysis and Detection of IoT Network Attacks Using Recon Host Discovery Traffic Dataset
Section: Articles Keywords: IoT Security, Network Traffic Analysis, Reconnaissance Attacks, Host Discovery, Intrusion Detection System, Feature Correlation, Traffic Profiling, Cybersecurity, Flow-Based Analysis, Machine Learning for IoT, TCP Flags Analysis, Data Visualization, Exploratory Data Analysis, Anomaly Detection, Correlation Heatmap, Packet Statistics, Flow Duration, Benign Traffic, Malicious Traffic, Hybrid Models Disciplines: cybersecurity, network, IoT The proliferation of Internet of Things (IoT) devices has introduced unparalleled interconnectivity and significant security challenges. Reconnaissance attacks, particularly Host Discovery, are often precursors to more severe cyber threats. In this study, we examine a labeled network traffic flow dataset to analyze patterns and identify key indicators of Recon Host Discovery attacks. Leveraging exploratory data analysis and feature correlation techniques, we uncover critical traffic behaviors, such as short flow durations and anomalous packet statistics, that distinguish benign from malicious activities. The findings lay the groundwork for developing robust detection mechanisms for IoT networks, emphasizing the importance of targeted feature selection and real-time analytics.
Fair and Ethical Resume Screening: Enhancing ATS with JustScreen the ResumeScreeningApp
Section: Articles Subjects: Artifical Intelligence, Human Resources Information Systems Abstract— In today's fast-paced job market, the efficiency and fairness of the resume screening process are paramount. "JustScreen" emerges as a cutting-edge solution leveraging advanced Natural Language Processing (NLP) to automate resume evaluation, thus eliminating biases and promoting merit- based candidate selection. This thesis explores JustScreen's innovative approach to integrating NLP and machine learning algorithms to enhance the recruitment workflow, ensuring a more streamlined, unbiased, and efficient candidate assessment process. The methodology involves several key components: data preprocessing, NLP information extraction, fairness metrics calculation, bias mitigation, and interpretability techniques. By utilizing frameworks such as spaCy for NLP tasks, JustScreen aims to overcome the challenges of traditional manual screening processes, improving both accuracy and fairness. This thesis explores the transition from developing a full Application Tracking System (ATS) to creating a powerful enhancement for existing ATS systems. The ResumeScreeningApp/ JustScreen integrates generative AI to provide comprehensive resume analysis, adding significant value to traditional ATS functionalities. Initial evaluations indicate a significant advancement in talent acquisition practices, promoting equal opportunities and reducing the impact of potentially discriminatory factors. This research signifies a transformative shift in recruitment, setting new standards for ethical and efficient hiring practices using Generative AI.
Artifical Intelligence, Human Resources Information Systems
Copyright (c) 2025 Gloribeth Navarro (Author) https://creativecommons.org/licenses/by/4.0
Cloud Computing: Evolution, Challenges, and Future Prospects
Section: Articles Keywords: Cloud Computing, Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS), Virtualization, Edge Computing, Serverless Architecture, Hybrid Cloud, Quantum Computing Subjects: Cloud Computing, Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS), Virtualization, Edge Computing Disciplines: Cloud Computing Cloud computing has redefined how individuals and organizations interact with computational resources. Providing scalable, flexible, and cost-efficient solutions has become a cornerstone of digital transformation in various sectors, including healthcare, education, finance, and artificial Intelligence. This paper examines the evolution of cloud computing, its architecture, service delivery models, benefits, challenges, and emerging trends. It also highlights critical aspects of cloud adoption and the implications of recent technological advancements. The study utilizes authentic sources to offer a holistic view of cloud computing's current and future trajectory.
Copyright (c) 2024 Safana alzide (Author) https://creativecommons.org/licenses/by/4.0
Enhancing Cloud Security through Intrusion Detection: A Comprehensive Study Using the ISOT-CID Dataset
Section: Articles Keywords: Cloud security, Intrusion detection system (IDS), ISOT-CID dataset, Cyber threat detection, Machine learning Disciplines: Cloud Security Cloud computing has revolutionized data management, offering unparalleled scalability, flexibility, and efficiency. However, its open and multi-tenant nature introduces significant security vulnerabilities, making it an attractive target for cyber threats. Intrusion Detection Systems (IDS) tailored for cloud environments are essential in mitigating these risks. Despite various IDS models, benchmarking datasets representing realistic cloud environments is a substantial limitation. This study utilizes the ISOT Cloud Intrusion Detection Benchmark Dataset (ISOT-CID), a publicly available dataset featuring a range of network and application layer attacks collected from a real production cloud environment. The research explores the dataset's structure, analyzes attack patterns, and evaluates IDS models' performance to provide actionable insights for enhancing cloud security. This work contributes to the field by presenting a systematic analysis of ISOT-CID, identifying effective IDS models, and proposing improvements for future cloud intrusion detection research.
Copyright (c) 2024 Safana Alzide (Author) https://creativecommons.org/licenses/by/4.0
The Impact of Generative AI on Student Engagement and Ethics in Higher Education
Section: Articles Keywords: Artificial, Intelligence, Education, Student, Perceptions , AI, Literacy, Ethics Disciplines: Artificial Intelligence The rapid adoption of Artificial Intelligence (AI) in higher education is reshaping students’ learning experiences, with tools such as ChatGPT, Grammarly, and Microsoft Copilot becoming integral to academic work. This study, informed by data from the Digital Education Council Global AI Student Survey 2024, examines the impact of AI on students, focusing on usage patterns, trust in AI-generated content, ethical awareness, and expectations for institutional support. Findings indicate that 86% of students use AI for various academic tasks, with a majority expressing concerns about trust, fairness, and over-reliance on AI. While students value AI’s benefits, only 5% are fully aware of institutional guidelines on AI use, and 72% desire more AI literacy courses, reflecting a significant need for comprehensive support in navigating AI responsibly. The study underscores the importance of clear ethical guidelines, faculty training, and student involvement in AI policy formation to foster responsible AI use and preserve academic integrity. These insights offer valuable guidance for educators and policymakers seeking to integrate AI ethically and effectively into higher education.
Copyright (c) 2024 Ahmed Al Zaidy (Author) https://creativecommons.org/licenses/by/4.0
Section: Articles Keywords: Human-Computer Interaction, Swift, Gamification, Fantasy, User-Centric Design, Positivie Psychology Subjects: Human-Computer Interaction, Swift, Gamification, Fantasy, User-Centric Design, Positivie Psychology Disciplines: Computer Science This paper presents MageMemo, an innovative productivity tool that integrates gamification and fantasy storytelling to enhance user engagement in task management. Traditional productivity applications often struggle to sustain long-term user interest, decreasing effectiveness over time. MageMemo addresses this challenge by creating a captivating experience where users earn achievements, level up, and uncover an unfolding narrative as they complete real-life tasks. By combining principles of Human-Computer Interaction (HCI) with a compelling fantasy theme, MageMemo offers a novel approach to improving productivity and motivation. Outlined are the design methodology, technical implementation, and evaluation metrics employed in the development of MageMemo, providing insights into its progression and potential to revolutionize how people use task management applications.
Human-Computer Interaction, Swift, Gamification, Fantasy, User-Centric Design, Positivie Psychology
Copyright (c) 2024 Hunter Winghart, Dr. Ahmed Al Zaidy (Author) https://creativecommons.org/licenses/by/4.0
Section: Articles Keywords: Cybersecurity, cybercrime, threats, attacks Subjects: analysis of the high cybercrime rates in Florida, focusing on the financial impact, prevalent cyber threats, and strategic countermeasures Disciplines: Cybersecurity This article explores Florida's high rate of cybercrime and the efforts being made to combat it. Florida has the second-highest rate of cybercrime events in the United States, with significant financial losses. The most frequent dangers are call center fraud, ransomware, investment fraud, and business email compromise. Strong defense tactics, including multi-factor authentication, safe password policies, cybersecurity training, frequent software upgrades, network segmentation, and dependable backups, are emphasized throughout the text. It also emphasizes the importance of efficient incident response procedures, promoting teamwork, and using continuous monitoring to lessen cyber threats. The essay discusses the vital role that international collaborations and law enforcement play in the battle against cybercrime.
analysis of the high cybercrime rates in Florida, focusing on the financial impact, prevalent cyber threats, and strategic countermeasures
Copyright (c) 2024 Ahmed Al Zaidy (Author) https://creativecommons.org/licenses/by/4.0/
Digital Crimes and Digital Terrorism: The New Frontier of Threats in Cyberspace
Section: Articles Keywords: Digital, Crimes, Terrorism, Cyberspace Disciplines: Cybersecurity This paper explores the rise of digital crimes and digital terrorism as emerging threats that exploit cyberspace to conduct illegal activities, impact society, and compromise individuals’ privacy and security. With a growing dependency on digital platforms, these crimes have become more complex, highlighting the need for strengthened security measures, regulatory frameworks, and coordinated global efforts to address these issues.