Journal of Information and Organizational Sciences (JIOS)
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Smart Real-time Attendance System for Nigerian Universities
No full textThis study proposes a Smart Real-Time Attendance System using face recognition technology to address challenges in traditional attendance systems in Nigerian universities. These challenges include proxy attendance, manual errors, and administrative inefficiencies. The system employs Convolutional Neural Networks (CNNs) and the ArcFace algorithm for facial feature extraction and identity verification. Key development tools included InsightFace, OpenCV, and Streamlit, with Visual Studio Code as the IDE. The system ensures high accuracy, with 94% face detection, 98% face recognition, and 96% overall attendance prediction accuracy. It automates essential tasks like attendance percentage calculation and report generation, ensuring compliance with the National Universities Commission (NUC) 75% attendance requirement for exam eligibility. Ethical compliance was a core design concern, including informed consent, data encryption, access control, and fairness across facial profiles. This system significantly reduces impersonation, administrative workload, and enhances operational efficiency, making it a scalable and secure solution for attendance management. Its deployment is recommended for improving academic monitoring and policy enforcement in Nigerian universities
The Impact of Generative AI on University Students’ Learning Experience: A Study on Cognitive and Affective Outcomes
No full textGenerative Artificial Intelligence (GenAI) is rapidly transforming higher education, yet its impact on learning experiences remains contested. Existing research often isolates either cognitive outcomes (e.g., comprehension, creativity) or affective outcomes (e.g., motivation, engagement), leaving a gap in integrated analyses that also account for heterogeneity across student groups. This study investigates both dimensions simultaneously by examining university students’ perceptions of GenAI, focusing on learning, creativity, motivation, and engagement, alongside perceived risks such as overreliance, ethical concerns, and difficulties in verifying accuracy. Data were collected from 93 students and analyzed through Spearman’s correlations and unsupervised clustering (k-means) with PCA visualization. Findings indicate low to moderate positive correlations between GenAI usage and learning outcomes, particularly problem-solving and motivation. Cluster analysis reveals diverse usage–perception profiles, including paradoxical cases where frequent users report limited cognitive benefit. These results align with Technology Acceptance Model (TAM) and UTAUT assumptions of perceived usefulness and performance expectancy, while also showing that digital literacy moderates these relationships, especially in critical thinking and responsible use. The study contributes by integrating cognitive and affective outcomes, revealing latent profiles beyond averages, and bridging adoption models with responsible AI frameworks. Practical implications highlight the need for AI literacy training, ethical policies, and instructional design to foster effective and responsible GenAI integration in higher education
A Semantic-Context Embedding Enhanced Attention Fusion BiLSTM: Unraveling Multilingual Sentiments in Product Reviews with Advanced Deep Learning
No full textFor natural language processing (NLP), sentiment analysis (SA) is crucial since it helps to understand users' feelings and opinions in a variety of contexts. Even though Deep Learning (DL) techniques are becoming more popular in SA, effective model optimization can be difficult because they frequently call for a great deal of hyperparameter tuning. However, because current models can't sufficiently capture the variety of review contexts, it introduces bias and inaccuracies, especially in product reviews. For Multilingual Sentiment Analysis (MSA) in product reviews, this research proposed a Semantic-Context Embedding Enhanced Attention Fusion BiLSTM (SCEEAF-BiLSTM). The proposed model combines Continuous Bag-of-Word (CBoW) and Skipgram techniques to extract semantic context after the preprocessing stages of tokenization, stop word removal, and case normalization. A novel Convolutional BiLSTM with Enhanced Attention (CoBLEA) architecture is introduced for multilingual sentiment prediction to extract comprehensive context representations. The model ultimately shows efficacy in dividing multilingual sentiments into positive, neutral, and negative states, providing a viable method for complex SA in several circumstances. The outcome signifies that the proposed approach obtains a high accuracy attained 0.987, precision attained 0.985, recall attained 0.978 and F1-Score attained 0.986 when compared with prior works. With practical applications in sentiment-driven platforms operating in multiple languages, the research presents a method for complex SA in e-commerce, social media, and customer feedback systems. It also emphasizes the significance of comprehending multilingual opinions for enhancing marketing strategies, driving business decisions, and improving customer satisfaction
Exploring the Access to the Static Array Elements via Indices and via Pointers — the Introductory C++ Case Expanded
No full textWe revisit the old but formally still unresolved debate on the time efficiency of accessing the elements of 1D arrays via indices versus accessing them via pointers. To analyze that, we have programmed benchmarks of minimal complexity in the C++ language and inspected the machine code of their compilation in the x86 assembly language. Before exploring the performance, we briefly compared a few methods used for the execution time measurements. The results on the Wintel platform show no significant advantage in using pointers over indices except for some benchmarks and array (data) types. In other cases, the exact opposite may be true. The cause of this inconsistency lies in the compilation of the source code into the rather nonorthogonal x86 instruction set. Furthermore, the execution speed does not clearly relate to the instruction length. The parallel aim of this work is to provide a ground for further analysis and measurements of this kind using different compilers, languages, and computer platforms
Organizational Homeostasis: A Quantum Theoretical Exploration with Bohmian and Prigoginian Systemic Insights
No full textThis study examines the complex interactions in organizational structures using Bohm's ‘wholeness’ and Prigogine’s equilibrium theories. Wave analogies and quantum principles like superposition, non-locality, and entanglement explain fluctuations in these systems. Thus, this research suggests a paradigm shift in organizational methods towards a balanced, scientific approach. Organizations need flexible tactics and behave like dissipative structures to maintain internal coherence in chaos. Heightened through mindful techniques, corporate consciousness provides insights into temporal dynamics, improving decision-making, market resilience, and an expanded organizational ethos founded in present awareness. This heightened consciousness and demand for organizational alignment and coherence empowers the corporate entities to succeed in present conditions and anticipate and address future obstacles. This study introduces ‘Mindful Corporate Entity’ (‘MCE’), emphasizing mindfulness as a critical tool for organizational well-being and sustainability. This change is proposed to close management gaps
Pure Strategy Saddle Points in the Generalized Progressive Discrete Silent Duel with Identical Linear Accuracy Functions
No full textA finite zero-sum game defined on a subset of the unit square is considered. The game is a generalized progressive discrete silent duel, in which the kernel is skew-symmetric, and the players, referred to as duelists, have identical linear accuracy functions featured with an accuracy proportionality factor. As the duel starts, time moments of possible shooting become denser by a geometric progression. Apart from the duel beginning and end time moments, every following time moment is the partial sum of the respective geometric series. Due to the skew-symmetry, both the duelists have the same optimal strategies and the game optimal value is 0. If the accuracy factor is not less than 1, the duelist’s optimal strategy is the middle of the duel time span. If the factor is less than 1, the duel solution is not always a pure strategy saddle point. In a boundary case, when the accuracy factor is equal to the inverse numerator of the ratio that is the time moment preceding the duel end moment, the duel has four pure strategy saddle points which are of the mentioned time moments. For a trivial game, where the duelist possesses just one moment of possible shooting between the duel beginning and end moments, and the accuracy factor is 1, any pure strategy situation, not containing the duel beginning moment, is optimal
A Review on Blockchain for Fintech using Zero Trust Architecture
No full textFinancial Technology (FinTech) has sparked widespread interest and is fast spreading. As a result of its continual growth, new terminology in this domain has been introduced. The name 'FinTech' is one such example. This term covers a wide range of practices that are repeatedly used in the financial technology industry. This processes were typically accomplished in careers or organizations to supply required services through the use of information technology-based applications. The word covers a wide range of delicate subjects, including security, privacy, threats, cyberattacks, and others. Several cutting-edge technologies, including those associated with a mobile embedded system, mobile networks, mobile cloud computing, big data, data analytics techniques, and cloud computing, among others, must be mutually integrated for FinTech to thrive. To be approved by its users, this new technology must overcome serious security and privacy flaws. This research gives a thorough analysis of FinTech by discussing the present as well as expected confidentiality and safety problems facing the financial sector to protect FinTech. Finally, it examines potential obstacles to ensuring financial technology application security and privacy
Proactive Detection of Malicious Webpages Using Hybrid Natural Language Processing and Ensemble Learning Techniques
No full textThe proliferation of malicious webpages presents a growing threat to online security, necessitating advanced detection methods to mitigate risks. This paper proposes a novel approach that integrates Natural Language Processing (NLP) techniques with an ensemble of machine learning models for the proactive detection of malicious web content. By leveraging semantic analysis, lexical patterns, and metadata extraction, the proposed framework enhances the identification of suspicious patterns in web page content. The ensemble model combines decision trees, random forests, and gradient boosting methods, optimizing classification accuracy and reducing false positives. A comprehensive evaluation using a large dataset of web pages, including both benign and malicious examples, demonstrates the superiority of the proposed method over traditional single-model approaches. With accuracy rates exceeding 98%, this framework achieves a robust, scalable solution for real-time web content analysis, providing a critical tool for cybersecurity professionals to detect and block malicious webpages before they can cause harm. Future directions include the integration of deep learning architectures and adaptive filtering techniques to further refine detection capabilities
Comparative Analysis of Machine Learning Techniques for Cryptocurrency Price Prediction
No full textThe significant increase in cryptocurrency trading on digital blockchain platforms has led to a growing interest in employing machine learning techniques for the effective prediction of highly nonlinear and nonstationary data, becoming increasingly popular among both individual and institutional market participants. The aim of this research is to deal with the challenging task of predicting the closing prices of two prominent cryptocurrencies, Binance Coin (BNB) and Ethereum (ETH), utilizing machine-learning techniques. This study evaluates the efficacy of various machine learning models in predicting cryptocurrency prices, with a particular focus on Support Vector Machines for Regression (SVR), least-squares Boosting (LSBoost), and Artificial Neural Networks and Adaptive Neuro-Fuzzy Inference System (ANFIS). These models are compared under various metrics. ANFIS models exhibited superior predictive performance on both training and testing datasets based on diverse performance metrics. Comparatively, SVR with a linear kernel demonstrated strong generalization capabilities, particularly on the testing set. LSBoost, while showing promise in training accuracy, indicated results with higher test errors. ANN models maintained a balance between training and testing. This comparison showed the models’ effectiveness, particularly the robustness of ANFIS in capturing the volatile cryptocurrency market trends. The experimental data suggest that certain of the above models can be utilized to predict the ETH and BNB closing price in real time with promising accuracy and experimentally proven profitability
Effect of Electronic Surveillance on Task Performance: Mediating Role of Digital Transformation Moderating Role of Perceived Organizational Support
No full textPurpose: Digital transformation is of increasing relevance for both practitioners and scholars. Modern digital technologies, services, and systems are extremely important for social development because they are currently taking place in the economy, production, and society as a whole. This study delves into the crucial domain of electronic surveillance within the healthcare sector, assessing its influence on employee task performance. It investigates the interplay between electronic surveillance, perceived organizational support, and digital transformation. Our research aims to unravel how perceived organizational support moderates the relationship between electronic surveillance and task performance while also examining the mediating role of digital transformation in this dynamic.
Design: This cross-sectional study utilizes purposive sampling to collect data from 428 participants from the healthcare sector of the province of Punjab, Pakistan.
Findings: The findings indicate that electronic surveillance positively influences task performance, with digital transformation acting as a mediator. Additionally, perceived organizational support moderates this relationship, emphasizing its vital role in optimizing task performance.
Theoretical Implications: This study advances understanding of workplace dynamics by elucidating how electronic surveillance, digital transformation, and perceived organizational support interact. It contributes valuable insights for organizational and management theories, emphasizing the need to consider these multifaceted factors in optimizing task performance.
Practical Implications: This research provides valuable insights to healthcare organizations by shedding light on these multifaceted dynamics seeking to optimize task performance amid evolving technological landscapes and increased surveillance.
Originality: This study pioneers the exploration of the intricate interplay between electronic surveillance, perceived organizational support, and digital transformation in the context of task performance in the healthcare sector of society