The Asian Bulletin of Big Data Management
Recognization by HEC's Journals Recognition System (HJRS) The Asian Bulletin of Big Data Management (ABBDM) is a peer-reviewed journal recognized by the Higher Education Commission of Pakistan in Y category . The Asian Bulletin of Big Data Management (ABBDM) is a peer-reviewed journal for research in social science and related disciplines. The Journal seeks to include empirical, conceptual, and methodological articles across the full range of green management in Asian countries. The ABBDM provides a forum for academics and practitioners to share the latest developments and advances in the knowledge and practice of global business dimensions. It publishes scholarly articles of an interdisciplinary nature with contemporary global relevance. It also aims to foster the exchange of ideas on a range of important international issues and, in doing so, provide a stimulus for further research and the development of global perspectives. To this end, our objective is to attract quality scholarship that can practically benefit corporations and other institutions in their overall development as well as society at large. We encourage contributions from around the globe, with a special emphasis on emerging economies. The Asian Bulletin of Big Data Management (ABBDM) © 2021 by ASIAN ACADEMY OF BUSINESS AND SOCIAL SCIENCE RESEARCH is licensed under Attribution 4.0 International
ASIAN ACADEMY OF BUSINESS AND SOCIAL SCIENCE RESEARCH
Expanded Entity Coverage and Machine-Annotated Pre-Training for Urdu Named Entity Recognition
Named Entity Recognition (NER) for low-resource languages remains challenging due to limited annotated corpora and complex linguistic characteristics. Urdu is a morphologically rich Indo-Aryan language written in a cursive right-to-left script, increases these challenges in contrast with high-resource languages in NER performance. This paper presents a data-efficient framework for Urdu NER that combines large-scale machine-generated annotations with high-quality human-annotated data to mitigate annotation scarcity. Firstly, a new gold-standard human-annotated Urdu NER corpus is built comprising 49,040 tokens and 5,839 named entities across 13 fine-grained entity categories, including newly introduced types such as Sports, Food, and Color. To complement this dataset, a large machine-annotated corpus was created using a bootstrapped ensemble of Conditional Random Field models with confidence-based filtering. A two-stage training strategy in which multilingual transformer models, Multilingual BERT (mBERT) and XLM-RoBERTa (XLM-R) are pre-trained on the machine-annotated corpus and subsequently fine-tuned on the human-annotated data. Experimental results demonstrate that machine-annotation pre-training consistently improves NER performance for both models, yielding micro-averaged F1-score gains from 0.84 to 0.86 for mBERT and from 0.86 to 0.88 for XLM-R. Detailed per-class and confusion matrix analyses further show notable improvements for low-frequency and specialized entity types. The findings confirm that integrating weakly supervised machine annotation with multilingual transformer-based learning provides a practical and scalable solution for improving NER in Urdu and other low-resource languages. Keywords: NER, machine-annotated corpus, low-resource languages, Urdu NER corpus
Automatic, text summarization, natural language processing, deep learning.
Copyright (c) 2026 Dr.Nazish Basir, Dr.Mumtaz Qabulio, Dr.Muhammad Suleman Memon, Mr.Danish Nazir Arain, Dr.Sehrish Basir Nizamani, Dr.Saad Nizamani https://creativecommons.org/licenses/by-nc-nd/4.0
Assessing application of Blockchain, Artificial Intelligence, and Cloud-Computing in e-Governance
Administrative setups in developing countries face hurdles due to traditional manual processing of daily government affairs. Further, lack of transparency, delayed service and irregularities aggravate these challenges despite partial computerization. Conventional, data storages are subject to unauthorized modification, inefficient data registers and lack of coordination among administrative agencies. Establishment of decentralized, transparent, fraud-resilient and digitized e- governance infrastructure becomes inevitable to address these challenges. In this study, we outline e-governance model that particularly allows workflow automation of administrative affairs leveraging blockchain-based immutability, AI powered data analytics and cloud-computing services. The proposed architecture is further expanded to determine its efficacy in healthcare, electoral process and supply chain management. We conducted an analytical study to formulate modular architecture of these critical e-governance service. We also carried out experimental evaluation using blockchain based implementation for tamper- resistant, secure and anonymized public record registration. Results of this study shall help towards development scalable, interoperable and digitized e-governance model.
Copyright (c) 2026 Mohsin Shaikh, Abdul Aziz Memon, Khalil Rauf, Ashfaq Ahmed, Shah Nawaz Khuhro https://creativecommons.org/licenses/by-nc-nd/4.0
Industry 5.0, Human-Centric Innovation, and Green Operational Efficiency: Evidence from Smart Sustainable Workflows
This study analyses the effect of eco-based automation and human-centred innovation associated with Industry 5.0 on the green operational efficiency with attention to the mediating factor of smart sustainable workflow integration. A transition of Industry 5.0, based on a paradigm that opposes the technology-oriented paradigm of Industry 4.0, practiced by incorporating human creativity, ethics, and high-level automation, is part of bringing a more sustainable and efficient industrial production. Eco-motivated automation is supposed to decrease the energy use, waste and better utilization of resources and human-oriented innovation reclaims human agency in the production process, as the workers able to cooperate with the machines to achieve a greater sustainability. The research design is quantitative research, which involves the use of surveys and secondary data of the industries that have already implemented Industry 5.0 technologies. The hypothesis that eco-motivated automation and human-centric innovation has a positive influence on green operational efficiency is supported with the help of regression analysis, and the Structural Equation Modeling (SEM) proves that smart sustainable workflow integration mediates this correlation. The results indicate that industries that use these technologies have the potential to enhance performance with regard to environmental performance as well as productivity. This study builds on the knowledge of the potential of adopting Industry 5.0 practices to turn the conventional manufacturing to green and sustainable operations and presents some practical information to industries intending to maximize green efficiencies. The study also identifies future research opportunities, including investigating the long-term effects of the adoption of Industry 5.0 and experimenting with other mediating variables that may make industrial processes even more sustainable.
Copyright (c) 2026 Fahad Asghar , Nijah Akram , Khawar Abbas, Muhammad Hammad Abbasi, , Sudhair Abbas Bangash https://creativecommons.org/licenses/by-nc-nd/4.0
An Efficient AI and Deep learning Assisted Self-Healing Network Approach: Analysis on Fault Detection Response and Recovery to Mitigate Threats in IoT-Security Ecosystem
Artificial intelligence, machine learning (ML) is transforming the self-healing systems. These are mechanisms that are able to detect issues, anticipate on their occurrence and repair themselves without the assistance of an individual. In this paper, the author discusses how AI and ML may be applied to state-of-the-art self-healing systems with the help of predictive analysis, anomaly discovery, and automated recovery of the network. We discuss artificial intelligence methods such as neural networks, decision trees and reinforcement learning that are employed to predict and model failures in systems. In learning with historical and real-time data, it is possible to make predictions of a problem and begin correcting it before such a problem occurs, utilizing modern networks using ML algorithms. The article also compares the existing processes and outlines a framework for how predictive analytics and recovery algorithms can work together in real-time. As evidence of successful and secure AI-driven self-healing computer systems, we consider various practical applications and case studies in such fields as self-driving automobiles, cloud computing, and industrial automation. A few of the issues that arise include model accuracy, data quality and moral issues. Tables and figures present the performance of the proposed architecture and various models of ML. The integration of machine learning and self-healing capabilities indicates a significant step towards the creation of adaptable AI systems. The article contributes to the novel artificial- intelligence and machine-learning framework of reliable Internet-of-Things (IoT) networks, autonomic computing via a MAPE-K loop, reinforcement learning and bio-inspired concepts to achieve proactive defense mechanisms with over 90% detection rates and a recovery time of less than 5 minutes. The paper is characterized by the quality of literature assessment, clear statement of research issue, problematics and the presentation of hybrid solutions addressing some of the most common IoT threats, including distributed denial-of-service (DDoS) and zero-day exploits. However, the paper is significantly reliant on the simulation-based assessment to a significant degree and only slight empirical verification of hardware and scaling issues in the heterogeneous deployments can be observed. The current review includes quantitative studies, the most critical tableaux, derivational mathematics and constructive suggestions such as improved empirical testing and interdisciplinary harmonization that will help improve the viability of the system in the real world. Summing up, this work has an important contribution to the area of IoT security but it requires additional improvement to gain a more practical applicability and influence. The paper is concluded with the future opportunities and the possibility of the popularization of AI usage in the process of developing self-healing ecosystems.
Copyright (c) 2026 Muhammad Hamza Akhtar, Umair Ghafoor, Osama Imran, Nasir Ayub , Mian Muhammad Abdullah , Hamayun Khan https://creativecommons.org/licenses/by-nc-nd/4.0
Design and Implementation of Decentralized Student Management System on Blokchain Infrastructure
Database security is a critical concern in the information and communication technology industry, emphasizing the need for data availability, integrity, and confidentiality. Traditional centralized databases, commonly used in academic institutions for managing student records, are vulnerable to unauthorized access and system failures. To address these challenges, we propose a decentralized database using blockchain technology, specifically the Hyperledger Fabric platform, combined with CouchDB to store student information distributed among permissioned nodes. This approach leverages the cryptographic encryption features of blockchain to create an immutable and tamper-proof system. The architecture relies on a network of peer nodes across academic institutions, maintaining ledger copies and executing smart contracts. An ordering service ensures consistent transaction processing, while the Membership Service Provider (MSP) manages identities and access control. Authorized personnel can add student records through endorsed transactions, validated and added to the blockchain, ensuring data integrity and security. We analyzed the performance of our model using the Hyperledger Caliper tool, demonstrating its effectiveness in enhancing the security and reliability of student record management systems.
Design, Decentralized, Student Management System, Blockchain Infrastructure.
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
Folk Wisdom: The Role of figurative language in the Saraiki identity through animal imagery in poetry of Khawaja Ghulam Fareed
This study uses V.V. Vorobyev’s model of culturematic and linguocultural analysis to examine Khawaja Ghulam Fareed's kafis employs animal-based folk wisdom. It explores the ways in which animals, birds and wild creatures are utilized as socially embedded meanings that convey moral principles, religious beliefs, and collective folk wisdom, rather than just as artistic fun. Based on religious philosophy each animal represents a specific set of attitudes and convictions. The animals serve as carriers of collective knowledge, as demonstrated by the research, because these depictions are conveying folk wisdom. This study illustrates the use of folk wisdom in form of animals that are transmitters, narrators, and symbols of religious. Findings of the study shows that Fareed's Kafis shows religious with depictions of animal’s imagery playing a key role in the creation of ethical and philosophical discourses. Animals and birds serve as culturemes small messengers representing village life influenced by everyday encounters with the natural world. The research comes to the conclusion that Fareed's poetry is a prime example of folk wisdom is deeply woven into animal imagery.
Folk wisdom, culturemes, Linguacultural, animal imagery.
Exploring the theory and research in the area of language attitude
Language attitude is an important research area in sociolinguistics as well as language planning and policy. It has the predictive potential to forecast the future of a language and this predictive potential is often explored using several factors influencing language attitude of a language community. The construct of language attitude has been widely explored by sociologists, psychologists, linguistics, sociolinguists and others. All disciplines have tried to define the concept from the cannons of their respective discipline. There are two approaches used in the study of language attitude. The first approach called behavioristic sets to collect linguistic data to provide findings on the language attitude of speakers. The second approach called mentalist approach considers attitude as a mental state and employs questionnaires, interviews and matched guise techniques for collecting data and inferring language attitude from that. The importance of this research areas is reflected in its potential to draw upon the theories from multiple disciplines and consequently to provide directions for the progress of these disciplines.
Register of Cricket Commentary: Variation Across Spoken and Written Sportscasting
This corpus-based comparative study explores two modes of cricket commentary, that is, television commentary (SAT) and online written commentary (OWC), from the register perspective. The data collected from the ICC Twenty-20 World Cup, June 2009: Final Match, Inning 1 is analyzed in the light of Biber and Conrad (2009) framework. The results substantiate that, in comparison to OWC, the situation of SAT occasions more versatile and diverse functions thus compelling for more resourceful and frequent linguistic features. Although the relatively formulaic nature of OWC substantially limits the variety and productivity of linguistic features owing to more impelling situations, it strives to replicate SAT, which places it intermediate between the play-by-play and color versions of sportscasting. A lesser known and less-noticed (in register studies) register has been placed on record, which is envisaged as a healthy contribution to the studies on register variation.
Register, sportscasting, cricket, online written commentary, television commentary, play-by-play commentary, color commentary.
Exploring Semantic Functions of Reduplication: A Cross Linguistic Perspective from Kashmiri and English
phenomenon across human languages and is one of the significant areas of interest in the field of linguistics. This study is a comparative and descriptive analysis of reduplication in Kashmiri and English languages and aims to study the phenomenon at the morphological level, showing its semantic effects created in varying contexts. Reduplication mainly exists in two forms: full reduplication and partial reduplication and generally affects the content words in the languages with an augmentative or diminutive effect. The study reveals that speakers use reduplicated terms excessively as they are accompanied by a diversity of meanings, whereas in English, reduplicated terms are used in few contexts, accompanied by stress.
Reduplication, Semantics, Comparative Study, Kashmiri Language, Word Formation.
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
Application of Blockchain-Artificial Intelligence towards Computerized Land Record Management System. *AI based Blockchain System for Sindh, Pakistan
Traditional land registry systems have been sub- ject to duplicated entries, manipulation of land documents, and possible risk of legal dispute in urban areas of Sindh, Pakistan. Growing urban population demands preservation of land documentation with efficacy, reliability and transparency. Blockchain based systems have emerged in recent years as a promising solution to ensure immutable and duplication free on- chain storage of records in the form encrypted hashes. In this study, we have demonstrated the integration of blockchain and artificial intelligence for ensuring digitized land record system for province Sindh. We developed the experimental and analytical framework comprising an Ethereum-based smart contract and machine learning based verification mechanism to eradicate prevalent hindrances on land record management. We leverage experimental methodology with real Pakistani naming norms, geographical distributions across ten Sindh districts, utilized the dataset of 2000 residential records, having induced inconsistent records (duplicates: 2.0%, fraudulent entries: 1.85%, ambiguous data: 3.0%). AI layer application was accomplished through a random forest classifier pre-screened records with 95.2% accuracy, preventing dubious entries from being registered on the blockchain. The implementation ensures maintaining immutable records, successfully prevented the duplication and transparency was achieved with public availability of data records. With improved security, transparency, and fraud resistance, the system architecture provides a significant pathway for digitizing urban land records.
Copyright (c) 2025 Mohsin Shaikh, Abdul Aziz Memon, Khalil Rauf, Shah Nawaz Khuhro https://creativecommons.org/licenses/by-nc-nd/4.0
Impact of Performance Appraisal, Compensation, Benefit and Special Allowances on Employee Engagement
This study examines the impact of selected human resource management practices on employee engagement in private sector universities in Jalalabad city, Nangrahar Province, Afghanistan. Specifically, it investigates the effects of performance appraisal, reward and compensation, employee benefits, and employee special allowances on employee engagement. A quantitative research design was employed, and data were collected through a self-administered structured questionnaire comprising 27 closed-ended items measured on a five-point Likert scale. The study population consisted of 162 employees working in four private universities, from which a sample of 81 respondents was selected using convenience and simple random sampling techniques. Statistical analysis was conducted using SPSS, applying descriptive statistics, reliability analysis, correlation, and multiple regression techniques. The reliability results confirmed acceptable internal consistency for all constructs. The findings reveal that employee benefits have a statistically significant and positive impact on employee engagement, whereas performance appraisal, reward and compensation, and employee special allowances do not show a significant effect in the context of private universities in Jalalabad. The regression model explains a substantial proportion of the variance in employee engagement. Based on the findings, the study concludes that private universities should prioritize improving employee benefit packages to enhance engagement levels. The study contributes empirical evidence from a developing-country context and offers practical implications for university management and human resource policymakers.
Copyright (c) 2025 Hayat Ullah, Qeyam Seddiqi https://creativecommons.org/licenses/by-nc-nd/4.0
Anomaly Detection in IoT Using Machine Learning Techniques: A Comparative Study and Voting-Ensemble Approach
The widespread adoption of Internet of Things systems has connected large numbers of devices through shared networks and cloud services, which has improved automation, monitoring, and operational efficiency. However, this growing connectivity also enlarges the security boundary and creates more opportunities for attackers to inject harmful or abnormal traffic. As a result, it becomes increasingly important to detect malicious behavior early and reliably so that IoT-oriented networks can remain stable, trustworthy, and resilient. In this study, machine learning is used to identify malicious activity from network traffic data. Five supervised learning models are examined, including k nearest neighbor, decision tree, naive Bayes, multilayer perceptron, and support vector machine. Since each classifier has different strengths and weaknesses, an additional voting-based ensemble model is developed to combine their predictions into a single decision. The evaluation is carried out on the KDD Cup 1999 benchmark dataset using a conventional training and testing procedure. To ensure a fair comparison, performance is assessed through accuracy, precision, recall, and F score, which together provide a balanced view of detection quality. The experimental findings show that the voting ensemble model produces the most reliable performance among the tested approaches. In the full label evaluation setting, the ensemble achieves 98.87 percent accuracy, exceeding the best single classifier performance. In a filtered class setting that removes rare classes to reduce extreme imbalance, the ensemble accuracy increases to 99.87 percent. These findings indicate that combining diverse learners improves robustness and reduces the risk of relying on a single model under varying traffic patterns, providing a strong baseline for intrusion detection in IoT network environments.
Copyright (c) 2026 Mediha Maroof, Ayesha Maroof, Ayesha Bano https://creativecommons.org/licenses/by-nc-nd/4.0
Digital Twin-Enabled Predictive Intelligence Framework for Supply Chain 5.0: Hybrid Deep Reinforcement Learning Architecture for Real-Time Adaptive and Self-Optimizing Decision Management
The rapid evolution of global supply networks, characterized by increasing uncertainty, demand volatility, and systemic disruptions, has exposed the limitations of conventional optimization-based and rule-driven supply chain management systems. In response to these challenges, the emerging paradigm of Supply Chain 5.0 emphasizes human-centricity, resilience, sustainability, and intelligent autonomy through advanced cyber-physical integration. This study proposes a Digital Twin–enabled predictive intelligence framework for Supply Chain 5.0 that integrates real-time data synchronization, hybrid deep reinforcement learning, and adaptive decision management to achieve self-optimizing and resilient operational control. The proposed framework establishes a high-fidelity digital twin that continuously mirrors the physical supply chain by assimilating heterogeneous data streams from demand signals, inventory states, logistics operations, and disruption indicators. This cyber-physical representation serves as an interactive simulation environment for intelligent policy learning and scenario evaluation. At the core of the framework, a hybrid deep reinforcement learning architecture is developed by combining model-free policy learning with model-based optimization elements, enabling both strategic foresight and rapid tactical adaptation under dynamic conditions. The learning agent is designed to optimize multi-objective performance criteria, including operational cost efficiency, service-level reliability, disruption resilience, and sustainability-oriented metrics, while maintaining real-time responsiveness. Unlike static optimization or reactive control approaches, the proposed predictive intelligence mechanism enables proactive anticipation of demand fluctuations, transportation delays, and supply disruptions through continuous interaction with the digital twin. Furthermore, a human-in-the-loop governance layer is incorporated to ensure explainability, supervisory control, and ethical alignment of autonomous decisions, reinforcing the human-centric vision of Supply Chain 5.0. The effectiveness of the proposed framework is evaluated through a multi-echelon supply chain simulation under diverse uncertainty scenarios, including stochastic demand patterns and disruption events. Comparative analysis against traditional optimization and standalone deep reinforcement learning baselines demonstrates substantial improvements in decision adaptability, recovery speed, and overall system robustness. The results highlight the framework’s ability to dynamically reconfigure sourcing, inventory, and distribution strategies in real time while maintaining stability and performance. Overall, this study contributes a scalable and intelligent decision-making architecture that advances digital twin–driven autonomy in next-generation supply chains, offering significant theoretical and practical implications for resilient, adaptive, and human-centric supply chain management.
Copyright (c) 2026 Omar J. Alkhatib , Muhammad Irshad Hussain, Raza Ali Nawaz, Muhammad Umar Amin, Ajab Khan https://creativecommons.org/licenses/by-nc-nd/4.0
Teachers’ Social Intelligence and Classroom Management Practices: A Systematic Review
The systematic review is a synthesis of empirical evidence of the relationship between social intelligence of teachers in classroom management with regard to instructional management and behavioral management. With the growing emergence of teaching as a socially constructed practice, the role of social intelligence of teachers in the classroom management has emerged as an urgent subject of research. A search was carried out using PRISMA 2020 on the platforms of Scopus, Web of Science, ERIC, Science Direct, SAGE Journals, and Google Scholar. A qualitative thematic synthesis was performed using 24 studies that passed the screening and eligibility tests. The analysis presents trends in the conceptualization and measurement of teacher social intelligence as well as its correlation with classroom management strategies of instruction and behavior. The synthesis showed that the positive relationships between teacher’s social intelligence and classroom management outcomes were consistent. More effective instructional management was observed in teachers who employed greater levels of social intelligence: more understandable instruction, flexible scheduling, and smooth changeovers. They also demonstrated more behavioral control, especially in proactive discipline, good behavioral control, and reduced interruption in the classroom. Although there were differences in definitions and measurement techniques, the results of the studies were not much different. Such findings underscore the power of social smartness in teachers as one of the professional competencies that improve classroom management and teaching. The strength of the review is that it brings together the scattered findings and explains the importance of social intelligence in bringing about the important aspects of classroom management. The discussion of implications to teacher education, professional development, and future research is made to promote socially informed practice of teaching. .
Social intelligence of teachers; classroom management; instructional management; behavioral management; systematic review.
Teachers’ Social Intelligence as a Predictor of Instructional and Behavioral Management Practices
Social intelligence of teachers has received growing academic interest as a key interpersonal competence in the educational contexts; however, the future impact of this competence on classroom management among the tertiary schools has not been sufficiently investigated. The current research examined the relationship between social intelligence in teachers in universities as a predictor of instructional and behavioral classroom management strategies. Using a cross-sections quantitative design, 593 instructors working at the public universities located in Lahore, Okara and Sahiwal, Pakistan were sampled. The Tromso Social Intelligence Scale (TSIS) was the measure of social intelligence with the Behavior and Instructional Management Scale (BIMS) used to measure the classroom management practices. The variables were interrogated to find predictive associations using Partial Least Squares Structural Equation Modeling (PLS–SEM). The results showed that respondents gave moderately high scores in the social intelligence and classroom management practices. The structural model analyses apparently revealed that social intelligence had a strong predictive effect on overall classroom management, and also made salient predictor of instructional management and behavioral management. What these findings mean is that teachers with higher levels of social intelligence are more adept at organizing teaching experiences as well as controlling classroom behavior in higher education institutions. The research is valuable to the body of existing literature in that it will help expand the theoretical use of social intelligence into the realm of university teaching and empirically support the antecedent role of social intelligence in classroom management. The results highlight the importance of integrating social intelligence training in professional training and teacher education programs at higher institutions of learning.
social intelligence, classroom management, instructional management, behavioral management, higher education, PLS-SEM.
Teacher's Power of Expression under the Headship with Distributive Justice: A comparative study of Junior and Senior Special Education Teachers
ABSTRACT The study aimed to examine the power of expression of the junior and senior special education teachers under the headship with distributive justice in special education institutions. Distributive justice has been considered as one of the key element of organizational justice that emphasizes on the fair distribution of resources, tasks and rewards within institutions. The power of expression is the influence of teachers to demonstrate their professional opinions, to participate in the school decision making, and to discuss with the administration and other teachers. The study employed a quantitative comparative research design and the researchers gathered data of 200 special education teachers in Punjab in Pakistan. The modified standardized measures were in accordance with the scale of Distributive and Procedural Justice and Teacher Performance Evaluation Scale. To obtain the statistical outputs, such as descriptive statistics, independent samples t-test, and regression analysis, the SPSS (v25) was used. The results showed that distributive justice is important in enhancing the expressive capacity of the teachers where senior teachers exhibited a high expressive capacity than junior ones. The findings emphasize the importance of an open and equitable leadership that helps to promote teacher communication and empowerment.
Copyright (c) 2025 Saud Ur Rehman Khan, Shehzad Ahmed, Muhammad Tahir Khan Farooqi https://creativecommons.org/licenses/by-nc-nd/4.0
The Role of Leadership Styles in Enhancing Workforce Performance: An Empirical Study on Lesco
This research is based on an in-depth examination of the effects of leadership philosophies on worker performance. The study will examine the impact of leadership ideologies on employee performance at Punjab, Pakistan's LESCO energy distribution firm, using LESCO Pakistan as the main case study. Both a quantitative and qualitative technique were used in this study to examine the relationship between employee performance and leadership styles. By providing appropriate tests and a valid and reliable data collection tool, the study adds substantial value to the corpus of previous research on administration, especially when considering developing countries such as Pakistan. Additionally, the developed model has undergone extensive testing and validation within the Pakistani environment.
Copyright (c) 2025 Saba Qasim https://creativecommons.org/licenses/by-nc-nd/4.0
An Exploration of User-level Privacy-Preserving Federated Learning Technique: A Machine Learning Perspective on Classification, Threat Mitigations, and Exploring Federated Learning and Beyond
The federated learning (FL) concept makes sure that a machine learning model is created by multiple clients without them sharing their local data. The prohibition policies of data exchange among organizations provided by various governments have resulted in the necessity of privacy-preserved federated learning. Federated learning has developed this concept of modeling development in many industries to improve performance and accuracy. The following paper provides the classification of Federate Learning, covering the presence of aggregation algorithms, frameworks, aspects of implementation, and dataset repositories, making it a crucial source of information to researchers working in the area. The paper critically examines the available centralized and decentralized FL methods that have been proposed in the literature and provides an overview regarding the methodology, privacy techniques applied and constraints to inform other researchers in making improvements in the area of federated learning. The article addresses the importance of privacy-enhancing technology, such as differential privacy (DP), homomorphic encryption (HE), and secure multiparty computation (SMPC) in federated learning as they can achieve privacy as well as effectiveness in terms of communication efficiency and the cost of computation. The article examines federated learning applications in the privacy-sensitive field of natural language processing (NLP) and healthcare, as well as in the Internet of Things (IoT) and edge computing. We think that our contribution is a new one because it determines privacy evaluation metrics and highlights the measures according to the data privacy and correctness, cost of communication, cost of computation and scalability. Moreover, it presents an emergent need and offers prospective research areas of the federated learning field.
Copyright (c) 2025 Ammar Ahmed, Syed Muhammad Rizwan, Fasiha Ikram, Nasar Ahmed , Hamayun Khan , Muhammad Muhammad Zulkifl Hasan https://creativecommons.org/licenses/by-nc-nd/4.0
An Enhanced Textual Review Classification and Sentiment Analysis Approach based on Machine Learning: A Comprehensive Analysis for Text Categorization Approaches
Since interpretable feature-engineered pipelines, natural language processing (NLP) has developed into deep neural and transformer-based architectures, and finally, large language models (LLMs) that can generalize their results across tasks. This development has significantly enhanced the subjective language comprehension, such as sentiment, emotion, sarcasm, humor, stance, metaphor, intent and aesthetic judgments, and increased the requirements of explainability in high-stakes areas. Already trained transformers (e.g., encoder-only and decoder-only versions) and LLMs like BERT and GPT have established powerful baselines on text classification and zero/few-shot subjectivity modeling, but the opaqueness of these models drives language-based rationalization (extractive/abstractive natural-language justifications) and feature-attribution algorithms (e.g., LIME, Integrated Gradients, SHAP). The four strands brought together by this survey include: (i) sentiment-analysis methods and datasets (e.g., IMDb, Sentiment140, Twitter Airline, SemEval), (ii) transformer-era text classification, (iii) LLMs on subjective language, and (iv) rationalization of explainable NLP. We have provided a unified taxonomy of subjective tasks and have analyzed model families, starting with classical ML up to transformers/LLMs; have collected major data sets and benchmarks; and have systematized explainability methods, in particular rationalization, and their evaluations and classify open problems dataset bias and annotation ambiguity, constraints of faithfulness and explanatory comprehensibility, evaluation bias, compute cost, and ethical risks. The article will facilitate the formation of a coherent foundation of explainable, credible subjective NLP based on the functions of transformer/LLM and analyze feature extraction techniques methodically based on LLM. We have trained a Machine Learning classifier using 70% of the training data and 30% of the testing data. Based on our results, we find that the proposed ML based technique gives enhanced performance, with an improved accuracy of 99% in the UCI-ML reviews dataset and 96% in the Twitter Kaggle dataset. This study underscores the paramount significance of feature extraction in sentiment analysis, endowing pragmatic insights to elevate model performance and steer future research pursuits.
Copyright (c) 2025 Ali Ahmed, Nasar Ahmed, Umair Ghafoor, Syed Muhammad Rizwan , Rizwan Qureshi , Hamayun Khan , Muhammad Zunnurain Hussain https://creativecommons.org/licenses/by-nc-nd/4.0
The Incorporation of AI-Driven Study Support Tools to Promote Academic Self-Efficacy and Reduce Procrastination Behaviors in STEM Students
The current research examines how artificial intelligence (AI) devices that facilitate study aided academic self-efficacy and procrastination behaviors among students in science, technology, engineering, and mathematics (STEM). In a quasi-experimental design, 182 students enrolled in undergraduate programs from a large public university in Pakistan were assigned at random to either the treatment group (n = 91) who used AI devices for study, or control group (n = 91) using traditional techniques of study. The treatment programs lasted twelve weeks. Academic self-efficacy and procrastination were assessed by validated scales pre- and post-test. Results showed that students in the treatment group gained in self-efficacy significantly (from 3.64 to 4.32) whereas procrastination decreased significantly (from 2.73 to 2.18). Effect sizes were moderate to large (η² = 0.079 self-assess, η² = 0.064 procrastination). Control students exhibited slight changes in the variables. Mediation analyses indicated that self-efficacy mediated significantly the relation of the use of the AI devices with diminution in procrastination, showing significant indirect effect (β = -0.231, p < .001) with moderate effect size (η² = 0.15). AI learning tools can facilitate the creation of a more confident, motivated self-regulated workforce in STEM initiatives, thus lessening educational disparities and enhancing national innovation capabilities through better educational outcomes for students.
Copyright (c) 2025 Ayesha Ashraf, Farazia Gulshan, Ali Humza, Muhammad Aqeel Aslam https://creativecommons.org/licenses/by-nc-nd/4.0
An Efficient Big Data Security and Privacy in Healthcare for Enhancing Remote Sensing and Monitoring: A Technological Perspective based on ACL for Preserving Big Data Analytics in Cloud
The growing adoption of big data technologies in healthcare has significantly enhanced patient care, diagnostics, and system efficiency. Yet, this transformation brings with it serious concerns about the security and privacy of sensitive medical information. The health care industry is in a critical phase of intelligence with the rapid advancement of modern information technology. With the increased use of healthcare big data, the issue of information security is increasingly becoming critical in the management of smart healthcare care, including the leak of patient privacy, the most critical issue. Thus, the enhancement of information management of intelligent health care during the age of big data is a significant aspect of long-term sustainable development of the hospitals. This paper has initially determined the most influential indicators to influence the privacy disclosure of big data in managing health care and presented a comprehensive overview of the current landscape in healthcare data security and thereafter set the privacy and security based access control model, which has been applied on the security and management of big data in utilization of medical data, and solves the issue of actual data breach where actual problems are involved. Lastly, the model is contrasted with the state-of-the-art techniques. The paper offers a comparative analysis of proposed solution considering numerous parameters and highlights critical gaps for building more secure and trustworthy healthcare systems. The findings confirm that the model is useful in the evaluation of the existing safety threats and forecasting the scope of the various risk factors, by demonstrating that Network Segmentation and Cloud Usage (Hybrid) have significantly enhanced the results by 1.5% and 6.7% respectively and the User Access mechanism is upgraded by 1.5% and 6.7% respectively. The Audit Trail and Compliance is also improving as the proposed technique examines ACL and explores key global regulatory frameworks like the GDPR and HIPAA. The outcome of this study suggests that the proposed access control model is resistant to most cyber-attacks in big data, and it is also demonstrated that the offered framework can be used as a starting point in order to develop secure and safe medical big data solutions. Therefore, this study can be valuable to future scholars to understand the information about the security and privacy of big data in the medical field and ways to implement countermeasures.
Copyright (c) 2025 Irfan Farooq, Umair Ghafoor, Sania Umer, Arshad Ali, Abdul Karim Shahid, Hamayun Khan https://creativecommons.org/licenses/by-nc-nd/4.0
Image Noise Reduction Techniques Using Machine Learning
Image noise is a ubiquitous problem of digital imaging which reduces the quality of visual representation and affects the functionality of computer vision systems. Noise may exist due to a number of factors, such as sensor flaws, low-light processing, and transmission noise. The conventional methods of noise reduction, including filtering and transform-domain methods, tend to be non-restrictive to the details of the image, at the expense of noise reduction. Over the last few years, machine learning (ML) and deep learning systems have become useful tools in image denoising, where data-driven models can be used to restore data to a high quality. This paper evaluates and compares different noise reduction algorithms based on machine learning, such as supervised and unsupervised algorithms, and hybrid models with respect to their effectiveness, computational cost, and ability to address various types of noise. Other issues covered in the study are overfitting, generalization, and performance in practical application. The results show that deep convolutional neural networks (CNNs), autoencoders, and generative adversarial networks (GANs) are better at noise suppression and structural details conservation. The application of the ML-based denoising to the imaging sequence has shown great potential in the medical imaging, remote sensing, and surveillance fields, and further research is necessary in this field.
Copyright (c) 2025 Muhammad Uzair Baig, Muhammad Afzal Raja, Tanveer Ahmad, Nazia Azim, Asad Riaz https://creativecommons.org/licenses/by-nc-nd/4.0
Tax Evasion Hinders the Progress of Economy of Pakistan
Tax evasion s a critical issue that significantly hampers the profitable/economic growth and development of Pakistan. Despite the country’s eventuality for profitable expansion, the wide practice of tax evasion undermines government profit generation, limiting the capacity to fund essential public services, structure systems, and social weal programs. This issue is aggravated by a large informal sector, weak enforcement mechanisms, and a lack of taxpayer mindfulness/knowledge, which inclusively contribute to a low duty base and high situations of non-compliance. Tax evasion not only deprives the government of important- demanded profit but also creates an uneven playing field, where honest taxpayers bear the burden while evaders continue to escape their liabilities. As a result, the country faces challenges in achieving financial stability, promoting sustainable development, and addressing poverty. Effective results, including strengthening duty administration, enhancing public mindfulness, and expanding the formal frugality, are critical to addressing tax evasion and fostering a more robust and indifferent profitable terrain in Pakistan.
Copyright (c) 2025 Mohsin Khan, Danish Bashir, Nadeem Rind https://creativecommons.org/licenses/by-nc-nd/4.0
A Big Data–Driven Optimization Framework for Enterprise Financial Management: Enhancing Predictive Decision-Making, Risk Control, and Computational Efficiency
The exponential growth of financial, operational, and market data has transformed the landscape of enterprise financial management, exposing critical limitations in conventional forecasting, budgeting, and risk assessment practices. Traditional financial systems characterized by fragmented data silos, rule-based decision routines, and delayed reporting cycles are no longer capable of supporting the speed, complexity, and granularity required in dynamic business environments. To address these challenges, this study proposes a big data–driven optimization framework that leverages large-scale data integration, advanced analytics, and intelligent optimization models to strengthen predictive decision-making, enhance enterprise-wide risk control, and achieve high computational efficiency. The proposed framework is structured around four interconnected layers. First, a multi-source data acquisition and integration layer consolidates heterogeneous data streams including ERP financial records, transactional logs, market indicators, supply chain data, customer interactions, regulatory updates, and alternative external datasets. This unified data repository provides comprehensive contextual visibility required for both micro-level financial insights and macro-level strategic planning. Second, a scalable big data infrastructure layer is designed using distributed storage, parallel computing, and streaming architectures capable of processing high-volume and high-velocity financial workloads with minimal latency. Third, an analytics and optimization intelligence layer integrates machine learning models for cash flow forecasting, anomaly detection, credit risk scoring, and cost prediction, combined with optimization algorithms for liquidity allocation, capital structure management, investment decisions, and real-time risk mitigation. Finally, a decision-support and visualization layer translates analytical outputs into actionable insights through interactive dashboards, predictive alerts, scenario analyses, and explainable AI modules tailored for CFOs, controllers, auditors, and risk management units. Through the fusion of predictive analytics, real-time data orchestration, and algorithmic optimization, the framework enables enterprises to transition from descriptive financial reporting toward proactive, predictive, and prescriptive financial management. This transition improves forecasting accuracy, strengthens resilience against market shocks, identifies early risk signals, and supports data-driven financial governance. The study further highlights the computational efficiency benefits achieved through distributed processing, workload balancing, and model optimization, significantly reducing processing time and enabling real-time decision flows. Overall, this research contributes a robust conceptual foundation and a scalable architectural pathway for deploying intelligent, big data–powered financial management systems. The framework offers a transformative direction for organizations seeking to enhance financial agility, strengthen risk governance, and achieve operational excellence in increasingly volatile and data-intensive business ecosystems
Big data analytics, Enterprise financial management, Predictive decision-making, financial risk control, Computational efficiency, Machine learning–based forecasting, Real-time financial intelligence, Decision support systems.
Copyright (c) 2025 Nazia Noor , Farah Arzu , Shah E Yar Qadeem, Muhammad Essa Siddique, Alamgir Safi, Aleena Qamer https://creativecommons.org/licenses/by-nc-nd/4.0
Artificial Intelligence Assisted Autonomous Unmanned Aerial Vehicles (UAVs) and Aerial drones based on Machine Vision for Enhancing Remote Sensing of Precision crop Health Monitoring
With unmanned aerial vehicles (UAVs), agricultural monitoring has developed into a new phase of innovation providing remedies to precision farming. The common traditional agricultural methods are based on manual inspection and few observations on the ground using sensors that may be inaccurate and time-consuming. New technologies such as drones and AI provide us with an opening of large scale, early detection, but most systems currently only seek pests or diseases and are usually specific to a single type of crop in controlled laboratory conditions. Drone-operated AI system, which combines RGB and, where feasible, multispectral cameras and a YOLOv8 pipeline to detect pests and crop diseases simultaneously across a variety of crops. We are developing it to be used in the real world: we load in data fields, laboratories, and the internet, perform preprocessing, transfer learning, and make the inference to be lightweight enough to execute on edge computers. The introduction of agricultural monitoring systems based on the use of UAVs builds on the peculiarities of quadcopters and fixed-wing UAVs. Quadcopters are used when conducting detailed field surveys or spot checks, allowing high-resolution imaging to be used in order to complete precise inspections, whereas fixed-wing UAVs are used when it comes to covering extensive areas and long-range capabilities. These UAVs can gather extensive data and conduct biological and chemical analyses due to sophisticated IoT devices and sensors, such as multispectral and hyperspectral cameras, GPS modules, and real-time communication tools. Our hybrid machine learning model (HMLM) has more accuracy and predictive capabilities, with an amazing score of 98.74 and hence, our machine learning model is doing the right job of 98.74 accurate classification and thereby yielding high accurate yields by predicting crop management. This research will contribute to the sustainability of agricultural practices as well as yield protection by providing timely, precise and scalable detection. The model proposed can potentially enable farmers with action-oriented insights, losses can be alleviated, and food security objectives can be achieved in areas where there are high susceptibility rates to pests and diseases.
UAV, AI, YOLOv8, crop disease detection, pest detection, precision agriculture, transfer learning
Copyright (c) 2025 Hamad Ullah Niaz, Qais Bin Qadeer Qadeer, Humera Niaz , Hanan Mansib, Muhammad Awais, Hamayun Khan https://creativecommons.org/licenses/by-nc-nd/4.0
An Efficient of Artificial Intelligence based Brain Tumor Diagnosis and Classification: An Advance Medical Diagnosis Approach
As a central regulatory system of the body, the human brain is susceptible to the abnormal proliferation of cells that may cause brain tumors and become a significant threat to health and life. The accurate classification and early identification of these tumors are critical to attaining effective treatment schemes and better prognosis of these patients. Although the traditional diagnostic methods, including biopsies and radiological (CT, PET and MRI) are still good, they have some shortcomings, such as invasive nature, subjective and interpreter’s variability. As a solution to these shortcomings, machine learning (ML)-based and deep learning (DL)-based computer-aided diagnostic systems have become potent tools in automated tumor classification, detection and segmentation across different medical imaging modalities. The present review discusses the current trends of ML and DL models, focusing especially on MRI-based segmentation and classification. It compares the conventional ML systems, which include Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Random Forests, and Extreme Learning Machines, with the contemporary DL systems, which include Convolutional Neural Networks (CNNs), ResNet, and transformer-based systems. Moreover, it discusses popular datasets, metrics of evaluation, and segmentation, starting with simple thresholding and clustering to more sophisticated DL-based architectures. The paper makes a comparative evaluation of these methodologies, research gaps and emphasizes the increasing significance of 3D modeling techniques and attention mechanisms in enhancing diagnostic performance. The results showed that DL-based methods especially CNN-based models are always better at tumor detection and segmentation than traditional ML methods and provide more reliable, efficient, and clinically useful solutions to brain tumor diagnosis.
Copyright (c) 2025 Mian Muhammad Abdullah, Umair Ghafoor, Qais Bin Qadeer, Farhan Khadim, Haider Sher Khan, Ammar Ahmad, Hamayun Khan https://creativecommons.org/licenses/by-nc-nd/4.0
Flavone Hydrazide Thiourea Derivatives: Synthesis, anti-diabetic, and in silico QSAR Analysis
This study reports synthesis of Flavone Hydrazide Thiourea Derivatives 2-21 with diverse functionalities for the cure of diabetic mellitus and their α-glucosidase inhibitor and in silico (computational) studies. In this regard, Flavone derivatives 2-21 has synthesized according to scheme and characterized by various spectroscopic techniques. These compounds showed significant potential towards α-glucosidase enzyme inhibition activity and found to be many fold better active than the standard Acarbose (IC50 =39.45±0.11µM). The IC50 values ranges 1.09-38.1µM. Among these, Compound 2 (IC50 = 3.16 ±0.29 µM) and 3 (IC50 = 2.12 ±0.78 µM) showed marvelous inhibition activity. Both these derivatives have –OH substitution at ring D. The molecular docking analysis revealed that these molecules have high potential to interact with the protein molecule and have high ability to bind with the enzyme. Furthermore, the computational study regarding drug likeness of molecules were also performed. The physicochemical and pharmacokinetics study showed that these compounds have excellent parameters required for drug likeness. The derivatives have high ability by GI absorption.
Copyright (c) 2025 Javeria Shaikh https://creativecommons.org/licenses/by-nc-nd/4.0
An Integrated Machine Learning Framework for Structural Health Monitoring of Bridges: A Case Study on Soan Bridge
The failing of bridges in develop and developing world requires AI based monitoring systems to ensure safety, prolonged existence, and economic sustainability. This research proposes an integrated Structural Health Monitoring (SHM) framework that uses machine learning (ML) to check in real-time and predict when the maintenance of bridge is required. The proposed system uses data from sensors (accelerometers, strain gauges, LVDTs) along with visual images captured using cameras. A complete model which consists of data acquisition, preprocessing, Finite Element Model (FEM) validation, and use of multiple ML models such as Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and Linear Regression. The proposed framework is implemented and validated through a step-by-step procedure and using the Soan Bridge, Pakistan as implementation bridge. The results obtained through evaluations show that SVM model achieved 96% accuracy in damage classification, however, the CNN was observed to be successful in the surface cracks. Further, linear regression model forecasted an alarming 79% loss in moment capacity by the year 2035, if no precautions measures are taken. The study concludes that the proposed ML-driven SHM system offers a strong, practical, and scalable solution for bridges or other infrastructure management, ultimately enhancing safety and optimizing maintenance resources.
Copyright (c) 2025 Usman Imtiaz, Bilal Ahmad, Muhammad Hamza Sajid, Qazi Abbas, Muhammad Ali Qureshi, Sajid Rasheed, Ajab Khan https://creativecommons.org/licenses/by-nc-nd/4.0
Real-Time Financial Fraud Detection: An Intelligent Data-Driven Framework Integrating Machine Learning, Stream Processing, and Big Data Analytics for High-Velocity Transaction Monitoring
The exponential growth of online financial transactions has significantly increased the vulnerability of banking and e-commerce systems to fraudulent activities, demanding intelligent, adaptive, and real-time detection mechanisms. This study presents an intelligent data-driven framework integrating machine learning, stream processing, and big data analytics for high-velocity transaction monitoring. The proposed architecture harnesses distributed data ingestion pipelines and stream-oriented processing engines to capture and analyze massive, continuously generated financial data streams with minimal latency. Feature engineering modules are designed to extract transactional, behavioral, and temporal features from heterogeneous data sources, while big data technologies such as Apache Spark and Kafka enable scalable real-time data handling. At the analytical core, the framework employs a hybrid ensemble of supervised and unsupervised learning models Random Forest (RF), Gradient Boosting (GBM), and Autoencoders to achieve robust detection of both known and novel fraud patterns. The models are trained on large-scale transactional datasets using feature selection and hyperparameter optimization strategies to ensure accuracy, interpretability, and generalization across dynamic environments. Streaming analytics and online learning components allow continuous model adaptation to evolving fraudulent behaviors without retraining from scratch. Experimental evaluations conducted on benchmark and synthetic datasets demonstrate the superior performance of the proposed framework in terms of detection rate, false-positive reduction, and computational efficiency compared with conventional batch-learning systems. The system achieves real-time throughput exceeding 50,000 transactions per second with sub-second decision latency, illustrating its suitability for deployment in large-scale financial ecosystems. In addition, explainable AI (XAI) modules are integrated to interpret model predictions and provide transparency in decision-making, thereby facilitating regulatory compliance and user trust. This research contributes to the ongoing advancement of intelligent financial security systems by merging data-driven learning with scalable stream analytics. The proposed framework offers a practical and generalizable solution for banks, payment gateways, and fintech platforms to identify fraudulent transactions proactively and adaptively in dynamic, high-velocity data environments. Future work will focus on integrating blockchain-based audit trails and federated learning for enhanced privacy and cross-institutional fraud intelligence sharing.
Copyright (c) 2025 Farah Arzu, Muhammad Khurram Zahur Bajwa , Obaidullah, Abdul Waheed, Farooq Alam, Muhammad Ali, Ajab Khan https://creativecommons.org/licenses/by-nc-nd/4.0
Blockchain-Driven Cybersecurity Framework for Smart Homes: Integrating IoT and Machine Learning for Secure Automation
With the increasing use of Internet Things (IoT) devices in smart homes has resulted in issues like data security, privacy, and how we manage everything. The Centralized architecture which we have been using can fail easily and bring down the whole system, in this research we are using a new approach where the smart home devices are handled using blockchain. This approach makes use of the decentralized approach and data replication of the blockchains so that the system is not controlled by one central server instead data is stored in distributed ledger so it would not be changed by the smart home data. With the use of IoT sensors and machine learning models (like Support Vector Regression, Linear Regression, Random Forest, and ARIMA), appliance monitoring and predictions are more correct, reaching an accuracy of 95.77%. The system uses tough security with cryptographic hashing and stores data across a decentralized network using IPFS. The system implementation costs and benefits analysis in Pakistani Rupees (PKR) confirms that this idea is worth it financially, paying itself off in 25 months. The plan comes with an easy-to-use dashboard for homeowners to watch and control things as they happen. Tests confirm that the system does a good job at boosting security, saving energy, and giving homeowners useful information.
Copyright (c) 2024 Usman Imtiaz, Shanza Malik, Ajab Khan https://creativecommons.org/licenses/by-nc-nd/4.0