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
AI-Driven Cybersecurity for Industrial Control Systems Using Graph and Time-Series Learning
Industrial Control Systems (ICS) are increasingly exposed to cyber threats because of growing connectivity between operational technology and digital networks. Traditional security solutions mostly lack the ability to record sophisticated patterns of attacks that are dynamic and can disseminate across interdependent components. The purpose of the study was to create and test an AI-assisted cybersecurity framework of ICS based on graph learning, and time-series learning to enhance anomaly and intrusion detection. A quantitative experimental design was taken with preprocessed ICS data with normal and attack records. This was tested on three models including a time series model, a graph learning model, and a hybrid model that unites both the branches using feature fusion. Accuracy, precision, recall, F1-score, false positive rate, and detection delay, were used to measure performance. The hybrid model had the highest performance with the accuracy of 97.99%, precision 97.85, recall 97.11, and F1-score of 97.48. It also had the lowest false positive rate of 1.42% and the shortest detection delay of 3.1 seconds, compared to the standalone time-series and graph models. Temporal and structural learning can greatly empower ICS cybersecurity. The hybrid framework is a more operationally secure, fast, and more accurate way of detecting cyber threats in the industrial community and cut back the attacks missed as well as the consistency in detection across the board.
Industrial Control Systems, cybersecurity, anomaly detection, graph learning, time-series learning, hybrid model, intrusion detection
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
Recall Timeline: An Open-Source Forensic Timeline Reconstruction Tool for Windows Recall EXIF Artifacts
The artificial intelligence (AI)-powered snapshot capability of Copilot+ PCs, known as Windows Recall, is a major new source of forensic artifacts. Snapshots are stored as JPEGs with detailed EXIF MakerNote metadata at tag 0x927C and the ukg.db, a SQLite database that records the window title, file path, URLs, and timestamp. Despite its potential importance in forensic investigations such as insider threat or data exfiltration attacks, no open-source tools currently exist to automatically capture, reconstruct timelines, score anomalies, or report on Windows Recall artifacts. This paper introduces RecallTimeline, an open-source Python tool for forensic recovery of Windows Recall artifacts. RecallTimeline combines three artifact sources: the ukg.db, ImageStore JPEG directory, and flat-folder snapshot images using a novel four-strategy EXIF MakerNote decoder to decode structured metadata in disparate encoding schemes. An automated anomaly detection engine uses 29 keyword-based rules to detect confidential file access, cloud uploads, Living-off-the-Land Binary (LOLBin) launches, and burst patterns. Testing on Case_E001, a corporate insider threat case, yielded the decoding of 65 events across 37 days, detection of 34 anomalies (52.3%), and verification of 25 events (38.5%) using independent Autopsy filesystem artifacts. Analysis took less than five seconds, approximately 180 times faster than manual Exif Tool based processing. To the best of our knowledge, RecallTimeline is the first open-source DFIR tool for Windows Recall forensics and is a necessary addition to the DFIR arsenal in 2025–2026 as Recall interest expands into enterprises through Microsoft Intune deployment policies. The remainder of this paper is organized as follows.
Windows Recall, Digital Forensics, EXIF MakerNote, Timeline Analysis, Open-Source Forensics, Insider Threat Detection.
The Phantom Borrower: Machine Learning Detection of Synthetic Identities and the Econometrics of Precision-Recall Trade-Offs in Digital Credit Markets
Digital lending platforms face escalating synthetic identity fraud—fabricated identities combining real and fictitious data—causing an estimated $20 billion in U.S. losses in 2020. Traditional verification systems fail to detect these "phantom borrowers," creating severe information asymmetries in credit markets that undermine portfolio quality and financial stability.This study compares machine learning algorithms to detect synthetic identities and explore the econometrics impact of precision-recall trade-offs in fraud detection systems, and includes evidence-based recommendations for selecting optimal threshold. This is secondary research, which is a synthesis of empirical evidence from recent literature. Asymmetric information theory and cost-sensitive learning approaches (2019-2024). Ensemble methods (XGBoost: AUPRC 0.847) and graph neural networks (AUPRC 0.891) outperform logistic regression (AUPRC 0.724). Feature importance analysis reveals identity consistency (28.4%), network/relational patterns (24.1%), and behavioral biometrics (22.7%) as primary detection signals. Optimal operating points vary by context: high-value loans require recall of 0.88-0.92, while small-dollar products prioritize precision at 0.82-0.90. Contextual factors significantly influence threshold selection, with F-beta scores ranging from 0.71 to 0.79 across scenarios. Machine learning is an effective method for detecting synthetic identities but thresholds should be optimized according to the costs of the product. To prevent fraud effectively, there needs to be a balance between the accuracy of fraud detection and the financial inclusion objectives, which means there is not a universal threshold for fraud. Regulatory framework should support a range of operating points to ensure financial stability and equitable access to credit.
Synthetic Identity Fraud, machine Learning, Digital Credit, Precision-recall trade-off, Fraud Detection, Financial Econometrics.
An Intelligent Forensic Framework for Hybrid Crypto Steganographic Image Analysis
Hybrid crypto-steganography involves embedding a hidden message within random voice, which is often encrypted, making it very hard to detect without analysing the second-order statistics of the image’s pixels or known tool signatures. This paper presents a multi-modal forensic steganalysis framework, that systematically reveals the subtle artefacts created by the encrypt-then-hide pipeline. The framework extracts twenty-six features, classified in five complementary forensic domains: pixel-domain regularities features, colour channel cross-correlation features, the Laplacian noise-residual moments, Grey-Level Co-occurrence Matrix texture descriptors and, for the first time in an open source detector, explicit encryption block signatures features including 128-bit windowed LSB entropy and autocorrelation at the AES block boundary. A self-calibrating anomaly detector calculates the mean absolute Z-score of those signals relative to a ”clean” reference baseline, yielding an easy-to-explain suspicion score, without requiring Labeled data or pre-trained models. The system is coded and tested in Python with a benchmark of clean and OpenStego created stego images with AES encrypted payload. Experimental results show that the framework can effectively detect stego images that are missed by existing tools using a single domain, such as StegExpose or StegSpy, while having a low false positive rate for clean images. The output is transparent and includes a complete feature set which allows forensic examiners to recognize the grounds for suspicion in the material, and in doing so, much of the gap between academic steganalysis and practical digital investigation is bridged. To the best of the authors’ knowledge, the proposed framework is the first publicly available tool dedicated to detecting hybrid cryptographic steganographic embedding in a unique manner by combining some new image statistics with some encryption aware new features.
Hybrid crypto-steganography, forensic steganalysis, LSB steganography detection, multi-modal feature extraction, encryption-block signatures, anomaly-based detection, digital image forensics.
CardioRiskNet: A Convolutional Neural Network CNN Meta Analysis: Effectiveness of Two-stage classification of an AI framework for personalized diagnosis, risk prediction and prognosis in cardiovascular diseases
The leading cause of death worldwide is heart attack, also called cardiovascular disease. Therefore, an accurate and effective early prediction system for heart attack is required. In this paper, a hybrid deep learning model with a combination of Convolutional Neural Network (CNN) and Support Vector Machine (SVM) is proposed to predict heart attack. CNN is used to automatically learn the deep features from the dataset, and SVM is used to perform effective classification with a better hyperplane. Artificial intelligence-based heart attack prevention systems provide a novel approach that has the potential to revolutionize early diagnosis, tailored prevention, and treatment. This work is done by using the UCI Heart Disease Dataset with common preprocessing and hyperparameter tuning techniques. It is observed that the proposed CNN-SVM model outperforms all Machine Learning models and Deep Learning models by showing 97% of accuracy, 94.2% of precision, 92.6% of recall, 93.4% of f1-score, and 0.96 of AUC. This work concluded that the hybrid models are very beneficial for improved accuracy and prediction. There is a possibility that recuperation and therapy won't be necessary under certain circumstances. Further research, development, clinical trials, and interdisciplinary collaboration are required to fully realize the benefits that AI-based heart attack preventive systems offer. These actions would be able to solve the issues that result from this problem.
Heart Disease Prediction, Convolutional Neural Network (CNN), Support Vector Machine (SVM), Hybrid Deep Learning, Predictive Analytics, Clinical Decision Support System.
From Compliance to Classroom Impact: How Policy Governance and Quality Assurance Shape Student Success in Modern Education
This article examines the relationship between policy governance and quality assurance in modern education systems and their collective influence on student success. Educational institutions worldwide are increasingly adopting governance policies and quality assurance mechanisms to improve accountability, transparency, and academic performance. The study explores how structured governance frameworks contribute to better classroom outcomes and institutional effectiveness.
Education, Quality assurance, Policy governance, accountability, classroom, institutional effectiveness.
An Efficient Approach for Security and Privacy Preserving based on Machine Learning and Federated Learning (FL): Analysis and Performance Optimization for Secure Multiparty Computing
Federated Learning (FL) is an approach that allows numerous users to train a single machine learning model with the oversight of a central server, and with their training data stored locally on their devices. The approach is relevant in alleviating the risks associated with violations in data privacy. It is a process by which a pool of clients collaborates towards solving machine learning problems, with a central coordinator being the one who coordinates the entire process. The paper will review the latest advances in privacy-preserving federated learning and discuss it in the context of machine learning. It assesses privacy-related solutions, which are already in existence, such as secure aggregation, meta-learning, blockchain technology, decentralized training, searchable encryption, and data privacy mechanisms and zero-knowledge proofs. Federated learning (FL) is an emerging technology that can be used in the realm of the intelligence of the Internet of Things. However, the information that is model-related can be shared in FL and reveal the sensitive data of the participants. In this regard, we propose a new privacy-preserving FL framework, which is founded on a new chained secure multiparty computing technique, which we call chain-PPFL. The scheme we are proposing is based mostly on two mechanisms: 1) a single-masking mechanism, which protects the information that is exchanged between participants in a serial chain frame and 2) a chained-communication mechanism, which allows the masked information to be communicated between participants in a serial chain frame. We run large-scale experiments with respect to simulation by comparing the training accuracy and the leak defence to other state-of-the-art schemes with two publicly available data sets (MNIST and CIFAR-100). We established data sample distributions (IID and NonIID), and training models (CNN, MLP and L-BFGS) in our experiments. The experiment results show that the chain-PPFL scheme can offer a realistic privacy preservation (which is the same as the various privacy with ϵ to near zero) to FL at the cost of communication, and without compromising the accuracy and convergence rate of the training model.
Machine Learning, Collaborative Learning, Zero Knowledge Proofs, Blockchain Technology, Decentralized Learning, Federated Averaging.
A Comparative Study of a Co-authorship System of MURJ and JLT Using Network Approach
A variety of complex real-world systems are analyzed as networks to better understand their heterogeneous and dynamic behaviors. This research implements a network science approach to model the co-authorship datasets of MURJ and JLT. Utilizing metrics such as closeness, betweenness, and clustering coefficients, the study determines that these collaboration networks are highly clustered with minimal path lengths between researchers. Unlike random networks, these journals possess an inhomogeneous distribution where a small number of central authors maintain a dominant influence across multiple issues. These structural characteristics highlight the non-random, organized nature of academic partnerships within these journals.
Network analysis, Complex Systems, Closeness, Betweenness, Clustring Coefficient.
Impact of Financial Inclusion, Green Growth, and Environment Sustainability on the Firm Performance: Mediating Role of Institutional Quality
The research study discusses how financial inclusion, green growth, and environmental sustainability affect the firm performance with specific reference made to the mediating effect of the institutional quality in the context of the Small and Medium Enterprises (SMEs) in Pakistan. The study has a quantitative design that is grounded on a positivist paradigm, and is founded on primary data that was gathered among 480 SME managers in Lahore and Karachi. The relationships between the variables are analyzed with the help of Structural Equation Modeling (SEM). The results indicate that financial inclusion, green growth, and environmental sustainability positively affect the performance of firms significantly. The most influential of these is the green growth, and environmental sustainability and financial inclusion. The institutional quality becomes the most important variable, and it not only has a strong direct impact on firm performance but also has a powerful mediating role in the relationships among the independent variables and firm performance. This demonstrates that the success of financial and sustainability strategies highly relies on the institutional frameworks. Moreover, the findings reveal that there are regional variations in the relationships, with Karachi showing higher relationships than Lahore, which may be due to different institutional and economic factors. The study is a contribution to the extant literature by incorporating the institutional quality as a key linking mechanism between financial and environmental variables and the performance of firms. The results have valuable implications on policy makers, financial institutions and SME managers in fostering sustainable and inclusive economic growth.
Advanced Persistent Threats, Stealth Falcon, OpenCTI, MITRE ATT&CK, Cyber Threat Intelligence, Behavioral Detection, Random Forest, Machine Learning, MITRE CALDERA, Adversary Emulation.
DFF-Net: Single Image Dehazing with Attention-based Deep Feature Fusion Network
We suggest end-to-end convolution neural network for recovering a haze-free image from contaminated image. The feature extraction module enables network to extract the features at various levels. The network provides additional flexibility in dealing with different types of information and focuses more on important information using mixed convolution attention mechanism. To improve the dehazing performance, multi-level features are fused and further refined using feature fusion block. The good kernel estimation can recover a sharp image. Moreover, DFF-Net has ability to capture sharp textural and semantic information, and recover high-quality haze-free image. Furthermore, semantic differences in deep features are measured by deep semantic loss. The experimental findings demonstrate that our suggested method exhibits superior performance compared to other haze-removal methods on both synthetic and real-world images.
Convolution layer, Mixed convolution attention, Channel attention, Feature fusion, Feature extraction, Semantic loss.
Reducing the Impact of Path Loss in Internet of Health Things Based on Residual Energy Calculation
Internet of Health Things is a sub field of Internet of Things that deals with the human physiological monitoring. It is a specialized field comprising on small state of the art devices that are used to record human vital signs and changes that occur. The recorded data transmission is wireless in nature so there are factors like path loss that come along to make wireless transmission ineffective. Energy efficiency and reliable communication are most critical challenges in Internet of Health Things. Path loss is a critical parameter which directly impacts on reliability as well as on energy consumption of wireless communication systems, influencing quality of communication between sensor nodes and sink. This study analyzes the evolution of path loss over network rounds to evaluate the stability and energy performance of an adaptive routing scheme. This research proposed in this paper investigates variations in path loss across occurred in multiple transmission rounds in an Internet of Health Things environment also focusing on achieving the efficiency in terms of energy in a routing protocol.
IoT, Sensors, Routing Protocol, Efficiency.
Bitcoin Exposure and the Cost of Debt Financing: Evidence from U.S. Publicly Listed Firms
This study investigates whether Bitcoin exposure affects the cost of debt financing among U.S. publicly listed firms, extending the emerging literature on corporate cryptocurrency exposure from firm risk and equity sensitivity to creditor pricing and borrowing costs. Motivated by the growing integration of digital assets into corporate financial policy, the paper examines whether creditors price Bitcoin exposure as a risk-enhancing firm characteristic. Drawing on the literature on creditor pricing, firm risk, and financing frictions, the study argues that Bitcoin exposure may increase debt-financing costs by amplifying perceived volatility, opacity, and uncertainty in firm-level financial positions. Using a panel-data framework, the analysis estimates pooled OLS, fixed-effects, random-effects, firm-clustered fixed-effects, lagged-exposure, dynamic fixed-effects, and two-step system GMM models. The results consistently show that Bitcoin exposure is positively and significantly associated with the cost of debt financing. In the preferred fixed-effects specification with firm-clustered robust standard errors, firms with Bitcoin exposure face materially higher effective debt costs than non-exposed firms. This finding remains robust in the dynamic fixed-effects and system GMM specifications, indicating that the main relationship is not driven by a single estimation approach. Diagnostic tests support the use of fixed effects, confirm the presence of heteroskedasticity, and show that multicollinearity is not a concern. By contrast, lagged Bitcoin exposure is not significant, and the dynamic specifications do not support a conventional positive persistence pattern in debt-financing costs. Overall, the findings suggest that debt markets treat Bitcoin exposure as a financially relevant source of firm-level risk and incorporate it into borrowing costs. The study contributes to the emerging literature on corporate cryptocurrency exposure by extending it from equity-market and firm-risk outcomes to a core corporate-finance variable: the cost of debt financing
Bitcoin exposure; cost of debt financing; creditor pricing; corporate finance; U.S. publicly listed firms; fixed effects; system GMM.
Integrating Generative AI into Software Development Lifecycles: A Comparative Qualitative Study of Engineering Practices in Pakistan and the USA
This study explores the integration of generative AI tools into the Software Development Life Cycle (SDLC) in Pakistan and the USA, examining the impact of AI on software engineering practices, productivity, and quality. By comparing the experiences of 20 software engineers from both countries, this qualitative study identifies key challenges and benefits of AI adoption across cultural and technological contexts. In the USA, the advanced integration of AI tools has significantly accelerated development cycles and enhanced code quality. In contrast, Pakistan faces barriers such as limited resources, infrastructure gaps, and cultural resistance to AI adoption. The study highlights the disparity in AI readiness between developed and developing countries, focusing on the operational and ethical challenges associated with AI in software engineering. This research contributes to understanding the role of generative AI in reshaping development practices and offers recommendations for improving AI adoption strategies, particularly for countries like Pakistan, where technological infrastructure is still developing.
Generative AI, Software Development Life Cycle, AI adoption, Pakistan, USA.
Determinants of E-Government Portal Adoption in Pakistan: Evidence from Diffusion of Innovations Theory
Electronic government is the use of the internet to provide government facilities and services, at any time and from anywhere. Successful e-government implementation through portals necessitates a thorough grasp of the system as well as the resolution of technical and non-technical difficulties from the standpoint of citizens. Several countries, notably developing countries, are implementing e-government systems; yet, the utilization pattern remains unclear. The ultimate goal of this study was to discover the determining factors for the adoption of e-government portals in Pakistan from citizens' viewpoints, using the Diffusion of Innovation Theory. To collect user perception, the study used a quantitative cross-sectional technique, with a Likert scale questionnaire survey. The survey included 345 residents from the urban areas of Sindh, Pakistan. Participants were chosen based on their familiarity with e-government portals, relevance, and convenience. The study used path analysis with AMOS software and structural equation modeling to examine the complex relationships between variables. The results show that behavioral intentions to use e-government portals are positively and significantly impacted by the Trialability, Observability, and Relative Advantage variables. The Compatibility and Complexity variables did not, however, appear to have a positive impact on behavioral intentions for the adoption of e-government sites. The study suggests that in order to boost e-government portal adoption, the government should make successful e-government portal usage more visible, offer opportunities for users to test out such services, and clearly communicate the perceived advantages of e-government portals over traditional methods.
E-government, e-government portals, e-government portals adoption, Diffusion of Innovation Theory, Behavioral intentions to use e-government.
Homomorphic Encryption Techniques: Advances, Challenges, and Future Directions in Distributed Computing Security
The emergence of Big Data and cloud computing have increased the need to have a strong cryptographic system capable of securing data throughout the processing process and not just at rest or in transit. One way to meet this need is through homomorphic encryption (HE), which allows arbitrary computations to be performed on ciphertexts and ensures that the result obtained on the plaintexts is semantically equivalent. This essay describes and revisits in detail the homomorphic encryption methods: multiplicative, XOR-based (GoldwasserMicali), additive (Paillier), and fully homomorphic encryption (FHE); focusing on their mathematical basis, real-world applications and real-world uses in healthcare, electronic voting, private information retrieval, Big Data analytics, drone security and privacy-preserving machine learning. We also analyse the security of critical infrastructure systems to intrusion threats and the complement of HE to the advanced intrusion detection frameworks. The major issues of computational overhead, noise growth and key-size expansion, and limited expressiveness of Somewhat Homomorphic Encryption (SHE) are examined, and the recent progress in Learning with Errors (LWE)- and Ring-LWE (RLWE)-based FHE algorithms are outlined. Open research directions and convergence of HE with federated learning, blockchain and IoT security are also described in the paper. This work contains 95 cross-disciplinary references to foundational cryptography and up-to-date applied systems, and has become a point of entry and a reference to those researchers constructing privacy-preserving data-processing pipelines.
Homomorphic encryption; fully homomorphic encryption; Big Data security; cloud computing; Paillier cryptosystem; RSA; ElGamal; Goldwasser–Micali; Learning with Errors; Ring-LWE; privacy-preserving computation; intrusion detection systems; IoT security.
Enhancing IoT Cybersecurity through Multi-Technique Data Anonymization: A Differential Privacy Framework Using Public IoT Datasets
The proliferation of Internet of Things (IoT) deployments in critical domains such as smart homes, healthcare, and industrial control has significantly expanded the attack surface of modern networks. While the security research community increasingly relies on public IoT network traces to design intrusion detection systems (IDS), the release and sharing of such datasets raise serious privacy concerns for end-users, operators, and infrastructure providers. Building on recent work on scalable anonymization and privacy-preserving big data analytics, this paper proposes a unified data anonymization framework that combines ɛ-differential privacy (DP) with classical k-anonymity and l-diversity to protect sensitive information while preserving the utility of IoT cybersecurity datasets. We instantiate the framework conceptually on three widely used public benchmarks Bot-IoT, TON_IoT, and IoT-23 and design a reproducible pipeline to study the privacy-utility trade-off under different privacy budgets. Informed by recent advances in IoT security, big data analytics, and differential privacy for multimedia and sensing, we focus on moderate privacy budgets (e.g., ɛ in the range 0.5-1.0) that are known to offer a favorable balance between privacy protection and model utility. The paper contributes (i) a single-column description of the anonymization and evaluation pipeline, (ii) a multi-technique anonymization framework tailored to public IoT datasets, and (iii) design guidelines for dataset publishers and security practitioners grounded in existing literature on scalable anonymization.
IoT, Cybersecurity, Multi-Technique, Differential Privacy
A Unified Trust Architecture for Secured Voting Systems and a Hedge Against Inflation: An Advanced Framework Towards Sustainable Blockchains
The dual crises of electoral integrity and monetary stability share a common cause: the loss of trust in centralized systems. This paper develops a unified approach for the study of blockchain-based systems for trust, verification, and scarcity logic in two domains: voting systems and inflation hedging. By analyzing the properties of blockchain-based systems for trust, verification, and scarcity logic, we show that blockchain enables the transition from trust-based to verification- based systems. In voting systems, blockchain-based systems offer cryptographic verification for the integrity of voting outcomes without compromising ballot secrecy through the use of advanced cryptography. In monetary systems, blockchain-based systems offer transparent scarcity logic and verification for the constraints imposed by the monetary supply. Our analysis, however, reveals the limitations of blockchain-based voting systems for coercion resistance and the limitations of blockchain-based monetary assets for long-term credibility. The unified approach identifies the components of trust architecture for identity, recording, verification, coordination, and governance as key determinants for the success of blockchain-based systems. The comparative analysis reveals that the key benefit of blockchain-based systems is not the properties of the system but the ability to distribute trust among verification mechanisms. We develop a conceptual model for the design of hybrid systems for the integration of blockchain-based verification and institutional systems. The unified approach contributes to the literature on blockchain by providing tools for the evaluation of blockchain-based systems for various applications in the domains of governance and economics.
Blockchain, Voting Systems, Inflation Hedge, Trust Architecture, Cryptographic Verification, Decentralized Governance, Scarcity Mechanism, Electoral Integrity, Monetary Credibility, Distributed Consensus.
Escalating Cyber Threats: Analyzing Trends, Risks, and Countermeasures in Critical Sectors
During the period between 2021 and 2026, the situation regarding cyber-attacks against the government bodies, banking platforms, telecom networks, and identity related systems steadily escalated in Pakistan. This paper examines 239 reported cases during the same time and establishes an increment in the frequency of attacks by 294.4 percent between 2021 and 2025. Data breaches that comprise 32.6 percent of all incidents and accumulated privacy exposure of about 744.6 million compromised records shows that the evidence gathered is comprehensive. To go beyond description, the research uses a Privacy Risk Score (PRS) tool in six types of attacks, namely, data breaches, ransomware, phishing and social engineering, distributed denial-of-service attack, website defacement, and advanced persistent threat. The review of the literature provides the basis of the analysis upon Pakistani legal, governance, and technical setting, whereas the experimental section examines the framework of a layered countermeasure structure guided by anonymization, adaptive privacy protection, AI-facilitated detection, and embedding of IoT security. The simulated PRS comparison indicates that privacy risk has been decreased by 84.2 percent when the suggested controls bring down the aggregate Privacy Exposure Index by 92.88 to 14.64. This paper thus provides a policy-relevant and data-supported framework of comprehension on cyber-attacks in Pakistan and its connection of sectoral exposure to the actual countermeasures.
Copyright (c) 2026 Muhammad Imran Ghafoor, Muhammad Sohaib Roomi, Mehmood Baryalai,, Zubair Zaland, Iqra Tabassum, Aliza Sohail https://creativecommons.org/licenses/by-nc-nd/4.0
Pomegranates Fruit Disease Classification Using EfficientNet Deep Learning Model
Pomegranate stands among the most important valuable fruits globally because it contributes significantly to food security and economic development throughout agricultural communities. The combination of diseases including alternaria alongside anthracnose bacterial blight and cercospora creates severe problems that lead to 75% yield reduction and deteriorated quality and substantial financial loss for growers. Manual inspections along with expert consultations currently fail to detect diseases effectively because they consume significant time while being subjective and their responses are usually delayed for managing the condition. The proposed research introduces the EfficientNet deep learning model for pomegranate disease classification because it exhibits high accuracy alongside efficient computing capabilities. A model based on the EfficientNet has been devised to classify disease affected fruits. The model was train and tested using Pomegranates fruits diseases dataset for deep learning model. The study's experimental results indicate that our proposed work attains an accuracy of 98.73%. The Proposed Solution functions as one of many agricultural technology developments through its deep learning system that delivers scalable accessible classification of pomegranate diseases while achieving high performance. Additionally the models proposed in this research can be now expanded to enable predict in real-time the quality of vegetable and fruits, by use of IoT devices. Moreover, the proposed model could be progressed into an android app where smartphone would enable farmers to take images of their plants or fruits in real time to receive instant classification reports related to plant or fruits disease.
Copyright (c) 2026 Waqar Hussain , Kifayat Ullah, Shafiq Ahmad, Wasim Ahmad, Taseer Ullah https://creativecommons.org/licenses/by-nc-nd/4.0
Design Of A 3d Model for Smart Vertical Farming
This research suggests a Vertical farm design idea with well-known goals to explain addition in state-of-the-art is taken surroundings. Exposing new methods to farming has enhanced a powerful order existing. The always-growing need for food results and a chance to meet the supplies of the increasing public form a powerful determination for these available new patterns. These likewise intend benefits over usual farming in the way that a requirement for lean districts of land, the privilege to atmosphere changes, and a smooth approach to city countryside are completely superior to an increase in value. A suitable instance of this is a plan of hopeful vertical cultivation, trained in a household atmosphere. The effectiveness of these farms is improved by computerization and the use of machines. The work focuses on cultivating a standard electrical arrangement original for use on a vertical farm. It considers the design and happening of key orders a depository whole accompanying upright levels for home the plant modules, popular as the vertical Stack System (VSS). It’s still appearance is sure machinelike parts benefit a smooth transfer of these modules completely. The work likewise examines a movable machine, grown to guide along the route, often overwatering the farm atmosphere and transporting the plant modules to and from the VSS. The machine’s act in core and here and now is given using an imitation model, grown utilizing the kinematics of the movable machine, and a computational model, grown utilizing sensor response, individually. The happening of a clever method named the Stacker is again bestowed. The Stacker act boards the traveling machine mainly being the reason for converting plant modules. Additional work is bestowed for combining computerization into the arrangement, through a sensor arrangement for listening and newsgathering sure atmosphere variables encircling the plant and through a certain movement. The architecture design of each of these methods is explained, and the control approaches, functional arrangement, and results are bestowed later. In this research, I will first test abundant finishes and electronics that are second hand for the design of smart vertical farming. Secondly, a 3D model utilizing Blender will perform for a smart upright land scheme. Lastly, creating an inclusive and adept 3D Vertical farm culture plan adjusted for plant progress and overall range of capabilities. Finally, the exercise of a productive listening and control scheme will allow the raiser to amend the development environments and increase crop yield. The results concerning this research show that a well-devised three-dimensional (3D) cultivation plan can specify an available and effective method of bearing new, prime crops while underrating incidental impact and maximizing financial returns.
Structural Health Monitoring, Machine Learning, Bridge Infrastructure, Predictive Maintenance, Support Vector Machine, Convolutional Neural Network, Finite Element Model.
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
Early Detection Model for Stroke Patients Through Machine Learning
Machine learning (ML), a subfield of artificial intelligence in computer science and engineering, enables data-driven insights and predictive analysis across various domains. In healthcare, ML is increasingly applied to clinical services, therapy, and patient diagnosis, offering critical support for medical decision-making, particularly in neurological disorders. This study presents a predictive model applied to a secondary, cloud-based healthcare dataset containing clinical records. Five ML algorithms were implemented: Logistic Regression (LR), Linear Support Vector Machine (SVM), Decision Tree Classifier (DTC), Random Forest Classifier (RFC), and Gaussian Classifier (GC). The dataset was preprocessed by converting categorical string values into numerical format, and descriptive statistics and variable correlations were analyzed. Hypertension was identified as the primary risk factor for stroke. Model performance was evaluated using confusion matrix metrics, including accuracy, precision, recall, and F1-score. Among the algorithms, RFC achieved the highest accuracy of 92.6%, followed by DTC at 87%, LR at 83%, and both SVM and GC at 81%.
Stroke Prediction, Machine Learning, Random Forest Classifier, Healthcare Analytics, Early Detection, Clinical Decision Support.
Internet Of Things Intrusion Detection Based on Deep Learning
The fast-growing Internet of Things (IoT) has exposed more attack vectors of the connected devices and heightened the necessity of effective intrusion detection systems (IDS). Nevertheless, the three main challenges that still limit the practical use of IoT intrusion detection include the high complexity of models that are only runnable on resource-constrained devices, low generalization in the presence of small and unbalanced labelling, and the lack of privacy-concerning collaborative detection systems among heterogeneous settings. Here, it is possible to identify a multi-stage research program based on three complementary frameworks that consolidate these challenges in this paper. The main shortcoming of the paper is that the authors first propose lightweight intrusion detection model, LIIDXC, to describe binary XNOR-based convolution of a two-layer long short-term memory (LSTM) and entropy-guided feature selection with a focus on computational overhead reduction without losing detection power. Second, a superior framework of transfer learning, EMTD-SSC, combines a residual convolutional autoencoder, multilayer multi-kernel maximum mean discrepancy (MLMK-MMD) along with fine-tuning techniques to enhance cross-domain transfer when the sample size is small. Third, a federated contrastive learning framework, ID-CFL, enables collaborative intrusion detection without raw data sharing and improves robustness under non-IID client distributions by adaptive node-correlation aggregation. Experiments on N-BaIoT, CIC-DDoS2019, IoT-23, ToN-IoT, and BoT-IoT demonstrate that LIIDXC achieves 87.4% accuracy with approximately fivefold training acceleration, EMTD-SSC reaches up to 94.8% accuracy and maintains 82.8% accuracy with only 100 samples per class, and ID-CFL attains 84.79% average personalized local-client accuracy while improving non-IID collaborative detection performance by up to 38% relative to conventional federated baselines. Taken together, the results show that lightweight computation, cross-domain knowledge transfer, and privacy-preserving distributed learning can be integrated into a coherent IoT IDS research agenda that is efficient, robust, and scalable.
Copyright (c) 2026 Fauzia Talpur , Mir Rahib Hussain Talpur, Adeel Kamran, Shakir Hussain Talpur, Syed Baig Ali Shah, Khan Muhammad Maher https://creativecommons.org/licenses/by-nc-nd/4.0
Ai-Enabled Acne Diagnosis And Smart Dermatologist Referral System
Facial acne is a very common dermatosis with an economic and psychological cost for the person, which makes the proper severity graduation a must for adequate therapeutic performance. In this study, we proposed a new grading schema for acne that accommodates different acne morphologies and introduced a quantitative metric for a proper evaluation of the severity. Faced with the challenge of separating between acne lesions with similar visual properties and their count being critical, we extracted patches of skin from the images of the face and tested a number of deep learning architectures, such as YOLOv8, YOLOv7, YOLOv6, and a conventional convolutional neural network (CNN). YOLOv8 improves the fidelity of images by median filter and histogram equalization, feature representation by channel attention module, and reduces class imbalance by focal loss region. In addition, we conducted model pruning and knowledge distillation using features to decrease the computational overhead. Following inference, the dermatoses were bound to generate boxes around, and the grading improved using ancillary patient metadata. The complete pipeline was implemented on a mobile platform, which results in an application that gives users a dermatologist-level evaluation of acne severity. Comparative evaluation between the four models used, evaluated on the basis of confusion matrix, F1-score curve, Precision-Recall Curve, Recall curve, and Precision Curve, showed better results of YOLOv8 over its counterparts, and it showed better diagnostic performance.
Facial Acne, therapeutic, skin, deep learning, YOLO.
Latest Trends in Parallel and Distributed Computing
The exponential growth of digital data and computational requirements has significantly increased the demand for advanced computing paradigms capable of processing large-scale workloads efficiently. Parallel and distributed computing have emerged as key technologies that enable high-performance processing by utilizing multiple processors and interconnected computing nodes. These paradigms play an essential role in modern applications such as artificial intelligence, big data analytics, cloud computing, and scientific simulations. This paper provides a comprehensive overview of recent developments in parallel and distributed computing, focusing on heterogeneous computing architectures, distributed machine learning frameworks, cloud-native infrastructures, and energy-efficient computing techniques. Additionally, major technical challenges including communication overhead, resource management, and system scalability are discussed. Finally, potential future research directions are explored, including edge computing integration, intelligent scheduling mechanisms, and hybrid computing environments. The study highlights how emerging technologies continue to reshape large-scale computing systems and improve computational efficiency across various domains.
Parallel computing, distributed systems, heterogeneous architectures, high-performance computing, distributed machine learning
Latest Trends in Chip Technology in Pakistan: Emerging Opportunities and National Priorities
Semiconductor technologies are critical to modern digital infrastructure and economic development. Countries worldwide are investing in semiconductor research, chip design, and manufacturing to strengthen technological independence and economic competitiveness. Pakistan has recently initiated several national programs aimed at building domestic capabilities in semiconductor design and research. This paper reviews the present state of chip technology development in Pakistan, highlighting government initiatives, academic research activities, and emerging industry trends. Key challenges such as infrastructure limitations, skill shortages, and research capacity gaps are also examined. Finally, future opportunities and strategic priorities are discussed to outline pathways for Pakistan’s participation in the global semiconductor value chain.
Copyright (c) 2026 Kavita Tabbassum, Saima Shaikh, Suhni Abbasi, , Shahnawaz Farhan Khahro https://creativecommons.org/licenses/by-nc-nd/4.0
Design and Development of an IoT-Based Dynamic Self-Stabilizing Mobile Platform for Healthcare Applications
Lifestyle has been much enhanced through the internet of things (IoT) and the internet of things multimedia (Name IoT smart Healthcare, IoT smart Homes and IoT Smart transport). we have a self-adaptive security model of the IoT multimedia and we describe one of the scenarios in the environment of a mobile e-health system where we utilize a knowledge adaptation loop, a type of loop of thought an adaptation model (monitor-analyze-plan- execute) in order to determine the demand of the adaption to the level of confidence of the various security factors. This is dictated by the adaptation requirement that indicates that within a constantly unstable and sustainable e-health IoT multimedia space, one will be required to adapt ionize the security issues to stabilize the IoT multimedia space. The given paper includes the construction and design of a two degree of freedom self-balancing mobile platform that is not only dynamic, but is also constructed using low-cost materials. This research covered self-stabilizing control system is not only applicable to the vast majority of medical, military and logistical systems but it is impossible not to attribute the importance of this control system in operation in the case where the surface of the ground cannot be considered even or smooth. The platform is developed in a mechanical manner boasting a two-degree freedom of motion. The whole stab system of the stabilization control is developed on the basis of Arduino UNO microcontroller. The longitudinal movement, and the lateral movement in reference to X and Y axis can be controlled with the help of the servomotors. This is an algorithm that was developed in order to convert the digital information of the gyroscope to the angular position of the system and apply the complementary filter and proportional controller on the same. The scale is further employed in determining the tilt of the platform by the comparison between the scale and a familiar functional. This angle of tilt is then converted to a rotation angle which would allow working of the servomotors.
IoT, Self-Stabilizing Platform, Healthcare, Arduino UNO, Gyroscope, Servomotor, Two Degrees of Freedom.
The impact of artificial intelligence technology and customer engagement on consumer repurchase intention: The mediation and moderation approach
This research examines the role of artificial intelligence technology (AIT) in customer repurchase intention (CRI) in the digital commerce sector of Pakistan. It specifically considers the mediating role of customer engagement on social media (CESM) and the moderating role of trust to address the contextual research gaps on emerging markets. Primary data was taken from a sample of online grocery store customers who booked services online using structured questionnaires and analyzed using Partial Least Squares Structural Equation Modeling. The structural model shows significant explanatory power in CRI. Key results are that AIT is found to have a strong and significant positive direct influence on both CRI and CESM. In addition, trust is a significant positive predictor of CRI. Importantly, the path from CESM to CRI as well as the hypothesized moderating effect of trust on the relationship between AIT and CRI were found to be statistically insignificant. These findings offer actionable insights for digital retailers, which may indicate that businesses need to focus investments in AIT as a key driver for relational outcomes, and transparent data practices to build trust. In contrast, resources directed to only the increment in social media engagement as a strategy to increase repurchases may need to be re-evaluated due to its insignificant impact on customer repurchase intention.
Artificial Intelligence Technology, Customer repurchase intention, Customer engagement on Social Media, Trust, Digital Commerce, PLS SEM, Pakistan.
Copyright (c) 2026 Minhaj Ikram, Afshan Anwar https://creativecommons.org/licenses/by-nc-nd/4.0
Integrated Agriculture Management with IoT, AIoT, and Mobile Decision Support: An Analytical Review and Design
The paper is an overview of the shift in the agricultural IoT research focus away from isolated monitoring prototypes to management systems that are capable of facilitating timely action, traceability, and accountability of operations. Instead of viewing smart farming as a scattering of scattered sensors, the discussion is bracketed in terms of the decisions that the farm managers need to make: at what times and at what points do they receive the first signal, how to verify the field work and how to maintain the operation in case of poor connectivity. One years of literature are reviewed by combining the author analysis, comparison by system-layer analysis, and mapping of management-functions in order to synthesise a representative body of literature between 2020 and early 2026. A clear shift in the types of device-centric studies to architectures that bring sensing, edge coordination, analytics, and mobile workflows in addition to more digital-twin or AIoT components can be seen in the review. The paper suggests a visionary framework, AgriTwinOps, as a connection between field telemetry, a farm digital twin, decision intelligence, and proof-of-completion work flows, enclosed within a single implementable operating model, based on that shift. The value is thus two-fold; a realistic literature review on the basis of realistic agriculture, and a blueprint of future readiness design to scientists or creators who desire to construct the following generation of farm management websites.
An Enhanced Machine Learning & Deep Learning based Intrusion Detection System for Intelligent Network Security: A Comprehensive Analysis to Avoid Intrusions in Big Data-based IoT Ecosystem
The data growth is measured in exponential rates that are estimated at zettabytes to petabytes globally in the past decade in computer networks and Internet of Things (IoT) networks. The network growth has therefore also caused security issues. Nonetheless, it is difficult to monitor intrusion in this type of big data. Other advanced applications of the emerging networks are smart homes, smart cities, smart grids, smart devices, objects, e-commerce, e-banking, e-government, etc. The security and privacy threats facing most computer networks have led to the development of many Intrusion Detection Systems (IDS) in the recent past. The damage to data confidentiality, integrity, and availability will be experienced in the case of failure of the IDS prevention. The traditional methods are ineffective to match the sophisticated attacks. Rapid advancements in Internet of Things (IoT) infrastructure and Cloud Computing have expanded the digital threat landscape, necessitating a shift from outdated defensive frameworks. First, traditional signature-based systems are unable to identify zero-day attacks. In addition, classical Machine Learning (ML) systems cannot efficiently filter the 5G networks’ encrypted traffic. Competitionally, Deep Learning (DL) systems can automate feature extraction, but individual systems have specific weaknesses. Convolutional Neural Networks (CNN) overlook the importance of temporal dependencies; Recurrent Neural Networks (RNN) are burdened by the vanishing gradient problem, excessive computation costs, and temporal dependencies. Such weaknesses can be alleviated using Hybrid Deep Learning (HDL) systems like CNN-LSTM, CNN-GRU, and Transformers. This paper systematically and critically assesses the recent literature on the “Efficient HDL-Based IDS.” More than just descriptive summaries, we put forth a framework for a taxonomy of Sequential, Parallel, and Auxiliary architectures, which we assess using a Hybrid Efficiency Score (HES). We claim the existence of the “Efficiency-Accuracy Pareto Frontier.” For instance, we position Parallel Ensembles at the bottom, imposing a 63% efficiency cost and Transformer-based and Sequential-Cascading hybrids at the top as real-time ready “Tier 1” systems. We finish the review by providing a reproducibility checklist and a “Green AI” roadmap to support sustainable network security.
Intrusion Detection System (IDS); Hybrid Deep Learning; Network Security; Convolutional Neural Networks (CNN); Long Short-Term Memory (LSTM); Anomaly Detection; Cyber Threat Intelligence; Transformers; Big Data; Explainable AI (XAI).
HopeShield: A User-Informed Mobile Application for Reporting Harassment and Domestic Violence in Pakistan
HopeShield a smartphone App is designed to help protect people from domestic violence and gender-based harassment using the power of an anonymous reporting tool and linkages to support services (e.g. crisis centers). It is built on the belief that while there is significant technological potential to improve victims' safety through mobile phones, these technologies have been lacking in terms of being culturally grounded and aware of trauma experienced by many victims. A two-phase process was used to develop HopeShield. First a survey of 74 participants who were primarily women between the ages of 18 to 30 years old was conducted to assess their abuse experiences as well as their needs for technology in this regard. Second, a mobile application and dashboard system were designed using Agile principles based upon the input received from users in Phase One. Data collected during Phase One identified very high levels of both psychological and emotional abuse among the surveyed group; more than 70% of the women surveyed had never reported abuse or violence to anyone. The women surveyed also stated that they would use mobile tools that allowed them to report abuse anonymously, track locations, capture evidence, and link directly to support services. The data collected in Phase One was then used to inform the design of HopeShield's major features including one tap SOS alert buttons, secure audio and video recording, geolocation tagging, and a verifiable administrative dashboard for non-governmental organizations (NGOs) and law enforcement agencies. Testing of HopeShield was done internally to validate its functionality. Informal feedback about HopeShield has indicated that it is usable and relevant to real life situations and that its development represents a participatory design process which incorporates the ability of technology to foster trust and institutional integration. Overall, HopeShield provides a viable and scalable model for a national safety platform and addresses the need to bridge the gap between victim/survivors and response systems in resource-constrained environments.
gender-based harassment; mobile safety application; participatory design; digital reporting; trauma-aware technology, Pakistan
Copyright (c) 2026 Nazish Basir , Faisal Rehman, Sarwat Nizamani, Faizan Panhwer, Laveet Kumar, Saqib Ali Khaskheli https://creativecommons.org/licenses/by-nc-nd/4.0