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
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
The Convergence of AI and Cybersecurity: Opportunities for Resilience in the Digital Era
The aim of the paper is to address the intersection between Artificial Intelligence (AI) and cybersecurity by evaluating the awareness, adoption, opportunities, challenges, and prospects of analytics profession in the technology and security sectors of Pakistan. Their purpose was to assess how AI introduction would influence cyber defense systems and clarify the key issues that have the biggest influence on its integration into critical infrastructures. The quantitative descriptive research design was adhered to, and the assistance of a structured close-ended questionnaire was used whereby 200 professionals in the game of IT, cybersecurity, academia, government/defense and banking/finance were sent the questionnaire. Data analysis was performed on the basis of the Statistical Package of the Social Sciences (SPSS) that employed the descriptive statistics such as frequencies, percentages, means, medians and modes. The findings were discussed as reflecting the existing tendencies and perspectives on AI application in cybersecurity activities. The results have shown that the awareness of AI-based systems and the adoption of AI-based system in terms of cybersecurity is high, and respondents were in a firm agreement on the fact that AI would enhance fraud detection systems, predictive threat detection and resistance against advanced attacks. However, certain significant challenges have also been seen including adversarial AI risks, ethical and privacy concerns, high costs of implementation, absence of skilled experts. Despite these obstacles, the future AI in cybersecurity held a lot of hope among the respondents, and additional investment, regulation, and multi-sector collaboration is needed to promote responsible and efficient usage. The paper provides practical implication to the policy-makers, cybersecurity decision-makers and technology strategists by identifying both the strategic benefit and operational restrictions of AI implementation. It also highlights the importance of ethical governance, capacity building and continuous training in order to achieve safe and sustainable implementation of AI in cybersecurity facilities. This study contributes to the existing body of AI-based cybersecurity research on the developing country prism, which considers the example of Pakistan. It offers an opportunity to put into perspective the promising and challenging part of the opportunities of innovation and ethical implementation of AI in the critical national systems and offers a context-oriented framework of increasing the technological resilience.
Copyright (c) 2025 Asma Javaid, Shamsa Mansab, Fuhmida Suduf, Ishrak Alim, Jawaid Iqbal https://creativecommons.org/licenses/by-nc-nd/4.0
Image Processing Methods: A Systematic Literature Review of Classical and Modern Approaches
In image processing, there are many different methods used to process pictures. This includes denoising, enhancement, segmentation, feature extraction, and classification. All of them join together to solve different problems and understand the changes in images. They are useful in many ways, like in medicine, security, photography, and robotics. Where images need to be studied or improved. Drawing on visual information, these methods help us in comprehending images, extracting key data, and making informed choices. There are two main ways to process the image, which are through traditional image-processing methods and deep-learning models. Usually, traditional techniques depend on manually designed algorithms and rules. Which uses fixed steps to process images. In contrast, deep learning models learn features directly from the information itself, enabling them to automatically detect distant details that traditional techniques could miss. The things that help the image processing methods to proceed are like Self2Self NN, Denoising DFT-Net CNNs, and MPR-CNN, which help remove unwanted noise from images in denoising. However, they still face difficulties with data preparation and adjusting model settings. While in an image enhancement, R2R and LE-net are employed to enhance the image’s visual quality, through which they can deal with complex real-world images and help them to look natural. On one hand, in the segmentation, PSP Net and Mask-RCNN methods accurately separate objects in an image; however, they can face problems with overlapping objects and ensuring reliable performance. In the method of feature extraction, models like CNN and HLF-DIP can automatically detect important image details, though they can be hard to interpret and sometimes complex to use. In the classification method, Residual Networks and CNN-LSTM are the approaches, which are effective at accurately identifying image categories; however, they require enormous computing power and can be difficult to fully understand. This review gives a clear overview of the advantages and disadvantages of different methods, which can help people choose the best approach for real-world use. As image processing continues to develop, solving problems like high computing needs and ensuring reliable performance will be important to make these techniques work at their best.
Copyright (c) 2025 Ayesha Bano, Muqaddas Salamat https://creativecommons.org/licenses/by-nc-nd/4.0
Green Banking Activities and Corporate Reputation of Banks: A Moderating Role of Bank Environmental Performance
The banking sector plays a pivotal role in promoting a sustainable environment in the country. This study aims to gain a deeper understanding of the role of green banking activities in shaping the corporate reputation of banks, with a focus on the moderating effect of the bank's environmental performance. The purposive sampling technique was used to collect the data, and structural equation modeling by the PLS-SEM approach was adopted to analyze the relationships of study variables, where data was collected using the survey method. A significant and influential impact of the green banking activities was found in shaping the corporate reputation of banks. Results also reveal that the environmental performance of banks plays a moderating role in the relationship between green banking activities and with corporate reputation of banks. The findings of the study provide implications for policymakers, regulators, and society. This research study contributes to the literature on green banking by exploring the factors that impact the corporation of banks.
Copyright (c) 2025 Saeed Ahmad Sabir https://creativecommons.org/licenses/by-nc-nd/4.0
A Synergetic Deep Learning Framework for Enhanced Lumbar Spine MRI Segmentation Using ResNet-50 and U-Net Architectures
Lumbar spine disorders are a leading cause of disability worldwide, significantly affecting patients’ quality of life. Traditional diagnosis methods rely on manual interpretation of MRI scans by radiologists, a process that is time-consuming, prone to human error, and susceptible to inter-observer variability. To address these limitations, this study proposes an automated segmentation approach for lumbar spine MRI images using advanced deep learning models. Specifically, U-Net and ResNet-50 architectures are employed to accurately segment critical spinal structures, thereby improving diagnostic precision and consistency. The models were trained and evaluated using a publicly available Lumbar Spine MRI dataset, and their performance was assessed using multiple metrics, including Accuracy, Precision, Recall, Intersection over Union (IoU), and Inference Time. Experimental results demonstrate that ResNet-50 outperforms U-Net in most metrics, offering higher accuracy and faster inference. This automated framework provides a reliable and efficient solution for lumbar spine analysis, with the potential to enhance clinical decision-making and reduce diagnostic delays in real-world healthcare settings.
Copyright (c) 2025 Afia Zafar, Mohsina Abid, Shahneer Zafar, Noushin Saba, Hina Ayaz https://creativecommons.org/licenses/by-nc-nd/4.0
A Robust Model for Phishing URL Classification and Intrusion Detection using Machine Learning Techniques
Phishing is one of the most prevalent and risky online threats. It works when hackers deceive internet users into providing personal information, such as passwords, login credentials, and credit card numbers, in order to obtain data that is frequently used against them. Victims are often sent phishing URLs (Uniform Resource Locators) via email. These URLs send users to fraudulent websites, phishing, spam, drive-by download attacks, and other hazardous websites. It's critical to accurately classify each URL as harmful or legitimate in order to prevent consumers from accessing malicious URLs. Phishing URL categorization helps in avoiding visits to harmful websites beforehand. To recognize intrusion attacks and classify phishing URLs, we provide a deep neural network-based method. Three sources of information were used: Kaggle, PhishTank, and Alexa. Term Frequency Inverse Document Frequency (TF-IDF) properties of a Support Vector Machine (SVM) are used to classify the phishing URLs in the first place. Second, we detect intrusions using a deep neural network. Finally, we evaluate our proposed model against previous approaches. Our research indicates that the SVM algorithm using TF-IDF produces an accuracy rate of 97.14% and a false positive rate of 2.8%. The model's intrusion detection predictions using validation data yielded promising results. We achieved an F1 score of 5.873%. With the exception of NMAP and a few other assaults, we obtained an accuracy rate greater than 95%. The main contributions of this study are: 1) improving phishing URL classification by combining SVM and TF-IDF, 2) utilizing a DNN model for efficient intrusion detection, and 3) conducting a thorough evaluation across multiple datasets to illustrate the reliability and robustness of the proposed method. The findings of the experiment indicate that the suggested model considerably enhances cybersecurity defensive systems, outperforming existing strategies in terms of accuracy, false positive rate, and detection precision.
Copyright (c) 2025 Muhammad Imran Ghafoor, Paras Pervaiz, Saima Tareen, Mehmood Baryalai, Shariqa Fakhar, Shariqa Fakhar, Shah Noor https://creativecommons.org/licenses/by-nc-nd/4.0
Decomposition of Stein AG-groupoid via congruence relations
The In this paper, we examine the class of Stein AG-groupoids S, establishing some features in terms of powers and exploring some basic aspects of elements. We demonstrate the associative properties of S. powers without the additional left identity, weak associative laws, and local associativity requirements seen in other AG-groupoids subclasses such as AG^(**), LAD, RAD, and Cheban AG-groupoids. We establish the following relations on S: ζ, ξ, ϕ, and β. We also demonstrate the equivalence of the relations ξ,ϕ. Additionally, we demonstrate that the S/ζ is a maximal separative commutative image of S that is also anti-separative. Furthermore, the maximal separative commutative image of S is S/ϕ.
Stein AG-groupoid, power associative, congruence relation, separative relation anti-separative relation.
Copyright (c) 2024 https://creativecommons.org/licenses/by-nc-nd/4.0
an AG-groupoid. A groupoid is said to be AG-groupoid if holds for all An AG-groupoid is called regular if there exist an element such that . Soft ideals via regular AG-groupoids have been discussed in this paper. Relationship between bi-ideals and soft bi-ideals, regular LA-demigroups, soft quasi ideals and soft two sided ideals have been discussed and characterized. The properties of bi-ideals, interior ideals, generalized bi-ideals, soft interior ideals, Quasi ideals are studies for a regular soft AG-groupoid.
AG-groupoid, LA-semigroups, paramedial, abelian distributive groupoid, inner and outer repeated.
A Spatial Attention GAN (SPA-GAN) Model for Robust Cloud Removal in Multispectral Imagery
Remote sensing imaging is widely employed in a variety of sectors including environmental science, national military security and its excellent resolution and stable geometric elements make it ideal for weather monitoring. When a remote monitoring sensor on a robotic satellite collects terrestrial data it is affected by the climate notably clouds. Cloud cover influences the precision of optically remote sensing images. Existing cloud removal techniques for Sentinel-2 data usually rely on basic image processing approaches which are vulnerable to diverse cloud patterns and struggle with accurate reconstruction. Cloud removal from high-resolution remote sensing satellite images is an important pre-processing step before analysis. Addressing the issue of cloud contamination in Sentinel-2 imagery. Sentinel-2 data is becoming more useful in a variety of disciplines, including environmental monitoring, resource management and disaster response. In the proposed framework, the deep learning model was used for removing clouds from satellite imagery. Using the SPA-GAN model, Sentinel-2 multispectral images were produced without the presence of clouds. The proposed model was implemented for image-to-image translation challenges. Moreover, the SPA-GAN model produced realistic and high-quality images by successfully preserving spatial characteristics. The experimental results showed that the proposed model produced cloud-free imagery and enabled precise observation of Earth. The proposed model helps the researcher by identifying the cloud area to generate high-quality cloudless imagery which enhances the visual data's dependability and clarity.
Copyright (c) 2025 sadia Abdullah , Saif Ur Rehman, Hina Akbar , Madnia Tariq, Rimla Anwar , Nida Fatima https://creativecommons.org/licenses/by-nc-nd/4.0
A nexus between customer citizenship behavior, green purchasing, and green brand image: insights from Pakistan’s apparel sector
The main purpose of this study is to explore how green buying behavior (GBB) influences customer citizenship behavior (CCB) and to check the mediating role of green brand image (GBI) in this link. The data were collected through convenience sampling and analyzed by using Partial Least Squares Structural Equation Modeling (PLS-SEM). This method helped to test the relationships among variables and to measure the mediation effect. The results show that GBB has a positive and strong impact on CCB, and GBI also has significant effect on GBB. The mediation test using VAF indicates that GBI partially mediates this relationship with a value of 61.5%. This means green brand image plays an important connecting role between green buying and customer citizenship behavior. The study gives useful insight for marketers to understand how customers’ green purchase actions can develop stronger brand image and motivate citizenship behavior in garment retail sector. It also guides policymakers to make strategies that support green shopping and increase the use of sustainable products. This research is one of the first to combine green purchasing behavior as an attitude-based factor and green brand image as a cognitive factor to explain four parts of customer citizenship behavior: tolerance, feedback, helping, and advocacy. Future studies can use systematic sampling and longitudinal data to get deeper understanding and more general results.
Copyright (c) 2025 Amen Fiaz, Syed Zeeshan Haider, Fatima Tu Zuhra https://creativecommons.org/licenses/by-nc-nd/4.0
Impact-Based Forecasting of Pre- and Post-COVID-19 GDP Conditions in Pakistan: A Comparative Analysis
This study investigates the impact of COVID-19 on Pakistan’s economic trajectory by conducting a comparative analysis of GDP conditions before, during, and after the pandemic (FY2017–FY2024). Employing a multiple regression framework and sectoral segmentation based on Pakistan Bureau of Statistics (PBS) weights, the research examines the contributions of agriculture, industry, and services to overall GDP growth. Findings reveal that the COVID-19 pandemic caused Pakistan’s first economic contraction in seven decades (-0.5% in FY2020), disproportionately affecting the industrial and services sectors, while agriculture demonstrated relative resilience with 2% growth. Regression results (R² = 0.9998) indicate that the services sector remained the principal driver of GDP (β = 0.448, p < 0.001), followed by industry (β = 0.332) and agriculture (β = 0.175). Sector-specific analyses highlight volatility in textiles (-22.7% in 2020), recovery in IT services (18% in FY2021), and consistent contributions from livestock (3–6.5%). Despite stabilization at 4.7% GDP growth by FY2024, structural challenges—including inflation, energy dependency, and limited agricultural R&D investment—continue to hinder sustainable growth. The study underscores the need for sectoral rebalancing, diversification of industrial exports, climate-resilient agricultural practices, and digital transformation of services. By providing empirical evidence on Pakistan’s economic resilience and vulnerabilities, this research contributes to the broader discourse on pandemic economics in developing countries and offers policy-relevant insights for strengthening long-term economic sustainability.
Pakistan economy, GDP growth, COVID-19 impact, Sectoral analysis, Sustainable growth
Copyright (c) 2025 Muhammad Zohaib, Imtiaz Husain, Muhammad Adnan Khursheed https://creativecommons.org/licenses/by-nc-nd/4.0
The Importance of Enhancing Technology in Accounting and Finance
Accounting and finance have changed quickly thanks to digital technology. Companies require innovative solutions to improve accuracy, efficiency, and decision-making. This study upgrades accounting and finance technology, focusing on internal controls, automated data processing, electronic accounting, and automated reporting. Financial management systems evaluate technology uptake and performance based on these features. The study shows that technology-driven accounting solutions increase compliance, transparency, and risk management. The research found that sophisticated technology simplifies real-time financial reporting, improves data reliability, and reduces human error, boosting management decision-making. Accounting software and automation boost internal controls and operational resilience. This research found that technology boosts accounting and finance's competitiveness, sustainability, and creativity.
Copyright (c) 2025 Asghar Ali Arshad, Faiza Aziz, Zarmeena Imran, Ayesha Fazal, Muhammad Faisal Gulzar https://creativecommons.org/licenses/by-nc-nd/4.0
Enhancement of IoT Intrusion Detection: Evaluating ML and DL Approaches with NetFlow Datasets
The Internet of Things (IoT) is a concept that involves integrating diverse objects to enable seamless interaction between real-world and virtual entities. IoT is now connecting the physical world to networks. IoT devices can sense, process, transmit, and store data collected from the physical world. However, these devices are resource-constrained, creating significant security vulnerabilities in many IoT applications. Implementing effective security measures on such devices is challenging without compromising their performance or potentially causing damage. Consequently, there is a substantial gap between the security capabilities of current IoT devices and their security requirements. Computer security principles, namely Confidentiality, Integrity, and Availability (CIA), can be compromised by malicious intrusions or attacks on computers and information databases. This study proposed and compared 1D CNN and XGBoost for detecting malicious attacks in the IoT environment. The proposed techniques were evaluated on the five variants of NetFlow datasets. The experiments shows that the proposed techniques outperform the Ensemble Tree classifier, achieving better performance in binary and multi-class classification. The results for 1D CNN and XGBoost were compared on the basis of F1 measure, AUC, recall, correctness value, and ppv.. The comparison shows that XGBoost is the better-performing model across the NetFlow datasets. XGBoost's ability to capture complex patterns and optimize the classification task makes it robust and effective.
Copyright (c) 2025 Mohsin Suleman, Noushin Saba , Afia Zafar, Mohsina Abid https://creativecommons.org/licenses/by-nc-nd/4.0
Cyber-Resilient Mobile Edge Computing: A Deep Neural Approach for Secure and Efficient Task Offloading
Mobile edge computing (MEC) pushes cloud resources including computation and storage in vicinity of end devices. This effectively reduces communication latency. However, the minimum battery power of end devices limits the MEC performance. Integration of Wireless power transfer (WPT) with MEC enhances the performance by charging the end devices simultaneously while computation offloading. The problem lies in the variation of network state and channels while this offloading, which reduces the overall computation rate. To this end, in this paper, we aim to maximize the computation rate of whole MEC network using deep learning. We use a Deep Neural Network which learns from multiple episodes and decides whether to offload the task or not based on network state. This binary decision leads to remote execution of the task in case of optimal network state, and if not optimal, then leads to local execution. The experiments and results validate the effectiveness of our proposed framework.
Copyright (c) 2025 Syed Mehtab Hussain Shah, Fahad Amin, Ajab Khan https://creativecommons.org/licenses/by-nc-nd/4.0
Seed Quality Detection Using Deep Learning Though RGB Camera
Maize plays a vital role in Pakistan's agriculture, but its seeds are susceptible to damage and mold during transportation and storage, making it essential to strengthen the quality identification of maize seeds to increase crop yield and quality. Conventionally, seed recognition has remained inefficient and subjective despite the improvement in machine vision technology and digital image processing method employed in agricultural context. However, seed testing is still the most important aspect of seed technologies. The project was to identify pure and broken seeds with the help of deep learning, with an RGB camera which had high precision, low error, and accurate outputs at minimum space and cost. In order to solve this issue, various CNN models were used to identify mixed maize seeds as pure or broken seeds whereby the accuracy rate of CNN Sequential Model, ResNet50 and VGG16 was 86%, 80%, and 93%, respectively. The seed identification with the CNN models was much better than the conventional methods, as the complex features of the seed images are extracted and this minimizes the subjectivity of the conventional methods. The achievements of the CNN models in the project demonstrate the potential of deep learning in the classification of mixed maize seeds as pure and broken. This advancement in technology of seed identification may be a major boost in the efficiency and reliability of seed tests in the Pak agriculture production sector.
Copyright (c) 2025 Tehreem Fatima, Maleeha Saqlain, Sonia Raoof, Farwa Raoof, Nimra Razzaq, Zartasha Kiran https://creativecommons.org/licenses/by-nc-nd/4.0
Real-time Bubble Sheet Detection and Evaluation Using Object Recognition
Optical Mark Recognition (OMR) is a technology widely used in education to digitize large amounts of paper data. It is often used for scoring multiple-choice tests. This can be time-consuming when manually grading. OMR technology facilitates this process by providing a controlled format for students to submit their answers. Without the need for manual scoring, OMR is a well-known data entry method and an important technique in human-computer interaction. It is widely used in assessment tests in universities, colleges, questionnaire forms, and competitive examinations. There are many applications for computer image processing and recognition in today's technology-driven world. Existing OMR techniques have limitations, such as the need for special paper, reliance on high quality scanning to achieve accurate results. and to overcome these challenges this research introduces a new image-based OMR method. This reduces the need for special paper and increasing the detection accuracy of the proposed system which is highly reliable by using advanced object recognition techniques that can detect partially filled bubbles with some Marks are slightly blurry or blurred. Additionally, the research includes an automated questionnaire creation system based on specific needs. The generated question paper comes with an answer key. The system will evaluate the completion of the bubble sheet and create the final output file in Excel format. This technique allows for cost-effective optimization of question papers and guarantee effective evaluation in standard reports. Tests have shown the system's accuracy to be more than 99%, effectively scanning and analyzing filled, partially filled bubbles.
Copyright (c) 2025 Nimra Razzaq, Infal Farooq, Khadija Noor, Sadia Abdullah, Rana Hassan Ajmal , Ali Murtaza https://creativecommons.org/licenses/by-nc-nd/4.0
Role of Big Data Analytics Capabilities on Green Supply Chain Performance in Pakistan
This research delves into the impact of Big Data Analytics Capabilities (BDAC) on the performance of green supply chains in Pakistan. The investigation relies on primary data obtained from the manufacturing sectors, utilizing a sample size of 129 for the final analysis. Structural equation modeling (SEM) through SmartPLS 4 was employed for data analysis. The findings reveal a statistically significant and positive correlation between BDAC and Green Innovation (GI), Green Supply Chain Integration (GSCI), and Green Supply Chain Performance (GSCP). Notably, the study underscores the more pronounced impact of BDAC on Green Innovation compared to its effects on Green Supply Chain Integration and Performance. Several limitations were encountered during the study, including reliance on a questionnaire for primary data collection, the utilization of cross-sectional data, and the focus on a singular context within one country. Despite these constraints, the results offer valuable insights for stakeholders and policymakers in Pakistan's manufacturing sector, supporting effective management of organizational competencies related to big data analytics to enhance both green innovation and green supply chain performance.
Big Data Analytics Capabilities (BDAC); Green Innovation (GI); Green Supply Chain Integration (GSCI) ; Green Supply Chain Performance (GSCP) and Green Supply Chain (GSC).
Copyright (c) 2025 Minhaj Ikram, Ali Ismail https://creativecommons.org/licenses/by-nc-nd/4.0
Legitimacy Types, Platform Reputation, and Public Engagement:A Mediated Analysis of Cryptocurrency Markets
This paper discusses the aspects of the normative, the regulative, and the cognitive aspects of legitimacy and their role in influencing the population to participate in the cryptocurrency market in Pakistan as well as the moderating effect of the reputation of crypto platforms. The article aims to define the perceptions of the legitimacy of users with respect to their readiness to adopt and use electronic financial devices in a semi-regulated, dynamic market. It had a quantitative research design with the use of a sample of 199 Pakistani respondents in the data collection process. The statistical analysis was done in SPSS. The research has used reliability assessment, correlation and regression as well as mediation analysis via Hayes PROCESS Model 4. The findings indicate that normative and cognitive legitimacy have high positive influence on the involvement of the population; whereas regulative legitimacy does not have any significant influence. Such results demonstrate that social acceptance and cognitive understanding are the direct factors that influence people to engage in cryptocurrency operations instead of legal institutions. Moreover, when the mediation analysis is conducted, the reputation of the crypto platform is a mediator between the relationship between the legitimacy and the involvement of the population. This highlights the level of trustworthiness, transparency in platforms, and credibility in encouraging participation towards the user when the state regulation is still emerging. The end of the paper states that legitimacy is the requisite underlying factor in relation with engaging with the platform, and that platform reputation is a system that has the capacity of converting the appreciation of legitimacy into actual behavioral reactions. The observations made can be added to the theory of institutional and legitimacy theory because they demonstrate that some gaps within the regulatory framework of developing economies like Pakistan can be covered using the trust-based institutions.
Copyright (c) 2025 Asghar Ali Arshad, Syed Faiq Niaz, Afifa Jamil, Ibrar Haider, Sarmad Saeed https://creativecommons.org/licenses/by-nc-nd/4.0
Unlocking Brand Loyalty In Islamic Banking: The Impact Of Quality, Emotion, And Financial Perception Via Brand Image
This research paper examines the antecedents of brand loyalty in the Islamic banking industry in Pakistan, and the study will cover the roles of perceived quality, emotional value, and financial perception, with a brand image as the intermediary variable. Using a structured questionnaire as the method of collecting data with 266 respondents, the research uses regression and correlation analyses to investigate the correlation between these constructs. The results indicate that all the three aspects of perceived value are important sources of brand loyalty, and that brand image is the key mediator of reinforcing these relationships. The work will add to the literature by presenting a holistic framework that will combine marketing and faith-based approaches and offer valuable practical information that could be applied by Islamic financial institutions aimed at improving customer retention and brand differentiation in an increasingly competitive and digitally transformative environment.
Copyright (c) 2025 Asghar Ali Arshad , Hafiz Muhammad Awais Raza, Sameer Mujahid Butt, Muhammad Rehan Khan , Rehan Khan , Syed Muzahir Ali Zaidi https://creativecommons.org/licenses/by-nc-nd/4.0
Fintech Adoption in Pakistan’s Banking Sector: The Nexus of Trust, Perceived Security, Social Influence and Financial Inclusion
This research tries to identify the banking sector of Fintech use: trust, perceived security, social influence, and financial inclusion. The structured questionnaire used for collecting data from 231 participants, including students, company employees, bank employees and the self-employed. This study applied Pearson correlation and multiple regression analysis for relationships between independent variables and Fintech use. The study used the software IBM SPSS for multiple regression analysis, which explained 78.5% of the variation in Fintech use. The linear regression analysis is applied, and the results show that social influence and financial inclusion have a strong and positive relationship with the Fintech Use. However, Trust and Perceived Security have positive trends but they are statistically insignificant in Fintech use. The findings indicate that Fintech providers ought to prioritize enhancing financial inclusion and leveraging social influence to boost fintech adoption. The findings indicate that strategies centered on social engagement and improved financial inclusion infrastructure could significantly augment the use of Fintech in Pakistan's banking sector.
Copyright (c) 2025 Muhammad Rehan Khan , Muhammad Imran, Amna Tariq, Asghar Ali Arshad, Saalma Khalid Khalid https://creativecommons.org/licenses/by-nc-nd/4.0
ProjecTree: An AI Powered intelligent Project Management System
To ensure projects are completed on schedule, resources are used efficiently, and teams work together smoothly, strong project management is essential. However, the intricacy of today's projects, which involve many different tasks, dependencies, and teams, frequently leads to inefficiency, delays, and poor coordination. ProjectTree is a cutting-edge, AI-powered project management system created to tackle these issues. By using Natural Language Processing (NLP), the platform analyzes project details to sort tasks into frontend, backend, and database categories, and intelligently assigns them to employees based on their skills, experience, and availability. This smart resource allocation maximizes resource utilization and reduces bottlenecks. ProjectTree also includes a real-time communication tool to improve team interaction and uses role-based access control (RBAC) to limit user access to only the necessary resources and tasks, thereby improving security and data integrity. To protect sensitive project data, the system also incorporates strong security measures. This paper details the design, structure, and implementation of ProjectTree, comparing it to current project management tools, and showing how it can improve task assignment, team coordination, and overall productivity. The results show the system's ability to lessen project delays, improve decision-making, and offer a secure, collaborative environment for modern businesses.
Copyright (c) 2025 Umar Farooq, Farooq Ali, Moniba Iqbal, Asif Raza , Altaf Khan, Ali Raza https://creativecommons.org/licenses/by-nc-nd/4.0
Influential Nodes Detection in Ethereum Blockchain Network Using Machine Learning
Ethereum blockchain is the market leading platform for decentralized applications and smart contracts that have powered the new age of financial ecosystem. In order to improve security and performance, identify influential nodes, and understand network dynamics on Ethereum it is critical to identify influential nodes in Ethereum. This study explore machine learning techniques for discovery of these nodes using graph based algorithms, centrality measures and clustering methods. It studies the impact of a node in terms of frequency of usage, connectivity and computational power for a node. Finally, this study compare performance of proposed methodology combining supervised learning and graph neural networks to their traditional counterparts and demonstrate approach outperforms existing methods. The study demonstrate that highly influential nodes engage in unique patterns of behavior, which are detectable and categorizable. This study contribute to understanding of the network structure of Ethereum, along with a scalable approach to monitoring and optimising blockchain ecosystems. Moreover the study discuss the implications for network robustness, fraud detection and protocol enhancements, and demonstrate the promise of machine learning for blockchain analytics.
Copyright (c) 2025 Nazia Azim, Khair Ul Burria, Muhammad Zain Asghar, Zeeshan Raza, Mehran Ali, Sheraz Ali Jan https://creativecommons.org/licenses/by-nc-nd/4.0
From Anthropomorphism to Brand Love: A Study on S-O-R Framework Perspective in E-Retailing
The rapid growth of e-retailing has intensified the challenge of fostering stronger emotional connections with consumers. This study investigates how consumers develop a passion for e-retail brands by exploring the role of anthropomorphism in shaping brand love. Specifically, we explore how anthropomorphic cues on e-retail platforms influence brand love through consumer brand engagement, while considering the moderating effect of consumers’ technology self-efficacy. Under the light of the Stimulus-Organism-Response (S-O-R) framework, we employed purposive non-random sampling, and structural equation modeling helped to analyze the research findings. The findings show that brand anthropomorphism has a significantly positive effect on brand love. Furthermore, consumer brand engagement partially mediates the relationship between anthropomorphism and brand love, while technology self-efficacy significantly strengthens the link between anthropomorphism and consumer brand engagement. These findings extend existing literature by applying the S-O-R framework to a novel context, highlighting how boundary conditions shape consumer responses. The study also provides practical implications for marketing managers, suggesting that humanizing e-retail platforms can enhance consumer engagement and foster more profound emotional attachment, ultimately cultivating a more passionate consumer base.
Copyright (c) 2025 asmarafique, Muhammad Amir Rashid, Syed Muhammad Irfan https://creativecommons.org/licenses/by-nc-nd/4.0
Antecedents to Responsible Financial Management Behavior among Young Adults: The Moderating Role of Financial Risk Tolerance
The main objective of this study is to investigate the impact of financial knowledge, financial attitude, and locus control impact on financial management behavior in the context of Pakistan. The data has been collected in a quantifiable manner and PLS-SEM analysis has been conducted using SmartPLS version 3.2.8. In addition, the results have showed that financial attitude has a positively significant effect on financial management behavior while financial knowledge has positively significant effect on financial attitude. Lastly, locus of control has positively significant effect on financial management behavior. However, some of the recommendations for managers presented in this study are as follow: Firstly, Public policymakers and educational institutions can devise programs to improve financial literacy and promote positive financial attitudes. Individuals' financial health can benefit from these services, which come in the form of basic courses and seminars. Furthermore, they can assist young adults in developing financial management skills and promoting positive financial attitudes that emphasize the importance of saving rather than spending money. This is particularly critical in a developing country like Pakistan, where savings rates are poor.
Copyright (c) 2025 Ameenullah Aman, Irshad Ali Khan https://creativecommons.org/licenses/by-nc-nd/4.0
Analysis of Deep Learning Models on VQA(Toloka)Dataset
Visual Question Answering (VQA) is a recent task at the intersection of computer vision and natural language processing. In this task, instead of giving phrase images, machines will go on to learn and interpret them. It is also their job to answer any question in turn. Instead of the usual "Images ten questions", it relies on phrases reversed in order and asks unrelated questions about pictures. One way which VQA current challenges itself picks up new trends is with this issue for the model to predict an image and where provide bounding box as output, given input image together with a questioning on natural language sentence. Unlike traditional VQA tasks, where the model outputs a textual answer, this challenge presents a more complex scenario by not explicitly mentioning the object name in the question, making it more demanding than typical VQA and visual grounding tasks. A comparative analysis is performed using the Toloka VQA challenge dataset, which has seen limited exploration in existing research. Our findings show that a combination of the DINO and T5 models achieved a high Intersection over Union (IoU) score of 0.67, demonstrating its effectiveness in this unique context. Similarly, the BERT combined with Swin Transformer model yielded a competitive IoU score of 0.60. In contrast, models like MobileNet-v2 and GPT with ResNet-50 + BERT struggled, displaying lower IoU values and highlighting the challenges posed by this dataset.
Toloka VQA, IoU, Visual Question Answering, Object detection.
Copyright (c) 2025 Fariha Shoukat , Muhammad Naveed, Nazia Azim, Arooj Imtiaz Imtiaz https://creativecommons.org/licenses/by-nc-nd/4.0