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
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
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
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
Investigation of a SiC/Si Heterojunction as a Potential Solar Cell: Performance under Illumination
The integration of wide bandgap semiconductors with silicon has attracted significant interest for advancing photovoltaic technologies. In this study we experimentally fabricated is potential as a solar cell. The heterojunction was developed by depositing carbon on p-type silicon substrates using Plasma Enhanced Chemical Vapor Deposition (PECVD) at 700℃ leading to a formation of a SiC layer and establishment of a p-n junction. Mechanical hardness and surface morphology were examined using a vickers hardness tester and optical microscopy confirming the structural integrity of the fabricated junction. Electrical behavior was studied under both dark and illuminated conditions using Keithley 2400 source meter. In the dark, the junction exhibited diode-like rectifying characteristics with a reverse saturation current density of 4.58×10‑5 A/cm2 and an ideality factor of 10.19 suggesting recombination-dominated current transport. Under standard illumination (AM 1.5, 100 Mw/cm2) the heterojunction displayed clear photovoltaic activity, with a short-circuit current density (Jsc) of 13.12 mA/cm2 an open- circuit voltage (Voc) of 0.91V, a fill factor (FF) of 61% and a conversion efficiency (ƞ) of 12%. These results demonstrate that the SiC/Si heterojunction can be successfully fabricated via PECVD and operated as a functional solar cell. The study provides experimental validation of and operated as a functional solar cell. The study provides experimental validation of SiC/Si structures as promising candidates for efficient and thermally stable photovoltaic devices complementing earlier simulation-based reports and paving the way for further optimization toward higher efficiencies
Copyright (c) 2025 Nizam Ahmad, Muhammad Tanveer , Arooj Fatima, Faisal Amin , Iqra Ashraf , Nabeel Akbar, Farooq Haider https://creativecommons.org/licenses/by-nc-nd/4.0
Unlocking AI's Potential: Zero-Shot and Few-Shot Learning for Voice and Image Recognition
The zero-shot and few-shot learning paradigms have emerged as promising solutions to the shortcomings of traditional deep models that require large volumes of labeled data for training. In the present paper, a full-fledged experimental study of the usage of zero-shot and few-shot learning methods in voice and image recognition tasks is provided. We analyze and compare several state-of-the-art architectures, such as CLIP, Whisper, and prototypical networks, in benchmark datasets including ESC-50 for audio classification and mini-ImageNet for image recognition. The experiments are designed to evaluate the generalization ability of models in situations where classes are unseen or sparsely represent during training. Our results show that multimodal models that are pre-trained on large-scale datasets have a high rate of performance in zero-shot scenarios, whereas metric-based few-shot approaches allow greater accuracy when only a small amount of supervision is provided. We also discuss cross-modal transfer ability, and examine how acquired representations in one modality (e.g., voice) can be used in the other (e.g., images). The findings highlight critical trade-offs between model complexity, data efficiency, and recognition accuracy which provide practical information to the deployment of lightweight and scalable AI systems in resource-limited settings. The paper develops the concept of generalized recognition systems and provides a base on how the concept will be researched on in the future in a low-resource learning environment. The main contributions of this work include a comparative study of ZSL and FSL methods, analysis of cross-modal transfer and identification of key trade-offs that lay the foundation for future research in generalized and adaptive AI.
Copyright (c) 2025 Adnan Ali, Shoaib Farooq , Muhammad Zeeshan Shafi, Muhammad Talha Tahir Bajwa , Jamil Ur Rehman, Hanifullah https://creativecommons.org/licenses/by-nc-nd/4.0
Emotion in Action: A Thematic Study of How Build Loyalty Through Emotional Marketing
This thesis explores the role of emotional marketing in shaping consumer loyalty, focusing on how emotional strategies influence long-term consumer-brand relationships. Drawing on a qualitative research design and secondary data, the study conducts a comparative analysis of three globally recognized brands: Dove, Nike, and Apple, each of which has successfully leveraged emotional marketing to build and sustain loyalty across different consumer demographics and market contexts. The research is grounded in key psychological and marketing theories, including Maslow’s hierarchy of needs, affective events theory, emotional attachment theory, and the dual-process model of brand loyalty. Through thematic analysis, the study identifies four recurring emotional mechanisms: identity alignment, authentic storytelling, emotional continuity, and social resonance. These themes were evident across all three case studies and found to be central to the creation of both attitudinal and behavioral loyalty. Findings suggest that emotional marketing is most effective when it aligns with the consumer’s self- concept, reflects shared values, and is perceived as consistent and authentic over time. Campaigns that evoke self-transcendent emotions, such as pride, empathy, or empowerment, were particularly effective in fostering brand attachment and advocacy. The study offers theoretical contributions by advancing a multidimensional view of emotional loyalty and providing insight into how emotional branding strategies can be adapted across identity narratives and industry sectors. Practical recommendations are provided for marketers, including the importance of emotional coherence, psychographic targeting, cultural adaptability, and emotion-driven performance monitoring. Emotional marketing emerges as a powerful and ethically significant strategy for cultivating deep, lasting consumer loyalty. It is not merely a tactic to drive short-term engagement, but a relational framework that embeds brands into the emotional and symbolic lives of consumers.
: emotional marketing, brand loyalty, consumer behavior, emotional branding, brand attachment, identity alignment
Copyright (c) 2025 Saba Qasim , Waleed Tayyab https://creativecommons.org/licenses/by-nc-nd/4.0
Mobile Banking and Financial Inclusion: A Potential Catalyst for Growth in Pakistan
The recent studies are examining how mobile banking can help enhance financial inclusion and even economic empowerment of Pakistan particularly in rural environment. Mobile banking is now a significant tool in ensuring the bridge of financial inclusion, especially in the remote marketes where credit coverage to traditional banking service is limited. The study examines an adoption trend of mobile banking, financial accessibility in an attempt to determine its effect on the economic behavior of the users through a coded survey among urban and rural population in Pakistan in an attempt to get a deep-seated explanation of the penetration and adoption pattern of mobile banking. The results indicate that mobile banking contributes a lot to the access of savings, credit, insurance services mainly in the rural area where the conclusion can be made that the traditional banking infrastructures are not accessible in most areas. Additionally, there are increased financial behaviors of utility of mobile banking; improved savings, improved credit access, and increased business investments. Despite the above benefits as it has been indicated, the study has its challenges that will most likely make it difficult in broader adoption among the rural community that could include digital literacy, connectivity and security concerns. According to the results, another concern that should be addressed by policy makers, as well as financial institutions, is the need to improve digital literacy, Internet infrastructure and regulatory policies to enable the abilities of mobile banking regarding their financial inclusivity and economic enabling capabilities.
Copyright (c) 2025 Ahmad Zeb , Sadia Saeed , Syed Noman Mustafa, Muhammad Arshad Saleem , Muhammad Ali https://creativecommons.org/licenses/by-nc-nd/4.0
Impacts of Technology on The Performance of Students at College Level in Sindh
To meet the challenges in the educational field it is inevitable for all associated persons of education, either they can be students or educationist without any unfamiliarity of technology relevant with the education, they cannot compete with the other nations in the modern age. In the most recent time there is growing use of artificial intelligence with the help of various apps in everyday life including for teaching and learning. Despite of all such type of technological revolution, there is lack of provision for all learners in the Sindh to fulfill the educational needs with these facilities. For the quality education, there is great role of Technology. The outdated method of teaching and learning make the students boring and difficult to achieve the objectives of education. Target population is based on the District Badin and Sample size is 160 teachers from the Government Degree Colleges (Boys and Girls). The study was carried out by the quantitative method consists of closed ended questionnaire. Present study analysed the relationship between predictor and outcome variables, the use of technology and the students’ performance. The data was analysed by the multiple regression and correlation. The analyses revealed, there is the relationship between the academic performance of students and independent variables, difficulty facing by students while use of technology, lack of facilities, health and no proper training of use of technology.
Copyright (c) 2025 Aijaz Ali Soomro, Saba Qasim, Qurra Tul Ain https://creativecommons.org/licenses/by-nc-nd/4.0
A Transformer-Based Framework for Accurate and Interpretable PM2.5 Forecasting through Integration of Country-Level Embeddings and Explainable AI for Enhanced Environmental Decision-Making
Air quality prediction is increasingly vital to environmental health, particularly for urban and rural regions, where dangerous particulate matter hazards often exist. This study presents a novel methodology for estimating PM2.5 concentrations using a transformer model with country-level embedding and explainable AI (XAI). The proposed approach is superior to conventional machine learning and deep learning techniques as it provides high accuracy and is interpretable and applicable to various geographic regions. Given the country-specific embeddings, the transformer architecture models the replacements caused by time and location variations in pollutants' concentrations, consequently allowing accurate predictions even for regions having sparse data. Furthermore, SHAP and LIME elucidate the model's tendency to predict, providing policymakers with valuable insights. Overall, the proposed architecture presents a stronger predictive power than other forecasting models, with an R-squared value of 0.98 and a mean absolute error of 0.011. Also, using country embedding has helped improve accuracy and the ability to apply to different regions. Hence, this research offers a plausible framework to forecast air pollution and evidence-based government policymaking and planning about air pollution and its health and environmental effects.
Copyright (c) 2025 Syed Azeem Inam, Hassan Hashim, Asif Mehmood Awan , Syeda Wajiha Naim, Haider Rajput, Saddam Umer https://creativecommons.org/licenses/by-nc-nd/4.0
MLAFF-Net: Multi-level Attention-based Feature Fusion Network for Single Image Dehazing
We propose a convolution neural network to recover a dehazed image titled Multi-level Attention-based Feature Fusion Network (MLAFF-Net). MLAFF-Net consists of feature extraction blocks, pixel attention, feature fusion blocks, and mixed convolution attention mechanism. Feature extraction block is employed to extract the features. MLAFF-Net has ability to focus on significant features employing pixel attention mechanism. Feature fusion block fuses and refines the significant features of various levels for next fusion block. The accurate estimation of the kernel may recover a sharp image. Moreover, MLAFF-Net has ability to acquire both the high-level and low-level significant features, reduce the feature redundancy and boost the further internal feature representations employing the mixed convolution attention module. Further, multi-level supervision learning method is employed to compute the loss at various resolution levels. The experimental findings show that MLAFF-Net exhibits outstanding performance when compared to existing single image dehazing methods for both synthetic and real-world images.
Copyright (c) 2025 Sanaullah Memon, Rafaqat Hussain Arain, Farheen Mirza, Syed Rizwan, Tahira Qadeer https://creativecommons.org/licenses/by-nc-nd/4.0
Big Data Analytical Capabilities as an Enabler of Marketing Agility: Evidence from the Fintech Sector in Pakistan
The fintech sector is growing rapidly in Asia, but in Pakistan, its growth is steady due to dynamic market conditions and customers’ needs. Thus, Fintech companies or startups in Pakistan must develop marketing agility to cope with the dynamic market and customers’ needs. This research aimed to highlight the influence of big data analytical capabilities on the marketing agility of Fintechs. The data were gathered from 440 top and middle-level managers and analyzed by using SPSS and SmartPLS4. The results highlighted that Fintech companies or startups aiming to develop marketing agility must adopt big data analytical capabilities. The research directed the attention of marketing managers towards the significance of big data analytical capabilities for developing marketing agility.
Copyright (c) 2025 Aamir Abbas, Imran Arshad, Zohaib Irshad, Hafiz Muhammad Waqas https://creativecommons.org/licenses/by-nc-nd/4.0
Synergizing Cloud Computing and AI for Next-Generation Smart Cities
Modern societies have rapidly urbanized, which has led to the urgent need for sustainable, efficient and intelligent infrastructure. To overcome these issues, smart cities are striving to combine digital solutions to streamline city services, improve citizen experiences, and better manage resources. Cloud computing and artificial intelligence (AI) are among the most disruptive technologies that can make this vision possible. Cloud computing can offer scalable, flexible, and affordable frameworks to store and process large urban data, and AI can offer highly sophisticated capabilities in real-time analytics, decision-making and automation. This article discusses how cloud computing and AI can be synergistically integrated to enable the next generation of smart cities. It looks at the application of cloud-based AI in the optimization of transportation systems, energy management, healthcare services, and public safety, as well as discusses concerns related to data privacy, latency, and interoperability. The research identifies the potential of cloud-AI synergy to generate resilient, sustainable, and citizen-centric urban ecosystems by analyzing current research trends, implementation frameworks, and case studies. Ultimately, this work contributes to the understanding of how cloud computing and AI can collaboratively transform cities into more adaptive, efficient, and intelligent spaces for future generations.
Copyright (c) 2025 Hira Yaseen , Ayesha Quyyum, Muhammad Talha Tahir Bajwa , Marwa Afzal , Rida Fatima , Alia Akhtar https://creativecommons.org/licenses/by-nc-nd/4.0