Journal of Engineering and Computational Intelligence Review
type: channel
description: Welcome to The Journal of Engineering and Computational Intelligence Review (JECIR), ISSN: 3105-4811 The Journal of Engineering and Computational Intelligence Review (JECIR) is a premier open-access, double-blind peer-reviewed journal dedicated to advancing cutting-edge research in Computer Science and Engineering. Committed to academic excellence, JECIR provides a global platform for researchers, scholars, and industry professionals to share innovative findings and fundamental advancements in these dynamic fields. Our rigorous peer-review process ensures the publication of high-quality, original research that contributes to both theoretical foundations and practical applications. JECIR aims to bridge the gap between academia and industry by fostering interdisciplinary collaboration and promoting solutions to real-world engineering challenges. The journal encourages contributions that explore novel methodologies, intelligent systems, and emerging technologies, with a focus on scalability and societal impact. We cover a broad spectrum of topics, including artificial intelligence, machine learning, robotics, data analytics, mechatronics, and sustainable technologies. Our scope extends to interdisciplinary research, integrating engineering disciplines such as mechanical, electrical, and civil engineering with computational intelligence. We prioritize studies that develop innovative algorithms, optimize intelligent systems, and address critical challenges in healthcare, energy, automation, and beyond. By publishing original research articles, critical reviews, and technical notes, JECIR serves as a vital resource for advancing knowledge and technological progress. We invite researchers and practitioners to submit their work and join us in shaping the future of engineering and computational intelligence. Through open-access dissemination and a commitment to scholarly excellence, JECIR strives to be a catalyst for innovation, driving meaningful advancements that benefit a…
publisher: Inverge Consultancy (Pvt) Ltd.
language: en
publication name: Journal of Engineering and Computational Intelligence Review
issn: 3105-4811
items: Combined Geophysical and Geochemical Evaluation of the Kirthar Fold Belt for Mineral and Hydrocarbon Resources
items: Integrating Blockchain-Enhanced Enterprise Systems for U.S. Banking, Pharmaceutical Supply Chains, Economic Resilience, and Secure Operational Models
items: Leveraging Machine Learning Techniques for Enhancing Signature Verification to Unveiling the Forgery
items: Performance Evaluation of Battery Management Systems in Lithium-Ion Battery-Powered Electric Vehicles
items: Design and Control of an Autonomous Drone Navigation System Using Embedded AI
items: A Trust Management-based Energy Efficient Message Scheduling Algorithm in the Internet of Things System
items: AI and Blockchain Integration: Driving Strategic Business Advancements in the Intelligent Era
items: The Role of Threat Intelligence in Preventing Financially Motivated Cyberattacks
items: The Role of Artificial Intelligence in Carbon Pricing Policies: Economic and Environmental Implications
items: Cybersecurity in the Modern Era: An Overview of Recent Trends
items: A Comprehensive Review of Blockchain Applications across Diverse Domains
items: Big Data Analytics with Machine Learning: Challenges, Innovations, and Applications
items: A Review on the Role of IoT in Modern Electrical Engineering Education
items: A Comprehensive Review of Emerging Challenges in Cloud Computing Security
items: A Systematic Review of Electric Vehicle Innovations and Implementation Barriers
items: The Role of Quantum Computing in Future Big Data Processing: A Comprehensive Review
items: Solar Energy Development in Pakistan: Overcoming Barriers through Policy Reform and Stakeholder Engagement
items: AI-Powered Radiology: Innovations and Challenges in Medical Imaging
items: The Convergence of Artificial Intelligence and 6G Networks: A Transformative Shift in Computing and Communications
items: The Impact of Different Computing Tools in Electrical Engineering Learning
items: The Internet of Things (IoT): A Comprehensive Review of Technologies, Applications, Challenges, and Future Trends
items: Smart Grids: A Comprehensive Review of Technologies, Challenges, and Future Directions
items: The Transformative Role of Block chain Technology in Supply Chain Management
items: Advancements and Transformative Applications of Blockchain Technology
items: AI and Cybersecurity: Defending Data and Privacy in the Digital Age
items: Adoption Potential of Artificial Intelligence and Machine Learning in Islamabad’s Academic Libraries
items: Internet of Things in Food Supply Chains: Enhancing Quality and Safety through Smart Technologies
items: ChatGPT and the Future of Academic Writing: Enhancing Productivity and Creativity
items: The Strategic Influence of Cloud Computing on Contemporary Marketing and Management Practices
items: Computational Analysis of Geotherma-Enhanced Heavy Oil Production

Combined Geophysical and Geochemical Evaluation of the Kirthar Fold Belt for Mineral and Hydrocarbon Resources
type: item
description: This research seeks to characterize the mineral and hydrocarbon potential of the Kirthar Fold Belt (KFB) using an integrated approach of geophysics and geochemistry. KFB, which is located in the southwestern part of Pakistan, is one of the most tectonically active regions of the country and is made of a multi-tectonic lithology and diverse structural configurations. The geophysical approach using seismic, gravity, magnetic-resistivity surveys, and hydrocarbon and oil geochemical halo maps as well as major and trace, isotope, and hydrocarbon biomarker geochemical analysis helped to characterize potential mineralized and hydrocarbon-accumulating regions of the KFB. Interpreting both geophysical as well as geochemical information from the KFB helped in architectural reconstruction of the subsurface and identification of high probability prospective regions. Some of the highlighted findings include the identification of fault zones, as well as flexures, that are anticlines, having associated mineralized and hydrocarbon signatures indicating the presence of both resources in these zones. These findings, in conjunction with the study, serve as a precursor to the planning of integrated, resource-targeted, and technologically advanced development surveys. In this respect, higher tier energy and resource development of the BKC will be obtained from road and resource-based development of the surveyed areas which will minimize environmental strain. Higher resolution surveys and subsequent exploratory drilling are the next logical steps in research to enable resource assessment and development. REFERENCES [1]. M. B. Allen and S. R. Allen, Geology of the Kirthar Fold Belt and the Impact of Tectonic Processes on Resource Formation. Geological Society of Pakistan, 2010. [2]. J. L. Brown and T. E. Smith, “The role of seismic surveys in fold-and-thrust belt exploration: A case study from the Zagros Mountains, Iran,” Journal of Petroleum Geology, vol. 38, no. 2, pp. 123–140, 2015, do…
subject: Articles
  topic: section
subject: Kirthar Fold Belt
  topic: keywords
subject: Geophysical Methods
  topic: keywords
subject: Geochemical Exploration
  topic: keywords
subject: Mineralization
  topic: keywords
subject: Hydrocarbon Potential
  topic: keywords
creator: Sayed Qaisar Abbas
creator: Muhammad Naveed Khalil
rights: Copyright (c) 2025 Sayed Qaisar Abbas, Muhammad Naveed Khalil (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-12-10
publication date: 2025-12-10
starting page: 115
ending page: 128

Integrating Blockchain-Enhanced Enterprise Systems for U.S. Banking, Pharmaceutical Supply Chains, Economic Resilience, and Secure Operational Models
type: item
description: Background: Blockchain technology has emerged as a transformative force with the potential to revolutionize enterprise systems beyond its cryptocurrency origins. Critical U.S. sectors, including banking and pharmaceutical supply chains, face persistent challenges related to fraud, transparency, traceability, and cybersecurity, for which blockchain offers promising decentralized solutions. However, the adoption landscape remains fragmented, hindered by significant barriers and varying levels of awareness. Objective: This study aims to investigate the integration of blockchain-enhanced enterprise systems within the U.S., focusing on assessing awareness levels, evaluating its perceived impact on banking and pharmaceutical supply chains, analyzing its contribution to economic resilience and secure operational models, and identifying key adoption challenges and future prospects. Methods: A quantitative research design was employed, utilizing a structured questionnaire administered to 300 professionals from the banking, pharmaceutical supply chain, IT/technology, and government/regulatory sectors. The instrument demonstrated strong reliability, with Cronbach's Alpha scores ranging from 0.78 to 0.86. Data analysis included descriptive statistics, one-way ANOVA, and correlation analysis to examine sectoral differences and interrelationships between key constructs. Results: Findings indicate a moderate to high level of optimism, with the "Future Prospects" construct scoring the highest (M=3.71). A significant variance in awareness was observed across sectors (F=3.42, p=0.018), with pharmaceutical and banking professionals reporting higher awareness than their government/regulatory counterparts. Correlation analysis revealed strong positive relationships between blockchain adoption, economic resilience (r=0.52, p<0.01), and future prospects (r=0.49, p<0.01). However, "Adoption Barriers" such as cost and regulatory uncertainty were confirmed as significant impediments. Conclus…
subject: Articles
  topic: section
subject: Blockchain Technology
  topic: keywords
subject: Enterprise Systems
  topic: keywords
subject: U.S. Banking
  topic: keywords
subject: Pharmaceutical Supply Chain
  topic: keywords
subject: Economic Resilience
  topic: keywords
subject: Secure Operational Models
  topic: keywords
subject: Adoption Barriers
  topic: keywords
subject: Digital Transformation
  topic: keywords
creator: Arafat Hossain Khan Choain
creator: Chinmoy Majumder
creator: Md Abu Nasir Nasir
creator: Nasrin Sultana
rights: Copyright (c) 2025 Arafat Hossain Khan Choain, Chinmoy Majumder, Md Abu Nasir Nasir, Nasrin Sultana (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2023-12-31
publication date: 2023-12-31
starting page: 51
ending page: 63

Leveraging Machine Learning Techniques for Enhancing Signature Verification to Unveiling the Forgery
type: item
description: These days, computer vision and machine learning researchers are actively studying handwritten signature verification. It makes sense to define signature verification as a machine-learning task. This is accomplished by figuring out if the signature is real or fake. It is therefore regarded as a two-class classification problem. Given the prevalence of handwritten signatures in legal documents and financial transactions, researchers must carefully consider which machine-learning technique to apply in order to authenticate handwritten signatures and prevent forgeries that could result in significant losses for their clients. Thus far, the application of machine learning algorithms has produced excellent results in terms of equal computation error rates. The research aims to develop a model of classification capable of effectively categorizing forged verified signatures using input data. The primary objective is to explore the creation of a robust signature verification classification model using machine learning and algorithms. The research design involves a step-by-step approach design includes the gathering of data, Preprocessing, classification algorithms, and evaluation of models. The process of verifying the authenticity of a signature by use of machine learning techniques is called signature verification. The present project concentrates on off-line signature verification, however signatures might be of either online or offline form. This project intends to create a methodology that uses writer-independent characteristics to differentiate between authentic and faked signatures. In order to collect signatures from people, execute signature verification, and display the outcomes, we want to develop a whole end-to-end hardware/software system. To achieve this, a number of Machine Learning approaches for off-line signature verification were created and evaluated on benchmark datasets. Our proposed technique outperforms offline signature verification approaches such …
subject: Articles
  topic: section
subject: Offline and Online signature
  topic: keywords
subject: Handwritten Signature Verification
  topic: keywords
subject: Machine Learning Algorithms
  topic: keywords
subject: Classification
  topic: keywords
creator: Shumaila Ejaz
rights: Copyright (c) 2025 Shumaila Ejaz (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-11-10
publication date: 2025-11-10
starting page: 96
ending page: 114

Performance Evaluation of Battery Management Systems in Lithium-Ion Battery-Powered Electric Vehicles
type: item
description: This work provides the first exhaustive quantitative assessment of Battery Management Systems (BMS) in electric vehicles (EVs). This work evaluates BMS as each attempts to establish the State of Charge (SOC) and State of Health (SOH) estimation, cell balancing, thermal control, and fault safeguarding efficiency. This work evaluates real-world scenario BMS efficiency considering different driving conditions, environmental conditions, and scenarios to age the battery pack. A substantial amount of work has been done to assess the battery pack's cell voltages, currents, temperatures, State of Charge/State of Health, and fault logs in a 48V, 60Ah lithium-ion battery pack. The work has primarily focused on measuring the SOC error, SOH drift, balancing efficiency, and thermal deviation. This work demonstrates the BMS SOC estimation efficiency of 3% error, while SOH estimation accuracy dropped by 5% after 1000 cycles of charge and discharge. BMS cell balancing works efficiently at average driving conditions; however, it becomes much less efficient with aggressive driving. The BMS thermal regulation suffered greatly in extreme heat (45°C) as the temperature regulation control was ±5°C from the ideal temperature of 5°C. Fault BMS fault detection was remarkably fast, detecting overvoltage and overcurrent conditions in less than 200 ms. This work provides a summary of important BMS efficiency bottlenecks while offering solutions to advance BMS safety, BMS longevity, and BMS powered EVs efficiency. REFERENCES [1] M. Chen, S. Li, and Y. Zhang, “Impedance spectroscopy for monitoring state of health in lithium-ion batteries,” J. Power Sources, vol. 450, p. 227758, Feb. 2020, doi: 10.1016/j.jpowsour.2019.227758. [2] Cyient, “State of charge (SOC) estimation challenges in BMS technology,” 2020. [Online]. Available: https://www.cyient.com [3] Cyient, “Fault detection in Battery Management Systems for electric vehicles,” 2021. [Online]. Available: https://www.cyient.com [4] S. Lohum, “…
subject: Articles
  topic: section
subject: Battery Management System (BMS)
  topic: keywords
subject: State of Charge (SOC)
  topic: keywords
subject: State of Health (SOH)
  topic: keywords
subject: Cell Balancing
  topic: keywords
subject: Thermal Regulation
  topic: keywords
creator: Basit Azam
creator: Sanam Mehar
rights: Copyright (c) 2025 Basit Azam, Sanam Mehar (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-11-10
publication date: 2025-11-10
starting page: 81
ending page: 95

Design and Control of an Autonomous Drone Navigation System Using Embedded AI
type: item
description: This research study the design and the development of an autonomous drone navigation system utilizing embedded artificial intelligence (AI) capable of real-time decision making, obstacle avoidance, and path planning. The focus of this research is the application of neural networks and reinforcement learning techniques integrated with the drone’s onboard computer and the fully autonomous navigation of the drone in sophisticated and rapidly changing environments. The stability, precision, and flexible response of the drone’s navigation system within the complex environmental frameworks is attained by the combination of classical control methods (PID, LQR) and the control frameworks provided by artificial intelligence. Other than the developments in the optimization of path planning algorithms (A*, RRT) the paper discusses the incorporation of AI for dynamic obstacle avoidance which enables real-time handling of rapid environmental changes. A balanced coverage of sensor fusion methods (EKF, SLAM) which provides accurate localization in environments where GPS is not available outlines the importance of localization in autonomous drones. The research is developed and demonstrated in multiple environments, and the system’s performance is evaluated based on speed, accuracy, and reliability in comparison with classical systems. This research has significance in demonstrating the operationalization of embedded AI with real-time decision making in improving drone autonomy for applications in delivery, surveillance and, search-and-rescue missions. REFERENCES [1] M. Aghazadeh and M. Gohari, “Real-time obstacle avoidance in autonomous drones: A survey of vision-based navigation methods,” Robot. Auton. Syst., vol. 133, p. 103617, Nov. 2020, doi: 10.1016/j.robot.2020.103617. [2] M. Al-Dhaifallah and M. Ghanem, “Sensor fusion for autonomous drone navigation in GPS-denied environments,” IEEE Trans. Ind. Electron., vol. 67, no. 4, pp. 2935–2943, Apr. 2020, doi: 10.1109/TIE.2019.29584…
subject: Articles
  topic: section
subject: Autonomous Drones
  topic: keywords
subject: Embedded AI
  topic: keywords
subject: Navigation Systems
  topic: keywords
subject: Reinforcement Learning
  topic: keywords
subject: Path Planning
  topic: keywords
subject: Obstacle Avoidance
  topic: keywords
subject: Sensor Fusion
  topic: keywords
subject: Real-Time Decision Making
  topic: keywords
creator: Muhammad Taha Abbas
creator: Farrukh Hanif
rights: Copyright (c) 2025 Muhammad Taha Abbas, Farrukh Hanif (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-11-10
publication date: 2025-11-10
starting page: 68
ending page: 80

A Trust Management-based Energy Efficient Message Scheduling Algorithm in the Internet of Things System
type: item
description: Internet of Things (IoT) is an emerging concept in internet development that connects diverse objects by transmitting information from there to a central hub for interpretation. Though much work has been done individually either on message scheduling or provisioning of a secure environment to assure data accuracy in IoT systems, this paper focuses on a Trust Management-Based Scheme to defend against internal attacks while parallel handling Message Scheduling to improve overall IoT system efficiency. Choosing, secure cluster heads based on residual energy and node density to balance the network load through a trust-value scheme and operating the message scheduler on cluster heads to decide the transmission of messages is a salient feature of the proposed system. Managing traffic intensity for scheduling messages in a secure environment made the proposed algorithm more reliable against malicious attacks and energy consumption. In this IoT environment, things/nodes are placed into subgroups with a trusted cluster head responsible for successful message delivery from the group to the receiver of the sensed data. The simulation results show the effectiveness and efficiency of the proposed algorithm. REFERENCES Abdullah, M. N. Asghar, M. Ashraf, and N. Abbas, "An energy-efficient message scheduling algorithm with joint routing mechanism at network layer in Internet of Things environment," Wireless Personal Communications, vol. 111, no. 3, pp. 1821–1835, 2020, doi: 10.1007/s11277-019-06959-x. Abdullah and K. Yang, "An energy-efficient message scheduling algorithm in Internet of Things environment," in Proc. 2013 9th Int. Wireless Commun. Mobile Comput. Conf. (IWCMC), 2013, pp. 311–316, doi: 10.1109/IWCMC.2013.6583578. O. Abu Salem and N. Shudifat, "Enhanced LEACH protocol for increasing a lifetime of WSNs," Personal and Ubiquitous Computing, vol. 23, no. 5-6, pp. 901–907, 2019, doi: 10.1007/s00779-019-01205-4. Ali et al., "An efficient cryptographic technique using modifie…
subject: Articles
  topic: section
subject: Internet of Things
  topic: keywords
subject: Wireless Sensor Network
  topic: keywords
subject: Scheduling
  topic: keywords
subject: Energy Efficient
  topic: keywords
subject: Message Scheduling
  topic: keywords
subject: Trust Management
  topic: keywords
creator: Shanza Ijaz
creator: Muhammad Talah Zubair
rights: Copyright (c) 2025 Shanza Ijaz, Muhammad Talha Zubair (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-11-09
publication date: 2025-11-09
starting page: 51
ending page: 67

AI and Blockchain Integration: Driving Strategic Business Advancements in the Intelligent Era
type: item
description: This research examines the adopted artificial intelligence (AI) and blockchain integration in contemporary companies systems and their capabilities to enhance efficiency, security, and innovation in the finance, healthcare, and supply chain sectors. It also seeks to analyze the adoption patterns, operational advantages, and critical challenges that obstruct the wide-spread deployment of the technology. The study’s framework is quantitative, taking the structured survey method of data collection, the target population being 384 professionals, inclusive of business professionals, technology professionals, and scholars. The sample size was calculated using the Cochran formula, achieving a 95% confidence level and 5% margin of error. The collected data were processed, and a descriptive analysis was done, with the results being displayed in charts, graphs, and tables, highlighting the trends, barriers, and quantifiable impacts of technology adoption in specific industries. The results show that 69.2% of organizations reported enhanced operational efficiency with the adoption of AI-Blockchain solutions, with fraud detection (64.6%), smart contracts (54.7%), and data analytics (57.6%) cited as the primary use cases. However, the study identified high implementation costs (71.9%), talent shortages (60.2%), and regulatory uncertainty (51.6%) as the primary barriers. Interestingly, 80.5% of the respondents expect these technologies to become the de facto industry standards by the year 2030, and 81.3% confirmed that adoption of these technologies would lead to a competitive advantage. This research closes the gap between theoretical concepts and actual application by offering tailored recommendations for organizations at various levels of digital transformation. It shows the cross-sectional adoption dynamics of sectors AI and Blockchain and develops integration hurdles—strategic suggestions on adoption, phased integration milestones, and recommendations on policy and talent fr…
subject: Articles
  topic: section
subject: Artificial Intelligence (AI)
  topic: keywords
subject: Blockchain Technology
  topic: keywords
subject: Business Innovation
  topic: keywords
subject: Digital Transformation
  topic: keywords
subject: Operational Efficiency
  topic: keywords
creator: Ishrak Alim
creator: Nafiz Imtiaz
creator: Abdullah Al Prince
creator: MD Asif Hasan
rights: Copyright (c) 2025 Ishrak Alim, Nafiz Imtiaz, Abdullah Al Prince, MD Asif Hasan (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-08-14
publication date: 2025-08-14
starting page: 38
ending page: 50

The Role of Threat Intelligence in Preventing Financially Motivated Cyberattacks
type: item
description: The escalating prevalence of financially motivated cyberattacks poses critical risks to global financial institutions, driving urgent demand for proactive defense mechanisms (Haruna et al., 2022). This study employs a quantitative analysis of cyber incidents (2018-2023) across 50 financial entities to evaluate the efficacy of threat intelligence (TI) in mitigating attacks. We develop a predictive logistic model to assess breach probability, offering a robust framework for risk assessment in an increasingly digital financial landscape. Our analysis correlates TI maturity with 33.7% reduced breach likelihood (p < 0.01) and 28% faster incident response. The model further quantifies how regulatory compliance (e.g., GDPR/PSD2) and advanced authentication mechanisms amplify TI’s protective effect (Ali et al., 2024). Results demonstrate that institutions with TI maturity > 0.8 experienced ≥45% lower financial losses versus peers (p = 0.003). This research provides a validated framework for financial entities to prioritize TI investments, optimize cyber-resilience, and preempt evolving threats (Bouveret, 2018; Moon et al., 2022). To enhance the model’s applicability, we incorporated additional variables such as the frequency of security audits, the diversity of threat intelligence feeds and the integration of artificial intelligence-driven analytics. These factors significantly bolster the predictive power, enabling institutions to anticipate emerging threats with greater precision. Furthermore, the study highlights the importance of continuous training for cybersecurity teams, ensuring they can effectively leverage TI tools in real-time scenarios. The analysis also considers the impact of geopolitical factors and economic conditions, which can influence the sophistication and frequency of cyberattacks. By addressing these multifaceted dimensions, the framework offers a comprehensive approach to cybersecurity strategy development. Future research could explore the long-term…
subject: Articles
  topic: section
subject: Threat intelligence
  topic: keywords
subject: Financially Motivated Cyberattacks
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: Collaboration
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: U.S. Organizations
  topic: keywords
subject: Cyber Defense
  topic: keywords
subject: Organizational Factors
  topic: keywords
creator: Sanjida Alam Eshra
creator: Fatema Tuz Zohora
creator: Sonia Akter
creator: Iftekhar Rasul
creator: Amjad Hossain
rights: Copyright (c) 2025 Sanjida Alam Eshra, Fatema Tuz Zohora, Sonia Akter, Iftekhar Rasul, Amjad Hossain (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-08-05
publication date: 2025-08-05
starting page: 20
ending page: 37

The Role of Artificial Intelligence in Carbon Pricing Policies: Economic and Environmental Implications
type: item
description: The use of AI in carbon pricing policies can offer both opportunities and future challenges for the United States as it acts on climate change. This study examines experts’ perspectives on AI’s potential to enhance the effectiveness, enforcement, and equity of carbon pricing. Based on a survey of 200 U.S. experts, the research explores key factors shaping support for AI-driven climate policies, including technological feasibility, public acceptance, and regulatory hurdles. Statistical analyses, including chi-square, ANOVA, correlation, multiple regression, and logistic regression, reveal that AI’s policy role is most influenced by knowledge and trust, whereas algorithmic bias, data limitations, and legal uncertainties remain significant barriers. The findings highlight urgent U.S. priorities in climate resilience, digital infrastructure, and environmental justice, offering critical insights into the social and technical readiness for AI-integrated carbon policies. Furthermore, the study identifies disparities in expert opinions, with technologists emphasizing innovation while policymakers stress accountability. Public-private collaboration and interdisciplinary research are deemed essential to overcoming implementation challenges. The study also underscores the need for transparent governance frameworks to address ethical concerns, mitigate risks of automation bias, and foster public trust. By identifying key drivers of policy acceptance, this research provides actionable recommendations for lawmakers, including adaptive regulations and stakeholder engagement strategies. Ultimately, this study advances understanding of AI’s role in climate action, informing both policy practitioners and academic researchers while paving the way for responsible innovation in carbon pricing mechanisms. Future research should explore real-world case studies and international comparisons to refine best practices for AI-enabled climate governance. REFERENCES Alshater, M. M, Kampouris, I,…
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Carbon Pricing
  topic: keywords
subject: Climate Policy
  topic: keywords
subject: United States
  topic: keywords
subject: Environmental Justice
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Trust in AI
  topic: keywords
subject: Algorithmic Bias
  topic: keywords
subject: Policy Support
  topic: keywords
subject: Digital Climate Governance
  topic: keywords
creator: Mohammad Rabidul Islam
creator: MD Maruf Islam
creator: Istiaque Ahmed Badhan
creator: MD Nurul Hasnain
rights: Copyright (c) 2025 Mohammad Rabidul Islam, MD Maruf Islam, Istiaque Ahmed Badhan, MD Nurul Hasnain (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-07-05
publication date: 2025-07-05
starting page: 1
ending page: 19

Cybersecurity in the Modern Era: An Overview of Recent Trends
type: item
description: In the rapidly evolving digital landscape, cybersecurity has emerged as a critical concern for organizations, governments, and individuals alike. This article provides a comprehensive overview of contemporary cybersecurity challenges and solutions, examining the dynamic interplay between technological advancements, human factors, and regulatory frameworks. We analyze the evolving threat landscape, including sophisticated ransomware attacks, AI-powered threats, and supply chain vulnerabilities, while exploring cutting-edge defensive strategies such as zero trust architectures, behavioral analytics, and predictive security models. The discussion highlights sector-specific cybersecurity challenges in finance, healthcare, critical infrastructure, and government operations, emphasizing the growing risks posed by remote work and cloud adoption. Special attention is given to the human dimension of cybersecurity, including social engineering threats and the importance of security awareness training. The article also evaluates emerging regulatory landscapes and compliance requirements across different jurisdictions. Finally, we identify key future directions in cybersecurity, including quantum-resistant cryptography, AI-enabled defense systems, and international cooperation frameworks. By synthesizing current research and practical insights, this article offers valuable perspectives for cybersecurity professionals, policymakers, and organizational leaders navigating the complex challenges of digital security in an increasingly interconnected world. The findings underscore the urgent need for adaptive, multi-layered security approaches that balance technological innovation with human-centric solutions and robust governance models. References [1] J. Kaur and K. R. Ramkumar, "The recent trends in cyber security: A review," J. King Saud Univ.-Comput. Inf. Sci., vol. 34, no. 8, pp. 5766-5781, 2022. [2] K. M. Rajasekharaiah, C. S. Dule, and E. Sudarshan, "Cyber security challenges…
subject: Articles
  topic: section
subject: Cybersecurity
  topic: keywords
subject: Threat Landscape
  topic: keywords
subject: Zero Trust
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Regulatory Compliance
  topic: keywords
subject: Risk Management
  topic: keywords
creator: Asma Shaheen
rights: Copyright (c) 2023 Asma Shaheen (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2023-12-31
publication date: 2023-12-31
starting page: 39
ending page: 50

A Comprehensive Review of Blockchain Applications across Diverse Domains
type: item
description: Blockchain technology has emerged as a disruptive force with transformative potential across multiple industries. This comprehensive review examines blockchain applications in finance, healthcare, supply chain, government, and emerging sectors, while analyzing key challenges and future directions. The study reveals how blockchain's core features - decentralization, transparency, and immutability - address critical pain points including financial intermediation, data security, provenance tracking, and bureaucratic inefficiencies. Notable implementations demonstrate 90% reductions in land registry disputes, 97% faster food traceability, and new paradigms for digital identity management. However, significant barriers persist, particularly regarding scalability limitations, excessive energy consumption, regulatory uncertainty, and interoperability issues between competing platforms. The review highlights promising convergence with AI and IoT, yielding innovative solutions in smart contracts and asset tokenization. Future research directions emphasize the need for quantum-resistant cryptography, energy-efficient consensus mechanisms, and standardized frameworks to facilitate mainstream adoption. For practitioners, the findings suggest a strategic approach favoring hybrid models that combine blockchain with legacy systems, while policymakers must balance innovation with consumer protection. As blockchain transitions from experimental technology to enterprise infrastructure, its ultimate value may lie in enabling trust-minimized systems and redistributing power from centralized intermediaries to network participants. This analysis provides both a structured assessment of current applications and a forward-looking perspective on blockchain's evolving role in the digital economy. References [1] U. Bodkhe et al., "Blockchain for industry 4.0: A comprehensive review," IEEE Access, vol. 8, pp. 79764–79800, 2020. [2] J. Abou Jaoude and R. G. Saade, "Blockchain applications–usage…
subject: Articles
  topic: section
subject: Blockchain Applications
  topic: keywords
subject: Smart Contracts
  topic: keywords
subject: Decentralized Systems
  topic: keywords
subject: Supply Chain Transparency
  topic: keywords
subject: Digital Identity
  topic: keywords
subject: Emerging Technologies
  topic: keywords
creator: Kamil Khan
creator: Sabeeha Alvi
rights: Copyright (c) 2023 Kamil Khan, Sabeeha Alvi (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2023-12-31
publication date: 2023-12-31
starting page: 23
ending page: 38

Big Data Analytics with Machine Learning: Challenges, Innovations, and Applications
type: item
description: The convergence of big data analytics and machine learning (ML) has revolutionized decision-making across industries, enabling breakthroughs in healthcare diagnostics, financial forecasting, smart infrastructure, and personalized services. This article explores the transformative potential of these technologies while addressing critical challenges such as scalability, data quality, and ethical implications. We examine cutting-edge innovations, including distributed learning frameworks, real-time analytics, and automated machine learning (AML) systems, which enhance computational efficiency and model performance. The discussion highlights applications across healthcare, finance, retail, smart cities, and social media, demonstrating how organizations leverage large-scale data for predictive insights and operational optimization. Additionally, the paper identifies emerging research directions, such as neuro-symbolic artificial intelligence (AI) integration and responsible AI governance, which aim to balance technological advancement with societal accountability. By synthesizing current trends and future opportunities, this article provides a comprehensive roadmap for researchers and practitioners navigating the evolving landscape of data-driven intelligence. References [1] I. K. Nti, J. A. Quarcoo, J. Aning, and G. K. Fosu, “A mini-review of machine learning in big data analytics: Applications, challenges, and prospects,” Big Data Mining and Analytics, vol. 5, no. 2, pp. 81–97, 2022. [2] K. Vassakis, E. Petrakis, and I. Kopanakis, “Big data analytics: Applications, prospects and challenges,” in Mobile Big Data: A Roadmap from Models to Technologies. Springer, 2017, pp. 3–20. [3] L. Zhou, S. Pan, J. Wang, and A. V. Vasilakos, “Machine learning on big data: Opportunities and challenges,” Neurocomputing, vol. 237, pp. 350–361, 2017. [4] A. L’heureux, K. Grolinger, H. F. Elyamany, and M. A. Capretz, “Machine learning with big data: Challenges and approaches,” IEEE Access, …
subject: Articles
  topic: section
subject: Big Data Analytics
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Real-Time Processing
  topic: keywords
subject: Ethical AI
  topic: keywords
subject: Predictive Modeling
  topic: keywords
subject: Distributed Computing
  topic: keywords
creator: Sameer Niazi
rights: Copyright (c) 2024 Sameer Niazi (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2024-06-30
publication date: 2024-06-30
starting page: 38
ending page: 48

A Review on the Role of IoT in Modern Electrical Engineering Education
type: item
description: The rapid evolution of Internet of Things (IoT) technologies has significantly transformed electrical engineering education, creating new paradigms for experiential learning and industry-aligned skill development. This review paper systematically examines the integration of IoT in modern electrical engineering curricula, analyzing its implementation challenges, pedagogical benefits, and future directions. Through comprehensive evaluation of case studies and current research, we demonstrate how IoT-enabled smart laboratories, project-based learning approaches, and remote experimentation platforms enhance student engagement and practical competencies in key areas like power systems, automation, and renewable energy technologies. The paper highlights successful implementations at leading institutions while addressing critical barriers including infrastructure costs, faculty training requirements, and cyber security concerns. Emerging trends such as AI-powered digital twins, 5G-enabled labs, and edge computing applications are explored as future growth areas. Practical recommendations are provided for educators and policymakers to effectively incorporate IoT into electrical engineering programs, emphasizing curriculum design, industry partnerships, and sustainable implementation strategies. The findings underscore IoT's crucial role in preparing the next generation of electrical engineers for Industry 4.0 challenges, while identifying key research gaps that require further investigation. This review serves as both a roadmap for academic institutions seeking to modernize their programs and a call to action for broader adoption of IoT technologies in engineering education worldwide. References [1] R. S. Abd-Ali, S. A. Radhi, and Z. I. Rasool, "A survey: the role of the internet of things in the development of education," Indones. J. Electr. Eng. Comput. Sci., vol. 19, no. 1, pp. 215–221, 2020. [2] D. D. Ramlowat and B. K. Pattanayak, "Exploring the internet of things (IoT…
subject: Articles
  topic: section
subject: Internet of Things
  topic: keywords
subject: Electrical Engineering Education
  topic: keywords
subject: smart laboratories
  topic: keywords
subject: Industry 4.0
  topic: keywords
subject: project-based learning
  topic: keywords
subject: engineering pedagogy
  topic: keywords
creator: Muhammad Hamza Iqbal
rights: Copyright (c) 2023 Muhammad Hamza Iqbal (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2023-12-31
publication date: 2023-12-31
starting page: 14
ending page: 22

A Comprehensive Review of Emerging Challenges in Cloud Computing Security
type: item
description: The rapid expansion of Big Data has strained classical computing systems, necessitating innovative solutions for efficient data processing. Quantum computing, leveraging principles like superposition and entanglement, presents transformative potential for addressing complex, large-scale problems that are intractable for classical methods. This review explores the role of quantum computing in Big Data processing, examining key algorithms such as Grover’s search, Shor’s factorization, and quantum machine learning techniques. It discusses foundational concepts like qubit operations, quantum complexity classes, and hybrid quantum-classical architectures, while critically assessing hardware limitations such as de-coherence and error rates in NISQ-era devices. Promising applications in optimization, secure communication, and high-dimensional data analysis are highlighted, alongside challenges in data encoding, algorithmic readiness, and practical implementation. Emerging research directions, including near-term NISQ applications, fault-tolerant quantum computing, and cross-disciplinary opportunities in NLP and IoT, are also explored. By synthesizing theoretical advances with practical constraints, this review provides a balanced perspective on quantum computing is potential to reshape Big Data analytics, emphasizing both its revolutionary capabilities and the significant barriers to widespread adoption. While quantum advantage remains limited to specific use cases today, continued progress in hardware and algorithms may redefine large-scale data processing in the future. References [1]. C. Stouffer, "23 cloud security risks, threats, and best practices," Norton, 2023. [Online]. Available: https://us.norton.com/blog/privacy/cloud-security-risks [2]. A. I. Tahirkheli et al., "A survey on modern cloud computing security over smart city networks: Threats, vulnerabilities, consequences, countermeasures, and challenges," Electronics, vol. 10, no. 15, p. 1811, 2021. [3]. Biz Tec…
subject: Articles
  topic: section
subject: cloud security
  topic: keywords
subject: zero-trust architecture
  topic: keywords
subject: post-quantum cryptography
  topic: keywords
subject: confidential computing
  topic: keywords
subject: AI-powered threats
  topic: keywords
subject: security misconfiguration
  topic: keywords
creator: Nayab Arshad
rights: Copyright (c) 2024 Nayab Arshad (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2024-06-30
publication date: 2024-06-30
starting page: 27
ending page: 37

A Systematic Review of Electric Vehicle Innovations and Implementation Barriers
type: item
description: The global transition to electric vehicles (EVs) represents a critical pathway toward sustainable transportation and net-zero emissions. This comprehensive review synthesizes 150+ studies to analyse EV innovations, adoption barriers, and future trends across technological, economic, and policy dimensions. Key findings reveal that while battery energy density has improved by 200% since 2010 (reaching 270 Wh/kg) and costs have dropped 89%, significant challenges persist: inadequate charging infrastructure (requiring 40 million global chargers by 2030), supply chain vulnerabilities for critical minerals, and consumer range anxiety. The study highlights successful case studies like Norway (90% EV market share) and Shenzhen (16,000 electric buses), demonstrating that policy consistency and public investment drive adoption. Emerging technologies, including solid-state batteries (500+ Wh/kg) and vehicle-to-grid networks promise to address current limitations, but require coordinated standards and workforce development. Three critical gaps are identified: (1) recycling systems recovering <60% of battery materials, (2) grid capacity constraints at 30% EV penetration, and (3) equity issues in emerging markets (<3% adoption). The review proposes a tripartite framework for accelerated deployment: technology (solid-state batteries, AI-optimized charging), policy (harmonized standards, critical mineral strategies), and social (inclusive financing, skilled labour programs). With transport contributing 24% of global CO₂ emissions, these insights provide policymakers and industry leader’s actionable strategies to overcome adoption barriers while ensuring an equitable transition. The analysis concludes that systemic integration of EVs with renewable energy and smart infrastructure not just vehicle substitution will determine their climate impact. References [1] B. M. Sopha, D. M. Purnamasari, and S. Ma'mun, "Barriers and enablers of circular economy implementation for electric-vehicl…
subject: Articles
  topic: section
subject: Electric vehicles
  topic: keywords
subject: battery technology
  topic: keywords
subject: charging infrastructure
  topic: keywords
subject: decarbonisation
  topic: keywords
subject: policy incentives
  topic: keywords
subject: circular economy
  topic: keywords
creator: Muhammad Ali Shah
rights: Copyright (c) 2024 Muhammad Ali Shah (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2024-06-30
publication date: 2024-06-30
starting page: 18
ending page: 26

The Role of Quantum Computing in Future Big Data Processing: A Comprehensive Review
type: item
description: The rapid growth of Big Data has pushed classical computing systems to their limits, necessitating innovative approaches to data processing. Quantum computing, with its inherent parallelism and entanglement capabilities, offers transformative potential for solving complex, large-scale problems that are intractable for classical systems. This review paper examines the role of quantum computing in future Big Data processing, analyzing key quantum algorithms such as Grover's search, Shor's factorization, and quantum machine learning techniques. We explore fundamental concepts including qubit operations, quantum complexity classes, and hybrid quantum-classical architectures, while critically assessing current hardware limitations like de-coherence and error rates in NISQ-era devices. The paper highlights promising applications in optimization, secure communication, and high-dimensional data analysis, alongside significant challenges in data encoding, algorithmic readiness, and practical implementation. Emerging research directions are discussed, including near-term NISQ applications, fault-tolerant quantum computing prospects, and cross-disciplinary opportunities in NLP and IoT. By synthesizing theoretical advances with practical constraints, this review provides a balanced perspective on quantum computing's evolving role in Big Data, offering insights into both its revolutionary potential and the substantial barriers that must be overcome for widespread adoption. The findings suggest that while quantum advantage remains limited to specific use cases today, continued progress in hardware and algorithms may eventually redefine the landscape of large-scale data processing. References [1] S. S. Gill et al., "Quantum computing: A taxonomy, systematic review and future directions," Softw. Pract. Exper., vol. 52, no. 1, pp. 66-114, 2022. [2] R. Ur Rasool et al., "Quantum computing for healthcare: A review," Future Internet, vol. 15, no. 3, p. 94, 2023. [3] S. Zhu et al., "Int…
subject: Articles
  topic: section
subject: Quantum computing
  topic: keywords
subject: Big Data
  topic: keywords
subject: quantum algorithms
  topic: keywords
subject: quantum machine learning
  topic: keywords
subject: NISQ
  topic: keywords
subject: hybrid computing
  topic: keywords
subject: quantum advantage
  topic: keywords
creator: Maria Ilyas
creator: Rabia Ilyas
rights: Copyright (c) 2024 Maria Ilyas, Rabia Ilyas (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2024-06-30
publication date: 2024-06-30
starting page: 9
ending page: 17

Solar Energy Development in Pakistan: Overcoming Barriers through Policy Reform and Stakeholder Engagement
type: item
description: Pakistan is facing an escalating energy crisis due to its heavy reliance on imported fossil fuels and an outdated energy infrastructure. With an increasing population and industrial growth, the demand for energy is rising rapidly, placing significant pressure on the country’s energy system. Solar energy, with its vast potential, has emerged as a promising solution to address the energy deficit, yet its adoption remains limited due to various barriers. This study examines the barriers hindering the widespread adoption of solar energy in Pakistan and explores the policy reforms needed to overcome these challenges. The primary objective of this research is to identify and analyze the key barriers to solar energy adoption in Pakistan. These barriers are explored from both technical and financial perspectives, as well as through institutional and social lenses. The research also aims to propose evidence-based policy recommendations that could facilitate the growth of solar energy in the country, aligning with Pakistan’s commitments to sustainable energy and climate action goals. A mixed-methods approach was employed to collect both qualitative and quantitative data. The qualitative data were gathered through semi-structured interviews with solar vendors, government representatives, and energy experts, while the quantitative data were collected through surveys administered to consumers and vendors. The qualitative data were analyzed thematically to identify key barriers, while the quantitative data were analyzed using descriptive and inferential statistics to quantify the adoption trends and barriers. The integration of both methods allowed for a comprehensive understanding of the barriers to solar energy adoption in Pakistan. The findings highlight several key barriers to solar energy adoption in Pakistan, including high upfront costs, limited financing options, insufficient skilled labor, outdated grid infrastructure, and fragmented policy frameworks. The research also …
subject: Articles
  topic: section
subject: Solar energy
  topic: keywords
subject: barriers to adoption
  topic: keywords
subject: policy reform
  topic: keywords
subject: financial constraints
  topic: keywords
subject: public awareness
  topic: keywords
subject: renewable energy
  topic: keywords
subject: sustainable development
  topic: keywords
creator: Razi Ud Din
creator: Nimra Bibi
rights: Copyright (c) 2025 Razi Ud Din, Nimra Bibi (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-05-19
publication date: 2025-05-19
starting page: 82
ending page: 98

AI-Powered Radiology: Innovations and Challenges in Medical Imaging
type: item
description: Artificial intelligence (AI) is revolutionizing radiology by enhancing diagnostic accuracy, optimizing workflows, and enabling personalized medicine. This review explores the evolution of AI in medical imaging, from early rule-based systems to contemporary deep learning applications, while examining both ground-breaking innovations and persistent challenges. Key advancements include AI's superior performance in detecting tumours, fractures, and hemorrhages; workflow improvements through automated segmentation and prioritized case triage; and cutting-edge techniques like low-dose imaging reconstruction and radio genomics. However, significant barriers remain, including data quality issues, "black box" algorithm limitations, clinical adoption resistance, and ethical concerns regarding bias and privacy. The future of AI in radiology points toward explainable AI (XAI) for transparent decision-making, federated learning for privacy-preserving collaboration, and integration with emerging technologies like augmented reality. Successful implementation will require addressing technical, regulatory, and socioeconomic challenges while maintaining human oversight. As AI continues to transform medical imaging, its ultimate measure of success will be its ability to improve patient outcomes across diverse healthcare settings while upholding the highest standards of safety, equity, and clinical relevance. References [1] K. Devarapu, “Blockchain-driven AI solutions for medical imaging and diagnosis in healthcare,” Technol. & Manag. Rev., vol. 5, no. 1, pp. 80–91, 2020. [2] N. K. Mishra, J. Agrawal, and M. H. Baba, “AI-powered COVID-19 health management based on radiological imaging: Bio-ethics perspective,” JK Practitioner, vol. 27, 2022. [3] C. Han et al., “Bridging the gap between AI and healthcare sides: Towards developing clinically relevant AI-powered diagnosis systems,” in Artificial Intelligence Applications and Innovations, Springer, 2020, pp. 320–333. [4] S. M. Orthi et al.…
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Radiology
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Medical Imaging
  topic: keywords
subject: Explainable AI
  topic: keywords
creator: Qurat Ul Ain Nazir
rights: Copyright (c) 2023 Qurat Ul Ain Nazir (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2023-12-31
publication date: 2023-12-31
starting page: 7
ending page: 13

The Convergence of Artificial Intelligence and 6G Networks: A Transformative Shift in Computing and Communications
type: item
description: This article investigates the intersection of Artificial Intelligence (AI) with 6G networks and their potential impact on computing and communication systems. It aims to analyze the effects of telecommunications, healthcare, smart cities, and other industries by showcasing how AI integration will transform 6G technology and its subsequent network optimization and management. This article takes a conceptual approach by assessing available literature AI technology’s trends and research concerning 6G networks. It assesses the foundational technologies of 6G like AI and THz communication, massive MIMO, edge and quantum computing, and how these technologies amplify the impact of future networks on society. Further, it discusses the role of AI in the different domains of AI powered 6G and considers the major implications for security. Incorporating AI into 6G networks is expected to provide unprecedented benefits like ultra-high speed connection, real-time decision-making, and unparalleled data handling. This will give rise to autonomous systems, transform healthcare with proactive monitoring, boost industrial automation, and facilitate the development of smart cities. The study however does highlight challenges posed by complex AI algorithms, issues of security and privacy, and stringent governance policies. This text explains the impact of Artificial Intelligence with respect to its impact across different sections of 6G networks, as well as with the possibilities and problems this integration might pose. This research captures how important AI is for managing 6G networks alongside the interrelated issues of security and ethics. This publication increases the existing literature on emerging communication systems and IN the same time provides thoughtful recommendations for research, policy development, and industrial practice. References [1] M. Abd Elaziz et al., "Evolution toward intelligent communications: Impact of deep learning applications on the future of 6G techno…
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: 6G networks
  topic: keywords
subject: Autonomous systems
  topic: keywords
subject: Smart cities
  topic: keywords
subject: Network security
  topic: keywords
creator: Imran Nazeer Butt
creator: Sameer Shah
rights: Copyright (c) 2025 Imran Nazeer Butt, Sameer Shah (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-05-16
publication date: 2025-05-16
starting page: 68
ending page: 81

The Impact of Different Computing Tools in Electrical Engineering Learning
type: item
description: The objective of this study is to assess the use, impact, and issues related to the application of computing devices in Electrical Engineering learning by students in Pakistan. With the ever-increasing infusion of technology in engineering disciplines, it becomes necessary to examine the contribution of computing tools like MATLAB, Simulink, Python, etc. on understanding, application, and readiness for industrial demands. The current study adopted a quantitative strategy by using a pre-tested questionnaire with 400 randomly chosen students enrolled in the Electrical Engineering program across government and private universities in Pakistan. The survey recorded demographic information, frequency of tool utilization, methods of learning, perceived effectiveness of the methods, and challenges encountered. Data analysis procedures were conducted using SPSS Version 29 where frequencies and percentages were computed along with the generation of bar and pie charts, radar diagrams, and tables, all of which fell under descriptive statistics. The results suggest that MATLAB (80%) and Simulink (62.5%) are the most popular and considered the best tools in engineering courses. Most of the students completed these tools to be helpful or extremely helpful in learning; however, significant issues such as no training (62.5%), software cost (50%), and complexity (45%) posed problems. Although most students reported university courses as their primary learning source (75%), they also resorted to self-study, online videos, and studying with friends. Furthermore, students proposed including additional modern technologies like sophisticated Python for AI/ML and PLC programming in the curriculum. This research examines the relevant literature and integrates computing resources within the context of Pakistani Electrical Engineering programs, which has previously been documented in the region. It documents the gaps in tool-imbedded pedagogy, and offers engineering education improvement reco…
subject: Articles
  topic: section
subject: Electrical Engineering Education
  topic: keywords
subject: Computing Tools
  topic: keywords
subject: MATLAB
  topic: keywords
subject: Quantitative Study
  topic: keywords
subject: Curriculum Integration
  topic: keywords
creator: Asif Raza Bali
creator: Yousaf Mughal
rights: Copyright (c) 2025 Asif Raza Bali, Yousaf Mughal (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-05-15
publication date: 2025-05-15
starting page: 52
ending page: 67

The Internet of Things (IoT): A Comprehensive Review of Technologies, Applications, Challenges, and Future Trends
type: item
description: The Internet of Things (IoT) has emerged as a revolutionary paradigm, transforming industries and daily life through interconnected smart devices and data-driven automation. This paper explores IoT's architecture, core technologies, and diverse applications across sectors such as healthcare, agriculture, manufacturing, and smart cities. It highlights significant benefits, including operational efficiency, cost reduction, and enhanced quality of life, while addressing critical challenges like security vulnerabilities, privacy concerns, and interoperability issues. Case studies demonstrate real-world success stories, from precision farming that reduces water usage by 40% to smart city initiatives that optimize urban services. Future trends, such as AI-powered autonomous systems and quantum-secured networks, point toward an increasingly intelligent and secure IoT ecosystem. The paper concludes with recommendations for standardization, security enhancement, and policy development to ensure IoT's sustainable and equitable growth. By bridging technological innovation with societal needs, IoT holds immense potential to drive economic progress and solve global challenges in the coming decades. References [1] A. Khanna and S. Kaur, “Internet of things (IoT), applications and challenges: A comprehensive review,” Wirel. Pers. Commun., vol. 114, pp. 1687–1762, 2020. [2] R. Hassan et al., “Internet of Things and its applications: A comprehensive survey,” Symmetry, vol. 12, no. 10, p. 1674, 2020. [3] S. A. H. Mohsan et al., “Recent advances, future trends, applications and challenges of internet of underwater things (IoUT): A comprehensive review,” J. Mar. Sci. Eng., vol. 11, no. 1, p. 124, 2023. [4] H. Verma, N. Chauhan, and L. K. Awasthi, “A comprehensive review of ‘Internet of Healthcare Things’: Networking aspects, technologies, services, applications, challenges, and security concerns,” Comput. Sci. Rev., vol. 50, p. 100591, 2023. [5] K. Shafique et al., “Internet of things …
subject: Articles
  topic: section
subject: Internet of Things
  topic: keywords
subject: IoT applications
  topic: keywords
subject: smart devices
  topic: keywords
subject: data analytics
  topic: keywords
subject: security challenges
  topic: keywords
subject: future trends
  topic: keywords
creator: Asma Shaheen
rights: Copyright (c) 2024 Asma Shaheen (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2024-06-30
publication date: 2024-06-30
starting page: 1
ending page: 8

Smart Grids: A Comprehensive Review of Technologies, Challenges, and Future Directions
type: item
description: This paper reviews the revolutionary concept of smart grid, a digitally enabled electrical grid that utilises advanced communication, computation, sensing and control technology in order to improve the efficiency and reliability of electricity production and distribution. The purpose of this technical profile is to present a comprehensive overview of smart grid architectures, key technological enablers, challenges of their implementation, and future research topics geared towards global energy transitions. A systematic literature review approach was used to review academic, technical, and industry reports. The findings integrate insights from several disciplines including electrical engineering, computer science, energy policy, and behavioral economics. The analysis is classified into key technology areas, operations support, security, demand side management, and renewal interaction. Smart grids provide benefits including monitoring in real-time, increased energy efficiency, demand response, renewable integration and strengthening grid resiliency. Some key technologies are AMI, DERs, IoT devices, and AI-powered analytics. However, while the technology has matured, implementation has been constrained by issues involving interoperability, high capital cost, cyber-vulnerability, and regulatory resistance. Public accept­ance and customer participation are the other keys to success for smart grid. This review provides an extensive and interdisciplinary overview of smart grid technologies and their impacts. It focuses on research challenges, intelligent programming of smart controllers, AI-driven energy optimization, blockchain-based energy trading, quantum safe cybersecurity and the like. The article suggests practical implications for future innovation, policy making, and the implementation strategies of the inclusive smart grid. References [1] S. Z. Abbas et al., "Review of smart grid and nascent energy policies: Pakistan as a case study," Energies, vol. 15, no. 19, p.…
subject: Articles
  topic: section
subject: Smart Grids
  topic: keywords
subject: Renewable Energy Integration
  topic: keywords
subject: Demand Side Management
  topic: keywords
subject: Grid Modernization
  topic: keywords
subject: Cybersecurity
  topic: keywords
creator: Rameez Amin
creator: Abdullah Ali Kazmi
rights: Copyright (c) 2024 Rameez Amin, Abdullah Ali Kazmi (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2024-12-31
publication date: 2024-12-31
starting page: 45
ending page: 70

The Transformative Role of Block chain Technology in Supply Chain Management
type: item
description: This study analyzes the impact of blockchain technology in transforming the discipline of supply chain management (SCM) focusing on its benefits and challenges, as well as its level of readiness for implementation across different sectors. The objective is to explore how blockchain will resolve problems regarding transparency, security, and efficiency in modern supply chains. A quantitative strategy was selected for this research, which was in the form of a survey directed to 250 supply chain managers from different sectors. The survey captured data regarding participants’ perception, awareness, and readiness relating to the adoption of blockchain technology in SCM. Data analysis was conducted with the help of SPSS-29 and the primary outputs were presented through frequency distributions, bar graphs, pie charts, and tables illustrating the primary trends and insights. Data collected noted that respondents perceive blockchain technology as a revolutionary solution that improves the transparency, traceability, and security of the supply chain. However, insufficient adoption is driven by severe implementation cost, regulatory ambiguity, and technological sophistication. Despite these prospective scenarios, industries such as food and pharmaceuticals exhibit keen interest in the technology’s possibilities alongside logistics. Adoption acceleration was identified by respondents to require stronger government initiatives, industry case studies, and enhanced training programs. This work adds to the existing scholarship on the application of blockchain technology in supply chain management by examining the advantages and obstacles to adoption. It details the level of adoption of blockchain by various processes and the basic actions that need to be taken to address organizational challenges. The findings of the study will be useful to companies and other stakeholders interested in policy and academic research intending to improve supply chain performance and practices throug…
subject: Articles
  topic: section
subject: Blockchain
  topic: keywords
subject: Supply Chain Management
  topic: keywords
subject: Transparency
  topic: keywords
subject: Adoption Barriers
  topic: keywords
subject: Smart Contracts
  topic: keywords
creator: Muhammad Atif
rights: Copyright (c) 2024 Muhammad Atif (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2024-12-31
publication date: 2024-12-31
starting page: 31
ending page: 44

Advancements and Transformative Applications of Blockchain Technology
type: item
description: The purpose of the study is to assess particular blockchain applications in supply chain management, healthcare, and finance to determine their transformational benefits and potential issues across various industries. This investigation is defined by a specific question: what are the impacts of blockchain on transparency, security, and efficiency of operations, considering the impediments to its adoption? The methods in the study are quantitative, and the primary data was obtained using a formal structured online survey through Google Forms and Survey Monkey. Three hundred and fifty professionals from the technology, finance, and supply chain domains formed the sample. The survey solicited opinions regarding the impact of blockchain, barriers to adoption, and organizational willingness to embrace blockchain technology. For quantitative data analysis, statistical packages SPSS and R were utilized while Excel was used for creating charts and diagrams for easy interpretation. Findings of the study indicate that efficiency and transparency in supply chain management and healthcare are two sectors where blockchain technology promise great value. Still, its adoption is hindered by serious constraints such as technical scalability, regulatory ambiguity, and insufficient expertise. In addition, emerging technologies like Artificial Intelligence (AI) and the Internet of Things (IoT) are identified as critical in increasing blockchain scalability and efficiency as well. This research helps understand the present stage of blockchain technology adoption, discussing its revolutionary possibilities alongside the practical challenges that organizations confront. This research adds to existing knowledge regarding the effects of blockchain technology on various sectors and also proposes solutions to adoption hurdles, helping organizations use blockchain technology for growth. References [1] R. Abujamra and D. Randall, "Blockchain applications in healthcare and the opportunities and …
subject: Articles
  topic: section
subject: Blockchain technology
  topic: keywords
subject: scalability
  topic: keywords
subject: supply chain management
  topic: keywords
subject: healthcare
  topic: keywords
subject: decentralized finance (DeFi)
  topic: keywords
creator: Abbas Ali
rights: Copyright (c) 2025 Abbas A;i (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-05-14
publication date: 2025-05-14
starting page: 36
ending page: 51

AI and Cybersecurity: Defending Data and Privacy in the Digital Age
type: item
description: This research investigates the rapidly evolving relationship between Artificial Intelligence (AI) and cyber security, particularly how AI is impacting the cyber defense technologies. It seeks to examine the ambivalent nature of AI as a cybersecurity system’s defender and assailant in a digital world where data and privacy is increasingly exposed. Utilizing an analytical qualitative approach, this review attempts to document existing literature, case studies, and practical applications of AI in cybersecurity functions. The research assesses the advantages of AI tools such as predictive analytics, automated threat identification, and real-time responsive changes to the cyber defense systems, alongside new dangers like adversarial AI, deep fakes, and privacy violations. The claims are substantiated by specific case studies illustrating the application of AI in various domains. The application of AI in cybersecurity frameworks improves the ability to identify, respond to, and adapt to threats in a timely manner and augments systems agility. AI is instrumental in the detection of phishing scams, analysis of malware, prevention of unauthorized access, and fraud detection. Unfortunately, these same technologies are now available to the ‘bad guys’ to conduct sophisticated AI-driven cyber-attacks. It uncovers, though, serious ethical and regulatory concerns around the breach of privacy, the algorithmic bias of AI, and absence of governance structures. This paper offers a thorough synthesis of the current literature regarding AI in cybersecurity, analyzing its transformative potential against its associated risks. It adds to the discourse on the development of artificial intelligence by advocating for responsible AI, the necessity of international governance frameworks, and the application of human intervention in automated security systems. The research focuses on the application of AI in a manner that fosters confidence and safety within digital environments. References [1]…
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: Data Privacy
  topic: keywords
subject: Predictive Analytics
  topic: keywords
subject: AI-Powered Threats
  topic: keywords
creator: Muhammad Danish
creator: Malik Muhammad Siraj
rights: Copyright (c) 2025 Muhammad Danish (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-05-12
publication date: 2025-05-12
starting page: 25
ending page: 35

Adoption Potential of Artificial Intelligence and Machine Learning in Islamabad’s Academic Libraries
type: item
description: This research intends to determine the preparedness of academic libraries of Islamabad with respect to the adoption of Artificial Intelligence (AI) and Machine Learning (ML) technologies, looking especially into the awareness, attitude, and infrastructure of the library professionals concerning these technologies. The research employs a quantitative design and gathered data using a structured questionnaire from a sample of 250 university librarians within Islamabad. The questionnaire incorporated items relating to the participants’ awareness of AI/ML, their access to technological infrastructure, and the barriers to the adoption of AI/ML. The data were processed using SPSS-29, and for presenting the findings, tables were used with descriptive statistics and frequency distribution. The outcomes of the study indicate that while librarians have some general knowledge of AI/ML technologies, their specific applicability to the running of libraries is poorly understood. Readiness of infrastructure differs from one library to another; a good number of them do not have supporting IT infrastructure and personnel for full integration. There is, however, a positive attitude towards AI/ML adoption, with respondents claiming they were willing to be trained and actively support the adoption of AI/ML in their institutions. This study adds to the growing literature on the adoption of AI and ML in developing countries like Pakistan. The study highlights challenges and prospects AI/ML technology offers to academic libraries, along with suggestions to address identified problems. These findings assist the administration and policy makers in understanding how to equip libraries in advance for the evolving transformation of library services. References [1] M. Ahmed, M. Mukhopadhyay, and P. Mukhopadhyay, "Automated knowledge organisation: AI/ML-based subject indexing system for libraries," DESIDOC J. Lib. Inf. Technol., vol. 43, no. 1, 2023. [2] S. M. Ali, A. Razzaque, M. Yousaf, and R. …
subject: Articles
  topic: section
subject: Artificial Intelligence (AI)
  topic: keywords
subject: Machine Learning (ML)
  topic: keywords
subject: Library Professionals
  topic: keywords
subject: AI Adoption
  topic: keywords
subject: Infrastructure Readiness
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subject: Academic Libraries
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creator: Kanwal Islam
creator: Ayesha Shamshad
creator: Muhammad Usman
rights: Copyright (c) 2025 Kanwal Islam, Ayesha Shamshad, Muhammad Usman (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-05-12
publication date: 2025-05-12
starting page: 12
ending page: 24

Internet of Things in Food Supply Chains: Enhancing Quality and Safety through Smart Technologies
type: item
description: The integration of IoT technologies into food supply chains (FSCs) has redefined quality control and safety monitoring through real-time tracking, predictive analytics, and automated decision-making. This review synthesizes 30 peer-reviewed studies to examine how IoT-combined with AI, machine learning, and smart packaging enhances traceability, reduces waste, and improves food safety across production, logistics, and retail. Key applications include precision agriculture sensors, block chain-based traceability, and dynamic cold chain monitoring, which collectively minimize spoilage risks and ensure regulatory compliance. However, challenges like lack of interoperability between IoT systems, cybersecurity threats, and high implementation costs hinder widespread adoption, particularly among small-scale producers. Future advancements require standardized protocols, robust data encryption, and cost-effective IoT solutions tailored for diverse supply chain actors. By addressing these gaps, IoT can unlock a transparent, efficient, and sustainable food ecosystem, bridging the divide between technological potential and real-world implementation. Collaborative efforts among researchers, industries, and policymakers will be crucial to maximize IoT’s transformative impact on global food security. REFERENCES Z. Afshar, "Exploring factors impacting organizational adaptation capacity of Punjab Agriculture & Meat Company (PAMCO)," Int. J. Emerg. Issues Soc. Sci., Arts Humanit. (IJEISSAH), vol. 2, no. 1, pp. 1–10, 2023. Ben-Daya, E. Hassini, Z. Bahroun, and B. H. Banimfreg, "The role of internet of things in food supply chain quality management: A review," Qual. Manag. J., vol. 28, no. 1, pp. 17–40, 2020, doi: 10.1080/10686967.2020.1838978. Bouzembrak, M. Klüche, A. Gavai, and H. J. P. Marvin, "Internet of Things in food safety: Literature review and a bibliometric analysis," Trends Food Sci. Technol., vol. 94, pp. 54–64, 2019, doi: 10.1016/j.tifs.2019.11.002. K. Gayatri, J. Jayasa…
subject: Articles
  topic: section
subject: Internet of Things
  topic: keywords
subject: food supply chain
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subject: food safety
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subject: quality management
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subject: intelligent packaging
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subject: traceability
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subject: smart farming
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creator: Salma Museera
creator: Huma Khan
rights: Copyright (c) 2023 Salma Museera, Huma Khan (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2023-12-31
publication date: 2023-12-31
starting page: 1
ending page: 6

ChatGPT and the Future of Academic Writing: Enhancing Productivity and Creativity
type: item
description: This research evaluates the impacts of ChatGPT on productivity and creativity in academic writing among tertiary education learners in Pakistan. In particular, it seeks to understand how the students interacted with the AI program in relation to essay writing, ideation, editing, and addressing issues of output reliability, ethical use, institutional support, and workload. As part of a quantitative approach, a stratified survey was conducted for 343 selected students studying across various universities in Pakistan. The data set were analyzed in SPSS to compute descriptive statistics and the results graphically represented in the form of bar diagrams, pie diagrams, tables, and other illustrative forms with the intention to interpret the patterns, benefits, and concerns regarding ChatGPT in academics. Results showed that most students have integrated ChatGPT into their day-to-day activities, and most especially in academic writing and research. Students noted that their productivity increased significantly, agreeing with the notion that ChatGPT reduced drafting time, language hurdles, and increased creative output. However, some issues came up regarding the reliability of citations, ethically problematic use without disclosure, and absence of institutional guidelines. Irrespective issues, most students supported the inclusion of AI in the classroom, perceiving ChatGPT as constructive for academic work. This analysis is the first to provide empirical evidence on the implementation of ChatGPT in higher education in a developing country context. It emphasizes how AI is augmenting shifts in students' writing practices, and also calls for the urgent development of ethical guidelines, AI instructional policies, and faculty development programs on responsible use. The data gathered is useful for educators and policymakers who strive to navigate between innovation and academic ethics. REFERENCES Z. Afshar, "Exploring factors impacting organizational adaptation capacity of Pun…
subject: Articles
  topic: section
subject: ChatGPT
  topic: keywords
subject: Academic Writing
  topic: keywords
subject: Artificial intelligence
  topic: keywords
subject: Higher Education
  topic: keywords
subject: Student Productivity
  topic: keywords
creator: Mahnoor Afzal
creator: Nayab Arshad
creator: Asma Shaheen
rights: Copyright (c) 2025 Mahnoor Afzal, Nayab Arshad, Asma Shaheen (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-05-10
publication date: 2025-05-10
starting page: 1
ending page: 11

The Strategic Influence of Cloud Computing on Contemporary Marketing and Management Practices
type: item
description: The focus of this particular study is to evaluate the impact cloud computing has on the practices of marketing and management. The study looks into the ways cloud technology facilitates computing toward making decisions, CRM, cooperation, scalability, and cost within the different business environments. A mixed-method approach integrating both qualitative and quantitative frameworks was applied. Quantitative data was collected using surveys from 120 marketing professionals, while 25 marketing executives were interviewed in order to capture their perceptions regarding strategic relevance and implementation complexity around cloud adoption. Data was graphically analyzed using regression and thematic coding. It was discovered that cloud computing positively influences the marketing effectiveness level, campaign engagement, and overall customer interaction. Other benefits include lower marketing resource spending, increased customer retention by 40% due to cloud-CRM implementations, and lowered response time for marketing actions. There still remains apprehension over data security and system integration not being issues that can be resolved as obstacles to adoption. The body of knowledge on transformation in digital marketing is bolstered by this study, which documented facts for the first time. This study has also show-cased the importance of cloud computing vis-a-vis competitive advantage using AI automation, real-time analysis, and extendable marketing system infrastructure. These constituents present foundational details when studying cloud-based infrastructural innovations for marketers and researchers in the cloud-enabled environment. REFERENCES Afshar, M. Z. (2023). Exploring Factors Impacting Organizational Adaptation Capacity of Punjab Agriculture & Meat Company (PAMCO). International Journal of Emerging Issues in Social Science, Arts and Humanities (IJEISSAH), 2(1), 1-10. Attaran, M., & Woods, J. (2019). Cloud computing technology: improving small business pe…
subject: Articles
  topic: section
subject: Cloud Computing
  topic: keywords
subject: Digital Marketing
  topic: keywords
subject: CRM
  topic: keywords
subject: Scalability
  topic: keywords
subject: Data Analytics
  topic: keywords
creator: Sajjad Ahmad
creator: Salma Museera
rights: Copyright (c) 2024 Sajjad Ahmad, Salma Museera (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2024-12-31
publication date: 2024-12-31
starting page: 21
ending page: 30

Computational Analysis of Geotherma-Enhanced Heavy Oil Production
type: item
description: This study seeks to research the incorporation of geothermal energy with steam injection for better recovery of heavy oil in offshore and thermally challenging reservoirs. The goal is to determine the effects of steam temperature, well spacing, and oil mobility on reservoir pressure and energy efficiency in long-term production supply scenarios. A comprehensive numerical simulation approach was applied utilizing CMG-STARS software to simulate Enhanced Geothermal Systems (EGS) with steam injection. The case study relied on Bohai NB35-2S offshore heavy oil reservoir. A number of operational parameters were tested: steam temperatures of 150°C, 200°C, and 250°C, along with well distances of 100m, 150m, and 200m. These were analyzed for their impact on oil recovery, pressure changes during production, and production stability over a simulated period of 10 years. The results indicate increased temperatures did lower oil viscosity and short-term daily oil production rates; however, these outcomes did not influence cumulative oil production. Lower temperatures (especially 150°C) sustained bottom-hole pressures and provided longer production phases, especially at closer well spacing. Wider well configurations had higher heat loss, unstable pressure, and reduced effectiveness. During sensitivity analysis, higher temperatures also increased steam-oil ratio (SOR) with operational risk, which limits long-term sustainability. Optimal recovery was reached with a leveled moderate thermal input and compact well spacing. It presents a new hybrid geothermal-steam recovery model that addresses the particular difficulties posed by offshore heavy oil reservoirs. By integrating considerations of thermal efficiency with spatial arrangement, the research provides a valuable guideline for the design of energy-efficient and operationally resilient oil recovery systems. It contributes to the operational needs of field engineers and energy strategists in the context of enduring enhanced oil rec…
subject: Articles
  topic: section
subject: Geothermal energy
  topic: keywords
subject: Steam injection
  topic: keywords
subject: Heavy oil recovery
  topic: keywords
subject: Reservoir simulation
  topic: keywords
subject: Enhanced oil recovery (EOR)
  topic: keywords
creator: Danish Ali
creator: Tanim Ahmed
rights: Copyright (c) 2024 Danish Ali, Tanim Ahmed (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2024-12-31
publication date: 2024-12-31
starting page: 10
ending page: 20