Forthcoming Articles

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the SSURJET standard. Moreover, titles, authors' number, abstracts and keywords may change before publication.

Volume 15 Issue 1 Year 2025

1. Title: Car Price Prediction And Recognition Using Deep Learning and Computer Vision Algorithms.
   Authors: Hira Farman, Saad Ahmed, Muhammad Hussain Mughal, Qurat-ul-ain Mastoi and Govarishankar Lalwani.
   Accepted on: February 13, 2025                                                                                                             

   Abstract: Purchasing a used vehicle might be difficult. As with many other consumer items, the cost of a used automobile has increased dramatically in recent years. Growing interest rates and petrol prices have also exacerbated the unpleasant experience of being a car owner. One of the major and fascinating fields of investigation is automobile price prediction. As the number of automobiles on the road has increased significantly, more and more people are unable to purchase newly manufactured vehicles due to various factors, including excessive costs, limited supply, insufficient funds, and so on. As a result, the used automobile industry is expanding globally, although it is still in its infancy and is primarily controlled by the unorganized sector in Pakistan. Along with the outcomes of the experiments in this study to predict the price of the new cars, the prediction of the resale price of used cars from the features extracted from the number of parameters, such as the car's model, year of production, mark, selling type, fuel type, and current pricing, a variety of regression algorithms based on supervised machine learning were utilized in this study. Two key areas of the automotive domain, vehicle price prediction and automobile recognition are addressed by car price prediction and recognition initiatives, which blend supervised machine learning with computer vision methods. This work aims to equip users with the knowledge and skills necessary for making intelligent choices when buying cars and to identify cars through picture capture. The primary source of information for the forecasts in this study is the Kaggle website[1] . In all investigated models, random forest demonstrated high accuracy 95.1%, and low root mean square error 68.4%. Our suggested model's performance shows the usefulness and efficiency of the proposed investigation.

2. Title: Anomaly Detection in Blockchain Systems Leveraging Immutable Audit Trails for Enhanced Security.
   Authors: Hafiz Gulfam Ahmad Umar, Kashif Iqbal, Sajida Raz Bhutto, Asad Raza and Ammar Oad.
   Accepted on: May, 2025                                                                                                           

   Abstract: Blockchain technology supports recording financial transactions as well as other kinds of data using a distributed ledger, showing safe and immutable exchange. Computerized business transactions or the running of logic in blockchain networks are supported by smart contracts. Identity management systems have greatly relied on blockchain technology recently. The objective is the development of new anomaly detection techniques in streaming data using prominent features from blockchain technology. While the advantages of using a blockchain outweigh its usage, algorithms for real-time anomaly detection are still disadvantaged when talking about performance. This paper presents the integration of Apache Isis with Ethereum-based smart contracts for the development of an automated audit trail system. The proposed solution is going to ensure data integrity, transparency, and nonrepudiation and provide a reliable framework for anomaly detection. Some of the key objectives include investigating how and to what extent immutable audit trails are effective toward security, enhancing security and transparency in such systems, and applying machine learning techniques to improve anomaly detection. The resultant research would lead to the development of more secure and efficient blockchain-based systems that can benefit a wide range of industries, especially those where data security and integrity are of utmost importance.

3. Title: Anomaly Detection in Blockchain Systems Leveraging Immutable Audit Trails for Enhanced Security.
   Authors: Mujeeb Ur Rehman, Akbar Hussain, Imtiaz Hussain, Usman Sardar and Muhammad Hasnain.
   Accepted on: May, 2025                                                                                                             

   Abstract: Author ranking is an essential metric for assessing academic performance and facilitating comparisons within citation networks. While several indexing methods have been proposed to enhance author ranking, existing approaches often fail to capture the true contribution of authors to the literature. This study introduces two novel weighted citation methods to improve author ranking in academic citation networks. The first method, Normalized Weighted Citations (NWC), evaluates the rankings of co-authors within a publication. The second method, Topic-Sensitive Weighted Citations (TSWC), assigns scores to authors based on their relevance to specific research domains. Our goal is to identify both highly influential and less prominent authors within citation networks on a global scale. We evaluate our methods using scholarly publication datasets and compare them with a baseline approach. Experimental results demonstrate that the proposed methods outperform traditional ranking techniques, highlighting the importance of accurately assessing researchers’ impact in citation networks.
   
   

4. Title: Awareness and Barriers to Building Information Modeling (BIM) Adoption in the Construction Industry of Balochistan: A Case Study of Public Sector Projects.
   Authors: Shahid Ali.
   Accepted on: May, 2025                                                                                                             

   Abstract: Building Information Modeling (BIM) is an intelligent 3D model-based process that empowers Architecture, Engineering, and Construction (AEC) professionals to plan, design, construct, and manage buildings and infrastructure more efficiently, economically, and sustainably. As a futuristic technology, BIM addresses complex challenges, enhances productivity, and reduces rework risks in large-scale construction projects. This study investigates the awareness and barriers to BIM implementation in Balochistan's construction industry, focusing on public sector projects. Using qualitative and quantitative methodologies, data were collected from AEC professionals. Findings reveal that only 9.8% of respondents are aware of BIM, with minimal knowledge and experience. Key barriers include nonexistence of awareness, BIM managers, leadership, training, research and development, and resistance to change, alongside unclear BIM advantages and insufficient organizational and educational support. The study highlights critical challenges hindering BIM adoption in the region.

5. Title: Ushering Circular Economy Principles to Mitigate Environmental Hazards and Support Sustainable Development Goals Progress in an Urban Environment.
   Authors: Reena Majid Memon.
   Accepted on: May, 2025                                                                                                             

   Abstract: Urban areas are becoming more susceptible to complex environmental hazards, air pollution, heat waves, flooding, and waste upsurge, aggravated by climate change, overpopulation, excess resource consumption, and rapid urbanization. This study aims to analyze how circular economy (CE) practices can be leveraged as adaptive strategies to mitigate ecological damage while accomplishing sustainable development goals (SDGs). A multi-case study approach is embraced to examine the breadth to which CE initiatives accepted in Karachi, such as circular construction, resource-from-waste systems, and renewable energy initiatives, are engaged in reducing ecological hazards and supporting progress toward SDGs. In this regard, the paper designs a conceptual framework coupling CE interventions with environmental risks and SDGs. Tangible findings signify integral factors to construct CE are waste recycling, eco-friendly resources and products, and collaborations, whereas persistent barriers are governance, policy, technology, costs, and consumer behaviors. The study proffers actionable insights for researchers, industrialists, and urban policymakers to scale CE practices as a strategic pathway for climate-adaptive and sustainable urban futures, attempting to save society, the economy, and the environment.