Societal Transformation: AI and Big Data Journal


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ISSN: 3007-9349 (Online) ISSN: 3007-9330 (Print)

About the Journal

Societal Transformation: AI and Big Data Journal (AIBD) is an open-access, double-blind peer-reviewed journal published by the Department of Computer Science at IQRA University. The journal is issued biannually and is freely accessible online to a global audience.

AIBD is dedicated to publishing high-quality research in artificial intelligence and big data that drives meaningful societal transformation. The journal welcomes submissions from researchers worldwide and serves a diverse and interdisciplinary readership. Its primary aim is to promote novel methodologies, algorithms, applications, theories, and discoveries that contribute to large-scale societal impact through advancements in artificial intelligence.

The journal maintains rigorous academic standards through a comprehensive quality assurance process. All submissions undergo plagiarism screening (via iThenticate/Turnitin), with a maximum allowable similarity index of 19%. Each manuscript is evaluated through a double-blind peer-review process by at least two independent experts in the relevant field--preferably PhD-qualified reviewers--ensuring objectivity, originality, and scholarly excellence.


IMPORTANT LINKS:


Submissions via OJS:https://journals.iqra.edu.pk/ojs/index.php/aibd/index
Author Agreement/ Copyright form:Download

For any queries, email to the editor:noman.islam@iqra.edu.pk

AIBD publishes a wide range of contributions, including empirical studies, theoretical research, scientific surveys, and innovative application-based work, without preference for any specific methodology.

All accepted papers are published under a Creative Commons license and made freely available online immediately upon publication, ensuring unrestricted access and long-term availability.

Importantly, AIBD does not charge any article processing fees, submission fees, or editorial handling charges, making it an inclusive platform for researchers across the globe.



Subject Areas: Artificial neural network, Deep learning, Machine learning, Natural language processing, Computer vision, Robotics, Reinforcement learning, Data science, Algorithm, Recommender system, Knowledge representation and reasoning, Analytics, Pattern recognition, Speech recognition, Multi-agent system, Computational social science, Cognitive science, Big data.


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Systematic Literature Review of Smart Library Management and a Deep Learning Solution 0

Abstract: This work presents a systematic literature review of smart library management system. By studying different papers for past 10 years to address several research questions, the findings are presented. A smart library is based on employing smart information and communication technologies (ICT) to facilitate readers. Five major research questions are answered. What is smart library, the major research challenges in implementing smart library, the advantages of smart library, and the enabling technologies for smart library. Finally, this work presents a smart library management system based on deep learning technique. An android mobile application is developed that digitizes the book [...]

2025
By Zeeshan Islam
Keywords: Keywords: Smart Library, Library Automation, Character Recognition, OCR, Digitization
Comparative Analysis of LSTM and GRU Models for e-Commerce Sales Prediction 0

Abstract: This paper talks about a novel comparison of two deep learning models namely Long short-term memory (LSTM) and Gated recurrent unit (GRU). The target domain is e-commerce sales prediction applied on behavioral data from a Shopify store. The dataset comprises 87000 records stepped by hours. The dataset contains details about various features such as session duration, bounce rate, cart addition, checkout activity. It has been found that LSTM and GRU both were able to learn the spatiotemporal dependencies present in the data. These models predict with good accuracy the probability of making a purchase in a session. By means [...]

2025
By Syed Farasat Ali
Keywords: Keywords: e-Commerce, Sales Forecasting, LSTM, GRU, Session Level Forecasting
Cybersecurity Risk Assessment: Best Practices for Mitigating the Impact of Cyber-Attacks 0

Risk assessment is one of the complex issues that determines the probability of occurrence of the events. The purpose of this paper is to present the various types of cyber risks and attacks that can significantly affect organizational performance. This research study is qualitative research in which data has been collected from various respondents operating in the field of cyber security in Pakistan’s telecommunication sector. This paper follows the convenience sampling technique to collect data from various participants. The sample size of 5 participants consisting of cyber security experts has been chosen for this research. In-depth qualitative interviews were conducted [...]

2025
By Talha Amir ,Umair Saleem
Keywords: Keywords: Cyber Risks, Risk Assessment, Mitigation, Phishing, DOS
Social Media Data for Mental Health Screening: Perspectives on Age Bias 0

Abstract: During the past few years, there have been a surge in the use of social media. Various social media platforms have emerged such as Twitter, WhatsApp, Facebook and LinkedIn. The existing studies reveal that there has been a relation between social media usage and mental health. The objective of this study is to explore age biasness related to mental health data on social media platforms. Some studies have already been done on analysis of the effect of social media platforms on mental health. However, it has been observed that there is an issue of lack of inclusiveness towards the [...]

2025
By Hamdah Hamid
Keywords: Age Bias; Mental health; Social Media; Data Bias; Demographic Analysis
ASPECT-BASED SENTIMENT ANALYSIS: TAXONOMY, RECENT TRENDS & FUTURE DIRECTIONS 0

Abstract: With the expansion of online platforms, new rooms are opening to extract full insights from huge textual data. Aspect-Based Sentiment Analysis (ABSA) is rising as an essential Artificial Intelligence (AI) discipline that employs Natural Language Processing (NLP) to associate sentiments with specific attributes of entities. In this systematic review, we focused on ABSA studies published between 2020 and 2024, encompassing 66 primary research articles focusing on advancements in AI methodologies, including transformer-based models and generative AI techniques. The novel ABSA taxonomy developed in this study defines methodological categories and analyzes the use of commercial and domain-specific datasets. This research [...]

2025
By Afsheen Maroof , Shaukat Wasi
Keywords: Keywords: systematic literature review, ABSA task, ABSA datasets, ABSA trends, opinion term extraction, sentiment classification, aspect categorization, sentiment Analysis
Drag & Build: Democratizing App Development through a No-Code Drag-and-Drop Platform 0

Abstract: The focus of this study is the design, development, and impact of an example of no-code software application builder using drag and drop. By using programming visually, automating workflows, and integrating autonomously with different backends, this research also mitigates the traditional software development and the non-programmer spends on software development, simplifying and making app building development speedier, easier, and more scalable. This study focuses on architectural structures and the logic building processes coupled with the database, and aims at custom tailoring unmodifiable software that belt on security worries, unreachable deployment, and more, toward problematic customization, unchecked triangulation, unbounded cooperation, [...]

2025
By Kashif Laeeq
Keywords: Key Words: No-code development, drag-and-drop interface, workflow automation, scalability
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