Innovative Computing Review (ICR) is an international journal being published by the School of Systems and Technology (SST), University of Management and Technology (UMT), Lahore, Pakistan. ICR is committed to publishing high-quality studies in computing and related fields and is widely circulated, both nationally and internationally. It is focused on the publication of original research work and reviews in form of articles under the umbrella of computing science and it aims to introduce the latest developments in this rapidly growing subject.
University of Management and Technology Lahore, Pakistan
A Systematic Review of Augmented Reality Adoption in Libraries: Benefits, Barriers, and User Engagement
Augmented Reality has transformed our perception of reality over the past few decades. In libraries, AR is changing how and what individuals perceive when consuming knowledge. The review study examines the use of augmented reality in libraries, including its advantages, obstacles, and user involvement. The PRISMA standard is employed, in which literature is gathered from five databases and the Google search engine. The study uses 26primary papers published between 2010 and 2024. At the same time, studies demonstrate that AR significantly increases user engagement and learning outcomes, enables new ways to access relevant content in libraries, supports creative activities at makerspaces and different forms of experiential learning, and finally offers librarians a more efficient tool for library management. ShelvAR and ARLib are augmented reality applications that assist users in locating books and showing a virtual tour of the library. AR integrates devices into the real world to enhance authenticity. To employ AR in libraries more effectively, it takes preparation, budget, technical expertise, understanding, and knowledge. Worried about privacy, keeping things updated, and providing content are more items to consider. To sum it up, AR can convert libraries into innovative learning venues. It gets users involved by adding videos and other features, which aligns well with how people learn today. For libraries to use AR effectively, they need to plan, train staff, and team up with others to address any problems. Future studies should examine how AR supports learning and user happiness, ensure that anyone can use it, and identify the best ways to integrate AR into libraries.
Copyright (c) 2026 Naweed-e-Sehar, Muhmmad Rameez Sheikh, Alishba Asif https://creativecommons.org/licenses/by/4.0
Explainable Deep Learning Approach: Interpretation Between Detection and Root Cause Analysis of Distributed Log Anomalies with TLAN-EX
The deeper analysis into modern distributed banking environments has discovered that nowadays distributed log is facing challenges. This is because transaction data comes from multiple networked nodes, which makes a complete system behavior check difficult. This shows how complications grow when multiple system components produce logs at the same time. This gives rise to data inconsistencies that cause anomaly detection to be more difficult. The detection systems used nowadays show lower reliability due to high log volumes and logs that do not follow the standard format, and events showing no clear order, due to which the system raises alarms and fails to catch the real anomalies. Research into log anomaly detection by using Deep Learning (DL) models has shown positive results. However, distributed systems make it hard and difficult for real-life implementation. The current sequence models used in anomaly detection systems only identify one pattern type at a time between short-term and long-term patterns. This results in lower accuracy when different nodes produce dependent log data. The existing detection methods fail to give post-hoc explanations. This makes it hard for the analysts to understand the causes of anomalies, their starting point, and the cause. The current research is based on previous researches. The study implemented the TCN-Transformer hybrid system, which would be able to find patterns at both short and long-time scales in distributed log data. The system employs Parameter Entity Labeling (PEL) to enhance the working and quality of the preprocessing operations of the logs. The model adds three new elements, which include a post-hoc attribution module through SHAP and Integrated Gradients, a propagation analysis layer, and an overhead evaluation module. The model would result in improving the transparency and performance and might be able to cope with the issues as they arise. The designed (TLAN-EX) Temporal-Logical Attention Network with Explainability model has the capability to achieve high accuracy and present clear explanations and low response time. The study revealed that the hybrid learning approach assists in enhancing multi-node anomaly detection and SHAP and Integrated Gradients help to justify the causes of anomalies. The propagation analysis is used to demonstrate the spread of system anomalies between nodes to indicate the detailed working pattern of the system. The system is analyzed to be at its optimal level as the model is highly functioning when the system is considered to be handling high traffic. The upgraded explanation capabilities and speed of detection of the model depict improved detection results and suit well in distributed financial systems.
Copyright (c) 2026 Iman Nasir https://creativecommons.org/licenses/by/4.0
Stable Election with Sink Mobility Routing Protocol, a Stochastic Model for Clustering in Wireless Sensor Network
The best routing protocol is a requirement of any application that incorporates a wireless sensor network (WSN) to determine the best path, minimize congestion, route optimization, minimum traffic delays, effective data transfer, and network stability. In this research we discuss the design challenges of WSN and its architecture that might impact routing protocols. We also discussed various routing protocols for WSN that could be divided into four categories: location based, hierarchical, quality of service (QoS)-based and data-centric. We also presented the new routing protocol model for a heterogeneous randomly deployed wireless sensor network named Stable Election with Sink Mobility (SESM). This routing protocol can work for such applications where the sink node is mobilized on a predefined path within the area of the network. Sensor nodes selects cluster head (CH) by-election based on residual energy and probability of being selected. Our proposed protocol results prove that there is an increase in network stability time, throughput, and network lifetime. We have compared our results and it has outperformed the existing famous protocols, Stable Election Protocol (SEP) and Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol.
Copyright (c) 2026 Khawar Iqbal Malik https://creativecommons.org/licenses/by/4.0