Volume 17, Issue 1, January 2026

Evaluating Spatio-Temporal Changes of Flood Risk Area in the Case of Chemoga Watershed, Amhara Region, Ethiopia

Mekoyet Fekadu, Hamere Yohannes, Beka Adugna Jima

Department of Civil and Environmental Engineering, Master of Science in Geodesy and Geomatics Program (Specialization in Geomatics), Addis Ababa Institute of Technology (AAiT), Addis Ababa University
Agricultural Machinery Engineering program, Department of Natural Resource Management, College of Agriculture and Natural Resource Management at Salale University, Fitche, Ethiopia

Abstract- Flooding is a frequent natural disaster, often caused by rivers exceeding their storage capacity and inundating nearby low-lying areas. This study analyzed flood risk using a 30×30 m digital elevation model (DEM), along with land use/land cover (LULC), precipitation, and soil data. LULC changes were assessed for 1993, 2003, 2013, and 2023 using a semi-automatic plugin in Quantum GIS, with flood risk trends analyzed through Multi-Criteria Decision-Making Analysis. In 1993, agricultural land (41.56%) and water bodies (30.51%) were the most dominant. By 2003, settlements (29.52%) and forests (23.53%) were highest. In 2013, bare land (40.99%) and agricultural land (26.86%) were prevalent. In 2023, agricultural land accounted for 54.14%, with settlements and bare land each at approximately 16.5%. Classification accuracy ranged from 91.4% to 93.48% across the years. High flood risk areas increased over time: 66.56% (1993), 77.29% (2003), 77.09% (2013), and 79.37% (2023). Conversely, low-risk areas declined from 1.83% in 1993 to just 0.38% in 2023, highlighting a growing vulnerability to flooding.

Keywords- Chemoga Watershed, Flood Risk Area and Land Use Land Cover Change

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Ethics, Governance, and Policy Frameworks for IoT Ecosystems: The Ethical Dilemmas in Data Sharing and Consent (Focus: IoMT)

Moeid Ahmed

Department of Computer Science, University of Engineering and Technology, Lahore, Pakistan

Abstract- The Internet of Medical Things (IoMT) poses serious ethical questions about data sharing and informed consent while also promising revolutionary advancements in diagnostics, remote monitoring, and individualized care. In order to identify the main ethical conundrums related to ongoing health data collecting, fragmented governance regimes, and the emerging role of AI in decision-making, this study summarizes recent studies (2022–2025). We examine the governance models in the EU, the US, and Asia and suggest elements for a single, moral IoMT governance model that incorporates patient rights, data privacy, and AI ethics. Lastly, we propose recommendations for governments, healthcare providers, and designers for future research topics, including privacy-by-design, blockchain-based consent mechanisms, and ethical audits for medical IoT devices.

Keywords- Data Privacy and Confidentiality, Security Vulnerabilities, Accountability and Liability, Data Integrity and Availability, Regulatory Compliance and Ethical Guidelines Development

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An Efficient IoT-Enabled Dual-Mode Learning System for Blind and Deaf-Blind Students

Nimra Nisar, Ammar Shafiq, Ali Burhan

Department of Computer Science, University of Engineering and Technology, Lahore, Pakistan

Abstract- This review paper critically examines the research article “IoT-Driven Accessibility: A Refreshable OCR-Braille Solution for Visually Impaired and Deaf-Blind Users through WSN” by Reddy et al. (2024), which proposes an integrated assistive framework combining the Internet of Things (IoT), Optical Character Recognition (OCR), Wireless Sensor Networks (WSN), and refreshable Braille technology. The primary objective of the reviewed system is to enhance information accessibility for visually impaired and deaf-blind individuals by enabling the real-time conversion of printed textual content into tactile Braille output. By leveraging IoT-enabled communication and WSN-based connectivity, the system ensures low-latency, synchronized interaction between input devices, processing modules, and output interfaces, thereby supporting seamless and autonomous user interaction. This review systematically analyzes the architectural design, methodological workflow, and technological components of the proposed system, with particular emphasis on OCR accuracy, Braille translation efficiency, system responsiveness, and communication reliability. In addition to evaluating the technical implementation, the paper presents a comparative analysis with existing assistive technologies, highlighting how the IoT–OCR–Braille integration addresses limitations commonly associated with conventional text-to-Braille and audio-based systems, such as delayed feedback, limited adaptability, and lack of scalability. The findings indicate that the reviewed approach achieves improved recognition accuracy, significantly reduced processing latency, and enhanced adaptability to dynamic environments compared to earlier frameworks.

Keywords- IoT, Optical Character Recognition (OCR), Wireless Sensor Networks (WSN), Braille Display, Assistive Technology, Accessibility, Visually Impaired, Deaf-Blind, Real-Time Translation, Inclusive Education, Tactile Communication and Smart Devices

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A Comprehensive Survey of Next-Generation IoT-Based Energy Monitoring and Management: Innovations and Challenges

Arooj Fatima

Computer Science Departmrnt, University of Engineering and Technology, Lahore

Abstract- Abstract— The integration of Internet of Things (IoT) technologies into modern energy systems has significantly altered the idea of conventional approaches to energy monitoring and management. In recent years, IoT-based solutions have been increasingly in numbers and adopted across smart homes, microgrids, industrial facilities, and large-scale power networks. This survey examines research published from 2021 to 2025 on next-generation IoT-based energy monitoring and management, with a particular focus on those that incorporate edge computing, machine learning, and smart grid architectures. This review paper examines and analyzes the area in terms of system architectures, application domains, and underlying technological trends. A clear shift toward distributed intelligence is observed, and seen and as many recent solutions move data processing closer to the network edge to address latency constraints and support real-time decision-making. There are many in practice hybrid edge–cloud architectures that are commonly employed, which combine local responsiveness with the scalability of centralized cloud platforms. Machine learning and deep learning techniques shows strong potential in energy consumption forecasting, demand response, and load optimization. Several studies further explore the integration of IoT platforms with digital twin models. Digital twin enables the virtual representations of physical energy assets to support simulation, performance analysis, and operational planning. Despite these advances, persistent challenges remain. Issues related to cybersecurity, interoperability among heterogeneous devices, data privacy, system scalability, and the energy overhead of large IoT deployments continue to limit largescale adoption. Based on these observations, this review paper identifies key research gaps and outlines future directions toward more secure, interoperable, and intelligent IoT-driven energy management systems.

Keywords-IoT, Smart Energy, Energy Management, Edge Computing, Machine Learning, Smart Grid and Survey

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A Review Based on Methods and Context to Classify Legal and Illegal Traffic

Muhammad Kamran Khan, Asifa Munsif Ali, Muhammad Junaid Arshad

Department of Computer Science, UET, Lahore

Abstract- Abstract— This study presents a comprehensive review of methodologies for classifying legal and illegal network traffic in the evolving context of cybersecurity. It examines traditional, machine learning (ML), deep learning (DL), and hybrid approaches, emphasizing their performance, adaptability to encrypted data, and scalability under real-world conditions. Port-based and Deep Packet Inspection are traditional approaches that do not work well with encrypted traffic, and ML models (even though effective) have issues with feature drift and poor generalization. DL architectures especially CNN, LSTM and reinforcement learning-based models are more accurate and resilient but require more computational resources. Hybrid networks such as CNN-GRU provide a tradeoff between their performance and efficiency, attaining almost optimal accuracy at moderate complexity. The review demonstrates significant issues with the reliability of the dataset, variability of features, and the processing of encrypted traffic as critical research gaps. Results highlight that deep and hybrid learning methods have the potential to boost secure real-time traffic classification under dynamic network setups.

Keywords-Network Traffic Classification, Machine Learning, Deep Learning, Encrypted Traffic, Hybrid CNN-GRU Model and Cybersecurity

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