Volume 16, Issue 3, July 2025

Deep Learning Techniques for Skin Cancer Diagnosis: A Review

Muhammad Jafar, Bushra Inayat, Ayesha Inayat, Amna Inayat

Department of Computer Science, University of Engineering and Technology, Lahore, Pakistan
College of Nursing, King Edward Medical University, Lahore, Pakistan
College of Nursing, Lahore Institute of Science Technology , Lahore, Pakistan

Abstract - Skin cancer remains a significant global health concern due to its rising incidence and associated mortality. The integration of Deep Learning (DL) into diagnostic practices has demonstrated remarkable promise, particularly in enhancing early detection accuracy. This systematic review critically examines the application of DL methods—primarily Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), and Transfer Learning frameworks—in the context of skin cancer detection. Emphasis is placed on model performance, including ensemble learning, lesion segmentation techniques, and the incorporation of metadata to improve diagnostic precision. Studies utilizing benchmark datasets such as ISIC and HAM10000 report classification accuracies ranging from 83% to 99%. The review also highlights current challenges in traditional diagnostic approaches and discusses how DL technologies can bridge gaps, particularly in resource- constrained settings with limited access to dermatological expertise.

Index Terms - Skin Cancer Detection, Deep Learning, Convolutional Neural Networks (CNNs), Transfer Learning and Medical Image Analysis

The Convergence of Blockchain and IoT: A Review of Potentials and Future Pathways

Pages: 9-14 (6) | [Full Text] PDF (323K)

Lea Yue Wang, Chou Kung Yi

Department of Computer Science and Information Technology (CS&IT), Korea

Abstract - The Internet of Things (IoT) has extended internet connectivity beyond traditional devices and users to include a wide array of everyday objects. With the ability to connect billions of devices simultaneously, IoT significantly enhances data exchange and contributes to improving quality of life. Despite its vast potential, IoT faces several real-world adoption challenges, primarily due to its centralized client-server architecture. This model introduces issues related to scalability and security, as all devices must connect and be authenticated through a central server, creating a single point of failure. To overcome these limitations, a shift toward a decentralized architecture is seen as a promising solution. Blockchain technology, known for its decentralized and distributed nature, offers a robust framework that can address many IoT-related challenges, particularly in security and trust management. This article presents a comprehensive overview of blockchain integration with IoT, highlighting its benefits, associated challenges, and future research directions. We argue that the convergence of blockchain and IoT can pave the way for innovative business models and decentralized applications.

Index Terms - Scalability, Client-Server Architecture, Distributed Systems, Blockchain-IoT Integration, Smart Devices, Future Technologies, Trust Management and Data Integrity

Network Protocols in IoT Systems: An Overview and Comparative Study

Pages: 15-25 (11) | [Full Text] PDF (522K)

Syed Muhammad Saad Ali Shah, M. Junaid Arshad

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

Abstract - Devices with limited resources use IoT by interacting through a varied set of protocols on the OSI stack. This paper gives an extensive review of the main IoT communication protocols, featuring applications layer choices (MQTT, CoAP), adaptations at network/transport (6LoWPAN, RPL, IPv6/UDP), and link-layer methods (BLE, Zigbee, LoRaWAN, NB-IoT, etc.). We evaluate their strengths and usual uses in IoT environments, and talk about challenges like scalability, power consumption, latency, security, and compatibility each protocol must solve. We also review both academic and technological methods meant to tackle these issues, including better protocols, optimizations, and modern solutions such as edge computing and security systems. The article is written in an IEEE-recommended format and contains sections on background, comparative analysis, challenges, solutions, and a conclusion. We found that each IoT protocol has limitations; on the contrary, diverse networking approaches are employed, where each protocol specializes in a subarea and further research focuses on boosting large-scale IoT systems’ performance and interoperability.

Index Terms - Internet of Thing (IoT), MQTT, COAP, 6LOWPAN, BLE, Zigbee, LORAWAN, NB-IoT and Interoperability.

A Comprehensive Review of the Role of Mobile Technology in Enhancing E-Learning for Higher Education: Transforming Digital Education

Pages: 26-30 (5) | [Full Text] PDF (267K)

Kashaf Shakoor, Syeda Masooma Marriam

Data Science Department, University of Engineering and Technology, Lahore

Abstract - This review synthesizes insights from a range of recent studies examining how mobile technologies—particularly social media, artificial intelligence (AI), machine learning (ML), the Internet of Things (IoT), and deep learning—are reshaping higher education. While social media presents mixed outcomes, often linked to reduced academic performance due to distractions [5, 20], AI and IoT-based tools show promise for enhancing student engagement, real-time feedback, and personalized learning . This paper critically examines these technological interventions, highlighting key advancements, current limitations, and future prospects in digitally driven education.

Index Terms - Mobile Technology, E-Learning System, Key Advancement and Limitations

AI-Driven Edge Computing for IOT: Enhancing Intelligence, Efficiency, and Security

Pages: 31-37 (7) | [Full Text] PDF (438K)

Muhammad Amir, Muhammad Junaid

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

Abstract - The Internet of Things revolutionizes industrial practices through automated connected systems. The conventional cloud-centric IoT models encounter multiple barriers including elevated latency and bandwidth constraints while dealing with security flaws. Edge computing with Artificial Intelligence capabilities resolves data processing problems by operating near IoT hardware devices which produces instant decisions and enhanced security in addition to efficient operations. This research investigates Al-driven edge computing for IoT applications through a study of architectural models and practical uses alongside security considerations and foreseeable research concepts. Recent developments in Artificial Intelligence have introduced TinyML and Federated Learning as optimized AI models for edge intelligence. The research provides final conclusions about how 5G-powered Edge AI, Neuromorphic Computing and Quantum AI will advance intelligent IoT systems into the future.

Index Terms - Internet of Thing (IoT), Edge Computing, Artificial Intelligence, TinyML, Federated Learning, Smart Systems and AI-driven IoT

Revolutionizing Telecommunications: The Impact of IoT

Pages: 38-44 (7) | [Full Text] PDF (387K)

Abdullah Haseeb, M.J. Arshad

Department of Computer Science, UET, Lahore, Pakistan

Abstract - The internet of things (IoT) has emerged as a transformative pressure in the telecommunications zone, revolutionizing how networks are based, operated, and utilized. As the number of connected devices continues to grow exponentially, telecommunication networks must evolve to satisfy the needs of this hyper-connected environment. This observation explores the profound effect of IoT at the telecommunications enterprise, specializing in the way it drives innovation, optimizes community performance, and allows new enterprise models. The IoT is predicated closely on sturdy telecommunications infrastructure to make certain seamless verbal exchange between billions of devices. The arrival of the 5G era plays an important role in this modification, presenting ultralow latency, excessive bandwidth, and the potential to support massive tool connectivity. As a result, telecom operators are moving from conventional communique models to IoT-specific services, including low-energy extensive-vicinity networks (LPWAN) and better facts analytics talents. Furthermore, IoT programs, ranging from smart homes and cities to business automation and healthcare, place a giant strain on telecom networks to deliver faster, more reliable, and greater relaxed connectivity. Telecommunications groups are responding by investing in network virtualization, side computing, and records analytics, which permit them to address the surge in IoT visitors and offer real-time insights for companies and purchasers. The examine additionally examines the emerging opportunities for telecom providers, inclusive of new sales streams via IoT as a provider (IoTaaS) and partnerships with IoT device manufacturers. Moreover, it addresses the challenges of scalability, safety, and information privacy in IoT ecosystems, emphasizing the role of telecom in ensuring the strong safety of interconnected gadgets. In the end, the IoT is essentially reshaping telecommunications, supplying enormous demanding situations and thrilling opportunities for growth. Collaboration among telecom operators, IoT developers, and clients will ultimately decide the achievement of this technological revolution.

Index Terms - IoT, Telecommunication, Network, Connectivity, 5G and Smart Devices

Volume 16, Issue 3, July 2025

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