Journal of Emerging Technology and Digital Transformation

QUANTUM POWERED FORENSICS: A NEW AGE OF CYBER INVESTIGATION

Muhammad Ahsan Naeem — 2025-07-08

The evolution of quantum computing presents both critical risks and opportunities for digital forensics. This study addresses the urgent need to adapt forensic science in response to quantum threats, especially the vulnerabilities in classical encryption standards exposed by quantum algorithms like Shor’s and Grover’s. This conceptual paper develops an integrated framework that links Quantum Computing Capability (QCC), Quantum-Resistant Cryptography (QRC), Digital Evidence Integrity (DEI), and Forensic Analysis Accuracy (FAA), moderated by Investigator Readiness and Skills (IRS) and Legal and Regulatory Adaptability (LRA). The research builds on theories from quantum mechanics and forensic science to model how QCC can both challenge and enhance forensic processes. Key findings highlight that QRC plays a crucial mediating role in preserving evidence authenticity under quantum threats. The originality of the study lies in its holistic framework, combining legal, technological, and human readiness dimensions. This framework offers strategic value for policymakers, legal institutions, and forensic professionals aiming to secure digital evidence in a post-quantum era. Future work may test this model empirically through SEM/PLS-SEM to guide technological adoption and regulatory reforms. Ultimately, the study contributes a forward-looking roadmap for resilient, quantum-compatible forensic systems.

Keywords: Quantum Computing Capability, Digital Forensics, Quantum-Resistant Cryptography, Evidence Integrity, Legal Adaptability, Post-Quantum Security.

A COMPARATIVE ANALYSIS OF FUNDAMENTAL CONCEPTS OF OPERATING SYSTEM

Asna Riaz — 2025-07-22

This paper provides a comparative review of various fundamental cores of operating system, including Time Sharing, Multitasking, Kernel mode, User mode, Threads, File systems, Virtual memory, Paging, Paging and Swapping, Page hit, Page miss. The review synthesizes findings from 40 research and review papers, examining these fundamental core’s performance across different applications, methodologies, and optimization tasks. The discussion highlights key findings, identifies research gaps, and suggests future research.

Keywords – Time Sharing, Multitasking, Kernel mode, User mode, Threads, File systems, Virtual memory, Paging, Paging and Swapping, Page hit, Page miss.

THE EVOLUTION OF SDMS: TRENDS, TRADE-OFFS, AND FUTURE DIRECTIONS

Abdulrehman Arif — 2025-07-22

Software development has undergone significant paradigm shifts, transitioning from rigid, linear models to adaptive, iterative methodologies. This paper critically examines Software Development Methodologies (SDMs) by evaluating key developments, their associated strengths and limitations, and future trajectories. Unlike prior literature, which often presents uncritical endorsements of Agile methods, this study focuses on real-world implementation, contextual fit, and long-term sustainability. Drawing from academic sources, industry reports, and hybrid case studies, the analysis challenges mainstream success narratives and highlights the methodological trade-offs involved in SDM selection. Particular attention is given to the limitations of Agile in distributed and highly regulated environments, where hybrid models such as DevOps and SAFe have emerged as more context-appropriate solutions. The study advocates empirical, future-oriented approaches to guide the development and application of SDMs in increasingly complex and dynamic software engineering settings.

Keywords: Software Development Methodologies (SDMs), Agile vs. Waterfall, Hybrid Approaches, Methodological Trade-offs, Future Trends in Software Engineering

REVOLUTIONIZING DERMATOLOGY: ADVANCED DEEP LEARNING TECHNIQUES FOR AUTOMATED SKIN DISEASE DETECTION

Faseh Ur Rehman — 2025-07-29

Skin is the largest tissue in the humanoid body, and it protects body from external damage, regulates body temperature, and provides the sense of touch. It is made up of three layers: the epidermis, dermis, and subcutaneous tissue. Skin cancer is a disorder in which tissue cells grow abnormally and would extent to additional portions of body. It is typically caused by solar system or tanning bed exposure to ultraviolet (UV) radiation. Early detection of skin cancer is crucial because it increases the likelihood of successful cure and treatment. Although advanced stages of the disease are more difficult to treat and have a worst diagnosis. Regular skin self-examinations, combined with professional skin checks, can aid in the early detection of skin cancer. Machine learning has been used for detection of skin cancer. But it works on features, for which we must first extract the features from the raw data. Feature engineering is a time consuming and challenging task. Deep learning is an emerging area and subfield of artificial intelligence, which extracts useful features from raw data on its own. But it is quite time-consuming task to train such complex deep learning methods and requires huge computational resources. In this work, we have proposed a deep learning model based on ensemble learning for detection of skin cancer. In this model, we used the ensemble learning method for combining four different popular CNN architectures namely InceptionV3, ResNet50, Efficient net and Xception. In this study, we use HAM10000 dataset for our experiments. First, when we train these classifiers, our proposed model was not performing better due to presence of unbalancing in the data. After balancing the dataset, our proposed model was performing better in terms of accuracy, precision, recall and F1-score. On balanced dataset, our proposed model gave an accurate score of 0.94, Precision score of 0.93, Recall and F1-score of 0.94.

Keywords—Skin Cancer, Machine Learning, Deep Learning, HAM10000 dataset, Medical Imaging, Convolutional Neural Networks (CNN), Cancer Diagnosisy

DEEP CONVOLUTIONAL NEURAL NETWORKS FOR CAVITY AND PIT DETECTION IN DENTAL IMAGES

Muhammad Maaz Ali Khan — 2025-07-29

If it is not addressed, several oral issues can arise from dental caries, one of the most prevalent oral disorders. However, access to professional dental care is often limited, particularly in underserved communities. Our proposed AI-based solution empowers individuals to monitor their oral health and detect early signs of cavities. Children are particularly susceptible to pits and caries in permanent molars, which mostly arise in the cavities on the occlusal, buccal, and palatal surfaces of molars. To address this challenge, we propose developing an AI-powered solution that utilizes smartphone cameras to capture and analyze dental images for cavity detection. This research enables image processing techniques and computer vision algorithms to identify and classify various cavities, including early-stage lesions and enamel defects. Using the Teeth Cave Convolutional Neural Network (CAVTee-CNN), which was particularly designed for dental cavity detection, we were able to minimize information loss during the preprocessing of images. This custom network was developed through extensive experimentation with convolutional layers, activation functions, and pooling mechanisms adapted to highlight dental features. The CAVTee-CNN Model enhances both efficiency and accuracy; it yields the best result of 85.2. By providing timely and accurate cavity detection, the application can empower users to make informed decisions about their oral health and seek appropriate dental care when necessary.