Muhammad Adnan Khan

• Doctor of Engineering
• Professor (Research) at Gachon University

Brain tumor classification is critical for therapeutic applications that benefit from computer-aided diagnostics. Misdiagnosing a brain tumor can significantly reduce a patient's chances of survival, as it may lead to ineffective treatments. This study proposes a novel approach for classifying brain tumors in MRI images using Transfer Learning (TL)...

Background: Industry 4.0's development requires digitalized manufacturing through Predictive Maintenance (PdM) because such practices decrease equipment failures and operational disruptions. However, its effectiveness is hindered by three key challenges: (1) data confidentiality, as traditional methods rely on centralized data sharing, raising conc...

In the field of Natural Language Processing (NLP), the task of text summarization plays a vital role in understanding textual content and producing concise summaries. Text summarization approaches can be categorized as either extractive or abstractive, with the latter largely unexplored in the Arabic script languages. Previous research on abstracti...

This special issue introduces the topic "Digital Transformation and Cybersecurity Challenges," in which the author discusses the intricate relationship between the fast growth in technology and the increase in the demand for strong cybersecurity measures. As entities across a broad spectrum of sectors are increasingly resorting to digital transform...

Higher education in the Gulf Cooperation Council (GCC) is going through big changes as universities try to meet the needs of 21st-century students and society. New technologies give both opportunities and challenges for Arab region universities to develop sustainably. This paper looks at ways to successfully use artificial intelligence (AI) and big...

In modern smart grids and decentralized systems, smart buildings face several key energy management challenges , including data privacy concerns, the need for accurate real-time decisions, the complexity of managing Distributed Energy Resources (DERs), and the lack of transparency in Artificial Intelligence (AI) systems, which erodes user trust. Tr...

Brain Tumours are highly complex, particularly when it comes to their initial and accurate diagnosis, as this determines patient prognosis. Conventional methods rely on MRI and CT scans and employ generic machine learning techniques, which are heavily dependent on feature extraction and require human intervention. These methods may fail in complex...

Artificial Intelligence (AI) innovations in digital health offer unprecedented opportunities to facilitate human health and provide tools and techniques that reduce overall costs. This book discusses the use of AI to improve diagnostic accuracy, patient monitoring, the use of remote diagnostic tools, identification of life-threatening diseases, med...

Cyber threats have become a global concern, and attackers use more efficient, hidden, and destructive techniques. Traditional signature- and rule-based systems struggle to encounter these latest threats, exposing organizations facing severe risks to their financial well-being and national security risks. Although Artificial Intelligence (AI) provid...

For detecting and diagnosing a wide range of ophthalmological diseases, fundus images are used as a primary and basic tool. Early and accurate diagnosis of these ocular diseases can substantially improve the quality of treatment as well as important for preventing permanent vision loss. Changes in the anatomical structures like the optic disc, macu...

Brain tumor detection is essential for early diagnosis and successful treatment, both of which can significantly enhance patient outcomes. To evaluate brain MRI scans and categorize them into four types—pituitary, meningioma, glioma, and normal—this study investigates a potent artificial intelligence (AI) technique. Even though AI has been utilized...

Parkinson’s disease ranks as the second most prevalent neurological disorder after Alzheimer’s disease. Convolutional neural networks (CNNs) have been extensively employed in Parkinson’s disease (PD) detection using MR images. However, CNN models generally focus on local features while prone to capture global representations. On the other hand, the...

This study explores the performance of deep learning models, specifically Convolutional Neural Networks (CNN) and XGBoost, in predicting alpha and beta thalassemia using both public and private datasets. Thalassemia is a genetic disorder that impairs hemoglobin production, leading to anemia and other health complications. Early diagnosis is essenti...

Cardiovascular disease (CVD) is rising as a significant concern for the healthcare sector around the world. Researchers have applied multiple traditional approaches to making healthcare systems find new solutions for the CVD concern. Artificial Intelligence (AI) and blockchain are emerging approaches that may be integrated into the healthcare secto...

Alzheimer’s disease (AD) is one of the primary causes of dementia in the older population, affecting memories, cognitive levels, and the ability to accomplish simple activities gradually. Timely intervention and efficient control of the disease prove to be possible through early diagnosis. The conventional machine learning models designed for AD de...

Accurate load forecasting is crucial for the optimized operation and planning of smart power grids. However, the increasing penetration of renewable energy sources and the emergence of flexible loads like electric vehicles create significant uncertainties and complexities in load patterns. Traditional centralized forecasting models struggle with da...

The increasing sophistication of fraud has rendered rule-based fraud detection obsolete, exposing banks to greater financial risk, reputational damage, and regulatory penalties. Financial stability, customer trust, and compliance are increasingly threatened as centralized Artificial Intelligence (AI) models fail to adapt, leading to inefficiencies,...

Facial expression recognition (FER) has garnered significant attention due to advances in artificial intelligence, particularly in applications like driver monitoring, healthcare, and human-computer interaction, which benefit from deep learning techniques. The motivation of this research is to address the challenges of accurately recognizing emotio...

The current COVID-19 epidemic is responsible for causing a catastrophe on a global scale due to its risky spread. The community's insecurity is growing as a result of a lack of appropriate remedial measures and immunization against the disease. In this case, social distancing is thought to be an effective barrier against the spread of the contagion...

The novel Coronavirus (COVID-19) spread rapidly around the world and caused overwhelming effects on the health and economy of the world. It first appeared in Wuhan city of China and was declared a pandemic by the World Health Organization (WHO). Many researchers, as well as experts in clinical and artificial intelligence experts, are working togeth...

Diabetic retinopathy stands as a leading cause of blindness among people. Manual examination of DR images is labor-intensive and prone to error. Existing methods to detect this disease often rely on handcrafted features which limit the adaptability and classification accuracy. Thus, the aim of this research is to develop an automated and efficient...

Network security is crucial in today’s digital world, since there are multiple ongoing threats to sensitive data and vital infrastructure. The aim of this study to improve network security by combining methods for instruction detection from machine learning (ML) and deep learning (DL). Attackers have tried to breach security systems by accessing ne...

Skin diseases impact millions of people around the world and pose a severe risk to public health. These diseases have a wide range of effects on the skin’s structure, functionality, and appearance. Identifying and predicting skin diseases are laborious processes that require a complete physical examination, a review of the patient’s medical history...

This Special Issue focuses on advancing research on algorithms, with a particular emphasis on feature selection techniques [...]

The worldwide health epidemic of anaemia which is a condition with low levels of red blood cells or haemoglobin requires accurate prediction models to act promptly and improve patient outcomes because it is widespread and has different causes. The effective management of anaemia is piled with obstructions, which may include the variability of diagn...

Chronic disease significantly affects health on a global scale. Deep machine learning algorithms have found widespread application in the diagnosis of chronic diseases. Early diagnosis and treatment reduce the chance of a disease getting worse and, as a result, raise related mortality. The main objective of this work is to present a deep machine le...

This study introduces a novel approach to traffic congestion detection using Reinforcement Learning (RL) of machine learning classifiers enhanced by Explainable Artificial Intelligence (XAI) techniques in Smart City (SC). Conventional traffic management systems rely on static rules, and heuristics face challenges in dynamically addressing urban tra...

Problem Skin lesions are the major indicator for diagnosing different skin diseases, which are caused by the abnormal growth of skin cells. Skin cancer, one of the most fatal types of cancer in the world, relies on the proper diagnosis of skin lesions and other relevant disease indicators for early detection, which can enhance treatment and increas...

The expanding importance of technology, particularly mobile banking, in the financial industry, is examined in this literature review, as well as the crucial role that cybersecurity knowledge plays in protecting online transactions. Users now have the flexibility to conduct payments whenever and wherever they wish thanks to the advent of mobile ban...

Urban traffic congestion presents a range of vital difficulties requiring precise prediction models in order to facilitate traffic management for Autonomous Vehicles. This work introduces a novel framework that regulates a Long Short-Term Memory (LSTM) system with methods provided by Explainable Artificial Intelligence (XAI) to explain traffic cong...

Energy is fundamental to all significant human endeavors and is crucial for sustaining life and realizing human potential. With the advent of smart homes, energy consumption is increasing as new technologies are introduced, leading to shifts in both lifestyle and societal norms. This scenario presents a unique energy challenge that requires extraor...

Background: X-chromosomal short tandem repeats (X-STRs) are crucial in forensic applications, particularly in complex kinship cases, and play an important role in population genetics. However, there is limited data on X-STR variation in Pakistani populations, especially among ethnic groups like Kashmiri and Punjabi. Methodology: This study investig...

The early prediction of ocular disease is certainly an obligatory concern in the domain of ophthalmic medicine. Although modern scientific discoveries have shown the potential to treat eye diseases by using artificial intelligence (AI) and machine learning, explainable AI remains a crucial challenge confronting this area of research. Although some...

Diabetic retinopathy is one of the most common causes of vision complications and blindness which pose considerable diagnostic difficulties because of its diverse and faint manifestations. Some of them include the fact that the disease displays a non-uniform pattern, where patients present different symptoms; the requirement of highly qualified spe...

In the recent era, the practical implementation of Autonomous Vehicular Networks (AVNs) with the vulnerable Vehicle-to-Vehicle (V2V) communication of autonomous vehicles and inadequate intelligent decision-making systems has become a primary concern in smart city mobility. This has led to a worsening traffic congestion nightmare with its associates...

Understanding human activities in daily life is of utmost importance, especially in the context of personalized and adaptive ubiquitous learning. Although existing HAR systems perform well-identifying activities based on their inter-spatial and temporal relationships, they lack in identifying the importance of accurately detecting postural transiti...

Business process modeling is used to model business processes using Business Process Modeling Notation (BPMN), which is a widely accepted standard for process modeling. BPMN elements are visually represented by the existing model, but the expressiveness of elements in terms of communication between the participants of the business process is a prob...

Emerging Industry 5.0 designs promote artificial intelligence services and data-driven applications across multiple places with varying ownership that need special data protection and privacy considerations to prevent the disclosure of private information to outsiders. Due to this, federated learning offers a method for improving machine-learning m...

Histopathology images are visual representations of tissue samples that have been processed and examined under a microscope in order to establish diagnoses for various disorders. These images are categorized by deep transfer learning due to the absence of big annotated datasets. There are some classifiers such as softmax and Support Vector Machine...

🔒SCI: Call for Papers-Advanced Algorithms for Feature Selection in Machine Learning Journal: CMC-Computers, Materials & Continua (SCI IF=2.0) 🔗SI URL: https://www.techscience.com/cmc/special_detail/feature_selection 📅 Submission Deadline: 30 July 2025 🌟 Guest Editors: Dr. Muhammad Adnan Khan, Gachon University, Republic of Korea 🔍 Summary: Thi...

This study addresses the increasing problems of traffic congestion in smart cities by introducing a Secure and Transparent Traffic Congestion Control System using federated learning. Traffic congestion control systems face key issues such as data privacy, security vulnerabilities, and the necessity for joint decision-making. Federated learning, a t...

The precise prediction of energy consumption is crucial for businesses, companies and households especially when it comes to planning energy purchases. An underestimated or overestimated forecast value may result in the use of energy inefficiently. The companies will face financial consequences for inefficient energy usage because energy production...

In recent years, the infrastructure of Wireless Internet of Sensor Networks (WIoSNs) has been more complicated owing to developments in the internet and devices’ connectivity. To effectively prepare, control, hold and optimize wireless sensor networks, a better assessment needs to be conducted. The field of artificial intelligence has made a great...

The field of fiber optic networks has seen significant growth and advancements in recent years, particularly with the emergence of new technologies that require higher bandwidth and low-latency connections. The design and implementation of a flexible and reconfigurable fiber optic network is essential to cater to the increasing demands of modern co...

Air pollution, which is both environmental and social, is a serious issue that affects people's health as well as ecosystems and the environment. Air pollution currently poses a number of health problems to the ecosystem. The most important factor that has a direct impact on disease occurrence and decreases people's quality of life is city and metr...

The agricultural sector is pivotal to food security and economic stability worldwide. Corn holds particular significance in the global food industry, especially in developing countries where agriculture is a cornerstone of the economy. However, corn crops are vulnerable to various diseases that can significantly reduce yields. Early detection and p...

Sentiment Analysis is a crucial area of study within the realm of Computer Science. With the rapid advancement of Information Technology and the prevalence of social media, a substantial volume of textual comments has emerged on web platforms and social networks such as Twitter. Consequently, individuals have become increasingly active in dissemina...

This study examines the vital role of accurate load forecasting in the energy planning of smart cities. It introduces a hybrid approach that uses machine learning (ML) to forecast electricity usage in homes, improving accuracy through the extraction of correlated features. The accuracy of predictions is assessed using loss functions and the root me...

Dear Colleagues, For this Special Issue, we seek papers concerning current advances in feature selection algorithms for high-dimensional settings, as well as review papers that will motivate ongoing efforts to grasp the challenges commonly faced in this field. High-quality articles that address both theoretical and practical challenges relating to...

Cervical cancer is an intrusive cancer that imitates various women around the world. Cervical cancer ranks in the fourth position because of the leading death cause in its premature stages. The cervix which is the lower end of the vagina that connects the uterus and vagina forms a cancerous tumor very slowly. This pre-mature cancerous tumor in the...

A stable and sustainable food supply chain is only possible with effective agriculture management. The application of technology in agriculture has recently produced encouraging outcomes in terms of improving agricultural yield and quality. Early detection and characterization of plant leaf diseases, which have a substantial influence on agricultur...

Artificial intelligence (AI) is poised to play an increasingly pivotal role in reshaping and enhancing the way we communicate, fostering more intuitive, efficient, and personalized interactions across various domains. In this chapter, we delve into the future of AI in communication, exploring its transformative potential across a spectrum of domain...

Healthcare 5.0 represents the next phase in healthcare evolution. It aims to harness the creativity and expertise of healthcare professionals, integrating them with efficient, intelligent, and precise technologies. This integration allows for resource-efficient and patient-centered approaches, surpassing previous paradigms in healthcare. To provide...

Cardiotocography measures the fetal heart rate in the fetus during pregnancy to ensure physical health because cardiotocography gives data about fetal heart rate and uterine shrinkages which is very beneficial to detect whether the fetus is normal or suspect or pathologic. Various cardiotocography measures infer wrongly and give wrong predictions b...

The abnormality of haemoglobin in the human body is the fundamental cause of thalassemia disease. Thalassemia is considered a common genetic blood condition that has received extensive investigation in medical research globally. Likely, inherited disorders will be passed down to children from their parents. If both parents are beta Thalassemia carr...

A kidney stone is a solid formation that can lead to kidney failure, severe pain, and reduced quality of life from urinary system blockages. While medical experts can interpret kidney-ureter-bladder (KUB) X-ray images, specific images pose challenges for human detection, requiring significant analysis time. Consequently, developing a detection syst...

Recently, the improvement of network technology and the spread of digital documents have made the technology for automatically correcting English texts very important. In English language processing, finding and fixing mistakes in the meaning of words is a very interesting and important job. It is also important to fix wrong data in cleaning data....

Dibenzyltoluene (H0-DBT), a Liquid Organic Hydrogen Carrier (LOHC), presents an attractive solution for hydrogen storage due to its enhanced safety and ability to store hydrogen in a concentrated liquid form. The utilization of machine learning proves essential for accurately predicting hydrogen storage classes in H0-DBT across diverse experimental...

Energy management is an inspiring domain in developing of renewable energy sources. However, the growth of decentralized energy production is revealing an increased complexity for power grid managers, inferring more quality and reliability to regulate electricity flows and less imbalance between electricity production and demand. The major objectiv...

The strain on healthcare systems is growing dramatically due to the significant rise in heterogeneous chronic disease incidence. Even though frequent patient monitoring is complex and expensive, analyzing chronic illness transitions and predicting patients at risk of developing ailments is crucial to healthcare. This paper proposes an innovative pr...

Nanotechnologists and medical researchers are working hard to develop new and innovative ways to use nanorobots as nanomedicine to improve healthcare outcomes and revolutionize the field of therapeutics. Nanotechnology has the potential to revolutionize healthcare by providing new ways of treating chronic diseases in the field of medicine. A “Gold...

Digital technologies present unrivaled opportunities to improve healthcare services worldwide. Medical devices and hospitals are now using innovative techniques to diagnose cancer patients. Despite the vast amount of data generated, stored, and communicated to the cloud and edge devices, patient data privacy remains a crucial concern. Federated lea...

The perhydro-dibenzyltoluene (H18-DBT) exhibits promising potential as a viable option for hydrogen production purposes. There are several important features for hydrogen generation predictions including dosage of H18-DBT, temperature, concentration of catalyst, and stirring speed. This study presents the Hydrogen Production Prediction System Empow...

In this study, a weighted federated learning approach is proposed for electrocardiogram (ECG) arrhythmia classification. The proposed approach considers the heterogeneity of data distribution among multiple clients in federated learning settings. The weight of each client is dynamically adjusted according to its contribution to the global model imp...

Sentiment Analysis is a crucial area of study within the realm of Computer Science. With the rapid advancement of Information Technology and the prevalence of social media, a substantial volume of textual comments has emerged on web platforms and social networks such as Twitter. Consequently, individuals have become increasingly active in dissemina...

This paper delves into the crucial need for robust security measures to protect web services from cyber threats that can compromise their availability, veracity, and privacy. The introduction highlights the inadequacy of existing solutions that are limited to specific use areas and emphasizes the importance of web services being able to respond eff...

Road torments are ample cause of deterioration of pavements structure. As they are not assessed and recovered on time. Progress can be easily assessed by the strong infrastructure of roads. But this infrastructure requires a lot of effort not only in construction but also in maintenance. In this paper our cause to detect main cause of road destruct...

Over the past few decades, home automation systems have gained significant popularity due to their ability to enhance comfort and improve the quality of life. However, with the increasing reliance on internet connectivity, these systems face challenges in coping with the expanding attack surfaces and the corresponding attacks. This paper provides a...

In response to the demand for impartial and precise personality testing, this study presents a unique multi-modal method for predicting personality traits in collaborative settings. Conventional approaches that depend on surveys frequently create biases, which has led to the investigation of raw, subconscious open writing as a rich source of person...

The application of artificial intelligence (AI) in the medical field has received a great deal of interest in recent years. AI has demonstrated positive outcomes in a variety of healthcare applications, including diagnostics, drug development, medical image analysis, and personalized therapy, among others. On the other hand, the lack of transparenc...

Breast cancer (BC) is the most widespread tumor in females worldwide and is a severe public health issue. BC is the leading reason of death affecting females between the ages of 20 to 59 around the world. Early detection and therapy can help women receive effective treatment and, as a result, decrease the rate of breast cancer disease. The cancer t...

The title of our book is AIoT Innovations in Digital Health: Emerging Trends, Challenges, and Solutions. Our book mainly focuses on the techniques of green computing, big data, AI, IoT, machine learning, deep learning, fuzzy logic, ANN, and optimization techniques that can be used in digital healthcare industry. However, this proposed book offers n...

Technology improvements have benefited the medical industry, especially in the area of diabetes prediction. In order to find patterns and risk factors related to diabetes, machine learning and Artificial Intelligence (AI) are vital in the analysis of enormous volumes of data, including medical records, lifestyle variables, and biomarkers. This make...