M. Shamim Kaiser

• PhD, MS | Senior Member-IEEE
• Professor at Jahangirnagar University

Optical coherence tomography (OCT) scanning is crucial for the diagnosis of widespread ophthalmic diseases. Traditionally, experts manually identify diseases and biomarkers from OCT scans. Recently, modern medical imaging practices have increasingly utilized deep learning techniques to speed up and improve diagnostic accuracy in ophthalmology. Howe...

Students with disabilities often face challenges in participating in classroom activities with normal students. Assistive technologies powered by Artificial Intelligence (AI) or Machine Learning (ML) can provide vital support to ensure inclusive and equitable learning environments. In this paper, we identify AI or ML-powered inclusive education too...

Automatically generating image captions poses one of the most challenging applications within artificial intelligence due to its integration of computer vision and natural language processing algorithms. This task becomes notably more formidable when dealing with a language as intricate as Bengali and the overall scarcity of Bengali-captioned image...

In a dynamic environment, path planning for Unmanned Aerial Vehicles (UAVs) is challenging due to obstacles and changing conditions. To solve this, bio-inspired metaheuristic algorithms such as Ant Colony Optimization (ACO) have shown promising result in solving optimization problems. In this paper we have presented ACO-based path planning algorith...

Alzheimer’s Disease (AD) is a progressive neurological disease that severely impairs cognitive function. Early detection is critical for effective treatment and management. Machine Learning (ML) methods are often used to ensure early detection and prediction. However, ML has various issues, including the data island problem. The fragmentation that...

The paper presents a framework of microservices-based architecture dedicated to enhancing the performance of real-time travel reservation systems using the power of predictive analytics. Traditional monolithic systems are bad at scaling and performing with high loads, causing backup resources to be underutilized along with delays. To overcome the a...

The rapid growth of the travel industry has increased the need for real-time optimization in reservation systems that could take care of huge data and transaction volumes. This study proposes a hybrid framework that ut folds an Artificial Intelligence and a Microservices approach for the performance optimization of the system. The AI algorithms for...

The increasing demand for scalable, efficient resource management in hybrid cloud environments has led to the exploration of AI-driven approaches for dynamic resource allocation. This paper presents an AI-driven framework for resource allocation among microservices in hybrid cloud platforms. The framework employs reinforcement learning (RL)-based r...

The growing complexity of the operations of airline reservations requires a smart solution for the adoption of novel approaches to the development of quick, efficient, and adaptive reservation systems. This paper outlines in detail a conceptual framework for the implementation of edge computing microservices in order to address the shortcomings of...

This research proposes the development of a next generation airline reservation system that incorporates the Cloud microservices, distributed artificial intelligence modules and the blockchain technology to improve on the efficiency, safety and customer satisfaction. The traditional reservation systems encounter issues related to the expansion of t...

This research investigates the implementation of a real-time, microservices-oriented dynamic pricing system for the travel sector. The system is designed to address factors such as demand, competitor pricing, and other external circumstances in real-time. Both controlled simulation and real-life application showed a respectable gain of 22% in reven...

In today’s world of software development technologies, serverless cloud computing with microservices is a big deal. Microservices gives modularity, agility and scalability, while serverless allows to code without having to maintain any internal hardware infrastructure, so processes are streamlined and costs are reduced by 60% compared to traditiona...

This paper introduces a microservices architecture for the purpose of enhancing the flexibility and performance of an airline reservation system. The architectural design incorporates Redis cache technologies, two different messaging systems (Kafka and RabbitMQ), two types of storages (MongoDB, and PostgreSQL). It also introduces authorization tech...

Fake news detection is a critical challenge in the digital age, where misinformation spreads rapidly, causing real-world harm. In the context of the Bangla language, this problem is exacerbated by the scarcity of labeled data for model training. This paper introduces an enriched dataset of Bangla fake news, containing 7000 authentic and 1000 fake n...

Alzheimer's disease is a significant public health concern, and early detection is crucial for effective intervention. In this paper, we explore the application of ensemble learning approaches to classify Alzheimer's disease in brain imaging data(MRI images). We employed several pre-trained deep learning models, including VGG-19, ResNet-152, Effici...

Performance evaluations are conducted with the intention of establishing each worker's level of commitment to the company. Numerous businesses are challenged by the issue of employee attrition, which occurs when talented workers with years of expertise leave the firm on a regular basis. On the other hand, employee turnover can be caused by a wide v...

Skin cancer poses a significant threat to human health, particularly when early identification and accurate diagnosis are lacking. Early detection is pivotal for successful treatment, given the potential lethality of this disease. While skin cancer is commonly associated with sun-exposed areas, it can also manifest on regions shielded from sunlight...

Chronic Kidney Disease (CKD) poses significant health risks, particularly for elderly and middle-aged individuals, leading to gradual kidney damage and reduced renal function. CKD's impact on AQ1 morbidity and mortality rates underscores the urgent need for early diagnosis. This study proposes a machine learning-based prediction system leveraging k...

The aftermath of primary cancer treatment presents a multitude of challenges for patients, necessitating prolonged recovery periods that can span months or even years. Survivors contend with a range of debilitating side effects, including fatigue, constant pain, lym-phedema, weight fluctuations, swallowing difficulties, and menopause symptoms. Thes...

The detection of varied hand gestures in heterogeneous settings plays a crucial role in enhancing human-computer interaction. AQ1 Leveraging the YOLO-V4 model for object detection offers the potential for faster performance and improved accuracy, which are paramount in the field of Artificial Intelligence. This paper presents an approach AQ2 to tra...

The integration of cutting-edge technologies such as the Internet of Things (IoT), robotics, and machine learning (ML) has the potential to significantly enhance the productivity and profitability of traditional fish farming. Farmers using traditional fish farming methods incur enormous economic costs owing to labor-intensive schedule monitoring an...

Methylation is considered one of the proteins’ most important post-translational modifications (PTM). Plasticity and cellular dynamics are among the many traits that are regulated by methylation. Currently, methylation sites are identified using experimental approaches. However, these methods are time-consuming and expensive. With the use of comput...

A brain tumor is a dangerous condition that can be challenging to reliably identify using conventional techniques, such as by looking at MRI scans. To solve this problem, our convolutional neural network (CNN) and transfer learning models were developed to distinguish between the three types of brain cancers that are most frequently found: gliomas,...

Stroke is a disease that affects the arteries leading to and within the brain. Detecting Stroke early and conveniently is much more difficult as there is no portable system to detect it. Most of the time the expensive diagnosis method of stroke is out of reach for low-and middle-income countries like ours. Hence, there is a significant necessity fo...

A brain tumor is a dangerous condition that can be challenging to reliably identify using conventional techniques, such as by looking at MRI scans. To solve this problem, our convolutional neural network (CNN) and transfer learning models were developed to distinguish between the three types of brain cancers that are most frequently found: gliomas,...

Stroke is a disease that affects the arteries leading to and within the brain. Detecting stroke early and conveniently is much more difficult as there is no portable system to detect it. Most of the time the expensive diagnosis method of stroke is out of reach for low- and middle-income countries like ours. Hence, there is a significant necessity f...

The biomedical profession has gained importance due to the rapid and accurate diagnosis of clinical patients using computer-aided diagnosis (CAD) tools. The diagnosis and treatment of Alzheimer’s disease (AD) using complementary multimodalities can improve the quality of life and mental state of patients. In this study, we integrated a lightweight...

Brain hemorrhage refers to a potentially fatal medical disorder that affects millions of individuals. The percentage of patients who survive can be significantly raised with the prompt identification of brain hemorrhages, due to image-guided radiography, which has emerged as the predominant treatment modality in clinical practice. A Computed Tomogr...

Explainable artificial intelligence is beneficial in converting opaque machine learning models into transparent ones and outlining how each one makes decisions in the healthcare industry. To comprehend the variables that affect decision-making regarding diabetes prediction that can be accounted for by model-agnostic techniques. In this project, we...

The Underwater sensor network (UWSN), also known as Marine Sensor Network (MSN), is gaining increasing attention due to its applications in the monitoring of the marine environment and assisting Marine Intelligent Transportation Systems (MITS). Such systems provide in-vehicle assistance services (i.e., traffic monitoring and driver alerts) by gathe...

Background According to the World Health Organization (WHO), dementia is the seventh leading reason of death among all illnesses and one of the leading causes of disability among the world’s elderly people. Day by day the number of Alzheimer’s patients is rising. Considering the increasing rate and the dangers, Alzheimer’s disease should be diagnos...

Recent advancements in the manufacturing and commercialisation of miniaturised sensors and low-cost wearables have enabled an effortless monitoring of lifestyle by detecting and analysing physiological signals. Heart rate variability (HRV) denotes the time interval between consecutive heartbeats.The HRV signal, as detected by the sensors and device...

Fall causes trauma or critical injury among the geriatric population which is a second leading accidental cause of post-injury mortality around the world. It is crucial to keep elderly people under supervision by ensuring proper privacy and comfort. Thus the elderly fall detection and prediction using wearable/ non-wearable sensors become an active...

Alzheimer’s disease (AD) is a progressive and irreversible neurological disorder that affects millions of people worldwide. Early detection and accurate diagnosis of AD are crucial for effective treatment and management of the disease. In this paper, we propose a transfer learning-based approach for the diagnosis of AD using magnetic resonance imag...

Alzheimer’s disease (AD) is a common form of dementia that affects brain regions that control cognition, memory, and language. Globally, more than 55 million people are suffering from dementia. Given these troubling statistics, predicting AD is critical for future medications and treatment. Mild Cognitive Impairment (MCI) is a vital stage for patie...

The Internet of Things (IoT) has transformed not only the way we communicate and operate our devices, but it has also brought us significant security challenges. A typical IoT network architecture consists of four levels: a device, a network, an application, and a service, each with its own security considerations. There are three types of IoT netw...

Skin disease is a common health condition of the human body that greatly affects people’s life. Early and accurate disease diagnosis can help the patients in applying timely treatment thus resulting in quick recovery. Recent developments in deep learning-based convolutional neural networks (CNN) have significantly improved the disease classificatio...

Blockchain technology enables a distributed and decentralized environment with no more central authority. To increase the accuracy of electronic healthcare records (EHRs) and establish a secured patient-centric approach, Blockchain can be a smart solution in this case. Blockchain is a distributed ledger technology that allows for the secured transf...

Alzheimer’s disease is a neurocognitive disease that results from the brain shrinking and brain tissue dying over time. It gradually erodes memory, thinking skills, and the ability to carry out the most basic tasks. The use of an MRI to evaluate brain atrophy is thought to be a reliable way to diagnose and track the progression of Alzheimer’s disea...

Machine learning (ML), sensors networks, and Internet of Things (IoT) are the most important contributor in the newest revolution in the industry. It is going towards a fully automated industrial environment where all the components including post production, pre production, supply chain and quality control would be automatically managed. Human wil...

The availability of large-scale datasets, massively parallelizable GPUs and a wide spectrum of open-source tools have immensely capacitated Artificial Intelligence (AI) field in accurate analytics of medical data. Although it is a challenging task, the application of powerful AI-based computer-aided diagnosis has enabled health practitioners to pre...

Cloud storage helps clients reduce the burden of data management and storage, attracting more data owners to keep their private information on the cloud. Among the massive amounts of confidential information that are being created rapidly nowadays, storage efficiency has become a primary concern for reliable data deduplication. Numerous deduplicati...

Among the tons of articles that are published every year, a considerable number of substandard articles are also published. One of the primary reasons for publishing these substandard articles is due to applying ineffective and/or inefficient reviewer selection processes. To overcome this problem, several reviewer recommender systems are proposed t...

A mortality prediction model can be a great tool to assist physicians in decision making in the intensive care unit (ICU) in order to ensure optimal allocation of ICU resources according to the patient’s health conditions. The entire world witnessed a severe ICU patient capacity crisis a few years ago during the COVID-19 pandemic. Various widely ut...

Stress is a condition that causes a specific physiological response. Heart rate variability (HRV) is a critical aspect in identifying stress. It is crucial for those who want to keep track of their wellness. Currently, numerous research is being conducted on stress prediction from HRV. The existing works in this field cover different data sets to i...

The plasmonic antenna probe is constructed using a silver rod embedded in a modified Mach-Zehnder interferometer (MZI) ad-drop filter. Rabi antennas are formed when space-time control reaches two levels of system oscillation and can be used as human brain sensor probes. Photonic neural networks are designed using brain-Rabi antenna communication, a...

Gaussian Process Regression (GPR), a Bayesian nonparametric machine learning modelling technique, is gaining interest in recent times in many fields as a practical and powerful approach. To plan for economic services for any nation, projections of future Life Expectancy (LE) are required. In our research, we have proposed a model to forecast LE usi...

Over the years, physiological signals have shown its efficiency in emotion recognition. Galvanic skin response (GSR) is a quantifiable physiological signal generated from the change of skin conductance in response to emotional stimulation. Understanding human emotions through GSR signals can be a challenging task because of the characteristic’s com...

Facial expression recognition is an intriguing research area that has been explored and utilized in a wide range of applications such as health, security, and human-computer interactions. The ability to recognize facial expressions accurately is crucial for human-computer interactions. However, most of the facial expression analysis techniques have...

The application driven technology wireless sensor networks (WSNs) are developed substantially in the last decades. The technology has drawn the attention for application in the scientific as well as in industrial domains. The networks use multifunctional and cheap sensor nodes. The application of the networks ranges from military to the civilian ap...

COVID-19 has affected many people across the globe. Though vaccines are available now, early detection of the disease plays a vital role in the better management of COVID-19 patients. An Artificial Neural Network (ANN) powered Computer Aided Diagnosis (CAD) system can automate the detection pipeline accounting for accurate diagnosis, overcoming the...

Epilepsy is a neurological condition affecting around 50 million individuals worldwide, reported by the World Health Organization. This is identified as a hypersensitive disease by clinical associations. The unique characteristics of Electroencephalography have proven to be stable and universal; therefore, researchers have a lot of credibilities. S...

The novel coronavirus disease (COVID-19) pandemic is provoking a prevalent consequence on mental health because of less interaction among people, economic collapse, negativity, fear of losing jobs, and death of the near and dear ones. To express their mental state, people often are using social media as one of the preferred means. Due to reduced ou...

Polycystic Ovary Syndrome (PCOS) is a critical hormonal disorder of women that significantly impacts life. In this new generation, women are more prone to PCOS. It is the cause of various problems, including infertility. Early detection of PCOS can reduce complexity. Therefore, an early and proper PCOS detection system is essential to minimize comp...

Mental health has become a major concern in recent years. Social media have been increasingly used as platforms to gain insight into a person's mental health condition by analysing the posts and comments, which are textual in nature. By analysing these texts, depressive posts can be detected. To facilitate this process, this work presents an attent...

The Coastal Patrol and Surveillance Application (CPSA) is developed and deployed to detect, track and monitor water vessel traffic using automated devices. The latest advancements of marine technologies, including Automatic Underwater Vehicles, have encouraged the development of this type of applications. To facilitate their operations, installatio...

With the rapid growth of scientific publications, researchers often find difficulty in discovering appropriate articles that can mitigate the knowledge gaps to understand a target article (a.k.a., base article in this paper). In this case, reference articles can play an important role. It may happen that a researcher may have to read several levels...

Internet scams have been a major concern for everyone over the past decade. With the advancement of technology, attackers have formulated different kinds of contemporary fraudulent procedures to obtain user’s sensitive information. Phishing is one of the oldest and common fraudulent attempts by which every year millions of internet users fall victi...

According to the World Health Organisation, depression is the prime contributor to mental disability worldwide. Depression is a severe threat to people’s public and private lives because it causes catastrophic alterations in feelings and emotions. The recent rise in mental health issues and major depressive disorder has spurred many depression dete...

Autism Spectrum Disorder (ASD) refers to a spectrum of conditions characterised mainly by impairments in social interaction, speech and nonverbal communication, and restricted—repetitive behaviour. The lack of physical testing, done primarily via behaviour analysis, makes ASD diagnosis more difficult. The emergence of Computational Intelligence tec...

This special issue editorial introduces the latest development in emerging technologies of biomedical engineering, including big medical data, artificial intelligence, cloud/fog computing, federated learning, ubiquitous computing and communication, internet of things, wireless technologies, and security and privacy. In this special issue, nine manu...

Stress is identified as one of the most common human responses to physical, mental or emotional pressure. Long-term stress can cause cardiovascular diseases, depression, anxiety and even death. Stress can be recognized by observing physiological activity data and social media posts of individuals. This explorative study is performed to find the eff...

Coronavirus disease (COVID-19) is a major concern now. According to the Globe Health Organization, the coronavirus (COVID-19) epidemic is straining healthcare systems worldwide (WHO). Early-stage detection using artificial intelligence of this virus will help in the fast recovery. Early identification of this infection utilizing artificial intellig...

Parkinson’s disease (PD) is a neurological condition that causes tremors, stiffness, and difficulty walking, balancing, and coordinating. There is no cure for PD, and that is why it is important to look for early signs so that it can be mitigated right from the beginning. Previous works have used different data to detect PD, but we have focused on...

Social media has become an integral part of our day-to-day life. In our activities or posts on social media, the presence of hate speech written in the native language or English has increased significantly. It often leads to the spread of negativity, depression, or even sometimes considered cybercrime. In this paper, a hybrid deep learning approac...

The health monitoring for disease diagnosis and prognosis in a desired smart medical structure is realized by interpreting the health data. The advances in sensor technologies and biomedical data acquisition tools have led to the new era of big data, where different sensors collect massive medical data every day. This special issue explores the lat...

Mental health has become a major concern in recent years. Social media have been increasingly used as platforms to gain insight into a person’s mental health condition by analysing the posts and comments, which are textual in nature. By analysing these texts, depressive posts can be detected. To facilitate this process, this work presents an attent...

Schizophrenia (SZ) is a major neuropsychiatric disorder. Neuroimaging studies have provided compelling evidences of both structural and functional brain abnormalities in SZ. However, most existing studies are based on functional connectivity (FC) of brain regions under an implicit assumption of stationary of FC throughout the time. Recent studies h...

Heart disease is the lead cause of death of a human, and pre-detection can save lives and reduce future health-related complexities. A machine learning (ML) based algorithm can find a pattern among different health-related parameters and provide an accurate prediction of a heart attack. This paper investigates various ML algorithms which suit most...

Brain signals are recorded using different techniques to aid an accurate understanding of brain function and to treat its disorders. Untargeted internal and external sources contaminate the acquired signals during the recording process. Often termed as artefacts, these contaminations cause serious hindrances in decoding the recorded signals; hence,...

Underwater robots, also known as autonomous underwater vehicles (AUVs), are amazing machines that are becoming increasingly important in a variety of disciplines, including sea environment exploration, data collection (for climate change), industry, and the military. Wireless nodes and relay nodes are randomly distributed and incorrectly selected,...