Browsing by Author "Saha, Arpa"

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A Comprehensive Review of Green Computing
(IEEE, 2023-08-01) Paul, Showmick Guha; Saha, Arpa; Arefin, Mohammad Shamsul; Bhuiyan, Touhid; Biswas, Al Amin; Reza, Ahmed Wasif; Alotaibi, Naif M.; Alyami, Salem A.; Moni, Mohammad Ali
Green computing, also called sustainable computing, is the process of developing and optimizing computer chips, systems, networks, and software in such a manner that can maximize efficiency by utilizing energy more efficiently and minimizing the negative environmental influence on the surrounding. The term “green computing” refers to practices that lessen the negative effects of technology on the environment. Due to the improvements in modern technology, various devices, mechanisms, and software have been developed, and lots of studies have been conducted to optimize and increase those technologies’ green computing abilities. Thus, review and summarization of green computing-based studies are required to identify the current advancements, challenges, and future research opportunities. This study reviewed and summarized green computing in each area studies, by exploring green computing’s twelve areas. Current research trends, datasets or testing mechanisms, and the construction or implementation of various technologies to accomplish green computing and sustainable development have been discussed. This study, after conducting a thorough comparison and analysis, provides responses to the proposed state-of-the-art research questions. Furthermore, this study presents the current challenges and future research opportunities with respect to each green computing area. This study will provide organizations, researchers, and institutions conducting research on green computing with insights and ideas. Furthermore, environmental organizations, companies, and government agencies concerned with reducing carbon emissions and energy consumption will also benefit from this review study.
A Real-Time Application-Based Convolutional Neural Network Approach for Tomato Leaf Disease Classification
(Elsevier, 2023-07-26) Paul, Showmick Guha; Biswas, Al Amin; Saha, Arpa; Zulfiker, Md. Sabab; Ritu, Nadia Afrin; Zahan, Ifrat; Rahman, Mushfiqur; Islam, Mohammad Ashraful
Early diagnosis and treatment of tomato leaf diseases increase a plant’s production volume, efficiency, and quality. Misdiagnosis of disease by farmers can lead to an inadequate treatment strategy that hurts the tomato plants and agroecosystem. Therefore, it is crucial to detect the disease precisely. Finding a rapid, accurate approach to take care of the issue of misdiagnosis and early disease identification will be advantageous to the farmers. This study proposed a lightweight custom convolutional neural network (CNN) model and utilized transfer learning (TL)-based models VGG-16 and VGG-19 to classify tomato leaf diseases. In this study, eleven classes, one of which is healthy, are used to simulate various tomato leaf diseases. In addition, an ablation study has been performed in order to find the optimal parameters for the proposed model. Furthermore, evaluation metrics have been used to analyze and compare the performance of the proposed model with the TL-based model. The proposed model, by applying data augmentation techniques, has achieved the highest accuracy and recall of 95.00% among all the models. Finally, the best-performing model has been utilized in order to construct a Web- based and Android-based end-to-end (E2E) system for tomato cultivators to classify tomato leaf disease.
A Real-Time Deep Learning Approach for Classifying Cervical Spine Fractures
(Elsevier, 2023-09-24) Paul, Showmick Guha; Saha, Arpa; Assaduzzaman, Md
The first seven vertebrae of our spine are called the cervical spine. It supports the weight of our head, encloses and safeguards our spinal cord, and permits a variety of head motions. The seven cervical vertebrae are joined at the rear of the bone by a kind of joint known as a facet joint. These joints enable us to move our necks forward, backward, and twist. Fractures of the cervical spine are a medical emergency that may lead to lifelong paralysis or even death. If left untreated and undetected, these fractures can worsen over time. Using computed tomog- raphy, a cervical spine fracture in individuals can be accurately diagnosed. Given the scarcity of research on the practical use of deep learning methods in detecting spine fractures in persons, it is imperative to address this gap. This study uses a dataset containing fracture and normal cervical spine computed tomography images. This study proposed modified transfer-learning-based MobileNetV2, InceptionV3, and Resnet50V2 models. An ablation study was also conducted to determine the optimal custom layers for models and data augmentation techniques. In addition, evaluation metrics have been used to analyze and compare the model’s performance. Among all the approaches, MobileNetV2 with augmentation has achieved the highest accuracy of 99.75%. Furthermore, the best-performing model has been deployed in a smartphone-based Android application
A Systematic Review of Graph Neural Network in Healthcare-Based Applications: Recent Advances, Trends, and Future Directions
(Scopus, 2024-01-16) Saha, Arpa; Hasan, Md. Zahid; Noori, Sheak Rashed Haider; Moustafa, Ahmed
Graph neural network (GNN) is a formidable deep learning framework that enables the analysis and modeling of intricate relationships present in data structured as graphs. In recent years, a burgeoning interest has arisen in exploiting the latent capabilities of GNN for healthcare-based applications, capitalizing on their aptitude for modeling complex relationships and unearthing profound insights from graph-structured data. However, to the best of our knowledge, no study has systemically reviewed the GNN studies conducted in the healthcare domain. This study has furnished an all-encompassing and erudite overview of the prevailing cutting-edge research on GNN in healthcare. Through analysis and assimilation of studies, current research trends, recurrent challenges, and promising future opportunities in GNN for healthcare applications have been identified. China emerged as the leading country to conduct GNN-based studies in the healthcare domain, followed by the USA, UK, and Turkey. Among various aspects of healthcare, disease prediction and drug discovery emerge as the most prominent areas of focus for GNN application, indicating the potential of GNN for advancing diagnostic and therapeutic approaches. This study proposed research questions regarding diverse aspects of GNN in the healthcare domain and addressed them through an in-depth analysis. This study can provide practitioners and researchers with profound insights into the current landscape of GNN applications in healthcare and can guide healthcare institutes, researchers, and governments by demonstrating the ways in which GNN can contribute to the development of effective and efficient healthcare systems
Combating COVID-19 Using Machine Learning and Deep Learning: Applications, Challenges, and Future Perspectives
(IEEE, 2023-03-15) Paul, Showmick Guha; Saha, Arpa; Biswas, Al Amin; Zulfiker, Md. Sabab; Arefin, Mohammad Shamsul; Rahman, Md. Mahfujur; Reza, Ahmed Wasif
COVID-19, a worldwide pandemic that has affected many people and thousands of individuals have died due to COVID-19, during the last two years. Due to the benefits of Artificial Intelligence (AI) in X-ray image interpretation, sound analysis, diagnosis, patient monitoring, and CT image identification, it has been further researched in the area of medical science during the period of COVID-19. This study has assessed the performance and investigated different machine learning (ML), deep learning (DL), and combinations of various ML, DL, and AI approaches that have been employed in recent studies with diverse data formats to combat the problems that have arisen due to the COVID-19 pandemic. Finally, this study shows the comparison among the stand-alone ML and DL-based research works regarding the COVID-19 issues with the combinations of ML, DL, and AI-based research works. After in-depth analysis and comparison, this study responds to the proposed research questions and presents the future research directions in this context. This review work will guide different research groups to develop viable applications based on ML, DL, and AI models, and will also guide healthcare institutes, researchers, and governments by showing them how these techniques can ease the process of tackling the COVID-19.