Article
Green & Sustainable Science & Technology
Karpagam Sundararajan, Kathiravan Srinivasan
Summary: Drought has a direct impact on environmental sustainability. Predicting drought early on can help in implementing drought mitigation plans. In India, the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) are used to predict meteorological drought. This study evaluates the ability of these indices to predict meteorological drought in Tamil Nadu using 62 years of data.
Review
Computer Science, Information Systems
Shriniket Dixit, Khitij Bohre, Yashbir Singh, Yassine Himeur, Wathiq Mansoor, Shadi Atalla, Kathiravan Srinivasan
Summary: Parkinson's disease is a devastating neurological disease that requires the development of a faster, less expensive diagnostic instrument. This article provides a thorough analysis of AI-based ML and DL techniques used to diagnose PD and their influence on developing additional research directions.
Review
Medicine, General & Internal
Saransh Bhachawat, Eashwar Shriram, Kathiravan Srinivasan, Yuh-Chung Hu
Summary: Degenerative nerve diseases like Alzheimer's and Parkinson's have become a global concern, affecting approximately 1/6th of the world's population. Early detection through machine learning algorithms, which can infer based on patient data and history, is crucial for effective treatment. The use of machine learning and deep learning in the diagnosis of these diseases has shown promising results.
Article
Public, Environmental & Occupational Health
Venkatesan Rajinikanth, P. M. Durai Raj Vincent, Kathiravan Srinivasan, G. Ananth Prabhu, Chuan-Yu Chang
Summary: Cancer rates in the kidney are on the rise, and accurate detection and management are crucial. This study focuses on developing a framework to classify renal CT images using deep-learning schemes, with a pre-processing scheme to improve accuracy. The experimental results show that the KNN classifier achieves 100% detection accuracy with the pre-processed CT slices, making it clinically significant.
FRONTIERS IN PUBLIC HEALTH
(2023)
Article
Public, Environmental & Occupational Health
Sanchit Vijay, Thejineaswar Guhan, Kathiravan Srinivasan, P. M. Durai Raj Vincent, Chuan-Yu Chang
Summary: Brain tumor diagnosis has been time-consuming, but automating the segmentation process can speed it up. This paper introduces SPP-U-Net, a model that replaces residual connections with a combination of Spatial Pyramid Pooling (SPP) and Attention blocks, allowing for greater context and scope in the segmentation. The proposed approach achieves comparable results to existing literature without increasing training parameters.
FRONTIERS IN PUBLIC HEALTH
(2023)
Article
Engineering, Aerospace
Arindam Bhattacharyya, Shvetha M. Nambiar, Ritwik Ojha, Amogh Gyaneshwar, Utkarsh Chadha, Kathiravan Srinivasan
Summary: The recent trend of creating an interconnected world through satellites has reignited interest in satellite communications. Private and government-funded space agencies are making progress in developing satellite constellations, and the introduction of 5G technology has brought attention to the concept of a fully connected world. Satellites are seen as solutions to establish high-speed and low-latency connections in remote and inaccessible areas. However, the increasing number of satellites in Earth's orbit has raised concerns and calls for highly adaptive and flexible satellite systems. This review explores the utilization of Machine Learning (ML) and Deep Learning (DL) in satellite communications, covering the implementation of satellite communication subsystems and other applications through Artificial Intelligence (AI), as well as ongoing challenges and future directions.
INTERNATIONAL JOURNAL OF SATELLITE COMMUNICATIONS AND NETWORKING
(2023)
Review
Green & Sustainable Science & Technology
Amogh Gyaneshwar, Anirudh Mishra, Utkarsh Chadha, P. M. Durai Raj Vincent, Venkatesan Rajinikanth, Ganapathy Pattukandan Ganapathy, Kathiravan Srinivasan
Summary: Deep learning models have proven to be effective in drought forecasting, providing more accurate and timely predictions to mitigate the impacts of drought on crop failure, water shortages, and economic losses.
Review
Oncology
Navneet Melarkode, Kathiravan Srinivasan, Saeed Mian Qaisar, Pawel Plawiak
Summary: This research aims to explore the use of deep learning and machine learning techniques for diagnosing skin cancer. It discusses the challenges and future directions in this field, as well as compares widely used datasets and review papers on skin cancer diagnosis using AI. The authors of this study aim to establish a benchmark for further research in this field and address the limitations and benefits of historical approaches.
Review
Medicine, General & Internal
Shriniket Dixit, Anant Kumar, Kathiravan Srinivasan
Summary: Recent advancements in machine learning techniques in the field of cancer diagnosis have provided valuable insights for efficient and accurate treatment decision-making. Gene expression data for cancer detection has improved due to advancements in sequencing technologies. Oral cancer, affecting different parts of the body, is the sixth most prevalent form of cancer globally. Excessive tobacco use and smoking are the main causes of oral cancer. Early detection of oral cancer can save many lives. Artificial intelligence can assist in early cancer detection through accurate analysis of large datasets from various imaging modalities. This review explores the implementation of AI for early cancer detection and treatment, along with performance evaluations of deep learning and machine learning models. The limitations and potential applications of AI in oral cancer research are also discussed.
Article
Medicine, General & Internal
A. Angel Nancy, Dakshanamoorthy Ravindran, Durai Raj Vincent, Kathiravan Srinivasan, Chuan-Yu Chang
Summary: The fast-paced technology trend has led to continuous transformation, with cloud computing being the prime provider of various services on a pay-per-use basis. Cloud computing supports the internet of things (IoT) by providing computation and storage capabilities. The inclusion of decentralized fog computing addresses latency and connectivity issues in the cloud-IoT interaction. In the healthcare domain, a fog-assisted smart healthcare system combining fuzzy inference system (FIS) and recurrent neural network (RNN) variants has shown significant improvements in performance, achieving a classification accuracy of 99.125%.
Article
Medicine, General & Internal
Venkatesan Rajinikanth, P. M. Durai Raj Vincent, C. N. Gnanaprakasam, Kathiravan Srinivasan, Chuan-Yu Chang
Summary: This research aims to develop an efficient deep-learning-based brain-tumor detection scheme using FLAIR- and T2-modality MRI slices. The scheme includes preprocessing, deep-feature extraction, tumor segmentation, feature optimization, and binary classification. Experimental results show that the integrated feature-based scheme achieves a classification accuracy of 99.6667% when using a support-vector-machine classifier.
Article
Medicine, General & Internal
Jayakumar Kaliappan, Apoorva Reddy Bagepalli, Shubh Almal, Rishabh Mishra, Yuh-Chung Hu, Kathiravan Srinivasan
Summary: Intrauterine fetal demise is a significant issue in developing and underdeveloped countries, and machine learning models can help detect it. This study used 22 features from fetal heart rate obtained from CTG for 2126 patients and applied various cross-validation techniques to enhance the performance of ML algorithms. Gradient Boosting and Voting Classifier achieved 99% accuracy after cross-validation.
Review
Medicine, General & Internal
Somit Jain, Dharmik Naicker, Ritu Raj, Vedanshu Patel, Yuh-Chung Hu, Kathiravan Srinivasan, Chun-Ping Jen
Summary: Cancer is a dangerous disease that can have negative consequences for the body, is a leading cause of death, and is difficult to detect. Doctors use different methods, including imaging tests, to diagnose cancer. This article evaluates computational-intelligence approaches in cancer diagnosis using machine learning and deep learning models, and explores their advantages and disadvantages. Despite some clinical issues, artificial intelligence has significant potential to enhance cancer imaging and diagnosis.
Article
Chemistry, Analytical
Erana Veerappa Dinesh Subramaniam, Kathiravan Srinivasan, Saeed Mian Qaisar, Pawel Plawiak
Summary: The emergence of the Internet of Medical Things (IoMT) has enabled remote patient diagnosis and treatment using mobile-device-collected data. However, concerns about patient privacy arise when using traditional AI systems in this context. To address this issue, we propose a privacy-enhanced approach for illness diagnosis within the IoMT framework, improving IoT network performance and ensuring data confidentiality using various techniques. Our approach shows substantial enhancements in network performance metrics compared with previous works, emphasizing the effectiveness of our method in enhancing IoT network interoperability and protection, and improving patient care and diagnostic capabilities.
Review
Health Care Sciences & Services
Ritik Kumar, Divyangi Singh, Kathiravan Srinivasan, Yuh-Chung Hu
Summary: Blockchain technology has experienced substantial growth in the past decade, finding applications in various fields for its security and privacy features. In the healthcare industry, blockchain has been used for secure data logging, transactions, and maintenance with smart contracts. This review explores the integration of artificial intelligence (AI) with blockchain and discusses its applications in healthcare, including EHR management, telemedicine, genomics, drug research, specialized imaging, and outbreak prediction.
