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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Mohankumar, Nagarajan
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Advancing chronic pain relief cloud-based remote management with machine learning in healthcare Mohankumar, Nagarajan; Reddy Narani, Sandeep; Asha, Soundararajan; Arivazhagan, Selvam; Rajanarayanan, Subramanian; Padmanaban, Kuppan; Murugan, Subbiah
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1042-1052

Abstract

Healthcare providers face a significant challenge in the treatment of chronic pain, requiring creative responses to enhance patient outcomes and streamline healthcare delivery. It suggests using cloud-based remote management with machine learning (ML) to alleviate chronic pain. Wearable device data, electronic health record (EHR) data, and patient-reported outcomes are all inputs into the suggested system’s data analysis pipeline, which combines support vector machines (SVM) with recurrent neural networks (RNN). SVM’s powerful classification skills make it possible to classify patients’ risks and predict how they will react to therapy. RNNs are very good at processing sequential data, which means they may identify trends in patient symptoms and drug adherence over time. By integrating these algorithms, healthcare professionals may create individualized treatment programs that consider each patient’s preferences and specific requirements. Early intervention and proactive treatment of pain symptoms are made possible by the system’s ability to monitor patients in real-time remotely. The system is further improved by using predictive analytics to identify patients who could benefit from extra support services and to forecast when they will have acute pain episodes. The proposed approach can change the game regarding managing chronic pain. It provides data-driven, individualized treatment that improves patient outcomes while cutting healthcare expenses.
Deep learning for skin melanoma classification using dermoscopic images in different color spaces Manikandan, Sankarakutti Palanichamy; Narani, Sandeep Reddy; Karthikeyan, Sakthivel; Mohankumar, Nagarajan
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp319-327

Abstract

Skin cancer begins in the skin cells. The damage to the skin cells can cause genetic mutations that lead to uncontrolled growth and the formation of tumors. It is estimated that millions of people are diagnosed with skin cancer of different kinds each year. The earlier a skin cancer is diagnosed, the better the patient's prognosis and the lower their chance of complications. In this work, an efficient deep learning classification (EDLCS) to classify dermoscopic images is developed. The importance of color in the diagnosis of skin melanoma has caused color analysis to attract considerable attention from researchers of image-based skin melanoma analysis. Three different color spaces such as red-green-blue (RGB), hue-saturation-lightness (HIS) and LAB are investigated in this study. The obtained dermoscopic images are in RGB color space. The RGB dermoscopic images are first converted into HSV and LAB spaces to investigate the HSV and LAB color spaces for melanoma classification. Then, the color space converted image is fed to the proposed EDLCS to evaluate their performances. Results show that the proposed EDLCS provides 99.58% accuracy while using the LAB color model to classify preprocessed images while other models provide 99.17%.
Deep learning for infectious disease surveillance integrating internet of things for rapid response Sumithra, Subramanian; Radhika, Moorthy; Venkatesh, Gandavadi; Lakshmi, Babu Seetha; Jancee, Balraj Victoria; Mohankumar, Nagarajan; Murugan, Subbiah
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp1175-1186

Abstract

Particularly in the case of emerging infectious diseases and worldwide pandemics, infectious disease monitoring is essential for quick identification and efficient response to epidemics. Improving surveillance systems for quick reaction might be possible with the help of new deep learning and internet of things (IoT) technologies. This paper introduces an infectious disease monitoring architecture based on deep learning coupled with IoT devices to facilitate early diagnosis and proactive intervention measures. This approach uses recurrent neural networks (RNNs) to identify temporal patterns suggestive of infectious disease outbreaks by analyzing sequential data retrieved from IoT devices like smart thermometers and wearable sensors. To identify small changes in health markers and forecast the development of diseases, RNN architectures with long short-term memory (LSTM) networks are used to capture long-range relationships in the data. Spatial analysis permits the integration of geographic data from IoT devices, allowing for the identification of infection hotspots and the tracking of afflicted persons' movements. Quick action steps like focused testing, contact tracing, and medical resource deployment are prompted by abnormalities detected early by real-time monitoring and analysis. Preventing or lessening the severity of infectious disease outbreaks is the goal of the planned monitoring system, which would enhance public health readiness and response capacities.
Evaluating tumor heterogeneity in oncology with genomic-imaging and cloud-based genomic algorithms Gurulakshmanan, Gurumoorthi; Amarnath, Raveendra N.; Lebaka, Sivaprasad; Reddy, Munnangi Koti; Mohankumar, Nagarajan; Muthumarilakshmi, Surulivelu; Srinivasan, Chelliah
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp2427-2435

Abstract

The goal of this initiative is to rethink how oncology is traditionally practiced by integrating novel approaches to genomic imaging with cloud-based genomic algorithms. The research intends to give a thorough knowledge of cancer biology by focusing on the decoding of tumor heterogeneity as its primary objective. It is possible to get a more nuanced understanding of the intricacy of tumors via the integration of high-resolution imaging tools and sophisticated genetic analysis. It is a pioneering use of cloud computing, which enables the quick analysis of large genomic information. The major goal is to decipher the complex genetic variants that are present inside tumors in order to direct the creation of individualized treatment strategies. This discovery marks a significant step forward, since it successfully bridges the gap between genetics and imaging. Diagnostic accuracy and treatment effectiveness have both been improved. This innovative technique permits real-time analysis, which in turn enables treatment tactics to be adjusted in a timely manner. It makes a significant contribution to the continuous development of oncological research as well as its translation into better clinical outcomes for cancer patients.
Convolutional neural network based encoder-decoder for efficient real-time object detection Rajasekaran, Mothiram; Sabapathy Ranganathan, Chitra; Mohankumar, Nagarajan; Sampathrajan, Rajeshkumar; Merlin Inbamalar, Thayalagaran; Nandhini, Nageshvaran; Sujatha, Shanmugam
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp1960-1967

Abstract

Convolutional neural networks (CNN) are applied to a variety of computer vision problems, such as object recognition, image classification, semantic segmentation, and many others. One of the most important and difficult issues in computer vision, object detection, has attracted a lot of attention lately. Object detection validating the occurrence of the object in the picture or video and then properly locating it for recognition. However, under certain circumstances, such as when an item has issues like occlusion, distortion, or small size, there may still be subpar detection performance. This work aims to propose an efficient deep learning model with CNN and encoder decoder for efficient object detection. The proposed model is experimented on Microsoft Common Objects in Context (MS-COCO) dataset and achieved mean average precision (mAP) of about 54.1% and accuracy of 99%. The investigational outcomes amply showed that the suggested mechanism could achieve a high detection efficiency compared with the existing techniques and needed little computational resources.
Hybrid semantic model based on machine learning for sentiment classification of consumer reviews Rajidurai Parvathy, Palaniraj; Mohankumar, Nagarajan; Shobiga, Rajendran; Mitra Thakur, Gour Sundar; Bandaru, Mamatha; Sujatha, Velusamy; Sujatha, Shanmugam
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp2001-2011

Abstract

Digital information is regularly produced from a variety of sources, including social media and customer service reviews. For the purpose of increasing customer happiness, this written data must be processed to extract user comments. Consumers typically share comments and thoughts about consumable items, technological goods, and services supplied for payment in the modern period of consumerism with simple access to social networking globe. Each object has a plethora of remarks or thoughts that demand special attention due to their sentimental worth, especially in the written portions. The goal of the current project is to do sentiment prediction on the Amazon Electronics, Kindle, and Gift Card datasets. In order to predict sentiment and evaluate utilizing many executions evaluates admitting accuracy, recall, and F1-score, a hybrid soft voting ensemble method that combines lexical and ensemble methodologies is proposed in this study. In addition to calculating a subjectivity score and sentiment score, this study also suggests a non-interpretive sentiment class label that may be used to assess the sign of the evaluations applying suggested method for sentiment categorization. The effectiveness of our suggested ensemble model is examined using datasets from Amazon customer product reviews, and we found an improvement of 2-5% in accuracy compared to the current state-of-the-art ensemble method.
Co-Authors Amarnath, Raveendra N. Arivazhagan, Selvam Asha, Soundararajan Bandaru, Mamatha Gurulakshmanan, Gurumoorthi Jancee, Balraj Victoria Karthikeyan, Sakthivel Lakshmi, Babu Seetha Lebaka, Sivaprasad Manikandan, Sankarakutti Palanichamy Merlin Inbamalar, Thayalagaran Mitra Thakur, Gour Sundar Murugan, Subbiah Muthumarilakshmi, Surulivelu Nandhini, Nageshvaran Narani, Sandeep Reddy Padmanaban, Kuppan Radhika, Moorthy Rajanarayanan, Subramanian Rajasekaran, Mothiram Rajidurai Parvathy, Palaniraj Reddy Narani, Sandeep Reddy, Munnangi Koti Sabapathy Ranganathan, Chitra Sampathrajan, Rajeshkumar Shobiga, Rajendran Srinivasan, Chelliah Sujatha, Shanmugam Sujatha, Velusamy Sumithra, Subramanian Venkatesh, Gandavadi