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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
Sujatha, Shanmugam
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Breast cancer prediction using genetic algorithm and sand cat swarm optimization algorithm Sangeetha, Velu; Vaneeta, Maniyambadi; Mamatha, Arjuna; Shoba, Muniya; Ramamurthy Deepa, Sugatur; Sujatha, Velusamy; Sujatha, Shanmugam
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.pp849-858

Abstract

Breast cancer is the second leading type of cancer, which is mainly found in women and which increases the death rate among women. Early detection and diagnosis of breast cancer can reduce its occurrence and the death rate. Unfortunately, even if cancer treatment is initiated quickly after diagnosis, cancer may relapse because cancer cells may continue to exist in the body, which is also a major problem faced by women who fear facing the same treatment twice. So, detecting cancer at its early stage and predicting the recurrence of it is a major issue in the medical field that needs to be solved. Machine learning (ML) algorithms such as support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), and voting classifier (VC) are used for breast cancer prediction. Due to high-dimension data, the predicted results using Machine learning algorithms will increase the errors and decrease the accuracy. So, bioinspired algorithms such as the genetic algorithm (GA) and sand cat swarm optimization (SCSO) are used to reduce the data dimension. Convolutional neural network (CNN) is used for feature extraction from the image dataset. CNN algorithms are used for feature selection, which selects the important features for classification and prediction by applying 10 cross-validation methods. The proposed model using bioinspired optimization algorithms outcomes will yield high accuracy and the best solution.
Predictive modeling for healthcare worker well-being with cloud computing and machine learning for stress management Sudha, Muthukathan Rajendran; Malini, Gnanamuthu Bai Hema; Sankar, Rangasamy; Mythily, Murugaaboopathy; Kumaresh, Piskala Sathiyamurthy; Varadarajan, Mageshkumar Naarayanasamy; Sujatha, Shanmugam
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.pp1218-1228

Abstract

This paper provides a new method for stress management-focused predictive modeling of healthcare workers' well-being via cloud computing and machine learning. The need for proactive measures to track and assist healthcare workers' mental health is highlighted by the rising expectations placed on them. Using various data sources, our system compiles information from surveys, social media, electronic health records, and wearable devices into a single location for analysis. Predictive models that predict healthcare workers' stress levels and well-being are developed using gradient boosting, a strong machine learning (ML) technique. This work is suitable for gradient boosting due to its resilience to overfitting and capacity to process many kinds of data. Healthcare organizations may improve the health of their employees by using our technology to detect stress patterns and identify the causes of that stress. It can use specific treatments and support systems to alleviate that stress. Widespread adoption and real-time monitoring are made possible by the scalability, flexibility, and accessibility of cloud computing infrastructure. This method shows promise in the direction of proactive solutions driven by data for controlling the stress of healthcare workers and improving their general well-being.
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.
SVM algorithm-based anomaly detection in network logs and firewall logs Jesudasan Peter, John Benito; Rakesh, Nitin; Rekha, Puttaswamy; Sreelatha, Tammineni; Sujatha, Velusamy; Muthumarilakshmi, Surulivelu; Sujatha, Shanmugam
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1642-1651

Abstract

The purpose of many advanced forms of cyberattack is to deceive the monitors, and as a result, these attacks often involve several kinds, levels, and stages. Existing anomaly detection systems often examine logs or traffic for indications of attacks, ignoring any additional analysis regarding attack procedures. This is done to save time. For example, traffic detection technologies can only identify the attack flows in a general sense. Still, they cannot reconstruct the attack event process or expose the present condition of the network node. In addition, the logs kept by the firewall are significant sources of evidence; nevertheless, they are still challenging to decipher. This paper introduces support vector machine algorithm-based Anomaly detection (SVMA) in network logs and firewall logs to provide robust security against cyberattacks. This mechanism consists of three modules: preprocessing, feature selection and anomaly detection. The genetic algorithm (GA) selects the better feature from the input. Finally, the support vector machine (SVM) isolates an anomaly powerfully. The investigational outcomes illustrate that the SVMA minimizes the required time to select the features and enhances the detection accuracy.
Co-Authors Bandaru, Mamatha Jesudasan Peter, John Benito Kumaresh, Piskala Sathiyamurthy Malini, Gnanamuthu Bai Hema Mamatha, Arjuna Merlin Inbamalar, Thayalagaran Mitra Thakur, Gour Sundar Mohankumar, Nagarajan Muthumarilakshmi, Surulivelu Mythily, Murugaaboopathy Nandhini, Nageshvaran Rajasekaran, Mothiram Rajidurai Parvathy, Palaniraj Rakesh, Nitin Ramamurthy Deepa, Sugatur Rekha, Puttaswamy Sabapathy Ranganathan, Chitra Sampathrajan, Rajeshkumar Sangeetha, Velu Sankar, Rangasamy Shoba, Muniya Shobiga, Rajendran Sreelatha, Tammineni Sudha, Muthukathan Rajendran Sujatha, Velusamy Vaneeta, Maniyambadi Varadarajan, Mageshkumar Naarayanasamy