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2nd International Conference on Emerging Trends in Information technology and Engineering, ic-ETITE 2024

作者： Ranathive, S. Vidhya, R. Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur India

ISBN: (纸本)9798350328202

Wireless sensor networks (WSNs) have arisen as a feasible technology in healthcare applications, due to recent developments in wireless networking, wireless sensors, and low-power systems with networks. Securing these networks is essential for practical deployment in healthcare applications since the data transferred in these wireless body area networks (WBAN) frequently consists of sensitive and important patient health and personal information. The objective of this study is to create and develop sophisticated intrusion detection methods that will increase protection in WBAN. In this study, we offer an intrusion detection system (IDS) that utilises Ens-learnHealth to offer the best identification of attacks for such environments. The suggested approach ensures that the intrusion detection process uses exactly the features required for identifying a particular attack, reducing the difficulty of computation. As a result, it is found that Ens-learnHealth achieves 99.7% of accuracy, 98.4% of precision, 95% of recall,96% of F1-score, and 21% of miss rate. © 2024 IEEE.

关键词： Wireless sensor networks

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Customized Transfer Learning Models for Oral Squamous Cell Carcinoma Classification and Detection using Histopathological H&E Stained Images

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SN Computer science 2024年第7期5卷 1-17页

作者： Ramya, Singaraju Minu, R.I. Magesh, K.T. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Oral and Maxillofacial Pathology SRM Kattankulathur Dental College and Hospital Chennai India

According to global statistics, “oral cancer” is one of the most prevalent forms of cancer worldwide. India ranks second globally in mouth cancer incidence, accounting for approximately 33% of all cases. Various factors such as tobacco use, excessive alcohol consumption, poor oral hygiene, betel quid usage, and viral infections influence oral squamous cell carcinoma (OSCC), the most common type of “oral cancer”. Early detection of OSCC greatly enhances treatment outcomes and therapeutic interventions. In our research, we developed a Transfer Learning (TL) model to efficiently and accurately detect cancer lesions promptly. We evaluated and compared a range of advanced pre-trained TL models, including ResNet50, DenseNet121, InceptionResNetV2, VGG16, MobileNetV2, Xception, VGG19, and InceptionV3, along with customized models. After customization, the DenseNet121 and InceptionResNetV2 models demonstrated superior classification performance compared to the other models. The proposed models demonstrated exceptional training, testing, and validation ACC of 99%, 98%, and 97%, respectively. The results show that the method using the DenseNet121 and InceptionResNetV2 models is excellent at automatically sorting data about OSCC. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Histopathological images Normal images Oral cancer OSCC images TL

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A review on lung carcinoma segmentation and classification using CT image based on deep learning

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International Journal of Intelligent Systems technologies and Applications 2022年第5期20卷 394-413页

作者： Poonkodi, S. Kanchana, M. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Tamil Nadu Kattankulathur India

Lung carcinoma was the first leading cause of death when compared to all other cancer. Early-stage detection of a lung nodule is an important step to prevent death and to increase the survival rate of patients with lung cancer. Various types of radiology techniques are used to acquire the image of lung nodules. Among the radiology technique, computed tomography (CT) is an effective method for diagnosing lung carcinoma at its early stage, thus reducing the mortality rate. Radiologists have faced a challenging task, that is, to calculate the accuracy of images due to the exponential growth of CT images. Nowadays, various computer vision techniques are available for the prediction and detection of carcinoma. Deep learning (DL) model provides a high level of services to the healthcare sector. DL techniques are becoming more efficient to detect and predict diseases at an early stage. This study reviews current work on the segmentation and classification of lung nodules in CT using DL models. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Diseases

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Evaluation of Data Mining Classification Algorithm by Monitoring Fall Detection Anomaly using Discriminative Approach 14

Evaluation of Data Mining Classification Algorithm by Monito...

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14th International Conference on Advances in computing, Control, and Telecommunication technologies, ACT 2023

作者： Poorni, R. Madhavan, P. School of Computing SRM Institute of Science and Technology Tamilnadu Kattankulathur India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Tamilnadu Kattankulathur India

Data mining plays a vital role in various sectors such as health care, finance, business, intrusion detection, scientific analysis and research. The user health behavior can be classified using various data mining models so that the analysis can be used for future predictions on the healthcare unit. The proposed model compares and evaluates various discriminative classification algorithms and shows the result of best classification model by applying it to the fall detection anomaly dataset. © Grenze Scientific Society, 2023.

关键词： Data mining

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Detecting a suitable path method for assisted localization techniques in transmission of data over wireless sensor networks

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International Journal of Information technology (Singapore) 2025年第4期17卷 2235-2242页

作者： Navaneethan, S. Arun, A. Department of Networking & Communications School of Computing SRM Institute of Science and Technology SRM Nagar Tamilnadu KattanKulathur 603203 India Department of Networking & Communications School of Computing Faculty of Engineering & Technology SRM Institute of Science and Technology Tamilnadu KattanKulathur 603203 India

In many Wireless Sensor Networks (WSNs) applications, the relevant sensor node’s location information is essential in determining where the event or situation occurs. Therefore, localization is one of the critical challenges in WSNs. Mobile Anchor Node Assisted Localization (MANAL) is one of the promising solutions for the localization of statically deployed sensors. The main problem in MANAL is that the path planning of the Mobile Anchor Node (MAN) should be done in a way that the localization error in the network will be minimal and that all unknown nodes in the network are covered. In this paper, a new path planning approach called Nested Hexagons Curves (NH-exCurves) is proposed for MANAL. NH-exCurves guarantees that it will receive messages from at least three non-collinear anchors to locate all unknown nodes in the network. The proposed model is compared to six different path planning schemes using Accuracy Priority Trilateration (APT) under different evaluation criteria. In these comparisons, first of all, localization errors of the models are compared using some statistical concepts. Secondly, the variation of the localization error according to parameters such as resolution (R) and the standard deviation of noise (σ) are observed. The simulation results present the superiority of the proposed work over existing path planning models used in this study. © Bharati Vidyapeeth's institute of Computer Applications and Management 2025.

关键词： Accuracy priority trilateration (APT) Localization Mobile anchor node assisted localization (MANAL) Static path planning Wireless sensor networks (WSNs)

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Determination and Classification of Diabetes Mellitus Applying Ensemble Machine Learning Classifiers 1

Determination and Classification of Diabetes Mellitus Applyi...

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1st International Conference on Cognitive, Green and Ubiquitous computing, IC-CGU 2024

作者： Patel, Siddharth Hassan, Zayd Iniyan, S. SRM Institute of Science and Technology Department of Computer Science and Engineering Kaatankulathur Tamilnadu Chennai India School of Computing SRM Institute of Science and Technology Department of Computing Technologies Kaatankulathur Tamilnadu Chennai India

ISBN: (纸本)9798350361780

This paper addresses the accurate and early prediction of the onset and progression of diabetes, an important aspect in the management of this chronic metabolic disease given a variety of information like the history of the diseased person and his day-to-day routine factors by using state-of-the-art machine learning techniques to obtain predictive dynamic models. The proposed work not only demonstrates the effectiveness of different algorithms but also highlights the importance of feature engineering to refine predictions. This work uses ensemble classifiers and voting classifier algorithms to develop better accuracy for diabetes prediction. Combining the results of multiple base classifiers like KNN, SVM, RFC, and decision tree ensemble methods aims to enhance the predictive ability and chances of improvement in the patient. The treatment adopted is different from patient to patient based on the result of the treatment. In this research, we utilize a combination of ensemble techniques and a voting classifier to construct a comprehensive diabetes prediction model. This model is evaluated using real-world data. Our findings reveal the exceptional effectiveness of ensemble classifiers and the voting classifier, underscoring their potential to achieve precision and the possibility of recovery and probability. These outcomes not only enable early intervention but also offer valuable insights into the care of individual patients. © 2024 IEEE.

关键词： Decision trees

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An effective object detection and tracking using automated image annotation with inception based faster R-CNN model

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International Journal of Cognitive computing in Engineering 2024年第1期5卷 343-356页

作者： Vijiyakumar, K. Govindasamy, V. Akila, V. Department of Computing Technologies School of computing SRM institute of Science and Technology Kattankulathur campus Tamilnadu Chengalpattu district India Department of Information Technology Pondicherry Engineering College Pondicherry India Department of Computer Science and Engineering Pondicherry Engineering College Pondicherry India

The present study advances object detection and tracking techniques by proposing a novel model combining Automated Image Annotation with Inception v2-based Faster RCNN (AIA-IFRCNN). The research methodology utilizes the DCF-CSRT model for image annotation, Faster RCNN for object detection, and the inception v2 model for feature extraction, followed by a softmax layer for image classification. The proposed AIA-IFRCNN model is evaluated on three benchmark datasets: Bird (Dataset 1), UCSDped2 (Dataset 2), and Under Water (Dataset 3), to determine prediction accuracy, annotation time, Center Location Error (CLE), and Overlap Rate (OR). The experimental results indicate that the AIA-IFRCNN model outperformed existing models regarding detection accuracy and tracking performance. Notably, it achieved a maximum detection accuracy of 95.62 % on Dataset 1, outperforming other models. Additionally, it achieved minimum average CLE values of 4.16, 5.78, and 3.54, and higher overlap rates of 0.92, 0.90, and 0.94 on the respective datasets (1, 2 and 3). Hence, this research work on object detection and tracking using the AIA-IFRCNN model is essential for improving system efficiency and fostering innovation in the field of computer vision and object tracking. © 2024

关键词： Image annotation

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Improving Early Detection of Lung Disorders: A Multi-head Self-Attention CNN-BiLSTM Model

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Journal of The Institution of Engineers (India): Series B 2024年第3期105卷 595-607页

作者： Indumathi, V. Siva, R. School of Computing Department of Computational Intelligence SRM Institute of Science and Technology Tamil Nadu Kattankulathur603 203 India

Respiratory system diseases are a leading cause of increased mortality, morbidity, and disability rates globally. Lung disorders occur due to constant exposure of the lungs to harmful agents present in the ambient air. Early diagnosis is the only prevention measure to avoid the risk of health issues. In recent times, numerous technologies exist to treat pulmonary disease in the early stages. However, evaluating pulmonary microstructural variations is considered a challenging task in routine clinical practice. In this paper, we propose a novel automated pulmonary disease prediction system using the convolutional neural network-based bidirectional neural network (CNN-BiLSTM) with multi-head self-attention layer approach to address the above-mentioned issue. We implemented this novel approach on four medical image datasets, namely, chest X-ray 14, contrast-enhanced CT (CECT), PET/CT, and the Japanese Society of Radiological technology (JSRT) databases. The proposed approach predicts diseased instances from the image datasets and classifies them as normal cases and predicted diseases of multiple classes, which include pneumonia, lung cancer, COVID-19, asthma, etc. To attain high classification accuracy, we preprocess the datasets using normalization and standardization pipelines. The existing methods such as DLSA, VDSNet, SLIC, and DNN models offer accuracy of 92.5%, 93.7%, 93.2%, 93.5%, and 91.8%, 93.8%, 94%, and 91% on the chest X-ray and JSRT datasets, respectively. The proposed CNN-BiLSTM with multi-head self-attention layer approach offers an accuracy of 94.9%, 97.8%, 97.9%, and 94.6% in the chest X-ray, PET/CT, CECT, and JSRT datasets, respectively. © The Institution of Engineers (India) 2024.

关键词： Pulmonary diseases

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Agri-Ontology: A Novel Ontology Framework for Agricultural Domain 3

Agri-Ontology: A Novel Ontology Framework for Agricultural D...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Deepa, R. Jayaraman, Venkatesh Srinivasulu, Senduru Umamageswari, B. Surendran, R. Srm Institute of Science & Technology School of Computing Department of Computing Technologies Kattankulathur Tamilnadu Chennai603203 India Chennai Institute of Technology Department of Computer Science and Engineering Chennai600069 India Sathyabama Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai600119 India Srm Institute of Science & Technology School of Computing Department of Computing Technologies Chennai600119 India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331509675

The agricultural information system deals with massive amounts of data from heterogeneous sources. It helps the farmers gain accurate information by providing better insights. A significant issue in agricultural data is its unstructured form, which can be resolved through ontology - a novel framework known as AgriOntology is proposed to address this issue for the Agrovoc dataset. The data in the unstructured form is initially pre-processed through the Natural Language Processing approach to generate features for ontology construction. The BERT and Jaccard similarity are applied to establish a relationship between the features used to construct the Agri-Ontology. Post ontology construction, the BiGAN framework is used to analyze the performance. The performance of the constructed ontology is observed to have an accuracy of 94.64%, which is higher than the existing models. © 2025 IEEE.

关键词： Data assimilation

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Markov enhanced I-LSTM approach for effective anomaly detection for time series sensor data

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International Journal of Intelligent Networks 2024年第1期5卷 154-160页

作者： Shanmuganathan, V. Suresh, A. Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur ChengalpattuTamil Nadu603203 India

Users could engage and interact with their immediate surroundings without effort in smart settings. The emergence of intelligent technologies along with software-based services has made this possible. It is clear that technical advancements have ushered in a new era for both computer processing and sensor technology, facilitating the concept of smart surroundings. Even though their implementation faces a number of obstacles, numerous expansive projects are working to advance their adoption. The problem of anomalies in the sensor data could result inappropriate decisions and could lead unamicable situations to the users. Many such algorithms are already there, which does not provide satisfactory predictions for the sensor data for the time series data. Time series anomaly detection problems are typically stated as finding outlier data points in comparison to some norm or typical signal. Better anomaly detection in time series data is provided by the proposed Markov and enhanced LSTM technique. The Markov model and the enhanced LSTM offer accurate predictions for extra- and short-term data, which is highly useful in situations involving intelligent environments. When compared to the KNN algorithm, the technique offers reduced MAE, RMSE, MSE and MAPE errors. The algorithm also performs better than other LSTM and RNN methods. The proposed algorithm provides 0.00047 reduced error in humidity data, 0.00416 reduced error in temperature and 0.01771 reduced MAE value in case of light intensity when comparing with the KNN algorithm. © 2024 The Authors

关键词： Anomaly detection

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