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ABES: attention bi-directional ensemble SVM for early detection of brain tumors

Neural Computing and Applications 2024年第26期36卷 16179-16193页

作者： Subramaniam, Erana Veerappa Dinesh Krishnasamy, Valarmathi Department of Computer Science and Business Systems Ramco Institute of Technology Rajapalayam India Department of Electronics and Communication Engineering P.S.R. Engineering College Sivakasi India

Brain tumor is the most serious and deadly disease, and it is formed due to abnormal cell production. There are two different sorts of tumors including benign (non-cancerous) and malignant (cancerous), and the third level of a brain tumor is cancerous, which is a highly deadly form of cancer. Detecting brain tumors early is crucial for accurate diagnosis and efficient treatment planning. Although numerous techniques are established, the prediction techniques are time-consuming, have higher computational complexity, prone to overfitting, and have limited accuracy. Thus we proposed an Attention Bi-directional Gated Recurrent Unit Ensemble Support Vector Machine (ABES model) for detecting brain tumors. Here, the Bi-GRU layer learns the most important context information for every image. The attention layer also uses the attention mechanism to assign weights to each Bi-GRU layer output. The ensemble SVM classifier performs the categorization of brain tumors. Then, the training set is used to train the Attention BiGRU model and Ensemble SVM classifiers. The ABES classifier weights are optimized by utilizing the improved whale optimization algorithm. To demonstrate the efficiency of our proposed method, we compare it to four existing methods including Fully Automatic Heterogeneous Segmentation-based Support Vector Machine, Whale Harris Hawks Optimization-based Deep Learning, Machine learning-based back propagation neural networks, and Deep Convolutional Neural Network. Also, the ABES model is validated using the MRI dataset and the Figshare brain tumor dataset. Accuracy, precision, specificity, and sensitivity are the performance metrics used to evaluate classification performance, which achieves 97.2% accuracy, 98.61% Precision, 96.48% Specificity, and 97.34% Sensitivity, respectively. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Support vector machines

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Hybrid technique for lung disease classification based on machine learning and optimization using X-ray images

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Multimedia Tools and Applications 2024年 1-23页

作者： Poloju, Naresh Rajaram, A. Department of Computer Science and Engineering E.G.S Pillay Engineering College Tamilnadu Nagapattinam611002 India Department of Electronics and Communication Engineering E.G.S Pillay Engineering College Tamilnadu Nagapattinam611002 India

The remarkable growth of machine learning has shown that it can perform at an expert level in a number of difficult tasks, such as medical decision-making and image processing. The goal of this research is to take advantage of this potential by creating a new and precise technique for chest X-ray analysis-based lung disease identification. The goal of this work is to research the methods currently used for creating the architecture of machine learning and create our own model, which will then be used for the best possible detection of lung illness using digital medical (X-ray) pictures. Therefore, the focus of this work is on employing various machine learning approaches to diagnose lung illness. The proposed methodology's optimization section consists of 2 primary phases. In the first phase, bagging, voting ensemble learning and boosting techniques are utilized to enhance performance of the support vector machine (SVM), K-nearest neighbours (KNN), and naïve Bayes (NB) algorithms. Emperor Penguin Optimization (EPO) was then used to the system to optimise the parameters and improve the overall system performance. The outcome shows the efficacy of the proposed methodology that compared to other hybrid techniques, the combination of ensemble learning and EPO with SVM produced the highest accuracy of 97.50%. This result suggests that the proposed system has the potential to greatly increase the precision of conventional machine learning methods in the identification of lung illness using chest X-rays. This development may lead to more accurate diagnoses and better patient outcomes. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image classification

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Energy-aware multi-attribute trust modal for secure MANET-IoT environment

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Multimedia Tools and Applications 2024年第38期83卷 85637-85662页

作者： Aravindan, S. Rajaram, A. Department of Computer Science and Engineering E.G.S. Pillay Engineering College Nagapattinam611 002 India Department of Electronics and Communication Engineering E.G.S. Pillay Engineering College Nagapattinam611 002 India

A self-organizing network with dispersed functionalities is known as a mobile-adhoc network (MANET). Modern technology has made integration, MANET, and the Internet of Things (IoT) very desirable. The MANET-IoT architecture that has been suggested uses a multi-cluster setup. Hybrid K-Mode Clustering (HKMC) is a method that has been suggested for use in cluster building. An approach called Spider Monkey Optimization (SMO) is used in each cluster to identify the best cluster head (CH). This algorithm takes into account numerous objective functions. An Energy-aware Multi-Attribute Trust (EMAT) model is proposed to ensure trusted environment. The EMAT algorithm works on twofold: (i) first, multi-attribute based trust value is computed by Multi-Agent Reinforcement Learning (MA-RL) algorithm and (ii) next, optimal route is selected by Secure & Energy Score (SES) based methodology. For data security, Multi-Attribute Cryptography (MAC) approach is presented. On the whole, the energy efficiency and security is improved for MANET-IoT environment. There is an improvement to the network in terms of residual energy, protection level, throughput, routing overhead, energy efficiency, and packet delivery ratio (PDR) according to the testing findings. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Reinforcement learning

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PSO-DBNet for Peak-to-Average Power Ratio Reduction Using Deep Belief Network

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computer Systems science & engineering 2023年第5期45卷 1483-1493页

作者： A.Jameer Basha M.Ramya Devi S.Lokesh P.Sivaranjani D.Mansoor Hussain Venkat Padhy Department of Computer Science and Engineering Hindusthan Institute of TechnologyCoimbatore641032India Department of Computer Science and Engineering Hindusthan College of Engineering and TechnologyCoimbatore641032India Department of Electronics and Communication Engineering Kongu Engineering CollegeErode638060India Department of Computer Science and Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India School of Computing Science&Engineering VIT Bhopal UniversityBhopal466114India

Data transmission through a wireless network has faced various signal problems in the past *** orthogonal frequency division multiplexing(OFDM)technique is widely accepted in multiple data transfer patterns at various frequency bands.A recent wireless communication network uses OFDM in longterm evolution(LTE)and 5G,among *** main problem faced by 5G wireless OFDM is distortion of transmission signals in the *** transmission loss is called peak-to-average power ratio(PAPR).This wireless signal distortion can be reduced using various *** study uses machine learning-based algorithm to solve the problem of PAPR in 5G wireless *** transmit sequence(PTS)helps in the fast transfer of data in wireless *** is merged with deep belief neural network(DBNet)for the efficient processing of signals in wireless 5G *** indicates that the proposed system outperforms other existing ***,PAPR reduction in OFDM by DBNet is optimized with the help of an evolutionary algorithm called particle swarm ***,the specified design supports in improving the proposed PAPR reduction architecture.

关键词： 5G wireless network orthogonal frequency division multiplexing signal distortion peak to average power ratio partial transmit sequence deep belief network

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ISAC-Fi: Enabling Full-Fledged Monostatic Sensing over Wi-Fi Communication

IEEE Journal of Selected Areas in Sensors

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IEEE Journal of Selected Areas in Sensors 2024年 1卷 139-153页

作者： Chen, Zhe Hu, Chao Zheng, Tianyue Cao, Hangcheng Yang, Yanbing Chu, Yen Jiang, Hongbo Luo, Jun Fudan University Institute of Space Internet School of Computer Science Shanghai200437 China Sichuan University College of Computer Science Chengdu610017 China Nanyang Technological University School of Computer Science and Engineering Singapore639798 Singapore Hunan University College of Computer Science and Electronics Engineering Changsha410012 China University of Electronic Science and Technology of China Chengdu610054 China

Although Wi-Fi communications have been exploited for sensing purpose for over a decade, the bistatic or multistatic nature of Wi-Fi still poses multiple challenges, hampering real-life deployment of integrated sensing and communication (ISAC) within the Wi-Fi framework. In this article, we aim to redesign Wi-Fi so that monostatic sensing (mimicking radar) can be achieved over the multistatic communication infrastructure. Specifically, we propose, design, and implement ISAC-Fi as an ISAC-ready Wi-Fi prototype. We first present a novel self-interference cancellation scheme, in order to extract reflected (radio frequency) signals for sensing purpose in the face of transmissions. We then subtly revise the existing Wi-Fi framework so as to seamlessly operate monostatic sensing under the Wi-Fi communication standard. Finally, we offer two ISAC-Fi designs: 1) a Universal Software Radio Peripheral (USRP)-based design emulates a totally redesigned ISAC-Fi device and 2) another plug-and-play design allows for backward compatibility by attaching an extra module to an arbitrary Wi-Fi device. We perform extensive experiments to validate the efficacy of ISAC-Fi and also to demonstrate its superiority over existing Wi-Fi sensing proposals. © 2024 IEEE.

关键词： Wi-Fi

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Hybrid attention residual deep convolution learning network for bio medical image analysis

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Journal of Intelligent and Fuzzy Systems 2025年第4期48卷 537-554页

作者： Sundar Raj, A. Naidu, Paruchuri Chandra Babu Sivakumar, S. Senthilkumar, S. Department of Biomedical Engineering E.G.S. Pillay Engineering College Tamilnadu Nagapattinam India Department of Electrical and Electronics Engineering BSA Crescent Institute of Science and Technology Vandalur Tamilnadu Chennai India Department of Computer Science and Engineering Nehru Institute of Engineering and Technology Tamilnadu Coimbatore India Department of Electronics and Communication Engineering E.G.S. Pillay Engineering College Tamilnadu Nagapattinam India

Biomedical image analysis has progressed significantly with the integration of artificial intelligence, presenting new opportunities for early diagnosis and treatment of diseases with high mortality rates, such as skin cancer. This research work introduces a novel Hybrid Attention Residual Deep Convolution Learning (HARDCL) Model designed to enhance the accuracy of skin lesion classification, which is often challenging due to the subtle characteristics of lesions. The HARDCL Network employs an innovative hybrid attention mechanism within a residual learning framework, effectively distinguishing between benign and melanoma skin lesions with a high degree of precision. The encoder-decoder network segments the lesion from dermoscopic images, followed by feature extraction and classification, achieving a maximum accuracy of 97.11%, precision of 98.10%, recall of 98.81%, and an F1-score of 98.45%. This performance surpasses conventional deep learning algorithms such as AlexNet, InceptionV3, and ResNet18, demonstrating the effectiveness of the attention-based features in identifying critical lesion characteristics. However, despite its strengths, the HARDCL Network’s computational intensity and the need for a substantial dataset for optimal training highlight areas for further refinement. The proposed network advances the field of skin cancer diagnostics, offering a robust tool for medical professionals and setting a foundation for future enhancements in automated biomedical image analysis techniques. © 2025 – IOS Press. All rights reserved.

关键词： Deep learning

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Ecological Surveillance and Crisis Management: Early Forest Fire Prediction and Endangered Species Identification 6

Ecological Surveillance and Crisis Management: Early Forest ...

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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Ashok, K. Chatterjee, Pratik Jha, Aman Kumar Herle, Akshay Gupta, Shaksham Das, Saumyadeep New Horizon College of Engineering Computer Science and Engineering Bangalore India New Horizon College of Engineering Electronics and Communication Engineering Bangalore India

ISBN: (纸本)9798350306613

In the realm of ecological surveillance and crisis management, a pioneering approach combining some of the advanced machine learning techniques with bioacoustic analysis is to be delved. The proposed system integrates the predictive capabilities of Mel-frequency cepstral coefficients (MFCC) and other sophisticated models of Deep Learning like CRNN which is a hybrid and composite neural network architecture that combines elements of both the convolutional layers and recurrent layers, for the dual objectives of early forest fire prediction and endangered species identification. The soundscapes of avian habitats are leveraged for early forest fire prediction, wherein a classification system discerns sounds produced by birds into two categories: Panic and normal. This real-Time analysis enables timely intervention strategies in the event of potential fire outbreaks. Simultaneously, the study addresses the critical issue of endangered species identification. By employing MFCC and other sound-based models, the system recognizes and classifies species-specific vocalizations during predefined observation periods. The quantification of hits, where a detected sound precisely matches a trained species' vocal signature, serves as a decisive metric in determining the endangered status of a species. © 2024 IEEE.

关键词： Convolutional Recurrent Neural Networks Mel frequency cepstral coefficients

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Advanced Breast Cancer Detection Using Spatial Attention and Neural Architecture Search (SANAS-Net)

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SN computer science 2025年第1期6卷 1-12页

作者： Souza, Melwin D. Ananth Prabhu, G. Kumara, Varuna Department of Computer Science & Engineering Sahyadri College of Engineering & Management Mangalore India Department of Electronics and Communication Engineering Moodlakatte Institute of Technology Kundapura India

This paper introduces a novel spatial attention neural architecture search network (SANAS-Net), which incorporates a spatial attention mechanism to enhance the model’s ability to focus on critical regions within mammograms. By utilizing multi-head attention within transformer blocks, the model captures diverse spatial relationships and interactions between features. Positional embeddings are incorporated into the transformer blocks, further enriching the model’s capacity to understand global spatial context. Extensive experiments were conducted to validate the proposed methodology. SANAS-Net was shown to successfully identify attentive networks that demonstrate high performance in distinguishing malignant from benign breast cancer cases. The model achieved a test accuracy of 89.95%, significantly surpassing the performance of previously proposed algorithms for breast cancer detection using mammography images. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Breast cancer detection Deep learning Mammography Neural architecture search Spatial attention

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Early Parkinson's Disease Detection and Classification using Machine Learning and Neutrosophic Sets

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Neutrosophic Sets and Systems 2025年 82卷 873-886页

作者： Oudah, Hasan H. Metwaly, Ahmed A. Eassa, Mohamed Abdelhafeez, Ahmed Nagm, Ahmad M. Salama, Ahmed S. College of Arts Department of Journalism Ahl Al Bayt University Karbala Iraq Department of Computer Science Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Engineering and Electronics Cairo Higher Institute for Engineering Computer Science and Management New Cairo Egypt

Optimizing therapy and rehabilitation for Parkinson's disease (PD) requires early identification and precise evaluation of the illness's course. However, there is disagreement about the best way to use gait analysis to categorize the severity of motor symptoms and identify early-stage Parkinson's disease. The precision of machine learning (ML) models in identifying early and intermediate stages of Parkinson's disease was assessed in this study. Six ML models are used in this study for the prediction of PD. Different metrics are used to evaluate ML models such as accuracy, precision, recall, f1-score, and AUC score. Then we propose a multi-criteria decision-making methodology (MCDM) to evaluate the ML models and select the best one. Two MCDM methods are used such as CRITIC method to compute the criteria weights and the TOPSIS method to rank the alternatives. These methods are used under the bipolar neutrosophic sets (BNSs) to deal with uncertainty information. The results show the support vector machine is the best ML model for the prediction of PD. © 2025, University of New Mexico. All rights reserved.

关键词： Neurodegenerative diseases

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Comparative Study of Deep Learning Models to Classify Arrhythmia 8

Comparative Study of Deep Learning Models to Classify Arrhyt...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Kulkarni, Nitya N. Nagaraja, G.S. Sudarshan, B.G. Kumar, S. C. Prasanna Venkatesh, S. Rv College of Engineering Department of Computer Science and Engineering Bangalore India Rv College of Engineering Department of Electronics Instrumentation and Engineering Bangalore India

ISBN: (纸本)9798331505462

An irregular heart rhythm that can result from various heart-related conditions are arrhythmia. Globally an estimate of 3% to 5% of population is affected by arrhythmia. If it is not diagnosed and treated quickly, it can lead to sudden cardiac death (SCD). Hence early detection of SCD is essential for patient safety. Detection of arrhythmia has improved with the recent advancements in deep learning (DL). This research paper examines arrhythmia classification based on the AAMI EC57 standard, using several prominent deep learning models applied to the lead II Electrocardiogram heartbeat data from the well-known MIT- BIH Arrhythmia dataset. The models trained and tested in this research work are Artificial Neural Network (ANN), Long Short- Term Memory (LSTM), Convolutional Neural Network (CNN), Residual Network (ResNet), and Temporal Convolution Network (TCN). The results showed that both the ResNet and TCN models performed the best, achieving an impressive accuracy of 96% and precision of 97% and 98% respectively. These models could be further fine-tuned to achieve better generalization and improve performance © 2024 IEEE.

关键词： Contrastive Learning

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