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Optimized Neural Network for Automated Cardiovascular Disease Classification: Meta-heuristic Enabled Model

SN computer science 2025年第1期6卷 1-14页

作者： Kusuma, S. Jothi, K.R. Department of Computer Science & Engineering (Data Science) Madanapalle Institute of Technology & Science Madanapalle India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

Cardiovascular disease remains a major issue for mortality and morbidity, making accurate classification crucial. This paper introduces a novel heart disease classification model utilizing Electrocardiogram (ECG) signals. The model consists of two main steps: feature extraction using a refined Lion Algorithm with New Mutated evaluation (LA-NM) and classification through an ensemble approach combined with a Neural Network (NN). The proposed model significantly outperforms traditional classifiers. At 80% training data, it achieves accuracy improvements of 8.4% to 3.11% over Neural Network (NN), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and ensemble models. At 90% training data, it shows precision improvements ranging from 1.03% to 0.31% better than the comparison models and specificity improvements ranging from 13.92% to 0.52%. These results underscore the effectiveness of the model and suggest its potential for further development in personalized treatment recommendations based on patient history. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Ensemble models Feature extraction Heart disease classification Neural network Optimization

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A Study of Federated Learning with Internet of Things for Data Privacy and Security using Privacy Preserving Techniques

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Recent Patents on engineering 2024年第1期18卷 1-17页

作者： Shakeer, Shaik Mahamad Rajasekhara Babu, M. School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore India

To address the privacy concerns that arise from centralizing model training on a large number of IoT devices, a revolutionary new distributed learning framework called federated learning has been developed. This setup of devices works together to train models without compromising the security of individual data streams. To solve machine learning problems, a federated learning architecture relies on many clients working together through a central aggregator. To protect each device's privacy, we only access the training data at random. Because of its focus on decentralized processing and model transmission, federated learning considerably reduces systemic privacy risks and costs in comparison to conventional centralized machine learning systems. All client data is encrypted before being stored in a local database. Each user's device collects data locally for training during a federated learning session, and then uploads the final model to a centralized server. To provide a comprehensive assessment and inspire more research, this paper explores into the inner workings of federated learning from five different vantage points: data partitioning, privacy approaches, machine learning models, communication architectures, and system heterogeneity. Then, suggestions for further research are given, and a list is compiled of the most recent problems in federated learning. We conclude with an examination of the present applications of federated Learning knowledge. © 2024 Bentham science Publishers.

关键词： Privacy-preserving techniques

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A Novel Enhanced Approach for Security and Privacy Preserving in IoT Devices with Federal Learning Technique

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SN computer science 2024年第6期5卷 1-17页

作者： Moeed, Syed Abdul Karnati, Ramesh Ashmitha, G. Mohammad, Gouse Baig Mohanty, Sachi Nandan Department of Computer Science and Engineering Kakatiya Institute of Technology and Science Department of Computer Science and Engineering Vardhaman College of Engineering School of Computer Science & amp Engineering (SCOPE) VIT-AP University

The Internet of Things (IoT) has revolutionized our lives, but it has also introduced significant security and privacy challenges. The vast amount of data collected by these devices, often containing sensitive information, makes them prime targets for cyberattacks. Traditional security methods struggle to keep pace with the evolving threat landscape of the interconnected IoT world. In response, this paper explores the transformative potential of federated learning (FL) in safeguarding both the privacy and security of IoT devices. FL keeps data on individual devices, only sharing updated models, not raw data, thus protecting user privacy and eliminating the need for a central data storage server, which reduces the risk of data breaches and security vulnerabilities. The effectiveness of the proposed model is evaluated using established open-source datasets like NSL-KDD and UNSW NB15, ensuring real-world applicability. Analysis of the dataset's features enabled the development of a model utilizing federated learning. Notably, the proposed model achieved superior performance with FL in detecting attacks on IoT networks. Furthermore, this research investigates the transformative potential of FL to address the inherent security and privacy challenges plaguing traditional, centralized data collection methods in the ever-expanding realm of IoT devices. FL empowers collaborative learning on distributed datasets, allowing devices to collectively improve security without compromising user privacy. The findings of this study demonstrate the promise of FL as a secure and privacy-preserving solution for the future of IoT. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cyberattacks Federal learning Internet of Things Machine learning Security and privacy

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Lung disease classification using deep learning and genetic algorithm

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Neural Computing and Applications 2024年 1-16页

作者： Chutia, Upasana Tewari, Anand Shanker Singh, Jyoti Prakash Computer Science and Engineering National Institute of Technology Patna Bihar Patna India

Lung disorders are medical conditions that disrupt the lungs and their capacity to function normally. One fatal lung disease is a collapsed lung where the lung collapses partially or fully due to diseases like pneumothorax and atelectasis. The conventional approach to detecting such diseases is time-consuming, often requiring extensive manual analysis by trained experts, leading to delays in diagnosis and treatment. computer-aided diagnostics have the potential to aid doctors in enhancing the consistency of diagnoses while also optimizing time efficiency. In this study, we enhance each lung X-ray image with three image enhancement techniques (i) contrast-limited adaptive histogram equalization (CLAHE), (ii) discrete wavelet transform (DWT), and (iii) gamma correction (GC) in parallel. A 3-channel convolutional neural network (CNN) then uses those enhanced images to extract features. The extracted features are further optimized using a genetic algorithm to improve the efficiency of the classification models. Our proposed model is validated with a dataset containing 9391 X-ray images to achieve an average precision, recall, and F1-score of 98, 97, and 98% and best accuracy of 99.33% which shows an improvement of 2.03% in terms of accuracy over the existing state-of-the-art methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Lung cancer

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Brain haemorrhage classification from CT scan images using fine-Tuned transfer learning deep features

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International Journal of Business Intelligence and Data Mining 2024年第2期24卷 111-130页

作者： Ghosh, Arpita Soni, Badal Baruah, Ujwala Department of Computer Science and Engineering National Institute of Technology Assam Silchar India

Classification of brain haemorrhage is a challenging task that needs to be solved to help advance medical treatment. Recently, it has been observed that efficient deep learning architectures have been developed to detect such bleeding accurately. The proposed system includes two different transfer learning strategies to train and fine-Tune ImageNet pre-Trained state-of-The-Art architecture such as that VGG 16, Inception V3 and DenseNet121. The evaluation metrics have been calculated based on the performance analysis of the employed networks. Experimental results show that the modified fine-Tuned Inception V3 performed well and achieved the highest test accuracy. © 2024 Inderscience Enterprises Ltd.. All rights reserved.

关键词： Image classification

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Hierarchical Patch Selection: An Improved Patch Sampling for No Reference Image Quality Assessment

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第2期5卷 541-555页

作者： Nandhini, C. Brindha, M. National Institute of Technology Department of Computer Science and Engineering Tiruchirappalli620015 India

Quality degradation due to the compression and the transmission of images is a significant threat to multimedia applications. Blind image quality assessment (BIQA) is a principal technique to measure the distortion and dynamically set the optimal parameters for developing image compression standards, image restoration algorithms, etc. Insufficient training data with quality scores are a challenge for image quality assessment (IQA) tasks. The existing solutions to deep-convolution-neural-network-based BIQA, which rely on patchwise training, struggle to find an ideal set of patches consistent with the human visual system. To address these issues, HIerarchical paTch Selection (HITS) is proposed. HITS keeps the patches with considerable details in each quadrant according to their intensity variance scatter ratio (IVSR). IVSR identifies the best nonhomogeneous patches by computing patch intensity variance. Extensive trials are conducted, and the performance reveals that the proposed approach achieves excellent performance on synthetic and authentic distortion datasets with less memory and processing power. Moreover, the proposed approach outperforms other BIQA methods that adapt patchwise training strategies. © 2023 IEEE.

关键词： Feature extraction

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Multi-scale Vision Transformer toward improved non-invasive anaemia detection using palm video

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Multimedia Tools and Applications 2024年第38期83卷 85825-85848页

作者： Kesarwani, Abhishek Das, Sunanda Kisku, Dakshina Ranjan Dalui, Mamata Department of Computer Science and Engineering National Institute of Technology Durgapur Durgapur India

Anaemia, a condition characterised by reduced haemoglobin levels, exerts a significant global impact, affecting billions of individuals worldwide. According to data from the World Health Organisation (WHO), India exhibits notably high anaemia prevalence in comparison to other developing nations. The urgency arises for the development of non-invasive, cost-effective, and accurate anaemia screening technologies, primarily due to the substantial costs and logistical complexities associated with invasive screening methods, which impede their broad adoption on a global scale. The current endeavor aims to establish a robust anaemia detection system through the integration of cutting-edge computational methodologies with the traditional yet less precise approach of assessing haemoglobin levels based on observation of palm pallor. The proposed method employs time-domain analysis to establish a correlation between blood haemoglobin concentration and the observed changes in palm colour resulting from the application and release of pressure. This involves employing a custom device to capture and record the complete sequence of palm colour changes using a smartphone camera sensor, followed by data processing and analysis. The Multi-scale Vision Transformer is then employed to extract features from dominant frames. The approach further incorporates the application of a multi-layer perceptron (MLP) network as a regression model based on the reduced feature outcomes. The proposed system accurately estimates blood haemoglobin levels with RMSE, sensitivity, specificity, and accuracy of 0.509, 96.88%, 86.39%, and 92.59%, respectively based on 531 video samples of palm evidence. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Diagnosis

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Information Freshness for Monitoring and Control over Wireless Networks

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Performance Evaluation Review 2024年第3期51卷 62-65页

作者： Tripathi, Vishrant Massachusetts Institute of Technology Electrical Engineering and Computer Science United States

Brief Biography: Vishrant Tripathi obtained his PhD from the EECS department at MIT, working with Prof. Modiano at the Lab for Information and Decision Systems (LIDS). He is currently working on building efficient data center networks at Google. His research interests primarily lie in the optimization of resources in resource constrained networked systems. The main applications of his work are in multi-agent robotics, federated learning, edge computing, cloud infrastructure, and monitoring for IoT. More recently, he has also been working on software defined networking and next-generation wireless networks. In 2022, he won the Best Paper Runner Up Award at ACM MobiHoc. Copyright is held by author/owner(s).

关键词： Constrained optimization

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TREAT: Temporal and Relational Attention-Based Tensor Representation Learning for Ethereum Phishing Users

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IEEE Transactions on Services Computing 2025年第3期18卷 1838-1851页

作者： Ghosh, Medhasree Halder, Raju Chandra, Joydeep Indian Institute of Technology Patna Deprtment of Computer Science and Engineering India

The Ethereum blockchain platform has witnessed a surge in crypto-cybercrimes, particularly phishing attacks, resulting in significant financial losses. Analyzing the Ethereum transaction network to detect phishing users poses a set of critical challenges, including network sparsity, dynamic network fluctuations, large-scale data and significant class imbalance. Existing literature in this area primarily leverages traditional feature engineering or network representation learning to retrieve crucial information from transaction records to identify suspected users. However, these methods mainly rely on manually handcrafted features or conventional node representation learning while ignoring the inherent network sparsity and dynamic fluctuations. Hence, to alleviate these challenges, this paper introduces a novel tensor-based representation learning framework, TREAT (Temporal and Relational Attention-based Tensor Representation Learning). TREAT models the Ethereum transaction network as a 3-dimensional tensor to preserve structural, transactional, and temporal aspects in a standalone architecture, thereby observing the rich correlation among these. The framework is coupled with a two-way self-attention mechanism alongside a rank-based tensor decomposition to comprehend the underlying evolving transaction interaction patterns while addressing the network sparsity. A Graph Neural Network layer with edge attention elevates the final representation quality, thereby yielding a 3%~4% improvement in F1-Score with respect to the existing baselines. © 2008-2012 IEEE.

关键词： Phishing

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Hybrid CGAN-based plant leaf disease classification using OTSU and surf feature extraction

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Neural Computing and Applications 2024年第23期36卷 14395-14407页

作者： Saraswathi, E. Banu, J. Faritha Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai600089 India

Agriculture encompasses a way of life and a profession for the general population. Most global traditions and cultures revolve around agriculture. With the help of advanced farming, agriculture may become more profitable, dependable, and able to use resources and time more effectively. The proposed hybrid CGAN using OTSU and SURF (CGAN-OF) model provides a novel framework for plant leaf disease classification. In proposed CGAN-OF model, contrast-limited adaptive histogram equalization is used for image preprocessing and enhancement. The proposed model utilizes OTSU algorithm to speed up the image segmentation without prior knowledge of the images and SURF algorithm to extract the local features using scale-invariant feature transformation. CGAN increases the input plant village dataset using image generation method and identifies the various plant leaf diseases and classifies them. There are three classifications of leaf diseases: fungi, viruses, and bacteria. Furthermore, these classifications contain approximately 300 diseases. The proposed work identified a minimum of 200 diseases in 18,161 major and minor crop species. The investigational investigations are carried out using the Python Jupyter app with the Kaggle Plant Village Dataset and also leaf samples collected from farmers. The proposed framework achieves 99.2% accuracy. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Image enhancement

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