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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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Advancing emotion recognition via EEG signals using a deep learning approach with ensemble model

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Journal of Intelligent and Fuzzy Systems 2024年第1-2期47卷 143-154页

作者： Immanuel, Rajeswari Rajesh Sangeetha, S.K.B. Department of Computer Science and Engineering SRM Institute of Science and Technology Faculty of Engineering and Technology Vadapalani Campus Tamil Nadu Chennai India

Human emotions are the mind's responses to external stimuli, and due to their dynamic and unpredictable nature, research in this field has become increasingly important. There is a growing trend in utilizing deep learning and machine learning techniques for emotion recognition through EEG (electroencephalogram) signals. This paper presents an investigation based on a real-time dataset that comprises 15 subjects, consisting of 7 males and 8 females. The EEG signals of these subjects were recorded during exposure to video stimuli. The collected real-time data underwent preprocessing, followed by the extraction of features using various methods tailored for this purpose. The study includes an evaluation of model performance by comparing the accuracy and loss metrics between models applied to both raw and preprocessed data. The paper introduces the EEGEM (Electroencephalogram Ensemble Model), which represents an ensemble model combining LSTM (Long Short-Term Memory) and CNN (Convolutional Neural Network) to achieve the desired outcomes. The results demonstrate the effectiveness of the EEGEM model, achieving an impressive accuracy rate of 95.56%. This model has proven to surpass the performance of other established machine learning and deep learning techniques in the field of emotion recognition, making it a promising and superior tool for this application. © 2024 - IOS Press. All rights reserved.

关键词： Convolutional neural networks

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Revolutionizing signature scheme: the enhanced Edward Elgamal extreme performance accumulate signature approach for IoT and blockchain applications

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Soft Computing 2025年第3期29卷 1473-1496页

作者： Anusha, R. Saravanan, R. School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore632014 India

Digital signatures, essential for establishing trust in the digital realm, have evolved in their application and importance alongside emerging technologies such as the Internet of Things (IoT), Blockchain, and cryptocurrency. These advancements necessitate improvements in performance, security, and efficiency. This article examines and compares the Elliptic Curve Digital Signature Algorithm with the Hyper Elliptic Curve Digital Signature Algorithm and the Edwards Curve Digital Signature Algorithm. We highlight its superior capabilities for blockchain and IoT applications and advocate for its potential to deliver immediate enhancements in security and performance. Our study introduces a novel digital signature scheme specifically designed to enhance non-repudiation in blockchain ecosystems. Utilizing the Optimized Extreme Performance Edwards Curve Accumulated Signature scheme, our approach significantly reduces signing and verification times by 10% and 13%, respectively, compared to traditional signatures. Additionally, it offers a 10% boost in transaction throughput and block validation efficiency. Experiments conducted within various blockchain-integrated IoT setups demonstrate the scheme's effectiveness, consistently achieving improvements across diverse IoT sensor data. This highlights the innovative contribution of our scheme to the efficiency and security of blockchain technology. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Blockchain Edward curve Elliptic curve Hyperelliptic curve IoT Optimization

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CRAQL: a novel clustering-based resource allocation using the Q-learning in fog environment

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International Journal of Cloud Computing 2024年第3期13卷 243-266页

作者： Ahlawat, Chanchal Krishnamurthi, Rajalakshmi Department of Computer Science and Engineering Jaypee Institute of Information Technology Noida India

Fog computing is an emerging paradigm that provides services near the end-user. The tremendous increase in IoT devices and big data leads to complexity in fog resource allocation. Inefficient resource allocation can lead to resource starvation and unable to complete the task assignment within a specific time. Hence, to enhance the efficiency of the fog resources, it is critical to perform proper resource allocation. This work targets to provide the solution to the resource allocation problem with a novel clustering-based resource allocation using the Q-learning (CRAQL) model. For this purpose, the problem is defined as a decision-making problem and formulated as Markov decision process (MDP). Next, to find the optimal resource, an enhanced optimal resource allocation (EORA) algorithm is proposed and detailed study is performed to analyse the impact of various performance parameters. Simulation results show the comparison of the EORA versus conventional Brute force method by varying the performance parameters such as learning rate and number of trials. The experimental results exhibit optimal solutions with significant improvement in learning rate at an average probability of 0.5 within limited epochs. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Reinforcement learning

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Convergence of various computer-aided systems for breast tumor diagnosis: a comparative insight

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Multimedia Tools and Applications 2025年第16期84卷 16709-16756页

作者： Singh, Saket Kumar Patnaik, K. Sridhar Department of Computer Science and Engineering Birla Institute of Technology Mesra Ranchi835215 India

Breast Cancer, with an expected 42,780 deaths in the US alone in 2024, is one of the most prevalent types of cancer. The death toll due to breast cancer would be very high if it were to be totaled up globally. Early detection of breast cancer is the only way to decrease the mortality caused by it. In order to diagnose breast cancer, even the most competent and qualified pathologists and radiologists have to examine hundreds of high-resolution images, which is a massive burden on them. Compared to the number of cases, very few experts are available to manage this burden. Additionally, as humans are more prone to mistakes, the likelihood of finding false positive cases is also high. Numerous AI techniques, including machine learning and deep learning, are ideally suited to address these issues, inspiring many researchers to introduce novel computer-aided detection systems. In this study, we have comprehensively reviewed pre-existing literature aimed at developing computer-aided systems based on using machine learning, deep learning, and vision transformers to identify and classify breast cancer. We have discussed numerous imaging modalities for detecting breast cancer, along with the widely used data pre-processing approaches, machine learning and deep learning models, as well as ensemble learning methods suitable for the task. Popular datasets and their sources are also listed for future referencing. Finally, we have identified a few gaps and addressed potential future research directions with an intent of aiding researchers select approaches tailored to case-specific needs. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： computer aided diagnosis

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Global-local term fusion for optimised community Q&A topic modelling

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International Journal of Innovative Computing and Applications 2024年第1期15卷 50-69页

作者： Prabha, Sneh Sardana, Neetu Department of Computer Science and Engineering and Information Technology Jaypee Institute of Information Technology Noida India

Community question and answer (Q&A) websites have become invaluable information and knowledge-sharing sources. Effective topic modelling on these platforms is crucial for organising and navigating the vast amount of user-generated content. To address these challenges, we propose a novel global-local term fusion with optimised community (GLOCOM) Q&A topic modelling approach that leverages both local and global term importance to enhance topic modelling on community Q&A websites. GLOCOM combines term frequency-inverse document frequency for local importance and entropy for global importance. Further, we employ fuzzy clustering to enhance the representation of multifaceted topics. Furthermore, clustering results are optimised using a genetic algorithm (GA) to refine cluster assignments and centroids. We compared the proposed model with baseline models LDA and FLSA. GLOCOM has performed consistently well for all topic numbers. It has shown an improvement of 8.86% in silhouette score as compared to LDA and excelled for datasets with size > 3 MB. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Inverse problems

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2P3FL:A Novel Approach for Privacy Preserving in Financial Sectors Using Flower Federated Learning

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computer Modeling in engineering & sciences 2024年第8期140卷 2035-2051页

作者： Sandeep Dasari Rajesh Kaluri School of Computer Science Engineering and Information Systems Vellore Institute of TechnologyVellore632014India

The increasing data pool in finance sectors forces machine learning(ML)to step into new *** data has significant financial implications and is *** users data from several organizations for various banking services may result in various intrusions and privacy *** a result,this study employs federated learning(FL)using a flower paradigm to preserve each organization’s privacy while collaborating to build a robust shared global ***,diverse data distributions in the collaborative training process might result in inadequate model learning and a lack of *** address this issue,the present paper proposes the imple-mentation of Federated Averaging(FedAvg)and Federated Proximal(FedProx)methods in the flower framework,which take advantage of the data locality while training and guaranteeing global *** improves the privacy of the local *** analysis used the credit card and Canadian institute for Cybersecurity Intrusion Detection Evaluation(CICIDS)***,recall,and accuracy as performance indicators to show the efficacy of the proposed strategy using FedAvg and *** experimental findings suggest that the proposed approach helps to safely use banking data from diverse sources to enhance customer banking services by obtaining accuracy of 99.55%and 83.72%for FedAvg and 99.57%,and 84.63%for FedProx.

关键词： Federated learning FedAvg FedProx flower framework privacy preservation financial sectors

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An Exploration of Deep Learning Techniques for the Detection of Grape Diseases

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Recent Advances in computer science and Communications 2024年第2期17卷 1-12页

作者： Pandey, Kavita Chandak, Abhimanyu Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Noida India

Plant diseases are one of the major contributors to economic loss in the agriculture industry worldwide. Detection of disease at early stages can help in the reduction of this loss. In recent times, a lot of emphasis has been done on disease detection due to the overall increase in production as well as the loss of grape number. With deep learning, having a promising future and having the advantages of automatic learning and feature extraction, the use of these techniques has now been widely spread. This paper reviewed the existing deep-learning techniques available for grape disease detection. Firstly, covering the various steps in a grape disease detection model ranging from the various sources of image acquisition, the different image augmentation techniques and the various models used, and the parameters required to evaluate. Secondly, the study summarizes the im-portant findings of all literature available on the theme. The paper also tries to highlight the various challenges faced by the researchers and the common trend among them, so that future research on the topic can achieve higher performance. © 2024, Bentham science Publishers. All rights reserved.

关键词： Losses

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Farm-Level Smart Crop Recommendation Framework Using Machine Learning

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Annals of Data science 2025年第1期12卷 117-140页

作者： Bhola, Amit Kumar, Prabhat Department of Computer Science and Engineering National Institute of Technology Patna Bihar Patna India

Agriculture is the primary source of food, fuel, and raw materials and is vital to any country’s economy. Farmers, the backbone of agriculture, primarily rely on instinct to determine what crops to plant in any given season. They are comfortable following customary farming practices and standards and are oblivious to the fact that crop yield is highly dependent on current environmental and soil conditions. Crop recommendations involve multifaceted factors such as weather, soil quality, crop production, market demand, and prices, making it crucial for farmers to make well-informed decisions. An improper or imprudent crop recommendation can affect them, their families, and the entire agricultural sector. Modern technologies like artificial intelligence, machine learning, and data science have emerged as efficient solutions to combat issues like declining crop production and lower profits. This research proposes a Smart Crop Recommendation framework that leverages machine learning to empower farmers to make informed decisions about optimal crop selection. The framework consists of two phases: crop filtration and yield prediction. Crops are filtered in the first phase using an artificial neural network based on local input parameters. The second phase estimates yield for filtered crops, considering the season, farm area, and location data. The final recommendation provides farmers with crops aimed at maximizing profit. The remarkable 99.10% accuracy of the framework is demonstrated through experimentation using artificial neural networks and the 0.99 R2 error metric for the random forest. The uniqueness of this framework lies in its distinctive focus on the farm level and its consideration of the challenges and various agricultural features that change over time. The experimental results affirm the effectiveness of the framework, and its lightweight nature enhances its practicality, making it an efficient real-time recommendation solution. © The Author(s), under exclusi

关键词： Crops

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A secure and privacy preserving model for healthcare applications based on blockchain-layered architecture

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International Journal of computers and Applications 2024年第12期46卷 1206-1218页

作者： Sanober, Adla Anwar, Shamama Department of Computer Science and Engineering Birla Institute of Technology Ranchi Mesra India

Blockchain is one of the emerging technologies that are applied in various fields and its application in Healthcare 4.0 is crucial to handle the vast amount of health records that are growing continuously everyday. This paper explores the application of blockchain technology-based layered architecture integrated with Interplanetary File System (IPFS) storage to provide a secure and robust sharing platform for healthcare system. A multilayer security is provided in the proposed system including a 128-bit Random Deoxyribonucleic Acid (DNA) encoding sequence with a modified version of the 128-bit Advanced Encryption Standard (AES) key expansion symmetric key technique is being used before storing the records in the IPFS storage, Rivest–Shamir–Adleman (RSA) 2048 digital envelope is used for secure transmission of symmetric keys, RSA 1024 is used for digital signature, and Secure Hash Algorithm 256-bit (SHA-256) is used for hashing the record files. Comparative analysis with existing state-of-the-art in blockchain-based E-healthcare system is discussed. The performance of various assessment metrics is evaluated, including encryption and decryption time for EHR records, mining time, record retrieval time, smart contract execution time and network latency. Additionally, a reputation-based filtering mechanism specifically designed in the proposed model to detect and mitigate the various attacks. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Electronic health record

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