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

SN computer science 2024年第6期5卷 750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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iTrustBD: Study and Analysis of Bitcoin Networks to Identify the Influence of Trust Behavior Dynamics

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SN computer science 2024年第5期5卷 476页

作者： Islam, Md. Jahidul Islam, Md. Rakibul Basar, Md. Abul Department of Computer Science and Engineering Chandpur Science and Technology University Chandpur 3600 Bangladesh Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Dhaka 1000 Bangladesh

The concept of cryptocurrency is a significant advancement in digital currencies. “Cryptocurrency” refers to a form of electronic or virtual currency that is secured through the application of encryption. It is a common practice to trade cryptocurrencies on decentralized exchanges where neither governments nor financial institutions can exert any form of authority over them. This being the case that cryptocurrencies are not regulated either by the government or financial institutions presents a potential risk for investors. Although a few nations have authorized cryptocurrency, the vast majority of nations, including Bangladesh, have not recognized it due to concerns over the safety of the cryptocurrency system. In this paper, we analyze cryptocurrency networks, “Bitcoin Alpha trust weighted signed network” and “Bitcoin OTC trust weighted signed network”. Following an investigation into the characteristics of the networks, we came to the conclusion that they are robust and reliable, as well as capable of withstanding any kind of attack. Additionally, we forecast the future condition of the trader’s conduct in terms of trustworthiness;as a consequence, we identified more trustworthy behaviors on the behalf of traders. As a result, this work has the potential to make a contribution to the process of legalizing cryptocurrency transactions. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Behavior dynamics Cryptocurrency Epidemic spread Network analysis Trusty

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Performance Evaluation of Natural Language Processing Algorithms for Sentiment Analysis

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SN computer science 2024年第6期5卷 724页

作者： Silviya, S. H. Annie Faith, S. Julia Seetha, R. Hemalatha, M. Department of Computer Science and Engineering Rajalakshmi Institute of Technology (RIT) Department of Information Technology S.A.Engineering College Department of Information Technology Vellore Institute of Technology Department of Computer Science and Engineering RMD Engineering College

Sentiment analysis can be used to identify if a text’s sentiment is neutral, positive, or negative. One type of natural language processing is sentiment analysis. An interdisciplinary field encompassing linguistics, computer science, and artificial intelligence called “natural language processing” studies how computers and human language interact, with a focus on how to teach computers to process and analyze vast volumes of natural language data. Sentiment analysis, sometimes referred to as opinion mining or emotion artificial intelligence, is the methodical identification, extraction, quantification, and study of affective states and subjective data using natural language processing, text analysis, computational linguistics, and biometrics. Sentiment analysis is frequently used in online and social media platforms, voice of the customer materials (such as reviews and survey replies), and healthcare materials for marketing, customer service, and clinical medicine purposes. This paper discusses different sentiment analysis methods and selects the best approach for a certain task. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Biometrics Computational linguistics Natural language processing Sentiment analysis Text analysis

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A New Resilient Configuration of Wound Rotor Resolver in Mechanical Fault Conditions

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IEEE Sensors Journal 2025年第11期25卷 19069-19076页

作者： Zare, F. Tootoonchian, F. Nasiri-Gheidari, Z. Isfahan University of Technology Department of Electrical and Computer Engineering Isfahan Iran Iran University of Science and Technology Electrical Engineering Department Tehran Iran Sharif University of Technology Electrical Engineering Department Tehran Iran

Resolvers are widely employed as position sensors to detect both rotational and linear displacement. Among the various types, wound rotor resolvers offer superior accuracy in absolute measurements, especially under mechanical fault conditions, but they present significant manufacturing challenges. This paper introduces a novel winding arrangement designed to simplify the manufacturing process of wound rotor resolvers and enhance the accuracy of the sensor under mechanical fault conditions. The performance of the resolver with this new winding configuration is analyzed, with a particular focus on its resilience to eccentricity faults. To validate the high fault tolerance of the proposed design, a prototype of the optimized configuration was developed and tested. The performance of the prototype under these conditions was comprehensively evaluated through experimental measurements. © 2001-2012 IEEE.

关键词： Failure (mechanical)

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Parkinson’s disease Severity Detection Based on Fused features with tuned Residual Deep Convolutional Neural Network

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Multimedia Tools and Applications 2025年第14期84卷 13111-13131页

作者： Vishwakarma, Gagan Nandanwar, Amit Kumar Thakur, Ghanshyam Singh Computer Science & Engineering Indian Institute of Information Technology MP Bhopal India Computer Science & Engineering Maulana Azad National Institute of Technology MP Bhopal India Department of Computer Applications Maulana Azad National Institute of Technology MP Bhopal India

Parkinson's disease (PD) diagnosis involves the assessment of a variety of motor and non-motor symptoms. To accurately diagnose PD, it is necessary to differentiate its symptoms from those of other conditions. During the diagnosis process, clinicians must consider gait abnormalities as one of the key symptoms. However, relying solely on clinical judgment for the diagnosis of PD may have limitations. In this work, we have proposed a Multikernel SVM (MK-SVM) based Parkinson's severity analysis method. Initially, an adaptive median filter is used to reduce the noise in the gait image. Then, to extract the features, we propose a dual-layer method that employs stacked autoencoders (SAEs) for local feature extraction and residual deep convolution neural networks (RDCNN) for global feature extraction. The hyper-parameters from the RDCNN are optimized using the Enhanced walrus optimization algorithm (EWO). In the final step, we use MK-SVM to classify PD according to severity. This model is evaluated using the GAIT-IT and GAIT-IST datasets. With the help of Python, we evaluated the proposed model in terms of precision, accuracy, recall, specificity, and F-scores, and the model achieved a maximum accuracy of 97.3%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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An ensembled-deep-learning paradigm trained with a self-improved coyote optimization algorithm (SI-COA) for crop disease detection

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Multimedia Tools and Applications 2025年第4期84卷 1697-1724页

作者： Shukla, Preeti Chandanan, Amit Kumar Department of Computer Science and Information Technology Guru Ghasidas Vishwavidyalaya CG Bilaspur India Department of Computer Science & Engineering Guru Ghasidas Vishwavidyalaya CG Bilaspur India

In recent times, drastic climate changes have caused a substantial increase in the growth of crop diseases. This causes large-scale demolition of crops, decreases cultivation, and eventually leads to the financial loss of farmers. Due to the rapid growth in a variety of diseases and adequate knowledge of farmers, identification, and treatment of the disease have become a major challenge. The leaves have texture and visual similarities which are attributes for the identification of disease type. Hence, computer vision employed with deep learning provides a way to solve this problem. In this research work, a novel voting-based ensembled-deep-learning paradigm is developed for crop disease detection. Initially, the collected raw images are pre-processed via Laplacian Filter and morphological operations. From, the pre-processed image, the ROI region is identified using K-means Clustering. Then, from the ROI regions, the features like texture feature (Chi-squared Local Binary Pattern (CLBP)), Scale Invariant Feature Transform (SIFT), Gray-Level Co-occurrence Matrix (GLCM)), color features, and shape features are extracted. Then, the optimal features are selected by using Self-Improved Coyote Optimization Algorithm (SI-COA) is an extended version of the standard Coyote Optimization Algorithm (COA) and is a metaheuristic for optimization that is inspired by the canis latrans species. For crop disease detection, the voting-based ensembled-deep-learning paradigm was used which includes the Attention-based Bi-LSTM, Recurrent Neural Networks (RNNs), and Optimized Deep Neural Networks (O-DNN). The weight of DNN is optimized via Self-Improved Coyote Optimization Algorithm (SI-COA). The overall outcome of this process is evaluated by using various performance metrics such as Precision, Recall, F1-Score, Accuracy, Sensitivity and Specificity, NPV, FNR, FPR, and MCC, as well. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2

关键词： Long short-term memory

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Understanding Brain Network Stimulation for Emotion Analyzing Connectivity Feature Map from Electroencephalography

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International Journal of Advanced computer science and Applications 2025年第4期16卷 542-551页

作者： Maria, Mahfuza Akter Akhand, M.A.H. Kamal, Abdus Samad Department of Computer Science and Engineering Khulna University of Engineering & Technology Khulna9203 Bangladesh Graduate School of Science and Technology Gunma University Kiryu376-8515 Japan

In understanding brain functioning by Electroencephalography (EEG), it is essential to be able to not only identify more active brain areas but also understand connectivity among different areas. The functional and efficient connectivity networks of the brain have been examined in this study by constructing a connectivity feature map (CFM) with four widely used connectivity methods from the Database for Emotion Analysis Using Physiological Signals (DEAP) emotional EEG data to research how this connectivity’s patterns are influenced by emotion. According to the investigation results, emotions are mainly related to the parietal, central, and frontal regions. The parietal region is more responsible for emotion alteration among these three regions. Positive emotions are associated with more direct correlations and dependencies than negative ones. When experiencing negative emotions, the regions of the brain function more synchronously as well as there are less flow of information. Whether direct or inverse, there is less correlation between brain regions in the higher frequency band than in the lower frequency band. Higher frequencies are associated with increased dependence and directed information transfer between brain regions. Generally, the electrodes in the same lobe show stronger connectivity than those in different lobes. At a glance, the present study is a comprehensive analysis to understand brain network stimulation for emotion from EEG, and it significantly differs from the existing emotion recognition studies typically focused on recognition proficiency. © (2025), (science and Information Organization). All rights reserved.

关键词： Emotion Recognition

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An efficient deep learning model for early disease detection in vegetable crops

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Neural Computing and Applications 2025年 1-20页

作者： Bhola, Amit Kumar, Prabhat School of Computer Science and Engineering Sharda University Greater Noida201310 India Department of Computer Science and Engineering National Institute of Technology Patna Bihar Patna800005 India

Plant diseases significantly threaten global food security and economic stability by reducing crop yields, increasing production costs, and exacerbating food shortages. Early and precise detection of plant diseases is essential for mitigating these risks. This study introduces a lightweight deep learning model for early and efficient disease detection in key vegetable crops, including eggplant, potato, tomato, and soybean. The model leverages a comprehensive dataset containing 23 classes of healthy and diseased plant images. With only 388,055 parameters and a model size of 1.48 MB, it achieves an impressive accuracy of 92.75%, outperforming 17 state-of-the-art deep learning models in terms of performance, efficiency, and size. The novelty lies in the model’s compact architecture, making it highly suitable for deployment in resource-constrained environments such as mobile and edge devices. The primary objective is to provide a scalable and cost-effective solution for early disease detection to enhance agricultural productivity. The findings underscore the model’s superiority over traditional methods and emphasize its potential for real-world applications in smart and sustainable agriculture systems. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Smart agriculture

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

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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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PLDMLT:Multi-Task Learning of Diabetic Retinopathy Using the Pixel-Level Labeled Fundus Images

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computers, Materials & Continua 2023年第8期76卷 1745-1761页

作者： Hengyang Liu Chuncheng Huang Department of Computer Science and Engineering Chongqing University of TechnologyChongqing400054China

In the field of medical images,pixel-level labels are time-consuming and expensive to acquire,while image-level labels are relatively easier to ***,it makes sense to learn more information(knowledge)from a small number of hard-to-get pixel-level annotated images to apply to different tasks to maximize their usefulness and save time and training *** this paper,using Pixel-Level Labeled Images forMulti-Task Learning(PLDMLT),we focus on grading the severity of fundus images for Diabetic Retinopathy(DR).This is because,for the segmentation task,there is a finely labeled mask,while the severity grading task is without classification *** this end,we propose a two-stage multi-label learning weakly supervised algorithm,which generates initial classification pseudo labels in the first stage and visualizes heat maps at all levels of severity using Grad-Cam to further provide medical interpretability for the classification task.A multitask model framework with U-net as the baseline is proposed in the second stage.A label update network is designed to alleviate the gradient balance between the classification and segmentation *** experimental results show that our PLDMLTmethod significantly outperforms other stateof-the-art methods in DR segmentation on two public datasets,achieving up to 98.897%segmentation *** addition,our method achieves comparable competitiveness with single-task fully supervised learning in the DR severity grading task.

关键词： DR lesion segmentation pseudo labels grading task class activation heat map update label network

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