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Integration of cloud-based scheme with industrial wireless sensor network for data publishing in privacy of point source

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International Journal of Cloud Computing 2024年第2期13卷 124-138页

作者： Ravindhar, N.V. Sasikumar, S. Bharathiraja, N. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu India Department of Computer Science and Engineering Saveetha Engineering College Tamil Nadu India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

Wireless sensor networks (WSNs) are normally conveyed in arbitrary regions with no security. The source area uncovers significant data about targets. In this paper, a plan dependent on the cloud utilising data publishing in privacy of point source is proposed to resolve the issue of source area security. Then, at that point, a cloud-moulded phony area of interest is made to add counterfeit parcels into the WSN to confound the enemy and give a far-reaching protection area. Every important parcel is steered through a way that is very hard for the area of interest finding enemy to discover straightforwardly. Recreation results represent that the plan can forestall antagonistic catch and keep a significant degree of security insurance simultaneously. The energy utilisation in this plan applies restricted impact on the organisation lifetime contrasted and a cloud-based plan and an all-course irregular steering calculation conspire. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Wireless sensor networks

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Cloud Computing Network in Remote Sensing-Based Climate Detection Using Machine Learning Algorithms

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Remote Sensing in Earth Systems sciences 2025年第2期8卷 400-408页

作者： Srinivas, Jhade Raju, Ch VV Narasimha Sasikala, C. Bhaskar, Parumanchala Panyala, Amarendra Reddy Priya, Divya Department of Computer Science and Engineering - Data Science K G Reddy College of Engineering and Technology Moinabad Telangana Hyderabad India Information Technology Marri Laxman Reddy Institute of Technology and Management Dundigal Telangana Hyderabad 500043 India Dept of CSE Srinivasa Ramanujan Institute of Technology (Autonomous) Andhra Pradesh Ananthapuramu India Department of Computer Science and Engineering Santhiram Engineering College Nandyal India Department of Computer Science and Engineering (Data Science) Malla Reddy University Telangana Hyderabad India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Effective monitoring of the environment over a large area will require mobilization of a considerable amount of information. Otherwise, the use of traditional methods will prove to be costly and would take up so much time. Also, assessing the land cover changes between two times is one of the other methods which can be adopted to measure the environmental sustainability. Using ML techniques for predictive modeling in remote sensing has emerged as a useful approach in solving some of the environmental and climatic challenges. This research proposes a novel method in remote sensing image-based climate detection utilizing cloud computing and machine learning algorithms. Here, the cloud computing module is used in the collection of remote sensing images for the detection of climate changes. Then, the collected image is processed for noise removal as well as normalization. The processed image is segmented, and features are extracted utilizing a fuzzy K-clustering-based contour Gaussian model with graph cut Boltzmann CNN. Experimental analysis is carried out for various remote sensing-based climate change datasets in terms of training accuracy, random precision, AUC, recall, and F-measure. The proposed technique obtained 97% of training accuracy, 95% of random precision, 96% of recall, 93% of AUC, and 94% of F-measure. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Climate detection Cloud computing Contour Gaussian model Machine learning algorithms Remote sensing images

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Joint Awareness and Congestion Control in Vehicular Networks

Joint Awareness and Congestion Control in Vehicular Networks

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Chen, Ying Zhang, Fuxin College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

ISBN: (纸本)9781665410205

In vehicular networks, vehicles frequently broad-cast vehicle states information to track the movement of their neighbors. A large number of vehicles get access to the shared channel resources to broadcast their states information, which may lead to channel congestion. The existing channel congestion solutions mainly focus on the media access control layer state to optimize channel resource utilization, without considering the impact of the physical layer on successful packet reception performance. In this article, we propose a packet reception model to represent the successful reception rate of state packets under the influence of interference signals and noise, and design an application-specific utility function. This utility considers the states of the physical layer and the media access control layer to balance reducing the physical layer interference and meeting application requirements. We then propose a distributed joint power and rate control algorithm that uses on-demand mode to allocate channel resources to meet the safety application requirements of each vehicle. The simulation results show that our work can effectively prevent channel congestion and improve the safety performance of vehicles in different driving scenarios. © 2024 IEEE.

关键词： Congestion control (communication)

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ART-Net: An Attention-Based Hybrid ResNet-Transformer Network for 12-lead ECG Signal Classification

ART-Net: An Attention-Based Hybrid ResNet-Transformer Networ...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Liu, Kun Yang, Ruiping Qi, Liang Luan, Wenjing Zhang, Zhen College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

ISBN: (纸本)9781665410205

Electrocardiogram (ECG) signal classification is an important task in healthcare as it plays a vital role in early prevention and diagnosis of cardiovascular diseases. In this work, we propose an attention-based hybrid ResNet-Transformer network (ART-Net) for 12-lead ECG signal classification. It is comprised of a stacked multi-scale attention-based ResNet and self-attention-based Transformer. At first, ECG signals are divided into several signal segments with the same length. Then multi-scale features are extracted by attention-based Resnet through signal segments, and attention mechanisms are used to adjust the weight of different channel features based on their importance. Next, these multi-scale features from a same ECG signal are integrated in chronological order as input to the Transformer network. In this end, extracting and fusing contextual information based on self-attention mechanism, and extracting the correlation between beats at different positions. The experimental results on CPSC2018 indicate that our model outperforms three state-of-the-art methods, and achieve 85.27% of accuracy, 86.01% of sensitivity and 85.59% of specificity, respectively. © 2024 IEEE.

关键词： Cardiology

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High-Fidelity Real-Time Radar Modeling with Consideration of False and Missed Alarms for Autonomous Driving Simulation

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-12页

作者： Huang, Kaibo Deng, Weiwen Wang, Ying Zhao, Rui Ding, Juan School of Transportation Science and Engineering Beihang University Beijing China College of Computer Science and Technology Jilin University Jilin China PanoSim Technology Company Limited Jiaxing Zhejiang China

Simulation is crucial for autonomous driving technology evolution. Radar, as an essential sensor in this field, significantly influences decision-making with its outputs. High-fidelity autonomous driving simulations require radar models that replicate radar outputs, including false alarms, missed alarms, and measurement errors, both in real-time and with high fidelity. The radar detection process is highly complex, and false and miss alarms add significant uncertainty to the detection results. Current radar models cannot accurately predict radar outputs. To address these issues, this study introduces a data-driven radar modeling approach. Initially, an analysis of factors influencing radar detection outcomes was conducted. Then proposes a labeling method for radar output objects, identify the corresponding scene targets, and distinguish between ghost and real objects. Following this, it introduces a modeling technique that separates radar output status and parameters, aiming to accurately predict radar outputs in the presence of false and missed alarms. It further decouples output parameters to boost prediction accuracy. Radar data is then collected to create a dataset. The radar model is developed and validated against conventional models. The model achieves a 96.5% accuracy in predicting false and missed alarms, with its predictions for radar output parameters closely approximating actual values. Compared to traditional models, there are improvements exceeding 70.60% and 93.68% respectively. Its 5-millisecond processing speed is substantially faster than actual radar speeds. This demonstrates the method's ability to create high-fidelity, real-time models. IEEE

关键词： Radar cross section

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Predictive Health Assistant: AI-Driven Disease Projection Tool 11

Predictive Health Assistant: AI-Driven Disease Projection To...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Anchitaalagammai, J.V. Kavitha, S. Murali, S. Gururaj, K. Lochen Kiruthik, J.P. Pranav Velammal College of Engineering and Technology Department of Cse Madurai India Velammal College of Engineering and Technology Computer Science and Engineering Madurai India

ISBN: (纸本)9798350350357

This study emphasizes the potential of chatbots in revolutionizing healthcare, particularly in the context of infectious disease management. While hospitals have long been the primary source of medical check-ups, diagnoses, and treatment recommendations worldwide, the rise of chatbots, powered by artificial intelligence, is changing the landscape of healthcare delivery. Chatbots enable users to seek medical information and assistance in a manner like conversing with a human. They are gaining rapid adoption, especially in computer chat platforms. The paper highlights the challenges faced by individuals with busy schedules, who often spend considerable time waiting for doctor appointments, particularly for diseases that require prolonged treatment, such as heart disease. Heart disease remains a prevalent global health issue, causing numerous fatalities every day. The future of healthcare depends on remote data collection, accurate distance diagnoses, and AI-driven data analysis, all of which can significantly enhance both healthcare efficiency and outcomes. The proposed chatbot system aims to empower users by facilitating daily health check-ups, raising health awareness, and encouraging proactive health measures. Despite its potential, this research reveals that such systems are underutilized, and public awareness remains limited. Implementing the proposed framework can reduce the time-consuming process of visiting hospitals, offering a convenient and accessible healthcare solution. © 2024 IEEE.

关键词： Python

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Abnormal Traffic Detection for Internet of Things Based on an Improved Residual Network

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computers, Materials & Continua 2024年第6期79卷 4433-4448页

作者： Tingting Su Jia Wang Wei Hu Gaoqiang Dong Jeon Gwanggil School of Computer Science and Technology Xinjiang UniversityUrumqi830046China School of Cyber Science and Engineering Jinling Institute of Technology CityNanjing210000China College of Information Technology Incheon National UniversityIncheon22012Korea

Along with the progression of Internet of Things(IoT)technology,network terminals are becoming continuously more *** has been widely applied in various scenarios,including urban infrastructure,transportation,industry,personal life,and other socio-economic *** introduction of deep learning has brought new security challenges,like an increment in abnormal traffic,which threatens network *** feature extraction leads to less accurate classification *** abnormal traffic detection,the data of network traffic is high-dimensional and *** data not only increases the computational burden of model training but also makes information extraction more *** address these issues,this paper proposes an MD-MRD-ResNeXt model for abnormal network traffic *** fully utilize the multi-scale information in network traffic,a Multi-scale Dilated feature extraction(MD)block is *** module can effectively understand and process information at various scales and uses dilated convolution technology to significantly broaden the model’s receptive *** proposed Max-feature-map Residual with Dual-channel pooling(MRD)block integrates the maximum feature map with the residual *** module ensures the model focuses on key information,thereby optimizing computational efficiency and reducing unnecessary information *** results show that compared to the latest methods,the proposed abnormal traffic detection model improves accuracy by about 2%.

关键词： Abnormal network traffic deep learning residual network multi-scale feature extraction max-feature-map

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Comparison of Optimizers for Lung Cancer Classification using Deep Convolution Neural Network 2

Comparison of Optimizers for Lung Cancer Classification usin...

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2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Dev, R.B. Shanthi, S. Brindha, V. Arunkarthi, T. Ashwinth, K. Kuppuchamy, R. Kongu Engineering College Department of Computer Science and Engineering Perundurai India PSNA College of Engineering and Technology Department of Computer Applications Dindigul India

ISBN: (纸本)9798350349221

The process of identifying and categorizing lung cancer in its early stages is difficult, yet doing so will improve patient survival rates. There is a wealth of research that segments and categorizes lung nodules using convolution neural networks (CNN). Numerous factors, including batch size, epochs, learning rate, optimizers, activation function, and weight initialization, affect the model's performance. To categorize CT scan images as benign, malignant, or normal, a deep CNN model is developed. Optimizers are crucial to the training process of the model because they adjust the weight and learning rate of the network in each epoch, which lowers loss and raises classifier accuracy. Using ReLU and selu as activation functions, this work offers a thorough analysis of the performance for a number of optimizers, including Nesterov Adaptive Momentum (Nadam), Adaptive Delta (Adadelta), Adaptive Max Pooling (Adamax), Stochastic Gradient Descent (SGD), Adaptive gradient (Adagrad), Adaptive Momentum (Adam), Adaptive Gradient Descent (Adagrad), and Root Mean Square Propagation (RMSProp)[1][1][1][1][1][1]. The best training accuracy for the ReLu activation function is 92.67%, with a validation accuracy of 97.92, and for the Selu activation function, it is 95.42%, with a validation accuracy of 96.88, according to the Adam optimizer. The study aims to assess various optimization techniques in deep CNNs for lung cancer classification using CT scan images. It analyzes different optimizers to understand their impact on model convergence, accuracy, and generalization. This insight is crucial for selecting the most effective optimization methods in lung cancer diagnosis. © 2024 IEEE.

关键词： Biological organs

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Malicious Activities Prediction Over Online Social Networking Using Ensemble Model

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Intelligent Automation & Soft Computing 2023年第4期36卷 461-479页

作者： S.Sadhasivam P.Valarmathie K.Dinakaran Department of Computer Science and Engineering K.S.R College of EngineeringThiruchengodeIndia Centre for Artificial Intelligence Chennai Institute of TechnologyChennaiIndia Department of Computer Science and Engineering S.A.Engineering CollegeChennaiIndia

With the vast advancements in Information technology,the emergence of Online Social Networking(OSN)has also hit its peak and captured the atten-tion of the young generation *** clone intends to replicate the users and inject massive malicious activities that pose a crucial security threat to the original ***,the attackers also target this height of OSN utilization,explicitly creating the clones of the user’s *** clone detection mechanisms are designed based on social-network *** instance,monitoring the occur-rence of clone edges is done to restrict the generation of clone ***,this assumption is unsuitable for a real-time environment and works optimally during the simulation *** research concentrates on modeling and efﬁ-cient clone prediction and avoidance methods to help the social network activists and the victims enhance the clone prediction *** model does not rely on ***,an ensemble Adaptive Random Subspace is used for clas-sifying the clone victims with k-Nearest Neighbour(k-NN)as a base classiﬁ*** weighted clone nodes are analysed using the weighted graph theory concept based on the classiﬁed *** the weighted node’s threshold value is high-er,the trust establishment is terminated,and the clones are ranked and sorted in the higher place for ***,the victims are alert to the clone propaga-tion over the online social networking end,and the validation is done using the MATLAB 2020a simulation *** model shows a better trade-off than existing approaches like Random Forest(RF),Naïve Bayes(NB),and the standard graph *** performance metrics like True Positive Rate(TPR),False Alarm Rate(FAR),Recall,Precision,F-measure,and ROC and run time analysis are evaluated to show the signiﬁcance of the model.

关键词： Online social network decision tree weighted measure clone attack predictive measures

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Distributed and Adaptive Message Dissemination for Vehicle Platooning in Hybrid Traffic

Distributed and Adaptive Message Dissemination for Vehicle P...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Li, Xiang Zhang, Fuxin College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

ISBN: (纸本)9781665410205

In vehicular networks, vehicle in the platooning relies on dissemination of beacons to perceive the status of neighbor vehicles and then take control low to maintain a constant inter-vehicle distance. Vehicle platooning communication has stringent high-reliability and low-latency requirements. In this paper, we focus on resource allocation strategies for vehicle platoons in hybrid scenarios. First, we propose a two-dimensional Markov model to describe the channel contention (back-off) processes for platoons and individual vehicles. Based on the model, we then can derive the transmission probabilities of beacons and event messages for platoons and individual vehicles. Finally, we design a distributed and adaptive beacon control scheme to determine the optimal beacon rates for vehicles in the platoons. The simulation results demonstrate that our efforts substantially enhance platooning communication reliability and ensure optimal performance for vehicle safety applications. © 2024 IEEE.

关键词： Markov processes

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