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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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Molecular Generation and Optimization of Molecular Properties Using a Transformer Model

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Big Data Mining and Analytics 2024年第1期7卷 142-155页

作者： Zhongyin Xu Xiujuan Lei Mei Ma Yi Pan School of Computer Science Shaanxi Normal UniversityXi'an 710119China Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Generating novel molecules to satisfy specific properties is a challenging task in modern drug discovery,which requires the optimization of a specific objective based on satisfying chemical ***,we aim to optimize the properties of a specific molecule to satisfy the specific properties of the generated *** Matched Molecular Pairs(MMPs),which contain the source and target molecules,are used herein,and logD and solubility are selected as the optimization *** main innovative work lies in the calculation related to a specific transformer from the perspective of a matrix *** intervals and state changes are then used to encode logD and solubility for subsequent *** the experiments,we screen the data based on the proportion of heavy atoms to all atoms in the groups and select 12365,1503,and 1570 MMPs as the training,validation,and test sets,*** models are compared with the baseline models with respect to their abilities to generate molecules with specific *** show that the transformer model can accurately optimize the source molecules to satisfy specific properties.

关键词： molecular optimization transformer Matched Molecular Pairs(MMPs) logD solubility

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A privacy and compliance in regulated anonymous payment system based on blockchain

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Journal of Ambient Intelligence and Humanized Computing 2024年第8期15卷 3141-3157页

作者： Elfadul, Issameldeen Wu, Lijun Elhabob, Rashad Elkhalil, Ahmed School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China School of Computer and Information Engineering Qilu Institute of Technology Shandong Jinan250200 China Faculty of Computer Science and Information Technology Karary University Khartoum Sudan

Decentralized Anonymous Payment Systems (DAP), often known as cryptocurrencies, stand out as some of the most innovative and successful applications on the blockchain. These systems have garnered significant attention in the financial industry due to their highly secure and reliable features. Regrettably, the DAP system can be exploited to fund illegal activities such as drug dealing and terrorism. Therefore, governments are increasingly worried about the illicit use of DAP systems, which poses a critical threat to their security. This paper proposes Privacy and Compliance in Regulated Anonymous Payment System Based on Blockchain (PCRAP), which provides government supervision and enforces regulations over transactions without sacrificing the essential idea of the blockchain, that is, without surrendering transaction privacy or anonymity of the participants. The key characteristic of the proposed scheme is using a ring signature and stealth address to ensure the anonymity of both the sender and receiver of the transaction. Moreover, a Merkle Tree is used to guarantee government supervision and enforce regulations. Our proposed scheme satisfies most of the stringent security requirements and complies with the standards of secure payment systems. Additionally, while our work supports government regulations and supervision, it guarantees unconditional anonymity for users. Furthermore, the performance analysis demonstrates that our suggested scheme still remains applicable and effective even when achieving complete anonymity. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Blockchain

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Manhattan-based k-member clustering and enhanced rabbit optimization algorithm for k-anonymization in social network

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Multimedia Tools and Applications 2024年第30期83卷 74739-74756页

作者： Sivasankari, K. Maheswari, K. M. Uma Department of computer science and engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of computing technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India

Online social networks are becoming more and more popular, according to recent trends. The user's primary concern is the secure preservation of their data and privacy. A well-known method for preventing individual identity in publicly accessible datasets is K-anonymization. Existing anonymity techniques' primary drawback is that they're susceptible to similarity and attribute disclosure attacks. Furthermore, a large amount of data has been lost in the given database. To address this limitation, the proposed model integrated an anonymizing algorithm with Manhattan-based k-member clustering and an enhanced rabbit optimization algorithm (MKCAEROA). The Manhattan distance-based k member (MKCA) clustering algorithm is employed in this case to cluster the initial database. The resulting clusters are then further optimized, and the original database is made anonymous, using the enhanced rabbit optimization algorithm (EROA). In the suggested model, EROA is included to satisfy the specified anonymity constraints while concurrently minimizing the clustering error rate and the generated information loss. When compared to existing approaches, the experimental results demonstrate that the suggested method provides the highest clustering accuracy with the least amount of information loss. The MATLAB workbench will be used to implement the suggested model. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Database systems

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Hardware and Software Integration of Machine Learning Vision System Based on NVIDIA Jetson Nano 1

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Future of Information and Communication Conference, FICC 2024

作者： Manolescu, Denis Reid, David Secco, Emanuele Lindo Robotics Lab School of Mathematics Computer Science and Engineering Liverpool Hope University Liverpool United Kingdom

ISBN: (数字)9783031540530

ISBN: (纸本)9783031540523

This study investigates the capabilities and flexibility of edge devices for real-time data processing near the source. A configurable Nvidia Jetson Nano system is used to deploy nine pre-trained computer vision models, demonstrating proficiency in local data processing, analysis and providing real-time feedback. Additionally, the system offers deployment control via a customized Graphical User Interface (GUI) and proves very low-latency inference re-stream to other local devices using the G-Streamer framework. The Machine Learning models which cover a wide range of applications, including image classification, object recognition or detection, depth estimation and semantic segmentation, show potential for IoT and industrial applications. Further, the fusion of these capabilities with AI and machine learning algorithms unveils a promising perspective for substantial industrial redevelopment. This research underscores the strategic significance of edge devices in modern computational frameworks and their potential role in future technological advancements. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： computer vision

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Latent-Factorization-of-Tensors-Incorporated Battery Cycle Life Prediction

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 633-635页

作者： Minzhi Chen Li Tao Jungang Lou Xin Luo the College of Computer and Information Science Southwest University the Huzhou Yangtze Delta Region Institute of Intelligent Transportation the School of Information Engineering Huzhou University IEEE

Dear Editor,This letter presents a latent-factorization-of-tensors (LFT)-incorporated battery cycle life prediction framework. Data-driven prognosis and health management (PHM) for battery pack (BP) can boost the safety and sustainability of a battery management system (BMS),which relies heavily on the quality of the measured BP data like the voltage (V), current (I), and temperature (T).

关键词： Batteries Tensors Prognostics and health management Data models Degradation Battery charge measurement Vectors Distributed databases Computational modeling Buildings

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An Overview of LoRa Localization Technologies

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computers, Materials & Continua 2025年第2期82卷 1645-1680页

作者： Huajiang Ruan Panjun Sun Yuanyuan Dong Hamid Tahaei Zhaoxi Fang Institute of Artificial Intelligence Shaoxing UniversityShaoxing312000China Department of Computer Science and Engineering School of Mechanical and Electrical EngineeringShaoxing UniversityShaoxing312000China

Traditional Global Positioning System(GPS)technology,with its high power consumption and limited perfor-mance in obstructed environments,is unsuitable for many Internet of Things(IoT)*** paper explores LoRa as an alternative localization technology,leveraging its low power consumption,robust indoor penetration,and extensive coverage area,which render it highly suitable for diverse IoT *** comprehensively review several LoRa-based localization techniques,including time of arrival(ToA),time difference of arrival(TDoA),round trip time(RTT),received signal strength indicator(RSSI),and fingerprinting *** this review,we evaluate the strengths and limitations of each technique and investigate hybrid models to potentially improve positioning *** studies in smart cities,agriculture,and logistics exemplify the versatility of LoRa for indoor and outdoor *** findings demonstrate that LoRa technology not only overcomes the limitations of GPS regarding power consumption and coverage but also enhances the scalability and efficiency of IoT deployments in complex environments.

关键词： LoRa localization IoT TDoA RSSI application hybrid models

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An Interpretable CNN for the Segmentation of the Left Ventricle in Cardiac MRI by Real-Time Visualization

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computer Modeling in engineering & sciences 2023年第5期135卷 1571-1587页

作者： Jun Liu Geng Yuan Changdi Yang Houbing Song Liang Luo Robotics Institute School of Computer ScienceCarnegie Mellon UniversityPittsburghPA15217USA Department of Electrical&Computer Engineering College of EngineeringNortheastern UniversityBostonMA02115USA Security and Optimization for Networked Globe Laboratory(SONG Lab) Embry-Riddle Aeronautical UniversityDaytona BeachFL32114USA School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu610054China

The interpretability of deep learning models has emerged as a compelling area in artificial intelligence *** safety criteria for medical imaging are highly stringent,and models are required for an ***,existing convolutional neural network solutions for left ventricular segmentation are viewed in terms of inputs and ***,the interpretability of CNNs has come into the *** medical imaging data are limited,many methods to fine-tune medical imaging models that are popular in transfer models have been built using massive public Image Net datasets by the transfer learning ***,this generates many unreliable parameters and makes it difficult to generate plausible explanations from these *** this study,we trained from scratch rather than relying on transfer learning,creating a novel interpretable approach for autonomously segmenting the left ventricle with a cardiac *** enhanced GPU training system implemented interpretable global average pooling for graphics using deep *** deep learning tasks were *** included data management,neural network architecture,and *** system monitored and analyzed the gradient changes of different layers with dynamic visualizations in real-time and selected the optimal deployment *** results demonstrated that the proposed method was feasible and efficient:the Dice coefficient reached 94.48%,and the accuracy reached 99.7%.It was found that no current transfer learning models could perform comparably to the ImageNet transfer learning *** model is lightweight and more convenient to deploy on mobile devices than transfer learning models.

关键词： Interpretable graphics training visualization image segmentation left ventricle CNNs global average pooling

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Hybrid approach for the detection of epileptic seizure using electroencephalography input

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International Journal of Information Technology (Singapore) 2024年第1期16卷 569-575页

作者： Basha, Niha Kamal Surendiran, B. Benzikar, Amutha Joyal, S. School of Computer Science and Engineering Vellore Institute of Technology (VIT) Vellore India Department of Computer Science and Engineering National Institute of Technology (NIT) Puducherry Karaikal India School of Computer Science and Engineering Vellore Institute of Technology (VIT) Chennai India Saveetha Engineering College Chennai India

In the early days, it was difficult to study bio-electric signals, but now a days these problems have been solved by many hardware devices which are available at low cost. Even then there is a need for technical improvements to process bio-electric signals. Enormous effort has been taken for the detection of epileptic seizure from EEG signal. Considerable evidence available on Bonn university EEG dataset for epileptic seizure detection. However, deep learning algorithms have not applied often on Bonn university data like other machine learning algorithms for the detection of epileptic seizure due to the less availability of data. This work adopted machine learning and deep learning models for the detection of epileptic seizure. The data consists of a hundred subjects with sampling rate as 173.61 Hz of ‘S’ (ictal) and ‘Z’ (normal) dataset. The results show how the choice of healthy and ictic subjects through high order functional math decision like variance (STD), Power, Skewness, and Kurtosis values are analyzed to extract epileptic features. The performance of classifiers has been evaluated based on the evaluation metrics within which CGRU-SVM outperforms all other models with 97.54% accuracy. The features are extracted based on the statistical measures to detect epileptic seizure. Further, the proposed work provides more evidence on the epileptic seizure features, and it shows the new possibility for using deep learning models for epileptic seizure detection. In future, an analysis must be performed with dynamic sets of input and different analysis techniques. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer Applications and Management.

关键词： Confusion matrix Deep learning model Electroencephalography Epilepsy Ictal subject Rhythmic frequency Statistical feature

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Reinforcement learning of non-additive joint steganographic embedding costs with attention mechanism

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science China(Information sciences) 2023年第3期66卷 273-286页

作者： Weixuan TANG Bin LI Weixiang LI Yuangen WANG Jiwu HUANG Institute of Artificial Intelligence and Blockchain Guangzhou University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen Institute of Artificial Intelligence and Robotics for Society School of Computer Science and Cyber Engineering Guangzhou University

Image steganography is the art and science of secure communication by concealing information within digital images. In recent years, the techniques of steganographic cost learning have developed rapidly. Although the existing methods can learn satisfactory additive costs, the interplay of different pixels' embedding impacts has not been considered, so the potential of learning may not be fully exploited. To overcome this limitation, in this paper, a reinforcement learning paradigm called Jo Po L(joint policy learning) is proposed to extend the idea of additive cost learning to a non-additive situation. Jo Po L aims to capture the interactions within pixel blocks by defining embedding policies and evaluating contributions of embedding impacts on a block level rather than a pixel level. Then, a policy network is utilized to learn optimal joint embedding policies for pixel blocks through interactions with the environment. Afterwards,these policies can be converted into joint embedding costs for practical message embedding. The structure of the policy network is designed with an effective attention mechanism and incorporated with the domain knowledge derived from traditional non-additive steganographic methods. The environment is responsible for assigning rewards according to the impacts of the sampled joint embedding actions, which are evaluated by the gradient information of a neural network-based steganalyzer. Experimental results show that the proposed non-additive method Jo Po L significantly outperforms the existing additive methods against both feature-based and CNN-based steganalzyers over different payloads.

关键词： information hiding non-additive steganography steganalysis cost learning image processing

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