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International Conference on Circuit, Power and computing Technologies (ICCPCT)

作者： D. Saisanthiya Naman Saxena Pratham Saini Networking and Communications School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

The rapid growth of the Internet of Things (IoT) has underscored the critical need for robust information exchange mechanisms, especially within cloud services. However, this advancement also introduces significant data security risks due to the potential for misuse. To address these concerns, this research proposes an innovative method to bolster data security in cloud environments using proxy re-encryption. Our method employs identity-based encryption, enabling data owners to securely upload encrypted data to the cloud. Authorized users can then access this data through a proxy re-encryption process. To handle computationally demanding tasks, an edge device functions as a proxy, thereby alleviating the limitations of typical IoT devices. A key feature of our system is the integration of blockchain technology, which decentralizes data sharing and alleviates the bottlenecks inherent in centralized systems, while also enabling fine-grained access control. A thorough data security analysis and review indicate that our solution effectively ensures data confidentiality, integrity, and public *** of Form, Bottom of Form.

关键词： Access control Cloud computing Data privacy Protocols Reviews Scalability Encryption

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Underwater Photography Noise Cancellation 5

Underwater Photography Noise Cancellation

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5th International Conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Mohanty, Devjyoti Jain, Vaibhav Nithya Paranthaman, R. Dept. of Networking and Communications SRM Institute of Science and Technology Kattankulathur Chennai India Dept. of Networking and Communications School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9798400709418

Underwater photography has been a well-known aspect in the tech industry to both professionals and researchers. The most challenging part in taking pictures underwater shows up hustles like the noise caused by water turbidity, lightning conditions along with camera settings. Meanwhile the core part is the image whose quality can be reduced by the noise caused. Algorithms have been developed using deep learning and artificial intelligence to identify and remove noise from underwater images and also helps in the enhancement of their visual appeal. In this research paper, we have analysed the current state of underwater photography noise cancellation using Artificial Learning and Deep Learning techniques. © 2023 ACM.

关键词： Object detection

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Enhanced Privacy in Data Aggregation with Secret Sharing Techniques

Enhanced Privacy in Data Aggregation with Secret Sharing Tec...

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computing and Data science (ICCDS), International Conference on

作者： A.C.K. Yugandhar Mandyam Hemanth S. Gnanavel Dept of Computing Technologies Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India

ISBN: (数字)9798350365337

ISBN: (纸本)9798350365344

The effectiveness of wireless sensor networks is greatly impacted by wireless spoofing attacks, which are simple to initiate. The difficult issues in Wireless Sensor Networks are locating many adversaries and eliminating them, identifying spoofing attackers, and estimating the number of attackers. The spoofing attackers are located and detected using the clustering approach. This method is unable to correctly identify the attackers. Intrusion Detection System (IDS) is suggested as a solution to this issue in order to identify spoofing attackers. In order to detect the misbehaving nodes in a wireless sensor network, VANET IDS monitors node behavior in their car, such as packet transfer. The simulation results clearly show that the proposed strategy effectively and consistently detects accidents in wireless sensor networks.

关键词： Wireless communication Wireless sensor networks Data privacy Simulation Vehicular ad hoc networks Transforms Standardization

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Classification Based on Texture Analysis for Synthetic Hazy Image from Mount Kelud Haze Image Density 8

Classification Based on Texture Analysis for Synthetic Hazy ...

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8th International Conference on Electrical, Electronics and Information engineering, ICEEIE 2023

作者： Nugroho, Fresy Basid, Puspa Miladin Safitri A. Arif, Yunifa Miftachul Diah, Norizan Mat Bahtiar, Firma Sahrul Nurhayati, Hani Kurniawan, Fachrul Priandani, Nurizal Dwi Dermawan, Dodik Arwin Universitas Islam Negeri Maulana Malik Ibrahim Informatics Engineering Faculty of Science and Technology Malang Indonesia School of Computing Sciences College of Computing Informatics and Media Universiti Teknologi Mara Shah Alam Malaysia Library and Information Science Universitas Islam Negeri Maulana Malik Ibrahim Faculty of Science and Technology Malang Indonesia Manajemen Informatika Program Vokasi Universitas Negeri Surabaya Surabaya Indonesia

ISBN: (纸本)9798350340501

This paper classifies the simulations of homogeneous synthetic images, heterogeneous synthetic hazy images, and original hazy images taken from CCTV (Close Circuit Television) of Mt. Kelud crater using the GLCM (Gray Level Co-Occurrence Matrix) method. The average feature values obtained using the GLCM (Gray Level Co-Occurrence Matrix) method are used to compare the similarity of gray feature values of the three and then classify thin, medium, and thick images. The results for classifying thin haze, medium haze, and thick haze on the homogeneous synthetic hazy image test data obtained an accuracy value of 50%, a precision value of 46%, and a sensitivity value of 65%. As for the classification of thin, medium, and thick fog on heterogeneous synthetic hazy images, test data obtained an accuracy value of 42%, a precision value of 32%, and a sensitivity value of 48%. © 2023 IEEE.

关键词： Textures

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Automated File Labeling for Heterogeneous Files Organization Using Machine Learning

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Computers, Materials & Continua 2023年第2期74卷 3263-3278页

作者： Sagheer Abbas Syed Ali Raza MAKhan Muhammad Adnan Khan Atta-ur-Rahman Kiran Sultan Amir Mosavi School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Computer Science GC University LahorePakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Software Pattern Recognition and Machine Learning LabGachon UniversitySeongnam13120Korea Department of Computer Science College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam31441Saudi Arabia Department of CIT The Applied CollegeKing Abdulaziz UniversityJeddah31261Saudi Arabia John von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaBratislava81107Slovakia Faculty of Civil Engineering TU-DresdenDresden01062Germany

File labeling techniques have a long history in analyzing the anthological trends in computational *** situation becomes worse in the case of files downloaded into systems from the ***,most users either have to change file names manually or leave a meaningless name of the files,which increases the time to search required files and results in redundancy and duplications of user ***,no significant work is done on automated file labeling during the organization of heterogeneous user files.A few attempts have been made in topic ***,one major drawback of current topic modeling approaches is better *** rely on specific language types and domain similarity of the *** this research,machine learning approaches have been employed to analyze and extract the information from heterogeneous corpus.A different file labeling technique has also been used to get the meaningful and`cohesive topic of the *** results show that the proposed methodology can generate relevant and context-sensitive names for heterogeneous data files and provide additional insight into automated file labeling in operating systems.

关键词： Automated file labeling file organization machine learning topic modeling

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Evaluation of mechanical property of banana HDPE composite through experiment and artificial neural network (ANN)

Evaluation of mechanical property of banana HDPE composite t...

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2023 International Conference on Advances in Materials, Mechanics, Mechatronics and Manufacturing, IC4M 2023

作者： Jores, Jon Bonnar anak Jayamani, Elammaran Lai, Tasha Sie Ming Subramanian, Jeyanthi Rejeesh, C.R. Faculty of Engineering Computing and Science Swinburne University of Technology Sarawak Campus Sarawak Kuching93350 Malaysia Vellore Institute of Technology Chennai Campus Vandalur-Kelambakam Road Tamil Nadu Chennai600127 India Federal Institute of Science and Technology Kerala Angamala India

In this study, two post-curing techniques using microwave and oven were utilized to examine the mechanical characteristics and behavior of composites reinforced with banana fibers with its matrix made of high-density polyethylene polymer. The research involved experimental work through tensile tests and predictive modelling using the artificial neural network (ANN). The first part of the research involved the collection of datasets which were gathered from the tensile tests based on the ASTM D638 using dog bone specimens of various fiber percentages to compare the mechanical properties such as the specific energy and maximum load after post-curing using an oven and microwave at various powers and time. The trend and behavior of the composite due to the fiber percentages and the effects of post curing methods were analyzed. The second part of the research involved the ANN approach to predict the maximum gage length based on the data collected from the experiments using inputs such as the fiber content, maximum load, and displacement. Further optimization was done on the ANN model by fine-tuning variables such as the learning function, transfer function, and number of neurons to predict the stress and strain values. The accuracy of the results was calculated using statistical evaluation equations. The optimized settings were utilized to generate the predicted results and the deviation from the experimental results were evaluated. Copyright © 2024 Elsevier Ltd. All rights reserved.

关键词： Tensile testing

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GACL-Net:Hybrid Deep Learning Framework for Accurate Motor Imagery Classification in Stroke Rehabilitation

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Computers, Materials & Continua 2025年第4期83卷 517-536页

作者： Chayut Bunterngchit Laith H.Baniata Mohammad H.Baniata Ashraf ALDabbas Mohannad A.Khair Thanaphon Chearanai Sangwoo Kang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing100190China School of Computing Gachon UniversitySeongnam13120Republic of Korea Computer Science Department Faculty of Information TechnologyThe World Islamic Sciences and Education UniversityAmman11947Jordan Intelligent Systems Department Faculty of Artificial IntelligenceAl-Balqa Applied UniversityAl-Salt19117Jordan IT Infrastructure Department Qatrana Cement CompanyAmman11821Jordan Division of Industrial and Logistics Engineering Technology Faculty of Engineering and TechnologyKing Mongkut’s University of Technology North BangkokRayong CampusRayong21120Thailand

Stroke is a leading cause of death and disability worldwide,significantly impairing motor and cognitive *** rehabilitation is often hindered by the heterogeneity of stroke lesions,variability in recovery patterns,and the complexity of electroencephalography(EEG)signals,which are often contaminated by *** classification of motor imagery(MI)tasks,involving the mental simulation of movements,is crucial for assessing rehabilitation strategies but is challenged by overlapping neural signatures and patient-specific *** address these challenges,this study introduces a graph-attentive convolutional long short-term memory(LSTM)network(GACL-Net),a novel hybrid deep learning model designed to improve MI classification accuracy and ***-Net incorporates multi-scale convolutional blocks for spatial feature extraction,attention fusion layers for adaptive feature prioritization,graph convolutional layers to model inter-channel dependencies,and bidi-rectional LSTM layers with attention to capture temporal *** on an open-source EEG dataset of 50 acute stroke patients performing left and right MI tasks,GACL-Net achieved 99.52%classification accuracy and 97.43%generalization accuracy under leave-one-subject-out cross-validation,outperforming existing state-of-the-art ***,its real-time processing capability,with prediction times of 33–56 ms on a T4 GPU,underscores its clinical potential for real-time neurofeedback and adaptive *** findings highlight the model’s potential for clinical applications in assessing rehabilitation effectiveness and optimizing therapy plans through precise MI classification.

关键词： Motor imagery EEG stroke rehabilitation deep learning brain-computer interface

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AEGAN: Generating imperceptible face synthesis via autoencoder-based generative adversarial network

AEGAN: Generating imperceptible face synthesis via autoencod...

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作者： Che, Aolin Yang, Jing-Hua Guo, Cai Dai, Hong-Ning Xie, Haoran Li, Ping Faculty of Innovation Engineering Macau University of Science and Technology China School of Computing and Information Engineering Hanshan Normal University Chaozhou China Department of Computer Science Hong Kong Baptist University Hong Kong Department of Computing and Decision Sciences Lingnan University Hong Kong Department of Computing The Hong Kong Polytechnic University Hong Kong School of Design The Hong Kong Polytechnic University Hong Kong

Face recognition (FR) systems based on convolutional neural networks have shown excellent performance in human face inference. However, some malicious users may exploit such powerful systems to identify others' face images disclosed by victims' social network accounts, consequently obtaining private information. To address this emerging issue, synthesizing face protection images with visual and protective effects is essential. However, existing face protection methods encounter three critical problems: poor visual effect, limited protective effect, and trade-off between visual and protective effects. To address these challenges, we propose a novel face protection approach in this article. Specifically, we design a generative adversarial network (GAN) framework with an autoencoder (AEGAN) as the generator to synthesize the protection images. It is worth noting that we introduce an interpolation upsampling module in the decoder in order to let the synthesized protection images evade recognition by powerful convolution-based FR systems. Furthermore, we introduce an attention module with a perceptual loss in AEGAN to enhance the visual effects of synthesized images by AEGAN. Extensive experiments have shown that AEGAN not only can maintain the comfortable visual quality of synthesized images but also prevent the recognition of commercial FR systems, including Baidu and iKLYTEK. © 2023 John Wiley & Sons Ltd.

关键词： Face recognition

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Multi-Modal Data Fusion Approach Using the Structured and Sparse Principal Component Analysis (MMSSCA)

Multi-Modal Data Fusion Approach Using the Structured and Sp...

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2023 IEEE International Conference on Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Kandpal, Jyoti Jonitha, A. Sivakumar, Janaki Ray, Rejon Kumar School of Computing Graphic Era Hill University Uttarakhand Dehradun India Universiti Malaysia Faculty of Data Science & Computing Kelantan Malaysia Global College of Engineering and Technology Computer Science and Creative Technologies Muscat Oman Gannon University Department of Business Analytics PA Erie United States

ISBN: (纸本)9798350329773

There are many different sorts of data that can be gathered and analyzed, including pictures, videos, texts, speeches, music, and other noises, Video content, for example, generally includes minimum some types of audible sounds or any type of visual image, and often also useful text data that can be of some specific type of titles or subtitles, which may consist of some unique type text language which is different type of speech. 'Modality' is basically defines each different type of acquisition strategy. As natural phenomena tend to be quite complex, it is unusual for a single modality to provide exhaustive understanding of the phenomenon of interest. The process of combining and interpreting the useful data between the same modal or in different modal, and also interaction with the human beings with multi-modal data fusion &the accurate and deep interpretation and understanding of data obtained from the combining process from it, are central to the research challenge on multimodal data analysis. We contend that many of these challenges are shared by researchers in different fields. Both the 'why' and the 'how' of data fusion are addressed in this paper. Various examples from the realms of science and technology serve as inspiration for the first problem, which is then suggested by a various types of mathematical based model framework demonstrating the utility of data fusion. To solve the second problem, we introduce the idea of 'diversity' as a central concept and discuss several data-driven solutions based on matrix and tensor decompositions, highlighting how they take into account diversity in the underlying data sets to address the aforementioned issues, this research suggests a novel approach for modeling multi data fusion implemented using sparse technique and structural analysis. © 2023 IEEE.

关键词： Modal analysis

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Comparative study of IoT-and AI-based computing disease detection approaches

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Data science and Management 2025年第1期8卷 94-106页

作者： Wasiur Rhmann Jalaluddin Khan Ghufran Ahmad Khan Zubair Ashraf Babita Pandey Mohammad Ahmar Khan Ashraf Ali Amaan Ishrat Abdulrahman Abdullah Alghamdi Bilal Ahamad Mohammad Khaja Shaik Department of Computer Application Lovely Professional UniversityPhagwaraPunjab144411India Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationGunturAndhra Pradesh522502India School of Computing Science and Engineering VIT Bhopal UniversityKothrikalanSehore466114India Babasaheb Bhimrao Ambedkar University Lucknow226025India College of Commerce and Business Administration Dhofar UniversitySalalah211Oman Faculty of Computer Studies Arab Open University18211Bahrain Department of Computer Application Shri Ramswaroop Memorial UniversityLucknow225003India College of Computing and Information Technology Shaqra UniversityShaqra11961Saudi Arabia

The emergence of different computing methods such as cloud-,fog-,and edge-based Internet of Things(IoT)systems has provided the opportunity to develop intelligent systems for disease *** to other machine learning models,deep learning models have gained more attention from the research community,as they have shown better results with a large volume of data compared to shallow ***,no comprehensive survey has been conducted on integrated IoT-and computing-based systems that deploy deep learning for disease *** study evaluated different machine learning and deep learning algorithms and their hybrid and optimized algorithms for IoT-based disease detection,using the most recent papers on IoT-based disease detection systems that include computing approaches,such as cloud,edge,and *** analysis focused on an IoT deep learning architecture suitable for disease *** also recognizes the different factors that require the attention of researchers to develop better IoT disease detection *** study can be helpful to researchers interested in developing better IoT-based disease detection and prediction systems based on deep learning using hybrid algorithms.

关键词： Deep learning Internet of Things(IoT) Cloud computing Fog computing Edge computing

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