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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Monica, K.M. Parameswaran, T. Swapna, J. Prabu, S. Kaliappan, S. Vit University School Of Computer Science And Engineering Chennai India Cmr University Lakeside Campus School Of Engineering And Technology Department Of Computer Science And Engineering Bangalore India Veltech Rangarajan Dr.Sagunthala R&d Institute Of Science And Technology Department Of Computer Science And Engineering Tamilnadu Chennai India Mahendra Institute Of Technology Department Of Ece Namakkal India Lovely Professional University Division Of Research And Development Punjab Phagwara India

ISBN: (纸本)9798350381665

Cloud computing has fundamentally transformed the accessibility and payment models of services within distributed system environments. In response to heightened security concerns, cloud service providers offer diverse resources spanning Infrastructure, Software, Platform, and Database services, supported by security measures such as Service Level Agreements (SLAs), access control rules, and cryptographic methods. This paper introduces a novel data encryption technique, termed Data Security in Cloud Computing using Maritime Search and Rescue Algorithm (DSCC-MSRA). By integrating an enhanced cypher block chaining algorithm with logical substitution and the Maritime Search and Rescue Algorithm (MSRA) for key generation, this method provides a unique approach to encryption. Through rigorous optimization and analysis, it has undergone significant refinement, with comparisons against industry-standard algorithms showcasing its efficacy. The primary objective is to elevate encryption procedures and introduce complexity to cryptanalysis, ultimately enhancing data security in cloud computing. © 2024 IEEE.

关键词： Cryptography

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An Automatic Error Recognition approach for Machine Translation Results based on Deep Learning 2

An Automatic Error Recognition approach for Machine Translat...

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Rekha, M.N. Prasad, Mudarakola Lakshmi Mukherjee, Saptarshi Nikam, Sudam Vasant Sharma, Swati Reddy, Pundru Chandra Shaker Jss Science and Technology University Electrical and Electronics Engineering Sri Jayachamarajendra College of Engineering Karnataka Mysore India Institute of Aeronautical Engineering Computer Science and Engineering Telangana Dundigal India Maulana Abul Kalam Azad University of Technology M.Tech-Bioinformatics West Bengal India Bharati Vidyapeeth College of Engineering Instrumentation Engineering Navi Mumbai India Presidency University School of Computer Science Engineering and Information Science Karnataka Bangalore India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal506371 India

ISBN: (纸本)9798350387933

At present, the speedy growth of natural-language-processing(NLP) has produced considerable evolution for the topic of machine-translation. There have been many deep neural network-based machine-translation(MT) methods that have become increasingly broad. Still, there currently lacks reliable automatic-error-detection(AED) systems for MT outcomes. One important metric for gauging the efficacy of an engineering English translation application is the accuracy of the corresponding deep learning model's translation. This research presents a deep learning-based solution for automatic mistake identification in machine translation results as a means of closing this gap. In this research, we propose a deep generative model to synthesis the training data that will be utilized to train a model to correct grammatical errors in English used in international trade. Next, the learner's corpus is corrected using the grammatical fault alteration approach, and 'error-correct' sentence pairs are created using the exacted aim sentences and by hand interpreted standard sentences and fed back to the error generation technique for retraining. The model's error detection and correction capabilities are enhanced through the establishment of a connection amid the grammatical error recognition and correction model. The suggested error recognition method has been shown to considerably increase the stealthiest of the trigger, guarantee the efficacy of the backdoor assault, and make the trigger resistant to specific data augmentation operations in experiments using datasets like GTRSB. © 2024 IEEE.

关键词： Error detection

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A Novel Approach for Electricity Load Forecasting using Hybrid Deep Learning Models 2

A Novel Approach for Electricity Load Forecasting using Hybr...

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2nd IEEE International Conference for Women in Innovation, technology and Entrepreneurship, ICWITE 2024

作者： Shyam Prasath, J. Natarajan, B. Venkatraman, K. Nallusamy, M. Revathi, M. Muruganantham, T. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India VIT University School of Computer Science and Engineering Tamilnadu Chennai India Roever Engineering College Department of Computer Science and Engineering Tamilnadu Perambalur India University College of Engineering Bharathidasan Institute of Technology Campus Anna University Department of Computer Science and Engineering Tamilnadu Tiruchirappalli India K. Ramakrishnan College of Engineering Dept. of Ece Tamil Nadu Tiruchirappalli621112 India

ISBN: (纸本)9798350383287

The importance of electricity in both daily lives of people and the economy of the country has made it a vital commodity. Accurate forecasting of electricity load is crucial to regulate production and meet demands at different times. This research work uses the electricity load data of Spain which had samples for every hour in a day for four years and opted for the multivariate forecasting approach instead of treating the features as multiple univariate sequences. The data was broken down and windowed with features such as price of electricity, hour of the day, day of the week and so on. Numerous research studies have shown that traditional statistical methods fail to capture the complex relations, however neural networks are able to learn features through extraction of spatial relationships in the time series data. In order to model the dependencies, Recurrent Neural Networks (RNN) which are the base of Long Short-Term Memory Networks (LSTM) are commonly used. The proposed research uses multiple hybrid models such as stacked LSTMs with CNN (Convolutional Neural Networks) with and without skip connections. As shown by previous works this model can support long input sequences as it can be read as sub-sequences by the CNN and pieced together by the RNN thus capturing details and interconnections between multivariate factors. This research work analyzes and compare the performances of these models on complex multivariate time series data by making them predict energy loads for different time periods and calculating errors. This approach also deeply examined the strength and weakness of each of the models and their performance patterns and yields outstanding performance. © 2024 IEEE.

关键词： Long short-term memory

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Development of Deep Neural Framework for Human Activity Recognition 2

Development of Deep Neural Framework for Human Activity Reco...

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2nd IEEE International Conference for Women in Innovation, technology and Entrepreneurship, ICWITE 2024

作者： Kumar, Madisetty Tharun Natarajan, B. Murali, P. Chellammal, P. Dhavakumar, P. Muruganantham, T. Chennai601103 India VIT University School of Computer Science and Engineering Tamilnadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamilnadu Chennai India J J College of Engineering and Technology Department of Computer Science and Engineering Tamilnadu India K. Ramakrishnan College of Engineering Dept. of Ece Tamil Nadu Tiruchirappalli621112 India

ISBN: (纸本)9798350383287

In the domain of deep learning and computer vision, Human Activity Recognition (HAR) holds paramount importance in understanding human movements through sensor data. This research presents a robust HAR system leveraging the VGG-19 deep learning architecture. Acknowledging challenges like limited context understanding and dataset biases, the research emphasizes the ongoing need for mitigation through continued research. Despite these hurdles, the VGG-19 based system showcased promising applications in healthcare and smart environments, enriching human-machine interactions. This research work conducted a comparative analysis between VGG-19 and ResNet-50 using standardized images of 15 human activities, highlighting VGG-19's superiority with an impressive 98.44% accuracy on the test dataset. The research underscores the potential of deep learning, particularly VGG-19, in accurately identifying intricate human activities from images. However, challenges such as contextual understanding and biases persist, prompting the research to emphasize the ongoing efforts to enhance the reliability and ethical soundness of HAR systems. These advancements are crucial for diverse sectors like healthcare and surveillance, ensuring the effective and responsible deployment of HAR technology. © 2024 IEEE.

关键词： computer vision

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Sanet: Spatial Attention Network with Global Average Contrast Learning for Infrared Small Target Detection 48

Sanet: Spatial Attention Network with Global Average Contras...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zhu, Jiewen Chen, Shengjia Li, Lexiao Ji, Luping University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China

ISBN: (纸本)9781728163277

Infrared small target detection has always been a challenging theme, due to small target size, unconspicuous contour and texture, even low vision contrast to background. Because of these causes, some popular object detection methods, such as Faster-RCNN and YOLOV, could often lose effectiveness. Aiming to promote the comprehensive performance of detection, this paper proposes a Spatial Attention Network (SANet) with global average contrast learning specially for infrared small target. Different from the other detection strategies by pixel-level segmentation, our scheme extends traditional contrast methods to target detection framework of deep learning, so as to achieve robust performance. In feature extraction, a group of cross stage partial networks (CSPNet) is designed to capture the local semantic information, and a cluster of spatial attention modules with global average contrast (SAG) is devised to obtain global spatial semantics. Moreover, a series of selective kernel convolution (SKConv) is adopted to effectively fuse semantic and spatial features. For robust feature representation, an Spatial Pyramid Pooling (SPP) scheme is utilized in our detection model. The experiments on two public datasets show that our detection model could often obviously outperform current state-of-the-art ones. The source code is available at https://***/UESTC-nnLab/SANet. © 2023 IEEE.

关键词： Feature extraction

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Glass Wet Deep Etching for Fabricating Biomimetic Devices in Biosensing 15

Glass Wet Deep Etching for Fabricating Biomimetic Devices in...

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15th IEEE International Conference on ASIC, ASICON 2023

作者： Li, Yuxin Duan, Zhenxia Wang, Jie Zhou, Zijian Yang, Ming Wu, Enqi Du, Lin University of Shanghai for Science and Technology School of Mechanical Engineering Shanghai China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200433 China

ISBN: (纸本)9798350312980

The advancement of semiconductor fabrication methods has sped up the development of bionic structures and biosensing technologies. The device structure of an imitation lotus leaf and the digital biomolecular diagnostic technology are developed in this work using the wet etching procedure. The mold formed of high borosilicate glass BF33 has the benefit of low roughness and superior etching controllability over a calcium sodium glass, and the roundness parameter is raised by an average of more than 4% up to an etching depth of more than 100 μm. The produced droplet volume by device preparation can have a relative standard deviation as low as 4.5%, and the one-time filling rate is greater than 90%. The novel tool is used in biological Loop-Mediated Isothermal Amplification (LAMP) assessment to confirm that digital detection and biocompatibility are feasible. © 2023 IEEE.

关键词： BF33 glass droplet array LAMP wet deep etching

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Robust copy-move detection and localization of digital audio based CFCC feature

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Multimedia Tools and Applications 2025年第11期84卷 9573-9589页

作者： Wang, Dongyu Li, Xiaojie Shi, Canghong Niu, Xianhua Xiong, Ling Wu, Hanzhou Qian, Qing Qi, Chao School of Computer and Software Engineering Xihua University Chengdu610039 China The College of Computer Science Chengdu University of Information Technology Chengdu610225 China School of Information Shanghai University Shanghai200444 China School of Information Guizhou University of Finance and Economics Guiyang550000 China

Copy-move forgery is a common audio tampering technique in which users copy the contents of one speech and paste them into another region of the same speech signal, thus achieving the effect of tampering with the semantics. To verify the authenticity of the audio, this paper proposes a method to detect and localize audio copy-move forgery based on the cochlear filter of cochlear filter cepstral coefficients (CFCC) feature. The pitch tracking algorithm is used to distinguish the voiced and unvoiced segments in the audio, and then the CFCC coefficients are extracted for each voiced segment. The CFCC feature simulates the entire transmission process of signals in the cochlear basilar membrane using wavelet transformation. Finally, we use Pearson correlation coefficients (PCCs) and dynamic time warping (DTW) in combination to compare the similarity of voiced segments, accurately determining the tampered locations in the audio through threshold judgment. Through extensive experiments on relevant datasets, this algorithm achieves a precision rate of 98.39% and a recall rate of 98.00% in detecting audio without post-processing. Even when detecting audio that has undergone different post-processing, the precision and recall rates remain above 90%. Compared to existing methods, this approach not only achieves precise localization of replicated segments but also demonstrates superior experimental results. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Semantics

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User Centric Block-Level Attribute Based Encryption in Cloud Using Blockchains

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computer Systems science & engineering 2022年第8期42卷 605-618页

作者： S.Godfrey Winster A.Siva Kumar R.Ramesh Department of Computer Science and Engineering School of ComputingCollege of Engineering and TechnologySRM Institute of Science and TechnologyChengalpattu603203India Department of Information Technology Sri Sai Ram Engineering CollegeChennai600044India Department of Electronics and Communication Engineering Saveetha Engineering CollegeChennai602105India

Cloud computing is a collection of distributed storage Network which can provide various services and store the data in the efficient *** advantages of cloud computing is its remote access where data can accessed in real time using Remote Method Innovation(RMI).The problem of data security in cloud environment is a major concern since the data can be accessed by any time by any *** to the lack of providing the efficient security the cloud computing they fail to achieve higher performance in providing the efficient *** improve the performance in data security,the block chains are used for securing the data in the cloud ***,the traditional block chain technique are not suitable to provide efficient security to the cloud data stored in the *** this paper,an efficient user centric block level Attribute Based Encryption(UCBL-ABE)scheme is presented to provide the efficient security of cloud data in cloud *** proposed approach performs data transaction by employing the block *** proposed system provides efficient privacy with access control to the user access according to the behavior of cloud user using Data Level Access Trust(DLAT).Based on DLAT,the user access has been restricted in the cloud *** proposed protocol is implemented in real time using Java programming language and uses IBM *** implementation results justifies that the proposed system can able to provide efficient security to the data present in and cloud and also enhances the cloud performance.

关键词： Cloud blockchain data security ABE block-level

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Model-to-Circuit Cross-Approximation for Printed Machine Learning Classifiers

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2023年第11期42卷 3532-3544页

作者： Armeniakos, Giorgos Zervakis, Georgios Soudris, Dimitrios Tahoori, Mehdi B. Henkel, Jorg Karlsruhe Institute of Technology Karlsruhe76131 Germany National Technical University of Athens School of Electrical and Computer Engineering Athens15780 Greece University of Patras Computer Engineering and Informatics Department Patras26504 Greece Karlsruhe Institute of Technology Department of Computer Science Karlsruhe76131 Germany

Printed electronics (PEs) promises on-demand fabrication, low nonrecurring engineering costs, and subcent fabrication costs. It also allows for high customization that would be infeasible in silicon, and bespoke architectures prevail to improve the efficiency of emerging PE machine learning (ML) applications. Nevertheless, large feature sizes in PE prohibit the realization of complex ML models in PE, even with bespoke architectures. In this work, we present an automated, cross-layer approximation framework tailored to bespoke architectures that enable complex ML models, such as multilayer perceptrons (MLPs) and support vector machines (SVMs), in PE. Our framework adopts cooperatively a hardware-driven coefficient approximation of the ML model at algorithmic level, a netlist pruning at logic level, and a voltage overscaling at the circuit level. Extensive experimental evaluation on 12 MLPs and 12 SVMs and more than 6000 approximate and exact designs demonstrates that our model-to-circuit cross-approximation delivers power and area optimal designs that, compared to the state-of-the-art exact designs, feature on average 51% and 66% area and power reduction, respectively, for less than 5% accuracy loss. Finally, we demonstrate that our framework enables 80% of the examined classifiers to be battery-powered with almost identical accuracy with the exact designs, paving thus the way toward smart complex printed applications. © 1982-2012 IEEE.

关键词： Timing circuits

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A Geometry-Based Deep Learning Feature Extraction Method for Airfoils

A Geometry-Based Deep Learning Feature Extraction Method for...

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2023 International Conference on computer, Artificial Intelligence, and Control engineering, CAICE 2023

作者： Hu, Liwei Zhang, Guangbo School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9781510665040

The geometry of airfoils, crucial in predicting aerodynamic coefficients, can be perceived by three existing methods: manual definition of geometry parameters, polynomial and deep learning. The first two methods are capable of directly obtaining geometry-features or polynomial coefficients from airfoil coordinates, but they are insufficient to extract latent features. Current deep learning techniques are capable of extracting latent features from only Euclidean space. However, it has been proven that curves of airfoils are exists in Riemannian space. Inspired by deep learning and polynomial (especially the Bézier polynomial) and deep learning, we propose a geometry-based feature extraction method (GFE) to extract airfoil geometry-features from both Euclidean space and Riemannian space. By utilizing Bézier curve, GFE can extract geometry-features (i.e., manifold metrics) of airfoils in Riemannian space. Through an internal Auto-Encoder, GFE encodes airfoil coordinates and manifold metrics to generate a novel feature representation. The UIUC airfoil dataset is employed to test the feasibility of GFE. Experimental results show that smooth and realistic airfoils can be rebuilt based on the geometry-features extracted by GFE, and the average MSE of rebuilt airfoils is 38.66% lower than those rebuilt by existing Auto-Encoders. © 2023 SPIE.

关键词： Geometry

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