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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Kasturi, Soundarya Lahari Nivetha, S. Selva Rani, B. Vairamuthu, S. Tiwari, Sourabh Yadav, Devendra Shankarappa, Rashmi T. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India Voice Intelligence Group Samsung R&d Institute Bengaluru India

ISBN: (纸本)9798350370249

Through this research, we introduce a unique Audio Spoof Detection System for distinguishing between actual, fraudulent, and generated audio recordings. Strong detection algorithms are required since the spread of audio modification techniques poses a major threat to the integrity of audio-based systems. Combination of 7 key voice features from cepstral coefficients and spectrograms were used to cover all possible spoofing. We are proposing ensemble methods to enhance the accuracy of conventional machine learning models such as Random Forest, State Vector Classifier (SVC), K-Nearest Neighbors (KNN) etc. and deep learning models such as Recurrent Neural Networks (RNN), Time-Delay Neural Networks (TDNN), SqueezeNet etc. The proposed model is evaluated using the ASVspoof 2021 logical access (LA) and Deep Voice dataset. Experimental results show that our proposed model significantly elevates performance compared to the baseline and state-of-the-art models. © 2024 IEEE.

关键词： Audio Spoof Detection CNN Deep Learning Ensemble learning Machine learning Random Forest TDNN

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Integrated High Accuracy Laser Ranging and Communication Scheme Using Large Dynamic Range and Low-complexity DSP Algorithm

Integrated High Accuracy Laser Ranging and Communication Sch...

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2023 Asia Communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings, ACP/POEM 2023

作者： Tang, Jianwei Cui, Sheng Li, Xueyang Hao, Yaguang Yang, Yanfu Hu, Weisheng Department of Circuits and Systems Shenzhen China School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan China School of Electronic and Information Engineering Harbin Institute of Technology Shenzhen China

ISBN: (纸本)9798350312614

A novel integrated laser ranging and communication (ILRC) scheme is proposed, offering high ranging accuracy, a large dynamic range and low complexity. By redesigning the receiver-side DSP scheme and utilizing the output parameters of a low-complexity clock recovery and frame synchronization algorithm in the Rx-DSP procedure, the proposed schemes achieves millimeter-level ranging in the conventional optical communication system without introducing any additional hardware components. Simulation and experimental results validate the effectiveness of the proposed ILRC scheme that exhibits a dynamic range exceeding 20 dB. We experimentally demonstrate an ILRC system of a communication rate of 20 Gbps and high ranging accuracy of 1. 95\sim 1.28 mm at the low received power of -48 \sim -35 dBm. © 2023 IEEE.

关键词： clock recovery DSP dynamic range frame synchronization ILRC low-complexity

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Brain Tumor:Hybrid Feature Extraction Based on UNet and 3DCNN

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Computer systems science & engineering 2023年第5期45卷 2093-2109页

作者： Sureshkumar Rajagopal Tamilvizhi Thanarajan Youseef Alotaibi Saleh Alghamdi Center for System Design Chennai Institute of TechnologyChennai600069India Department of Information Technology Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062India Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Automated segmentation of brain tumors using Magnetic Resonance Imaging(MRI)data is critical in the analysis and monitoring of disease *** a result,gliomas are aggressive and diverse tumors that may be split into intra-tumoral groups by using effective and accurate segmentation *** is intended to extract characteristics from an image using the Gray Level Co-occurrence(GLC)matrix feature extraction method described in the proposed *** Convolutional Neural Networks(CNNs),which are commonly used in biomedical image segmentation,CNNs have significantly improved the precision of the state-of-the-art segmentation of a brain *** two segmentation networks,a U-Net and a 3D CNN,we present a major yet easy combinative technique that results in improved and more precise *** U-Net and 3D CNN are used together in this study to get better and more accurate estimates of what is going *** the dataset,two models were developed and assessed to provide segmentation maps that differed fundamentally in terms of the segmented tumour ***,the estimates was made by two separate models that were put together to produce the final *** comparison to current state-of-the-art designs,the precision(percentage)was 98.35,98.5,and 99.4 on the validation set for tumor core,enhanced tumor,and whole tumor,respectively.

关键词： Medical imaging segmentation U-net 3D CNN brain tumor

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A single-wavelength non-coherent photonic matrix multiplication circuit for optical neural networks

A single-wavelength non-coherent photonic matrix multiplicat...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Tang, Rui Okano, Makoto Toprasertpong, Kasidit Takagi, Shinichi Takenaka, Mitsuru Department of Electrical Engineering and Information Systems The University of Tokyo Tokyo113-8656 Japan National Institute of Advanced Industrial Science and Technology Ibaraki305-8568 Japan

We demonstrate a single-wavelength, non-coherent silicon photonic 4×4 matrix multiplication circuit for optical neural networks. An accuracy of 91.7% is obtained using this circuit in a simple neural network for ... 详细信息

ISBN: (纸本)9781957171395

关键词： Silicon photonics

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A new method for human activity identification using convolutional neural networks

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International Journal of Cloud Computing 2023年第2-4期12卷 191-200页

作者： Prakash, P.S. Balakrishnan, S. Venkatachalam, K. Balasubramanian, Saravana Balaji Department of Computer Science and Engineering Vivekanandha College of Engineering for Women Namakkal Tiruchengode637205 India Department of Computer Science and Business Systems Sri Krishna College of Engineering and Technology Tamilnadu Coimbatore India Department of Applied Cybernetics Faculty of Science University of Hradec Králové Hradec Králové50003 Czech Republic Department of Information Technology Lebanese French University Erbil Iraq

Body fitness monitoring applications are using mobile sensors to identify human activities. Human activity identification is a challenging task because of the wide availability of human activities. This paper proposes a novel technique that extracts the discriminative dimensions for human activity identification. Particularly, a novel technique with convolutional neural networks (CNN) is used for catching dependency. A deep convolutional neural network (DCNN) consists of two different types of layers, convolutional and pooling are used. The depth of each filter increases from left to right in the network. Three activities like walking, running, remaining still are collected from smart mobile sensors. The axis like x, y, and z information was transferred with column vector magnitude information and utilised for studying or training CNN. Experimental results show that CNN-based method achieves 93.67% accuracy than the baseline random forest approach's 89.20%. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Convolutional neural networks

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Knowledge-Driven Possibilistic Clustering with Automatic Cluster Elimination

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Computers, Materials & Continua 2024年第9期80卷 4917-4945页

作者： Xianghui Hu Yiming Tang Witold Pedrycz Jiuchuan Jiang Yichuan Jiang The School of Computer Science and Engineering Southeast UniversityNanjing211189China The School of Computer and Information Hefei University of TechnologyHefei230601China The Department of Electrical and Computer Engineering University of AlbertaEdmontonAB T6R 2V4Canada The Systems Research Institute Polish Academy of SciencesWarsaw00-901Poland The School of Information Engineering Nanjing University of Finance and EconomicsNanjing210023China

Traditional Fuzzy C-Means(FCM)and Possibilistic C-Means(PCM)clustering algorithms are data-driven,and their objective function minimization process is based on the available numeric ***,knowledge hints have been introduced to formknowledge-driven clustering algorithms,which reveal a data structure that considers not only the relationships between data but also the compatibility with knowledge ***,these algorithms cannot produce the optimal number of clusters by the clustering algorithm itself;they require the assistance of evaluation ***,knowledge hints are usually used as part of the data structure(directly replacing some clustering centers),which severely limits the flexibility of the algorithm and can lead to *** solve this problem,this study designs a newknowledge-driven clustering algorithmcalled the PCM clusteringwith High-density Points(HP-PCM),in which domain knowledge is represented in the form of so-called high-density ***,a newdatadensitycalculation function is *** Density Knowledge Points Extraction(DKPE)method is established to filter out high-density points from the dataset to form knowledge ***,these hints are incorporated into the PCM objective function so that the clustering algorithm is guided by high-density points to discover the natural data ***,the initial number of clusters is set to be greater than the true one based on the number of knowledge ***,the HP-PCM algorithm automatically determines the final number of clusters during the clustering process by considering the cluster elimination *** experimental studies,including some comparative analyses,the results highlight the effectiveness of the proposed algorithm,such as the increased success rate in clustering,the ability to determine the optimal cluster number,and the faster convergence speed.

关键词： Fuzzy C-Means(FCM) possibilistic clustering optimal number of clusters knowledge-driven machine learning fuzzy logic

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Optimal Deep Canonically Correlated Autoencoder-Enabled Prediction Model for Customer Churn Prediction

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Computers, Materials & Continua 2022年第11期73卷 3757-3769页

作者： Olfat M.Mirza GJose Moses R.Rajender E.Laxmi Lydia Seifedine Kadry Cheadchai Me-Ead Orawit Thinnukool Department of Computer Science College of Computers and Information SystemsUmm Al-Qura UniversityMakkahSaudi Arabia Department of Computer Science Engineering Malla Reddy Engineering College500100India Department of Computer Science Engineering Lendi Institute of Engineering&TechnologyDenkada535005India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeNorway Program in Statistics and Information Management Faculty of ScienceMaejo UniversityChiang Mai50290Thailand Department of Modern Management and Information Technology College of Arts Media and TechnologyChiang Mai UniversityChiang Mai50200Thailand

Presently,customer retention is essential for reducing customer churn in telecommunication *** churn prediction(CCP)is important to predict the possibility of customer retention in the quality of *** risks of customer churn also get essential,the rise of machine learning(ML)models can be employed to investigate the characteristics of customer ***,deep learning(DL)models help in prediction of the customer behavior based characteristic *** the DL models necessitate hyperparameter modelling and effort,the process is difficult for research communities and business *** this view,this study designs an optimal deep canonically correlated autoencoder based prediction(ODCCAEP)model for competitive customer dependent application *** addition,the O-DCCAEP method purposes for determining the churning nature of the *** O-DCCAEP technique encompasses preprocessing,classification,and hyperparameter ***,the DCCAE model is employed to classify the churners or ***,the hyperparameter optimization of the DCCAE technique occurs utilizing the deer hunting optimization algorithm(DHOA).The experimental evaluation of the O-DCCAEP technique is carried out against an own dataset and the outcomes highlighted the betterment of the presented O-DCCAEP approach on existing approaches.

关键词： Churn prediction customer retention deep learning machine learning archimedes optimization algorithm

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A Private Blockchain System based on Zero Trust Architecture 26

A Private Blockchain System based on Zero Trust Architecture

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26th International Conference on Advanced Communications technology, ICACT 2024

作者： Chang, Yao-Chung Lin, Yu-Shan Sangaiahc, Arun Kumar Wu, Hsin-Te National Taitung University Department of Computer Science and Information Engineering Taitung Taiwan National Taitung University Department of Information Science and Management Systems Taitung Taiwan National Yunlin University of Science and Technology Department of International Graduate School of Artificial Intelligence Yunlin Taiwan

ISBN: (纸本)9791188428120

During the outbreak of COVID-19, many enterprises massively created Virtual Private Networks (VPNs) for companies to cooperate;however, these accounts lacked efficient management after the epidemic, leading to data leakage or the suffering of malicious attacks. Consequently, several firms have started to build private blockchains for data preservation and verification. Private blockchains are usually built internally in companies;once a firm's internal network opens for massive external logins, many servers and private blockchains cannot work properly to protect data. In general, private blockchains store critical personal information or relevant confidential data;once a private blockchain opens to external access, all the information in the network nodes will be exposed, making private them lose their protection functions. This paper proposes a security mechanism using a private blockchain system based on zero trust architecture. The zero trust architecture tracks every user's network conditions and analyses whether their behaviors are authorized. Additionally, the system utilizes micro-segmentation to divide the private blockchain, preventing the system from malicious attacks. The proposed system employs user multi-factor authentication to identify users, and the zero trust architecture tracks and analyzes if users' behaviors are reasonable. This method effectively ensures corporate networks' security and enables private blockchain to filter legal and authorized users to access and verify. © 2024 Global IT Research Institute - GIRI.

关键词： Virtual private networks

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SELECTING FEATURES BY UTILIZING INTUITIONISTIC FUZZY ENTROPY METHOD

Decision Making: Applications in Management and Engineering

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Decision Making: Applications in Management and engineering 2023年第1期6卷 111-133页

作者： Pandey, Kiran Mishra, Arunodaya Raj Rani, Pratibha Ali, Jabil Chakrabortty, Ripon Department of Computer Science and Engineering Technocrats Institute of Technology Madhya Pradesh Bhopal India Department of Mathematics Government College Raigaon Madhya Pradesh Satna India Department of Engineering Mathematics Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India School of Engineering and Technology Department of Computer Science and Engineering Sharda University Uttar Pradesh Greater Noida India Capability Systems Centre School of Engineering and Information Technology UNSW Canberra Australia

Feature selection is the most significant pre-processing activity, which intends to reduce the data dimensionality for enhancing the machine learning process. The evaluation of feature selection must consider classification, performance, efficiency, stability, and many factors. Nowadays, uncertainty is commonly occurred in the feature selection process due to time limitations, imprecise information, and the subjectivity of human minds. Moreover, the theory of intuitionistic fuzzy set has been proven as an extremely valuable tool to tackle the uncertainty and ambiguity that arises in many practical situations. Thus, this study introduces a novel feature selection framework using intuitionistic fuzzy entropy. In this regard, new entropy for IFS is proposed first and then compared with some of the previously developed entropy measures. As entropy is a measure of uncertainty present in data (features), features with higher entropy values are filtered out, and the remaining features having lower entropy values have been used to classify the data. To verify the effectiveness of the proposed entropy-based feature selection, some experiments are done with ten standard benchmark datasets by employing a support vector machine, K-nearest neighbor, and Naïve Bias classifiers. The outcomes of the study validate that the proposed entropy-based filter feature selection is more feasible and impressive than existing filter-based feature selection methods. © 2023 by the authors.

关键词： Feature Selection

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Unsupervised Domain Adaptation for Action Recognition via Self-Ensembling and Conditional Embedding Alignment 24

Unsupervised Domain Adaptation for Action Recognition via Se...

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24th IEEE International Conference on Data Mining, ICDM 2024

作者： Ghosh, Indrajeet Chugh, Garvit Md Faridee, Abu Zaher Roy, Nirmalya University of Maryland Department of Information Systems Baltimore County United States Indian Institute of Technology Department of Computer Science and Engineering Jodhpur India Amazon United States

ISBN: (纸本)9798331506681

Recent advancements in deep learning-based wearable human action recognition (wHAR) have improved the capture and classification of complex motions, but adoption remains limited due to the lack of expert annotations and domain discrepancies from user variations. Limited annotations hinder the model's ability to generalize to out-of-distribution samples. While data augmentation can improve generalizability, unsupervised augmentation techniques must be applied carefully to avoid introducing noise. Unsupervised domain adaptation (UDA) addresses domain discrepancies by aligning conditional distributions with labeled target samples, but vanilla pseudo-labeling can lead to error propagation. To address these challenges, we propose μDAR, a novel joint optimization architecture comprised of three functions: (i) consistency regularizer between augmented samples to improve model classification generalizability, (ii) temporal ensemble for robust pseudo-label generation and (iii) conditional distribution alignment to improve domain generalizability. The temporal ensemble works by aggregating predictions from past epochs to smooth out noisy pseudo-label predictions, which are then used in the conditional distribution alignment module to minimize kernel-based class-wise conditional maximum mean discrepancy (kCMMD) between the source and target feature space to learn a domain invariant embedding. The consistency-regularized augmentations ensure that multiple augmentations of the same sample share the same labels;this results in (a) strong generalization with limited source domain samples and (b) consistent pseudo-label generation in target samples. The novel integration of these three modules in μDAR results in a range of 4-12% average macro-F1 score improvement over six state-of-the-art UDA methods in four benchmark wHAR datasets. © 2024 IEEE.

关键词： Deep learning

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