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Catalyzing EEG signal analysis: unveiling the potential of machine learning-enabled smart K nearest neighbor outlier detection

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International Journal of Information Technology (Singapore) 2024年 1-12页

作者： Aymen, Abid Khediri, Salim El Thaljaoui, Adel Miladi, Moahmed Kachouri, Abdennaceur Computer & amp Embedded Systems Lab National Engineering School of Sfax University of Sfax Sfax Tunisia Department of Information Technology College of Computer Qassim University Buraydah Saudi Arabia Department of Computer Science Faculty of Science of Gafsa University of Gafsa Gafsa Tunisia Preparatory Institute for Engineering Studies of Gafsa Gafsa Tunisia Department of Computer Science and Information College of Science at Zulfi Majmaah University Al-Majmaah 11952 Saudi Arabia Higher Institute of Computer Science and Multimedia of Sfax University of Sfax Sfax Tunisia Advanced Fluid Dynamics Energetics and Environment Lab National Engineering School of Sfax University of Sfax Sfax Tunisia

Electroencephalogram (EEG) data are susceptible to artifacts, such as lapses in concentration or poor imagination, which can significantly impact the accuracy of disease diagnosis in e-health applications. To mitigate this issue, the use of machine learning (ML) and potentially artificial intelligence (AI) solutions to accurately identify outliers becomes crucial. Unlike many AI methods that incorporate unnecessary or redundant input variables, our study focuses on detecting anomalous values in EEG data through the K nearest neighbor (KNN) process and Euclidean distance metric. Our proposed unsupervised non-parametric algorithm, known as the smart KNN outlier detector (SKOD), eliminates the need for initial parameter configurations such as the number of neighbors (K), while achieving high performance. Evaluation of SKOD using real EEG data from 140 trials demonstrated sensitivity and specificity exceeding 60%, with nearly perfect accuracy in detecting outliers reaching close to 100%. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Data analysis EEG signal Healthcare Knowledge-based systems Machine learning Outlier detection

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On Security-Constrained Bidding of Shared Transportation Hubs for Renewable Fuels and Refined Oil Participating Within Energy, Regulation and Pipeline Logistics Markets 7

On Security-Constrained Bidding of Shared Transportation Hub...

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7th IEEE Student Conference on Electric Machines and Systems, SCEMS 2024

作者： Wang, Shengshi Li, Hongyi Huang, Danji Li, Kun Cui, Shichang Ai, Xiaomeng Fang, Jiakun Wen, Jinyu School of Electrical and Electronic Engineering Huazhong University of Science and Technology State Key Laboratory of Advanced Electromagnetic Technology Wuhan China School of Engineering Cardiff University Cardiff United Kingdom Iowa State University Department of Electrical and Computer Engineering Ames United States University of Macau Department of Electrical and Computer Engineering China PetroChina ShenZhen New Energy Research Institute Co. Ltd. Shenzhen China

ISBN: (纸本)9798350366945

This paper investigates the security-constrained bidding strategy of the emerging shared transportation hubs for renewable fuels and refined oil (STH-RRs) participating within energy, regulation and pipeline logistics markets. Firstly, to excavate more flexible capability for the power system as well as to increase the revenue of the STH-RR operation company, the model for STH-RR participating in regulation market is established. Secondly, the security-constrained two-stage bidding model is first proposed for STH-RRs to balance the revenue across energy, regulation and pipeline logistics markets. Particularly, to guarantee secure operation of STH-RRs and meet the delivery demand, the rigorous multi-product transportation constraints are considered in both stages. Case studies based on the modified real-world multi-product pipelines in South China validate the proposed method, with the revenue and turnover increase of 14% and 4.7%, respectively, compared with conventional methods. © 2024 IEEE.

关键词： Pipelines

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Deep Learning PNN Based Fault Monitoring System for Three Phase Industrial Drive System 7

Deep Learning PNN Based Fault Monitoring System for Three Ph...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Chempavathy, B. Raju, K. David Mani, P.K. Vijayalakshmi, S. Janaki, N. Karthikeyan, D. New Horizon College of Engineering Department of Computer Science and Engineering Bangalore India Malla Reddy Engineering College for Women Department of Electrical and Electronics Engineering Hyderabad India Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Electrical and Electronics Engineering Chennai India K. Ramakrishnan College of Technology Department of Electrical and Electronics Engineering Tiruchirappalli Tamil Nadu Samayapuram India Technology and Advanced Studies Vels Institute of Science Department of Electrical and Electronics Engineering Tamilnadu Chennai India SRM Institute of Science and Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798350372816

Asynchronous motors are widely used in a variety of sectors. Even though induction motors are strong and dependable, they are susceptible to a variety of problems. Faults in induction motors can result in horrible events such as operator injuries, manufacturing disruption, and raw material loss. As a result, defect identification has become more crucial in induction motor management. Among the different problems that can occur in the motor, bearing failure is a severe issue that can cause catastrophic damage to the machine if not detected at an early stage of the fault. As consequently, the functioning of bushings in induction machinery must be checked on a regular basis. This research proposes a novel method for analyzing speed using discrete wavelet transform (DWT) and identifying bearing difficulties using Probabilistic Neural Network (PNN) optimized by improved salp swarm algorithm. The stator electricity is examined and classified when an induction motor is operating under varied loading conditions with good and troublesome bearings. Scale-Invariant Feature Transform (SIFT) is a feature extraction method that condenses visual content into a set of points that may be used to find similar patterns in other images. The proposed PNN classifier can classify different types of bearing faults, and the empirical findings validate the created approach. PNN-based motor bearing recognizing defects and diagnosis outperforms SVM and ANN classifiers in this regard. The Shortest path accuracy of the PNN Classifier is 98.5% respectively. © 2024 IEEE.

关键词： Discrete wavelet transforms

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Recognition of Deep Fake Voice Acoustic using Ensemble Bagging Model

Recognition of Deep Fake Voice Acoustic using Ensemble Baggi...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： Shaik Abdul Yunus Basha K.Ulaga Priya Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

As artificial speech generates technological evolution; fake voice information has become an increasingly prevalent avenue for fraud. Numerous investigations have been carried out into machine learning techniques, signal processing techniques, and the Automatic Speaker Verification System to address such issues. Since there are increasing methods to create fake information, techniques must be developed to determine when audio recordings employed as electronic proof are corrupted. Such recordings contain the potential to be manipulated. Hence, this research work identified the data regarding audio systems as either fake or real by i) extracting features from audio file metadata using oversampling techniques and ii) Developing an Ensemble Bagging Model appropriate for analyzing input audio signal followed by classification of fake (fraudulent) and real (authentic) audio. Our experimental results reveal that the ensemble bagging model attained an accuracy of 99.5%, precision of 99%, 99.6% recall, and 99.9% Fl-measure in the prediction of the audio system compared with traditional approaches.

关键词： Deepfakes Accuracy Audio systems Speech recognition Metadata Feature extraction Audio recording Real-time systems Fraud Bagging

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A hybrid technique for selection and minimization of test cases in regression testing

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Recent Advances in computer Science and Communications 2021年第2期14卷 477-488页

作者： Singh, Leena Singh, Shailendra N. Dawra, Sudhir Department of Computer Science and Engineering Amity University Noida India Department of Computer Application Tecnia Institute of Advanced Studies New Delhi India

Background: In today’s era, modifications in a software is a common requirement by customers. When changes are made to existing software, re-testing of all the test cases is required to ensure that the newly introduced changes do not have any unwanted effect on the behavior of the software. However, re-testing of all the test cases would not only be time consuming but also expensive. Therefore, there is a need for a technique that reduces the number of tests to be performed. Regression testing is one of the ways to reduce the number of test cases. Selection technique is one such method which seeks to identify the test cases that are relevant to some set of recent changes. Objective: It is evident that most of the studies have used different selection techniques and have focused only on one parameter for achieving reduced test suite size without compromising the performance of regression testing. However, to the best of our knowledge, no study has taken two or more parameters of cov-erage, and/or execution time in a single testing. This paper presents a hybrid technique that combines both regression test selection using slicing technique and minimization of test cases using modified firefly algorithm with combination of parameters coverage and execution time in a single testing. Methods: A hybrid technique has been described that combines both selection and minimization. Selection of test cases is based upon slicing technique while minimization is done using firefly algorithm. Hybrid technique selects and minimizes the test suite using information on statement coverage and execution time. Results: The proposed technique gives 43.33% much superior result as compared to the other hybrid approach in terms of significantly reduced number of test cases. It shows that the resultant test cases were effective enough to cover 100% of the statements, for all the programs. The proposed technique was also tested on four different programs namely Quadratic, Triangle, Next day, Co

关键词： Testing

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Generalized LRS Estimator for Min-Entropy Estimation

Generalized LRS Estimator for Min-Entropy Estimation

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作者： Woo, Jiheon Yoo, Chanhee Kim, Young-Sik Cassuto, Yuval Kim, Yongjune Department of Electrical Engineering Pohang37673 Korea Republic of Department of Electrical Engineering and Computer Science Daegu42988 Korea Republic of Chosun University Department of Information and Communication Engineering Gwangju61452 Korea Republic of Technion-Israel Institute of Technology Viterbi Department of Electrical and Computer Engineering Haifa32000 Israel Institute for Convergence Research and Education in Advanced Technology Yonsei University Seoul03722 Korea Republic of

The min-entropy is a widely used metric to quantify the randomness of generated random numbers, which measures the difficulty of guessing the most likely output. It is difficult to accurately estimate the min-entropy of a non-independent and identically distributed (non-IID) source. Hence, NIST Special Publication (SP) 800-90B adopts ten different min-entropy estimators and then conservatively selects the minimum value among these ten min-entropy estimates. Among these estimators, the longest repeated substring (LRS) estimator estimates the collision entropy instead of the min-entropy by counting the number of repeated substrings. Since the collision entropy is an upper bound on the min-entropy, the LRS estimator inherently provides overestimated outputs. In this paper, we propose two techniques to estimate the min-entropy of a non-IID source accurately. The first technique resolves the overestimation problem by translating the collision entropy into the min-entropy. Next, we generalize the LRS estimator by adopting the general Rényi entropy instead of the collision entropy (i.e., Rényi entropy of order two). We show that adopting a higher order can reduce the variance of min-entropy estimates. By integrating these techniques, we propose a generalized LRS estimator that effectively resolves the overestimation problem and provides stable min-entropy estimates. Theoretical analysis and empirical results support that the proposed generalized LRS estimator improves the estimation accuracy significantly, which makes it an appealing alternative to the LRS estimator. © 2005-2012 IEEE.

关键词： Frequency estimation

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Assessing a BERT-based model for analyzing subjectivity and classifying academic articles

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Multimedia Tools and Applications 2024年第42期83卷 90511-90532页

作者： Mehmood, Atif Shahid, Farah Khan, Rizwan Ahmed, Shahzad Ibrahim, Mostafa M. Zheng, Zhonglong School of Computer Science and Technology Zhejiang Normal University Jinhua321002 China Zhejiang Institute of Photoelectronics & Zhejiang Institute for Advanced Light Source Zhejiang Normal University Zhejiang Jinhua321004 China Faculty of Information Technology Beijing University of Technology Beijing100024 China Department of Electrical Engineering Faculty of Engineering Minia University Minia61519 Egypt

The metaverse concept extends beyond virtual worlds and can be applied to collaborative analysis environments. Data analysts worldwide may read academic article extracts in real-time in a shared digital workplace to mine and analyze data using the metaverse. Furthermore, a semantic metaverse in natural language processing might also involve creating a digital environment with linguistic and semantic connections. Many dedicated researchers navigating the complexities of natural language processing in the metaverse era have spent considerable time searching for relevant papers. However, online reviews and evaluations of articles are helpful for their assistance and may save the researcher time. In this work, human specialists manually produced a dataset from four conferences and evaluated subjectively using rule-based techniques. Subsequently, we aim to evaluate the effectiveness of pre-trained word embeddings and pre-trained BERT models seamlessly integrated with convolutional neural networks. This endeavor focuses on the subjective analysis and classification of contributions and previous work sentences extracted from academic literature. For comparison, various deep learning architectures were systematically employed, including long short-term memory-GloVe and bi-directional long short-term memory-GloVe, alongside classical machine learning methods. Our findings show that the proposed BERT model achieved state-of-the-art performance in classification and subjective analysis tasks with an accuracy of 91.50% and F1 score of 91.00%. Finally, we plan to utilize sentence similarity to identify contributions within abstracts, thus outlining potential avenues for future research. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Natural language processing systems

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Aloe Vera Tissue Modeling and Parameter Identification Using Meta-heuristic Optimization Algorithm

Aloe Vera Tissue Modeling and Parameter Identification Using...

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2023 International Conference on Microelectronics, ICM 2023

作者： Ghoneim, Mohamed S. Fathi, Dalia A. Said, Lobna A. Madian, Ahmed H. Bayoumi, Magdy A. University of Louisiana at Lafayette Department of Electrical and Computer Engineering LA United States Cairo Egypt Ncrrt Egyptian Atomic Energy Radiation Engineering Dept. Cairo Egypt University of Louisiana Center for Advanced Computer Studies LafayetteLA United States

ISBN: (纸本)9798350380828

The agricultural industry's use of non-invasive bioimpedance monitoring methods is expanding quickly. These measured impedance fluctuations reflect imperceptible biophysical and biochemical changes in living and non-living tissues. Bioimpedance circuit modeling is a valuable method for fitting the measured impedance in biology and medicine. A study on two samples of Aloe Vera leaves is conducted to identify the best model representing Aloe Vera leaves, and two different interelectrode spacing distances are used to measure each sample. An electrochemical station (SP150) is used to detect bio-impedance at frequencies between 80 Hz and 200 kHz. The effectiveness of the employed models is evaluated by fitting them to the measured data. Among all models used, the medical plant stem and simplified plant stem models show the best results for all inter-electrode spacing distances. © 2023 IEEE.

关键词： Timing circuits

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Performance Analysis of AODV Routing forWireless Sensor Network in FPGA Hardware

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computer Systems Science & engineering 2022年第3期40卷 1073-1084页

作者： Namit Gupta Kunwar Singh Vaisla Arpit Jain Adesh Kumar Rajeev Kumar Department of Computer Science&Engineering Uttarakhand Technical UniversityDehradun248007India Department of Computer Science B T Kumaon Institute of Technology DwarahatIndia Faculty of Engineering&CS Teerthanker Mahaveer UniversityMoradabadIndia Department of Electrical&Electronics Engineering School of EngineeringUniversity of Petroleum and Energy StudiesDehradun248007India

Wireless sensor network(WSN)is a group of interconnected sensor nodes that work wirelessly to capture the information of *** routing of the network is a challenging *** routing of WSN is classified as proactive,reactive,and *** on-demand distance vector(AODV)routing is an example of reactive routing based on the demand route formations among different nodes in the *** research article emphasizes the design and simulation of the AODV routing hardware chip using very-high-speed integrated circuit hardware description language(VHDL)programming in Xilinx integrated synthesis environment(ISE)14.7 *** performance of the chip is studied based on the field-programmable gate array(FPGA)hardware parameters such as slices,lookup table(LUTs),input/output block(IOB),flipflops,and memory for the different configurations of the network(N=10,20….100).The delay and frequency are also estimated on the Virtex-5 *** performance of the WSN with AODV routing is also analyzed based on the packet delivery ratio,throughput,delay,and control *** simulation test cases verified the 8-bit,64-bit,and 128-bit data communication within the nodes.

关键词： Wireless sensor network FPGA synthesis AODVrouting simulation and synthesis VHDL simulation

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Crow Search Optimization to Identify Adversary Nodes in Wireless Networks 2

Crow Search Optimization to Identify Adversary Nodes in Wire...

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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Rajaram, Gnanajeyaraman Thangaraj, T. Stephen Packialatha, A. Deepa, R. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Applied Machine Learning Tamil Nadu Chennai India Paavai Engineering College Department of Computer Science and Engineering Tamil Nadu Namakkal India Vels Institute of Science Technology & Advanced Studies Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350347579

Adversary node detection systems have become a vital component due to the malware attacks. These systems are very important for network security due to internet's widespread development and increased accessibility to global data systems. This paper presents a Crow Search Optimization Algorithm (CSOA) to detect the adversary node in a wireless network. The Rivest, Shamir, Adleman (RSA) algorithm transmits secure data at first. It uses the public key to encrypt the data and the private key to decrypt the data and the adversary nodes can't access or modify the real data. One of the significant problems is when an unknown attack occurs on the network due to the high volume of data while the detection's accuracy and false alarm rate decline. The proposed system aims to improve accuracy by identifying the adversary nodes using CSOA algorithm. From the results, it is observed that the CSOA mechanism reduces the loss ratio and improves throughput of the network. © 2023 IEEE.

关键词： Optimization

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