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2023 International Conference on Power, Energy and Innovations, ICPEI 2023

作者： Grout, Ian Mullin, Lenore University of Limerick Faculty of Science and Engineering Department of Electronic and Computer Engineering Limerick Ireland University at Albany SUNY College of Engineering and Applied Sciences Department of Computer Science AlbanyNY United States

ISBN: (纸本)9798350344585

In this paper, the Mathematic of Arrays (MoA) approach to solving complex multi-dimensional array computations using array indexing is considered with reference in its implementation in code targeting embedded systems applications using software programmed microcontrollers. The array computations, memory access, and communications requirements are considered with application to different software programming/scripting languages. The ideas can then be extended to realizations as digital logic circuit descriptions using Hardware Description Languages (HDLs). With reference to a range of tensor products for rank 2 tensors, the MoA derived algorithm is presented in different code formats. © 2023 IEEE.

关键词： Tensors

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Gabor Wavelet Based Fused Texture Features for Identification of Mungbean Leaf Diseases 1st

Gabor Wavelet Based Fused Texture Features for Identificati...

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1st International Conference on Machine Intelligence and Emerging Technologies, MIET 2022

作者： Majumder, Sarna Mazumder, Badhan Islam, S. M. Taohidul Faculty of Computer Science and Engineering Patuakhali Science and Technology University Patuakhali Bangladesh Department of Computer and Communication Engineering Patuakhali Science and Technology University Patuakhali Bangladesh Department of Computer Science and Engineering Dhaka International University Dhaka Bangladesh Department of Electrical and Electronics Engineering Patuakhali Science and Technology University Patuakhali Bangladesh

ISBN: (纸本)9783031346187

Early diagnosis of crop plant disease is crucial since many of these diseases pose a considerable threat not only to global food security but also towards agricultural productivity. Aiming that, for detection of mungbean leaf diseases at the beginning stage, we introduce a novel approach based on Gabor Wavelet Transform (GWT) and Cubic SVM in this paper. We perform GWT to decompose the given images into eighteen directional sub-bands and then extract fusion of Gabor Wavelet (GW) based texture features from each detailed GWT coefficient sub-band. Finally, Cubic SVM was deployed to classify three different disease classes by using these GW based features, conducting cross validation at 10 fold. Outcomes of our experimental evaluation using our self-prepared dataset of mungbean leaf diseases exhibit that our proposed method yields overall a sensitivity of 91.11%, a specificity of 95.56%, a precision of 91.39% and an accuracy of 91.11%. Moreover, outcomes obtained from our comparative analysis confirm the supremacy of our proposed framework over 3 currently existing approaches. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Crops

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Distance Metric-Based Classification Comparisons for a Brain-computer Interface Authentication

Distance Metric-Based Classification Comparisons for a Brain...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Lewis, Tyree Agarwal, Rupal Andujar, Marvin University of South Florida Department of Computer Science and Engineering University of South Florida United States Department of Computer Science and Engineering TampaFL33620 United States Faculty of Computer Science and Engineering TampaFL33620 United States

ISBN: (纸本)9798350337020

The rise of security concerns has spurred on-going research into Brain-computer Interfaces (BCI) based authentication. These applications utilize electroencephalogram (EEG) signals, due to their properties that can enhance security systems. In previous studies, EEG data has been incorporated into various authentication systems to compare the performance of new and existing classification methods. However, using EEG data to compare the performance of distance metrics in a P300-based BCI authentication system has not been explored yet. In this study, EEG data is used to determine the most effective distance metric for authenticating users in a closed-loop system. To accomplish this task, we conducted a longitudinal study to evaluate three distance metrics (Cosine, Correlation and Chebyshev) while participants interacted with our BCI authentication system. Our results indicated that the Cosine similarity outperformed all other distance metrics for each user. © 2023 IEEE.

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An Efficient Drowsiness Detection and Driver Alert System using OCNN 8

An Efficient Drowsiness Detection and Driver Alert System us...

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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Thirumurugan, S. Pradeepan, M.L. Sundhar, K. Dhanapal, R. Karpagam Academy of Higher Education Faculty of Engineering Department of Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350396638

To analyze traffic accident data and identify priority enhancement junctions, this research aims to build a high accident risk prediction model. To identify possible high accident risk locations for traffic management departments to use in developing countermeasures to reduce accident risk, an intersection accident risk prediction model was created using a variety of mechanical learning approaches. the creation and examination of an accident record. The research work focus on identifying the drowsiness using EEG signal. It identified environmental factors at intersections that affect accident risk levels using optimized CNN. An accident risk prediction model was developed using optimized Convolutional Neural Network (CNN)-Heuristic. To build up a drowsiness identification framework that can recognize weariness in drivers to forestall mishaps and the ground truth drowsiness detection system that is depending on the vigorous left, focus and right-AOEs and fixed back AOE (Area of eye vision). Additionally, this model can identify the crucial elements that influence the likelihood of high-risk crossings, giving traffic management organizations a strong foundation for choosing an intersection. This could be used to forecast future risk levels and aid in the reduction of traffic accidents by using the same climatic variables as high-risk crossings as model inputs. It can serve as a model for upcoming improvements to junction architecture and the surrounding area. © 2023 IEEE.

关键词： Forecasting

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Systematic Review on Real-Time Students Behavior Monitoring using Machine Learning 6

Systematic Review on Real-Time Students Behavior Monitoring ...

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Akash, S. Dinesh, V. Muthamil Selvan, S. Srikanth, R. Department of Computer Science and Engineering Faculty of Engineering Karpagam Academy of Higher Education Tamil Nadu Coimbatore India

ISBN: (纸本)9798350398496

Student behavior analysis is an important research area that aims to understand and improve student outcomes. Machine learning has emerged as a powerful tool for analyzing student behavior, allowing researchers to identify important patterns and predict future behavior. Further, student behavior analysis can help to identify individual student needs and preferences, allowing for personalized learning experiences that are tailored to the unique needs of each student. This can improve engagement and motivation, and help students reach their full potential. This study provides an overview of various methods for student behavior analysis using machine learning, including clustering, classification, time-series analysis, recommender systems, and natural language processing. Moreover, steps involved in using these methods, including data collection, preprocessing, feature engineering, model training, and model evaluation is elaborated. Furthermore, discussion on the architecture, and ethical considerations for using machine learning in student behavior analysis depicted. Finally, the article highlights the importance of carefully choosing the appropriate method for each research question and considering the potential impact of machine learning on students and society. © 2023 IEEE.

关键词： Machine learning

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An Improved Learning Experience for English Language Using Big Data Analytics and AAC 2

An Improved Learning Experience for English Language Using B...

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2nd International Conference on Sustainable Computing and Data Communication Systems, ICSCDS 2023

作者： Kumar, P.Vinoth Balamurugan, D. Suriya, K. Reshma, R. Faculty of Engineering Karpagam Academy of Higher Education Department of Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9781665491990

Teachers and students in the 21st century are shifting their attention from the classroom to digital education, aiming for meaningful, high-quality, and dynamic learning. With the introduction of digital learning, the educational system has undergone significant transformations. Some teachers/instructors, on the other hand, are still experiencing difficulties. For this reason, this research finds a solution named AAC integrated Big Data Analytics Framework (AACBDAF) for people with neural disorders in the English language. AAC-BDAF gathers all the linguistic data and categorizes it based on how frequently it is used. To input data, the AAC system uses eyeball tracking to display symbols and characters on a screen. To help the listener follow along, the selected option/word is converted into a text-to-speech paradigm. The detection algorithms of machine learning and deep learning are evaluated for word detection and suggest the convolutional neural network (CNN) support AACBDAF. According to simulation findings, the proposed AAC-BDAF recognizes phrases and predicts the word from their eye-gazing with higher accuracy of 98.63%. © 2023 IEEE.

关键词： Students

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Formation Control of Heterogeneous Multi-Agent Systems Under Fixed and Switching Hierarchies

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IEEE Access 2024年 12卷 97868-97882页

作者： Shamrooz Aslam, Muhammad Bilal, Hazrat Chang, Wen-Jer Yahya, Abid Anjum Badruddin, Irfan Kamangar, Sarfaraz Hussien, Mohamed China University of Mining and Technology Artificial Intelligence Research Institute Xuzhou221116 China University of Science and Technology of China Department of Automation Hefei230026 China Department of Marine Engineering Keelung202 Taiwan Botswana International University of Science and Technology Department of Electrical Computer and Telecommunications Engineering Palapye Botswana King Khalid University College of Engineering Department of Mechanical Engineering Abha Saudi Arabia King Khalid University Faculty of Science Department of Chemistry Abha61413 Saudi Arabia

Modern information technology is driving the rapid development of the information and automation industry through the combination of computer technology, communication technology, and control technology. Inspired by nature's cluster movement, such as birds, people proposed the concept of Multi-Agent Systems (MASs). This paper deals with the formation control problem for a class of heterogeneous multi-agent systems with time-delays. First, the formation control of heterogeneous multi-agent systems with communication delay in fixed and switching topologies is investigated. A heterogeneous multi-agent system model with input delay and communication delay is established. Furthermore, based on this model, the formation control protocol is designed and the stability of the proposed control protocol in fixed and switched topologies is analyzed. The first- and second-order multi-agent control system was formulated for both fixed and switched topology, respectively. Then, using the new LMI toolbox, the feasibility of the parameters is obtained and the example is created by MATLAB. The simulation results show that the proposed formation control protocol with delay in this paper can make the systems converge to the expected value under fixed and switched hierarchies. © 2013 IEEE.

关键词： Topology

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Wireless modulation classification based on Radon transform and convolutional neural networks

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Journal of Ambient Intelligence and Humanized Computing 2023年第5期14卷 6263-6272页

作者： Ghanem, Hanan S. Al-Makhlasawy, Rasha M. El-Shafai, Walid Elsabrouty, Maha Hamed, Hesham F. A. Salama, Gerges M. El-Samie, Fathi E. Abd Department of Electrical Engineering Faculty of Engineering Minia University Minia61111 Egypt Department of Computers and Systems Electronics Research Institute Cairo Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Electronics and Communications Engineering Egypt-Japan University of Science and Technology Alexandria Egypt Faculty of Engineering Egyptian-Russian University Cairo Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Convolutional Neural Networks (CNNs) are efficient tools for pattern recognition applications. They have found applications in wireless communication systems such as modulation classification from constellation diagrams. Unfortunately, noisy channels may render the constellation points deformed and scattered, which makes the classification a difficult task. This paper presents an efficient modulation classification algorithm based on CNNs. Constellation diagrams are generated for each modulation type and used for training and testing of the CNNs. The proposed work depends on the application of Radon Transform (RT) to generate more representative patterns for the constellation diagrams to be used for training and testing. The RT has a good ability to represent discrete points in the spatial domain as curved lines. Several pre-trained networks including AlexNet, VGG-16, and VGG-19 are used as classifiers for modulation type from the spatial-domain constellation diagrams or their RTs. Several simulation experiments are presented in this paper to compare different scenarios for modulation classification at different Signal-to-Noise Ratios (SNRs) and fading channel conditions. © 2022, The Author(s).

关键词： Fading channels

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Energy-Efficient Clustering Using Optimization with Locust Game Theory

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Intelligent Automation & Soft Computing 2023年第6期36卷 2591-2605页

作者： P.Kavitha Rani Hee-Kwon Chae Yunyoung Nam Mohamed Abouhawwash Department of Computer Science and Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India Department of ICT Convergence Soonchunhyang UniversityAsan31538Korea Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Wireless sensor networks(WSNs)are made up of several sensors located in a specific area and powered by a finite amount of energy to gather environmental *** use sensor nodes(SNs)to collect and transmit ***,the power supplied by the sensor network is ***,SNs must store energy as often as to extend the lifespan of the *** the proposed study,effective clustering and longer network lifetimes are achieved using mul-ti-swarm optimization(MSO)and game theory based on locust search(LS-II).In this research,MSO is used to improve the optimum routing,while the LS-II approach is employed to specify the number of cluster heads(CHs)and select the best *** the CHs are identified,the other sensor components are allo-cated to the closest CHs to them.A game theory-based energy-efficient clustering approach is applied to *** each SN is considered a player in the *** SN can implement beneficial methods for itself depending on the length of the idle listening time in the active phase and then determine to choose whether or not to *** proposed multi-swarm with energy-efficient game theory on locust search(MSGE-LS)efficiently selects CHs,minimizes energy consumption,and improves the lifetime of *** findings of this study indicate that the proposed MSGE-LS is an effective method because its result proves that it increases the number of clusters,average energy consumption,lifespan extension,reduction in average packet loss,and end-to-end delay.

关键词： Wireless sensor network clustering routing cluster head energy consumption network’s lifetime multi swarm optimization game theory

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Stock Price Prediction Using Sentiment Analysis on News Headlines 6th

Stock Price Prediction Using Sentiment Analysis on News Head...

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6th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2021

作者： Bhavsar, Hetal Jivani, Anjali Amesara, Sameer Shah, Smeet Gindani, Prashant Patel, Sohamkumar Department of Computer Science and Engineering Faculty of Technology and Engineering The Maharaja Sayajirao University of Baroda Gujarat Vadodara India

ISBN: (纸本)9789811935701

Recently, there has been rapid growth in the field of machine learning and deep learning for stock market prediction. This study evaluates and compares various deep learning algorithms like LSTM and CNN for predicting stock price with and without using sentiments gathered from the news headlines. The evaluation was conducted on the publicly available dataset of Infosys (BSE) stock closing prices from Yahoo Finance. For the sentiments, the data in the form of news headlines had been scraped from Google News and after evaluating the sentimental data from headlines using the VADER library, the final dataset has been prepared by combining historic data of stock and the evaluated sentimental data. This work finds the effect of sentiments while predicting the stock price. The final results came to be contradictory to our preliminary standing as we found a weak correlation between sentimental data and the stock price. Additionally, decline in the model’s performance to some degree was observed after including sentimental data from news headlines. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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