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Classification of Four-Class Motor-Imagery Data for Brain-Computer Interfaces 8th

Classification of Four-Class Motor-Imagery Data for Brain-Co...

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8th international conference on the Development of Biomedical Engineering, BME8 2020

作者： Ngan thanh, Luu Anh Hoang Lan, Duong Dung Xuan, Nguyen Khiet thi thu, Dang Chau Nu Ngoc, Pham thuong Hoai, Nguyen Huong, Ha thi thanh School of Biomedical Engineering International University Ho Chi Minh City Viet Nam Ho Chi Minh City Viet Nam

ISBN: (纸本)9783030755058

Brain-computer interface (BCI) provides opportunities for patients with severe motor impairment to regain their motor controls by decoding and converting Electroencephalogram (EEG) signals into the motor control signal. Existing processing schemes, including artifact removal and band-pass filtering, usually cause leakage of EOG artifacts. Hence, the preprocessing order in this paper was optimized for artifact removal in event-related desynchronization. To address this challenge, we implemented an algorithm to classify four different motor imagery tasks, including the left hand, right hand, both feet, and tongue movements. Here, we use Graz dataset A published in the BCI competition IV in 2008. Band-pass filter (7–30 Hz) with 5th order Butterworth IIR combined with linear regression was applied to remove the artifact of the signal. Mutual Information Best Individual Features (MIBIF) and Linear Discriminant Analysis (LDA) algorithms were used for selecting features and classifying the classes. the mean training kappa of our classifier is 0.57, and the mean evaluated kappa is 0.41. Besides, the application of the Common Spatial Pattern enhanced the kappa score (from 0.33 to 0.41). those results are comparable to those from the BCI competition (range from 0.29 to 0.57). Hence, our approach could be the foundation for further development of motor imagery based BCI research. For future works, we propose to create an algorithm for the automatic procedure by automatically choosing proper frequency bands for band power features. © 2022, Springer Nature Switzerland AG.

关键词： Electroencephalography

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An Optimized DDoS Attack Detection Using Deep Convolutional Generative Adversarial Networks

An Optimized DDoS Attack Detection Using Deep Convolutional ...

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international conference on Inventive Research in Computing Applications (ICIRCA)

作者： Chandra Mouli Venkata Srinivas Akana Arvind Kumar Mohit Tiwari Aslanov Zabit Yunus Vijayakumar E Mukesh Singh Bonam Venkata Chalamayya Engineering College Odalarevu School of Computer Applications Manav Rachna International Institute of Research and Studies Department of Computer Science and Engineering Bharati Vidyapeeth's College of Engineering Delhi India Azerbaijan State University of Economics - UNEC Baku Azerbaijan Department of Computer Applications KIT-Kalaignar Karunanidhi Institute of Technology Coimbatore Tamilnadu India Department of Computer Science and Engineering Graphic Era University Dehradun India

Attacks known as Distributed Denial-of-Service (DDoS) are rising as a result of recent, dramatic increase in demand for Internet access. When the amount and characteristics of network traffic, which may include harmful DDoS contents, expand dramatically, traditional basic algorithms using machine learning for classifying DDoS attacks frequently fail because it cannot automatically extract high-value characteristics. In order to effectively identify and classify DDoS attacks, a hybrid technique called DFNN-SAE-DCGAN that combines three deep learning-based models is suggested. the Deep Feed-Forward Neural Network (DFNN) and Stacked Auto encoder offers an efficient method for extracting features that identifies the most pertinent feature sets without the assistance of a person. To avoid the operational overhead and presumption associated with processing massive set of features with distortion and redundant characteristic values, the Deep Convolutional Generative Adversarial Networks (DCGAN) component of the proposed model classifies the attacks into various DDoS attack types using the restricted and minimized characteristic sets generated by the DFNN-SAE as inputs. the experimental results show a very high and resilient accuracy rate and an F1-score of 98.5%, which is higher than the performance of many similar approaches. these results were acquired by thorough and extensive trials on various performance aspects on the CICDDoS2019 dataset. this demonstrates that the suggested methodology may be utilized to defend against the increasing amount of DDoS attacks.

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Enhanced Agricultural Decision-Making: Merging Climatic Data with Machine Learning algorithms

Enhanced Agricultural Decision-Making: Merging Climatic Data...

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Data Intelligence and Cognitive Informatics (ICDICI), international conference on

作者： Snehal Rathi Shubham Wamanacharya Samkit Shah Vijayshri Khedkar Department of Computer Engineering BTACT’S Vishwakarma Institute of Information Technology Pune Department of Computer Science & Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

Climate change still implies risks for economic security, such as for agriculture and its productivity and, therefore, national economic potential. Although similar approaches have been used in publications concerning individual crops or soil types, the application of climatology parameters in agriculture and it limited indices have not been optimal. Owing to the recognized limitations of traditional linear productivity models, this thesis subjects’ agricultural productivity to a Cobb-Douglas multi-factorial modeling approach through intensive levels of data decomposition. the proposed approach enabled the researchers to adjust the impact of climate parameters during students’ evaluations in the secondary education curricula. Additionally, frameworks which relate temperature variations and crop growth do exist, and therefore the temperature and agriculture performance model effects determinants were determined using complex analysis of temperature relations and crop outputs. this highlights the various factors affecting production, its structure, and the relative importance of some conditions in agriculture and explains their dynamics depending on the changes in growth factors. Hence, a set of factors employed in the modeling resulted mainly from economic conditions, production system structures and advanced agricultural technology applications in the economy as prediction

关键词： Productivity Economics Support vector machines Temperature Biological system modeling Soil Predictive models Data models Long short term memory Meteorology

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A Review of Spectral Estimation in Underwater Acoustic Signal Processing

A Review of Spectral Estimation in Underwater Acoustic Signa...

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IEEE Advanced Information Management,Communicates,Electronic and Automation Control conference (IMCEC)

作者： Chao Peng International School of Microelectronics Dongguan University of Technology DongGuan China

ISBN: (纸本)9781665479691

this paper first introduces the direct method of classical power spectrum estimation, and then proposes two improved classical power spectrum estimation methods: Welch Method and MultiTaper (MTM) Method. then the Maximum Entropy Spectral Estimation (based on Burg) in parametric spectral estimation is introduced. then the power spectrum is extended in the spatial domain, and two kinds of spatial spectrum estimation methods, MUSIC Method and ESPRIT method, and their improved algorithms are introduced. All of the above methods have been simulated in Matlab environment. We have compared various spectrum estimation methods in detail, and analyzed and summarized their respective advantages and disadvantages. Finally, the paper points out the shortcomings of current power spectrum estimation and the future development direction.

关键词： Estimation Signal processing algorithms Process control Signal processing Entropy Multiple signal classification Information management

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Machine vision based potato species recognition 5

Machine vision based potato species recognition

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5th international conference on Intelligent Computing and Control Systems, ICICCS 2021

作者： Nuruzzaman, Md. Hossain, Md. Shahadat Rahman, Md. Mostafijur Shoumik, Ahete Shamul Haque Chowdhury Khan, Md. Abbas Ali Habib, Md. Tarek Daffodil International University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9781665412728

Potato is one of the tasteful vegetable in the list of our daily delicious food. At present, there are 42000 kind of potatoes available in the world. In Bangladesh, we cultivate 82 species every year. Potatoes are used for other purposes besides eating. So it is produced by thinking of other purposes besides eating. Different varieties of potatoes are used for different purposes. But People usually do not know which variety of potato is suitable for which work. We took this research step to solve this problem. there are currently some conventional common detection methods that are not very convenient. therefore, we have introduced Machine Vision Recognition (MVR) procedure to discover a suitable technique. As if, through this method people can easily identify the potato species. In this research paper, we want to show how to identify different varieties of potatoes in Bangladesh using machine vision approach. We have been collected total 1200 potato imagers from four fact for our experience. To reach our aim, several machine learning algorithms have been applied to the datasets, like Random Forest Classifier (RF), Linear Discriminant Analysis (LDA), Logistic Regression, Support Vector Machine (SVM), CART, NB, and KNN. Once we have been applied all the algorithms, different results have been shown by each algorithm. Logistic Regression shows the best result, which has an accuracy rate of 98%. In contrast, the lowest rate has been shown by the SVM. the accuracy rate of SVM is 33% which is not only a good fit for future research but also promising. © 2021 IEEE.

关键词： Support vector machines

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Optimisation of Random Forest Algorithm Based on Data Mining Analysis and Sparrow Search Analysis of the Impact of 'BM Innovative thinking System' on Innovation and Entrepreneurship Education in Colleges and Universities

Optimisation of Random Forest Algorithm Based on Data Mining...

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international conference on Computer Engineering and Applications (ICCEA)

作者： Xiangyun Su Qian Lai Nong Li Di Liang School of Languages Cultures and International Education Liuzhou Institute of Technology Liuzhou China School of Design and Arts Liuzhou Institute of Technology Liuzhou China School of Innovation and Entrepreneurship Liuzhou Institute of Technology Liuzhou China School of Logistics Management and Engineering Nanning Normal University Nanning China

ISBN: (数字)9798350386776

ISBN: (纸本)9798350386783

Artificial intelligence-generated content based on big language models has been widely used in marketing, customer service, education, entertainment, news and other fields. Business English programmes need to be integrated with innovation and entrepreneurship education to help students hone their international communication skills and innovative thinking to better meet the challenges of the modern business world. the BM Innovative thinking System developed in this paper is a web-based software based on artificial intelligence methodology designed to help undergraduate students in Chinese higher education institutions train in innovative and entrepreneurial thinking. the system employs advanced algorithms and technologies that can automatically adjust the learning content and difficulty based on user input and feedback to achieve optimal learning results. In this study, we investigated the role of practical projects in business English courses in improving innovation and entrepreneurship-related competencies and collected self-assessment data from a total of 1205 students. One-way ANOVA showed that there was a significant difference between the pre and post data, i.e., students believed that their innovative and entrepreneurial competencies were significantly improved through the course. the paper also develops a random forest algorithm optimised on the basis of an improved sparrow search algorithm that can predict students' experiences after using the innovative thinking system. In this paper, the model was trained and tested using relevant data, and the confusion matrices in the training and test sets show that the accuracy of the four classifications in the training set is 100%, and the accuracy of the four classifications in the test set is 96.26%, which can well predict the students' experience after using the system. this result has some reference value for the development of business English course design and practice programme.

关键词： Training Technological innovation Accuracy Entrepreneurship Globalization Prediction algorithms Search problems

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Risk Factor Prediction for Heart Disease Using Decision Trees 23

Risk Factor Prediction for Heart Disease Using Decision Tree...

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Proceedings of the 5th international conference on Information Management & Machine Intelligence

作者： Shanthi Kunchi Vijaya N. Aher Sharayu Ikhar Kishor Pathak Yatin Gandhi Kirti Wanjale Symbiosis Law School Symbiosis International (Deemed University) Pune India India E&TC VIIT India Department of Information Technology Vishwakarma Institute of Information technology Pune India Computer Vishwakarma Institute of Information Technology India Computer Competent Softwares India Department of Computer Engineering Vishwakarma Institute of Information Technology India

ISBN: (纸本)9798400709418

Global mortality rates are high due to heart disease, it is crucial to early analyzing the risk factors of prediction and diagnosis are crucial. Decision trees are a reliable machine-learning method for this problem. Four decision tree algorithms CART (Classification and Regression Trees), C5.0, M5P (Model Tree with Linear Models in Leaves), and XGBoost are tested for heart disease risk factor prediction. An extensive dataset of heart disease risk factors was used to evaluate decision tree algorithms. To assess model performance rigorously, the study used data preprocessing, feature selection, and stratified cross-validation. the results from the experiments were significant. Despite its simplicity, the CART algorithm predicted heart disease risk factors accurately. the C5.0 algorithm handled missing data well, a common healthcare dataset issue. M5P, which integrates linear models into leaf nodes, improves interpretability without compromising accuracy. XGBoost was the best model, predicting risk factors with 99.32% accuracy. the result shows the algorithm's durability and suitability for complex medical diagnostic tasks. Decision tree algorithms predict heart disease risk factors effectively and clearly. XGBoost was the best decision tree algorithm due to its accuracy and reliability. this technology's precision suggests it could help doctors make quick, accurate diagnoses, potentially saving lives. this study highlights the importance of using machine learning and decision trees to improve heart disease risk assessment and patient care. Further research could examine the integration of XGBoost and other decision tree models into clinical practice to improve heart disease detection and treatment.

关键词： C5.0

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Based on Navmesh to implement AI intelligent pathfinding in three-dimensional maps in UE4 22

Based on Navmesh to implement AI intelligent pathfinding in ...

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Proceedings of the 2022 5th international conference on algorithms, Computing and Artificial Intelligence

作者： Xinyuan Zhang Xinyou Zhang International Design Art College Inner Mongolia Normal University China

ISBN: (纸本)9781450398336

Abstract: the development of science and technology has led to the development of the game industry. As the mainstream development engine of the current three-dimensional game, UE4 is popular with game developers for its powerful rendering technology and model processing technology. there will be a large number of non-player characters (NPC, non-player character) in the game, and these AI-controlled characters are an important way for developers to interact with users. Among them, pathfinding is the most important part of the game's AI character, which gives the AI character vitality, so that the AI character can interact with the player, chase, and automatically complete the movement to the target point. Pathfinding is the core function of an AI character, and there are many current pathfinding methods, such as Dijstra algorithm, best-first search algorithm, A-star algorithm. Most of these pathfinding algorithms are used in 2D static maps, but there is no detailed description of 3D pathfinding by pathfinding algorithms in UE4. this paper first introduces and compares several map pathfinding models, then experimentally compares different pathfinding algorithms, and then uses blueprint programming to implement AI role pathfinding in the three-dimensional map in UE4 based on the optimal performance algorithm. Finally, the overall experimental results are summarized and sorted out, and the direction for future development is pointed out.

关键词： A-star

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Small-Object Detection Based on ITS-YOLOv5s-P2

Small-Object Detection Based on ITS-YOLOv5s-P2

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Computer Engineering and Intelligent Control (ICCEIC) international conference on

作者： Tao Li Xiaoyang Chen Xianfei Xie Fuchun Liu Ying Pan School of Automation Science and Engineering South China University of Technology Guangzhou China School of Compute Science Technology Beijing Normal University Hong Kong Baptist United International College Zhuhai China GuangDong CNIPM Ecological Environment Co. Ltd. Guangzhou China

ISBN: (数字)9798331507992

ISBN: (纸本)9798331508005

In small object detection tasks, the instances of small objects are numerous, multi-scale, densely distributed, and easily occluded between objects, often resulting in missed and false detections, making it difficult to improve detection accuracy. A new small object detection algorithm called ITS-YOLOv5s-P2 is proposed to address these issues. Firstly, a P2 detection layer is added to YOLOv5s, introducing high-resolution feature maps and rich detail information to improve the overall recognition performance of the model. Secondly, an improved adaptive anchor box strategy (IAAS) is proposed, allowing the model to learn better and fit the real bounding boxes, which is beneficial for multi-scale object recognition. then, a tiny object detection layer (TODL) is designed, incorporating texture information from shallow regions of the backbone network into the prediction head, enhancing the model's ability to perceive details of small objects. Finally, Soft-NMS is used instead of NMS for non-maximum suppression, effectively mitigating the issue of object occlusion that leads to missed detections caused by the aggressive filtering of traditional NMS. Experiments show that the ITS-YOLOv5s-P2 algorithm effectively improves the detection accuracy of small objects. On the public dataset VisDrone2019, the parameters and the number of floating-point operations of this algorithm increase by only 0.2M and 7.3G compared to YOLOv5s, while mAP@O.5 and mAP@[.5:.95] increase by 11.6% and 9.1%.

关键词： YOLO Adaptation models Accuracy Filtering Predictive models Prediction algorithms Feature extraction Object recognition Intelligent control

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5th international conference on Advances in Computing and Data Sciences, ICACDS 2021

5th International Conference on Advances in Computing and Da...

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5th international conference on Advances in Computing and Data Sciences, ICACDS 2021

ISBN: (纸本)9783030882433

the proceedings contain 38 papers. the special focus in this conference is on Advances in Computing and Data Sciences. the topics include: Recognition of Isolated Gestures for Indian Sign Language Using Transfer Learning;a Study of Five Models Based on Non-clinical Data for the Prediction of Diabetes Onset in Medically Under-Served Populations;representation and Visualization of Students’ Progress Data through Learning Dashboard;denoising of Computed Tomography Images for Improved Performance of Medical Devices in Biomedical Engineering;image Dehazing through Dark Channel Prior and Color Attenuation Prior;predicting the Death of Road Accidents in Bangladesh Using Machine Learning algorithms;numerical Computation of Finite Quaternion Mellin Transform Using a New Algorithm;predictive Modeling of Tandem Silicon Solar Cell for Calculating Efficiency;Text Summarization of an Article Extracted from Wikipedia Using NLTK Library;handling Class Imbalance in Electroencephalography Data Using Synthetic Minority Oversampling Technique;grapheme to Phoneme Mapping for Tamil Language;comparative Study of Physiological Signals from Empatica E4 Wristband for Stress Classification;an E-Commerce Prototype for Predicting the Product Return Phenomenon Using Optimization and Regression Techniques;crop Yield Prediction for India Using Regression algorithms;a Novel Framework for Multimodal Twitter Sentiment Analysis Using Feature Learning;an Iterative Approach Based Reversible Data Hiding with Weight Update for Dual Stego Images;lower and Upper Bounds for ‘Useful’ Renyi Information Rate;sign Language Recognition Using Convolutional Neural Network;Prediction of Stock Price for Indian Stock Market: A Comparative Study Using LSTM and GRU;early Prediction of Cardiovascular Disease Among Young Adults through Coronary Artery Calcium Score Technique.

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