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3rd international conference on Artificial-Business Analytics, Quantum and Machine Learning: Trends, Perspectives, and Prospects, Com-IT-Con 2023

3rd International Conference on Artificial-Business Analytic...

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3rd international conference on Artificial-Business Analytics, Quantum and Machine Learning: Trends, Perspectives, and Prospects, Com-IT-Con 2023

ISBN: (纸本)9789819748594

The proceedings contain 60 papers. The special focus in this conference is on Artificial-Business Analytics, Quantum and Machine Learning: Trends, Perspectives, and Prospects. The topics include: Particle Swarm Optimization for Efficient Data Dissemination in VANETs;a Reactive Approach for High-Accuracy and Data-Driven Customer Behaviour Analysis and Prediction;a Brief Survey on Fabric Defect detection;CNN Based Real Time detection of Words from Lip Movements and Automated into Text;application of Artificial intelligence for the Diagnosis of Dementia (Alzheimer): A systematic Evaluation;shortest Job First with Gateway-Based Resource Management Strategy for Fog Enabled Cloud Computing;software Vulnerability Analysis Based on Statistical Characteristics;hate Speech detection on Twitter: A Comparative Evaluation of Different Machine Learning Techniques;detection of Heart Disease Using Machine Learning;SVM-Based Framework for Breast Cancer detection;hand Gesture Recognition system Using Machine Learning;deep Learning Method for Plant Disease Recognition and Prediction;Lung Cancer detection by Using CNN Architecture Models;deep Fake Analyser: A Review Based on Detecting the Deepfakes;predicting the Outcomes of La Liga Matches;Artifact detection and Removal in EEG: A Review of Methods and Contemporary Usage;analyzing the Employee Attrition Rate: A Comparative Study of Various Machine Learning Approaches to Foresee Employee Attrition;satellite Image Classification Using Deep Learning for Big Earth Data;tackling Misinformation Through Tweets: A Comparative Study of Various Machine Learning Approaches;feature-Based Anomaly detection in Static Social Networks;framework for Optimising Supply Chain Analysis Using Machine Learning;evaluation of Load Forecasting in Intelligent Grid systems Through Machine Learning Techniques.

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Comparative Analysis of CNN and SVM Classifiers for Covid-Affected Lung Segmentation: A Novel Approach for High Accuracy detection 3

Comparative Analysis of CNN and SVM Classifiers for Covid-Af...

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3rd international conference on Advancement in Electronics and Communication Engineering, AECE 2023

作者： Singh, Manpreet Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research for Business and Technology Punjab India

ISBN: (纸本)9798350330724

Aim: The goal of this current research work is to identify the Covid affected lung portion with the help of CNN (Novel Convolutional Neural Networks) and compare the accuracy with SVM (Support Vector Machine). Materials and Methods: Two classifiers, namely CNN and SVM classifiers used to invent the accuracy value of Covid Spread in the lung portion with 10 sample sizes each to estimate this research study. Sample size value was computed using G power information with pretest power value at 0.8. Initially, models were trained using NN (Neural Network) approaches where the Covid Spread in lung portion model implements the system efficiently. Results: The accuracy rate of CNN is significantly increased by 95% than the SVM with 93% and there is a statistical significance value that is examined as 0.03 (p>0.05). Conclusion: CNN model has more accuracy rate than the SVM classifier. From the simulation, the CNN is significantly better than the SVM classifier. © 2023 IEEE.

关键词： Support vector machines

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TechSSN4@LT-EDI-RANLP2023: Depression Sign detection in Social Media Postings using DistilBERT Model 3

TechSSN4@LT-EDI-RANLP2023: Depression Sign Detection in Soci...

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3rd Workshop on Language technology for Equality, Diversity and Inclusion, LTEDI 2023

作者： Thannickal, Krupa Elizabeth Sanmati, P. Rajalakshmi, S. Angel Deborah, S. Department of Computer Science and Engineering Sri Sivasubramaniya Nadar College of Engineering Tamil Nadu Chennai603110 India

ISBN: (纸本)9789544520847

As world population increases, more people are living to the age when depression or Major Depressive Disorder (MDD) commonly occurs. Consequently, the number of those who suffer from such disorders is rising. There is a pressing need for faster and reliable diagnosis methods. This paper proposes a method to analyse text input from social media posts of subjects to determine the severity class of depression. We have used the DistilBERT transformer to process these texts and classify the posts across three severity labels-’not depression’,’moderate’ and’severe’. The results showed the macro F1-score of 0.437 when the model was trained for 5 epochs with a comparative performance across the labels. The team acquired 6th rank while the top team scored macro F1-score as 0.470. We hope that this system will support further research into the early identification of depression in individuals to promote effective medical research and related treatments. © 2023 LTEDI 2023 - 3rd Workshop on Language technology for Equality, Diversity and Inclusion, associated with the 14th international conference on Recent Advances in Natural Language Processing, RANLP 2023 - Proceedings. All rights reserved.

关键词： Social networking (online)

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3rd international conference on Artificial-Business Analytics, Quantum and Machine Learning: Trends, Perspectives, and Prospects, Com-IT-Con 2023

3rd International Conference on Artificial-Business Analytic...

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3rd international conference on Artificial-Business Analytics, Quantum and Machine Learning: Trends, Perspectives, and Prospects, Com-IT-Con 2023

ISBN: (纸本)9789819725076

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3D-aided image augmentation for occluded pedestrian detection based on parallel vision 3

3D-aided image augmentation for occluded pedestrian detectio...

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3rd IEEE international conference on Digital Twins and Parallel intelligence, DTPI 2023

作者： Bai, Songlin Du, Quancheng Tian, Yonglin Wang, Xiao University of Science and Technology Beijing Beijing100083 China University of Science and Technology Beijing Beijing100190 China Institute of Automation Chinese Academy of Sciences Beijing100190 China Anhui University Hefei230601 China Qingdao Intelligent Industry Technology Research Institute Qingdao266109 China

ISBN: (纸本)9798350318470

Deep learning algorithms rely heavily on diverse and high-quality datasets to excel in complex tasks. However, in intricate traffic scenarios, manual data collection often falls short in terms of diversity due to the challenges of replicating real-world scenes. While techniques such as Generative Adversarial Networks (GANs) have been used for image generation, they frequently overlook the impact of object relationships on data diversity. In this paper, our primary focus is on the perception of pedestrians, especially those who are occluded, within complex driving scenes. We introduce a novel 3D-aided image augmentation technique known as "Drive-CP,"which is based on the principles of parallel vision. This innovative approach enriches the dataset by generating instances of occluded pedestrians within the original images, thereby significantly enhancing the diversity of the training data. Experimental results consistently demonstrate that detectors trained on our augmented datasets outperform those relying solely on the original data. This work contributes to strengthening the robustness of deep learning algorithms in complex traffic scenarios, providing a more reliable foundation for practical applications. © 2023 IEEE.

关键词： Learning algorithms

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Research on Information Processing system Under Computer Artificial intelligence Big Data technology 3

Research on Information Processing System Under Computer Art...

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3rd international Signal Processing, Communications and Engineering Management conference, ISPCEM 2023

作者： Peng, Lei Fu, Qiang Li, Zhi Lin, Musong Qian, Yihua Electric Power Research Institute of Guangdong Power Grid Co. Ltd. Guangdong Key Laboratory of Electric Power Equipment Reliability Guangzhou China

ISBN: (纸本)9798350307023

In this paper, a virtual view image post-processing method using background information is proposed. Firstly, a depth-based method is used to deal with overlapping between two images. Secondly, the artifacts of the left and right virtual view are located by the motion direction of the observation point, and the artifacts are eliminated by the background pixels in the supplementary view Angle. Then the background image is filled with holes and combined with the foreground image to form a virtual perspective image. The experimental results show that the peak signal noise of this method is 0.6357 dB better than that of the traditional method. Experiments show that the method proposed in this paper can improve the rendering effect significantly. © 2023 IEEE.

关键词： Big data

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Autonomous Vehicle Lane detection Using Hyperbolic Neural Network with Bi-Directional Long Short-Term Memory 3

Autonomous Vehicle Lane Detection Using Hyperbolic Neural Ne...

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3rd IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Alzubaidi, Laith H. Almoussawi, Zainab Abed Jagadish, Sripelli Madhavan, Sowmya Alassedi, Zainab The Islamic University Najaf Iraq College of Islamic Science Ahl Al Bayt University Karbala Iraq School of Computer Science & Artificial Intelligence SR University Telangana Warangal India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India National University of Science and Technology Dhi Qar Iraq

ISBN: (纸本)9798350317022

Lane detection is a feature of autonomous vehicles and is an essential technology for environmental awareness. While many academics have successfully used Deep Learning (DL) for lane detection, their application cases were rather straightforward. When a lane is blocked, lost, or has other problems, the accuracy of lane recognition diminishes significantly. In this paper, to solve the above-mentioned problems, a Hyperbolic Neural Network (HNN) with a Bi-directional Long Short-Term Memory (B-LSTM) model is proposed for efficient autonomous vehicle lane detection. At first, hyperbolic space is used to identify the space changes in the frame, and the HNN is used to extract the spatial information in the frames. Then, finally, the B-LSTM is used to perform the sequential data to detect the autonomous vehicle lane in an effective way. The implemented HNN with the B-LSTM model demonstrates superior performance, achieving a Precision of 0.0311, Recall of 0.0253, F1-Score of 97.57%, and Accuracy of 96.74%. When compared to previous models like the Lightweight Attention Deep Neural Network (LA-DNN), ResNest and Self-attention Distillation (RS-Lane), and Convolutional Neural Network-Multi Layer Perceptron (CNN-MLP). © 2023 IEEE.

关键词： Autonomous Vehicle Deep Learning Hyperbolic Neural Network Long Short-Term Memory Spatial Information

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Closed-loop correction strategy for commutation deviation of BLDC motor based on internal power factor angle detection

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ENERGY REPORTS 2023年 9卷 635-645页

作者： Liu, Kun Zhang, Wenjuan Dai, Litao Gao, Jian Huang, Shoudao Hunan Univ Coll Elect & Informat Engn Changsha 410082 Peoples R China Changsha Univ Coll Elect & Elect Engn Changsha 410022 Peoples R China

The first-order inertia of the armature winding of the brushless DC motor will cause current offset, which will reduce the electromagnetic torque. In the sensorless drive of brushless DC motors, correction of commutation point is an effective solution to improve motor performance. This paper analyzes the relationship between the electromagnetic torque and the compensation angle in the static coordinate system, and it is concluded that the internal power factor angle (IPFA) is equal before and after the commutation. The dc component of the IPFA is constructed as a feedback variable, a novel iterative sliding mode observer is designed to improve the compensation accuracy, and the adaptive output of the optimal angle is realized through the PI regulator. The scheme is simple to implement, and has higher accuracy, stronger adaptability and robustness. Simulation and experimental results verify the effectiveness and feasibility of the proposed correction strategy. (c) 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Brushless DC motor Correction strategy Commutation deviation Internal power factor angle

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Research on the Application of Relationship Graphs in Data Analysis Algorithm Design 3

Research on the Application of Relationship Graphs in Data A...

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3rd IEEE international conference on Electronic technology, Communication and Information, ICETCI 2023

作者： Yancheng, Zhu Shenyang Aerospace University College of Artificial Intelligence Shenyang China

ISBN: (纸本)9798350398410

With the continuous development of statistics and computer science, people's demand for data mining and potential information analysis is increasing. There are a lot of relational data in the world. As the core of data analysis and mining, data analysis algorithms significantly impact the effectiveness and results of data analysis. A relationship graph is an essential tool for in-depth association analysis. This paper proposes a graph-based data screening and analysis evaluation system using the relationship graph. First, this paper proposes an entity retrieval and location algorithm to establish the entity and relationship network for the research. Then, a relationship graph is established to extract frequent data items for the relationship network. In addition, to facilitate the subsequent data scoring and analysis, we have established a graph-based data screening process and completed the above tasks by defining an evaluation matrix. Finally, we practice the constructed relationship graph in the application scenario of scientific and technological data analysis. The research is based on statistics and computer science, with in-depth research and applications on relationship graphs. It is expected to provide some references for designing and applying data analysis algorithms. © 2023 IEEE.

关键词： Data mining

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Machine Learning-based Prediction of Parkinson's Disease: A Comparative Analysis of Algorithms 3

Machine Learning-based Prediction of Parkinson's Disease: A ...

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3rd international conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Om Prakash, P.G. Reddy, Balineni Nehan Sai Kumar Lohith, Seedella Sai Mohankrishna SRM Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Kattankulathur India

ISBN: (纸本)9798350343632

Diagnosing Parkinson's disease (PD) is an important medical diagnosis that calls for the application of cutting-edge methods such as machine learning algorithms. This study explores the application of cutting-edge machine learning techniques for the early detection of Parkinson's disease using a diverse dataset that includes patient and clinical record data. The study calls for rigorous feature selection, model training, data preprocessing, and evaluation because it uses a complicated and nuanced dataset. Using rigorous cross-validation and extensive experimentation, this study assesses the sensitivity, specificity, and accuracy of these machine learning algorithms in identifying subtle patterns suggestive of symptoms of Parkinson's disease. Additionally, traditional diagnostic methods are thoroughly scrutinized for comparative analysis, augmenting the scope of the evaluation *** research findings shed important light on how well machine learning algorithms identify Parkinson's disease, which will significantly improve medical diagnostic methods. This work highlights the critical role that a range of sophisticated machine learning models and meticulous dataset curation play in enhancing the precision and robustness of Parkinson's disease detection systems. © 2023 IEEE.

关键词： Clinical Records Data Preprocessing Diagnostic Approaches Feature Selection Healthcare Parkinson's Disease detection Sensitivity Specificity Voice Data

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