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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

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Conversation-Driven Refinement of Knowledge Graphs: True Active learning with Humans in the Chatbot application Loop 4th

Conversation-Driven Refinement of Knowledge Graphs: True Act...

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4th international conference on Artificial Intelligence in HCI (AI-HCI) Held as Part of the 25th international conference on Human-computer Interaction (HCII)

作者： Buhl, Dominik Szafarski, Daniel Welz, Laslo Lanquillon, Carsten Heilbronn Univ Appl Sci D-74076 Heilbronn Germany

ISBN: (纸本)9783031358937;9783031358944

the value of knowledge-grounded cognitive agents is often limited by a lack of high-quality knowledge. Although advances in natural language processing have substantially improved knowledge-extraction capabilities, the demand for different types of knowledge fragments and the potential for error in extraction processes has created a next generation of the knowledge acquisition bottleneck. Instead of waiting for a perfect knowledge base, we propose a design for an agent that is aware of these issues and that actively seeks feedback from users in conversations to improve its knowledge and extraction processes. this approach allows for imperfection and incompleteness, and for the agent to improve over time. Any feedback provided by the users in this conversational application loop is used to not only refine the underlying knowledge graph, but also to improve the knowledge extraction processes. Eventually, the agent's knowledge and the quality of its answers rises while talking to its users.

关键词： Conversational AI Human-in-the-Loop AI Knowledge-Grounded Cognitive Assistants Knowledge Graph Refinement

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Research on Student Performance Prediction Model Based on Blended learning Data 4

Research on Student Performance Prediction Model Based on Bl...

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4th international conference on Big Data, Artificial Intelligence and Internet of things Engineering, ICBAIE 2023

作者： Zhang, Tong Yao, Hehua Duan, Yulang Zhang, Shun Xie, Yong An, Xiurui Qinghai University Department of Computer Technology and Applications Qinghai Xining810016 China

ISBN: (纸本)9798350343618

Abstract-Student performance prediction is a highly sought-after research direction in the field of education, and accurately predicting final exam scores is crucial for improving teaching quality. In this study, blended learning data from the 'Fundamentals of Programming (C)' course at Qinghai University from 2016 to 2019 was used. By combining SMOTE (Synthetic Minority Over-sampling Technique) oversampling and Bayesian optimization algorithm, four commonly used machine learning models were employed to evaluate students' final performance based on process-oriented learning data. the experimental results showed that these four models achieved accuracy rates of 86%, 84%, 85%, and 86% in predicting students' grade levels. Moreover, in predicting specific final exam scores, the root mean square errors of these models were 0.35, 0.28, 0.17, and 0.17, respectively. these findings indicate that these four models can provide accurate and effective references and guidance for students and teachers, further optimizing the teaching and learning process. By comprehensively analyzing students' process-oriented learning data, we can reveal the patterns of their academic performance in the course and use them as a basis for predicting students' grades. © 2023 IEEE.

关键词： Students

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Comprehending SMOTE Adaptations to Alleviate Imbalance in Intrusion Detection Systems 4

Comprehending SMOTE Adaptations to Alleviate Imbalance in In...

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4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Kaur, Ritinder Gupta, Neha Manav Rachna International Institute of Research & Studies Department of Computer Application Faridabad India

ISBN: (纸本)9798350300093

Intrusion detection System (IDS) are an essential component in the world of internet as they identify the malicious activities on the network and alert the network administrators. However, the effectiveness of an IDS is critically hampered due to the inherent imbalanced data problem prevalent in network traffic. An effective solution to this problem has been provided through the technique of Synthetic Minority Over-sampling Technique (SMOTE). this technique brings about balance in the dataset by generating synthetic data points that are similar to the minority class. Many variants of SMOTE have been proposed over the recent years which have been scientifically proven to improve the performance of IDS effectively. this research article provides a comprehensive introduction to the SMOTE (Synthetic Minority Over-sampling Technique) method. It delves into the underlying principles of SMOTE and its effectiveness in improving classification performance when dealing with imbalanced datasets. © 2023 IEEE.

关键词： Network security

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Enhancing Early Childhood learning using Image Processing for Sinhala Medium Education 5

Enhancing Early Childhood Learning using Image Processing fo...

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5th international conference on Advancements in Computing, ICAC 2023

作者： Saranga, R.G.V. Jayasinghe, A.H.N. Divyantha, U.A.S. De Silva, S.W.L.P. Swarnakantha, N.H.P. Ravi Supunya Kumari, P. K. Suriya Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350358131

the study aims to develop a mobile application for young children to learn Sinhala letters, shapes, colors, and storytelling incorporating machine learning models to evaluate and enhance educational activities. With the rise of online education during the COVID-19 pandemic, the familiarity of children with mobile devices provides an opportunity to create an engaging and educational experience. the application will teach Sinhala letters using object images, allowing children to upload their own images for feedback. It also includes a feature for children to practice writing letters and analyze their progress. Also, the application introduces colors and shapes in Sinhala, encouraging children to draw and track their improvement. Additionally, the application aims to generate stories in Sinhala to improve children's creativity and thinking knowledge. this research addresses a critical gap in existing Sinhala learning applications by integrating machine learning for activity assessment, promising to significantly impact and improve early language education for children. © 2023 IEEE.

关键词： Image enhancement

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Enhancing Driver Safety and Experience: Real-Time Facial Expression Detection in Smart Vehicles with Vision Systems 4th

Enhancing Driver Safety and Experience: Real-Time Facial Ex...

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4th international conference on Frontiers in Computing and Systems, COMSYS 2023

作者： Mishra, Siba Mishra, Soumya Ranjan Mahapatra, Hitesh Behera, Rishab Raj, Hemant School of Computer Engineering KIIT Deemed to be University Odisha Bhubaneswar751024 India

ISBN: (纸本)9789819726134

One of the primary causes of mortality nowadays is traffic accidents. Several of these accidents are brought on by drowsy driving, which has several detrimental implications. there are a few techniques proposed by different researchers like lane departure warning: this technique uses a camera to detect lane markings on the road and warns the driver if the vehicle drifts out of the lane without signalling. this can help prevent accidents caused by distracted or drowsy driving. Forward collision warning: this technique uses sensors to detect the distance between the vehicle and objects in front of it, and informs the driver if the vehicle is approaching an item or another vehicle too closely. By doing this, rear-end accidents may be avoided. In this study, we suggested a system that analyses a driver’s facial expression to conclude whether or not the driver is drowsy. this image-based system monitors the driver’s alertness and detects sleepiness by employing convolutional neural networks for computer vision and machine learning. To determine whether a person is feeling sleepy, this technology analyses facial characteristics. Index Terms Convolutional neural networks · machine learning · computer vision. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： machine learning

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A spatiotemporal multi-channel learning framework for PCMA signal detection and recognition algorithm 4

A spatiotemporal multi-channel learning framework for PCMA s...

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4th international conference on computer Engineering and application, ICCEA 2023

作者： Zhao, Yuanke Ran, Xiaomin Liu, Guangyi Jiao, Xiaolin Zhengzhou University Zhengzhou China Pla Strategic Support Force Information Engineering University Zhengzhou China

ISBN: (纸本)9798350347548

Paired Carrier Multiple Access signal (PCMA) signals use the same frequency to transmit the modulation waveforms of both sides of the communication. With the characteristics of high-frequency band utilization and strong anti-interception ability, PCMA signals are widely used in military and important commercial communication systems. Blind separation of PCMA signals is an important part of radio spectrum monitoring, high-precision modulation parameter information is the necessary basis for separation. In order to process the PCMA signal normally, it is necessary to distinguish the PCMA signal from the common single-channel satellite digital modulation signal, and then identify the modulation mode of the PCMA signal. the detection and modulation identification of the PCMA signal provides the necessary guidance for parameter estimation and blind processing. In this paper, the detection and modulation identification of the PCMA signal with low SNR are implemented by using the multi-channel feature to extract the different feature information of the signal, adopting the idea of feature fusion, and using the time and space features of the signal simultaneously. Simulation results show that the proposed algorithm can be widely applied to the blind detection and modulation identification of the PCMA signal with BPSK, QPSK, 8PSK and their mixtures, and the classification performance is better than the traditional deep learning network based on CNN and the traditional detection modulation identification algorithm. © 2023 IEEE.

关键词： Signal detection

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Predicting Parkinson's Disease Risk through Protein and Peptide Level Analysis: An Evidence from EDA and machine learning Based Approach 4

Predicting Parkinson's Disease Risk Through Protein and Pept...

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4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Gupta, Kushagra Lamba, Tripti Garg, Neeraj Maharaja Agrasen Institute of Technology College New Delhi India

ISBN: (纸本)9798350306118

Parkinson's disease (PD) is a disabling brain disorder that affects movement, cognition, and mood. Although there is no known cause or cure, it is possible to manage symptoms with treatment. PD affects about 7 million people worldwide and is projected to reach 10 million by 2030. Recent studies have explored protein and peptide levels as potential PD biomarkers that could impact Unified Parkinson's Disease Rating Scale (UPDRS) scores. In this research paper, we analyze data from 1,019 patients to predict the UPDRS score based on biomarkers. We leverage Exploratory Data Analysis (EDA) and machine learning to identify changes in protein and peptide levels that could serve as useful PD risk biomarkers. these analytical techniques utilize protein and peptide levels from general health screenings of patients for early PD detection to uncover patterns and correlations that enable accurate predictive models for UPDRS. this helps in the early detection of PD, allowing effective management and intervention. Our results show that several proteins and peptides are associated with PD risk. these proteins and peptides have a deep impact on the UPDRS score. We also find that the levels of these proteins and peptides are higher in people with PD than in healthy individuals. Finally, we obtain Symmetric Mean Absolute Percentage Error (SMAPE) scores from various machine learning algorithms to measure the accuracy of predictions. Analysis with SMAPE reveals the superiority of Random Forest as the best-performing machine learning technique for PD prediction, with a score of 0.37. In conclusion, PD is a complex disorder without a known cure. However, biomarker analysis with machine learning offers promise for early PD detection and gives clinicians and patients hope for early diagnosis and better treatment strategies. © 2023 IEEE.

关键词： Peptides

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Efficient & Sustainable Intrusion Detection System Using machine learning & Deep learning for IoT 4

Efficient & Sustainable Intrusion Detection System Using Mac...

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4th international conference on Computing, Mathematics and Engineering Technologies, iCoMET 2023

作者： Amir, Muhammad Sarim Bhatti, Gufran Anwer, Misbah Iftikhar, Yumna Karachi Institute of Economics and Technology Dept. of Computer Science Karachi Pakistan

ISBN: (纸本)9798350335316

Everything is evolving toward IoT (Internet of things) and online-based in our technological environment. the number of IoT devices and ubiquitous computing systems are growing exponentially. this also increases the risk of network breach. To cater this issue many researchers proposed different techniques and get great results but it can be better since everything in online and it's a matter of security and privacy. this paper presents an efficient and sustainable intrusion detection system by the concatenation of two well-known state of the art 'kitsune' datasets (ARP MITM and SSDP Flood). Random Forest, decision tree, and Bi-LSTM (Bi-Directional Long Short Term Memory) were implemented in different training and testing ratios and different numbers of layers. Performance measures show that all the models achieved over 99% accuracy but random forest outperforms both models on the concatenated dataset. Both attacks are determined by the given model hence increasing the performance and the system will notify in case of any malicious activity. © 2023 IEEE.

关键词： Internet of things

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Benchmarking of Deep learning Techniques in FDIA 4

Benchmarking of Deep Learning Techniques in FDIA

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4th international conference on Distributed Sensing and Intelligent Systems, ICDSIS 2023

作者： Alsaid, Basma Badway, Youssef Waraga, Omnia Abu Talib, Manar Abu Nasir, Qassim Department of Electrical Engineering University of Sharjah Sharjah United Arab Emirates Department of Computer Science University of Sharjah Sharjah United Arab Emirates Department of Computer Engineering University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9781839539954

False Data Injection Attacks (FDIA) are among the most significant security threats to critical systems. In this paper, we present a comprehensive benchmarking study to evaluate the performance of four deep learning models for detecting FDIA. the study covers an analysis of three diverse datasets representative of real-world scenarios. Our evaluation leverages a comprehensive set of metrics, including precision, recall, accuracy, F1 score, ROC analysis, and precision-recall plots. the primary objective is to identify the optimal model that exhibits robust performance across various attack scenarios. Subsequently, the selected model undergoes in-depth examination through the application of Principal Component Analysis (PCA) to ascertain the influence of dimensionality reduction on its efficacy. the results of this study enhance the understanding of deep learning capabilities in FDIA detection and lay the foundation for the development of advanced detection and defense mechanisms. © the Institution of Engineering & Technology 2023.

关键词： Contrastive learning

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Deep learning Approaches for Plant Disease Management: A State-of-the-Art Analysis 7

Deep Learning Approaches for Plant Disease Management: A Sta...

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7th IEEE international conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023

作者： Reddy E, Mahendra Nath Rananavare, Laxmi B School of Pecs University of Hertfordshire Hatfield United Kingdom School of Cse Reva University Bangalore India

ISBN: (纸本)9798350343144

Plant diseases pose a serious threat to agriculture, resulting in substantial financial losses and limiting the availability of food. Manually recognizing and classifying plant diseases may require an extensive amount of time and expertise in plant pathology. therefore, automated disease recognition is essential for efficient disease management. this review paper analyses the state-of-the-art in the detection and classification of the diseases in the plants, with a focus on the use of computer vision, machine learning, and other techniques. this paper offers an in-depth review of several disease detection and classification approaches, including deep learning-based applications like Yolo and Conventional Neural Networks (CNN). the limitations of the systems and techniques that are now in use are also addressed by this survey. According to the review, automatic diagnosis and classification of plant diseases have shown promising outcomes through the application of computer vision and deep learning techniques. However, there are still challenges like poor dataset quantity, quality, and generalization of the classification model. © 2023 IEEE.

关键词： computer vision

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