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Optimizing Mental Health Status Prediction Models Using Machine learning Algorithms

Optimizing Mental Health Status Prediction Models Using Mach...

引用

Data Analytics, Computing and Artificial Intelligence (ICDACAI), International conference on

作者： Dong Liu Guangdong Polytechnic of Science and Technology Zhuhai China

ISBN: (数字)9798331519032

ISBN: (纸本)9798331519049

In contemporary society, mental health concerns are gaining heightened attention, and accurately forecasting individuals’ mental well-being has become a pivotal research focus. Traditional predictive models for mental health often struggle with issues like the complexity of data processing and insufficient prediction precision. However, the application of machine learning algorithms introduces new approaches to address these challenges. In the age of artificial intelligence, creative works are high-quality data sources for machine learning, raising crucial questions about copyright regulations. Balancing these rules to foster innovation in both cultural and technological sectors is a pressing issue. This study employs various machine learning techniques, such as support vector machines, decision trees, and deep learning algorithms, to thoroughly examine and evaluate mental health data. As artificial intelligence technology evolves rapidly and reshapes its applications, new research themes and methodologies continuously emerge within machine learning, with advances in deep learning and reinforcement learning driving the field forward. This paper not only reviews the essential techniques in the optimization process but also explores future research avenues and potential applications, aiming to provide valuable insights for ongoing research and practical efforts in mental health prediction.

关键词： Training Machine learning algorithms Soft sensors Mental health Predictive models Feature extraction Data models Stability analysis optimization Thermal stability

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A Multi-Model Approach to Oral Pathology Classification and Dental X-Ray Analysis

A Multi-Model Approach to Oral Pathology Classification and ...

引用

intelligent Data Communication Technologies and Internet of Things (IDCIoT), International conference on

作者： M.R. Sumalatha M S Tejasree S Sowmiya D Parthasarathi M Sathish Kumar Department of Information Technology Anna University Chennai India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The timely detection and accurate localization of oral diseases, such as oral cavity cancer and dental anomalies, is critical for improving preventive dental care and patient outcomes. This paper presents an AI-based system that employs a dual-architecture approach for analyzing oral and dental x-ray images. An ensemble of ResNet-50, EfficientNet-B1, and InceptionResN etV2 is used for oral disease classification, leveraging their complementary strengths to ensure accurate predictions across various conditions. For dental X-ray analysis, the Detection Transformer (DETR) model is utilized to detect and localize abnormalities in complex radiographic structures. This integrated system enhances diagnostic accuracy and provides early detection, treatment suggestions, and clinical decision support through a user-friendly interface and chatbot. The proposed approach improves accessibility and efficiency in oral health diagnosis, contributing to better patient outcomes and facilitating early intervention in dental care.

关键词： Deep learning Pathology Analytical models Accuracy Medical services Transformers Chatbots Dentistry Ensemble learning X-ray imaging

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Effect of Cosine Decay Restart learning Rate Scheduler on Movie Recommender System 3rd

Effect of Cosine Decay Restart Learning Rate Scheduler on ...

引用

3rd International conference on Machine Intelligence and Smart Systems, MISS 2023

作者： Airen, Sonu Agrawal, Jitendra SOIT RGPV Bhopal India

ISBN: (纸本)9783031691140

Recommender System is an information filtering tool to filter the relevant information from given information in the present era of big data. Movie Recommender System is machine learning based autonomous tool that filters the movies from big movie database like Netflix, Amazon etc. according to user preferences. learning Rate Scheduler is optimization technique in Deep Neural Network. learning rate is most important hyper-parameter for Deep Neural Network training. The main focus of this paper is to propose learning Rate Scheduler for Movie Recommender System. The primary objective of this research article is to find effect of Cosine Decay Restart learning Rate Scheduler on Movie Recommender System. Test results obtained from the Movie database show that the proposed method can bring more accurate personalized recommendations for the movie as compared to existing methods of the order of 2.224%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Architecture Design of Flexible Production Line Control System for Reconfigurable Operations and Maintenance

Architecture Design of Flexible Production Line Control Syst...

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Cloud Computing, Big Data Applications and Software Engineering (CBASE), International conference on

作者： Yangyang Zhang Bo Xu Dong Wang Xing Dang Tianjin Institute of Aerospace Mechanical and Electrical Equipment Tianjin China

ISBN: (数字)9798331506582

ISBN: (纸本)9798331506599

Thereis an urgent need for flexible production lines and other control systems, such as uncertain production scale, rapid iteration and optimization of business processes and structured data packages. It is urgent to design and package flexible systems and services with business as the core, to achieve “on-demand use, on-demand adaptation”. This article starts with the requirements and architecture design of a flexible satellite batch production line control system, abstracts the physical system of intelligent equipment into a digital model, proposes a solution of “business domain+middleware platform+intelligent equipment information model”, designs an open software architecture for a microservice based control system, constructs a rule-based process configuration method, decouples business and hardware, and can efficiently match different business scenarios. The microservice partitioning of intelligent warehousing systems, intelligent distribution systems, and production management systems will be iteratively configured based on the business complexity of the system application scenarios and the microservice partitioning methods; The middleware platform is a bridge for information exchange between business domains and intelligent equipment. It digitizes the information model of intelligent equipment and interacts with business data for processing, obtaining standard and effective data for traceability and display purposes; The intelligent equipment information model forms a standardized solution for the interconnection and intercommunication of equipment and workshop information systems, ensuring the interconnection and intercommunication of on-site equipment and solving the problem of communication protocols not being open or standardized.

关键词： Satellites Software architecture Warehousing Microservice architectures Process control Production Computer architecture Control systems optimization Business

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optimization of Transmission Network Structure Based on Deep Neural Networks

Optimization of Transmission Network Structure Based on Deep...

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Energy Engineering and Power Systems (EEPS), International conference on

作者： Weiqi Zhang Lixin Li Yijun Yu Power Automation Department China Electric Power Research Institute Beijing China

ISBN: (数字)9798350375138

ISBN: (纸本)9798350375145

Real-time transmission line switching control is a cost-effective and efficient measure to alleviate line overload. However, due to the nonlinear characteristics of the transmission network structure optimization problem, conventional optimization methods often struggle to find solutions quickly. In recent years, data-driven methods have been widely applied in fields such as power system dispatch optimization. This paper proposes a method based on deep neural networks. By using historical and simulation data of the power system, the neural network is trained to perform exploratory learning and find optimal topology control strategies when line overload occurs. This method is trained and tested on the IEEE 14-bus open-source dataset, and the test results verify its effectiveness.

关键词： Adaptation models Network topology Power system dynamics Artificial neural networks Switches Transmission line measurements Real-time systems Power grids Topology Load modeling

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Antenna optimization based on genetic algorithm

Antenna optimization based on genetic algorithm

引用

Applied Intelligence and Computing (AIC), IEEE World conference on

作者： Onur Duzel Bilal Saoud Ibraheem Shayea Gulsim N. Tulepova Dept. Electronics & Communications Engineering Faculty of Electrical and Electronics Engineering Istanbul Technical University (ITU) Istanbul Turkey Dept. Electrical Engineering Faculty of Sciences and Applied Sciences LISEA Laboratory University of Bouira Bouira Algeria Dept. Intelligent Systems and Cybersecurity Astana IT University Astana Kazakhstan

ISBN: (数字)9798350384598

ISBN: (纸本)9798350384604

In modern communication systems, optimizing antenna phase is crucial for improving signal quality and reducing interference. Genetic algorithms (GAs) are frequently employed for this purpose due to their ability to avoid local minima and identify global solutions within large search spaces. This survey provides a comprehensive review of existing research on antenna phase optimization using genetic algorithms. It examines the strengths and weaknesses of current techniques, analyzes various methodologies and their outcomes reported in the literature, and critically compares the performance of genetic algorithms with other optimization methods. This survey aims to guide future research and serve as an extensive resource for engineers and researchers in the field.

关键词： Surveys Reviews optimization methods Interference Directive antennas Market research Computational efficiency Antennas Tuning Genetic algorithms

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An intelligent path selection algorithm based on deep reinforcement learning 3

An intelligent path selection algorithm based on deep reinfo...

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2021 3rd International conference on Artificial Intelligence Technologies and Applications, ICAITA 2021

作者： Sun, Mengchen College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211006 China

Path selection is the most important algorithm in intelligent devices such as robots. At present, the traditional path-planning algorithm has achieved some results, but it lacks the ability of environmental perception and continuous learning. In order to solve the above problems, this paper proposes an intelligent path selection algorithm based on deep reinforcement learning, which uses the learning ability of deep learning and the decision-making ability of reinforcement learning to realize the autonomous path planning of robots and other equipment. Simulation results show that the proposed algorithm has faster convergence, efficiency and accuracy. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Reinforcement learning

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Automated Pneumonia Detection using deep features in chest X-ray images

Automated Pneumonia Detection using deep features in chest X...

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International conference on intelligent Communication and Computational Techniques (ICCT)

作者： Taoufik Ouleddroun Ayoub Ellahyani Mohamed El Ansari Department of Computer Science Faculty of Sciences Informatics and Applications Laboratory University of Moulay Ismail Meknès Morocco Department of Computer Science LabSIV multidisciplinary Faculty Ibn Zohr University Ouarzazate Morocco Department of Computer Science LabSIV Faculty Of Sciences Ibn Zohr University Agadir Morocco

Pneumonia is swelling of the lungs that is usually caused by an infection. This disease is considered as one of the most common reasons for US children to be hospitalized. According to American Thoracic Society (ATS), the cost of treating pneumonia cases in hospitals reached 9.5 billion dollar. The appropriate treatment and recovery process for this disease are linked to early diagnosis. In this work a novel method is proposed for detecting the pneumonia and help the radiologists in their decision making process. First, histogram equalization (HE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) are calculated for chest X-ray images. Then, the images extracted are fed to a model consisting of two stream of Convolutional Neural Networks (CNN) that was trained on the Pneumonia Kermany dataset. Finally, several machine learning classifiers are employed to perform the detection process based on the deep features extracted. The proposed system achieves 97.86% in terms of accuracy on the Kermany dataset, which is satisfactory when compared to recently published works.

关键词： Histograms Pulmonary diseases Lung Machine learning Adaptive equalizers Streaming media Feature extraction

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intelligent Health Assessment System for Paddy Crop Using CNN 3

Intelligent Health Assessment System for Paddy Crop Using CN...

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3rd International conference on Signal Processing and Communication (ICPSC)

作者： Sankar, Pagadala Rohit Sai RamaKrishna, Siva D. . P. S. Rakesh, Mutyala Mani Venkata Raja, P. Vinh Truong Hoang Szczepanski, Cezary SASTRA Deemed Univ Sch Mech Engn Thanjavur 613401 Tamil Nadu India Ho Chi Minh City Open Univ Fac Comp Sci Ho Chi Minh City Vietnam Lukasiewicz Res Network Inst Aviat Warsaw Poland

ISBN: (纸本)9781665428644

Crop cultivation plays an essential role in the agricultural field. Presently, the loss of food is mainly due to infected crops, which reflexively reduces the production rate. Health monitoring and disease detection on plant is very critical for sustainable agriculture. It is very difficult to monitor the plant diseases manually. It requires a tremendous amount of work as well as expertise. Hence, an intelligent crop health assessment system using deep learning based on Convolutional Neural Networks (CNN) was proposed. The steps involved are image acquisition, pre-processing, data augmentation and classification. Image of the plant is captured using a smartphone with a camera. Captured images are pre-processed. Data augmentation has been done on the training data set. Implementation of CNN model yielded an accuracy of 85%. The model has been tested against a set of images collected manually.

关键词： Health monitoring Health assessment Deep learning Data augmentation Classification Training data set

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Design and optimization Algorithm of Interactive Education System Based on Virtual Reality Technology

Design and Optimization Algorithm of Interactive Education S...

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Artificial Intelligence and Autonomous Robot Systems (AIARS), International conference on

作者： Xiaoli Lei Tongcheng Teachers College Tongcheng Anhui China

ISBN: (数字)9798350376173

ISBN: (纸本)9798350376180

With the continuous improvement of education level, the implementation of interactive Virtual Reality (VR) teaching has become the most concerned issue. In the interactive education system, in order to provide students with more personalized learning experience and cognition, a large amount of data must be processed and analyzed. This includes students' historical performance, students' learning feedback, course design and data analysis, and the powerful computing and storage functions of computers, which provide conditions for this work. This paper constructs an interactive virtual education system based on virtual reality technology. The personalized interactive module in the system is conducive to improving learning efficiency and enhancing the interactivity, experience and practicality of the teaching platform. When the number of system response requests is 20, the system response delay in this paper is 0.34s, while the traditional system response delay is 0.58s. This paper provides some reference for the development of virtual reality teaching in schools, and provides some reference for the application of virtual reality technicians in practice.

关键词： Computers Technological innovation Data analysis Education Virtual reality Reliability theory Reliability engineering Cognition Delays optimization

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