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检索条件"任意字段=2023 International Conference on Computer Engineering and Distance Learning, CEDL 2023"

共 40628 条记录，以下是4981-4990 订阅

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Cotton Plant Disease Detection Using CNN 8

Cotton Plant Disease Detection Using CNN

引用

8th IEEE international conference for Convergence in Technology, I2CT 2023

作者： Bera, Soumyajit Kumar, Puneet Chandigarh University Department of Computer Science and Engineering Mohali India

ISBN: (纸本)9798350334012

The economy and everyday existence of our country are fundamentally reliant on agriculture, but diseases like anthracnose and crown gall, caused by fungi and bacteria, have recently reduced agricultural production. While these diseases can be treated if detected and addressed in a timely manner, it can be difficult to manually identify them over large areas of land. However, with the advent of intelligent and science-based technologies like machine learning and artificial intelligence (AI), it is now possible to develop quick and accurate methods for diagnosing agricultural diseases. In this study, we focus on using CNN modeling to identify and classify diseases affecting cotton plants. © 2023 IEEE.

关键词： Diagnosis

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Reinforcement learning Based Joint Resource Allocation and User Fairness Optimization in mmWave-NOMA HetNets 31

Reinforcement Learning Based Joint Resource Allocation and U...

引用

31st international conference on Electrical engineering, ICEE 2023

作者： Sobhi-Givi, Sima Nouri, Mahdi Shayesteh, Mahrokh G. Kalbkhani, Hashem Ding, Zhiguo Urmia University Electrical and Computer Engineering Department Urmia Iran Sharif University of Technology Elect. Eng. Dep. Iran Iran Tehran Iran Sharif University of Technology Wireless Research Lab Acri Elec. Eng. Dep. Tehran Iran Urmia University of Technology Faculty of Elec. Eng. Urmia Iran School of Electrical and Electronic Engineering The University of Manchester ManchesterM13 9PL United Kingdom

ISBN: (纸本)9798350312560

In this paper, we propose heterogeneous network (HetNet) with mmWave and hybrid non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) transmission, where uplink (UL) of macrocell users (MCUs) and downlink (DL) of small cell users (SCUs) share the same resource block (RB) to increase the network capacity. Also, an imperfect successive interference cancelation (I-SIC) decoding is considered due to hardware impairment of the real-world NOMA systems. We formulate the problem of joint power and resource block (RB) allocation to maximize rate-fairness and sum-rate by considering minimum quality of service (QoS) requirements. Reinforcement learning (RL) named Q-learning (QL) and deep Q-learning network (DQN) algorithms are employed to solve these problems. The results indicate that the proposed QL and DQN algorithms increase the fairness and sum-rate while low number of iterations is required. © 2023 IEEE.

关键词： Reinforcement learning

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Domestic Violence Detection System Using Natural Language Processing

Domestic Violence Detection System Using Natural Language Pr...

引用

2023 international conference on Innovations in engineering and Technology, ICIET 2023

作者： Fathima, K.S. Haroon, Rosna P. Fathima, Fitha Thajudeen, Thameesa Fudin, Muhammedshera Ilahia College of Engineering and Technology Department of Computer Science and Engineering Kerala Muvattupuzha India

ISBN: (纸本)9798350309980

Domestic violence is a widespread problem that leaves victims feeling helpless and trapped. It refers to a pattern of abusive behavior in an intimate relationship, where one partner seeks to exert power and control over the other. To address this issue, we proposed a system that incorporates several advanced technological aspects to address domestic violence and support victims. It includes a speech recognition module that identifies abusive behavior patterns through specific words and phrases associated with domestic violence. Natural language processing techniques analyze the context and tone of the language to understand the emotional state of the victim and detect instances of emotional abuse. A camera system with computer vision capabilities detects physical violence and abuse signs through object detection and tracking in video footage. The system is accessible through a mobile app, enabling victims to collect evidence of abuse and access necessary resources and support services. Machine learning algorithms continuously improve the system's accuracy and performance by learning from a large dataset of abusive language patterns and visual cues. When abusive behavior is detected, the system can automatically send alerts to law enforcement and emergency contacts, providing immediate assistance. Recorded voice and video footage, along with associated analysis results, are securely stored as evidence for legal proceedings. By integrating these technological aspects, the system aims to empower victims, improve evidence collection, and support efforts to combat domestic violence. © 2023 IEEE.

关键词： Object detection

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MBTI-based recommendation system for extracurricular activities for high school students 17

MBTI-based recommendation system for extracurricular activit...

引用

17th international conference on Electronics computer and Computation, ICECCO 2023

作者： Zhalgassova, Zhadyra Shaikym, Amina Sadyk, Ualikhan Kutzhan, Aruzhan Amirkumar, Mukhtar Assangali, Bekassyl Suleyman Demirel University Department of Computer Science Kaskelen Kazakhstan

ISBN: (纸本)9798350339611

This paper proposes MBTI-based recommendation system for extracurricular activities for high school students. The system extracts personality types from student profiles using feature engineering and utilizes machine learning algorithms to recommend relevant extracurricular activities. We evaluate the system's performance using precision, recall, and F1-score metrics and compare it with other state-of-the-art recommendation systems. Our results show that the MBTI-based recommendation system outperforms other systems and provides a more personalized approach to activity selection. We also discuss the potential impact and benefits of the system for high school students and identify areas for future research. Overall, this paper contributes to the growing body of literature on recommendation systems and highlights the importance of considering personality types in the recommendation process. © 2023 IEEE.

关键词： Recommender systems

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Blending Forest with Neural Networks: An Insight into Marula Leaf Disease Prediction 4

Blending Forest with Neural Networks: An Insight into Marula...

引用

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

作者： Kaur, Arshleen Kukreja, Vinay Manwal, Manika Joshi, Kireet Jain, Vishal Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun248002 India Graphic Era Deemed to Be University Computer Science and Engineering Uttarakhand Dehradun248002 India Sharda School of Engineering and Technology Sharda University Department of Computer Science and Engineering UP Greater Noida India

ISBN: (纸本)9798350306118

The economic value of Marula trees is investigated here by looking at the impact of disease on fruit production. The prediction of eight major Marula diseases is made possible with the introduction of a novel technology that combines Convolutional Neural Networks (CNNs) and Random Forests. This model incorporates the feature-extracting power of a CNN with the robustness of a Random Forests ensemble. It uses four ReLU-Activated convolutional layers and a max-pooling layer to process the data, which consists of photos of both healthy and diseased Marula leaves. These features are fed into Random Forest, which uses them to make diagnoses. The model's strong predictive capacity is demonstrated by evaluation measures showing precision and recall rates between 88.65% and 94.92%. Overall accuracy is 98%, and the Fl-Score indicates a well-balanced performance from the model. Metrics like 'Macro Average' show how much better the model performs than other approaches. The study demonstrates the potential of combining deep and ensemble learning for disease prediction, and hence makes a substantial contribution to agricultural disease management. It lays the groundwork for the sustainable production of Marula and highlights the broader implications for global food security and future agricultural innovation based on machine learning. © 2023 IEEE.

关键词： Forecasting

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Research on Human Body Features Extraction based on Attention Mechanism 4

Research on Human Body Features Extraction based on Attentio...

引用

4th international conference on computer Vision, Image and Deep learning, CVIDL 2023

作者： Wu, Yuheng Wu, Liangyu Feng, Hao Kong, Lei School of Management Guangdong University of Technology GuangZhou China School of Electro-mechanical Engineering Guangdong University of Technology GuangZhou China

ISBN: (纸本)9798350326444

Human body features are widely used in existing downstream applications including medical health, behaviour predictions. The existing features extraction methods are concentrated on the deep learning optimization process, which ignore the important locations on human body features and cause the trained learning model can not precisely extract the fundamental body features. In this work, we initially utilize objective detection algorithm to identify the target area of human body and submit the detection results to the feature extraction module. Subsequently, attention mechanism to concentrate graph convolutional neural network on the body joints and motivate the model to generate precise extraction results. Finally, we utilize the MSCOCO data-set to evaluate our proposed model and compare with most used feature extraction algorithms. From our extensive simulation results, we can significantly conclude that our proposed attention mechanism can achieve the effectiveness and high accurateness of body features extraction through comparing with existing extraction methods in the identical simulation environments and data-sets. © 2023 IEEE.

关键词： Feature extraction

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Designing User Experience (UX) for Special Education: Principles, Practices, and Challenges 17

Designing User Experience (UX) for Special Education: Princi...

引用

17th international conference on Electronics computer and Computation, ICECCO 2023

作者： Pradeep, Aneesh New Uzbekistan University Software Engineering Tashkent Uzbekistan

ISBN: (纸本)9798350339611

For educators, families, and education providers, the enrollment and education of pupils with disabilities in the general education classroom have been challenging and polarizing. The trend of enrolling disabled students in general and specialized education classes will continue, and educators are looking for teaching strategies that enhance inclusion. With a growing emphasis on inclusive education, it is important to ensure that learners with disabilities or special needs have equal access to educational resources and opportunities. However, designing UX for special education presents unique challenges that must be addressed to create a positive and effective learning experience for all learners. The purpose of this paper is to address the above concerns and give helpful strategies for inclusive education by using 'User-sensitive inclusive design' that is flexible enough to accommodate a greater spectrum of pupils and fulfill their educational needs regardless of their mental health. The importance of user-sensitive, inclusive design in special education is highlighted and provides insights and recommendations for designina effective and inclusive UX for all learners. © 2023 IEEE.

关键词： Human computer interaction

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Application of Deep learning Based Longitudinal Tear Detection in Intelligent Main Transportation 3

Application of Deep Learning Based Longitudinal Tear Detecti...

引用

3rd international conference on computer Science, Electronic Information engineering and Intelligent Control Technology, CEI 2023

作者： Yu, Zhiqiang Automation Co. Ltd. Changzhou213003 China Middling Coal Science and Engineering Group Changzhou Research Institute Co. Ltd. Changzhou213003 China

ISBN: (纸本)9798350372052

The current traditional device for detecting longitudinal tearing of belts has a low level of intelligence, and the triggering mechanism is inherently prone to missed and false detections. This paper designs a longitudinal tear detection system based on deep learning, which takes YOLOv5s model as the deep learning model and low power consumption edge computing module as the carrier. The industrial intrinsic safety camera is used to obtain the image, which is transferred to the edge computing module for tear feature detection. The longitudinal tear length is calculated by fusing the belt speed sensor data, and only the external detection results are finally output. When the detected longitudinal tear length exceeds a certain threshold, the belt conveyor collaborative controller will control the sound and light alarm to give an alarm, and at the same time issue a shutdown command to the specific single controller. In addition, the edge computing module uploads the video stream and alarm data after image processing to the control platform for reference. This system deepens the intelligence level of longitudinal tear detection for the main conveyor belt in coal mines, ensures the safety of the coal production process, improves production efficiency, and reduces economic losses. © 2023 IEEE.

关键词： Belt conveyors

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Adaptive Path Planning for Autonomous Vehicles in Complex Traffic Scenarios

Adaptive Path Planning for Autonomous Vehicles in Complex Tr...

引用

2023 international conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Durgabhavani, B. Reddy Muvva, Vijaya Bhaskar Leo Joseph, L.M.I. Babu, Duvvapu Mahesh Manohara, H.T. Malla Reddy Engineering College for Women It Department Hyderabad India University of Technology and Applied Sciences Department of Information Technology Shinas Oman Sr University School of Engineering Department of Ece Warangal India Cmr Technical Campus Department of Information Technology Hyderabad India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350313413

Over the past decades, an Autonomous Vehicles (AVs) driving functions have obtained the more interests from both the industry and academia. The AVs needed a basic planner for all the practicable scenarios and the recent research develops named a planner through the integrated scenario interpretation. Nevertheless, it may remarkably enhance the planner difficulty indeed in some simple operations. Examining the safety of AVs is the significant challenge in relation to their distribution on the roads because of large number of achievable conditions happened in traffic. In this research, the adaptive path planning approach of AV established multi-light trained Reinforcement learning (RL) is proposed, aimed to enhance the fuel cost as well as AV comfort. By utilizing a logical Deep Q-learning function, the training approach obtains the key setting data collected through the vehicles as inputs, eventually outcomes an acceleration that enhances a cumulative reward. The proposed method attains better results by using the performance metrices of average acceleration, distance and computational time values about 0.149, 0.98 and 0.49 respectively compared with Pertinent Boundary-based Unified Decision (PBUD), Adaptive Mode Predictive Control (MPC) and Dynamic Decoupling Lane-Changing (DDLC). © 2023 IEEE.

关键词： Adaptive Speed Planning Autonomous Vehicles Deep Q-learning Multi-light trained model Reinforcement learning

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Feature Selection in the Classification of Erythemato-Squamous Diseases using Machine learning Models and Principal Component Analysis 15

Feature Selection in the Classification of Erythemato-Squamo...

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15th Biomedical engineering international conference, BMEiCON 2023

作者： Akarajarasroj, Thumpussorn Wattanapermpool, Ohmthong Sapphaphab, Piyaon Rinthon, Oraya Pechprasarn, Suejit Boonkrong, Pichit Satriwithaya School Bangkok Thailand Rangsit University College of Biomedical Engineering Lak Hok Pathum Thani12000 Thailand

ISBN: (纸本)9798350345247

Erythemato-squamous diseases (ESD) are dermatological diseases that significantly impact the quality of life of an increasing number of patients worldwide. This study used a publicly available clinical dataset of 366 patients from the Department of computer engineering and Information Science, Bilkent University with 34 predictors contributing to the classification of ESDs. The data was curated to ensure unbiasedness and accuracy before applying principal component analysis and machine learning models to identify crucial factors in classifying the six main types of ESDs. 31 different machine learning models, including Tree, Linear Discriminant, Quadratic Discriminant, Naïve Bayes, SVM, KNN, Ensemble, Neural Network, and Kernel were trained, validated, and the classification accuracy was compared. The model that is the most adequate is the Fine KNN, which has the highest cross-validation classification accuracy at 100%. This model requires only eight predictors: itching, the Koebner phenomenon, follicular papules, fibrosis of the papillary dermis, spongiosis, inflammatory mononuclear infiltrate, bandlike infiltrate, and age. © 2023 IEEE.

关键词： Principal component analysis

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