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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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Revolutionizing Human–computer Interaction: Unraveling the Power of Deep learning Convolutional Neural Networks in Face Recognition 4th

Revolutionizing Human–Computer Interaction: Unraveling the ...

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4th international conference on Advances in Computational Science and Engineering, ICACSE 2023

作者： Baharum, Aslina Halamy, Suhaida Ismail, Rozita Abdul Rahim, Emelia Mat Noor, Noorsidi Aizuddin Deris, Farhana Diana Department of Computing and Information Systems School of Engineering and Technology Sunway University Bandar Sunway Selangor Malaysia School of Information Science College of Computing Informatics and Mathematics Universiti Teknologi MARA Cawangan Sarawak Kota Samarahan Malaysia College of Computing and Informatics Universiti Tenaga Nasional Putrajaya Campus Kajang Malaysia Faculty of Business Management Universiti Teknologi MARA Cawangan Sarawak Sarawak Kota Samarahan94300 Malaysia Mass Appraisal Housing and Planning Research Group Centre for Real Estate Studies Real Estate Department Faculty of Built Environment and Surveying Universiti Teknologi Malaysia Johor Johor Bahru Malaysia Faculty of Social Sciences and Humanities Universiti Teknologi Malaysia Johor Johor Bahru Malaysia

ISBN: (纸本)9789819729760

Face recognition, being one of the most effective applications of image analysis, has recently received a lot of attention due to the huge implication in human–computer interaction (HCI). As the availability and eligibility to detect a person’s facial features, face recognition technology has been used in biometric detection applications as uses certain aspects of a person’s physiology to identify them. In addition, Deep learning, under the subset of machine learning, can solve various problems, especially in image processing and face recognition. the advancement and development of Deep learning can also enhance the use of the Convolution Neural Network (CNN) as the predominant model in the field of face recognition. the paper discusses the systems based on CNN approaches and algorithms and provides a review of the CNN face recognition approach. Furthermore, each paper’s details, such as used datasets, techniques, architecture, and obtained findings, hence the application are fully summarized and analyzed. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Enhancing Maize Crop Health: Deep learning Approach for Disease Detection and Classification Using Leaf Images 4th

Enhancing Maize Crop Health: Deep Learning Approach for Dise...

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4th international conference on Artificial Intelligence and Knowledge Processing

作者： Nyange, Roseline Chipofya, Mapopa Gota Goel, Srishti Ashoka, S. B. Chola, Channabasava Jeonbuk Natl Univ Dept Comp Sci & Artificial Intelligence Jeonju Si 54896 South Korea Jeonbuk Natl Univ Dept Elect & Informat Engn Jeonju Si 54896 South Korea Kyung Hee Univ Dept Chem Engn Integrated Engn 1732 Deogyeong Daero Yongin 17104 Gyeonggi Do South Korea Maharani Cluster Univ Dept Comp Sci Maharani Sci Coll Women Bangalore Karnataka India Univ Mysore Dept Studies Comp Sci Mysore 570006 Karnataka India Univ Mysore Mysore Univ Sch Engn MUSE Dept Artificial Intelligence & Machine Learning Mysore 570006 Karnataka India

ISBN: (纸本)9783031734762;9783031734779

the agriculture sector in sub-Saharan Africa faces significant challenges in growing vital food security crops like maize due to crop diseases. In response, we introduce a robust deep learning model for accurately identifying diseased and healthy maize leaves using leaf images. Specifically, our model distinguishes betweenmaize leaves affected by Maize LethalNecrosis (MLN), Maize Streak Virus (MLV) and healthy ones. We utilize the ResNet architecture, a well-established convolutional neural network known for its exceptional performance in computer vision tasks. Our experiments demonstrate that using a pretrained ResNet model achieves remarkable accuracy, exceeding 99%, in distinguishing between diseased and healthy maize leaves. Importantly, we find that employing a pre-trained Deep learning (DL) model eliminates the need for extensive training from scratch, affirming the effectiveness of Transfer learning (TL) in this field. Furthermore, our model proves its robustness by effectively handling diverse variations in leaf appearance, lighting conditions, and disease symptoms. this research offers practical tools for early disease detection, aiding farmers, and agricultural experts, ultimately enhancing maize production. Our results highlight the potential of harnessing DL techniques to address critical challenges in agriculture and food security.

关键词： maize streak virus maize lethal necrosis convolutional neural networks deep learning

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Next-Gen Cucurbit Disease Diagnosis: A Federated learning CNN Approach 12

Next-Gen Cucurbit Disease Diagnosis: A Federated Learning CN...

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12th international conference on System Modeling and Advancement in Research Trends, SMART 2023

作者： Suryavanshi, Ankita Kukreja, Vinay Srivastava, Prateek Mehta, Shiva thapliyal, Siddhant Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science and Engineering Graphic Era Hill University Uttarakhand Dehradun India Computer Science and Engineering Graphic Era Deemed to Be University Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350369861

this study adopts a novel strategy using federated learning with a Convolutional Neural Network (CNN) to identify and categorise Cucurbit leaf diseases. the research includes information from six customers, each with access to distinctive, local datasets of Cucurbit leaf illnesses divided into four severity categories, ranging from 1% to 25% to 50% to 75% to 100%. the use of federated averaging, which combines local model updates into a global one, is a crucial component of this work. this approach highlights a significant development in machine learning by using dispersed data without data centralization. the findings show that the model performed well, with accuracy, recall, and F1-scores ranging from 85.93% to 94.85% across all customers, illuminating the model's strength and adaptability. the performance of the client model Cltt-3 was outstanding, with measures averaging a startling 95%. Additionally, findings from macro, micro, and weighted averages were similarly exceptional, demonstrating the model's adaptability to various class distributions and severity levels. In summary, this work shows the impressive effectiveness of federated learning with CNN in combating Cucurbit leaf diseases and sketches out possible application scenarios. the reliable findings confirm the efficacy of federated learning in a decentralized data environment while opening up fresh opportunities for the global model to be improved using lessons from local datasets. © 2023 IEEE.

关键词： Convolutional neural networks

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A Multi-Criteria Intelligence AID Methodology and IoT Based Data Protection Using machine learning 8

A Multi-Criteria Intelligence AID Methodology and IoT Based ...

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8th IEEE international conference on Science, Technology, Engineering and Mathematics, ICONSTEM 2023

作者： Manikandan, K.P. Anusha, P. Jayaj, Arockia Pundir, Sumit Rammohan, K. Adhikary, Priyabrata Madanapalle Institute of Technology & Science Department of Computer Science Engineering AndhraPradesh Madanapalle517325 India R.M.K. Engineering College Department of Electronics and Communication Engineering Tamilnadu Chennai India Idhaya Engineering College for Women Department of Computer Science and Engineering Tamilnadu Kallakurichi India Graphic Era Deemed to Be University Department of Computer Science Engineering Uttrakhand Dehradun India St.Martin's Engineering College Department of Computer Science and Engineering Telangana Secunderabad India New Horizon College of Engineering Department of Mechanical Engineering Karnataka Bangalore India

ISBN: (纸本)9798350347791

IoT-powered devices have become one of the key companions of humans in recent times. Almost every sector of the modern market is heavily reliant on IoT-powered devices such as smartphones, computers and other smart gadgets. this has resulted in the massive availability of digital data containing the personal information of the users. this has caused a massive issue regarding data privacy on IoT-powered devices in recent times. this has resulted in the application of ML-based technologies for fostering better data protection. the concerned study has focused on the role of the different ML-powered technologies such as federated learning and differential learning models. thai entre study has also focused on numerical analysis of the issues associated with the mentioned models during data protection. It has also provided survey data regarding different FL-based algorithms such as LM, NN, NM, DT and CM. this study has analyzed the issues and strengths of ML-based data protection on local and global data sets. © 2023 IEEE.

关键词： Data privacy

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Classification of Ethnic Dress Based on ResNet 4

Classification of Ethnic Dress Based on ResNet

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4th international conference on Advances in Electrical Engineering and computer applications, AEECA 2023

作者： Fang, Yangyang Zhang, Jie Fang, Jiemin Southwest Minzu University College of Electronic Information Sichuan Province Chengdu China Fujian Jiangxia University College of Electronic Information Science Fujian Province Fuzhou China

ISBN: (纸本)9798350308082

Deep learning is widely used to process picture information. Chinese ethnic culture is colorful, and ethnic costume is one of the most symbolic representatives. In order to solve the problem of time-consuming and laborious manual classification, this paper uses transfer learning to put forward a method of ethnic costume classification based on ResNet. the convolution neural network is used to extract the features of different ethnic costumes to realize the classification. In the experiment, we first used ResNet, AlexNet, LeNet and GoogLeNet for training, and found that ResNet had the best effect, but the initial recognition accuracy was not high, the primary reason was insufficient data, so we first expanded the data set, and established a national costume data set with about 10,000 pictures. 70% of them are used as the training set and 30% as the test set. After image preprocessing, it is cut to the size of 300∗300∗3. the learning rate decay strategy is then introduced along with a deeper ResNet network - ResNext that is stacked from Inception modules. the final experimental results show that the recognition accuracy of ResNet18 is the highest, and its accuracy rate, recall rate and f1-score are about 0.6. this experiment lays a good foundation for the information processing of national costumes, and also provides support for the inheritance and protection of national culture. © 2023 IEEE.

关键词： Deep learning

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Innovative Deep learning Model-based Stock Price Prediction using a Hybrid Approach of CNN and Gradient Recurrent Unit 8

Innovative Deep Learning Model-based Stock Price Prediction ...

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8th international conference on Communication and Electronics Systems, ICCES 2023

作者： Rao, B. Srinivasa Bhattacharya, Rajib Tiwari, Manoj Kumar Kumari, K. Aruna Devmane, Mahavir A. Singh, Kamlesh Gokaraju Rangaraju Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India Globsyn Business School Department of Pgdm Bishnupur JL No. 101 South 24 Paganas West Bengal Chandipur India Jecrc University Department of Computer Application Rajasthan Jaipur India University Mathematics Gss Visakhapatnam India Vppcoe & Va Department of Computer Engineering Maharashtra Mumbai India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350396638

the fluctuation in stock prices from industry to industry are a major source of concern in the market. As the market attracts more participants and stock prices play a larger role in more transactions, the ability to accurately predict stock price movements becomes more valuable. When making an investment, many people first look at the share price and then try to anticipate whether or not that price will go up or down in the future. the traditional problem of forecasting the stock market using machine learning tools and methodologies has been thoroughly studied. Time dependence, volatility, and similar complicated dependencies are interesting aspects that make this modeling non-trivial. To overcome this the proposed method in this work is a deep learning-based hybrid strategy for predicting stock prices. Preprocessing is performed after input is delivered to increase precision. Selecting MPSOA features is the next step. In the end, it's put to use in MC-GRU model training. the proposed method achieves better results than both the CNN and GRU models. © 2023 IEEE.

关键词： Investments

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Monitoring and Predicting Performance of Students in Degree Programs using machine learning 10

Monitoring and Predicting Performance of Students in Degree ...

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17th INDIACom;2023 10th international conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Kumar, G. Saravana De La Cruz-Camaco, Dante Ravichand, M. Joshi, Kapil Gupta, Zatin Gupta, Sakshi University Karnataka Bengaluru India Universidad Tecnológica Del Perú Peru School of Liberal Arts and Allied Sciences Mohan Babu University India Uttaranchal Institute of Technology Uttaranchal University India School of Computing Science and Engineering Galgotias University Uttar Pradesh Greater Noida India Graphic Era Deemed to Be University Department of Professional Communication Uttarakhand Dehradun248002 India

ISBN: (纸本)9789380544472

In higher education, it is frequently necessary to find a solution to the issue of student retention. Managers and scholars in the field frequently rely entirely and expertise to recognise potential risk factors and students. this study looks at the research on the approaches and metrics being used now in learning analytics. While there are techniques available, our research shows that they are not focused on identifying difficult pupils as soon as possible. With the use of contemporary computer vision tools and methodologies, we design a novel statistical tool for class participation and develop a predictive model. It shows how pupils may be identified with 97% accuracy as early about week three. Additionally, we place this finding in a suitable pedagogical framework to support its application as a component of a more complete student assistance system. there is a wealth of publicly available learning data that can be utilised to analyse individual student behaviour, address their issues, improve the instructional climate, and enable data-driven choices, thanks to the support of software learning platforms. Based on that, the paper focuses on developing a system that can effectively monitor and predict the performance of students in degree programs by using the evolving machine learning. As retention of students has becoming a serious concern, it was necessary to develop performance monitoring and predicting system. Using a comprehensive research methodology this study has analysed all relevant information to the topic to draw an effective conclusion at the end. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Students

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PE-FedAvg: A Privacy-Enhanced Federated learning for Distributed Android Malware Detection 21

PE-FedAvg: A Privacy-Enhanced Federated Learning for Distrib...

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21st IEEE international Symposium on Parallel and Distributed Processing with applications, 13th IEEE international conference on Big Data and Cloud Computing, 16th IEEE international conference on Social Computing and Networking and 13th international conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Tang, Junwei Xu, Zilong Ye, Luyao Peng, Tao He, Ruhan Hu, Xinrong Tian, Wenlong Wuhan Textile University Hubei Provincial Engineering Research Center for Intelligent Textile and Fashion China Wuhan Textile University School of Computer Science and Artificial Intelligence China University of South China School of Computer Science China Nanyang Technological University School of Physical and Mathematical Sciences Singapore

ISBN: (纸本)9798350329223

Android malware detection has become research hotspot in mobile security. When security service providers obtain feature information from target samples, they may involve user privacy information such as identity and behavioral habits, creating an urgent need for a distributed machine learning model to carry out malicious application detection with privacy protection. However, the information exchange between clients and servers in basic federated learning still poses risks of model data leakage, and the likelihood of inferring privacy information from the original feature data is high. therefore, this paper propose a privacy-enhanced federated learning system called PE-FedAvg for distributed Android malware detection. Firstly, Gaussian noise is added to the feature data uploaded from the client to the server, satisfying differential privacy characteristics while retaining data distribution for privacy protection. Secondly, an improved homomorphic encryption algorithm based on modular repeated squaring is used to encrypt and transmit the gradient weight information exchanged between federated learning clients and servers, preventing model-targeted attacks. By conducting the experiments on the Drebin and Malgenome datasets, our design has demonstrated exceptional performance compared to the state-of-the-art work, achieving accuracy rates of 98.61% and 99.3%, respectively. this achievement highlights that our approach not only enhances the security of the federated learning system, but also more effectively accomplishes the detection of Android malware. © 2023 IEEE.

关键词： Android malware

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Cryptocurrency Trading Strategies and Profitability Based on Deep learning Algorithms 4

Cryptocurrency Trading Strategies and Profitability Based on...

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4th international conference on Applied machine learning, ICAML 2022

作者： Qing, Yinghe Kong, Ying Sun, Jifeng Lin, Jianwu Tsinghua-Berkeley Shenzhen Institute Shenzhen China Tsinghua Shenzhen International Graduate School Guangdong Shenzhen China

ISBN: (纸本)9781665462655

Quantitative trading replaces the traditional subjective judgment mode based on modern digital model, so as to avoid irrational investment decisions under extreme conditions. this processing method has important research and application value in many fields such as stock and foreign exchange. Especially in the context of increasingly strong research on deep learning algorithms, judgment analysis on Cryptocurrency trading strategies and profitability has also received attention from all walks of life. therefore, on the basis of understanding the current development of the stock market and the overall state of China's economy, this paper combines the stock prediction model with deep learning as the core, and constructs corresponding trading strategies on the basis of understanding the prediction results to improve the actual profitability. © 2022 IEEE.

关键词： Profitability

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Production quality early warning method for firearms components based on cloud edge collaboration 4

Production quality early warning method for firearms compone...

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4th international conference on machine learning and computer application, ICMLCA 2023

作者： Sun, Jiawei Hao, Jia Zhang, Xiaoning School of Mechanical Engineering Beijing Institute of Technology Beijing10081 China Yangtze Delta Region Academy of Beijing Institute of Technology Jiaxing314019 China Key Laboratory of Industry Knowledge and Data Fusion Technology and Application Ministry of Industry and Information Technology Beijing Institute of Technology Beijing10008 China Tianjin Key Laboratory of Space Environment Simulation Technology Tianjing300450 China

ISBN: (纸本)9798400709449

Digital transformation in manufacturing has become a trend with the continuous development of cloud computing and edge computing. the production process of firearms components is highly challenging and quality problems in the production process may directly affect the safety of people's lives and property. thus, this paper proposes a production-quality early warning method for firearms components based on cloud-edge collaboration. Real-time early warning and rapid processing for abnormal conditions in the production process are realized based on cloud data analysis and edge intelligent prediction, thereby improving production efficiency and quality. the cloud edge collaboration framework is developed for cloud training of the long short-term memory models and real-time sample edge acquisition, enhancing the adaptability of quality early warning algorithms under specific conditions and the real-time quality of early warning for firearms components. © 2023 ACM.

关键词： Big data

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