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Severity Stratification of Brinjal Leaf Diseases: A Federated Learning CNNs Approach 4

Severity Stratification of Brinjal Leaf Diseases: A Federate...

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4th IEEE international conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2023

作者： Sharma, Vikrant Kukreja, Vinay Mehta, Shiva Choudhary, Ankur Graphic Era Hill University Computer Science And Engineering Uttarakhand Dehradun India Chitkara University Institute Of Engineering And Technology Chitkara University Punjab India Graphic Era Deemed To Be University Computer Science And Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350384628

A new method combining Federated Learning and Convolutional Neural Networks (CNNs) is explored in this study for the precise identification of Brinjal leaf diseases in agricultural settings. A total of 5448 photos were used to train and test the model, covering a wide range of illness severity from very mild (1-25%) to very severe (76-100%). the study of the data reveals interesting new perspectives. the results showed that nx-6 was the leading client, with a remarkable 94.77 per cent accuracy, 94.82 per cent recall and 94.79 per cent F1-score. Having achieved such a high degree of performance, the capacity to classify across all levels of severity is almost flawless. Client nx-2 had mediocre performance, on the other hand, with precision, recall, and F1-score all falling between 77% and 79% and accuracy at 89%. this indicates room for improvement in the model's sensitivity. the intermediate clients (nx-1, nx-3, nx-4, and nx-5) did very well, with precision and accuracy scores mostly in the mid-80s. this showed that they were good at finding illnesses and putting them into ***, the examination of average performance measures - Macro Average, Weighted Average, and Micro Average - across the customers offered a comprehensive assessment of the model's effectiveness. the weighted average varied from 77.64% (nx-2) to 94.78% (nx-6), and the micro average ranged from 77.72% (nx-2) to 94.78% (nx-6), highlighting the varying but usually excellent performance across different data circumstances. In summary, this research not only exemplifies the state-of-the-art in plant disease identification utilizing Federated Learning and CNNs, but it also ushers in new possibilities in precision agriculture, particularly in the context of Brinjal farming. © 2023 IEEE.

关键词： Diseases

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Assessment of road safety performance based on CRITIC-TOPSIS-Kmeans model 4

Assessment of road safety performance based on CRITIC-TOPSIS...

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4th international conference on Machine Learning and Computer Application, ICMLCA 2023

作者： Ding, Sophia He, Yuyang Sun, Haoyuan Song, Kunyu Xuan, Zehong Zhang, Jianwei Zhang, Mingshu Shenzhen College of International Education Guangdong Shenzhen518043 China Faculty of Art and Science University of Toronto TorontoONM5S 1A1 Canada Veritas Collegiate Academy ArlingtonVA22202 United States Polyprep Country Day School BrooklynNY11228 United States Peterhouse Boys School Marondera3741 Zimbabwe College of Arts and Sciences Georgia State University AtlantaGA30303 United States HD Beijing School Beijing100018 China

ISBN: (数字)9781510680265

ISBN: (纸本)9781510680258

the study assesses road safety performance in the Southeast Asian region by using the CRITIC-TOPSIS-Kmeans model. First, the weight of each indicator is obtained by CRITIC. then, the obtained weights are embedded into the TOPSIS model. Furthermore, the TOPSIS sores are put into the K-means unsupervised machine learning model. the proposed CRITIC-TOPSIS-Kmeans model is utilized to rank and group the road safety performance of Southeast Asian countries. Finally, radar and bar plots are utilized to deconstruct indicators and TOPSIS scores, providing valuable references for policymakers. Overall, the development of the CRITIC-TOPSIS-Kmeans model not only establishes a fresh foundation for evaluating road safety achievements but also offers potential solutions for other MCDM challenges. © 2024 SPIE.

关键词： Machine learning

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Pedestrian Trajectory Intention Prediction in Autonomous Driving Scenarios Based on Spatio-temporal Attention Mechanism 4

Pedestrian Trajectory Intention Prediction in Autonomous Dri...

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4th international conference on Electronic Information engineering and Computer Communication, EIECC 2024

作者： Wang, Yong Wan, Weixiang Zhang, Hanqing Chen, Chen Jia, Guancong University of Aberdeen Information Technology Aberdeen United Kingdom University of Electronic Science and Technology of China Chengdu China Trine University CA United States Nanjing University of Aeronautics and Astronautics Nan Jing China Rice University TX United States

ISBN: (纸本)9798331534622

In a mixed traffic environment of human and autonomous driving, an autonomous vehicle must predict the lane change intentions and trajectories of pedestrians that pose a risk to it. Due to the uncertainty of human intentions, accurately predicting pedestrian trajectory intentions is a great challenge. this paper proposes a novel spatiotemporal attention framework for pedestrian trajectory prediction in autonomous driving scenarios. the framework consists of three key components: a spatio-temporal feature extraction module, a multi-head attention mechanism for trajectory encoding, and an intention recognition module. the intention recognition module explicitly models the relationship between trajectory Extensive experiments on the Eth-UCY and Stanford Drone datasets demonstrate the effectiveness of our approach. the proposed method significantly improves over state-of-the-art methods, with a 12.8% reduction in average displacement error and 93% intention recognition accuracy. the framework maintains real-time performance capabilities, making it suitable for practical autonomous driving applications. © 2024 IEEE.

关键词： Prediction models

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the application and survey of emotion recognition technology in digital guide system 4

The application and survey of emotion recognition technology...

引用

4th international conference on Internet of things and Smart City, IoTSC 2024

作者： Li, Zijian Jia, Huiping College of Fine Arts and Art Design Qiqihar University Heilongjiang Qiqihar161000 China

ISBN: (纸本)9781510681767

the digital guidance system is widely used in urban planning, public transportation, and other fields. the lack of interactivity and the level of intelligence of this application need to be improved to become the focus of current research. Emotion recognition technology is a kind of emotion analysis technology based on artificial intelligence. It was presented in order to address these issues. this paper discusses the application of emotion recognition technology in digital navigation systems and its impact on user experience. Emotion recognition technology can better understand user needs and provide personalized recommendation services by analyzing user's facial expressions, voice tones, and other information, to improve the interactive and intelligent degree of the digital guidance system. Using the analytical hierarchy approach as a foundation, this paper determines the key role of emotion recognition technology in improving the interactivity and intelligence level of the system through weight analysis. the research results show that by analyzing the user's emotional state, the system is able to comprehend use's needs more effectively, provide more personalized and intelligent services, and thus improve user satisfaction and experience. Emotion recognition technology makes the guidance system more personalized and intelligent. At the same time, the technology can continuously optimize the system design and function through the analysis of user emotion, improve the innovation and adaptability of the system, and provide strong support for the maintenance and update of the system. the practical significance and application value of this research are significant in advancing the sustainable development of digital guiding systems. © 2024 SPIE.

关键词： Emotion Recognition

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An Efficient Local Search Solver for Mixed Integer programming 30

An Efficient Local Search Solver for Mixed Integer Programmi...

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30th international conference on Principles and Practice of Constraint programming, CP 2024

作者： Lin, Peng Zou, Mengchuan Cai, Shaowei State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783959773362

Mixed integer programming (MIP) is a fundamental model in operations research. Local search is a powerful method for solving hard problems, but the development of local search solvers for MIP still needs to be explored. this work develops an efficient local search solver for solving MIP, called Local-MIP. We propose two new operators for MIP to adaptively modify variables for optimizing the objective function and satisfying constraints, respectively. Furthermore, we design a new weighting scheme to dynamically balance the priority between the objective function and each constraint, and propose a two-level scoring function structure to hierarchically guide the search for high-quality feasible solutions. Experiments are conducted on seven public benchmarks to compare Local-MIP with state-of-the-art MIP solvers, which demonstrate that Local-MIP significantly outperforms CPLEX, HiGHS, SCIP and Feasibility Jump, and is competitive with the most powerful commercial solver Gurobi. Moreover, Local-MIP establishes 4 new records for MIPLIB open instances. © Peng Lin, Mengchuan Zou, and Shaowei Cai.

关键词： Mixed-integer linear programming

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Research on visualization of minority music knowledge graph based on Neo4j 24

Research on visualization of minority music knowledge graph ...

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7th international conference on Information science and Systems, ICISS 2024

作者： Mao, ChunXia Wen, ShuangBing Liu, Hong Hu, Tao Li, Jun College of Intelligent Systems Science and Engineering Hubei Minzu University China Hubei Engineering Research Center of Selenium Food Nutrition and Health Intelligent Technology Hubei Minzu University China School of Music and Dance Hubei Minzu University China The Key Laboratory of Performing Art Equipment and System Technology Ministry of Culture and Tourism Hubei Minzu University China

ISBN: (纸本)9798400717567

the digital age has created various channels for the sustainable development of traditional culture. A Neo4j-based knowledge graph for Tujia Intangible cultural heritage music was constructed to address the potential loss of Tujia Intangible cultural heritage music. the project involved integrating and consolidating Tujia Intangible cultural heritage music resources, organizing and cleaning the collected data, storing it in a Neo4j graph database, designing a knowledge base ontology model, and completing the construction of the Tujia Intangible cultural heritage music knowledge graph. the knowledge graph makes the knowledge of music and cultural heritage explicit from vast information resources, effectively expanding its dissemination breadth and promoting the protection and inheritance of intangible cultural heritage music in ethnic areas. © 2024 Copyright held by the owner/author(s).

关键词： Knowledge graph

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IoT and wireless sensor network -Based three- Tier Architecture for Continuous Cardiac Health Monitoring and Alert System 4

IoT and wireless sensor network -Based Three- Tier Architect...

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4th international conference on Expert Clouds and Applications, ICOECA 2024

作者： Gorde, Swapnil Gulabrao Singh, Archana Tamilselvi, M. Ravivarman, G. Rajendiran, M. Maranan, Ramya MIT College of Management MIT Art Design & Technology University Pune India Private University Maharashtra India Roever Engineering College Department of Computer Science and Engineering Tamil Nadu Perambalur India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Tamil Nadu Coimbatore India Panimalar Engineering College Department of Computer Science and Engineering Poonamallee Tamil Nadu Chennai India Saveetha School of Engineering SIMATS Department of Research and Innovation Tamil Nadu Chennai India

ISBN: (纸本)9798350385793

this study suggests a powerful three- Tier Architecture that combines machine learning models, Internet of things (IoT) devices, and sophisticated sensors to provide continuous cardiac health monitoring. A range of physiological data is provided by the sensors in the Data Collection Tier, which measure blood pressure, temperature, acceleration, and electrocardiogram (ECG). In the Data Processing Tier, a local server applies complex algorithms to efficiently assess the gathered data and provide insightful outcomes. By providing easy access, authentication, and alarm production, the Application Tier acts as the interface and guarantees efficient contact with healthcare providers. the unique aspect of the system is the integration of machine learning models, which were trained on recorded data for predictive analysis. k-Nearest Neighbors (KNN), artificial Neural Networks (ANN), Decision Trees (DT), and Recurrent Neural Networks (RNN) are some examples of these models. Impressive accuracy levels are seen in the results, with ANN leading at 96.44% the measures of accuracy, precision, recall, and F1 score all demonstrate the algorithms' capacity to anticipate possible health risks, which supports the models' effectiveness. this proactive paradigm in healthcare is made possible by the comprehensive approach of continuous heart health monitoring, which enables prompt interventions and customized treatment strategies. this study increases our knowledge of the dynamics of cardiovascular health and the rapidly growing field of health care technology by demonstrating the synergy of sensor-based monitoring, IoT connectivity, and machine learning analytics in a unified architecture. In the end, patient care is improved. © 2024 IEEE.

关键词： Blood pressure

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Naïve Bayesian Approach to Intruder Detection in Smart Homes Using Sensor Networks 4th

Naïve Bayesian Approach to Intruder Detection in Smart Ho...

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4th international conference on Advances in Computational science and engineering, ICACSE 2023

作者： Bolivar, Daisy Jane S. Gom-os, Dhong Fhel K. Department of Computer Science Center for Research in Intelligent Systems University of the Philippines Cebu Cebu6000 Philippines

ISBN: (纸本)9789819729760

We used camera, light, door, and window sensors, and day of the week, duration, and activation time parameters for a Naïve Bayesian approach to intruder detection in a smart home. A nine-week normal and a two-week intruder behavior data were extracted using sensor devices. Smartthings SmartApp was used to gather the sensor data which were published to Google Sheets, then exported to Excel and Python for pre-processing, providing a dataset size of 509, consisting of 284 normal and 225 intruder data. the data had an 80/20 train-test split and was fitted to the Naïve Bayes model. By identifying behavioral patterns and increasing parameters from the state-of-the-art, the model’s accuracy improved to 94.12% and achieved an F2-score of 93.18%. this study has shown that increasing sensor size could significantly improve intruder detection. Future work could include the analysis of the contribution of each sensor to provide informed decision to system designers. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Smart homes

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A Deep Retrieval Model for Revit Software Database Based on Keyword Mining Algorithm 4

A Deep Retrieval Model for Revit Software Database Based on ...

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4th international conference on Expert Clouds and Applications, ICOECA 2024

作者： Yang, Mingling Shang, Haijing Wuhan University of Engineering Science Hubei Wuhan430200 China

ISBN: (纸本)9798350385793

this research study explores the implementation of a deep retrieval model for the Revit software database, harnessing the power of keyword mining algorithms to enhance the search functionality and efficiency. By extracting and categorizing relevant keywords from voluminous textual data, this research study can effectively organize and condense information, paving the way for seamless text mining and information retrieval efforts. this methodology results in a robust and reliable database that greatly simplifies the process of searching and retrieving relevant information. For the model under consideration, this study defines a dual-core network to capture the weighted contributions of two core nodes to each other within a local network. Specifically, the dual-core network consists of nodes 1 and 2, which share a common relationship, along with their neighboring nodes that connect them. the edge weights between these nodes are carefully assigned to accurately reflect the relevance of the connected nodes to the keyword query throughout the retrieval process. By updating these edge weights, this study can achieve an effective retrieval model. To rigorously assess the performance of our proposed algorithm, this study adopted a collaborative programming approach that combines the strengths of Python and MATLAB. the results indicate that our algorithm exhibits remarkable proficiency, with an average time consumption of 6.3 seconds and an impressive accuracy level of 0.990. © 2024 IEEE.

关键词： Structured Query Language

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Text-Enhanced Reasoning for Question Answering over Incomplete Knowledge Base 4

Text-Enhanced Reasoning for Question Answering over Incomple...

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4th international conference on Computers and artificial Intelligence Technology, CAIT 2023

作者： Luo, Dan Tao, Zhiqiang Xie, Weixin Dou, Changjing Liu, Dachang Guangdong Southern Planning and Designing Institute of Telecom Co. Ltd. Shenzhen China Shenzhen University College of Electronics and Information Engineering Shenzhen China Shenzhen Power Supply Bureau Co. Ltd. Shenzhen China

ISBN: (纸本)9798350326710

the construction and maintenance of a structured Knowledge Base is too time-consuming and laborious to cover all the world's knowledge. When facing some questions, the Knowledge Base will lack its key evidence information or answers, resulting in ineffective reasoning of the Question Answering system. A prevalent solution to make up for an incomplete Knowledge Base is the utilization of Text Corpus. However, previous methods fail to thoroughly mine the topological structure information in Knowledge Bases, and their limited interaction between the two heterogeneous knowledge hinder the effective use of text knowledge in answering reasoning. To solve this problem, we propose a Text-Enhanced Reasoning Model for Question Answering over Incomplete Knowledge Base. It focuses on the topological structure present in the Knowledge Base, as well as the clues derived from the text corpus, and emphasizes the interaction and fusion of information between these two sources. We simulate incomplete scenarios with 10%, 30%, 50% and 100% Knowledge Base and conduct extensive experiments on an open dataset WEBQUESTIONSSP. the results surpasses the current state-of-the-art methods, demonstrating that our model effectively utilizes text information to enhance the reasoning ability of Question Answering over Incomplete Knowledge Base. © 2023 IEEE.

关键词： Topology

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