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Exploring the regional development trend in New York City

Exploring the regional development trend in New York City

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2023 international conference on Smart Transportation and City engineering, STCE 2023

作者： Yu, Tong Zhu, Haoran Wu, Xing Wang, Jianjia School of Computer Engineering and Science Shanghai University Shanghai200444 China

ISBN: (纸本)9781510673540

With rapid urbanization, cities around the world have undergone significant changes in their spatial and functional structures. Most previous studies focus on the division of urban regions, however, this cannot forecast the development of the city. In this paper, we propose a novel concept named regional intensity (RI) to indicate the functional intensity of each region and predict the regional development trend. In the end, we conduct extensive experiments using the temporal dataset of New York City to verify the effectiveness of our proposed approach. © 2024 SPIE.

关键词： Complex networks

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Clients Help Clients: Alternating Collaboration for Semi-Supervised Federated Learning 40

Clients Help Clients: Alternating Collaboration for Semi-Sup...

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40th IEEE international conference on Data engineering, ICDE 2024

作者： Jiang, Zhida Xu, Yang Xu, Hongli Wang, Zhiyuan Qiao, Chunming School of Computer Science and Technology University of Science and Technology of China China Suzhou Institute for Advanced Research University of Science and Technology of China China University at Buffalo Department of Computer Science and Engineering The State University of New York United States

ISBN: (纸本)9798350317152

Federated learning (FL) provides a distributed framework for multiple clients to collaboratively train models without exposing raw data. Most FL research assumes that all clients have fully labeled data, which is impractical for many real-world applications. To this end, we focus on semi-supervised FL (SSFL), where data samples of each client are partially labeled. However, existing SSFL methods ignore two inherent characteristics of FL: limited communication resources and heterogeneous data distribution, which severely hinder convergence stability and efficiency. This paper proposes a novel SSFL mechanism, called FedAC, to address the above two challenges by alternating client-to-client (C2C) collaboration. Specifically, we group all clients using different clustering strategies at two different training stages. During each global round, FedAC first performs similarity clustering based on local data distribution, which gathers the knowledge from similar clients to generate high-quality pseudo-labels for unlabeled data. Then the clients are re-grouped using dissimilarity clustering strategy to approximate the IID setting at the cluster level, thereby alleviating the bias induced by Non-IID data. FedAC adopts a reinforcement learning algorithm to achieve a balance between labeling assistance from similar clients and unbiased optimization from dissimilar clients. Extensive evaluations demonstrate that FedAC can improve model accuracy and save up to 59.65% of communication costs compared with existing benchmarks. © 2024 IEEE.

关键词： Efficiency

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Transforming Business Management Practices for Increased Production through Deep Learning and Cloud Computing integration 4

Transforming Business Management Practices for Increased Pro...

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4th international conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Kumar, P. Santhosh Singh, Anupam Soniya Farhana, Shaik Choudhary, Amar Biswas, Susmi Srm Institute of Science and Technology Ramapuram Faculty of Engineering and Technology Department of Information Technology Chennai India Graphic Era Hill University Department of Computer Science and Engineering India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Sanskar College of Engineering Department of Management Ghaziabad India CVR College of Engineering Department of Computer Science and Engineering Hyderabad India Alliance University Department Electronics and Communication Engineering Bengaluru India Haldia Institute of Management MAKAUT Department of Management Science India

ISBN: (纸本)9798350364729

This research is vital to meet the need for improved predictive maintenance in manufacturing, thus enabling predictive operations and minimizing expensive downtime. The aim is to assess and compare different machine learning models like Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Decision Trees( DT ) with respect to predicting maintenance requirements using historical sales data, social media trends, and machinery sensor readings. The methodology included gathering a dataset of 3,200 records from the industry and then the pre-processing phase to compute the preprocessing operations like data cleaning, normalization and feature selection. The various models were then trained and tested, using 70% of the data for training to generalise the model, with the rest of the unseen test set consisting of the remaining 30%. The models were evaluated based on performance metrics (accuracy, precision, recall, F1 score and Dice Similarity Coefficient or DSC). Results showed the proposed ANN model as the most accurate and efficient prediction mechanism in comparison with precision, recall and F1 score for remaining useful life (RUL) predictions. The SVM model also worked well, with an accuracy of 95.45% but low rates of false positives and negatives. Although the DT model was informative, its performance (93.34% accuracy) was less robust than that of our RF ensemble trainer. This research demonstrates the use of these findings to be applied in real-time maintenance prediction by ANN. This research would help industries and other manufacturing sectors to reduce downtime, achieve optimal planned maintenance scheduling and improve production efficiency. © 2024 IEEE.

关键词： Predictive maintenance

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Detecting Health Misinformation on Social Networking Sites Using Large Language Models and Deep Learning-based Natural Language Processing 2

Detecting Health Misinformation on Social Networking Sites U...

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2nd international conference on Foundation and Large Language Models, FLLM 2024

作者： Alshahrani, Amenah Misfer Farooq Ahmad, Hafiz Hussain, Jamil King Faisal University College of Computer Sciences and Information Technology Department of Computer Science Al-Ahsa Saudi Arabia Sejong University Department of Computer Science Seoul Korea Republic of

ISBN: (纸本)9798350354799

Health misinformation on social networking sites (SNS) is a critical issue, particularly during health crises like the COVID-19 pandemic. The spread of inaccurate health information can lead to severe outcomes, including reduced vaccine uptake and decreased trust in healthcare. This paper introduces a system using large language models (LLMs) and deep learning-based natural language processing (NLP) techniques to detect and mitigate health misinformation on SNS. The model incorporates transformer-based architectures, zero-shot (ZS) and few-shot (FS) learning, and prompt engineering to classify health-related content as true or false. A key feature is the integration of a knowledge graph that enhances verification capabilities. Applied to both English and Arabic datasets, the system's performance is evaluated using accuracy, F1-score, BLEU, and METEOR metrics, demonstrating its multilingual effectiveness in real-time misinformation detection. © 2024 IEEE.

关键词： Electronic health record

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The Necessary Requirements for Utilizing AI in Data Access Setting 15

The Necessary Requirements for Utilizing AI in Data Access S...

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15th international conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Swapnil, K. Sharma, Bhuvnesh Singh, Manpreet Yadav, Rakesh Kumar Devaraj Verma, C. Umarani, K. Vishwakarma Institute of Technology Department of Artificial Intelligence &data Science Pune India Vivekananda Global University Department of Mechanical Engineering Jaipur India Maharishi University of Technology Maharishi School of Engineering and Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Ramnagar District562112 India Chitkara University Institute of Engineering and technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350370249

AI has been an increasing number of adopted in records get admission to settings to improve the overall performance of statistics get admission to and extract significant insights, using extended productiveness and value-effectiveness. However, the success implementation of AI calls for positive essential requirements to be met, together with facts accuracy, connectivity, security, and scalability. First, facts accuracy is critical for AI applications in facts get entry to settings. information gathered need to be of excessive quality and accuracy, as AI working on erroneous facts can yield unreliable and probably harmful effects. Records should be of suitable granularity, taking pictures the most relevant elements, to make certain a complete and steady training performance.2nd, linked statistics assets, networks, and services are had to leverage the capability of AI in information get entry to settings. This gives an interconnected and up to date dataset at scale, while requiring included answers for uninterrupted get entry to. Moreover, single sign-on and alertness orchestration abilities are vital to decorate user revel in. © 2024 IEEE.

关键词： Connectivity Gathered Increasing Productiveness Security

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Presentation of Strategic information in the Agriculture Domain at the Agriculture Office of Garut Regency using Data Visualization

Presentation of Strategic Information in the Agriculture Dom...

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2023 international conference on Informatics engineering, science and Technology, INCITEST 2023

作者： Shidik, Muhammad Jafar Soeryanto Soegoto, Eddy Warlina, Lia Dharmayanti, Dian Universitas Komputer Indonesia Department of Informatics Engineering Bandung Indonesia Universitas Komputer Indonesia Department of Management Bandung Indonesia Universitas Komputer Indonesia Department of Urban & Regional Planning Bandung Indonesia

ISBN: (纸本)9798350317862

The purpose of this research is to produce the right visualization to help the Food Crops Sector obtain the information needed more quickly. The form of information presentation uses tables in Microsoft Excel, resulting in the field of food crops taking longer to get information. So that it hampers the understanding of information and policymaking. The method used in this research is data visualization. Based on research on data visualization, it is stated that effective data presentation is by transforming data into visual form. This research produces a visualization design and is implemented in the form of a prototype. The next stage is to test the results of the prototype using the usability testing method for food crop staff. Based on the test results, it can be concluded that the prototype of food crop data visualization can help the food crop field understand the information needed more quickly. © 2023 IEEE.

关键词： Data visualization

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Modified Recommender Algorithm based on Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) Methodology with Entropy Weighting Method (EWM) 4

Modified Recommender Algorithm based on Technique for Order ...

引用

4th international conference of science and information Technology in Smart Administration, ICSINTESA 2024

作者： Pacaon, Mark Reden S. Ballera, Melvin A. Technological Institute of the Philippines Graduate Program Manila Philippines Technological Institute of the Philippines Department of Computer Science Manila Philippines

ISBN: (纸本)9798350376111

This research exhibits an algorithm adapted from the Technique for Order of Preference by Ideal Solution (TOPSIS) methodology, integrating the Entropy Weighting Method (EWM) for objective and efficient recommendation processes. Decision-making methodologies have indicated gaps of subjective influence affecting performance values on several implementations with additional limitations on the flexibility of handling complex problems that require multiple factors while maintaining an acceptable running time. The study takes this into account and formulates an adaptable and efficient framework that offers objective and data-driven recommendations for diverse situations. Future endeavors include subjecting the framework to real-life scenarios and testing its effectiveness. © 2024 IEEE.

关键词： decision-making entropy weighting method modified algorithm multicriteria TOPSIS

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Machine Learning-Based Real-Time Data Analysis Techniques: A Comprehensive Study 15

Machine Learning-Based Real-Time Data Analysis Techniques: A...

引用

15th international conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Narmadha, T. Mohammed, Mehmood Ali Kalpana, B. Karyakarte, Mandar S. Vishnoi, Manish Dehankar, Pooja Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India University of The Cumberlands Department of Information Technology WilliamsburgKY United States Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Technology Department of Computer Engineering Pune India Vivekananda Global University Department of Civil Engineering Jaipur India Ajeenkya D. Y. Patil University School of Engineering Pune India

ISBN: (纸本)9798350370249

This paper gives a comprehensive take a look at of system learning-based real-time statistics analysis strategies. It discusses the various stages of the actual-time information analysis lifecycle, along with records collection, records preprocessing, feature engineering, model training, model assessment, and consequences interpretation. The paper offers an outline of dozens of machine getting to know strategies, along with supervised gaining knowledge of, unsupervised studying, semi-supervised mastering, and reinforcement studying strategies. tendencies within the development and application of gadget studying-primarily based real-time facts evaluation techniques are mentioned, with precise emphasis given to annular neural networks, deep mastering, and evolutionary-based totally algorithms. Different subjects of hobby consist of the cloud-based totally deployment of gadget studying fashions and the future of real-time records analysis. Finally, the paper offers a brief précis of episode research on how actual-time statistics analysis has helped improve business choices. © 2024 IEEE.

关键词： Adversarial machine learning

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Guided Exploration Reinforcement Learning for UAV Pursuit-Evasion Games 11th

Guided Exploration Reinforcement Learning for UAV Pursuit-E...

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11th international conference on Robot Intelligence Technology and Applications, RiTA 2023

作者： Kim, Dohyun Lee, Seungho Lee, Jayden Dongwoo Lee, Hohyeong Bang, Hyochoong Department of Electrical and Computer Engineering Seoul National University Seoul Korea Republic of Department of Mechanical Engineering Seoul National University Seoul Korea Republic of Department of Aerospace Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of

ISBN: (纸本)9783031706837

This paper proposes a novel pursuit strategy for a quadrotor-type UAV pursuit evasion game. The novel guidance law considering 3-dimensional and quadrotor UAV maneuvers is proposed as a baseline and expert knowledge for UAV pursuit-evasion games. In this paper, we propose guided exploration reinforcement learning (GE-DDPG) to enhance convergence time and exploration efficiency compared to pure DDPG with Gaussian noise and behavior cloning. The simulation results show that the proposed method outperforms the baseline guidance law and other DDPG methods in capture time and performance in UAV maneuvering scenarios. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Unmanned aerial vehicles (UAV)

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Optimized Machine Learning Model for Autonomous Robot Navigation Prediction 2

Optimized Machine Learning Model for Autonomous Robot Naviga...

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2nd IEEE international conference for Women in Innovation, Technology and Entrepreneurship, ICWITE 2024

作者： Payal Megha Verma, Khushboo Rani, Ritu Jaiswal, Garima Sharma, Arun Indira Gandhi Delhi Technical University Computer Science Delhi India Indira Gandhi Delhi Technical University Computer Science and Artificial Intelligence Delhi India Indira Gandhi Delhi Technical University Centre of Excellence- Artificial Intelligence Delhi India Bennett University Computer Science Engineering Greater Noida India

ISBN: (纸本)9798350383287

The primary problem at hand revolves around the domain of autonomous robot navigation. It is framed within the specific scenario of a robot, the SCITOS G5 navigating along the four walls of an enclosed room in clockwise direction for 4 rounds, equipped with 24 Ultrasound sensors circularly positioned around its waist, the dataset for which is acquired from Kaggle. This task, although seemingly simple is a non-linearly separable classification task and thus, linear classifiers are unable to learn and command the robot around the room without collisions. To address this challenge, a decision tree classifier model is employed, trained on a wealth of sensor data collected during the robot's navigation endeavours. The objective is to harness the power of machine learning and predictive analytics to enable the robot to and accurately predict its upcoming turns, thereby enhancing its navigation capabilities. The model's strengths emanate from its ability to expound upon decision trees, scrutinize the importance of features, and capture complex nonlinear relationships. This research serves as a conduit connecting predictive modelling with applications in robotics, providing valuable insights and avenues for enhancing the precision and resilience of turn prediction models. Prospects for forthcoming research include the exploration of ensemble methods, the integration of temporal dependencies, advanced methodologies for feature engineering, considerations for real-time deployment, and assessments spanning a variety of environmental conditions. © 2024 IEEE.

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