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检索条件"任意字段=1st International Conference on the Art, Science and Engineering of Programming, Programming 2017"

共 643 条记录，以下是81-90 订阅

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Development of Weather Forecast Application using API

Development of Weather Forecast Application using API

引用

Advances in Computing, Communication and Networking (ICAC2N), international conference on

作者： Shikha Chadha Neha Gupta Rosey Chauhan Department of Computer Science Engineering Sharda University Greater Noida ABES Engineering College Ghaziabad

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

This research paper presents the development of a weather forecasting model that incorporates real-time data through Application programming Interfaces. This model utilises simple algorithms to analyse meteorological parameters from diverse API data sources, aiming to improve forecast accuracy compared to traditional methods. Extensive datasets are used for training and validation, demonstrating the model’s effectiveness in providing more precise predictions. The study highlights the significance of API integration in advancing weather forecasting methodologies and fostering innovation in the field. The model created has yielded highly reliable results with minimal error.

关键词： Training Technological innovation Soft sensors Weather forecasting Predictive models programming Data models Real-time systems Reliability Application programming interfaces

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Optimization of IoT Devices in Smart Home to Minimize Cost Energy Conservation Based on Human Detector 1st

Optimization of IoT Devices in Smart Home to Minimize Cost E...

引用

1st international conference on Data engineering and Machine Intelligence, ICDEMI 2023

作者： Muthuvel, P. Raja Sekar, R. Rajkumar, T. Dhiliphan Mari Selvan, R. Department of Electronics and Communication Engineering Saveetha Engineering College Saveetha Nagar Thandalam India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Virudhunagar Krishnankoil India

ISBN: (纸本)9789819776153

Smart home systems are becoming an exciting new paradigm among researchers and homeowners. In modern society, the use of electrical appliances is an essential element for a comfortable life. However, in many classrooms or dorms, students may waste energy by leaving lighting and air conditioning systems on even when not in use and an optimized distribution of the used sensors is required for the resource-efficient (indoor) location of mobile humans. Indoor positioning, or the location of beacons or sensors inside buildings, may be a challenging and time-consuming process, particularly when done by hand. This topic has already been attempted to be solved by a number of researchers under various use cases and using various approaches. Nevertheless, none of the approaches that are now available use Boolean variables alone to do constraint programming. To address this issue, we are creating a smart IoT system that saves energy by turning on lights and fans when motion is detected in a specific part of a room. To be more precise, we want to optimize the beacons for an indoor locating system that uses trilateration. According to this method, three beacons have to be within range of each potential location inside the structure. The initially determined beacon placements are then further optimized in a subsequent stage. In order to do this, fewer beacons are used. Next, we assess the beacon placements’ quality by comparing them to a manually optimized beacon placement and assessing the quality of many sample places in an existing building. This system uses a PIR sensor for motion detection which makes it cost efficient. The electrical switch is controlled by a5V-1 channel relay connected to an Arduino board in the switchboard. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Smart homes

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ERODRL: Efficient ISP Routing Optimization via Deep Reinforcement Learning 21

ERODRL: Efficient ISP Routing Optimization via Deep Reinforc...

引用

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： Chen, Changrui Chen, Xiaolin Zeng, Deze Kunming University of Science & Technology School of Information Engineering & Automation Kunming China Qiongtai Normal University School of Information Science & Technology Haikou China China University of Geosciences School of Computer Science Wuhan China

ISBN: (纸本)9798350329223

Routing optimization is a critical issue to Internet Service Providers (ISPs) since it is highly related to the network traffic management efficiency. state-of-the-art approaches utilize Reinforcement Learning (RL) technology to minimize Maximum Link Utilization (MLU). However, these approaches have two significant shortcomings: 1) the rudimentary structure of Neural Networks (NNs) restricts their ability to capture network traffic patterns accurately, resulting in sub-optimal critical flow selection, and 2) the training algorithm is ineffective in utilizing experience, leading to training instability and prolonged duration. To overcome the above shortcomings, we propose ERODRL, a network traffic management scheme which incorporates a RL based critical flow selection strategy and a flow rerouting algorithm, towards the goal of MLU minimization. In details, the RL based critical flow selection strategy incorporates a Convolutional Neural Network (CNN) block to analyze traffic features and utilizes the GELU activation function to improve traffic distribution matching. It then introduces full connection layers to the actor and critic networks respectively, allowing for detailed traffic behavior analysis. To improve training efficiency, a trust region algorithm is also involved fast the training speed of RL agent. After the critical flow are identified, the flow routing problem is formulated into Linear programming (LP) form and a low-complexity rerouting solution is proposed to better fit the traffic patterns of ISPs. Extensive simulation results are conducted and the results demonstrate that ERODRL improves training speeds by 2.3 to 8.7 times compared to previous RL-based schemes, and reduces the MLU by 43.24% and 16.01% averagely compared to state-of-the-art solutions. © 2023 IEEE.

关键词： Reinforcement learning

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Open Market: A Proposal To Improve Engagement and Motivation Among students Towards Basic programming Subjects

Open Market: A Proposal To Improve Engagement and Motivation...

引用

Advanced Computing and Emerging Technologies (ACET), international conference on

作者： Vinesh Kumar Vartika Pandey Jay Prakash Maurya Abhishek Shrivastava School of Computing Science and Engineering VIT Bhopal University Sehore M.P India School of Mechanical Engineering VIT Bhopal University Sehore M.P India

ISBN: (数字)9798350367720

ISBN: (纸本)9798350367737

Gamification can be applied to grab the attention of people in wide range of environments. This work sheds light upon the prospects of its application in classroom to deliver the education related to computer programming is called “Open Market”. To study the impact upon students after applying this strategy, the level of motivation, engagement and learning were explored. This paper proposed a novel engagement model to improve learning level in student and comp are the impact of gamification method from traditional methods in terms of assessment methods for “Open Market”. This paper also encourages student’s agenda in student learning process by analyzing standard deviation in learning in number of parameters like age, subject, motivation type, and intrinsic method for engagement in programming subjects like C, C++, Python, Java etc. The outcome of the study contends in the support of open market strategy to be indulged in the teaching and learning mechanism.

关键词： Learning systems Knowledge engineering Java Internet Proposals Usability engineering education programming profession standards Python

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Voice-Based Virtual-Controlled Intelligent Personal Assistants 1

Voice-Based Virtual-Controlled Intelligent Personal Assistan...

引用

1st international conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

作者： Yadav, Satya Prakash Gupta, Amit Dos Santos Nascimento, Caio Hugo C. De Albuquerque, Victor Naruka, Mahaveer Singh Singh Chauhan, Sansar Department of Computer Science and Engineering U.P. GreaterNoida India Fortaleza-CE Brazil Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Federal University of Ceará Department of Teleinformatics Engineering Fortaleza/CE Fortaleza Brazil Gl Bajaj Institute of Technology & Management Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798350338027

Virtual assistants are improving and providing consumers with greater advantages. The comprehension and fulfilment of requests by virtual assistants will increase as voice recognition and natural language processing continue to grow. Virtual assistants are projected to be employed in more commercial activities as speech recognition technology advances. The main goal of developing personal assistant software (virtual assistant) is to use web-based semantic data sources, user-generated content, and knowledge from knowledge libraries. Basically, main objective of making this Voice-Based Virtual Assistant is to make life easier and having a personal assistant to everyone which can perform many tasks. As the end user interacts with a virtual assistant, the AI programming learns from the data provided and improves its ability to forecast the end user's needs. Virtual assistants are often used to do things like add tasks to a calendar, provide information that would normally be found in a website, and operate and monitor Smart Home devices like lighting and cameras and thermostats. Massive volumes of data are required to fuel virtual assistant technologies, which feed artificial Intelligence (AI) platforms such as machine learning, natural language processing, and speech recognition. Speech recognition has a lengthy history and has seen several key advancements. On smartphones and wearable devices, speech recognition for dictation, search, and voice commands has become a standard feature. Design of a small, large vocabulary speech recognition system that can run quickly, accurately, and with minimum latency on mobile devices. © 2023 IEEE.

关键词： Speech recognition

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Efficient Data Compression Algorithms for Cloud-Based Medical Imaging 1

Efficient Data Compression Algorithms for Cloud-Based Medica...

引用

1st international conference on artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Sharma, Himanshu Pundir, Sumit Deepak, A. Mayuri, K. Dwivedi, Shashi Prakash Kumar, Navneet Gla University Department of Computer Engineering and Applications Mathura India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamilnadu Chennai India Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India Lloyd Institute of Engineering and Technology Greater Noida India Lloyd Law College Greater Noida India

ISBN: (纸本)9798350330915

The storage as well as access of health-related imaging have been revolutionized by cloud-based systems. Algorithms for effective compression of information are essential for controlling the growing number of high-resolution photographs. The performance of compression techniques is assessed in this work using a deductive methodology employing a descriptive design, which is based on the interpretivist philosophy. For thorough analysis, secondary data gathering from several medical picture datasets is used. The study looks into well-known compression methodologies such wavelet-based methodologies, predictive computer programming, along with deep learning-based strategies. The results show wide variability in compression metrics, with wavelet-based techniques showing excellent spatial redundancies reduction performance. The tight equilibrium among compression overall diagnostic precision is highlighted by the slight artifacts introduced by some methods. The examination of computational resources reveals trade-offs between computing speed and memory needs, guiding real-world implementation. statistical comparison highlights algorithmic differences and offers fact-based conclusions. The use of deep learning integration, specialized algorithm improvement, and dynamic compression methods are suggested. Future studies on telemedicine applications should investigate real-time compressing and hybrid methodologies. Data privacy and security must continue to be guided by moral and legal principles. With implications for better storage and availability for health care providers, this study enhances the field by providing an in-depth comprehension of compression algorithms within stored in the cloud medical images. © 2023 IEEE.

关键词： Compression methods CT scans data transfer medical imaging radical

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The Design and Implementation of Python Knowledge Graph for programming Teaching 1

引用

1st international conference on artificial Intelligence Security and Privacy, AIS and P 2023

作者： Jiao, Xiaotong Yu, Xiaomei Peng, Haowei Gong, Zhaokun Zhao, Lixiang School of Information Science and Engineering Shandong Normal University Jinan China

ISBN: (数字)9789819997886

ISBN: (纸本)9789819997879

With the continuous development of information technology in education, Python course has a pivotal position in the current information technology curriculum system. But there are the following problems in the process of teaching: it is difficult for students to construct meaningful knowledge;the teaching materials cannot effectively stimulate the interest in learning;content taught in the classroom cannot meet the education needs either. Therefore, it is urgent to construct a Python Knowledge Gragh (KG) that covers heterogeneous data from multiple sources and enables knowledge visualization. We constructed a Python KG based on the Neo4j graph database and interactive graph framework from the perspectives of innovating how to present teaching content, improve learning interest, and expand teaching resources. We demonstrated by designing teaching experiments that the Python KG could address the issues of information overload and passive learning by making Python more accessible, engaging, and personalized. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Informatization

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Analyzing Forecasting Techniques for Optimizing Flow Releases in Multi-storage Systems for Flood Mitigation

Analyzing Forecasting Techniques for Optimizing Flow Release...

引用

Trends in engineering Systems and Technologies (ICTEst), international conference on

作者： Aswathy Ananthan Y stalin Jose Department of Civil Engineering Noorul Islam Centre for Higher Education Tamil Nadu India

ISBN: (数字)9798350307795

ISBN: (纸本)9798350307801

As floods become more frequent and severe globally, effective flood control measures are essential for mitigating the impacts of such events. Multi-storage systems, consisting of multiple reservoirs or storage facilities, offer a viable approach for managing floodwaters. However, determining the optimal flow releases from these systems to minimize flood risks requires accurate forecasting techniques. The review surveys relevant studies and research articles that focus on the forecasting of optimal flow releases in multi-storage systems for controlling floods. It examines various forecasting methods and models used in the literature, such as linear programming, dynamic programming, genetic algorithms, and particle swarm optimization. The strengths, limitations, and applications of these techniques are assessed, considering factors such as accuracy, lead time, and the ability to incorporate system constraints. This paper highlights the importance of accurate flow release forecasting for effective flood control in multi-storage systems. It identifies gaps and challenges in the previous works and suggests areas for further exploration. The outcomes of this review advance our knowledge of the state of the art in forecasting optimal flow releases for flood control in multi-storage systems, providing valuable insights for researchers, practitioners, and decision-makers involved in water resources management and flood risk reduction.

关键词： Accuracy Reviews Predictive models Control systems Reservoirs Linear programming Floods

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Respiratory Sound Classification Based on Swin Transformer 8

Respiratory Sound Classification Based on Swin Transformer

引用

8th international conference on Signal and Image Processing, ICSIP 2023

作者： Sun, Weiwei Zhang, Fan Sun, Ping Hu, Qishi Wang, Jianhong Zhang, Minghui Nanchang University Department of Electronic Information Engineering Nanchang China 1st Affiliated Hospital of Nanchang University Nanchang University Nanchang China

ISBN: (纸本)9798350397932

Various deep learning (DL) techniques have found application in the respiratory sound classification to effectively alleviate the diagnostic pressure of physicians. However, DL-based respiratory sound classification methods are mostly based on convolutional neural networks (CNN). Due to the inherent locality of convolution operations, CNN generally extracts only local features of 2D spectrogram representations. A respiratory sound has wide frequency bands and long time spans. It is important to obtain both global and local information. To address this problem, we propose a novel Swin Transformer network for respiratory sound classification. Experimental results on the ICBHI2017 dataset demonstrate that the proposed model achieves 77.3% specificity, 41.1% sensitivity, 59.2% ICBHI score and outperforms the state-of-the-art methods. © 2023 IEEE.

关键词： Convolution

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Respiratory Sounds Classification by Fusing the Time-Domain and 2D Spectral Features

Respiratory Sounds Classification by Fusing the Time-Domain ...

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2024 IEEE international conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Dong, Gaoyang Wang, Jianhong Shen, Yufei Zhang, Mingli Zhang, Minghui Sun, Ping Nanchang University School of Information Engineering Nanchang330031 China 1st Affiliated Hospital Nanchang University Nanchang330031 China

ISBN: (纸本)9798350386226

Automated respiratory sounds classification is a challenging task. Currently, the most commonly used feature extraction methods in the respiratory sounds classification are based on two-dimensional time-frequency representation, which reflect more information in the frequency domain but usually ignore the key information in the time domain of audio signals. To overcome this limitation, we proposed a novel respiratory sounds classification method that integrates the time-domain and two-dimensional time-frequency representation features of audio signals. Specifically, we first used a Mel encoder to extract the spectral features from the Mel-spectrum and used time domain feature extraction module to capture the time domain features of the original audio signal respectively. Then, we designed co-attention module for efficiently fusing these two different types of features. Finally, we inputted the fused features into the classifier for accurate classification of respiratory sounds. Experimental results on the ICBHI 2017 and SPRSound datasets demonstrate that our method achieved state-of-the-art performance. Specifically, it surpassed previous best results by 1.00% on ICBHI 2017 dataset and exceeded the best results in the four subtasks of SPRSound dataset by 3.01%, 4.89%, 5.70%, and 3.87%, respectively. © 2024 IEEE.

关键词： Frequency domain analysis

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