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共 5837 条记录，以下是321-330 订阅

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Comparative Analysis of Machine Learning Algorithms for Traffic Flow Prediction in intelligent Transportation Systems 4

Comparative Analysis of Machine Learning Algorithms for Traf...

引用

4th international conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Yadav, Mahima Kumar, Bijendra Netaji Subhas University of Technology Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350387490

As human society advances, the limitations of current transportation systems become more apparent, prompting a growing desire to leverage advanced technology for intelligent transportation solutions. However, low identification rates and complicated processing plague many of the vehicle detection techniques used in video analytics today. this paper gives a comparative analysis of multiple machine learning algorithms for traffic flow prediction in order to improve intelligent Transportation Systems (ITS). Using an extensive dataset of historical traffic flow and environmental variables, we evaluate the performance of several methods: Linear Regression (LR), Support Vector Machine (SVM), Decision Trees, Random Forests (RF) and deep Neural Networks (DNN). Numerous Variables are taken into account in the evaluation, including scalability, computing efficiency, and forecast accuracy. the findings show that while algorithms like RF and SVM give good performance with less computational overhead, DNN obtain the maximum accuracy. the work offers valuable information on the disadvantages of various machine learning (ML) techniques, which may be used to select suitable models for the prediction of traffic movement in real-world ITS. © 2024 ieee.

关键词： Random forests

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Using Artificial Intelligence to Fight Clickbait in Romanian News Articles 19

Using Artificial Intelligence to Fight Clickbait in Romanian...

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19th ieee international conference on intelligent computer communication and processing conference, ICCP 2023

作者： Pacurar, Aralda Oprisa, Ciprian Technical University of Cluj-Napoca Computer Science Department Cluj-Napoca Romania

ISBN: (纸本)9798350370355

On-line press is infested with poor quality content that uses misleading headlines in order to increase web traffic and gain popularity and profit. this phenomenon is called clickbait and impacts the usability and, in some cases, even the security by exposing the user to malicious advertisement. this paper proposes an automated solution for detecting clickbait by using Natural Language processing techniques to extract features from both the article title and the article content. the extracted features are used with a set of Machine Learning classifiers which are fine-tuned to improve the performance metrics. the resulting model shows good results and can be integrated in a news aggregator that selects only quality content for the user to read. © 2023 ieee.

关键词： Natural language processing systems

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Multi-Scale Optimized Feature Network for Polyp Segmentation 4

Multi-Scale Optimized Feature Network for Polyp Segmentation

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4th ieee international conference on Computing, communication, and intelligent Systems, ICCCIS 2023

作者： Mishra, Annu Chaturvedi, Ravi Prakash Sharma, Hirdesh Sharma, Ritu Asthana, Sameer Computer Science and Engineering JEMTEC Greater Noida India Computer Science and Engineering AKGEC Ghaziabad India Computer Science and Engineering UCER Greater Noida India

ISBN: (纸本)9798350306118

Polyps are one of the most common problems in human beings where some part on the surface of the body may bulk. this could be cancerous or non-cancerous. However, to segment these polyps it is very difficult to region them properly because of the different anatomy of human body, polyp shape, size etc. the smaller polyps are harder to segment than the bigger ones. thus, we have proposed a network which helps to segment the polyps despite of its shape and size. the network consists of two major modules. First module is the multi-scale feature fusion. the second module consists of attention module. the purpose of first module is to represent the image clearly as it works upon multiple dilations on different rate. the second module enhances the relevant channel in the feature map by learning weights. the combination of these modules helps in better representation and thus we can further segment it easily. © 2023 ieee.

关键词： Diseases

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Workout Mentor: Improving posture in real-time using computer vision 4

Workout Mentor: Improving posture in real-time using compute...

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4th ieee international conference on Computing, communication, and intelligent Systems, ICCCIS 2023

作者： Aiman, Umme Ahmad, Tanvir Jamia Milia Islamia Department of Computer Engineering New Delhi India

ISBN: (纸本)9798350306118

Fitness workouts are tremendously helpful in improving individual health and fitness;yet, if practiced incorrectly by the user, they can be unproductive and potentially harmful. these blunders occur when the person fails to apply the correct posture or position. We propose 'Workout mentor', an application for maintaining proper posture while performing workout routines or yoga. Current work on workout monitoring depends heavily on personal trainers or wearables. Drawing inspiration from medical advancements like robotic surgery, we created an innovative application called 'Workout mentor' that can direct workouts at the ease of your home using a single reference image. Workout mentor employs a machine learning approach to provide a real-time fitness instruction environment. Our research exploits the potential of posture estimation algorithms by providing real-time guidance for workout and yoga enthusiasts. Proposed model achieves 98.33% recognition accuracy. the model utilizes and learns from the crucial components that are necessary for all types of exercise. © 2023 ieee.

关键词： computer vision

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Leveraging the Strengths of Long Short-Term Memory and Gated Recurrent Unit Architectures for Indian Stock Market Prediction 4

Leveraging the Strengths of Long Short-Term Memory and Gated...

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4th international conference on Emerging Systems and intelligent Computing, ESIC 2024

作者： Jena, Prem Purusottam Das, Kaberi Mishra, Debahuti Lenka, Arundhati Mishra, Sashikala Mallick, Pradeep Kumar Siksha 'o' Anusandhan [Deemed to Be University Department of Computer Science and Engineering Bhubaneswar India Siksha 'o' Anusandhan [Deemed to Be University Department of Computer Application Bhubaneswar India Pune India Kiit Deemed to Be University School of Computer Engineering Bhubaneswar India

ISBN: (纸本)9798350349856

the study recognizes the multifaceted nature of the stock market and the challenges inherent in predicting market movements accurately. Focused on the Indian stock market, the research harnesses the capabilities of deep learning, specifically the long short-term memory (LSTM) and gated recurrent unit (GRU) architectures, known for their prowess in processing sequential data with predictions over a 10-day horizon. the models are systematically evaluated, considering parameters such as root mean square error (RMSE), mean absolute error (MAE), regression variance, and R2-score, with a comparative analysis highlighting the effectiveness of the proposed hybrid models - LSTM-GRU and GRU-LSTM. the experimental analysis reveals that the GRU-LSTM model demonstrates superior performance on the HDFC bank Ltd. dataset, while the LSTM-GRU model excels on the TATA Motors Ltd. dataset, aligning with the predictive performance and accuracy metrics observed in earlier experiments. © 2024 ieee.

关键词： Long short-term memory

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Application Research of computer intelligent Virus Prevention Technology in Network Security Big Data Analysis Platform 4

Application Research of Computer Intelligent Virus Preventio...

引用

4th ieee international conference on Electronic Technology, communication and Information, ICETCI 2024

作者： Wang, Dingbo Tian, Xianglan Wu, Yong Nie, Lin Guan, Shengtao China Southern Power Grid Digital Grid Group Information and Telecommunication Technology Co. Ltd. Guangzhou510700 China

ISBN: (纸本)9798350361643

the manuscript introduces a novel approach for constructing a network intrusion detection system (IDS) utilizing the PB neural network within the context of a big data ecosystem. First of all, through the big data under the network illegal behavior identification function is explained. the safety performance indexes under the condition of big data are studied and the corresponding hardware structures such as sampling chip, USB interface control chip, FPGA, power management chip are given. the system includes message acquisition module, message decoding module, preprocessing module, protocol analysis module, detection engine module and log alarm module. the method of vulnerability identification based on PB artificial neural network is studied. through the test of the algorithm, the fast implementation of the algorithm in the background of big data is verified. It can detect malicious attacks on the network in real time and accurately. It promotes the research and development in the field of network illegal intrusion. the system can be popularized on a large scale in other projects. © 2024 ieee.

关键词： Network security

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Automatic Irrigation and Crop Monitoring using Machine Learning and Internet of things 4

Automatic Irrigation and Crop Monitoring using Machine Learn...

引用

4th international conference on Sustainable Expert Systems, ICSES 2024

作者： Kaur, Inderpreet Patil, Snehal Pradeep Shankar, Bhukya Beenu, G. H. Kerinab Kumar, G. Suresh Reddy, D. V. Lokeswar Ramachandran, G. Rayat Bahra University Mohali India D. Y. Patil Institute of Master of Computer Applications and Management Maharashtra Pune India Cvr College of Engineering Department of Electronics and Communication Engineering Ibrahimpatnam Hyderabad India Velammal Engineering College Surapet India Vinayaka Mission's Kirupananda Variyar Engineering College Vinayaka Mission's Research Foundation Deemed to be University Salem India Jntu College Of Engineering Humanities and Basic Sciences Department Andhra Pradesh Kadapa516390 India

ISBN: (纸本)9798331540364

the Crop environmental monitoring system with intelligence multidimensional and multi-angle crop environment monitoring system is created to satisfy the demands of contemporary society for agricultural crop environmental monitoring. the system gathers, processes, and forwards data to the network module by connecting the appropriate data gathering sensors. Ultimately, the data is sent to the information processing center for accurate processing and analysis before being visually displayed. Ultimately, the system's ability to achieve the desired result, have a high application value, and effectively increase agricultural environment surveillance efficiency is fully demonstrated by the testing and verification of its operation. Low-cost intelligent modules for smart irrigation systems powered by IoT and AI In India, agriculture is the primary source of income for the majority of people. It is essential to use cutting edge technologies to safeguard priceless water supplies. In addition to serving as Industry 4.0's cornerstone, IoT improves smart agriculture. the goal of the research is to develop a cutting-edge, affordable system for intelligent irrigation. Self-talk and inter-device networking are made possible by IoT. © 2024 ieee.

关键词： Smart agriculture

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Design and Implementation of an intelligent Crawling Robot System 4

Design and Implementation of an Intelligent Crawling Robot S...

引用

4th ieee international conference on Electronic Technology, communication and Information, ICETCI 2024

作者： Chen, Guangyi Li, Danfeng Meng, Huishan Chen, Yuanji Ye, Jihua Qin, Yong Qin, Manping Wu, Chaopeng Hechi University School of Artificial Intelligence and Smart Manufacturing Hechi China Shenzhen Grandsun Electronic Co. Ltd. Shenzhen China

ISBN: (纸本)9798350361643

To improve the efficiency and safety of rescue and transportation of materials on unstructured terrain, this method reduces manufacturing costs, and adapts to complex environments. A simple intelligent crawling robot is designed to achieve high maneuverability in various terrains, landforms, and geological environments. Compared with traditional crawling robot systems, this intelligent crawling robot is designed using low-cost embedded chips, which have the advantages of small size, low power consumption, and high performance. It is designed using embedded chips and is easy to integrate into the system. this project utilizes the GD32F103C8T6 as the main control chip, along with a Bluetooth module, infrared module, ultrasonic module, voltage reduction module, servo module, and other peripheral configuration modules. through the collaboration of various modules, the robot completes the corresponding transportation and rescue functions. through testing and verification, this simple crawling robot can carry materials on unstructured terrain. Moreover, compared with traditional crawling robots, it significantly reduces manufacturing costs. Finally, the paper provides a summary and outlook on the future development trends of crawling robots. © 2024 ieee.

关键词： intelligent robots

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AC-BiLSTM: A Spatial Bidirectional LSTM with Multi-Head Self-Attention for SNR Estimation 4

AC-BiLSTM: A Spatial Bidirectional LSTM with Multi-Head Self...

引用

4th international conference on computer Systems, ICCS 2024

作者： Xu, Boyu Ding, Tengwei Guo, Li Qingdao266107 China College of Computer Science & Technology Qingdao University Shandong Qingdao China

ISBN: (纸本)9798350366341

the Signal-to-Noise Ratio (SNR) in wireless communication is an important indicator that effectively reflects the quality information of communication signals. Traditional SNR estimation methods have problems such as insufficient estimation accuracy and limited application range of signal modulation types. To solve the above problems, this paper proposes a novel architecture based on deep learning. We significantly enhanced the model's capacity for processing sequential data by integrating multi-head self-attention mechanisms into a spatial bidirectional LSTM (BiLSTM). Furthermore, we introduced convolutional layer to further optimize the extraction of input data features and enhance the model's ability to capture and represent critical information. the proposed architecture is called attention-based BiLSTM with convolution layer (AC-BiLSTM). We used simulation to construct a communication signal dataset containing information such as different SNR and modulation methods. Experiments on this dataset showed that compared with the baseline methods, the performance and robustness of our proposed method are better, and the modulation types are more applicable. © 2024 ieee.

关键词： Signal to noise ratio

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Object Detection Using thermal Imaging 10

Object Detection Using Thermal Imaging

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10th ieee international conference on Electronics, Computing and communication Technologies (ieee CONECCT)

作者： Akshay, S. Neelakandan, Shukoh Nath, Aswin G. Amrita Vishwa Vidyapeetham Sch Comp Dept Comp Sci Mysore Karnataka India Ruhr Univ Bochum Computat Engn Bochum Germany

ISBN: (纸本)9798350385939;9798350385922

Object recognition is a powerful innovation within computer vision. It identifies and tracks objects in images and videos, with applications like facial recognition, vehicle detection, pedestrian counting, self-driving vehicles, and security systems. ImageAI, a Python library, enables deep learning and computer vision applications. Single Shot Detectors (SSDs) efficiently detect multiple objects with speed and accuracy. Tools like YOLO, OpenCV, and Pillow enhance object recognition. this project improves image efficiency using IR thermal detection, overcoming low light and fog limitations. thermal cameras enable nighttime detection. By using filters like edge detection, negative image transformations, gray-scale adjustments, and contrast stretching, we enhance IR image details, improving object detection accuracy.

关键词： Object detection computer vision Image processing Infrared imaging (IR) thermal imaging YOLOv4

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