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检索条件"任意字段=2023 International Conference on Computer Science and Automation Technology, CSAT 2023"

共 68880 条记录，以下是1321-1330 订阅

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Technical Analysis of Stock Market using Data science and its tools 7

Technical Analysis of Stock Market using Data Science and it...

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2023 7th international conference On Computing, Communication, Control And automation, ICCUBEA 2023

作者： Pagar, Shrawani Jaiswal, Aditya Auti, Atharva Purohit, Vansh Mishra, Vivek Shah and Anchor Kutchhi Engineering College Department of Cybersecurity Mumbai India Shah and Anchor Kutchhi Engineering College Department of Computer Science Mumbai India

ISBN: (纸本)9798350304268

This paper presents a technical analysis approach for the stock market using data science and its associated tools. The objective is to develop a systematic framework that leverages data science techniques to analyze stock market data and make informed investment decisions. The proposed methodology involves data collection, preprocessing, feature engineering, and the application of various machine learning algorithms for predicting stock prices and identifying potential trading opportunities. The study demonstrates the effectiveness of data science in enhancing traditional technical analysis methods and provides insights into the potential benefits of integrating data-driven approaches into stock market analysis. Experimental results on historical stock data validate the proposed approach's accuracy and highlight its potential for improving investment strategies. © 2023 IEEE.

关键词： dataframe demand and supply zone explosive candles jupyter notebook python sql yfinance

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Motor Loosened Screws Diagnosing System Based on Vibration Detection and Neural Network 21

Motor Loosened Screws Diagnosing System Based on Vibration D...

引用

21st international computer conference on Wavelet Active Media technology and Information Processing, ICCWAMTIP 2024

作者： Dantong, Feng Ying, Zhong Yijie, Ai School of Automation and Electrical Engineering Chengdu Technological University Chengdu611730 China

ISBN: (纸本)9798331519254

Motor screw loosening is a common problem in motor operation, which, if not dealt with in time, may lead to part damage and even motor failure. However, few studies have focused on the diagnosis and warning of this problem. This study is based on a monitoring system for motor vibration detection, which uses three-axis vibration sensors to collect motor vibration displacement. Through algorithm processing and calculus, vibration velocity and acceleration can be obtained. The collected vibration signals are sent to the computer by serial port communication, and a deep learning model is trained with the abnormal vibration data to achieve fault diagnosis. The experiment results show that the motor vibration data based neural network can complete the health diagnosis and early warning of the motor in the early stage of motor end cap screw loosening and bracket screw loosening, reducing the loss caused by subsequent faults, and providing a new reference scheme for the motor fault diagnosis field. © 2024 IEEE.

关键词： Screws

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Intelligent Service Oriented Architecture (SOA) for State-of-the-Art IoT-DDoS Defense and Research Challenges

Intelligent Service Oriented Architecture (SOA) for State-of...

引用

2023 international conference on IoT, Communication and automation technology, ICICAT 2023

作者： Snehi, Manish Verma, Jyoti Kansal, Isha Popli, Renu Kumar, Rajeev Saini, Gurpreet Singh Punjabi University Department of Computer Science and Engineering Punjab Patiala India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Electronics and Electrical Engineering Lovely Professional University Punjab India

ISBN: (纸本)9798350302820

The Internet of Things (IoT) is rapidly surfacing that spans all spheres of life. However, IoT devices are less secure due to limited computational capabilities, making them easy for malware for various attacks. Distributed Denial of Service (DDoS) attacks focused on the Internet of Things are among the most devastating attacks that have gained attention as the number of intelligent devices is ever increasing. Several security protocols and defensive solutions have been developed to improve the security scenario in IoT networks, the frameworks are tightly coupled. The heterogeneity of network traffic is frequently increasing, making it challenging to keep the defense solutions updated for zero-day attacks. Additionally, the tight coupling makes scaling, upgrading, and migrating defense systems challenging. The state-of-the-art solutions cannot address the increased need for on-demand services and security concerns inherent in today's Internet. This article presented a novel service-oriented architecture (SOA) for security frameworks that can be supplied to tenants and customers of tenants to mitigate these attacks. The objective of service-oriented architecture is to organise application components into a system of loosely connected services in order to build adaptable, dynamical operational procedures and adaptable applications that transcends enterprises and computing systems. The proposed Security conceptual paradigm SOA employs an SDN-based security mechanism to detect and mitigate DDoS attacks that occur in IoT computer networks. As interfaces to services for defensive systems, programming networks enable the implementation of security. © 2023 IEEE.

关键词： Denial-of-service attack

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The WhatsApp Based Cost-Effective automation at Homes through Internet of Things 9

The WhatsApp Based Cost-Effective Automation at Homes throug...

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9th IEEE international conference on Smart Structures and Systems, ICSSS 2023

作者： Mohanraj, S. Karthi, J. Sridevi, N. Purushothaman, K.E. Kalaivanan, M. Sai Ganesh, B. Rajalakshmi Engineering College Ece Department Chennai India Sathyabama Institute of Science and Technology Cse Department Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Ece Department Chennai India Ck College of Engineering and Technology Ece Department Cuddalore India

ISBN: (纸本)9798350384208

This paper highlights the necessity for using lowcost methods in automating household appliances. Internet of Things (IoT) is the well-known technology for connecting devices over internet. Implementing entire house in automation through proprietary hardware is not acceptable. Owing to this problem, open hardware is the boon for full automation at low cost. NodeMCU boards work effectively with the things that are connected online. From user end, an application has to be interfaced with things that are connected through IoT. Hence Whatsapp, is used at user end which is a well-known application to every mobile user in today's era that can monitor and command the things through sensors and internet connectivity. In this work using Whatsapp, some of the home appliances are successfully connected and tested in the Whatsapp-IoT mode which will pave the way to implement the same for entire house in future. © 2023 IEEE.

关键词： Python

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Yolo: Human Detection-Based Intelligent Home automation using IOT

Yolo: Human Detection-Based Intelligent Home Automation usin...

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2023 international conference on computer Communication and Informatics, ICCCI 2023

作者： Nagaraj, P. Sathish, S. Mathan Kumar, M. Khan, Arsalan Ahmad Mohiuddin, Ahmad Syed Haroon, M. Kalasalingam Academy of Research and Education Krishnankoil Department of Computer Science and Engineering Virudhunagar India Kalasalingam Academy of Research and Education Krishnankoil Department of Electronics and Communication Engineering Virudhunagar India

ISBN: (纸本)9798350348217

As the Internet of Things (IoT) has revolutionized the way we live and interact with the world around us, the need for a fully automated home is greater than ever. Home automation allows for the control of electronic devices in your home through the internet, eliminating the need for manual intervention, which makes life much more convenient. In this paper, we present a model for implementing home automation (controlling and monitoring remotely). We also incorporate sensors based on the specific automation needs and utilize the YOLO algorithm to increase efficiency by suggesting the best usage schedule based on user patterns. Our proposed approach for intelligent home automation integrates the YOLO object detection algorithm, which is capable of accurately identifying and locating objects in images and videos, with IoT devices. By using YOLO to detect human presence, we can automate tasks such as turning off lights when no one is in the room, adjusting the thermostat based on occupancy and activating security measures when an unauthorized person is detected. We evaluate the performance of our system using a dataset of human images and demonstrate its high accuracy in detecting and classifying human presence. Our results show that the proposed approach has the potential to greatly enhance the efficiency and convenience of home automation. © 2023 IEEE.

关键词： Internet of things

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Crop Recommendation Systems using Machine Learning Algorithms

Crop Recommendation Systems using Machine Learning Algorithm...

引用

2023 international conference on Recent Advances in science and Engineering technology, ICRASET 2023

作者： Gokila Brindha, P. ReenaSri, S. Dhanushree, U. Sivadhanu, K. Kongu Engineering College Department of Computer Technology-UG Erode Perundurai India

ISBN: (纸本)9798350306927

Agriculture is the foundation of all the countries. Due to the decreased size of a farming parkland, it has become a most important issue in picking the maximum fitting crop based on current factors in a particular field. The difficulty of young farmers in India to estimate the ideal crop based on their needs is one of the most significant problems they face. This problem arises due to the ecological factors like rainfall, humidity, temperature etc. Machine learning (ML) a branch of AI enables computer to be trained based on the experience being clearly programmed. The goal of ML is to create computer programmes so as to access the data and exploit it to learn for themselves. The crop recommender assistant will suggest the proper crop considering the parameters such as NPK nutrients, humidity, temperature along with pH values with the assistance of Machine Learning algorithms. © 2023 IEEE.

关键词： Crops

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Diabetes Prediction Using Python Machine Learning Techniques

Diabetes Prediction Using Python Machine Learning Techniques

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2023 international conference on New Frontiers in Communication, automation, Management and Security, ICCAMS 2023

作者： Kiran, Ajmeera Khan, Mudassir Babu, J. Chinna Santosh Kumar, B.P. Ahmed, Sheik Jamil Khan, Zafar Ali Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad500043 India King Khalid University College of Science&Arts Tanumah Department of Computer Science Abha Saudi Arabia Annamacharya Institute of Technology and Sciences Department of Ece AP Rajampet India Yv University Ysr Engineering College Department of Ece AP Proddatur India Presidency University School of Computer Science and Information Science India

ISBN: (纸本)9798350317060

Diabetes is a disease that can originate in a person if their blood glucose levels are continually higher. Diabetes should not be neglected since, if left untreated, it can lead to complications with the heart, kidneys, blood pressure, and other systems in the body. Diabetes can be managed, however, if it is detected at an earlier stage. In order to achieve this goal, the work we are doing on this project will assist in the early prediction of diabetes in the body of a human being. The application of machine learning algorithms yields superior results when predicting whether or not a person will develop diabetes based on previous datasets compiled using the patient's medical history. To predict diabetes, this model will employ Machine Learning Classifier and ensemble approaches on a dataset. In this model, we will test the accuracy of several different machine learning algorithms, evaluate the accuracy of each method, and then select the algorithm that provides the best fit while having the highest accuracy among the available algorithms. Many machine learning algorithms exist, such as the Support Vector Machine, the K-Nearest Neighbors (KNN), and the Random Forest Classifier (SVM).Nonetheless, we are utilizing the Random Forest Classifier since, compared to several other methods, it provides higher accuracy. This is because it is composed of several different decision trees. In addition, it makes use of randomization to improve its accuracy, prevent overfitting, and deliver a higher level of precision. We will use this Random Forest Classifier on the Pima dataset, and then, based on this earlier data, we will predict whether or not the individual has diabetes. The differentiation of system to other existing models, this project demonstrates a higher degree of accuracy. Also, we are integrating our model into the front end, which is simple to operate. © 2023 IEEE.

关键词： Decision trees

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An AI Planning Approach to Supply Chain Logistics Planning and Scheduling 6

An AI Planning Approach to Supply Chain Logistics Planning a...

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6th international conference on Internet of Things, automation and Artificial Intelligence, IoTAAI 2024

作者： Tu, Mingjun Liang, Ruishi Zheng, Fanghao University of Electronic Science and Technology of China School of Computer Science and Engineering ChengDu China University of Electronic Science and Technology of China Zhongshan Institute School of Computer Science Zhongshan China

ISBN: (纸本)9798350386974

Fuelled by rapid advancements in logistics technology and the internet, the transaction volume in the food industry continues to soar, accompanied by escalating consumer expectations regarding the freshness and safety of various foods, notably perishable produce, chilled products, and pre-prepared dishes. As the key step ensuring the freshness and safety of food, efficient planning and optimization of supply chain logistics have become increasingly vital. This paper introduces an AI planning model for supply chain logistics planning and scheduling. Firstly, it extracts knowledge from the supply chain logistics process, with particular emphasis on controlling cargo damage and costs induced by temperature fluctuations in cold chain logistics. Secondly, incorporating elements of numerical and preference planning, a dual-objective optimization model is established, aiming at concurrently minimizing costs and reducing cargo losses during planning. Formulated using PDDL (Planning Domain Definition Language), this model is transformed into an AI-planning task. Ultimately, the model's effectiveness is demonstrated through scenario-based resolution by planning algorithms, showcasing its capability to make optimized decisions tailored to diverse objectives. It adeptly balances cost reduction with cargo damage mitigation, thereby enhancing customer satisfaction and furnishing invaluable guidance for supply chain logistics planning and scheduling. © 2024 IEEE.

关键词： Customer satisfaction

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A Lightweight Model for Object Detection in computer Motherboard Images 23

A Lightweight Model for Object Detection in Computer Motherb...

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7th international conference on Innovation in Artificial Intelligence, ICIAI 2023

作者： Wen, Dejun Zhou, Yinghua School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9781450398398

Directly benefiting from the deep neural network, object detection algorithm has played a key role in industrial automation in recent years. computer motherboard component detection is essential in the industrial automation of computer assembly or disassembly and custom computer maintenance scenarios. Existing public datasets like MS COCO do not focus on particular detection scenarios. In addition, the popular datasets (e.g., PCB-Aol, micro-PCB, etc.) committed to electronic component detection are limited in the number of categories, and diversity. So, we construct a multi-category and diversity dataset called CMC-14. The dataset contains 14 categories, 2,145 images, and 25,718 labeled instances with a large variance in appearance. Moreover, there is no deep neural network model focused on computer motherboard detection before our work. computer motherboard component detection exists two great challenges: (1) Some objects are small in scale and lack sufficient feature information;(2) Some objects are similar in appearance, but they do not belong to the same category. To cope with such challenges, we improved the YOLOv5n and designed a dense small object data augmentation algorithm. Extensive experiments and comprehensive evaluations on the CMC-14 benchmark dataset show the effectiveness of our work and methods. The dataset is available at https://***/CMC-14/CMC-14/releases/tag/CMC-14. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Object detection

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Heterogeneous Graph Convolutional Network for Visual Reinforcement Learning of Action Detection 8

Heterogeneous Graph Convolutional Network for Visual Reinfor...

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8th international conference on automation, Control and Robotics Engineering, CACRE 2023

作者： Wang, Liangliang Huang, Chengxi Chen, Xinwei Wuhan University of Technology School of Computer Science and Artificial Intelligence Luoshi Road 122 Wuhan430070 China -HIT Research Institute of Engineering and Technology Building 9 Software Park Quanzhou362008 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou350121 China

ISBN: (纸本)9798350302776

Existing action detection approaches do not take spatio-temporal structural relationships of action clips into account, which leads to a low applicability in real-world scenarios and can benefit detecting if exploited. To this end, this paper proposes to formulate the action detection problem as a reinforcement learning process which is rewarded by observing both the clip sampling and classification results via adjusting the detection schemes. In particular, our framework consists of a heterogeneous graph convolutional network to represent the spatio-temporal features capturing the inherent relation, a policy network which determines the probabilities of a predefined action sampling spaces, and a classification network for action clip recognition. We accomplish the network joint learning by considering the temporal intersection over union and Euclidean distance between detected clips and ground-truth. Experiments on ActivityNet v1.3 and THUMOS14 demonstrate our method. © 2023 IEEE.

关键词： Reinforcement learning

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