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A Novel Procedure for Recognising Human Activity Using Machine Learning 4

A Novel Procedure for Recognising Human Activity Using Machi...

引用

4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Singh, Neelam Bhatt, Mansi Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Department of Computer Application Dehradun India

ISBN: (纸本)9798350300888

In the past few years it is seen that there has been a boom in the usage of smart devices consisting of sophisticated sensors such as gyroscope or gyro and accelerometer. Human Activity Recognition (HAR) is one of these sensors' applications. Given the dependence on general Machine Learning (ML) techniques, Deep learning techniques are suggested by current research to be suitable for feature extraction from unprocessed sensor data. The study focuses on Human Activity Recognition (HAR) by means of data gathered from smartphone accelerometer sensors and employs Long-Short Term Memory (LSTM) networks in the time-series domain. Furthermore, a hybrid network consisting of a 2-layer RNN-LSTM is proposed to enhance the accuracy of recognition performance. For the input and output activation function in the neural network ReLU and Softmax function respectively are used. © 2023 IEEE.

关键词： Long short-term memory

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Analysis of Energy Quality Issues in the Solar Energy System 3

Analysis of Energy Quality Issues in the Solar Energy System

引用

3rd IEEE International Conference on Electrical Power and Energy Systems, ICEPES 2024

作者： Uniyal, Ishita Tiwari, Shweta Vishal Dhondiyal, Shiv Ashish Graphic Era Hill University Department of Electrical Engineering Dehradun India University Institute of Engineering Chandigarh University Department of Computer Science & Engineering Punjab Mohali India School of Computer Application Lovely Professional University Punjab Jalandhar India Graphic Era Deemed to be University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350390728

Photovoltaic systems are being used more and more in the production of electricity because of the high cost of fossil fuel-based energy. The main issue that happens between grid and end-user transmission lines is power quality. DSTATCOM is a power quality compensating device that corrects voltage sag and swell are examples of power quality concerns. In high voltage line for power transmission this can be happened. The study was carried out with MATLAB or Simulink software to resolve the voltage sag and swell, and the model was created. The simulation results show the problem of voltage sag and swell is minimize to very large extent. © 2024 IEEE.

关键词： Power distribution lines

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Outlier Detection Using Machine Learning Algorithms Integrated with Bayesian Optimization

Outlier Detection Using Machine Learning Algorithms Integrat...

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2022 International Conference on Algorithms, Data Mining, and Information Technology, ADMIT 2022

作者： Liu, Xihua Ma, Tao Gao, Wenjie Zhu, Xinglin Wen, Yong Pan, Wenqiang Institute of Computer Application China Academy of Engineering Physics Mianyang China

ISBN: (数字)9781665454728

ISBN: (纸本)9781665454728

This study aimed to discuss the performance of machine learning algorithms on the outlier detection problem and to investigate the effect of Bayesian Optimization Algorithm (BOA) on improving the performance of machine learning ***, Classification And Regression Tree (CART), Extreme Gradient Boosting (XGB), and Light Gradient Boosting Machine (LGBM) models were constructed. The models were trained using default parameters, and it was found that XGB and LSTM performed much better than CART. secondly, the best hyperparameters were searched using grid search, random search and Bayesian optimization, respectively, and the comparison revealed that BOA could search for the best hyperparameters in the shortest time, resulting in the maximum model performance. Finally, by analyzing the ROC and PRC curves of the three ML models tuned to the optimal hyperparameters using BOA, it is found that the combination of LSTM+BOA has the best detection effect on outlier points with an average precision of 0.911. Through the results of this study, it is confirmed that the machine learning model tuned with BOA can have a robust outlier detection capability, and the method can be applied to other method can be applied to other outlier detection problems, such as network intrusion detection. © 2022 IEEE.

关键词： Anomaly detection

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An Efficiency Evaluation Model of Reactor Safety Operation

An Efficiency Evaluation Model of Reactor Safety Operation

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2022 IEEE International Conference on Artificial Intelligence and computer applications, ICAICA 2022

作者： Fan, Huanhuan An, Baoran Wu, Huai Liu, Yunfei Institute of Computer Application China Academy of Engineering Physics Mianyang China

ISBN: (数字)9781665499910

ISBN: (纸本)9781665499910

Reactor operation may cause casualties, economic losses, environmental pollution and other adverse aftereffects. In this paper, a reactor operation safety evaluation model is built by combining subjective and objective combination weighting, traditional ideal solution and grey relational grade phase structure. The new relative closeness degree is calculated by modelling and simulation technology to judge the advantages and disadvantages of different reactor schemes. Compared with the expert score of the reactor operation scheme, the evaluation model can effectively evaluate the operation scheme, so that the staff can complete the task in the shortest possible time with less cost on the premise of ensuring safety. © 2022 IEEE.

关键词： Losses

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A Test Design and Coverage Measurement Method for Equipment Software System Testing 10

A Test Design and Coverage Measurement Method for Equipment ...

引用

10th IEEE Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2022

作者： Zhang, Jinkun Peng, Yanhong Yang, Hongqing Institute of Computer Application China Academy of Engineering Physics Mianyang China

ISBN: (数字)9781665422079

ISBN: (纸本)9781665422079

System testing has been widely used as an effective method for testing and evaluating equipment software in the software product qualification stage. However, due to the lack of practical test design and coverage measurement methods, it is still difficult for testers to answer the question of test design adequacy and coverage of software quality requirements after implementing system testing. To this end, this paper studies various test design techniques and proposes a test design and coverage measurement method for equipment software system testing. This method uses a uniform process for test design for different software quality characteristics. It can measure the test design coverage of a single test item or overall software quality requirements. In this paper, we also perform a system test analysis of equipment software based on a software product quality model to identify test items, test types to be conducted, and test design techniques to be applied to provide consistent input and guidance for system test design. We apply our method in the system testing of typical equipment software. The results show that this method can conduct test design by a uniform process for different software quality requirements and facilitates the measurement of test design coverage. It has important theoretical significance and practical engineering value. © 2022 IEEE.

关键词： Software testing

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A Comparative Analysis of Machine Learning Models for Colon Cancer Classification

A Comparative Analysis of Machine Learning Models for Colon ...

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2023 International Conference in Advances in Power, Signal, and Information Technology, APSIT 2023

作者： Swain, Rashmi Ranjan Nayak, Debasish Swapnesh Kumar Swarnkar, Tripti Siksha 'o' Anusandhan University Department of Computer Application Bhubaneswar India Siksha 'o' Anusandhan University Department of Computer Science and Engineering Bhubaneswar India

ISBN: (纸本)9798350339369

One of the trickiest machine learning challenges is the automatic classification of cancer cells. Colon Cancer can strike anyone at any age, but it often strikes older persons. In recent years, several investigations have focused on developing machine learning algorithms that can automatically classify genes of various types of a particular disease, especially colon cancer. The classification of cancer has improved due to improvements in computational methods like machine learning (ML), which required little human interaction and inefficient computers. Since the classification of gene expression data has been extensively studied in conjunction with the advancement of gene technology, a wide variety of techniques, primarily neural network-related, have been applied to the analysis of medical data, which is primarily concerned with the large dimension and very small in quantity. In this work, five different ML techniques were implemented for the classification of colon cancer in different age groups. The implemented ML techniques are Random Forest (RF), Decision Tree (DT), Support vector Machine (SVM), Naive Bayes (NB), and K-Nearest Neighbour (KNN). It is observed that most of the implemented model has an average accuracy of more than 95%. © 2023 IEEE.

关键词： Decision trees

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Conventional Neural Network for Maths Handwritten Digit Categorization 6

Conventional Neural Network for Maths Handwritten Digit Cate...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Garg, Shivya Kuchhal, Navya Barmola, Jigyasa Rawat, Vandana Singh, Neelam Graphic Era Deemed to Be University Department of Computer Application Dehradun India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350304480

Handwritten digit recognition plays a crucial role in various sectors such as document processing, automatic form identification, and postal automation. This task involves accurately identifying and classifying handwritten digits into their respective numerical values. This presentation provides a summary of current developments in machine learning techniques for handwritten digit recognition. The primary objective of this research is to develop an advanced system for recognizing handwritten digits by harnessing state-of-the-art machine learning techniques. To train and test the suggested system, a sizable dataset of handwritten digits is used. We are utilizing the MNIST dataset here. To improve the quality of the input pictures and guarantee uniform feature representation, preprocessing techniques like image enhancement and normalization are used. Experimental results indicate that the CNN model surpasses all other investigated techniques in accuracy, boasting an impressive accuracy rate of over 98%. The capacity of the CNN model to automatically deduce pertinent characteristics from the input pictures, catching both local and global patterns, is credited with its better performance. Additionally, the model can successfully handle spatial relationships in the digit pictures because to CNNs'usage of convolutional layers. A number of practical applications, such as computerized form processing, digital document analysis, and intelligent character recognition, are possible with the suggested handwritten digit recognition system. The CNN model's high accuracy enables accurate digit categorization, enhancing efficiency and accuracy across a variety of applications. Future research can concentrate on investigating ensemble approaches, data augmentation methods, and fine-tuning techniques to improve the handwritten digit recognition system's performance even further. © 2023 IEEE.

关键词： CNN digit recognition epochs Handwritten recognition hidden layers machine learning neural network

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Study on Penetration Testing Platform Oriented to CAN Bus Embedded System 7

Study on Penetration Testing Platform Oriented to CAN Bus Em...

引用

7th International Conference on Signal and Image Processing, ICSIP 2022

作者： Yang, Wansheng Hu, Chi Ma, Siyou Institute of Computer Application China Academy of Engineering Physics Mianyang China

ISBN: (数字)9781665495639

ISBN: (纸本)9781665495639

This paper designs a penetration testing platform on the CAN bus. The platform is characterized by the combined functions, such as, CAN bus data acquisition and transmission, data preprocessing and analysis, CAN news automatic detection and construction, CAN communication database establishment, various penetration and attack methods and recoding CAN bus arbitration ID. Aiming at the information security verification of the CAN bus protocol of the embedded system, the penetration testing platform can flexibly and quickly build a testing environment to successfully realize the connection with the system under test and the hidden connection of forged communication nodes, and then carry out fuzz testing verification experiments efficiently and conveniently. © 2022 IEEE.

关键词： Embedded systems

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Enhancing Relational Database Interaction through Open AI and Stanford Core NLP-Based on Natural Language Interface 5

Enhancing Relational Database Interaction through Open AI an...

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5th IEEE International Conference on Recent Trends in computer Science and Technology, ICRTCST 2024

作者： Kumar, Surjeet Alam, Md Shamsher Khursheed, Zeeshan Bashar, Shadan Kalam, Neha Government Engineering College Computer Science & Engineering Bihar Sheikhpura India R.V.S College of Engineering & Technology Computer Science and Engineering Jamshedpur India Jio Platform Limited DGM Mumbai India R.V.S College of Engineering & Technology Master of Computer Application Jamshedpur India R.V.S College of Engineering & Technology Computer Science and Engineering Student Jamshedpur India

ISBN: (纸本)9798350351378

In the contemporary digital era, data has evolved into the cornerstone of myriad sectors, encompassing academia, commerce, and medical research. Yet, the acquisition of insights from this expansive reservoir of information has historically demanded a level of proficiency in formal languages such as SQL, thereby imposing a substantial barrier for individuals lacking technical expertise. In response to this pervasive challenge, the advent of Natural Language Interface to Databases (NLIDB) has emerged as a promising and transformative solution. NLIDB offers users the capability to engage with databases through the utilization of their natural language, whether expressed in textual or verbal form. This paradigm shift empowers both seasoned experts and neophytes to effortlessly extract valuable information from databases without the prerequisite of specialized technical skills. In this research paper, we propose an innovative system that capitalizes on an intermediary SQL generation process, facilitating the seamless bridge between natural language input and subsequent database retrieval. This novel approach eliminates the need for users to possess awareness of the intricate underlying complexities of the database management process. By simplifying the interaction between users and databases, our system endeavours to democratize data access and promote the universal utilization of this invaluable resource across diverse domains. © 2024 IEEE.

关键词： Natural language processing systems

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Predicting the Spread of Covid-19 Using Machine as a Service (MaaS) Through Modified Apriori Algorithm

Predicting the Spread of Covid-19 Using Machine as a Service...

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International Conference on Smart Systems and Wireless Communication, SSWC 2024

作者： Sett, Samir Kumar Sett, Aishik Ghosh, Anupam Department of Computer Application Techno India Kolkata India ITC Infotech Kolkata India Department of Computer Science and Engineering and Department of Artificial Intelligence & Machine Learning Netaji Subhash Engineering College Kolkata India

ISBN: (纸本)9789819613472

The cloud computing and machine learning is one of growing technology in the field of computer science. In recent years, the word "Cloud" is like a cup of tea in our daily lives. Every day, a significant volume of data is produced by different IT sectors, banking sectors, e-commerce websites like flipkart. It provides the organization with consistency, scalability, and strong enumerating power. It also delivers eminence service by generating proper outcomes in a brief amount of time. The objective of this paper is to predict the growth of Covid-19 virus in near future using Machine as a Service (MaaS) through modified Apriori algorithm. In this paper, we will obtain the working graph of the algorithm. The result is also analyzed by obtaining the graphs for confidence against lift, support against confidence. At last, a running time comparative graph for modified apriori and apriori is shown for a certain duration of time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Apriori Cloud computing Confidence Covid-19 MaaS Support

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