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Hybrid Statistical and AI Methods for Synthetic Time Series Generation in Practical Applications 20

Hybrid Statistical and AI Methods for Synthetic Time Series ...

引用

20th ieee international conference on intelligent computer communication and processing conference, ICCP 2024

作者： Ungureanu, Iulia Iarina Portase, Raluca-Laura Dinsoreanu, Mihaela Lemnaru, Camelia Potolea, Rodica Computer Science Department Technical University Cluj-Napoca Romania

ISBN: (纸本)9798331539979

Time series are one of the most common data types in data mining, capturing sequential observations over long periods. Creating synthetic time series data involves generating a sequence of data points that maintain the statistical properties found in real-world datasets. this article addresses the generation of synthetic time series data from real univariate time series datasets. It presents two novel methodologies grounded in Seasonal-Trend decomposition using LOESS (STL). the first method involves estimating the probability distribution of the residual component and generating new residuals to substitute in the STL decomposition. the second method replaces the noise component with a randomized version and synthesizes seasonality using Long Short-Term Memory (LSTM) neural networks. We applied these methodologies to time series data representing the operating times of household appliances. the synthetic data produced were assessed both quantitatively and qualitatively using a range of statistical indicators. Moreover, we improved the classification performance on imbalanced real data with synthetic data. © 2024 ieee.

关键词： Adversarial machine learning

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Predicting the Severity of Chronic Kidney Disease with J48 4

Predicting the Severity of Chronic Kidney Disease with J48

引用

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

作者： Jaya Lakshmi, A. Joe Prathap, P.M. Lokesh, K. Dev Anand, D. Jagathieshwaran, J. Sneha, M. Gojan School of Business and Technology Department of Computer Science and Engineering Chennai India R.M.D. Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350306118

Chronic kidney disease (CKD) is an intensifying and immutable condition in which the kidneys slowly reduces is actual function. CKD can be effected and induced because of various aspects like high cholesterol, blood pressure, genetic disorders, kidney inflammations and infections. this health issue is affecting millions of people across the globe. the primary objective considered is to envision disease at an early stage by implementing different algorithms on the medical records of the patients. Since early stage of diagnosis may be helpful in reducing the severity of the disease. this study has a significant concern in deciding the suitable Machine Learning (ML) algorithm that suits best for analyzing CKD based on the accuracy rate. the demonstration work is performed with the commonly utilized machine learning classifiers. As a result of the demonstration work, it is revealed that J48 algorithm has higher rate of accuracy than other conventional machine learning techniques. © 2023 ieee.

关键词： Diagnosis

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Augmenting Cybersecurity and Fraud Detection Using Artificial Intelligence Advancements 4

Augmenting Cybersecurity and Fraud Detection Using Artificia...

引用

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

作者： Kumar, Keshaw Kuldeep Bhushan, Bharat Sharda University Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350306118

In 2023, the rapid advancement of Generative AI (GenAI) models, including ChatGPT and Google Bard, has reshaped the digital landscape. GenAI tools are now used both defensively and offensively, raising crucial questions about their societal, ethical, and privacy implications. Artificial Intelligence (AI) plays a pivotal role in the Fourth Industrial Revolution, enhancing the security of computer networks. this study investigates these connections, finding that e-Governance partially mediates the link between AI and cybersecurity, with stakeholders influencing this dynamic. AI's increasing integration into daily life, spanning healthcare, finance, and surveillance, emphasizes the importance of data privacy. the handling of extensive datasets by AI systems brings about unique privacy and ethical challenges, necessitating stringent safeguards. this concise abstract underscore the profound impact of GenAI models on cybersecurity in 2023, emphasizing their implications for society, ethics, and privacy. It highlights nuanced relationships between AI, e-Governance, and cybersecurity, while acknowledging the need for robust privacy protection. the paper calls attention to the potential of advanced techniques to fortify cybersecurity against ever-evolving threats. © 2023 ieee.

关键词： Cybersecurity

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Research on Vibration Signal Classification and Recognition System Based on computer Deep Learning Technology 4

Research on Vibration Signal Classification and Recognition ...

引用

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

作者： Fuxiang, Yu Zhenwang, Lv Zhaoran, Meng Bo, Liu Dalian Scientific Test and Control Technology Institute Dalian China

ISBN: (纸本)9798350361643

Reducing equipment downtime by detecting function decay signals. then the fault feature recognition technique of vector extreme learning machine is proposed. the mixed singular value decomposition method based on wavelet transform can effectively extract the vibration signal features containing impact noise. Finally, the obtained eigenmatrix is normalized and sent to the extreme learning machine for recognition. A representative sample of training was tested and the results showed that the accuracy of each training method improved. By comparing with BP neural network and support vector machine, the advantages of this algorithm in recognition accuracy and training speed are verified. Based on vibration analysis of intelligent fault diagnosis, BP neural network is used to extract eigenvalues of each type of fault to obtain diagnosis results of each type of fault. the results show that this method can identify the fault type effectively with high accuracy. the results show that the algorithm can accurately identify different fault types and fault degrees of equipment, and improve the reliability of equipment operation. © 2024 ieee.

关键词： Vibration analysis

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A Fuzzy License Plate Image processing Technology Based on MATLAB 4

A Fuzzy License Plate Image Processing Technology Based on M...

引用

4th international conference on computer Engineering and Application, ICCEA 2023

作者： Dang, LongFei. Yang, Miao. Si, Binghe. Yi, Kai. Sichuan Police College Intelligent Policing Key Laboratory of Sichuan Province LuZhou China Sichuan Police College Department of Computer Science and Technology LuZhou China Sichuan Police College Department of Criminal Technology LuZhou China

ISBN: (纸本)9798350347548

In recent years, the criminal digital image processing technology is becoming more and more important in the public security business, especially the static fuzzy license plate processing technology. It is particularly important to identify the vehicle used by the suspect after the hit-and-run, the vehicle used by the suspect before and after the crime in the theft case and the vehicle used to transport the drug in the drug case. Paper focuses on using MATLAB how to deal with fuzzy plates allow investigators to identify the license plate content of Chinese characters, Numbers and letters, allow investigators to identify the vehicle's license plate number, determine the relevant vehicle information, locking the vehicles, and then to carry out relevant investigation activities, use a combination of theory and the software for processing images, Finally achieve the desired results. © 2023 ieee.

关键词： MATLAB

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Integrating GAN to MFCC-Assisted RNN-LSTM Algorithms for Identifying Types of Tribes Based on English Pronunciation 4

Integrating GAN to MFCC-Assisted RNN-LSTM Algorithms for Ide...

引用

4th international conference on Electronic and Electrical Engineering and intelligent System, ICE3IS 2024

作者： Widodo, Agung Mulyo Baghban, Hojjat Rahaman, Mosiur Baharudin, Erwan Ulum, Muhamad Bahrul Aryani, Diah Universitas Esa Unggul Department of Computer Science Jakarta Indonesia Chang Gung University Department of Artificial Intelligence Taoyuan Taiwan Asia University Taichung Taiwan Universitas Esa Unggul Department of Communication Jakarta Indonesia Asia University Department of Foreign languages and Literature Taichung Taiwan

ISBN: (纸本)9798350378368

Many studies based on Natural Language processing (NLP) that have been carried out focus on the results of speech recognition on devices, but do not discuss the speech recognition process that is applied until the device works according to voice commands from the user. One of the factors for success in the classification process is the amount of unbalanced data. Imbalances in the data can impact the performance of the Deep Learning architecture used to create classifier models. therefore, it is proposed to integrate the Generative Adversarial Network (GAN) algorithm and the Mel Frequency Cepstral Coefficient (MFCC) algorithm assisted by RNN-LSTM which is used in Speech Recognition in English Pronunciation by Non-Native Speakers. the proposed model is able to produce ethnic recognition based on the pronunciation made by a speaker. the study was conducted using 2,722 voice samples from 4 indigenous tribes in Indonesia, namely, the Ambonese, Sundanese, Javanese and Betawi. By using the proposed method and after testing, a classifier model was obtained with an accuracy of 78.12%. © 2024 ieee.

关键词： Speech recognition

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Enhancing Road Safety in Diminished Vision Conditions: A Deep Learning Approach Using Faster MobileNet for Real-time Object Detection 4

Enhancing Road Safety in Diminished Vision Conditions: A Dee...

引用

4th international conference on intelligent Technologies, CONIT 2024

作者： Subbaiah, D. Venkata Sricharan, Byreddi Aman, Shaik Zeenath Gopinath, Kasarla Department of Computer Science and Engineering Mylavaram India

ISBN: (纸本)9798350349900

Despite significant enhancements in car safety technology, traffic accidents still happen, so finding creative ways to reduce the risk is necessary, particularly in situations with poor vision, like fog. this study presents a unique method using deep learning, namely the Faster MobileNet model, to reduce traffic accidents in situations where eyesight is impaired. the suggested solution makes use of a live video feed from the car's front-mounted camera and Faster MobileNet to identify and highlight things on the driver's display instantly. To improve image quality, the system incorporates image processing methods such as data augmentation, normalization, and scaling. Convolutional and pooling layers are used in feature extraction, together with a Region Proposal Network (RPN) to find potential object areas. Classifiers are used in object detection to recognize different kinds of objects and give the driver pertinent information. Non-Maximum Suppression (NMS) is incorporated into the implementation as a post-processing step to eliminate duplicate detections and improve the result. By acting as an intelligent helper, the created application greatly increases poor vision drivers' visibility and promotes a safer driving environment. © 2024 ieee.

关键词： intelligent systems

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EfficientAD: A Deep Learning Approach for Multi-Stage AD Classification Using Transfer Learning 4

EfficientAD: A Deep Learning Approach for Multi-Stage AD Cla...

引用

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

作者： thakran, Archana Gupta, Yogesh Kumar Banasthali Vidyapith Department of Computer Science Rajasthan India

ISBN: (纸本)9798350306118

Alzheimer's disease (AD) is a neurodegenerative disease that has affected millions of elderly people worldwide. It is the most common cause of dementia among the aged population. It affects the cognitive skills of a person making it difficult to perform daily chores. Due to the incurability and progressive nature of AD, it has garnered a huge interest from the researchers. Neurologists treating AD patients analyze various neuroimaging scans using computer-Aided Diagnosis (CAD) systems. these systems use DL algorithms to detect AD early and accurately. In this paper, various pretrained CNN models namely EfficientNetB7, VGG16, VGG19 and VGG Ensemble models were employed for binary and multistage classification of AD by using transfer learning. this paper achieved an accuracy of 97.88% using the EfficientNetB7, 91.36%, 92.81 % and 95.16% using VGG16, VGG19 and VGG Ensemble models respectively. © 2023 ieee.

关键词： computer aided diagnosis

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DFNet: Towards Real-Time Semantic Segmentation with Depthwise Separable Convolution 4

DFNet: Towards Real-Time Semantic Segmentation with Depthwis...

引用

4th international conference on intelligent Design, ICID 2023

作者： Huang, Zhiwei Wang, Luping Wu, Wuming Sun Yat-Sen University School of Electronics and Communication Engineering Shenzhen518000 China

ISBN: (纸本)9798350342574

Semantic segmentation refers to assigning a category label (such as pedestrian, car, building, road, sky) to each pixel in the image to help the computer understand the content of the image and conduct subsequent processing, such as the automatic driving car to identify roads, vehicles, pedestrians from the acquired images to make correct driving decisions, or help doctors diagnose diseases in medical image analysis. It also can provide semantic information for robot navigation. Running semantic segmentation algorithms in edge devices requires powerful hardware performance, most of the current deep neural networks is large, which seriously limits their application and deployment in practical scenarios. For this purpose, we propose a new convolutional network architecture named DFNet, which employ dilated convolution and depthwise separable convolution into segmentation networks, aiming to find the best balance between accuracy and speed. Tests on cityscapes dataset show that our model is almost as accurate as UNet, but model parameters are reduced by 4 times and the segmentation speed is increased 75%. © 2023 ieee.

关键词： Semantic Segmentation

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Classification of Plant Leaf Diseases Based on EfficientNet 4

Classification of Plant Leaf Diseases Based on EfficientNet

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4th international Signal processing, communications and Engineering Management conference, ISPCEM 2024

作者： Fan, Yi Zhang, Xing School of Computer Science and Technology Huaiyin Normal University Huaian Jiangsu223300 China

ISBN: (纸本)9798331528676

With the in-depth research of deep learning technology, convolutional neural networks have been widely applied in the field of image recognition. However, traditional diagnostic methods for plant leaf diseases often rely on expert experience and time-consuming manual detection, which is inefficient and limited in accuracy. Based on the above background, this paper mainly studies the plant leaf disease classification method based on EfficientNet. Firstly, the EfficientNet algorithm is reviewed, followed by a detailed description of the process of the plant leaf disease classification algorithm and then an analysis of the dataset used. In the simulation analysis, this paper explores the impact of different parameters on the performance of EfficientNet and demonstrates its effectiveness in plant leaf disease classification. the proposed method has efficient and accurate results, significant research significance, and application prospects in the fields of agricultural automation and intelligent diagnosis. ©2024 ieee.

关键词： Convolutional neural networks

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