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Time series anomaly detection based on TimeGAN and LSTM neural network 11

Time series anomaly detection based on TimeGAN and LSTM neur...

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11th International Conference on Advanced Cloud and Big Data, CBD 2023

作者： Han, Weixin Ying, Wenhao Hu, Wenjun Sun, Zhongqiang Huzhou University School of Information Engineering Huzhou China Changshu Institute of Technology School of Computer Science and Engineering Changshu China Soochow University School of Computer Science and Technology Suzhou China

ISBN: (纸本)9798350345346

Anomaly detection is one of the most mature applications in time series data analysis. However, due to the nonlinearity of time step and sequence, it often leads to abnormal state of a specific step in time series. On the one hand, previous GAN studies focused on the method of generating sequence data, but did not pay attention to the autocorrelation of sequences. After all, a good model should learn not only the distribution of features across each timestamp, but also the underlying relationships between different points in time. On the other hand, a good generation model should ensure the generation of high-quality synthetic data. Most of the researches on this aspect are based on RCGAN, so the TimeGAN applied in this paper is also conducted on this basis, and the visualization analysis of data is combined with T-SNE to evaluate the quality of the model qualitatively. Then LSTM neural network is used to evaluate the influence of periodicity, noise, step size and other factors on the model. The results show that both methods can detect anomalies effectively, but TimeGAN is better than traditional RCGAN, which verifies the feasibility of this method. © 2023 IEEE.

关键词： Anomaly detection

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Machine Learning Algorithms for Predicting House Prices in a Smart city using its Real-Time Data 2

Machine Learning Algorithms for Predicting House Prices in a...

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2nd International Conference on Electronics and Renewable Systems, ICEARS 2023

作者： Joel, M. Robinson Navaneethakrishnan, M. Sriram, K.P. Manonmani, S. Priskilla Kings Engineering College Department of Information Technology Chennai India St. Joseph College of Engineering Sriperumbudur Department of Computer Science and Engineering Chennai India Meenakshi Sundararajan Engineering College Department of Information Technology Chennai India

ISBN: (纸本)9798350346640

Housing prices are an important economic indicator, wherein the property values are important to both buyers and sellers. One of the least transparent industries across the world is real estate. In order to conduct the research, real-time housing data has been collected from Chennai City. Sine this study deals with the continuous outcome variable, regression techniques are highly preferred. The major goal of this research study is to predict housing values by using real-time information. This study performs various house price evaluation process based on various factors. Several regression models like Lasso and Ridge are utilized and their performances are compared in order to select the best model for resolving a specific issue. This study intends to develop an effective machine learning model to predict the price of a property based on input parameters and display the information in a website to reach out to people. © 2023 IEEE.

关键词： Machine learning

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Adaptive IoT Decision Making in Uncertain Environments 7

Adaptive IoT Decision Making in Uncertain Environments

引用

7th IEEE International Conference on Smart Internet of Things, SmartIoT 2023

作者： Wang, Yan Zheng, Liwei He, Jingjing Cui, Zhanqi Computer School Software Engineering Research Center Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9798350316575

The rapid growth of the Internet of Things (IoT) has led to widespread deployment of IoT systems in domains such as smart homes, healthcare, and transportation. However, IoT systems often operate under uncertainty, making it difficult to predict and control their behavior. In this paper, we propose an adaptive decision making approach for IoT systems in uncertain, dynamic environments. We present a framework with perception, decision and execution layers to handle uncertainty in IoT systems. The perception layer senses the environment and system state. The decision layer employs an optimized deep Q-network algorithm (Ad-DQN) specifically designed to handle uncertain environments, enabling it to make informed decisions based on learned experiences. The execution layer implements the actions. We demonstrate the framework on an intelligent air conditioning system as a case study of an IoT system operating under uncertainty. The Ad-DQN based decision layer adapts the air conditioning control policy to maximize comfort while minimizing energy usage. Experiments show our method outperforms traditional DQN method in uncertain environments. © 2023 IEEE.

关键词： Reinforcement learning

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AI-Driven Advanced Text Matching for Improved information Capture and Retrieval 3

AI-Driven Advanced Text Matching for Improved Information Ca...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Agrawal, Trapty Kulhar, Kuldeep Singh Singh, K. Ranjith Indira, G. Shelke, Priya M. Kannagi, A. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Civil Engineering Jaipur India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Chennai India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Department of Computer Science and Information Technology Karnataka Bangalore India

ISBN: (纸本)9798350319125

This paper presents an AI-pushed advanced text matching technique to improve facts capture and retrieval. The proposed approach makes use of deep getting to know and natural language processing algorithms to successfully compare texts and discover similarities and dissimilarities among them. The deep gaining knowledge of algorithm is trained on a textual content corpus that serves as enter statistics for the model. This corpus may be made from diverse styles of textual information depending at the goal of the assessment. To make certain accuracy, the model is outfitted with parameters consisting of word embedding, semantic similarity, and fuzzy string matching. Once skilled, the version is tested to discover false positives and fake negatives. Finally, the model is carried out to a hard and fast of statistics to quantify fits among texts and retrieve useful statistics. Consequences show a discount in false positives and a higher precision of the version in retrieving applicable statistics from given facts. © 2023 IEEE.

关键词： dissimilarities presents similarities statistics version

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A Deep reinforcement Learning Approach for Early Detection of tomato plant disease using CNNs and machine learning Techniques 4

A Deep reinforcement Learning Approach for Early Detection o...

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Kumar, Amit Ekvitayavetchanukul, Pongkit Donda, Prashanth Gupta, Anurag Sachdeva, Vrinda Singh, Bakshish Ajay Kumar Garg Engineering College Department of Information Technology Ghaziabad India The Board of Khonkaen University Affairs Khonkaen University Thailand CVR College of Engineering Department of Csit Hyderabad India Abes Engineering College Department of Computer Engineering Ghaziabad India G.L Bajaj Institute of Technology and Management Department of Computer Science and Engineering Greater Noida India Raj Kumar Goel Institute of Technology Department of Information Technology Ghaziabad India

ISBN: (纸本)9798350364729

The present research attempts to improve tomato plant disease early detection strategies by using a variety of machine learning models, such as Recurrent Neural Networks (RNN), K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Convolutional Neural Networks (CNN). Using the special advantages of these models, the major objective is to correctly diagnose diseases like Early blight, Bacterial leaf spot, Bacterial wilt, and Leaf curl. The work expands the foundation for disease detection by careful dataset construction, feature extraction, and preprocessing. The accuracy of CNN is the greatest at 97.5%, followed by SVM at 92.2%), KNN at 88.4%), and RNN at 86.2%. The results show remarkable accuracy. Our research demonstrates the promise of machine learning in precision agriculture by providing a useful tool for managing and diagnosing diseases early on, increasing crop output, and encouraging environmentally friendly farming methods. © 2024 IEEE.

关键词： Deep reinforcement learning

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Smoker Recognition from Lung X-ray Images using ML 26

Smoker Recognition from Lung X-ray Images using ML

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26th International Conference on computer and information technology, ICCIT 2023

作者： Chanda, Moumita Mazumder, Olive Karim Patwary, Md. Fazlul Jahangirnagar University Institute of Information Technology Savar1342 Bangladesh Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350359015

Smoking is the leading cause of death in Bangladesh, accounting for one in every five fatalities, according to scientific research. A number of researchers have developed cutting-edge techniques based on Deep Learning methods to ascertain a man's smoking status through image processing. As far as we are aware, no CNN-based system can distinguish between a smoker and a non-smoker from a lung X-ray image. In this work, we offer a novel CNN-based system that can detect, in real time, with high sensitivity and specificity, whether a man smokes or not by examining lung X-ray pictures. The employed data-set is divided into two groups: smokers and non-smokers. In this study, we provide a novel ML-based system that uses images of the guy's lungs taken in real time to detect, with high sensitivity and specificity, whether or not the man smokes. Hospitals may occasionally need to utilize this method to draw blood from a nonsmoker. Additionally, men who vape are not allowed to take college admissions exams or the army selection process. The efficacy of the proposed method for Smoker and Non-Smoker prediction was evaluated and contrasted with previous CNN systems based on multiple performance metrics. The proposed technique accurately detects smokers and nonsmokers from lung X-ray pictures with 91.50% accuracy, 92% precision, and 91% recall. We are certain that real-time system performance won't be affected, even if the recommended approach was trained on a picture dataset. This device might be used in a hospital, while choosing applicants for the police or army, or for university admission. © 2023 IEEE.

关键词： artificial intelligence CNN image augmentation InceptionMobileNetV2 non-smoker recognition smoker recognition X-ray Image detection

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Estimating Chlorophyll Content of Vegetation in Hyper Spectral Surface Images 5th

Estimating Chlorophyll Content of Vegetation in Hyper Spectr...

引用

5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Kannagi, A. Adhikari, Manoj S. Manjula, H.M. Agarwal, Trapty Karnataka Bangalore India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

The Estimating Chlorophyll content material of vegetation in Hyper Spectral floor photos aims to create an automatic approach to accurately degree the concentration of chlorophyll in plant life using hyperspectral floor pix. This studies makes use of spectral evaluation and feature extraction techniques, mixed with machine studying techniques, to discover and quantify the awareness of chlorophyll from photosynthetic plants. The experimental results show that the approach has an amazing capability to discover plant life chlorophyll concentrations. This approach has correct accuracy and is beneficial inside the subject of precision agriculture for tracking the fertilizer software and nutrient imbalance of crop. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Concentrations Evaluation Photosynthetic

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IoT Driven Air Quality Surveillance and Forecasting

IoT Driven Air Quality Surveillance and Forecasting

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2023 International Conference on Advances in Computation, Communication and information technology, ICAICCIT 2023

作者： Singh, Daljeet Pal Aruna, M. Somanna, M. N. Varun Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350344387

In order to lessen dependency on expensive sensors, this study introduces a low-cost model that makes use of computing power at the edge and the Internet of Things in an effort to develop affordable air quality monitoring and predictive platforms. The Raspberry Pi, also known as a device that provides edges with computing power, applies the Kalman Filter (KF) method to improve low-cost measurements by 27% at the boundary of the device. By combining data with related mistakes, the methodology uses the KF algorithm to predict the level of six pollutants in the air, including SO2, NO2, and PM2.5, with lightning speed. The KF procedure's higher predictive ability is shown by comparisons with three traditional prediction techniques: Autonomous Integrated Moving Standard, Increasingly Weighted Move Average, and Simple Moving Average, among others. The root-mean-square error measurement showed a significant decrease of 68.3% on averaged. These results highlight the system's potential to promote intelligent farming and are consistent with its potential for implementation in several domains where precise, immediate time quality of air monitoring is essential. © 2023 IEEE.

关键词： Air quality surveillance Edge computing Forecasting Internet of Things (IoT) IoT-driven Kalman Filter Low-cost sensors Real-time prediction and Machine learning algorithms Smart agriculture

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Wavelet-Based Time Series Forecasting for Wireless Sensor Networks 5th

Wavelet-Based Time Series Forecasting for Wireless Sensor Ne...

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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Murugan, R. Arya, Rakesh Kokila, S. Mishra, Awakash Karnataka Bangalore India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

Wavelet-primarily based time series forecasting has been an effective device for Wi-Fi sensor network (WSN) packages. This approach has proven to provide correct forecasts for a selection of situations whilst enabling strength performance through decreased statistics storage and transmission bandwidth necessities. By means of extracting the vital time-related functions from sensor facts streams, records compression is executed at the same time as retaining the accuracy of the forecasts. Furthermore, with the aid of taking advantage of the multi resolution decomposition belongings of wavelets, this method can appropriately seize the temporal dynamics of the authentic signal. This permits better approximation of the underlying signal’s temporal correlations while as compared to other kinds of forecasting strategies. Additionally, wavelet-primarily based methods permit robust and green adaptation of the prediction fashions to changing sensor records, for this reason adapting the overall performance of the forecasts to exclusive situations. All these homes make wavelet-based time series forecasting an appealing choice for WSN programs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Wireless sensor networks

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Spatial Filtering for Improved Target Identification from Hyper Spectral Images 5th

Spatial Filtering for Improved Target Identification from Hy...

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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Agarwal, Trapty Raghavendra, R. Goyal, Rohit Neekhra, Bhavesh Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819780426

Hyper Spectral Imaging (HSI) is a powerful approach used in far flung sensing packages that captures spatially contiguous set of spectrum statistics from a scene. HSI information incorporates beneficial facts about the goals within the scene;however, due to noise and various distortions, it can be tough to discover those objectives. Spatial filtering is a method used to reduce undesirable noise in images if you want to make goal identity easier and extra accurate. On this paper, we endorse a brand new technique evolved for applying spatial filters on HSI pics that utilizes a spatially adaptive sigma clear out to successfully lessen noise and improve target identity accuracy. The advanced method correctly carries spatial variability within the filter length and clear out parameters to obtains progressed goal discrimination overall performance. The proposed technique is evaluated the use of both simulated and actual HSI records sets and effects display progressed target accuracy when as compared to traditional spatial filters. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Wiener filtering

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