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2021 2nd international Symposium on Automation, Information and Computing, ISAIC 2021

2021 2nd International Symposium on Automation, Information ...

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2021 2nd international Symposium on Automation, Information and Computing, ISAIC 2021

The proceedings contain 136 papers. The topics discussed include: a comparative study on part-of-speech taggers' performance on examination questions classification according to bloom's taxonomy;pattern inversion as a pattern recognition method for machine learning;development of a face identification system's prototype;early estimation of daily reference evapotranspiration using machine learning techniques for efficient management of irrigation water;application of algorithm of fuzzy rule conclusions in determination of animal's diseases;using deep learning to detect the presence/absence of defects on leather: on the way to build an industry-driven approach;defect detection in the textile industry using image-based machine learning methods: a brief review;offloading decision making for workflow applications using an enhanced genetic algorithm in the edge environment;application of machine learning approaches in health care sector to the diagnosis of breast cancer;anomaly detection and analysis based on deep autoencoder for the storage tank liquid level of oil depot;research on knowledge transfer on multilayer networks based on link prediction algorithm;stock market prediction based on time-frequency analysis and convolutional neural network;and modality-invariant and -specific representations with crossmodal transformer for multimodal sentiment analysis.

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A machine learning Approach to Waiting Time Prediction in Queueing Scenarios 2

A Machine Learning Approach to Waiting Time Prediction in Qu...

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2nd ieee international conference on Artificial Intelligence for Industries (AI4I)

作者： Kyritsis, Athanasios, I Deriaz, Michel Univ Geneva Informat Sci Inst GSEM CUI Geneva Switzerland

ISBN: (纸本)9781728140872

Physically queueing is a reality on many industries that provide services or sell goods. Waiting in a queue can be stressful and exhausting for the clients because of the enforced idle time, and may lead to decreased customer satisfaction. Queueing theory has been widely used to assess client waiting times, to optimize staff schedules, and to increase the robustness of a queueing system against a variable demand for service. In this paper, we are exploring how multiple industries that require queues can benefit from machine learning to predict the clients' waiting times. We begin by predicting waiting times on bank queues, and then we propose how the procedure can be generalized to more industries and automatized. A publicly available dataset containing entries of people queueing in banks is initially utilized, and after training a fully connected neural network, a mean absolute error of 3.35 minutes in predicting client waiting times was achieved. We are then presenting a web application that is managing queues of different scenarios and industries. The queues may have unique parameters, and the system can adapt to each queue as it creates a per queue optimally trained neural network for waiting time prediction. The use and the capabilities of the system are validated with the use of a simulator. machine learning, therefore, proves to be a viable alternative to queueing theory for predicting waiting time.

关键词： machine learning pattern recognition queue system queueing analysis web application

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A novel method on summarization of video using Local Ternary pattern and Local Phase Quantization 2

A novel method on summarization of video using Local Ternary...

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2nd international conference on Range Technology, ICORT 2021

作者： Majumdhar, Jharna Nayak, Sasmita Kumari Min. of Defence Govt. of India Prof. of Eminence Odisha India Centurion University of Technology and Management Computer Science and Engineering Odisha India

ISBN: (纸本)9781665449564

In last decade, Video Summarization (VS) approach is playing a pivotal role in the analysis of the Video contents. The methodologies involved in Video Summarization have wide range of applications in the field of defense for video surveillance, intrusion, object detection, Video Browsing, Content-based Video Retrieval and Storage etc. In this study, we have proposed video summarization techniques to extract the frames of interest. Then, video summarization has determined by the advanced texture descriptors. Local Ternary pattern (LTP) Local Phase Quantization (LPQ) are the texture descriptor methods used to provide an efficient video summarization process. These methodologies are in conformity with the elimination of redundant frames in a video as well as the maintenance of user defined number of distinctive images. Then apply the clustering process, which is an unsupervised machine learning algorithms, such as, Affinity Propagation and BIRCH, are utilized to cluster the similar frames into one group. These methodologies confirm that the summary of video denotes the most distinctive frames of the input video, which results the same importance to preserve the continuousness of the summarized video. © 2021 ieee.

关键词： Object detection

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Fabric Weave pattern recognition and Classification by machine learning 2

Fabric Weave Pattern Recognition and Classification by Machi...

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2nd ieee international conference on Smart Systems and Emerging Technologies (SMARTTECH)

作者： Rauf, Muhammad Arslan Jehanzeb, Muhammad Ullah, Ubaid Ali, Usman Kashif, Muhammad Abdullah, Muhammad Univ Elect Sci & Technol China Sch Informat & Software Engn Chengdu Peoples R China Natl Text Univ Dept Comp Sci Faisalabad Pakistan Riphah Int Univ Riphah Coll Comp Faisalabad Pakistan

ISBN: (数字)9781665409735

ISBN: (纸本)9781665409735

The fabric pattern recognition and subsequently the classification is an imperative task in textiles. Currently, this is done manually, therefore, the need of the requirement is to develop a system that could recognize and classify the fabric weave patterns for ease of inspection and storage. The classification of woven fabrics in today's textile industry is generally manual, requiring significant human effort and a long time. Automatic and effective approaches for woven fabric classification are desperately required with the rapid development of computer vision. This paper proposes an automated and real-time classification technique to analyze three woven fabrics: plain, twill, and satin weave. To achieve the objective, ResNet pretrained Convolutional Neural Network architecture is used for classification. To obtain texture characteristics, the gray-level co-occurrence matrix and Gabor wavelet, are included in the technique. To eliminate redundancy and maximize main component feature vectors, Principal component analysis is then used to select feature vectors. The experimental result shows that with quicker training speed, the Deep CNN classifier can reliably and efficiently identify woven fabrics. Deep Convolutional Neural Network provides the best accuracy 96.15%.

关键词： Woven fabric Classification Gray-level co-occurrence matrix Gabor filters Deep convolutional neural network Principal component analysis

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machine learning-based Algorithm for Type of Wood Identification 2

Machine Learning-based Algorithm for Type of Wood Identifica...

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2nd ieee international conference on Emerging Research in Computational Science, ICERCS 2024

作者： Venugopal, D. Sandhiya, R. Swetha, S. Sredhar, R. Rohit, N. Shadrach, Finney Daniel KPR Institute of Engineering and Technology Department of ECE Coimbatore India KPR Institute of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798331534950

The remarkable developments in machine learning have made it feasible to use wood pictures to identify wood, a new area of research in wood science that is steadily working to the development for wood identification automatically to satisfy the demands of the market and the wood sector. There are many different tree species and the wood made from each of them has distinct structural, mechanical and physical characteristics. Due to the variations in the structure and characteristics of wood, a wide range of objects with various applications and aesthetics can be made from wood. It is necessary to employ the right species in the right places in order to use this priceless substance effectively. To help minimize misclassification of wood species in the wood industries, a novel application in the field of computer vision called intelligent wood species recognition is being researched. With the use of photographs taken of wood samples or features noticed, woods recognition implements the process of identifying the various species of wood. In this study, the LBP (Local binary pattern) histogram is utilized to identify the various wood species by extracting characteristics from improved wood photos. The objective of the wood recognition system is to determine whether it is feasible to develop a system that can automatically recognize wood based on its anatomy. Due to the technique's rotation invariance and tolerance for gray scale fluctuations the result achieved thus far shows a high recognition rate and accuracy indicating that it is very promising for practical applications. © 2024 ieee.

关键词： Local binary pattern

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STOCK MARKET PREDICTION USING LONG SHORT-TERM MEMORY (LSTM) 2

STOCK MARKET PREDICTION USING LONG SHORT-TERM MEMORY (LSTM)

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2nd ieee international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ieee ICAECT)

作者： Abu Nadif, Mohammad Samin, Towhidur Rahman Islam, Tohedul Amer Int Univ Bangladesh Dept Comp Sci Dhaka Bangladesh

ISBN: (纸本)9781665411202

The stock market is one of the most unpredictable and highly concerned places in the world. There is no fundamental way to forecast stock market share prices. So people think stock market prediction is a gamble. Nevertheless, it is possible to generate a constructive pattern by using different types of algorithms and predict the share price. But when the characteristics are complex, and the largest portion of these classification methods are linear, resulting bad performance in class label prediction. In this paper we suggest a non-linear technique based on the Long Short-Term Memory (LSTM) architecture. According to studies LSTM-based models predict time and sequential models better than other models and RNN is the first algorithm with an internal memory that remembers its input, making it perfect for sequential data machine learning issues. For our experiment we collected the share market data from a particular company named Beximco for the last 11 years. To reassert the effectiveness of the system different test data are used. This work introduces a robust method that can predict stock price accurately based on LSTM.

关键词： Time series analysis Stock Exchange machine learning pattern recognition Stock price prediction

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A Novel Deep learning Model for Automated Golf Swing pattern recognition in Video Footage 2

A Novel Deep Learning Model for Automated Golf Swing Pattern...

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2nd ieee international conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Goswami, Chahak Goswami, Niyati Israni, Priyanka Carroll Senior High School SouthlakeTX United States UNC-Chapel Hill Computer Science Major NC United States Independent AI-ML Researcher Gujarat Ahmedabad India

ISBN: (纸本)9798350372892

The automated classification of golf swing events is a crucial procedure for analyzing swings using video analysis. However, there has been a lack of initiative in applying machine learning (ML) algorithms to accurately determine and classify the various stages of a golf swing. This paper proposes a Customized Convolutional Neural Network (CNN) to classify the golf swing phases into eight primary phases, encompassing the pre and post-swing events, based on open source Kaggle dataset. The frames from the videos are extracted and converted into structured data along with the player's data details. The combined data is pre-processed using Linear discriminant analysis. The proposed customized CNN model is compared with the ML models like Support Vector machines and the CatBoost. The proposed CNN model outperforms by achieving an accuracy of 84.60%. Furthermore, the proposed approaches can separate all the swing phases only based on video data. This suggests that machine learning-based swing classification techniques may be utilized in different motion-analysis settings by splitting motion phases with more flexibility in video analytics in real time. © 2024 ieee.

关键词： Convolutional neural networks

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Atrial Fibrillation pattern recognition using Features of Second Order Dynamic System 2

Atrial Fibrillation Pattern Recognition using Features of Se...

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2nd international conference on Intelligent Cybernetics Technology and Applications (ICICyTA) - Cybernetic Technology for Better Future of Human Life

作者： Qi, Lee Wei Abdul-Kadir, Nurul Ashikin Heng, Wei Wei Othman, Mohd Afzan Safri, Norlaili Mat Embong, Abdul-Mutalib Univ Teknol Malaysia Johor Baharu Malaysia Univ Southampton Malaysia Johor Baharu Malaysia Univ Malaysia Terengganu Terengganu Malaysia

ISBN: (纸本)9798350399134

According to World Health Organization (WHO), an estimated 17.9 million people died from cardiovascular diseases (CVD) in 2019, representing 32 % of all global deaths. Of these deaths, 85 % were due to heart attack and stroke. The occurrence and prevalence of atrial fibrillation (AF) is growing worldwide. Limited tools are available to evaluate clinical outcomes and response to thrombolysis in stroke patients with AF. Therefore, this study analysed the ECG features of AF and the normal sinus rhythm (NSR) signals for AF recognition. The first objective is to extract AF features using second-order dynamic system (SODS) algorithm. The following objective is to investigate the effect of windowing length towards AF classification. Next, to compare the two-pattern recognition machine learning support vector machine (SVM) and artificial neural network (ANN) on the accuracy, specificity, and sensitivity of AF classification. In this study, the Physiobank database, included MITBIH Atrial Fibrillation Dataset and MITBIH Normal Sinus Rhythm Dataset were used. For signal pre-processing, butterworth filter are used to diminish the muscle noise and the features signals were extracted. Multiple episodes of the windowing size of 2s, 4s, 6s, 8s and 10s were included in this design to evaluate the appropriate windowing size for AF recognition. The pattern recognition machine learning of SVM algorithm has higher accuracy compared to ANN, which are 100 % with 4s windowing size. In conclusion, the 4s windowing size having the highest detection rate in AF classification system.

关键词： ANN atrial fibrillation ECG second order system

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Using Transfer learning and pattern recognition to Implement a Smart Waste Management System 2

Using Transfer Learning and Pattern Recognition to Implement...

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2nd international conference on Electronics and Sustainable Communication Systems, ICESC 2021

作者： Aggarwal, Vaibhav Gupta, Vaibhav Gupta, Shagun Sharma, Neeti Sharma, Kiran Sharma, Neetu Graphic Era University Department of Computer Science Engineering Uttarakhand Dehradun India Graphic Era University Department of Applied Sciences Uttarakhand Dehradun India

ISBN: (纸本)9781665428675

Smart cities are nowadays a very growing industry and a way of life that people want to experience. The technology that is used in the smart cities very well provide better living standards for the people. Smart city is most commonly utilised the benefits of information and communication technology. Internet of Things has opened a very vast space and has brought in many opportunities for all kind of research and development to integrate technology into people's life and hence make smart city is possible. this is a very vast topic to discuss as it has many different applications like water leak detection, gas leak detection, street lights that are smart and a very well designed traffic management system. also making use of this technology a very efficient way to collect waste can also be designed to maintain a very healthy lifestyle for the people and also minimizing the operation cost. This research paper presents a very well designed and completely new architecture In order to implement a smart waste management system which at its core implements artificial intelligence technique and machine learning with a combined focus on Internet of Things. By combining this approach with deep learning a system can be made which has a very minimal operation cost while not compromising on performance. © 2021 ieee.

关键词： Smart city

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Motor Imagery Analysis Based on Filter Bank Common Spatial pattern 2

Motor Imagery Analysis Based on Filter Bank Common Spatial P...

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2nd international conference on Artificial Intelligence and Computer Engineering, ICAICE 2021

作者： Du, Yixin Xu, Runtian Zhang, Jiting School of Software Engineering Nanjing Normal University Jiangsu Nanjing210023 China School of Automation East China University of Science and Technology Shanghai200237 China School of Automation Beijing Institute of Technology Beijing Beijing100081 China

ISBN: (纸本)9781665421867

This paper analyzes the EEG signals of left and right hand motor imagery and uses Filter Bank Common Spatial pattern to extract features from EEG signals. Compared with Discrete Wavelet Transform, Autoregressive mode, Power Spectral Density and Common Spatial pattern, it is found that FBCSP can significantly improve the recognition rate. While extracting the features of sub-frequency bands, this paper also considers ERD and ERS in motor imagery and the distribution of signal energy in different frequency bands. It is found that during the time of the motor imagery, the energy changes are generally concentrated on the μ rhythm, and the motor sensory area of the cerebral cortex has the nature of contralateral reflection of left and right hand motor imagery. Researchers generally use Support Vector machine classifiers for pattern recognition classification, but the classification effect of a traditional SVM classifier is not ideal. Therefore, the paper uses the integration algorithm AdaBoost and algorithm Gradient Boosting for classification. Compared with Adaboost, the error rate of Gradient Boosting algorithm drops more sharply with the increased number of iterations, and the accuracy of Gradient Boosting algorithm is higher. © 2021 ieee.

关键词： Filter banks

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