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Maintenance Optimization of Dormant Systems Submitted to Failure Based PM and Imperfect CM.

Maintenance Optimization of Dormant Systems Submitted to Fai...

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2023 International Conference on Information and Digital Technologies, IDT 2023

作者： Bris, Radim Jahoda, Pavel Vsb - Technical University of Ostrava Faculty of Electrical Engineering and Computer Science Department of Applied Mathematics Czech Republic

ISBN: (纸本)9798350305869

This paper deals with the exploration of a system which acts on demand in case of emergency matter. To verify functionality, each component of the system must be regularly inspected with a specified inspection interval and tested to find latent failures. A failure-based preventive maintenance (PM) is considered, in context with the imperfect corrective maintenance model. This means that PM brings full renew which is realized after a beforehand setting the number of failures. If a failure occurs, it is detected during the first follow-up inspection and the reparation process starts. The imperfect corrective maintenance model is intended where each repair deteriorates the system life-time whose probability distribution is gradually altered through rising failure rate. Reliability mathematics for unavailability analysis is briefly demonstrated. The new renewal process model, including the failure-based PM, has been termed as a real ageing process. The imperfect corrective maintenance as well as increasing length of the inspection interval result in unwanted increase of system unavailability function which can be eliminated as well as optimized by the properly selected failure-based PM applied to all system components. If the PM is fixed due to cost, inspection intervals of system components can be optimized to keep the maximal value of the unavailability function in its original level. Sensitivity analysis applied to the length of the optimal inspection intervals with regard to gradually changing PM is demonstrated. This optimization process is demonstrated on a four-component system adopted from references. © 2023 IEEE.

关键词： Preventive maintenance

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Explainability of Leverage Points Exploration for Customer Churn Prediction

Explainability of Leverage Points Exploration for Customer C...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Wang, Danny Y. C. Jordanger, Lars Arne Lin, Jerry Chun-Wei Western Norway University of Applied Sciences Electrical Engineering and Mathematical Sciences Department of Computer Science Bergen Norway

ISBN: (纸本)9798350324457

Customer churn is a most crucial challenge faced by various industries, especially for the telecommunications sector since it causes over 5 - 6 times cost to keep customers. Thus, customer churn prediction has experienced substantial expansion. While a lot of prediction models are becoming increasingly accurate, the capability to interpret these models and perform causal analysis has become a new challenge. Identifying the key reasons or patterns that truly influence customer churn is vital for enabling industries to make significant improvements. This study then introduced a comprehensive framework. That integrates causal graphs and explainable models such as SHAP and Shapley flow, and concepts from system dynamics to explore customer churn patterns and leverage points. Experimental results indicates that the designed model is more explainable and interpretable to explore the customer behaviors for customer churn prediction. © 2023 IEEE.

关键词： Customer Churn Prediction (CCP) Explainable Artificial Intelligence (XAI) Leverage Points SHapley Additive exPlanations (SHAP)

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Detection and Analysis of Gene Functioning and Manipulation using Deep Learning 4

Detection and Analysis of Gene Functioning and Manipulation ...

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

作者： Bhutto, Jameel Ahmed Sunkari, Venkateswarlu Patil, Amit Madhukar Vanathi, D. Loganayagi, S. Nayak, Manjushree College of Computer Science Huanggang Normal University Huanggang438000 China School of Information Technology and Engineering Addis Ababa Institute of Technology Addis Ababa University Ethiopia Bharati Vidyapeeth Deemed University Department of Applied Science Department of Engineering and Technology Navi mumbai400614 India Nandha Engineering College Department of Computer Science and Engineering Tamilnadu Erode India Department of Computer Science and Engineering Tamilnadu Chennai602105 India Department of Computer Science and Engineering Odisha Berhampur761008 India

ISBN: (纸本)9798350300888

In the field of genomics, the analysis of gene functioning and manipulation is critical for understanding the underlying mechanisms of genetic diseases and developing effective treatments. Deep learning algorithms have shown great potential in processing large volumes of genomic data and predicting gene function. However, deep learning models require large amounts of labeled data for training, which can be a challenge in genomics where data labeling is time-consuming and expensive. To get over this restriction, deep learning and evolutionary algorithms have been used to optimize model hyperparameters and boost accuracy. Through recurrent candidate selection and modification, genetic algorithms apply evolutionary biology principles to optimize complicated solutions. By using this method, the quantity of labeled data needed to train deep learning models can be greatly decreased, and gene function predictions can be made more accurately. Overall, deep learning and genetic algorithms provide a potent tool for the investigation and modulation of gene function. These methods could result in quicker drug development, more precise gene function predictions, and personalized therapy. To ensure the ethical use of these technologies, it is important to recognize and resolve the ethical issues raised by genetic alteration. Furthermore, by deciding on the best guide RNAs for the editing process, genetic algorithms can be utilized to optimize gene editing research like CRISPR-Cas9 gene editing. This strategy can improve the effectiveness and precision of gene editing while minimizing unwanted side effects and potential ethical issues. © 2023 IEEE.

关键词： Genome

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Performance Evaluation of SDN Controllers: RYU and POX for WBAN-based Healthcare Applications

Performance Evaluation of SDN Controllers: RYU and POX for W...

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2023 International Conference on Modeling and E-Information Research, Artificial Learning and Digital Applications, ICMERALDA 2023

作者： Alfaify, Lama Alnajem, Nujud Alanzi, Haya Almutiri, Rawan Alotaibi, Areej Alhazri, Nourah Alqahtani, Awatif College of Applied Studies and Community Service King Saud University Computer Science and Engineering Department Saudi Arabia

ISBN: (纸本)9798350369359

Wireless Body Area Networks (WBANs) have made it easier for healthcare workers and patients to monitor patients' status continuously in real time. WBANs have complex and diverse network structures;thus, management and control can be challenging. Therefore, considering emerging Software-defined networks (SDN) with WBANs is a promising approach since SDN implements a new network management and design approach. The SDN concept is used in this study to create more adaptable and dynamic network architectures for WBANs. The study focuses on comparing the performance of two SDN controllers, POX and Ryu. Mininet is used as an open-source simulation tool, to construct network topologies. The performance of the controllers is evaluated based on bandwidth, throughput, and round-trip time metrics for networks. The study finds that the choice of network controller can significantly impact network performance is crucial for optimizing network performance. The study provides valuable insights into the performance of SDN-based WBANs using POX and Ryu controllers and highlights the importance of selecting the appropriate network controller for a given network architecture. © 2023 IEEE.

关键词： Network architecture

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Diagnosing Spinal Abnormalities Using Machine Learning: A Data-Driven Approach 3

Diagnosing Spinal Abnormalities Using Machine Learning: A Da...

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3rd IEEE International Conference on Artificial Intelligence, ICAI 2023

作者： Ul Islam Nasir, Zia Khan, Khalil Asghar, Momna Islamabad Pakistan Pak-Austria Institute of Applied Science & Technology Department of Computer Software Engineering & Computer Science Haripur Pakistan University of Azad Jammu & Kashmir Department of Electrical Engineering A.K Muzaffarabad Pakistan

ISBN: (纸本)9798350322125

Low back pain is a predominant condition which can affects people from different diaspora. The goal of this work is to use machine learning approach to forecast spinal abnormalities. Extratreesclassifier is utilized as a data preprocessing stage to choose the dataset's most prominent features. On a dataset of 310 samples, spinal anomalies are diagnosed using machine learning algorithms like the Support Vector Machine (SVM) and the multilayer perceptron (MLP). The purpose of this study is to determine the most crucial factors that produce backbone abnormalities and to predict them using supervised machine learning techniques. The classification of normal and abnormal spinal patients is investigated in terms of various aspects, including testing and training accuracy, precision, and recall. The observed accuracies for SVM and MLP with 80% training data are 92% and 90%, respectively. The result shows that these models can achieve high accuracy in predicting spinal abnormalities, with the SVM model performing the better. The result suggest that this approach has the potential to significantly improve the efficiency and accuracy of spinal abnormality diagnosis, leading to better patient outcomes. © 2023 IEEE.

关键词： Support vector machines

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Analysis of Feature Parameters and Their Time-Transition for Shockable Arrhythmia and Prediction of the Effect of Electrical Defibrillation 3

Analysis of Feature Parameters and Their Time-Transition for...

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3rd IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2024

作者： Noguchi, Shunta Yoshikawa, Yuta Okai, Takayuki Oya, Hidetoshi Rahman, Md. Masudur Yoshida, Minoru Graduate School of Integrative Science and Engineering Tokyo City University Tokyo Japan Tokyo City University Department of Computer Science Tokyo Japan Noakhali Science and Technology University Department of Information and Communication Engineering Bangladesh Kanagawa University Department of Applied Systems and Mathematics Kanagawa Japan

ISBN: (纸本)9798331540319

In this paper, we propose a prediction system of the effect of electrical defibrillation. In order to develop the proposed system, we firstly analyze from pre-shock (immediately before defibrillation) ECGs (ElectroCardioGrams) by using Gabor Wavelet Transform, Poincaré plot analysis, Spectral entropy, and feature parameters are extracted by those extracted analysis results, we focus on time-transition feature parameter. Next, effective feature parameters are selected based on χ2 -test. Finally, 'Effective' and 'Ineffective' for electrical defibrillation are classified by using SVM (Support Vector Machine) with three kernels (Linear, Gaussian, and Polynomial) and regularization. In this way, we shows the effectiveness of the proposed feature extraction methods and prediction system. © 2024 IEEE.

关键词： Support vector machines

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Optimization of Artificial Neural Network Modelling for Improving Diabetic Diagnosis: A Promising Approach for Early Detection

Optimization of Artificial Neural Network Modelling for Impr...

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2024 International Conference on Brain computer Interface and Healthcare Technologies, iCon-BCIHT 2024

作者： Thumu, Muni Balaji Balajiraja, N. Yousoof, Muhammed Selvaraju, Sivamani PG and Research Department of Computer Science Pudukkottai India Pudukkottai India Dhofar University Salalah Oman College of Engineering and Technology Univarsity of Technology and Applied Sciences-Salalah Engineering Department Salalah Oman

ISBN: (纸本)9798331540067

Diabetes impacts food-to-energy conversion and is chronic. Diabetics store glucose in their blood instead of using it for energy, causing major health issues. The WHO reports that diabetes is rising worldwide, and early detection and treatment are crucial to preventing complications. Artificial intelligence (AI) and machine learning (ML) techniques have the potential to revolutionize the diagnosis of diabetes by making it possible to identify people at risk earlier and diagnose it more accurately. This work introduces a supervised learning approach in the artificial neural network (ANN) to improve the accuracy of diabetes diagnosis. Eight input variables from the National Institute of Diabetes and Digestive and Kidney Diseases dataset were used, including 2000 patients with 65.8% healthy and 34.2% diabetic. Altering the feedforward backpropagation neural network architecture improved the ANN's accuracy. Changes were made to hidden layers (1 to 3) and neurons (20 to 100) using R2 and MSE. The ANN model suited the dataset well with R2 and MSE of 99.1% and 7.38× 10-6 in the optimal architecture. These results imply that the suggested method might quickly detect diabetes. © 2024 IEEE.

关键词： Neural network models

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Integrating Artificial Intelligence and Multiple Intelligences for Advanced Educational Models

Integrating Artificial Intelligence and Multiple Intelligenc...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Palarimath, Suresh Maran, Pyingkodi Venkateswaran, R. Wilfred Blessing, N.R. Shiny, K.V. Renuga, S. University of Technology and Applied Sciences College of Computing and Information Sciences Salalah Oman Kongu Engineering College Department of Computer Applications Erode India University of Technology and Applied Sciences College of Computing and Information Sciences Ibri Oman Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798350378092

The aim of educational innovation is to foster students' creative and problem-solving skills via the integration of several disciplines, including science, technology, engineering, art, and mathematics. Efficiently identifying and fostering the various abilities of pupils continues to be a crucial obstacle. This research presents a sophisticated educational approach that combines the notion of multiple intelligences with artificial intelligence (AI) technology to tackle this problem. This concept improves the teaching environment by including intelligent auxiliary services for instructors and students via the use of smart speech and picture interaction. Artificial intelligence (AI) integrated into the multiple intelligence's framework enables the ability to observe, analyze, and implement personalized teaching tactics in real-time using machine learning. The suggested AI-assisted education paradigm aims to enhance teacher-student interactions and facilitate personalized instruction. The study provides evidence that this technique successfully facilitates personalized and captivating learning experiences, promoting students' diverse talents and augmenting their creativity and problem-solving capabilities. This approach seeks to fundamentally transform conventional educational techniques by addressing the disparity between existing educational practices and the future requirements of the workforce. © 2024 IEEE.

关键词： Adversarial machine learning

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Comparative Study of Optical Character Recognition Using Different Techniques on Scanned Handwritten Images 6th

Comparative Study of Optical Character Recognition Using Dif...

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6th International Conference on Microelectronics and Telecommunication engineering, ICMETE 2022

作者： Singh, Anshika Garg, Sharvan Kumar Department of Computer Science Shaheed Rajguru College of Applied Sciences for Women University of Delhi Delhi India Department of Computer Science and Engineering Subharti Institute of Technology and Engineering Swami Vivekan and Subharti University Meerut India

ISBN: (纸本)9789811995118

Optical Character Recognition (OCR) is always a prominent area of research and has gained attention in academia and industries, and more advancements are being performed to improve accuracy and quality of text and images. OCR is a technique that converts the digital or optical image (which can be image of typed or printed document or scanned handwritten document or scene photo) into machine-encoded format. OCR takes an image containing text as input, scans the text character by character, analyzes it, and generates a text corresponding to the text in the image, thus reducing the storage size and making accessibility of data easy and faster. In this paper, we will be studying OCR using different techniques like Convolutional Neural Network, Recurrent Neural Network, pytesseract, Keras-OCR, EasyOCR and comparing their results on handwritten scanned images. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Recurrent neural networks

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Optimized Smart Agricultural Production Systems using Enhanced Parallel Green Anaconda Convolutional Neural Network 8

Optimized Smart Agricultural Production Systems using Enhanc...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Meena, S. Gomathi Goyal, Himanshu Rai Lavanya Muthukumar, E. Megha, Mamtaben Kamleshbhai Parikh, Swapnil Peri College of Arts and Science Pg Department of Computer Science and Computer Applications Tamil Nadu Mannivakkam600048 India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun248 002 India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Viruthunagar626126 India Nehru College of Management Department of Mba Coimbatore Tamil Nadu Thirumalayampalayam641105 India Natubhai V. Patel College of Pure and Applied Sciences A Constituent College of Cvm University Department of Computer Science Gujarat Vallabh Vidyanagar388120 India Parul University Parul Institute of Engineering and Technology Department of Computer Science and Engineering Post Limda Gujarat Waghodia391760 India

ISBN: (纸本)9798350376425

Smart agriculture, driven by IoT technologies, is becoming increasingly essential due to global population growth and rising food demands. However, challenges such as energy supply constraints and routing issues often result in data transmission failures, high energy consumption, reduced network lifetime, and delays. To address these issues, this study proposes a novel solution: an enhanced smart agricultural production system based on an Optimized Smart Agricultural Production Systems using Enhanced Parallel Green Anaconda Convolutional Neural Network (EPar-GrAnCNN). This system enhances energy management by incorporating the Emperor Penguin Optimizer (EPO) for the formation and selection of cluster heads, aiming to increase power conservation and extend network lifespan. Additionally, the system leverages deep learning techniques, including the Enhanced Parallel Convolutional Neural Networks (EPar-CNN) and Green Anaconda Optimization (GAO), to analyze agricultural data and extract valuable insights. The EPar-GrAnCNN model accurately predicts crop health, the severity of water deficits, and other factors affecting crop growth, guiding farmers in the optimal utilization of resources. computer experiments conducted on the Python platform demonstrate that the proposed approach outperforms existing methods, achieving higher throughput (98.4%), a superior packet delivery ratio (99.3%), and lower energy consumption (0.30 mJ). These results highlight the efficiency of the approach and its potential for further advancement in smart agriculture. © 2024 IEEE.

关键词： Emperor Penguin Optimizer (EPO) Enhanced Parallel Convolutional Neural Network (EPar-CNN) Green Anaconda Optimization (GAO) Internet of Things (IoT) Smart Agricultural Systems

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