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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： U Naresh kumar K Madhana Mohan S. Pragadeeswaran Jobin M Scaria Baseera A D. Seenivasan Department of Artificial intelligence & Data science Annamacharya University Rajampet India Department of Electronics and Instrumentation Engineering Sri Sairam Engineering College Chennai India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Mt. Eriama Campus School of Information Technology IBS University Port Moresby Papua New Guinea School of Computing Science and Engineering VIT Bhopal University Sehore India Department of Computer Science and Business Systems M. Kumarasamy College of Engineering Karur India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

Food producers are under pressure to meet rising demand as the world's population and natural resources diminish. Overuse of pesticides and fertilizers has caused soil erosion and land degradation. Future food needs will require environmentally sustainable farming strategies. The suggested strategy includes model training, feature extraction, sensor integration, and model preparation. data preparation involves processing data before storing it machine-readable. SFFS and RFE select features. The model was trained using a hybrid ARIMA-LSTM technique, which blends deep learning and time-series forecasting. The ARIMA-LSTM model outperforms solo ARIMA and LSTM models with an average accuracy of 93.54%. Increased accuracy shows the model's capacity to anticipate sustainable agriculture approaches. data-driven and environmentally sustainable ways are needed to overcome global food production concerns, according to this study. When paired with good preprocessing and feature selection, ARIMA-LSTM can boost agricultural productivity while minimizing environmental impact.

关键词： Training Accuracy Food manufacturing Biological system modeling Soil Predictive models Feature extraction Agriculture Long short term memory Meteorology

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Predictive Maintenance of Produced Water Re-injection Pump Failure in the Field of Oil and Gas: A Review

Predictive Maintenance of Produced Water Re-injection Pump F...

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Cognitive Computing and Complex data (ICCD), International Conference on the

作者： Mohammed Adam Kunna Azrag Sharafeldin Kabashi Khatir Jasni Mohamed Zain Julius Beneoluchi Odili Institute for Big Data Analytics & Artificial Intelligence (IBDAAI) Universiti Teknologi Mara (UiTM) Shah Alam Malaysia Faculty of Computer Science and Information Technology (FSKTM) Universiti Tun Hussein Onn Malaysia (UTHM) Johor Malaysia Institute of Digital Humanities Anchor University Lagos Nigeria

Water production in an oil and gas flow station normally increases as fields mature and two main ways exist to deal with produced waste water depending on the pump situation. One is to dispose of the produced water into dedicated disposal wells; the other is to re-inject the produced waste water for pressure maintenance on sweep efficiency. In essence, pumps aid in the movement of process fluids from one location to another. For instance, a pump can be used to move crude oil from a well to a pipeline, and mud pumps can move drilling mud around a drill bit’s annulus before returning it to a storage tank for purification. Thus, pump failure can lead to costly downtime, extensive repair costs, and irredeemable damage. Understanding the causes of failure, on the other hand, can aid in the selection of pumping equipment to reduce the possibility of such damages and attendant costs occurring. Many businesses increase preventive maintenance and create aggressive inspection schedules to avoid these unexpected failures; others rely on condition-based maintenance, which focuses on maintenance performed after real-time data is monitored and unacceptable condition levels are detected. Using a predictive approach, past maintenance data and sensor measurements can be used to identify early signs of failure, allowing businesses to perform maintenance only when it is absolutely necessary. However, this paper intends to review the predictive maintenance of pump failure in flow-stations in the oil and gas industry.

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Responsible AI on National Cybersecurity Strategy with Enhancement of Cybersecurity in Education

Responsible AI on National Cybersecurity Strategy with Enhan...

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Intelligent & Innovative Practices in engineering & Management (IIPEM), International Conference on

作者： Bhagyashree Ashok Tingare G. Prasanna Lakshmi Vijayshri Khedkar Rakesh Thoppaen Suresh Babu Tarun Dhar Diwan Pravin B Khatkale Department of Artificial Intelligence and Data Science D Y Patil College of Engineering Akurdi Pune School of Computer Science and Engineering Sandip University Nashik Maharashtra Department of Computer Science & Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India Senior Technical Architect HEXAWARE TECHNOLOGIES INC New Jersey Assistant Professor and Controller of Examination (COE) Atal Bihari Vajpayee University Bilaspur India Sanjivani University Kopargaon Maharashtra India

ISBN: (数字)9798350390049

ISBN: (纸本)9798350390056

For national cybersecurity to keep up with the digital transition, AI is more important than ever. Responsible AI improves cybersecurity and education by emphasizing openness and ethics. Ethical design is stressed from the outset in responsible AI. This article analyzes how artificial intelligence (AI) influences the national cybersecurity strategy, focusing on cybersecurity education development and effectiveness. The National Cybersecurity Strategy will frame this article. The major results show that responsible AI improves threat detection and response, cybersecurity infrastructure, talent shortage filling, collaborative research, and public trust. Educational institutions may now offer innovative training programs thanks to artificial intelligence. Completing this aim requires closing the gap between conventional methodologies and modern challenges. Closing gaps is vital. Functional artificial intelligence improves national cybersecurity and educational quality. This analysis shows that this conclusion is possible. This allows experts to properly react to shifting dangers. This is why it matters.

关键词： Training Ethics Focusing Collaboration Threat assessment Filling artificial intelligence Computer security

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Use of Self-Organizing Kohonen Maps for Quantization of Tomography Images

Use of Self-Organizing Kohonen Maps for Quantization of Tomo...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： Mahesh Prasanna K Mary Selvan Deepa R D. Suganthi B. Uma Maheswari Parul Madan Department of Electronics and Communication Engineering Vivekananda College of Engineering and Technology Puttur Karnataka India Department of Artificial Intelligence and Data Science RMK College of Engineering and Technology Puduvoyal Tamil Nadu India Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science SIMATS Saveetha College of Liberal Arts and Sciences Chennai Tamil Nadu India Department of Computer Science and Engineering OMR St. Joseph's College of Engineering Chennai Tamil Nadu India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

In this work, a proof of concept is presented, which shows that EIT images reconstructed by EIDORS using methods based on the Finite Element Method can be transformed into less smooth versions using a combination of unsupervised classification and quantization. For that, Kohonen's Self-Organizing Maps were used. The objective is to group the image areas into well-defined regions by reducing the smoothing of the reconstructed image, thus allowing readings to be performed that are more strongly associated with the regions of interest. EIDORS is a reference tool that forms a comparative basis for research developments on the subject of EIT. The generation of this article is part of broader studies in the field of optimization of EIT tomographic images. Studies in the area of image reconstruction using Compressed Sensing combined with Computational intelligence techniques stand out, with the aim of reducing computational cost, increasing processing speed and increasing the quality of the reconstructed image.

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Decision Making and Recognition of String Characters using NN-Fuzzy Implication System

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Procedia Computer science 2025年 258卷 893-902页

作者： Santosh Kumar Henge Avinash Bhagat Sanjeev Kumar Mandal Nikhil Reddy Viraati Ravleen Singh K. Rushi Kedhar Associate Professor Department of Computer Science and Engineering School of Computer Science and Artificial Intelligence SR University Warangal 506371 India Associate Professor School of Computer Applications Lovely Professional University Phagwara Punjab India Assistant Professor Department of CS and IT Jain (Deemed-to-be University) Bangalore India Department of Data Insights Crisp Shared Services 7160 Columbia Gateway Dr Columbia United States of America Assistant Professor Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddewaram AP India BE-CSE RV Institute of Technology and Management Bengaluru India

This research proposes the neural network (NN)-fuzzy logic control (FLC)-based methodology, which is designed with two stages of execution. Stage-1 is composed with the NN approach; it takes the input from the scanned image, the input from the classified clustered group, processes it with hidden layers, and generates the final output, which is input to the FLC in stage 2. The input of stage 2 converts the input into fuzzified data (0s and 1s) and trains the FLC system with fuzzified set values. The trained data has been simulated for further stages in the FLC-based decision-making approach and integrated to identify the character patterns based on the trained dataset. FLC-based planting and normalization techniques are integrated for re-training the data. The dataset has been framed with more than 6200 handwritten image characters, which were written by 235 writers. It aligned with the pre-processing, classification, and filtering stages and achieved a 99.8% recognition rate.

关键词： Fuzzy Logic Control (FLC) artificial Neural Network (ANN) Multiple Layered Perceptron (MLP) Convolutional Neural Network (CNN) Feed Forward Neural Network (FNN) Optical Character Recognition (OCR) Handwritten (HW)

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Medical Image Fusion Solid Works Supporting Multiple Techniques with Feature-Level Transforms

Medical Image Fusion Solid Works Supporting Multiple Techniq...

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Sustainable Computing and data Communication Systems (ICSCDS), International Conference on

作者： Rakhi Mutha Devendra Nath Pathak Vandana Ahuja Prabhakar Ramesh Bhandari Pankaj Rahi Udit Mamodiya Department of Computer Application Lovely Professional University Punjab India Department of Computer Science and Engineering Poornima College of Engineering Jaipur Rajasthan India Department of Computer Science and Engineering Swami Vivekanand Institute of Engineering and Technology Punjab INDIA Department of Microbiology Sevadal Mahila Mahavidyalaya Umred Road Nagpur Maharashtra India Department of Artificial Intelligence & Data Science Poornima Institute of Engineering and Technology Jaipur Rajasthan India Department of Electrical Engineering Coordinator at PIET AICTE IDEA LAB Poornima Institute of Engineering and Technology Jaipur Rajasthan India

The use of group classifiers on similar features has resulted in computationally expensive and tedious, making them unsuitable for online applications. The clustering techniques are evaluated for both inter-subject and intra-subject variations, and it is discovered that intra-subject order execution is superior. Another approach to dealing with object recognition from medical images is considered. This is known as similarity matching, and it is a computationally simple process as compared to costly and complex characterization computations. The major challenges that occur in the image processing are high noise levels, limited image resolution, and geometric deformations throughout the image.

关键词： Technological innovation Three-dimensional displays Image resolution Deformation Transforms Solids Object recognition

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Analysis of Visual Expression of Light and Color in Movies based on Wavelet Neural Network

Analysis of Visual Expression of Light and Color in Movies b...

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Communication, Security and artificial intelligence (ICCSAI), International Conference on

作者： K. Murugan R. Mothi Retty George R. Esther Priya S. Nagarathinam S. Krishnakumar Department of Electronics and Communication and Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Biomedical Engineering Dr. N.G.P. Institute of Technology Coimbatore India Department of ECE SCMS school of engineering and technology Ernakulam Kerala Department of Artificial intelligence and Data Science Jansons Institute of Technology Coimbatore India Department of ECE Kumaraguru College of Technology Coimbatore Department of Robotics and Automation Sri Ramakrishna Engineering College Coimbatore India

Movies are in the era of “black, white and gray” ruling the screen, people have a strong desire for movies to truly reproduce colors, because the main rhythm of the “black, white and gray” film visual music is not clear enough, the momentum is not strong enough, the meaning is not rich enough, and the color is not enough. Visual aesthetics cannot be attractive… Aiming at the dual challenges of colorization quality and timing stability in film colorization, a generative adversarial network with a recurrent structure is proposed, which can be used for automatic colorization of films without any reference frames and artificial intervention. The network generates a confrontation network based on classical conditions: the generator is used to generate color images and complete the task of colorization; The discriminator is used to identify the authenticity and improve the performance of the generator. Cycle structure and timing consistency loss are introduced to integrate timing information and solve the stability problem of coloring. Experimental results show that this method can effectively reduce flicker in the generated movie sequence while ensuring the coloring of a single frame image. This paper studies and analyzes the visual expression of light and color in movies based on wavelet neural network.

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A data-Driven Approach Towards Software Regression Testing Quality Optimization

A Data-Driven Approach Towards Software Regression Testing Q...

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Information technology (ACIT)

作者： Abdallah Moubayed Nouh Alhindawi Jamal Alsakran MohammadNoor Injadat Mohammad Kanan Computer Engineering Department and Interdisciplinary Research Center for Intelligent Secure Systems King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia School of Computing and Augmented Intelligence Arizona State University Gilbert Arizona USA Department of Software Engineering Jadara University Irbid Jordan Fairleigh Dickinson University Canada Data Science & Artificial Intelligence Department Zarqa University Zarqa Jordan Department of Industrial Engineering University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Software testing is very important in software development to ensure its quality and reliability. As software systems have become more complex, the number of test cases has increased, which presents the challenge of executing all the tests in a limited time frame. Various test case prioritization techniques have been introduced to solve this problem. These methods aim to identify and implement the most critical tests first. In this paper, we propose an implementation of a dynamic test case prioritization strategy to improve software quality by increasing code coverage with special attention to edge case handling. Edge case test prioritization is a technique that improves test efficiency by selecting extreme case scenarios that can reveal critical bugs or unexpected behavior early in development, improving overall software reliability and dependability. In order to prioritize test cases, this paper presents a regression-based method that makes use of machine learning algorithms. The approach leverages previous performance data to optimize regression testing efficiency by examining variables like test time and execution status. Performance evaluations, when compared against industry standards and cutting-edge techniques, show how effective these algorithms are at correctly prioritizing test cases and identifying faults. This study offers simplified yet reliable solutions for regression testing optimization by shedding light on the efficacy of regression algorithms, such as Random Forest and decision trees.

关键词： Software testing Machine learning algorithms Software algorithms Software quality Software systems Software reliability Regression tree analysis Optimization Standards Software development management

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Anomaly Detection in Industrial Quality Control with Computer Vision and Deep Learning

Anomaly Detection in Industrial Quality Control with Compute...

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Distributed Systems, Computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： A. Jenita Mary V. Umesh Prerana Nilesh Khairnar R Sahana Sangeethaa SN Shanthi H J Department of Computer Science and Applications SRM Institute of Science and Technology Faculty of Science and Humanities Vadapalani City Campus Chennai India Department of IT Ksou Mysore Mysuru India Department of Electronics and Telecommunication Engineering Visvesvaraya Institute of Technology Chincholi Nashik India Department of Artificial Intelligence and Machine Learning Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Avadi Chennai India Department of Computer Science & Engineering Bannari Amman Institute of Technology Sathyamangalam India Department of Computer Applications Hindustan Institute of Technology and Science Deemed to be University Padur India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

This study examines the impact of the dynamic manufacturing environment on companies' efforts to improve customer satisfaction through product quality enhancement, production cost reduction, and sustainability realisation. The research focuses on the current trends and challenges companies face in adapting in response to the dynamic nature of the industrial industry. This study seeks to offer practical recommendations by analyzing these factors for companies to meet the evolving demands of their customers while ensuring long-term success and competitiveness. This study examines the impact of anomaly detection on product quality, specifically focusing on the commonly employed method of visual quality inspection. This study examines the methods of visual quality inspection for products, comparing manual and automatic approaches. This study examines the limitations of manual inspection as a monotonous task, which can result in the oversight of errors and subjective assessments. In order to keep up with the needs of smart production and the advent of Industry 4.0, manufacturing as a whole is actively seeking computerized quality inspection solutions. Innovative inspection systems can be created through the effective use of big data. This research abstract discusses the widespread use of machine learning in automated quality inspection systems to enhance performance and support data analytics methods. This study uses a convolutional neural network architecture to extract complex features and patterns from images. This enables the model to differentiate between normal and abnormal conditions effectively. This research presents a proposed model trained on a diverse dataset, including a comprehensive collection of potential defects and irregularities. This research abstract presents a proposed framework for validating effectiveness through extensive experiments on datasets from diverse industrial domains. This study presents the findings of a research project that evaluated

关键词： Training Deep learning Adaptation models Accuracy Computational modeling Quality control Production Inspection Convolutional neural networks Anomaly detection

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WiLoc: Encoding-based WiFi Indoor Localization

WiLoc: Encoding-based WiFi Indoor Localization

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International Conference on Indoor Positioning and Indoor Navigation (IPIN)

作者： Zhao Huang Mikko Valkama Juan Zhang Meng Xu Cunyi Yin Minglei Guan Computer and Information Science Northumbria University Newcastle UK Department of Electrical Engineering Tampere University Tampere Finland School of Information Technology & Management University of International Business and Economics Beijing China Centre for Intelligent Multidimensional Data Analysis Limited Hongkong China School of Artificial Intelligence Shenzhen Polytechnic University Shenzhen China

ISBN: (数字)9798350366402

ISBN: (纸本)9798350366419

WiFi Indoor localization plays a crucial role in an emerging application domain for tracking indoor people, however, the serious issue is that the WiFi signals from access points (APs) vary greatly over time and the deployment structure of APs may be changed, for example, some APs are replaced or removed over time, which cause localization accuracy reduced. To solve this problem, this paper presents WiLoc, a Long-term WiFi localization with Lightweight Siamese Neural Network. This method introduces a Siamese neural encoder-based framework to learn the similarity between three inputs, where the Siamese network only consists of three linear layers without any convolutional layer or transformer. The triplet loss function is utilized to supervise the training of the feature encoder. Then, the encodings from this encoder are input to K-Nearest Neighbors (KNN) to predict the user’s positions. Extensive experiments on the UJI dataset, show the proposed WiLoc can effectively relieve the degradation of localization accuracy over time compared to the state-of-the-art algorithms, the degradation is reduced from 51% to 12.1%, and the average localization error is 2.06 m.

关键词： Location awareness Degradation Training Accuracy Convolution Neural networks Nearest neighbor methods Transformers Real-time systems Wireless fidelity

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