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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Miranda Baldivieso, Thiago João da Silva Batista, Taise Grazielle Chagas, Fabio Suim Rodrigues Velho, Luiz Carlos Pacheco Ferreira Rosa, Paulo Fernando Defense Engineering Graduate Program Military Institute of Engineering Rio de Janeiro Brazil State Institute of the Environment Inea RJ Rio de Janeiro Brazil Department of Science and Industry systems University of South-Eastern Norway Kongsberg Norway Department of Computer Graphics Institute for Pure and Applied Mathematics Rio de Janeiro Brazil Electrical Engineering Graduate Program Military Institute of Engineering Rio de Janeiro Brazil

ISBN: (纸本)9781665464543

This working-in-progress paper aims to present a three-dimensional reconstruction using aerial images in different environments. The experiments were conducted with aircraft in both external and internal settings, starting with image acquisition, followed by the application of specific photogrammetry software-both commercial and open-source-and concluding with a qualitative evaluation of the results. © 2024 IEEE.

关键词： Aerial photography

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Transforming from Mathematical Model to ML Model for Meteorology in Macao's Smart City Planning 23

Transforming from Mathematical Model to ML Model for Meteoro...

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7th International Conference on E-Commerce, E-Business and E-Government, ICEEG 2023

作者： Ian, Vai-Kei Tse, Rita Tang, Su-Kit Pau, Giovanni Macao Meteorological and Geophysical Bureau Macao China Faculty of Applied Sciences Macao Polytechnic University Macao China Engineering Research Centre of Applied Technology on Machine Translation Artificial Intelligence of Ministry of Education Macao Polytechnic University Macao China Department of Computer Science and Engineering - Disi University of Bologna Bologna Italy Computer Science Department Ucla Los AngelesCA United States

ISBN: (纸本)9798400708398

Recently, with the rapid development and use of technology in network infrastructures, Macao is being transformed into a smart city progressively, even though it is recognized as a World heritage city for its architectural wonders and rich cultural traditions. The benefits of machine learning technology open up new avenues for environmental monitoring, such as weather forecasting, early extreme or severe weather alerts and safeguards, disaster mitigation enhancement, and technological revolution as part of overall smart city planning in Macao. This study focuses on the evolution of artificial intelligence, the important use of machine learning technology in the field of meteorology, and suggestions on the major future development trends in Macao to the migration of a smart *** discussed solutions tackling the problems in weather forecasting in Macao while creating advances in its transformation to be a smarter city, starting with the essence of machine learning and turning challenges into opportunities. © 2023 ACM.

关键词： Machine learning

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Effect of radiofrequency coil and primary magnetic field on radiolucent composite plates

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Noise and Vibration Worldwide 2023年第1期54卷 44-58页

作者： Gawande, Atul S Mukka Ramachanra, Shreyas Kamyab, Hesam Trung, Nguyen-Thoi Kattimani, Subhaschandra Department of Mechanical Engineering National Institute of Technology Karnataka Surathkal India Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia Kuala Lumpur Malaysia Laboratory for Applied and Industrial Mathematics Institute of Computational Science and Artificial Intelligence Ho Chi Minh City Viet Nam Faculty of Mechanical-Electrical and Computer Engineering Van Lang University Ho Chi Minh City Viet Nam

Magnetic Resonance Imaging (MRI) systems need a material compatible with the imaging technique with lesser attenuation and provide accurate images without distortion. Carbon fibers are the best-suited materials for x-ray applications because of their radiolucent properties and reduced attenuation characteristics. However, carbon fiber produces images with distortion in the MRI system by absorbing electromagnetic energy because of its conductive nature. In the present study, five different fiber-reinforced radiolucent composite plates are analyzed to predict their suitability for a radio frequency coil and the effect of a primary magnetic field of 1.5 T and 3.0 T on mechanical responses. Simulation models are built to explore the impact of electromagnetic waves and birdcage coil configurations on composite material and quantify the temperature changes caused due to energy absorption. A multiphysics coupling simulation is being used to understand the effect of stacking sequence, ply orientation, and boundary conditions on the response of composite plates under an electro-magneto-mechanical environment. © The Author(s) 2022.

关键词： Magnetic resonance imaging

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Satellite Image–Based Ecosystem Monitoring with Sustainable Agriculture Analysis Using Machine Learning Model

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Remote Sensing in Earth Systems Sciences 2024年第4期7卷 764-773页

作者： Mulakaledu, Ajjanna Swathi, Baswaraju Jadhav, Makarand Mohan Shukri, Shakeerah Mohd Bakka, Vinod Jangir, Pradeep Department of English Koneru Lakshmaiah Education Foundation AP Vaddeswaram India Department of Computer Science and Engineering (Data Science) New Horizon College of Engineering Bengaluru India Department of Electronics and Telecommunication NBN Sinhgad Technical Institutes Campus Pune 411041 India Management and Science University Selangor Shah Alam Malaysia Koneru Lakshmaiah Education Foundation AP Vaddeswaram India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602 105 India Applied Science Research Center Applied Science Private University Amman Jordan

Understanding the variations in soil fertility and crop growth across time and geography is crucial for understanding the agricultural environment. Satellite and unmanned aerial remote sensing are the two main types of remote sensing methods used in agroecosystem monitoring. Through the collection of remote sensing photos, it is able to monitor as well as control agro-ecosystem environment in real-time. Spatial choice constraints are lessened by high-decision satellites, and satellite imagery processing is improved by use of artificial intelligence (AI) and machine learning methods, which enables computerised crop identification, disease diagnosis, and yield calculation. This research proposes novel technique in satellite image–based sustainable agriculture analysis with ecosystem monitoring using machine learning techniques. Here, the input is collected as satellite image and processed for noise removal and normalisation. Then, this image feature has been selected for analysis of crop growth using Gaussian belief kernel component analysis and classified using recursive Bayes probabilistic vector neural networks. Experimental analysis has been carried out in terms of training accuracy, precision, sensitivity, F-1 score, and AUC for various satellite image dataset. Proposed technique attained precision of 94%, sensitivity of 96%, AUC of 93%, training accuracy of 97%, and F1-score of 95%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Crop Growth Analysis Ecosystem Monitoring Machine Learning Techniques Satellite Image Sustainable Agriculture Analysis

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LowResIR-Net: Lightweight Residual Network for ADL Recognition Using Low-Resolution Infrared Sensor Data

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Procedia computer Science 2025年 256卷 1358-1366页

作者： Sakorn Mekruksavanich Wikanda Phaphan Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao 56000 Thailand Research Group in Statistical Learning and Inference Department of Applied Statistics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok 10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok 10800 Thailand

Detecting activities of daily living (ADL) is crucial for supported living and medical monitoring. Traditional approaches rely on high-resolution sensors and computationally intensive algorithms, limiting their scalability and practical use in real-world settings. This research introduces LowResIR-Net, a compact deep residual network architecture specifically tailored for recognizing ADL utilizing low-resolution infrared sensor data. LowResIR-Net efficiently extracts key features from low-dimensional data by leveraging the advantages of infrared sensing, such as privacy protection and light resilience. Its architecture combines residual connections with depth-wise convolutions, enhancing feature learning while maintaining a compact model size. The model’s performance is evaluated using the Coventry-2018 dataset, containing low-resolution infrared data from three residential sensor locations. Our tests have shown that in all three cases, LowResIR-Net consistently achieves superior performance compared to other cutting-edge deep learning models, such as CNN, LSTM, BiLSTM, GRU, and BiGRU. LowResIR-Net attains the most excellent accuracies of 90.18%, 93.75%, and 93.30%, and F1-scores of 81.81%, 89.55%, and 88.92% for scenarios I, II, and III, respectively. Furthermore, it achieves these results with fewer parameters than most other models. LowResIR-Net’s strong performance highlights its ability to accurately capture complex patterns in low-resolution infrared data for ADL detection. This study demonstrates the potential of lightweight deep learning and low-resolution infrared sensors in developing privacy-focused and computationally efficient wellness monitoring systems.

关键词： Activities of daily living low-resolution infrared sensors deep residual networks lightweight architecture healthcare monitoring

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Optimization of Metal-Matrix Composites by W-EDM with CNT

Optimization of Metal-Matrix Composites by W-EDM with CNT

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2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Puthilibai, G. Tamilselvi, T. Priya, C. Chinnaraj, P. Balamuralitharan, S. Subhashree, A. Sri Sairam Engineering College Department of Chemistry Tamil Nadu Chennai India Sri Sairam Engineering College Department of Electronics and Instrumentation Engineering Tamil Nadu Chennai India Sri Sairam Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Psg Institute of Technology and Applied Research Department of Mathematics Tamil Nadu Coimbatore India Tamil Nadu Chennai India

ISBN: (纸本)9798350394580

The EDM procedure is typically used to machining the Al6061 metal matrix composites with silicon carbide (SiC) with carbon nanotube reinforcement (CNT). The Metal Removal Rate (MRR), Surface Roughness (SR) as well as Tool Wear Rate (TWR) of the work piece material could potentially be measured using this method. Fundamental machining characteristics impacting the process's performance includingpulse ON time, discharge current as well as duty cycle were input attributes using Taguchi's parameter formulation. For each possible arrangement, a performance criterion has indeed been computed. The MRR, SR and TWR for every combination were computed in this investigation. The acquired outcomes have been tabulated. The research indicates that by employing grey relational analysis (GRA) to optimise the EDM parameters, improved MRR, TWR, and SR could be attained. MRR, TWR, and SR were 0.301 g/m m3, 0.003 g/mm3, and 4.03 m, respectively, according to the optimum outcomes of this research. © 2023 IEEE.

关键词： Al6061 CNT EDM metal matrix composites Optimization SiC

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A machine learning-based framework to Classify X-rays anomality 5

A machine learning-based framework to Classify X-rays anomal...

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5th International Conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Saranya, V.S. Kanagajothi, D. Rajina Begam, S. Sharma, Sunil Dubey, Nilesh Rehan, Sumaiya School of Computing Department of Computing Technologies Srm Institute of Science and Technology Kattankulathur Chennai India Department of Mathematics Vel Tech Rangarajan Dr Sagunthala R & D Institute of science and technology Avadi. India Department of Computer Science and Engineering School of Engineering and Technology Dhanalakshmi Srinivasan university Tamil Nadu Trichy India Department of Computer Science and Engineering Vivekananda global University Jaipur India Department of Computer Science and Engineering Devang Patel Institute of Advance Technology and Research FTE CHARUSAT University India Department of Computer Science and Engineering Babu Banarasi Das University Lucknow India

ISBN: (纸本)9798400709418

In recent years, the intersection of healthcare and technology has witnessed transformative advancements, particularly in medical imaging. The integration of machine learning techniques has empowered the medical community to enhance diagnostic accuracy and streamline healthcare processes. This paper introduces a comprehensive machine learning-based framework designed for classifying X-ray anomalies within the dynamic and scalable environment of cloud computing. The motivation behind this framework stems from the need for scalable, accurate, and cost-effective solutions in healthcare. Leveraging cloud computing's elastic resources, our framework aims to accelerate the deployment of machine learning models for X-ray anomaly classification, ensuring accessibility and affordability across diverse healthcare settings. Gather a diverse dataset of X-ray images with both normal and abnormal cases. Ensure a balanced representation of different anomalies. Utilize the validation set to fine-tune hyperparameters and prevent overfitting. Develop a robust machine learning model capable of accurately classifying X-ray anomalies, leveraging advanced convolutional neural networks (CNNs) and transfer learning techniques. The hardware and software required are discussed in relation to the functioning and integration of Cloud Computing and Blockchain. Then we have gone through the model of machine learning for detection of anomality in X-rays(infection) step by step and in detail. The model discussed in the research paper can be of great importance to society as many people in society lose their lives to pneumonia every year. With the help of image classification, we can detect the areas of lung infected with infection, hence helping doctors in swift treatment of the patients. © 2023 ACM.

关键词： Cloud computing

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Deep Learning Techniques to Detect Learning Disabilities Among children using Handwriting 3

Deep Learning Techniques to Detect Learning Disabilities Amo...

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3rd International Conference on Intelligent Computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Vilasini, V. Banu Rekha, B. Sandeep, V. Charan Venkatesh, Vishnu Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Tamil Nadu Coimbatore India Psg College of Technology Department of Biomedical Engineering Tamil Nadu Coimbatore India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

In today's world, we come across children facing certain disabilities which pose as obstacles and hinder their academic growth. Some of these disabilities are explicitly visible to the common eye, whereas some are hard to find and need extra attention. One such condition is Motor Dysgraphia which challenges an individual's ability to write. The common practice that is followed to identify such a condition among children is quite expensive and creates a mental strain on them. There are many intelligent computational methods that have been proposed with bearing a wide range of performances, however they are not quite standardized for assessment. Fortunately the advancements in Deep Learning techniques have been proven beneficial in automating this identification task. In this study, Learning Disability Detection system is built using Deep Learning techniques. The project's application is mainly focused on the pre-school and primary school children. This model analyses the child's handwriting and classifies whether the child is subjected to such a disorder or not. Deep Learning models - Convolutional Neural Networks (CNN) and Vision Transformers are adapted and their Disability Detection performances are analyzed and compared. © 2022 IEEE.

关键词： Convolutional neural networks

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Enhancing Mild Cognitive Impairment Detection through Efficient Magnetic Resonance Image Analysis

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computers, Materials & Continua 2024年第8期80卷 2081-2098页

作者： Atif Mehmood Zhonglong Zheng Rizwan Khan Ahmad Al Smadi Farah Shahid Shahid Iqbal Mutasem K.Alsmadi Yazeed Yasin Ghadi Syed Aziz Shah Mostafa M.Ibrahim Department of Computer Science and Technology Zhejiang Normal UniversityJinhua321002China Zhejiang Institute of Photoelectronics&Zhejiang Institute for Advanced Light Source Zhejiang Normal UniversityJinhua321004China Department of Data Science and Artificial Intelligence Zarqa UniversityZarqa13100Jordan School of Chemical and Environmental Engineering College of ChemistryChemical Engineering andMaterials ScienceSoochow UniversitySuzhou215123China Department of Management Information Systems College of Applied Studies and Community ServiceImam Abdulrahman Bin Faisal UniversityDammam32244Saudi Arabia Department of Computer Science Al Ain UniversityAbu Dhabi999041United Arab Emirates Department of Electrical Engineering Faculty of EngineeringMinia UniversityMinia61519Egypt Healthcare Technology and Innovation Theme Faculty Research Centre for Intelligent HealthcareCoventry UniversityCoventryCV48BWUK

Neuroimaging has emerged over the last few decades as a crucial tool in diagnosing Alzheimer’s disease(AD).Mild cognitive impairment(MCI)is a condition that falls between the spectrum of normal cognitive function and ***,previous studies have mainly used handcrafted features to classify MCI,AD,and normal control(NC)*** paper focuses on using gray matter(GM)scans obtained through magnetic resonance imaging(MRI)for the diagnosis of individuals with MCI,AD,and *** improve classification performance,we developed two transfer learning strategies with data augmentation(i.e.,shear range,rotation,zoom range,channel shift).The first approach is a deep Siamese network(DSN),and the second approach involves using a cross-domain strategy with customized *** performed experiments on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset to evaluate the performance of our proposed *** experimental results demonstrate superior performance in classifying the three binary classification tasks:NC ***,NC ***,and MCI ***,we achieved a classification accuracy of 97.68%,94.25%,and 92.18%for the three cases,*** study proposes two transfer learning strategies with data augmentation to accurately diagnose MCI,AD,and normal control individuals using GM *** findings provide promising results for future research and clinical applications in the early detection and diagnosis of AD.

关键词： Alzheimer’s disease mild cognitive impairment normal control transfer learning classification augmentation

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Image Encryption Algorithm Based on New Fractional Beta Chaotic Maps

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computer Modeling in engineering & Sciences 2022年第7期132卷 119-131页

作者： Rabha W.Ibrahim Hayder Natiq Ahmed Alkhayyat Alaa Kadhim Farhan Nadia M.G.Al-Saidi Dumitru Baleanu Institute of Electrical and Electronics Engineers Kuala Lumpur59200Malaysia Information Technology College Imam Ja’afar Al-Sadiq UniversityBaghdad10001Iraq Department of Computer Technical Engineering College of Technical EngineeringIslamic UniversityNajaf192122Iraq Department of Computer Sciences University of TechnologyBaghdad10066Iraq Department of Applied Sciences University of TechnologyBaghdad10066Iraq Department of Mathematics Cankaya UniversityBalgatAnkara06530Turkey Institute of Space Sciences Magurele-BucharestR76900Romania Department of Medical Research China Medical UniversityTaichung40402Taiwan

In this study,a new algorithm of fractional beta chaotic maps is proposed to generate chaotic sequences for image *** proposed technique generates multi random sequences by shuffling the image pixel *** technique is used to blur the pixels connecting the input and encrypted images and to increase the attack *** proposed algorithm makes the encryption process sophisticated by using fractional chaotic maps,which hold the properties of *** fractional beta sequences are utilized to alter the image pixels to decryption *** experimental results proved that the proposed image encryption algorithm successfully encrypted and decrypted the images with the same *** output findings indicate that our proposed algorithm has good entropy and low correlation *** translates to enhanced security against different attacks.A MATLAB programming tool was used to implement and assess the image quality measures.A comparison with other image encryption techniques regarding the visual inspection and signal-to-noise ratio is provided.

关键词： Fractional calculus fractional beta function chaotic function image encryption

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