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An Innovative Design Of A Reusable Constellation Of CubeSats For Space Debris Removal 73

An Innovative Design Of A Reusable Constellation Of CubeSats...

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73rd International Astronautical Congress, IAC 2022

作者： Shukla, Aayush Thuluva, Sushmith Kodukula, Ananya Bharadwaj, Vyoma Jain, Alankriti Shadaksharaiah, Anushree Maligehalli Pulicallu, Greeshmanth Kadagadakai, Vishnurat Rai, Riddhi Mitra, Ruhi Prabhu, M. Nanditha Department of Artificial Intelligence & Machine Learning Ramaiah Institute of Technology MSRIT Post M S Ramaiah Nagar MSR Nagar Karnataka Bengaluru560054 India Department of Electrical and Computer Engineering Carnegie Mellon University 5000 Forbes Ave PittsburghPA15213 United States Department of Computer Science & Engineering Ramaiah Institute of Technology MSRIT Post M S Ramaiah Nagar MSR Nagar Karnataka Bengaluru560054 India Department of Electronics & Telecommunication Engineering Ramaiah Institute of Technology MSRIT Post M S Ramaiah Nagar MSR Nagar Karnataka Bengaluru560054 India Department of Electronics & Instrumentation Engineering Ramaiah Institute of Technology MSRIT Post M S Ramaiah Nagar MSR Nagar Karnataka Bengaluru560054 India Department of Mechanical Engineering Ramaiah Institute of Technology MSRIT Post M S Ramaiah Nagar MSR Nagar Karnataka Bengaluru560054 India Department of Artificial Intelligence & Data Science Ramaiah Institute of Technology MSRIT Post M S Ramaiah Nagar MSR Nagar Karnataka Bengaluru560054 India Department of Information Science & Engineering Ramaiah Institute of Technology MSRIT Post M S Ramaiah Nagar MSR Nagar Karnataka Bengaluru560054 India

Space debris is one significant setback in our attempt to explore space and enhance satellite technology. Eradicating this upshot is eminent to our future missions and a safe environment for our planet. The scale of the space junk in the low earth orbit is around few hundreds of thousands and more, sizing from 10s of centimeters in diameter and lower. Although it is inevitable for the space debris to reenter the atmosphere naturally, it is a matter of years and hence the rate is too slow. With advances in the CubeSat technology, a variety of designs are possible for CubeSat systems to function as per the mission requirements. The current paper hence nominates an idea to remove some of the inoperative satellites and it's corresponding components with the help of a constellation of CubeSats that are equipped with distributed actuators for propulsion. The CubeSats are so designed to function as a propulsive device alone with minimal subsystem requirements. The constellation is contained in a cuboidal station with CubeSats docked on all the lateral sides. Once a target body is locked and intended to be removed, the constellation releases one or more CubeSats en route to the target body. Once at site, the CubeSats attach themselves to the target and propel the body to reenter the atmosphere. This constellation station is so designed to be compatible for reusability and the maintenance of such a design is discussed in the paper. The entire approach is thoroughly analyzed investigating multiple designs for the CubeSats. An additional benefit of the proposed idea is to ensure an immediate solution for deorbiting any critically unstable debris in the future in an event of emergency. Copyright © 2022 by the International Astronautical Federation (IAF). All rights reserved.

关键词： Space debris

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Revolutioning IIot: A Smart Appliance of an Internet of Things Over Industrial Manufacturing Unit to Make Error-Free Circumstances

Revolutioning IIot: A Smart Appliance of an Internet of Thin...

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Cybernation and Computation (CYBERCOM), International Conference on

作者： Kiran Chand Ravi T. Vaishnavi Anni Princy B Vijay Anand Kandaswamy M Dinesh S Lakshmisridevi Department of ECE MLR Institute of Technology Hyderabad India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Machine Learning Panimalar Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering R.M.K. Engineering College Chennai Tamil Nadu India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai Tamil Nadu India Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331542344

ISBN: (纸本)9798331542351

A future where cyberspace and the real world are inseparable is the goal of the Internet of Things (IoT). The potential for the development of robust industrial services and applications, colloquially known as the Industrial Internet of Things (IIoT), is enormous. From the acquisition of raw materials to the provision of customer service and support, the Internet of Things (IoT) was utilized across numerous sectors by industries. The Internet of Things (IoT) offers a great opportunity to develop significant manufacturing-related services and applications, and it is widely regarded as a crucial technology of the industrial revolution. In addition, it facilitates communication between intelligent devices, allowing them to exchange information and data-crucial for complex systems to make decisions in the here-and-now. In this paper, a new method called Error-Free Smart Industrial Control System (EFSICS) is introduced to improve the efficiency of industrial operations through the use of IIoT. To test how well this method works, it is cross-validated with the traditional model called Classical Industrial Control System (CICS). Regarding the manufacturing aspects in particular, the suggested method is well-suited to the effects of the Internet of Things on sustainable development. Researchers are now at the forefront of cyber integrated manufacturing research, which will further illuminate the present state of the internet of things (IoT) in manufacturing.

关键词： Industries Industrial control Production Real-time systems Raw materials Manufacturing Maintenance Sustainable development Streams Industrial Internet of Things

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Development of a machine learning Model for pulmonary carcinoma Prediction using Computer Tomography (CT) Images

Development of a Machine Learning Model for pulmonary carcin...

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International Conference on Image Information Processing

作者： Jampani Chandra Sekhar Priyanshu Singh T. Rajesh Kumar G. Mahendran Katta Subba Rao Ramya Maranan Department of Computer Science and Engineering NRI Institute of Technology Guntur Andhra Pradesh India Department of Computer Science and Engineering R.V. College of Engineering Bengaluru Karnataka India Department of Artificial Intelligence and Machine Learning Sri Krishna College of Technology Coimbatore Tamil Nadu India Department of Mechanical Engineering R.M.K Engineering College Kavaraipettai Chennai Tamil Nadu India Department of Computer Science and Engineering B V Raju Institute of Technology Narsapur Medak (District) Telangana India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798350371406

ISBN: (纸本)9798350371413

In this study, computer tomography (CT) scans are used to build a machine learning model for predicting lung cancer. A dataset of 10,000 CT scan images from 900 patients was used to train a group of machine learning algorithms. The objective is to identify the most reliable algorithm for lung cancer detection. The algorithms CNN, CNN with gradient descent, VGG-19, and VGG-16 have all been used in real-world applications. Each algorithm is trained using the dataset to create a prediction model. Performance metrics including accuracy, precision, specificity, sensitivity, and F-Score are taken into account when assessing how effective the algorithms are. The results demonstrate that the CNN model outperforms the competition with a 97% accuracy rate. The superior accuracy, specificity, sensitivity, and F-Score of the CNN model enable accurate lung cancer detection in CT scan images. The outcomes demonstrate that, utilising the well-established machine learning paradigm, the CNN algorithm, in particular, has substantial potential for early lung cancer diagnosis and prediction. Correctly identifying lung cancer from CT scan images allows for the implementation of focused treatment programmes and prompt interventions, ultimately leading to better patient outcomes. The study emphasises the requirement for applying machine learning methods to identify relevant characteristics and patterns in CT scan images. machine learning models can now be used in clinical settings to support the precise detection and treatment of lung cancer. The use of powerful algorithms and extensive datasets allowed for this accomplishment. In order to validate and enhance the models that have been developed, more study is needed in this field. The models will therefore be more useful and suitable for use in actual clinical settings. Utilising larger datasets, deep learning architectures, and other possible machine learning advancements can significantly increase the precision and generalizability of

关键词： machine learning algorithms Sensitivity Computed tomography Computational modeling Lung cancer machine learning Predictive models

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HMM Classifier Object Recognizing System in Brain–Computer Interface 8th

HMM Classifier Object Recognizing System in Brain–Computer ...

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8th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2020

作者： Anupama, H.S. Jain, Raj V. Venkatesh, Revannur Cauvery, N.K. Lingaraju, G.M. Department of Artificial Intelligence and Machine Learning BMS Institute of Technology & Management Bangalore India Department of Computer Science and Engineering R. V. College of Engg Bangalore India Department of Information Science and Engineering R. V. College of Engg Bangalore India Department of Information Science M. S. Ramaiah Institute of Tech. Bangalore India

ISBN: (纸本)9789811557873

machine learning (ML) is the field that adds intelligence to devices providing them with capabilities to process and identify patterns in data just like human beings do. Programming devices in this manner can help in identifying those patterns which human beings often cannot. machine learning is based on modelling data mathematically. ML has been gaining a lot of attention in the last few decades, especially in fields of interdisciplinary research. Brain–Computer Interface (BCI) is an area where machine learning technology is been rapidly using. Also, machine learning techniques have to be used so that one can get a better result and more efficiency. Information Transfer Rate is the best way to measure the performance of the signals. The current research is mainly focused on achieving the systems with higher ITR. The focus of the proposed system is to get better and high Information Transfer Rate by merging two different approaches. The approach used in this work is (SSVEP), Visually Evoked Potential and (SSAEP) Auditory Evoked Potential by using Hidden Markova Model (HMM). The system which is to be developed checks whether the existing system has such facility if it has, does it provides accuracy which is of a higher rate and can put it in the real-world applications. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Hidden Markov models

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Discrete Hopfield Neural Network Model for Secure Electrical Routing in VANETs to Avoid Blackhole Attacks

Discrete Hopfield Neural Network Model for Secure Electrical...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： Chennaiah Kate Arvind Tudigani Sarala Raghavendra A. Lizy M. Malathi Lalit Johari Department of Computer Science and Engineering St.Peter’s Engineering College Hyderabad Telangana India Department of Computer Science University College of Science Hyderabad Telangana India Department of Computer Science and Engineering CMR Technical Campus Hyderabad Telangana India Department of Artificial Intelligence and Machine Learning Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Mathematics Bannari Amman Institute of Technology Sathyamangalam Tamil Nadu India Department of Computer Applications IFTM University Rajput Uttar Pradesh India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Intelligent Transportation Systems (ITS) depend heavily on Vehicle Ad Hoc Networks (VANETs) to establish vehicle-to-vehicle communication because these networks improve both safety performance on the roads and the reduction of traffic congestion. Black hole attacks represent one of the major cyber threats that affect the secure operation of Vehicle Ad Hoc Networks. Black hole attacks against VANET networks represent critical threats to security which can generate data packet losses along with network instability even with current security evolution efforts. Security measures based on conventional practices lead to substantial false detection situations while their response times remain slow thus preventing real-time mitigation strategies. This study develops a novel Discrete Hopfield Neural Network with Termite Alate Optimization (DHNN-TAO) model to address the mentioned issues. The Mountaineering Team-Based Optimization (MTBO) is the initial procedure that combines network nodes to establish enhanced network stability and effective transmission between nodes. Discrete Hopfield Neural Network (DHNN) identifies black hole attacks through effective detection of malicious nodes after the clustering process ends. The optimization process of hyperparameters in DHNN uses Termite Alate Optimization (TAO). After identification of malicious activity Light Spectrum Optimizer (LSO) works to find secure communication routes between attack-free network segments. The suggested DHNN-TAO attains 1600 kbps throughput, 0.42s routing delay, 7.4ms execution time, 99.1% accuracy, 0.96 packet delivery ratio, and 0.59 control overhead, surpassing the current techniques.

关键词： Accuracy Hopfield neural networks Vehicular ad hoc networks Routing Throughput Real-time systems Delays Vehicle dynamics Optimization Traffic congestion

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Real-Time Incident Detection and Response with Isolation Forest Algorithm and Cloud Infrastructure

Real-Time Incident Detection and Response with Isolation For...

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

作者： Chethan Chandra S Basavaraddi Chitra Sabapathy Ranganathan K. Sindhuja N. Mohankumar M. Jagadeeswaran S. Murugan Department of Artificial Intelligence and Machine Learning R&D Don Bosco Institute of Technology VTU Belagavi Karnataka India Mphasis Corporation Chennai Tamil Nadu India Department of Information Technology J.J College of Engineering and Technology Tiruchirappalli Tamil Nadu India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune Maharastra India Department of Computer Science and Engineering Vinayaka Missison’s Kirupanandha Variyar Engineering College Vinayaka Missions Research Foundation (Deemed to be University) Salem Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

This research proposes an innovative approach to make incineration facilities more secure using the Isolation Forest algorithm and cloud infrastructure to identify and respond to real-time incidents. Incineration plants present special security concerns because of the high-stakes nature of their work and the dangers to people and the environment that may result from an incident. Traditional security procedures aren't always quick enough to respond to new dangers. Using the Isolation Forest method to identify anomalies in operational data streams provides a proactive security system. Using a cloud-based architecture, it accomplishes the scalability, flexibility, and real-time processing capabilities necessary for quick threat detection and response. It shows that methods successfully detect anomalous occurrences with few false positives and confirm it experimentally using data from simulated incineration facilities. Real-world consequences architecture may be integrated with current security systems to protect vital infrastructure and improve incident reaction times. It provides a thorough solution to the rising security risks in incineration plants, advancing their security posture.

关键词： Incineration Scalability Forestry Computer architecture Real-time systems Threat assessment Security Reliability Streams Resilience

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Touchstone benchmark: are we on the right way for evaluating AI algorithms for medical segmentation? 24

Touchstone benchmark: are we on the right way for evaluating...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Pedro R. A. S. Bassi Wenxuan Li Yucheng Tang Fabian Isensee Zifu Wang Jieneng Chen Yu-Cheng Chou Saikat Roy Yannick Kirchhoff Maximilian Rokuss Ziyan Huang Jin Ye Junjun He Tassilo Wald Constantin Ulrich Michael Baumgartner Klaus H. Maier-Hein Paul Jaeger Yiwen Ye Yutong Xie Jianpeng Zhang Ziyang Chen Yong Xia Zhaohu Xing Lei Zhu Yousef Sadegheih Afshin Bozorgpour Pratibha Kumari Reza Azad Dorit Merhof Pengcheng Shi Ting Ma Yuxin Du Fan Bai Tiejun Huang Bo Zhao Haonan Wang Xiaomeng Li Hanxue Gu Haoyu Dong Jichen Yang Maciej A. Mazurowski Saumya Gupta Linshan Wu Jiaxin Zhuang Hao Chen Holger Roth Daguang Xu Matthew B. Blaschko Sergio Decherchi Andrea Cavalli Alan L. Yuille Zongwei Zhou Department of Computer Science Johns Hopkins University and Department of Pharmacy and Biotechnology University of Bologna and Center for Biomolecular Nanotechnologies Istituto Italiano di Tecnologia Department of Computer Science Johns Hopkins University NVIDIA Division of Medical Image Computing German Cancer Research Center (DKFZ) and Helmholtz Imaging German Cancer Research Center (DKFZ) ESAT-PSI KU Leuven Division of Medical Image Computing German Cancer Research Center (DKFZ) and Faculty of Mathematics and Computer Science Heidelberg University Division of Medical Image Computing German Cancer Research Center (DKFZ) and Faculty of Mathematics and Computer Science Heidelberg University and HIDSS4Health - Helmholtz Information and Data Science School for Health Shanghai Jiao Tong University Shanghai Artificial Intelligence Laboratory Division of Medical Image Computing German Cancer Research Center (DKFZ) Division of Medical Image Computing German Cancer Research Center (DKFZ) and Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Helmholtz Imaging German Cancer Research Center (DKFZ) and Interactive Machine Learning Group (IML) DKFZ School of Computer Science and Engineering Northwestern Polytechnical University Australian Institute for Machine Learning The University of Adelaide College of Computer Science and Technology Zhejiang University Hong Kong University of Science and Technology (Guangzhou) Hong Kong University of Science and Technology (Guangzhou) and Hong Kong University of Science and Technology Faculty of Informatics and Data Science University of Regensburg Faculty of Electrical Engineering and Information Technology RWTH Aachen University Faculty of Informatics and Data Science University of Regensburg and Fraunhofer Institute for Digital Medicine MEVIS Electronic & Information Engineering School Harbin Institute of Technology (Shenzhen) Shanghai Jiao Tong University and Beijing Academy of Artificial Intelligence (BAAI) S

ISBN: (纸本)9798331314385

How can we test AI performance? This question seems trivial, but it isn't. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified metrics, unfair comparisons, and short-term outcome pressure. As a consequence, good performance on standard benchmarks does not guarantee success in real-world scenarios. To address these problems, we present Touchstone, a large-scale collaborative segmentation benchmark of 9 types of abdominal organs. This benchmark is based on 5,195 training CT scans from 76 hospitals around the world and 5,903 testing CT scans from 11 additional hospitals. This diverse test set enhances the statistical significance of benchmark results and rigorously evaluates AI algorithms across out-of-distribution scenarios. We invited 14 inventors of 19 AI algorithms to train their algorithms, while our team, as a third party, independently evaluated these algorithms. In addition, we also evaluated pre-existing AI frameworks—which, differing from algorithms, are more flexible and can support different algorithms—including MONAI from NVIDIA, nnU-Net from DKFZ, and numerous other open-source frameworks. We are committed to expanding this benchmark to encourage more innovation of AI algorithms for the medical domain.

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Two-Stream Holographic Convolutional Network for Speech Emotion Recognition Using Korean Audio

Two-Stream Holographic Convolutional Network for Speech Emot...

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Ubiquitous Computing and Intelligent Information Systems (ICUIS), International Conference on

作者： N. Venkateswaran Balaji. Mudadla R. Rajitha Jasmine J Nalini M Rajasekar T. Jayasudha Department of Master of Business Administration Panimalar Engineering College Chennai Tamil Nadu India Department of Computer Science And Engineering (Artificial Intelligence & Machine Learning) CMR Technical Campus Hyderabad Telangana India Department of Information Technology R.M.K. Engineering College Kavaraipettai Tamil Nadu India Department of Computer Science Vignan's Institute of Engineering for Women Visakhapatnam Andhra Pradesh India Department of Computer Science And Application Saveetha College of Liberal arts and Sciences Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of English KPR Institute of Engineering and Technology Coimbatore Tamil Nadu India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

Speech Emotion Recognition (SER) has become an important area of research in the AI domain, helping systems comprehend people's feelings with speech, in such areas as healthcare, automated customer support, and virtual companionship. Emotion detection from Korean speech raises learning issues and demand an understanding of all high-frequency spectrum and time selective features. Despite that these layers work effectively, typical convolutional networks may lose phase information which is so important for emotion distinction especially in languages with tone and phonetics. For this research, the AI-Hub Speech Emotional Database, a large scale Korean speech dataset, is employed to create a Two-Stream Holographic Convolutional Network (TSHCN) for SER. However, the TSHCN architecture employed a two-path structure in which one branch encoded the spectral feature and the other branch encoded the temporal dynamics in sequence, while the phase information was retained using holodynamic convolutions for better classification of emotions. In order to increase the efficiency of the model, the Kookaburra Optimization Algorithm (KOA) is used on the parameters of the weight network, reducing the number of mistakes and the load of calculations as well as increasing the accuracy of the model. As more evident from the previously discussed results, SER performance is notably enhanced by the use of TSHCN with KOA; specifically, the accurate representation of Korean speakers' speech emotions will help pave the way for the development of SER for the Korean language and other complex languages as well. This study thus takes up the shortcoming in the conventional approaches to the task and proposes an optimal and language-independent SER model. The introduced approach attains higher accuracy as 99%.

关键词： Emotion recognition Accuracy Speech recognition Computer architecture Medical services Phonetics Feature extraction Convolutional neural networks Optimization Load modeling

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Application of Convolutional Neural Network techniques to the classification of lung illnesses

Application of Convolutional Neural Network techniques to th...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Shylaja S L Jyoti B K Santosh P P.N. Santosh Kumar V Sunil Kumar Department of Electronics and Communication East West Polytechnic Bengaluru India Department of Electronics & Communication Engg H. K. E. Society's S. L. N. College of Engineering Raichur India Department of Electronics and Communication Engineering Hyderabad Institute of Technology and Management Telangana India Department of CSE CMR Technical Campus Hyderabad India Department of Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India

The heterotypic perspective of cancer depicts solid tumors as ecosystems composed of aberrant epithelium tumor cells and a multitude of cell types together referred to as stromal cells. Macrophages, which are innate immune cells, are overrepresented in certain environments. Tumor-associated macrophages (TAMs) are macrophages found in the tumor microenvironment; they are derived from the blood's monocytes and are essential for tumor progression. TAMs acquiring tumorigenic qualities is dependent on a complicated interaction between TAMs and tumor cells. Using co-culture studies, we showed that tumor-derived secretory signals promote Tams' tumor-promoting characteristics, shaping up Tams' features in ways that are advantageous to the tumor. When model human monocytes (THP-1) were co-cultured with A549 cells, the A549 cells exhibited increased proliferation, migration, and invasiveness due to the secretion of tumor-promoting cytokines from the THP-1 cells. We showed that EDA-containing Fibronectin secreted by A549 cells reliably mediates the pro-inflammatory response of THP-1 monocytes in a paracrine manner. Ablation of such responses by the treatment of THP-1 cells with TLR-4 blocking antibody implicated Fibronectin-TLR4 axis in tumor-associated inflammation and suggests a paradigm wherein lung carcinoma cell derived EDA-containing Fibronectin drives a pro-inflammatory and pro-metastatic tumor microenvironment. Interestingly, autocrine proliferation, migration, and invasion were all boosted by EDA-containing Fibronectin secreted by A549 cells. Lastly, we demonstrated that the EDA in Fibronectin activates the epithelial-mesenchymal transition pathway in A549 cells, hence granting these cells the ability to metastasize.

关键词： Support vector machines Integrated circuits Sensitivity Lung Sensitivity and specificity Solids Classification algorithms

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IoT-Driven Predictive Maintenance for Energy-Efficient Industrial Systems

IoT-Driven Predictive Maintenance for Energy-Efficient Indus...

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Emerging technology (INCET), International Conference for

作者： Swathi Pai M VenkateswaraRao Cheekati V Nuthan Prasad KDV Prasad Syed Muqthadar Ali Hariprasad Tarigonda Dept. of Artificial Intelligence and Machine Learning NITTE (Deemed to be University) NMAM Institute of Technology Nitte Karnataka India Department of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram AndraPradesh India Department of Electronic and Communication M S R I T Bangalore Karnataka Department of Research Symbiosis Institute of Business Management Hyderabad Symbiosis International (Deemed University) Pune India Department of CSE CVR College of Engineering Hyderabad Telangana India Department of Mechanical Engineering School of Engineering Mohan Babu University Tirupati Andhra Pradesh India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

This research examines the deployment of the internet of things (IoT) technologies for predictive maintenance in industrial facilities in an effort to achieve energy efficiency and ensure the integration of renewable energy sources. Through the application of various IoT sensors and predictive algorithms at different industrial spaces, the study will show what the effect of predictive maintenance on operating costs, equipment life, and energy consumption. The results show that the reduction in downtime and energy expenditure, which, in turn, helps in the optimization of renewable energy usage in favor of environmental sustainability. The study brings to the foreground the practical obstacles, like the privacy of data and system integration problems, and puts forward the measures that would enhance their resolution. Such results support the potency of IoT focused preventive maintenance in constructing a ‘green’, smart and productive industrial sector.

关键词： Renewable energy sources System integration Prediction algorithms Energy efficiency Sensors Internet of Things Sustainable development

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