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A study on the systematic review of green IoT

A study on the systematic review of green IoT

引用

automation and Computation (AUTOCOM), international conference on

作者： Priyanshu Mittal Jiya Agrawal Rahul Chauhan Swati Devliyal Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

This research investigates the review of the Green Internet of Things (GIoT), a paradigm that incorporates ecofriendly principles into IoT design and operation. It discusses its current state, advancements, and potential applications in smart cities, agriculture, and healthcare. It also discusses ongoing projects and standardization efforts, challenges, opportunities, and prospects of Green IoT, emphasizing its potential for environmental sustainability.

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Underwater Photography Noise cancellation Using Artificial Intelligence and Deep Learning 2

Underwater Photography Noise cancellation Using Artificial I...

引用

2nd IEEE international conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Sanjiv, S. Kishore Harshan Kumar, R. Ranjith, R. Chandrasekar, A. Vijayakumar, K. St. Joseph's College of Engineering Artificial Intelligence and Data Science Chennai India St. Joseph's College of Engineering Computer Science and Engineering Chennai India

ISBN: (纸本)9798350315905

Cameras submerged underwater are widely used to see the ocean floor. Unmanned underwater automation, autonomous underwater vehicles, and in situ ocean sensors are common places to find them (AUVs). While being an essential sensor for keeping track of underwater landscapes, recent underwater camera sensors have several issues. Because of how light moves through water and the biological activity at the seafloor, underwater photographs are typically cluttered with scatters and noise. Over the past five years, a variety of tactics have been developed to Important facts of oceanographic study include image processing and underwater sensing. One prior challenge is light absorption with a scattering effect, which reduces the image quality in underwater conditions with respect to its ground truth. © 2023 IEEE.

关键词： Underwater photography

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Machine Learning-Based Detection of Blood Cancer from Microscopic Images: A Comparative Study 9

Machine Learning-Based Detection of Blood Cancer from Micros...

引用

9th international conference on Communication and Electronics Systems, ICCES 2024

作者： Manoj, Milan Sreekumar, Vishnu Reghuram, Surya Anjali, T. Ramlal, Nandakishor Prabhu Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India Altair Engineering Ab Stockholm Sweden

ISBN: (纸本)9798350377972

Early and accurate detection of blood cancer is critical for improving patient outcomes. This article is proposed to develop and evaluate the effectiveness of using machine learning techniques for automation in blood cancer detection based on microscopic images of blood cells. A comparison will be conducted using three of the most used models: Support Vector Machine, Decision Tree, and Random Forest on their capability in the classification process of distinguishing blood cells from benign and malig-nant groups. The methodology is divided into the following steps: image preprocessing, extraction of color histogram and texture features through the Gray-Level Co-occurrence Matrix (GLCM), and optimization of hyperparameters for model training. Evaluation metrics used are accuracy, precision, recall, and F1-score. Random Forest has been seen to perform with a high accuracy of 99.08% with all the categories showing robust classification. It was followed by SVM with an accuracy of 96.15 % and Decision Tree with 94.92%. The results present Random Forest as a valuable resource for the automated diagnosis of blood cancer. This paper reiterates the promise that machine learning holds in boosting diagnostic efficiency and encourages future studies into more advanced approaches such as deep learning for further improvement in clinical applications. © 2024 IEEE.

关键词： Diseases

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Research on surface defect detection algorithm of steel pipe weld seam based on deep learning

Research on surface defect detection algorithm of steel pipe...

引用

2024 international conference on Image Processing and Artificial Intelligence, ICIPAl 2024

作者： Wu, Qian Lu, Zhiyong Li, Lin Liu, Jiangbo Han, Yu College of Computer Science and Engineering Chongqing University of Technology Chongqing400054 China College of Automation Chongqing University Chongqing400044 China

ISBN: (纸本)9781510681514

An improved YOLOv5s model was proposed to address the issue of poor efficiency in steel pipe weld defect detection. First, the network's feature learning and localization capabilities are enhanced by introducing a coordinate attention mechanism. Secondly, compress the model through the lightweight Ghost module to effectively get fewer network parameters. Thirdly, standard convolution and depth-separable convolution are combined in the Neck module for convolution operations, aiming to reduce model complexity while maintaining accuracy. Finally, introduced a new WIoU loss function to speed up model convergence. Based on a self-created dataset, the detection of three types of surface defects, including overlap, spatter, and undercut, is conducted. The experimental findings demonstrate that the improved model achieves a 2.4% increase in mAP. Furthermore, the model also achieved a 22.4% reduction in parameter count. These findings indicate that the improved model can effectively meet the practical detection requirements. The experimental comparison confirms the feasibility and effectiveness of the proposed improved algorithm for detecting surface defects in steel pipe welds. © 2024 SPIE.

关键词： Convolution

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A Comprehensive Analysis on Clustering and Routing Techniques in Wireless Sensor Network

A Comprehensive Analysis on Clustering and Routing Technique...

引用

2024 international conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Samson Paul, K. Prabhu Chakkaravarthy, A. Veeresha, M. Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur India Dr.K.V.Subba Reddy Institute of Technology Department of Computer Science and Engineering Kurnool India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360660

Wireless Sensor Networks (WSN) is utilized as front-end communication systems in home automation, environment monitoring, industry, Internet of Things (IoT), etc. The extension of the lifetime of WSNs enhances the utilization of various applications and systems to communicate and interact efficiently. However, cluster heads (CHs) and relay nodes face the difficulties of energy balancing, nodes having limited capability for storage systems, communication, resource management and computational power which further results in corrupted nodes and data becoming vulnerable to improper access. The optimization algorithms namely Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Butterfly Optimization Mechanism (BOM), and Fuzzy Logic (FL)-based techniques are used for reduce the delay in CH selection and routing protocols. This research aims to research the strengths, relationships, and challenges of delay-aware clustering and routing for WSN-based approaches, thereby minimizing the EC and increasing network lifetime. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Leveraging Industrial IoT Infrastructure for Remote Sensing and Edge Computing in the Mining Sector 5

Leveraging Industrial IoT Infrastructure for Remote Sensing ...

引用

5th international conference in Electronic engineering, Information Technology and Education, EEITE 2024

作者： Eleftherios, Emmanouil Papatsaroucha, Dimitra Markakis, Evangelos K. Hellenic Mediterranean University Electrical and Computer Engineering Heraklion Greece

ISBN: (纸本)9798350372878

Mining 4.0 signifies a paradigm shift within the mining sector and is defined as an all-encompassing strategy for industry revolution by optimizing mining processes through the utilization of digital technologies, automation, and data analytics to streamline processes. Mining 4.0 aims to develop intelligent and networked mining systems that enhance resource utilization, improve operational efficiency, reduce costs, and mitigate environmental harm, all while prioritizing the safety and welfare of workers. The process of mining digitization encompasses the methodical collection, management, and monitoring of data derived from diverse sources, including sensors, equipment, vehicles, and more. It permits remote, realtime monitoring of environmental and operational conditions, as well as the integration of control systems designed to optimize and automate processes. With digital transformation, the mining sector aspires to overcome conventional obstacles and adjust to changing market requirements, therefore facilitating a more sustainable and efficient future. A critical challenge in the process of digitizing the mining sector is the need to provide reliable routes for transmitting and communicating data between physical and digital components within mining environments. This paper examines the convergence of Mining 4.0 and mining digitization, with a specific emphasis on the pivotal role that remote sensing, edge computing, and decentralized communication systems play in data transmission and real-time monitoring in light of enhancing mining operations' safety and efficiency. The study performed puts forward a European solution based on open standards and tools, revolving around an Industrial Internet of Things (IIoT) Gateway that is a core component of the proposed communication system. This gateway will function as an intermediary between the edge and cloud-based platforms and will facilitate real-time monitoring through the establishment of resilient wireless communi

关键词： Gateways (computer networks)

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PartDensE: Knowledge Graph Embedding with Joint Use of Specific Learning and Relation-aware Functions 6

PartDensE: Knowledge Graph Embedding with Joint Use of Speci...

引用

6th international conference on Internet of Things, automation and Artificial Intelligence, IoTAAI 2024

作者： Dan, Qiuyue Cui, Delong Peng, Zhiping Li, Qirui He, Jieguang Qiu, JinBo Jilin Institute of Chemical Technology School of Information and Control Engineering Jilin China Guangdong University of Petrochemical Technology School of Electronic and Information Engineering Maoming China Jiangmen Polytechnic Jiangmen China Guangdong University of Petrochemical Technology School of Computer Maoming China

ISBN: (纸本)9798350386974

Knowledge Graph Embedding (KGE) is important to the value of the Knowledge Graph (KG) in its application field. Currently, neural network-based models achieve the most advanced performance. However, these models rarely consider the potential information between entities and relations in KGs, and also suffer from the problems of complex structure and high consumption of computing resources. In this paper, we propose the simple and efficient model PartDensE by using dense network, combining specific sharing layer and relational awareness layer. Specifically, our model learns all entities and relationships individually and can mine hidden features to enrich global information. Also, learn all the information based on each relation and be able to accurately establish associations between entities and relations. PartDensE can capture global and local features and displayed and hide information, retaining as much valuable information as possible. We evaluated PartDensE with 4 indicators across on two baseline datasets. Numerous experimental data prove the effectiveness of our model. © 2024 IEEE.

关键词： Knowledge graph

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A Survey on Green IoT and Its Opportunities for Future Directions

A Survey on Green IoT and Its Opportunities for Future Direc...

引用

2023 international conference on Advanced Computing and Communication Technologies, ICACCTech 2023

作者： Upadhye, Sachin Shelke, Nilesh Thakur, Uma Patel Shri Ramdeobaba College of Engineering and Management Department of Computer Application Nagpur India Symbiosis Institute of Technology Nagpur Campus Symbiosis International Deemed University Pune India Jhulelal Institute of Technology Department of Computer Science and Engineering Nagpur India

ISBN: (纸本)9798350380880

The Internet of Things (IoT) has changed the way technology has evolved, integrating it with other aspects such as home appliances for home automation, smart city projects, e-waste management, and many more. But with the increase in the usage of IoT, there are several challenges that come along, like an increase in energy consumption. Due to this, the Green IoT comes into play. It looks for alternative solutions to the present style of implementing IoT technology, which leads to eco-friendly, sustainable, and cost-effective solutions. This survey points towards effective ways of implementing the IoT for eco-friendly, sustainable, and cost-effective management. The Internet of Things (IoT) has transformed the way technology is integrated into various aspects of life, including home automation, smart city projects, and e-waste management. However, the increasing usage of IoT technology has led to challenges, such as higher energy consumption. This has given rise to Green IoT, which seeks alternative solutions for implementing eco-friendly, sustainable, and cost-effective IoT technology. This survey focuses on effective ways to implement IoT technology for sustainable and eco-friendly cost management. It also explores the potential of IoT technology in new areas, its future sustainability, and its challenges. The adoption of green IoT practices can reduce pollution hazards, traffic waste, and energy consumption while enhancing public safety and quality of life. To create sustainable and eco-friendly cities, researchers, policymakers, and industry leaders must collaborate to address IoT implementation challenges. The survey concludes by highlighting opportunities for future research and innovation in this field. © 2023 IEEE.

关键词： Internet of things

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Research and Implementation of Key Technologies for Power Internet of Things Access Platform 4

Research and Implementation of Key Technologies for Power In...

引用

4th international conference on Energy engineering and Power Systems, EEPS 2024

作者： Song, Yinghua Fan, Qiaoyun Nari Group Corporation State Grid Electric Power Research Institute Nanjing211106 China Nari Technology Co. Ltd Nanjing211106 China China Mobile Group Jiangsu Co. Ltd. Jiangsu Nanjing210006 China

ISBN: (纸本)9798350366914

Based on the summary of the architecture of power automation systems and Internet of Things systems at home and abroad, further analyze the access requirements and functional requirements for the construction of power Internet of Things. In combination with the top-level architecture design of 'cloud-pipe-edge-terminal', the system architecture design of the power IoT access platform is proposed. The system architecture includes multiple sets of modules that support the platform, compatible with the underlying hardware on the bottom and supporting micro services and micro-applications on the top. In terms of key technologies for the implementation of the new power IoT back-end system, a design method for an access platform integrating multiple IOT access modules has been proposed in response to the coexistence of traditional power terminals and new IoT terminals;Drawing on existing risk prevention methods for power systems, a multilevel and multidimensional security protection scheme for IoT access platforms is proposed. Based on the above system architecture design and key technology research, the system has been demonstrated and applied in Nanjing, and the application has proved the feasibility and correctness of the design technology ideas. © 2024 IEEE.

关键词： Power systems computer aided design

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Comprehensive Analysis of Hand Radiographic based Person Authentication 7

Comprehensive Analysis of Hand Radiographic based Person Aut...

引用

7th IEEE Pune Section international conference, PuneCon 2024

作者： Tekade, Arti Vijayan, T. Karthik, B. Bharath Institute of Higher Education and Research Department of Electronics and Communication Chennai India Pimpri Chinchwad College of Engineering and Research College Ravet Department of Electronics and Tele-Communication Pune India

ISBN: (纸本)9798331527822

One of the most important components of the majority of computer vision automation systems is human authentication. It is impossible for conventional systems that are based on fingerprints, iris, face, and palm prints to identify a human being when these exterior biometric elements are destroyed due to things like rashes, wounds, severe burns, and other similar factors. The most important characteristics of any person authentication system are its security, resilience, privacy, and non-forgery capabilities. A comprehensive overview of numerous methods of person authentication based on hand radiographs is presented in this work. The technique, radiographic modality employed, dataset, benefits, and downsides of these methods are some of the topics that are discussed. It fills in the study gaps that were discovered during the survey and throws up the door to the possibility of future improvements to the authenticity of individuals based on radiography. Additionally the effectiveness of the system is analyzed for the different machine learning algorithms such as support vector machine (SVM), K-nearest neighbor (KNN), classification tree (CT) and random forest (RF). © 2024 IEEE.

关键词： Support vector machines

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