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Prediction of circularity of the holes in electrical discharge machining of inconel alloy using machine learning technique

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Interactions 2024年第1期245卷 1-10页

作者： Sujatha, M. Kavitha, B.C. Vimalarani, G. David, D. Beulah Muthukumar, K. Ram, S. Department of Electrical and Electronics Engineering Easwari Engineering College Chennai India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai India Department of Data Analytics Institute of Information Technology Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Thandalam Tamilnadu Chennai India Department of Electrical and Electronics Engineering Sri Krishna College of Engineering and Technology Coimbatore India

Accuracy and finishing of the component is playing an essential role in metal forming process. The quality of the product is depending on accuracy of the shape and size. The measurement of holes is a challenging task in electric discharge machining (EDM) of materials. Sometimes, the diameter of the holes are different and its seems to be conical shape. The circularity of the hole is not maintained due to over current, low voltage and improper electrode materials. In this article, the circularity of the holes and its dimensions are analyzed by coordinate measuring machine (CMM). The experimental results are compared with predicted results with the aid of machine learning technique through regularized random forest (RRF) model. Inconel 718 alloy is used as a work material for this experimental investigation. The material characterization and its performance against the EDM process are studied. Prediction of circularity of the holes by machining of inconel alloy is perfectly done by machine learning technique. It’s used to enhance the accuracy of the hole and performance of the process. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Metal forming

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Differentially Private Federated Stochastic Primal-Dual Learning for Internet of Vehicles

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IEEE Internet of Things Journal 2025年第11期12卷 17034-17050页

作者： Li, Yiwei Wang, Shuai Chang, Tsung-Hui Xiamen University of Technology Fujian Key Laboratory of Communication Network and Information Processing Xiamen361024 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China Chinese University of Hon g Kong School of Science and Engineering Shenzhen518172 China Shenzhen Research Institute of Big Data Shenzhen518172 China

Federated learning (FL) has the potential to empower Internet of Vehicles (IoV) networks by enabling smart vehicles (SVs) to participate in the learning process under the orchestration of a vehicular service provider while keeping data locally. In this article, we propose a novel federated stochastic primal-dual algorithm with differential privacy (FedSPD-DP) to ensure robust privacy protection for FL based IoV (FL-IoV) systems. The FedSPD-DP algorithm leverages multiple steps of local stochastic gradient descent (SGD) and partial client participation (PCP) to improve communication efficiency while incorporating differential privacy (DP) to ensure privacy protection. Our theoretical analysis explores the impact of these strategies on learning performance. Specifically, we demonstrate that the data sampling strategy and PCP enhance data privacy, while a larger number of local SGD steps may increase privacy leakage, revealing a nontrivial tradeoff between communication efficiency and privacy protection. Extensive experiments on real-world data validate the effectiveness of the proposed algorithm, showing superior performance compared to state-of-the-art FL algorithms, and confirming the analytical results and properties. © 2014 IEEE.

关键词： Federated learning

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Decentralized brokered enabled ecosystem for data marketplace in smart cities towards a data sharing economy

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Environment Systems and Decisions 2023年第3期43卷 453-471页

作者： Anthony, Bokolo Department of Applied Data Science Institute for Energy Technology Halden 1777 Norway Department of Computer Science and Communication Østfold University College BRA Veien 4 Halden 1757 Norway

Presently data are indispensably important as cities consider data as a commodity which can be traded to earn revenues. In urban environment, data generated from internet of things devices, smart meters, smart sensors, etc. can provide a new source of income for citizens and enterprises who are data owners. These data can be traded as digital assets. To support such trading digital data marketplaces have emerged. data marketplaces promote a data sharing economy which is crucial for provision of available data useful for cities which aims to develop data driven services. But currently existing data marketplaces are mostly inadequate due to several issues such as security, efficiency, and adherence to privacy regulations. Likewise, there is no consolidated understanding of how to achieve trust and fairness among data owners and data sellers when trading data. Therefore, this study presents the design of an ecosystem which comprises of a distributed ledger technology data marketplace enabled by message queueing telemetry transport (MQTT) to facilitate trust and fairness among data owners and data sellers. The designed ecosystem for data marketplaces is powered by IOTA technology and MQTT broker to support the trading of sdata sources by automating trade agreements, negotiations and payment settlement between data producers/sellers and data consumers/buyers. Overall, findings from this article discuss the issues associated in developing a decentralized data marketplace for smart cities suggesting recommendations to enhance the deployment of decentralized and distributed data marketplaces. © 2023, The Author(s).

关键词： data ecosystem data sharing economy Decentralized data marketplace Emerging technologies IOTA technology MQTT broker Smart cities Sustainable society

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Artificial intelligence in physiological characteristics recognition for internet of things authentication

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Digital communications and Networks 2024年第3期10卷 740-755页

作者： Zhimin Zhang Huansheng Ning Fadi Farha Jianguo Ding Kim-Kwang Raymond Choo School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing100083China Faculty of Informatics Engineering Aleppo UniversityAleppoSyria Department of Computer Science Blekinge Institute of Technology37179KarlskronaSweden Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Effective user authentication is key to ensuring equipment security,data privacy,and personalized services in Internet of Things(IoT)***,conventional mode-based authentication methods(e.g.,passwords and smart cards)may be vulnerable to a broad range of attacks(e.g.,eavesdropping and side-channel attacks).Hence,there have been attempts to design biometric-based authentication solutions,which rely on physiological and behavioral *** characteristics need continuous monitoring and specific environmental settings,which can be challenging to implement in ***,we can also leverage Artificial Intelligence(AI)in the extraction and classification of physiological characteristics from IoT devices processing to facilitate ***,we review the literature on the use of AI in physiological characteristics recognition pub-lished after *** use the three-layer architecture of the IoT(i.e.,sensing layer,feature layer,and algorithm layer)to guide the discussion of existing approaches and their *** also identify a number of future research opportunities,which will hopefully guide the design of next generation solutions.

关键词： Physiological characteristics recognition Artificial intelligence Internet of things Biological-driven authentication

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Prediction Of Chronic Kidney Disease On Ct Images Using Deep Learning: Resnet-34 And Vggnet-16

Prediction Of Chronic Kidney Disease On Ct Images Using Deep...

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2024 International Conference on Integration of Emerging Technologies for the Digital World, ICIETDW 2024

作者： Revathi, M. Nithiya, R. Ohm Shankar, S. Maheshwari, D. St.Joseph's Institute of Technology Artificial Intelligence & Data Science Chennai India Agni College of Technology BioMedical Engineering Chennai India Agni College of Technology Electronics & Communication Engineering Chennai India

ISBN: (纸本)9798331541170

Kidney is considered as the most essential and important organ of our human body. Many factors such as diabetes, high blood pressure, inherited kidney disease, abnormal structure of kidney etc., affects the filtration rate of the kidney and in turn leads to chronic kidney disease. İf disease is identified and properly treated at early stages, human life can be saved. But in most cases, it cannot be done since there is no proper symptoms in the early stages. Huge of data is produced in the medical industry daily and it is left unattended. If this data is utilized properly, many useful information can be found. The use of Artificial Intelligence to assist medical professionals is increasing day by day. In this research work, we have used two famous deep learning architectures: RESNET and VGGNET to predict the existence of Chronic Kidney Disease. To improve the prediction accuracy, Artificial Bee Colony algorithm is used for segmentation. Instead of giving the original image as input, segmented image is given. The experiments shows that the VGGNET architecture produces higher accuracy of 98% than the RESNET architecture which gives an accuracy of 96%. © 2024 IEEE.

关键词： Deep learning

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Multi-Agent Coordination of Autonomous Vehicles for Smart Logistics in Industrial Environments 13

Multi-Agent Coordination of Autonomous Vehicles for Smart Lo...

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13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Kirubhakaran Karthikeswaran, D. Arun, M. Chakravarthy, R. Arun Akshya, J. Sundarrajan, M. Choudhry, Mani Deepak Surya Engineering College Department of Computer Science and Engineering Erode India Nehru Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India KGiSL Institute of Technology Department of Electronics and Communication Engineering Coimbatore India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Networking and Communications Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798350380583

Dynamic effects in industrial logistics demand more creative solutions towards the pursuit of efficiency and agility in evolving environments. Though static path-planning and taskallocation algorithms have provided the basis for how autonomous vehicles are guided within structured environments, those approaches usually fail to contend with indeterminacy outside of their modelled world, where there is dynamic obstacle appearance and changing task requirements, thus raising inefficiencies and costs of operations. To address these issues, the paper introduces a new multi-agent coordination system that uses a hybrid A∗ algorithm, integrating path planning with sophisticated detection methods for dynamic obstacles and improving superior task allocation. The system is supposed to provide improvements in real-time coordination in industries of autonomous vehicles to achieve efficient management of tasks and paths in changing environments. We integrate the latest in machine learning techniques for improved and real-time updates within the changes of the vehicle routing process based on both immediate sensory input and environmental interactions. Simulations show that our system performs the task much faster than classic systems: up to 30% improvement in completing tasks compared with classic models, and an average improvement of 25% in allround energy efficiency. It intelligently integrates machine learning techniques with real-time data processing. With the ability to scale up or down, or to adapt itself, this system addresses the chief challenges existing methods face and could revolutionize logistics operations within any industry. © 2024 IEEE.

关键词： Motion planning

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Identification Of Medicinal Plants from Leaf Images 4

Identification Of Medicinal Plants from Leaf Images

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4th IEEE Global Conference for Advancement in technology, GCAT 2023

作者： Jhunjhunwala, Parth Dhawas, Riya Sanjay Tiwari, Shristi Sharma, Abhishek Electronics and Communication International Institute of Information Technology Naya Raipur India Data Science and Artificial Intelligence International Institute of Information Technology Naya Raipur India International Institute of Information Technology Naya Raipur India

ISBN: (纸本)9798350305258

Recognition quest for medicinal herbs is a continuous field of exploitation and is crucial in Ayurveda's industries. Incorrect identification leads to adverse effects. One of the most entrusted classification tools is Support Vector Machine (SVM), which deals with high dimensional feature space and is generally a crucial aspect of image data. This paper proposes a comparison of a novel multi-class prediction to segregate plant leaves based on an SVM classifier and deep learning based on Resnet50 and InceptionV3 for leaf classification implemented, each model was individually trained before comparison. The findings from the trained model can be integrated into a web application. The SVM model was built in four stages. Stage 1 involves data preprocessing using techniques like grayscaling, smoothing, thresholding, morphological transformation, and boundary extraction to make the data suitable for SVM-based classification. In Stage 2, shape-based, colour-based, and texture-based features are extracted, and a comma-separated delimiter file is generated that would be fitted in an SVM classification model. Stage 3 and 4 is the model-building and testing stage wherein classification, feature scaling, and parameter tuning is done to obtain optimal hyperparameters for the SVM classifier. Using the transfer learning approach parameters were initialized on the pre-trained Neural networks namely InceptionV3, and ResNet50. When training the models on the relevant dataset, the softmax layer coupled to the dense layer served as the classifier and was used to extract features from the input photos. The last stage involves integrating the solution into a web application. The real-time leaf images can be captured by the user utilizing a camera to obtain plant names from images and upload them to a web interface. The whole framework is compared with the existing frameworks for plant leaf classification. © 2023 IEEE.

关键词： ensemble feature extraction hyperparameters Inception V3 preprocessing Resnet50 SVM Transfer Learning

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Skin Cancer Prediction by Incorporating Bio-inspired Optimization in Deep Neural Network

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SN Computer science 2024年第8期5卷 1-20页

作者： Mundada, Monica R. Sowmya, B.J. Supreeth, S. Prabhu, Smitha G. Mahesh, K. Vishwanath, Y. Rohith, S. Department of Computer Science and Engineering M S Ramaiah Institute of Technology Bengaluru 560054 India Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru 560054 India School of Computer Science and Engineering REVA University Bengaluru 560064 India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Electrical and Electronics Engineering Sir M Visvesvaraya Institute of Technology Bengaluru India School of Computer Science & Engineering Presidency University Bengaluru 560064 India Department of Electronics & Communication Engineering Nagarjuna College of Engineering & Technology Bengaluru 562164 India

This study offers a unified surgical conception of skin neoplasms using deep learning and genetic optimization techniques to improve classification values and speed of diagnosis. In this case, the architecture of Convolutional Neural Networks such as MobileNet, Xception and Inception is enhanced on the basis of the creative algorithms of Particle Swarm Optimization and Bat Algorithm to adjust the hyperparameters. The Heroku hosted cloud solution permits registered users to upload pictures of skin lesions via the web interface, where the picture is then processed through a Deep Neural Network (DNN) model that classifies the lesion as either melanoma or non-melanoma. The Convolution Neural Network (CNN) models were trained and evaluated on large publicly available datasets of skin lesions like ISIC, HAM10000 and the model performance was evaluated based on the effect of varying optimizers. Among all the architectures, Xception with Bat Algorithm (BA) performed best because of the more complex and deeper network architecture and extreme inception modules, which made it possible to extract and classify more complicated features. As a cloud-based solution, this system provides dermatologists and health practitioners with a more practical and workable solution for the detection of skin cancers. Furthermore, it can be utilized in other fields, which implies that this approach could extend its applications beyond dermatological imaging or imaging of patients at risk for other diseases, thus creating an adaptable and effective tool for clinical diagnosis in many areas of medicine. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Bio-inspired optimization Deep neural network (DNN) Skin cancer prediction

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A Bi-LSTM Regression-Aided Estimation Technique for Time-Varying Multiple DoAs 9

A Bi-LSTM Regression-Aided Estimation Technique for Time-Var...

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9th International Conference on Business and Industrial Research, ICBIR 2024

作者： Inoue, Haruta Takano, Yasuhiro Su, Hsuan-Jung Ozawa, Seiichi Grad. School of Science and Technology Chitose Institute of Science and Technology Chitose Japan Chitose Institute of Science and Technology Dept. of Information Systems Eng. Chitose Japan Grad. Institute of Communication Eng. National Taiwan University Taipei Taiwan Center for Mathematical and Data Sciences Kobe University Kobe Japan

ISBN: (纸本)9798350383010

Motivated by mmWave applications in beyond 5th generation communication systems, direction of arrival (DoA) estimation techniques have re-attracted attention due to the line-of-sight propagation property of mmWave signals. Conventional DoA estimation algorithms require statistical signal models, which are not always available in practice. In addition, the conventional Root-MUISC can suffer from mean squared error (MSE) deterioration seriously in practical scenarios having multiple DoA parameters that change independently over time. Therefore, recently, supervised learning-based DoA estimation algorithms using Convolutional Neural Network (CNN) have attracted attention. Specifically, a conventional DoA classification method using Long Short-Term Memory (LSTM) networks can improve the aforementioned problems. However, it is difficult to improve MSE performance further than the resolution assumed in the classifier due to a finite number of classes. As a solution to these problems, this paper proposes a bi-directional LSTM regression-aided DoA estimation technique that obtains estimates by solving a regression problem from features captured in both the forward and backward directions of the observed sequence. Simulation results verify that, in a low signal-to-noise ratio (SNR) regime, the Bi-LSTM regression achieves a remarkable NMSE gain > 10 dB at normalized MSE = 1 over the Root-MUSIC and conventional Classification-CNN in a practical scenario. © 2024 IEEE.

关键词： Mean square error

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Automated Detection and Classification of Rice Leaf Diseases Using YOLOv8: A Deep Learning Approach for Enhanced Crop Monitoring 2

Automated Detection and Classification of Rice Leaf Diseases...

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2nd International Conference on Computer Vision and Internet of Things, ICCVIoT 2024

作者： Ferbin, F.J. Kumar, Nikhilesh Sasithradevi, A. Vijayalakshmi, M. Roomi, S. Mohamed Mansoor School of Mechanical Engineering Vellore Institute of Technology Chennai India Centre for Advanced Data Science Vellore Institute of Technology Chennai India School of Electronics Engineering Vellore Institute of Technology Chennai India Department of Electronics and Communication Engineering Thiagarajar College of Engineering Madurai India

ISBN: (纸本)9781837242672

Rice is a staple food crop, and its cultivation is crucial for ensuring global food security. However, rice plant diseases pose a significant threat to crop yields, emphasizing the need for effective disease detection and management strategies. With the rapid advancements in machine learning and computer vision techniques, there has been a growing interest in leveraging these technologies for automated disease detection in agriculture. Common diseases affecting rice plants, such as blast, brown spot, and bacterial blight, can cause substantial yield losses if left undetected and untreated. Early and accurate detection of these diseases is essential for implementing timely and targeted control measures, ultimately minimizing crop damage and maximizing yields. In this research, we propose the use of deep learning techniques, specifically the YOLOv8 (You Only Look Once) object detection algorithm, for the automated detection and classification of rice leaf diseases. The methodology involves training the YOLOv8 model on a carefully curated dataset comprising 2,304 annotated images of rice leaves, capturing diverse disease manifestations as well as healthy samples. The dataset was labeled, ensuring accurate ground truth for model training and evaluation. Experimental results demonstrate that the proposed deep learning model achieved an impressive accuracy of 68.2% in correctly classifying healthy rice leaf samples. This promising outcome highlights the potential of the YOLOv8 algorithm for practical applications in rice leaf disease detection and monitoring. © The Institution of Engineering & technology 2024.

关键词： Image annotation

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