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Enhanced Prediction of Semiconductor Wafer Defects through CNN Forest Fusion and Random Methods for Improved Efficiency

Enhanced Prediction of Semiconductor Wafer Defects through C...

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2024 international conference on advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Rawat, Savita Banerjee, Deepak Gupta, Amit Singh, Vijay Chitkara University Institute Of Engineering And Technology Chitkara University Punjab India Graphic Era Hill University Uttarakhand Dehradun India Graphic Era Deemed To Be University Uttarakhand Dehradun India

ISBN: (纸本)9798350371567

The abstract summarizes the research's key findings, presenting the robust approach to semiconductor wafer defect classification using a hybrid model that blends Convolutional Neural Network (CNN) and Random Forest techniques. The model has excellent precision scores ranging between 97.12% to 98.65%, indicating its accuracy in identifying specific fault categories, while recall scores ranging from 97.24% to 98.39% reflect its ability to capture genuine positive instances. The dataset of 5238 photos of semiconductor wafers shows the model's exceptional overall accuracy of 97.74%. The harmonized F1-Scores, with the Near-Full class scoring 97.26%, demonstrate a balanced performance that is critical for eliminating both false positives and false negatives. The model's flexibility to different defect kinds can be attributed to its training on a diverse and representative set, which is made possible by its uniform dataset distribution (Proportion = 0.20). The remarkable 99% total accuracy of the model indicates that it can accurately predict in every class, indicating its utility in semiconductor wafer defect classification. Aggregate measures, including Macro Average, Weighted Average, and Micro Average, provide comprehensive insights into the model's classification abilities over a range of fault categories and consistently validate the model's balanced & nuanced performance. Weighted average F1-Score of 97.75% indicates consistent recall and precision across all classes. A strong and accurate fault identification system is presented in this work, which significantly improves semiconductor quality control. The hybrid model's ability to effectively include features through CNN & fine-tune predictions using Random Forest demonstrates its capability to enhance production methods and ensure the integrity & quality of semiconductor wafers in real-world applications. © 2024 IEEE.

关键词： Defects

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A Hybrid Deep Learning Approach for Severity Classification of Pepper Leaf Mosaic Virus 4

A Hybrid Deep Learning Approach for Severity Classification ...

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4th international conference on advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Kaur, Arshleen Kukreja, Vinay Gopal, Lisa Verma, Garima Sharma, Rishabh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun248002 India Graphic Era Deemed to Be University Computer Science and Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350343670

The efficient administration of agricultural operations and the maximization of crop output both depend critically on an accurate assessment of the severity of plant diseases. This study aims to answer this need by providing a hybrid deep-learning model for the categorization of the severity of the Pepper Leaf Mosaic Virus (PLMV). This model mixes Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs). The suggested model makes use of the advantages offered by both the spatial feature extraction and the temporal dynamics modeling approaches. As a result, it is possible to gain an in-depth comprehension of the progression of PLMV severity. The study makes use of a dataset that contains twenty thousand photographs of pepper plants taken at various phases of PLMV infection. In order to guarantee precise classification, the architecture of the hybrid CNN-LSTM model has been painstakingly crafted to take into account the subtleties of the course of PLMV severity. The performance of the model can be made more reliable by doing exhaustive data preparation, which may include image scaling and enhancement. In order to prevent the model from becoming overfit, it is trained using stochastic gradient descent, which also includes dynamic learning rate scheduling and early stopping. The findings of this study shed light on the substantial potential that the hybrid CNN-LSTM model possesses in terms of the classification of PLMV severity. The findings may have repercussions for precision agriculture, which would benefit from having access to a more developed tool for making well-informed decisions and implementing timely interventions. The research lays the groundwork for further developments in illness severity assessment and management, despite the fact that it has some limitations, such as the fact that the accuracy of the model is affected by the influence of image quality and environmental conditions. © 2024 IEEE.

关键词： Deep Learning Disease Classification Long-short term memory Pepper leaf disease

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Smart Environment Monitoring Using Internet of Things and Big Data 3

Smart Environment Monitoring Using Internet of Things and Bi...

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3rd international conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Gupta, Neelesh Kumar Mishra, Achyutanand Shukla, Pavan Kumar Sharma, Ankur Pandey, Shweta Ajay Kumar Garg Engineering College Ghaziabad India Saupal Engineering College Supaul India Noida Institute of Engineering and Technology Greater Noida India Institute of Technology and Science Ghaziabad India Law College of Dehradun Uttaranchal University Dehradun India

ISBN: (纸本)9798350322842

With the advent of sophisticated technologies, big data, and the internet of things (IoT), environmental supervision has evolved it in to a smart environment monitoring (SEM) technology in recent decades. In light of that one, the current work seeks to complete a thorough assessment of meaningful advances and scientific researches on SEM that focus on the environmental monitoring, pollution levels, radiation contamination, or agricultural systems. In this study, tools rely on big data and the Internet of Things (IoT) for sensing devices is reviewed. The objective is to pinpoint the main application domains, modern developments, data structures, and persistent problems in these areas. The findings show that big data and IoT technology convergence opens up intriguing possibilities for actually smart corresponding input for detecting, safeguarding, and enhancing environmental capital. As a beginning point for future scientific research in IoT technology and surroundings, the study sums up the techniques most frequently utilised in big data & IoT as last step. © 2023 IEEE.

关键词： Big data

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Multi-Modal Tracking Using LiDAR and Visual Signals 11

Multi-Modal Tracking Using LiDAR and Visual Signals

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11th international conference on Electrical engineering, Computer science and Informatics, EECSI 2024

作者： Kumar, Sanjay Hassan, Mohamed Escudero-Vinolo, Marcos Hannan, Abdul Manzoor, Anam Godinho, Antonio Pires, Ivan Miguel Coelho, Paulo Jorge Autonomous University of Madrid Madrid Spain University of Bordeaux Bordeaux France University of Management and Technology Punjab Pakistan Polytechnic Institute of Coimbra Technology and Management School of Oliveira Do Hospital Oliveira Do Hospital Portugal Research Center for Arts and Communication Santarém Polytechnic University Santarém Portugal Instituto de Telecomunicações Escola Superior de Tecnologia e Gestião de Agueda Universidade de Aveiro Agueda Portugal School of Technology and Management Polytechnic University of Leiria Leiria Portugal Institute for Systems Engineering and Computers at Coimbra Coimbra Portugal

ISBN: (纸本)9798350355314

Multi-object tracking (MOT) is a crucial technique for detecting and tracking multiple objects over time in a scene. It involves locating objects in consecutive frames of a video or sequential observations and establishing correspondences to maintain their identity. MOT is essential in various applications, including autonomous vehicles, robotics, and video surveillance. In autonomous vehicles, it is typically performed using 3D models (LiDAR/stereo) or 2D models (one or more RGB cameras). The LiDAR 3D point-cloud provides a 360-degree view of its surroundings but has a low resolution compared to the camera's visual signals. This paper presents a sensor fusion approach for multi-object detection and tracking, combining object detection information from sensors, LiDAR, and cameras and applying data association algorithms like Hungarian algorithms for tracking to increase the overall system. The EagerMOT approach for 3D Multi-Object Tracking via Sensor Fusion achieves state-of-the-art results across several MOT tasks on the KITTI dataset, achieving an accuracy of 87.95 %. © 2024 IEEE.

关键词： Stereo image processing

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Efficiency Analysis of Convolutional Neural Netwoks in Multiclass Recognition of Skin Lesions via Dermoscopy Images 10

Efficiency Analysis of Convolutional Neural Netwoks in Multi...

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10th international conference on Artificial Intelligence and robotics, QICAR 2024

作者： Monajemi, Mahsa Shahdi, Seyed Omid Haji Nasiri, Seyed Saeed Shojaedini, Seyed Vahab Department of Biomedical Engineering Qazvin Branch Islamic Azad University Qazvin Iran Biomedical Engineering Department Iranian Research Organization for Science & Technology Tehran Iran

ISBN: (纸本)9798350348873

Although melanoma is considered as one of the deadliest types of cancer, however, detecting this disease in its early stages may lead to saving the patient's life. Biopsy is the most accurate method to diagnose this type of cancer, but due to its invasiveness trying to use non-invasive diagnostic modalities has always been of interest to researchers in this field. Dermoscopy is one of the common non-invasive modalities, in which it is determined whether the patient is healthy or cancerous by analyzing the images captured from the patient's skin. However, there may be several lesions on the skin that, despite their appearance, most of them are not cancerous and even harmless. In this way, recognizing melanoma lesions is the most important challenge in the field of analyzing dermoscopic images especially when it is desired to determine the type of other lesions in this classification process. In this article, the performance of the deep learning paradigm in the classification of multi-type lesions in dermoscopic images is evaluated and the challenges facing deep neural networks in this domain are analyzed. For this purpose, two well-known datasets of dermoscopy images known as ISIC2019 and HAM10000, which have 8 and 7 types of labeled lesions, respectively, were selected and the detection rate of lesions in them was evaluated by applying deep neural networks. The obtained results indicate that although the results obtained from the application of deep learning in the separation of lesions in both of examined datasets are significantly better than the conventional methods, but the resultant accuracy is highly dependent on the volume of data and balance between several classes of lesions. In this way, the use of data augmentation and data balancing methods should be considered as a priority for conducting any research in order to improve the analysis of dermoscopic images by using deep learning. ©2024 IEEE.

关键词： Deep neural networks

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Full-image acquisition and analysis of hot heavy rail incorporating multiple CCD 7

Full-image acquisition and analysis of hot heavy rail incorp...

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7th international conference on Mechatronics and Intelligent robotics, ICMIR 2023

作者： Mi, Zengzhen Zhang, Hongming Tang, Jing Xu, Xingyuan College of Mechanical Engineering Chongqing University of Technology No. 69 Hongguang Avenue Chongqing400054 China

ISBN: (数字)9781510668089

ISBN: (纸本)9781510668072

The rail transportation industry is moving towards heavy loads, high speeds as well as high density operation, which places higher demands on the quality of heavy rail. The quality inspection of rail defects using machine vision first requires obtaining clear surface/planar images of multiple surfaces of the hot heavy rail. Therefore, a vision inspection solution is proposed, which uses a 6-lane linear CCD to photograph the tread surface, bottom surface, and upper and lower waist surfaces of the heavy rail. A comprehensive comparison of various types of light sources was conducted through experiments on chromaticity, brightness, and spectral power. To solve the problem of image overexposure due to infrared radiation of the hot heavy rail, the spectral radiation characteristics of the hot heavy rail were analyzed, and the imaging effects of adding different filters were compared and analyzed. Finally, to solve the problem of out-of-focus due to oscillation of the heavy rail during rolling, the image is analyzed by an automatic focus search strategy, and the results are fed back to the camera to achieve automatic focus, thus adaptively obtaining good image quality and laying a good foundation for further defects detection. © 2023 SPIE. All rights reserved.

关键词： Light sources

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Research on natural environment protection based on blockchain technology to facilitate ecological balance and regional revitalization 4

Research on natural environment protection based on blockcha...

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4th international conference on Industrial IoT, Big Data and Supply Chain, IIoTBDSC 2023

作者： Wan, Chenye Fu, Taoran Wang, Haochen Wang, Xing Luo, Junyou Wang, Ziqian School of Information Science and Technology Nantong University Nantong China School of Computer and Communication Hunan Institute of Engineering Xiangtan China School of Computational Science and Electronics Hunan Institute of Engineering Xiangtan China School of Robotics Hunan University Changsha China

ISBN: (纸本)9798350341690

Now the Masai Mara population continues to expand, the wildlife in the reserve is facing serious threats to their survival. The purpose of this report is to establish a management strategy model for local protected areas that balances the mutual interests between local people and wildlife in ***, Masai Mara divides the protected area into three parts, planting area, protected area, and livestock area, and then conducts correlation analysis on different areas, and concludes that the reduction in the number of wild animals is closely related to the degree of population density, and the area of cultivated land and forest area is highly correlated. So a nonlinear programming model of multiple strategies is established to plan the planting area. Then establish a comprehensive evaluation model based on the entropy weight method, quantify various strategies, and use gray correlation prediction to obtain solutions. This article introduces blockchain related technologies and uses its decentralization, security and transparency to build an architecture that contributes to the actual government protection of wild animals. © 2023 IEEE.

关键词： Conservation

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Northern Cthulhu Algorithm Optimized VMD Combined with SVM for Fault Diagnosis

Northern Cthulhu Algorithm Optimized VMD Combined with SVM f...

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2022 international conference on Automation, robotics and Computer engineering, ICARCE 2022

作者： Cao, Dengxue Liu, Luyi Lin, Wei Shanghai Institute of Technology Department of Electrical and Electronic Engineering Shanghai201418 China

ISBN: (纸本)9781665475488

For a long time, in the face of complex signal processing, such as rolling bearings signals and other complex nonlinear signals, most of them are using traditional signal processing methods to extract signal features. However, it is difficult for general signal processing strategies to extract all the signal features contained in the signal one by one. With mature signal extraction methods like variational mode decomposition (VMD), the number of layers of signal decomposition determines the effect of final fault detection. To solve this problem, this paper proposes a northern goshawk optimization (NGO) algorithm to optimize the VMD and find the optimal decomposition parameter K, which further improve the detection effect. Finally, the experimental data simulated in the MATLAB software platform shows that the detection effect achieved by the optimized VMD of the NGO algorithm is improved by 6.4814%. © 2022 IEEE.

关键词： MATLAB

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An Overview of Artificial Intelligence Applications in Food Cold Chain Transportation 23

An Overview of Artificial Intelligence Applications in Food ...

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5th international conference on robotics Systems and Automation engineering, RSAE 2023

作者： Li, Shengyu Zhou, Yaping Zhang, Yunjie Zhang, Zirou College of Artificial Intelligence Tianjin University of Science and Technology China Department of Food Science and Engineering Tianjin University of Science and Technology China Department of Humanitier and Law Tianjin University of Science and Technology China School of Foreign Languages Tianjin University of Science and Technology China

ISBN: (纸本)9781450398510

In recent years, the rising income level of the population and the young consumer force brought about a continuous improvement and upgrading of the diet structure and rapid growth of the food cold chain logistics market. However, frequent outbreaks of food quality and safety issues have raised consumer concerns. As the key link of cold chain logistics, cold chain transportation has a direct and important impact on the freshness of food and even food safety. Facing the current situation of imperfect food cold chain transportation systems, it is necessary to make full use of artificial intelligence algorithms and convenient framework platforms in order to reduce food loss rates, strengthen transportation efficiency and improve customer satisfaction. In this paper, we study and summarize the application of artificial intelligence in cold chain transportation, and optimize the path planning problem of cold chain logistics delivery vehicles by using the bee colony algorithm. The environmental monitoring system and cold chain precise regulation cloud platform are used in the transportation process to monitor the external environment and guarantee the quality of food transportation. In the scheduling of cold chain transportation vehicles, the optimized ant colony algorithm is used to resolve the vehicle scheduling problem. The problem of specific vehicle search directionality from the scheduling source point to the target point of the logistics center is included in the optimization consideration, which will greatly improve the vehicle scheduling time and distance efficiency. The combination of artificial intelligence technology in cold chain transportation can reduce the average 10% of human and material resources and effectively improve the quality of transported food, thus reducing transportation costs and food loss and ensuring food safety and sustainable development. © 2023 ACM.

关键词： Population statistics

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An Optimized Combinatorial Approach For Stock Market Prediction Considering User Preferences 1

An Optimized Combinatorial Approach For Stock Market Predict...

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1st IEEE international conference on Cognitive robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Iyappan, P. Anuradha, K. Revathi, R. Sathish Kumar, L. Saravanan, D. Maragatharajan, M. Vellore Institute of Technology Tamil Nadu Vellore India Karpagam College of Engineering School of Computer Applications Tamil Nadu Coimbatore India Karpagam Academy of Higher Education Department of Computer Science Tamil Nadu Coimbatore India Vit Bhopal University School of Computing Science and Engineering Madhya Pradesh Kotrikalan India

ISBN: (纸本)9798350372748

The economy is often seen as the engine that propels a nation forward. It regulates the distribution of resources by keeping an eye on the complex relationship between production and consumption. To put it simply, the economic system is there to meet the demands of all the people and businesses that make up the economy. The stock market is an important part of any economic understanding since it is the setting in which activities such as the purchase and sale of shares and the issue of new shares by different types of limited liability companies (both public and private) take place in relation to market research and exchanges. Because it is so difficult for the average person to predict when stock prices will go up or down, it is critical to find the best stocks on the market and put their money into them. When it comes to intelligently and reliably meeting user demands, however, conventional processes fall short. An optimized stock prediction system that takes user preferences and demands into account is presented in this study as a new way to manage retrieval. This technique is crucial for properly satisfying user requirements. Essential aspects such as supply-demand dynamics, inflation, geopolitical events, technological advancements, natural catastrophes, company performance, and the efficiency of government data may be dynamically gathered using this technique. All of these things add together to make stock price swing predictions. Machine learning algorithms are crucial for stock prediction because of the massive amounts of data that must be collected, processed, cleaned, and analyzed. The proposed method, ML + LSTM, yields 96% accuracy. Comparing the existing methods, which are KNN, decision tree, and random forest, gives accuracy like 80, 92, and 94 percentages. In addition, the article highlights the use of ML techniques including k-nearest neighbour, decision tree, and random forest. Important financial indicators, including common factors, sector factors,

关键词： Decision trees

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