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Logistic Segmentation and Multistage Categorization based Predictive Modeling of Lung Cancer 2

Logistic Segmentation and Multistage Categorization based Pr...

引用

2nd international conference on Advancements in Electrical, Electronics, Communication, Computing and automation, ICAECA 2023

作者： Happila, T. Umamaheswari, S. Rajendran, A. Karpagam College of Engineering Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798350306811

The incidence of lung cancer is rising, and the fatal rate is beyond the measure. The treatment of cancer can be Successful if the condition is spotted at the earlier stage and found to be tiny and has not spread. Smokers and ex-smokers who are in the symptom-free stage are encouraged to get screened for lung cancer. X-rays are used to make cross-sectional images of the chest so as to do diagnosis of lung cancer. Symptoms to diagnose the disease are often non-existent and error prone in the early stages, making prediction is extremely challenging. Several computer-aided diagnostic methodologies and systems have been proposed, developed, and produced to detect the size and location and these methods leverage computer technology in various ways. The challenging characteristics of the lung cancer include high variability nodule classification and low decision making. In this paper machine learning based Multistage Categorization of Standard Segmentation (ML-MCSS) model has been proposed to assist clinicians deal with ambiguous pulmonary nodules found on the spot or through screening. Furthermore, the disease prediction accuracy of newly presented Logistic Segmentation Algorithms (LSA) is compared to that of the benchmark and data collected manually. The experimental result provides the step-by-step analyses of each method and their collective shortcomings were highlighted. © 2023 IEEE.

关键词： Diseases

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Multi-Agent hierarchical workflow for autonomous code generation with Large Language Models

Multi-Agent hierarchical workflow for autonomous code genera...

引用

2025 IEEE international Students' conference on Electrical, Electronics and computer Science, SCEECS 2025

作者： Akilesh, S. Sekar, Rajeev Om Kumar, C.U. Prakalya, D. Suguna, M. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331529833

This paper presents a multi-agent hierarchical workflow tailored for automating data analysis, code generation, and visualization, focusing specifically on user-provided CSV datasets. The workflow integrates AlphaCodium with LangChain, LangGraph, and GPT, enabling autonomous code generation, unit test development, and debugging. The system operates by analyzing the uploaded dataset, assigning agents that work sequentially: a programmer agent generates code using the Skeleton-Of-Code approach, a unit test agent verifies the code, and an executor agent runs the code. A debugging agent is also included to identify and resolve any issues. The AI agents involved are capable of dynamically accessing online resources, including documentation and references to enhance their decision-making processes. This work attempts to exemplify the application of AI in automating not only code execution but also the planning and validation stages by writing unit tests in the software development process to reflect the increasing role of AI in advancing automation for rapid code generation within the software industry. © 2025 IEEE.

关键词： Software design

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Condition Detection and Fault Diagnosis of Electrical engineering Equipment based on Deep Learning Algorithm

Condition Detection and Fault Diagnosis of Electrical Engine...

引用

2024 international conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Li, Shangrong The University of Melbourne Department of Engineering and It Melbourne Australia

ISBN: (纸本)9798350375442

Traditional fault diagnosis methods often rely on expert experience and simple statistical analysis, but in the field of modern electrical engineering, more efficient and accurate automatic diagnosis means are needed. Based on this background, this study proposes an innovative fault diagnosis method for electrical equipment, which improves the accuracy and efficiency of fault detection by combining Otsu threshold segmentation algorithm with Convolutional Neural Network. Otsu algorithm is used to preprocess the infrared thermal image of electrical equipment. The image is divided into foreground and background regions by calculating the global optimal threshold, which enhances the identifability of fault features. The CNN is then responsible for further feature extraction and fault classification. The image data preprocessed by the Otsu algorithm is input into the customized CNN model, and the weight is continuously adjusted by the back propagation algorithm to optimize the model performance. This framework not only improves the quality and interpretability of image data, but also enhances the training effect and diagnostic accuracy of the CNN model. Experimental results show that the proposed framework can identify fault features more accurately, reduce false positive rate and false negative rate, and improve the overall diagnostic accuracy when dealing with images with high background noise. This innovative framework is expected to be applied in a wider range of electrical engineering fields and provide strong technical support for industrial automation and intelligence. © 2024 IEEE.

关键词： Convolutional neural networks

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Inspection Methodology for Improving Wafer Quality in Semiconductor Production 9

Inspection Methodology for Improving Wafer Quality in Semico...

引用

Joint 9th international conference on Digital Arts, Media and Technology with 7th ECTI Northern Section conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2024

作者： Kwon, So-Young Kim, Young-Hyung Lee, Yong-Hwan Park, Jong-Ik Lee, Chang-Yong Kumoh National Institute of Technology Department of Electronic Engineering Gumi Korea Republic of Kumoh National Institute of Technology Defartment of It Convergence Gumi Korea Republic of Kumoh National Institute of Technology School of Electronic Engineering Gumi Korea Republic of Sejung Robot Gumi Korea Republic of

ISBN: (纸本)9798350318241

The edge grinding process refers to the process of polishing the edge of the wafer during the wafer manufacturing process. Visual inspection and rule-based vision inspection are used to detect defective conditions after the process. In the case of visual inspection, the detection rate of defects are different depending on the skill level of the operator, so the defect classification results may be inconsistent. When the rule-based method is used, defects may not be detected or may be over-detected. To solve this problem, this paper is proposed a method to develop an automated system for the grinding process through vision inspection using deep learning. The proposed method is expected to strengthen responses to low productivity and quality problems and improve the competitiveness of the semiconductor industry. Recently, the demand for automation equipment is increasing due to the growth of semiconductor wafer factories, and the methodology proposed in this paper is expected to make a positive contribution to this trend. © 2024 IEEE.

关键词： Defects

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Image Recognition and Similarity Retrieval with Convolutional Neural Networks 2

Image Recognition and Similarity Retrieval with Convolutiona...

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2nd international conference on automation, Computing and Renewable Systems, ICACRS 2023

作者： Dedeepya, P. Chiranjeevi, P. Narasimha, V. Shariff, Vahiduddin Ranjith, Jinugu Ramesh, Janjhyam Venkata Naga Computer Science & Engineering PVP Siddhartha Institute of Technology Vijayawada India Computer Science & Engineering Amrita Sai Institute of Science and Technology Bathinapadu India Computer Science & Engineering CMR College of Engineering & Technology Hyderabad India Computer Science & Engineering Sir C R Reddy College of Engineering Eluru India Computer Science & Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India

ISBN: (纸本)9798350340235

Image is a form of representation of data that is presented with visual beauty and has many patterns in it, in a database of irregular image datasets it is enough to make us humans who like structured, neat, and high readability data sets Therefore, the role of machine learning in helping to search for the same image will greatly help humans both in implementing search engines, tidying up image data structures automatically, etc. This research will focus on creating the same 5-image search system based on input from the user using the CNN algorithm & pretrained models such as ResNet with the expected results of model accuracy ranging from 80-95%. This was carried out using the CIFAR-10 dataset as a source. The CIFAR-10 dataset is a medium-scale image data set with a total of 60000 images, there are 10 categories in it, meaning each category contains 6000 images with the size of each image being 32x32. In general, a dataset has different image sizes, so this research study will first do an overview about whether the data for each image is enough with a size of 32x32. © 2023 IEEE.

关键词： Canadian Institute for Advanced Research (CIFAR) Dataset Convolutional Neural Networks (CNN) Deep Learning Deep Neural Network (DNN) Rectified Linear Units (ReLUs) Residual Neural Network (ResNet)

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Simulation of AGV Airport Path Planning Based on Improved Breadth-First Search Algorithm 2

Simulation of AGV Airport Path Planning Based on Improved Br...

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2nd IEEE international conference on Sensors, Electronics and computer engineering, ICSECE 2024

作者： Zhao, Shiwei Zhou, Yangyang Huang, Feilong Engineering Technology Training Center Civil Aviation University of China Tianjin China College of Electronic Information and Automation Civil Aviation University of China Tianjin China

ISBN: (数字)9798350373646

ISBN: (纸本)9798350373646

Aiming at the problems of incomplete search and low efficiency of traditional breadth-first search algorithm in path planning with grid obstacles, an improved breadth-first search algorithm is proposed. To solve the problem of incomplete search, the traversal direction has been improved by extending the original four directional traversal to omnidirectional traversal;at the same time, considering that the internal road network of the airport may become impassable over time, an optimization objective function was set to compare the travel time of different shortest paths in order to select the optimal path. The simulation experiment results show that in the grid map environment of passable and non passable runways starting from (39, 3), the improved BFS algorithm reduces the path length by 26.47%, and 18.18% respectively, and the search time by 13.68%, and 4.35% respectively compared to the traditional BFS algorithm. The improved BFS algorithm can effectively improve the efficiency of automatic guided vehicle path planning. The comprehensive performance indicators indicate that this algorithm is superior to traditional algorithms and has significant engineering practical value and theoretical research significance. © 2024 IEEE.

关键词： Automatic guided vehicles

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Research on Traffic Flow Prediction Based on GRU Model

Research on Traffic Flow Prediction Based on GRU Model

引用

2023 IEEE international conference on Electrical, automation and computer engineering, ICEACE 2023

作者： Huang, Xilong Maynooth International Engineering Collage Fuzhou University Fuzhou China

ISBN: (纸本)9798350309614

With the continuous development of social economy, the number of vehicles has increased year by year, resulting in a series of urban traffic problems such as traffic jams and frequent traffic accidents. Therefore, the prediction of traffic flow has gradually attracted people's attention. As machine learning and deep learning technologies develop rapidly, more and more methods are being used for traffic flow prediction. However, these methods have some shortcomings, such as not considering periodicity and too many parameters. In this paper, we propose the use of the Gate Recurrent Unit (GRU) model to predict traffic flow, using the open-access data set of England M20 motorway, finds a GRU model that is more consistent with traffic flow prediction by modifying the number of epochs, input sequence length, batch size, hidden units and learning rate through proper hyperparameter tuning, and the accuracy of prediction is evaluated by comparing RMSE and MAE with other models. The experimental results show that the solution we proposed can achieve higher accuracy in predicting traffic flow changes. © 2023 IEEE.

关键词： Deep Learning Gate Recurrent Unit Traffic Flow Prediction

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Smart Home Gardening with Robotic Assistance using Cloud-Connected Plant Care and Neural Networks Growth Analysis 1

Smart Home Gardening with Robotic Assistance using Cloud-Con...

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1st international conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Rajalakshmi, P. Dhivya, K. Anitha, D. Sandhya, Potturi Gnanavel, N. Muthulekshmi, M. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tami Nadu Chennai India SRM Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Department of Information Technology Tamil Nadu Namakkal India Department of Computer Science and Engineering Andhra Pradesh Bhimavaram India Department of Computer Science and Engineering Tamil Nadu Salem India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376517

This research introduces a new method for home gardening that uses neural networks to analyze plant development, cloud-connected plant care, and robotic help. The goal of combining these technologies is to make home gardening more efficient and productive while also allowing for remote management and monitoring. To understand how plants develop, our system collects data in real-time from a variety of sensors that measure things like temperature, humidity, and light levels, and then runs that data through neural networks. To further alleviate homeowners' workloads and guarantee plants get ideal care, robotic assistance is used to automate activities like watering, fertilizing, and pest management. Connecting to clouds allows customers to manage their garden from anywhere, get alerts when certain conditions are met, and get maintenance advice based on data. Through a series of battery tests, it can be observed that the proposed technique is superior to conventional gardening practices in terms of plant development and health. For eco-conscious green thumbs and busy gardeners, our system is the next big thing in smart home gardening, providing a practical and long-term solution to their problems. © 2024 IEEE.

关键词： automation Plant health Remote monitoring Robotic assistance Sustainable gardening

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A surgical dataset from the da Vinci Research Kit for task automation and recognition

A surgical dataset from the da Vinci Research Kit for task a...

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2023 international conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2023

作者： Rivas-Blanco, Irene Del-Pulgar, Carlos J. Pérez Mariani, Andrea Tortora, Giuseppe Reina, Antonio J. University of Malaga Dept. Systems Engineering and Automation Malaga Spain Scuola Superiore Sant'Anna The BioRobotics Institute Dept. Excellence in Robotics & Ai Pisa Italy ABzero Srl Smart Medical Theatre Lab Pisa Italy

ISBN: (纸本)9798350322972

The use of large datasets is essential in surgical robotics to advance in the field of recognition and automation of surgical tasks. Furthermore, public datasets allow the comparison of different algorithms and methods to evaluate their performance. The objective of this work is to provide a complete dataset of three common training surgical tasks performed with the da Vinci Research Kit (dVRK). The dataset contains a total of 206 trials performed by twelve different subjects. For each trial, the dataset includes 154 kinematic variables from the dVRK (both master and slave sides) together with the associated video recorded with a camera. All the data has been carefully timestamped and provided in a readable csv format. A MATLAB interface integrated with ROS for using and replicating the data is also provided. © 2023 IEEE.

关键词： Large dataset

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Home automation with Webserver Deployment with Raspberry Pi Pico 2

Home Automation with Webserver Deployment with Raspberry Pi ...

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2nd international conference on automation, Computing and Renewable Systems, ICACRS 2023

作者： Shabariram, C.P. Venkateshwaran, M. Abinesh, R. Manoranjith, R. Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Coimbatore India

ISBN: (纸本)9798350340235

Home automation system with web server deployment is developed using Raspberry Pi Pico and micropython, which turns ON the lights based on the count of persons inside that particular room. The system uses Infrared Red (IR) sensor, which detects the heat radiation in the environment. This heat radiation is not constant as it changes due to the movement of people in the sensing environment. This change is used as a measure to detect the changes in the environment which allows us to perform a particular action for the event. The data is sent to node which sends the data to the webserver running in the Raspberry pi pico. Based on the result, the light is turned ON or OFF. © 2023 IEEE.

关键词： Home automation Infrared sensor Micropython Raspberry Pi Pico Webserver

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