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Lung Cancer Prediction and Risk Assessment: A Machine Learning Approach Integrating Symptoms and Etiological Factors 3

Lung Cancer Prediction and Risk Assessment: A Machine Learni...

引用

3rd ieee International conference on Robotics, automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Hossain Sarkar, Md. Mosharrof Afrin, Sadiha Reza, Md Tuhin Bokshi, Md Abid Hasan Roni Mim, Sharmin Sultana Faridpur Engineering College Department of Computer Science and Engineering Faridpur Bangladesh Faridpur Engineering College Department of Electrical and Electronic Engineering Faridpur Bangladesh

ISBN: (纸本)9798331534400

Lung cancer requires accurate risk assessment and early detection due to its high prevalence and fatality rate. The simultaneous analysis of clinical symptoms and etiological factors using Machine Learning revolutionizes our understanding of lung cancer and patient outcomes. Machine Learning models are built on comprehensive patient data, including lifestyle, symptoms, and 12 variables. We used ten models for precise lung cancer diagnosis, including Decision Tree (DT), K-nearest neighbors (KNN), Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), Naive Bayes (NB), XGBoost, Gradient Boost, CatBoost and AdaBoost. At the National Institute of Cancer Research & Hospital (NICRH) in Mohakhali, TB Gate Road, Dhaka, Bangladesh, we collected 346 data directly from patients using questionnaires, with permission from the director. For every model, we performed parameter tuning to determine the ideal ratio of complexity to accuracy. Notably, with the best accuracy of 98.43% and an excellent area under the curve (AUC) of 0.983, the Decision Tree (DT) performed better than any other classifier. The motivation behind this research is to predict the risk assignment of lung cancer using various machine learning models. © 2024 ieee.

关键词： Lung cancer

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Machine Vision for Measurement Accuracy in Industrial automation 15

Machine Vision for Measurement Accuracy in Industrial Automa...

引用

15th International conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Ezhilarasan, G. Singh, Vaishali Upadhyay, Ramakant Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Himachal Pradesh India Department of Computer Science Engineering Sanskriti University Uttar Pradesh Mathura India

ISBN: (纸本)9798350370249

Commercial automation has been a powerful device for enhancing productivity, decreasing charges, and increasing performance. In particular, system vision systems have enabled the implementation of advanced automation answers, allowing accurate and rapid quality measurements and inspections. This paper examines the cutting-edge nation of the use of machine imaginative and prescient systems in industrial automation, highlighting the opportunities they provide in phrases of increasing dimension accuracy. It examines the specific sorts of device vision structures to be had, their advantages and boundaries, as well as their ability for improving the exceptional of business automation structures. It also addresses the challenges that ought to be overcome to make certain full adoption of such structures, as well as the strategies for fixing these challenges, inclusive of the improvement of progressed algorithms and laptop imaginative and prescient additives, and training applications for industrial engineers. This paper affords a comprehensive evaluation of associated studies and enterprise standards, permitting readers to gain a better expertise of the development of machine vision systems in industrial automation and to become aware of possibilities to improve business dimension accuracy. © 2024 ieee.

关键词： Office automation

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A Comparative Estimation of Media Manipulation Disclosure using Machine Learning Algorithms 2

A Comparative Estimation of Media Manipulation Disclosure us...

引用

2nd ieee International conference on IoT, Communication and automation Technology, ICICAT 2024

作者： Vidhya, R. Aswin Jaya Surya, M.J. Sri Krishna College of Technology The Department in Computer Science and Engineering Coimbatore Tamil Nadu Kovaipudur641042 India Sri Krishna College of Technology Computer Science and Engineering Coimbatore Tamil Nadu Kovaipudur641042 India

ISBN: (纸本)9798350368109

The suggested method to detect the accuracy level of fake information by applying a sequence of natural language processing (NLP), blockchain, and reinforcement learning approaches. The system applies NLP algorithms to assess the information of social media and their accuracy levels that is assigned a trust score and stored on a blockchain network. Reinforcement learning is used to train the algorithm and create more trust score predictions based on input from the blockchain network. The system is continually updated with new data, reinforcement learning algorithm improves the trust score predictions based on user feedback. The blockchain network maintains the security and immutability of the trust scores and assures that they cannot be tampered. The technology features a user interface that allows the users to verify the accuracy level of news data by verifying their trust rankings on the blockchain network. The technique has the potential to reduce the spread of false news by providing reliable and transparent mechanism to user and verify the accuracy of news data. The implementation of blockchain technology assures the security and integrity of the system, rendering it resistant to manipulation or tampering. © 2024 ieee.

关键词： Adversarial machine learning

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Real-time Detection of Working Status of Pneumatic Classification Equipment Based on Machine Vision 5

Real-time Detection of Working Status of Pneumatic Classific...

引用

5th International conference on Artificial Intelligence and Electromechanical automation, AIEA 2024

作者： Yu, Yuan He, Tianbai Beijing University of Chemical Technology College of Mechanical and Electrical Engineering Beijing China

ISBN: (纸本)9798350366174

A method to detect the working status of pneumatic classification equipment on-line based on machine vision is proposed addressing potential safety hazards, because the equipment cannot be shut down in a timely manner while the blockage of the feeding channel due to material consolidation and agglomeration during the classification process of pneumatic classification equipment. The shutdown time point can be obtained by preprocessing the working status images of the classifier collected by industrial cameras, introducing the image eigenvalue gradient detection algorithm to determine the current status of the classifier. Then the instructions will be sent to the classification equipment and the working status of pneumatic classification equipment can be detected online, which lays the foundation for improving the digitalization, automation, and visualization level of pneumatic classification equipment and its digital twins. © 2024 ieee.

关键词： Machine vision

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Leveraging Interquartile Range and Isolation Forest for Abnormal Power Consumption Prediction 6

Leveraging Interquartile Range and Isolation Forest for Abno...

引用

6th ieee Advanced Information Management, Communicates, Electronic and automation Control conference, IMCEC 2024

作者： Tan, Yi-Fei Zhao, Ge-Zhi Ooi, Chee-Pun Tan, Wooi-Haw Multimedia University Faculty of Engineering Cyberjaya Malaysia Chongqing University Technology Faculty of Engineering Chongqing China

ISBN: (纸本)9798350316520

The progression of time has brought in new challenges, particularly in power consumption and regeneration, affecting individuals, cities, and nations globally. From ancient fire fueling to modern electricity, energy development is integral to global advancements. Considerations such as global power consumption, economic growth, comprehensive energy systems, and mitigating global warming are important. Therefore, avoiding overuse or power wastage is essential. Through data collection and analysis, energy usage patterns can be forecasted using machine learning techniques. In this research, the authors build a model to predict the unusual power consumption. The model comprises three stages. The first stage involves labeling power consumption as normal or abnormal using the Interquartile Range and Isolation Forest. The second stage addresses the imbalanced data from the first stage using SMOTE. The third stage employs machine learning classifiers to construct the prediction model. Experimental results demonstrate that applying Isolation Forest labeling, SMOTE oversampling, and utilizing Random Forest classifier shows better results in performance evaluation. © 2024 ieee.

关键词： Decision trees

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Robot Learning of Assembly Tasks from Non-expert Demonstrations using Functional Object-Oriented Network 18

Robot Learning of Assembly Tasks from Non-expert Demonstrati...

引用

ieee 18th International conference on automation science and engineering (ieee CASE)

作者： Chen, Yi Paulius, David Sun, Yu Jia, Yunyi ABB US Res Ctr Raleigh NC 27606 USA Clemson Univ Dept Automot Engn Greenville SC 29607 USA Univ S Florida Dept Comp Sci & Engn Tampa FL 33620 USA

ISBN: (数字)9781665490429

ISBN: (纸本)9781665490429

Robot Learning from Demonstration (RLfD) is a research field that focuses on how robots can learn new tasks by observing human performances. Existing RLfD approaches mainly enable robots to repeat the demonstrated tasks by mimicking human activities, which usually requires efficient demonstrations for human experts. This paper proposes a new Function Object-Oriented Network (FOON) based approach to make robots learn and optimize assembly tasks from non-expert demonstrations. It first proposes an assembly FOON construction approach with automatic subgraph creation and merging algorithms to extract information from multiple non-expert demonstrations. It then proposes an assembly task tree retrieving approach with a robot execution optimization process to make the robot learn and generate the best possible task execution plan from the constructed FOON. The proposed approaches are validated through experiments with a dual-arm YuMi robot and the experimental results illustrate the effectiveness and advantages of the proposed approach.

关键词： Knowledge engineering Computer aided software engineering automation Merging Robot learning Data mining Task analysis

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Classifying Atrial Fibrillation Using Deep Residual Networks (ResNet) on ECG Signals 2

Classifying Atrial Fibrillation Using Deep Residual Networks...

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2nd ieee International conference on IoT, Communication and automation Technology, ICICAT 2024

作者： Shukla, Aasheesh Ankam, Sreejyothsna Hussein, Layth Durgadevi, G. Ashwini, S. Arunachalam, P. GLA University Department of Electronics & Communication Engineering Mathura India GRIET Bachupally Department of CSBS Telangana Hyderabad India College of Technical Engineering Department of Computers Techniques Engineering The Islamic University Najaf Iraq New Prince Shri Bhavani College of Engineering and Technology Department of ECE Tamil Nadu Chennai600073 India CMR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Department of Biomedical Engineering TamilNadu Chennai600062 India

ISBN: (纸本)9798350368109

Atrial Fibrillation (AF) is a complex most prevalent arrhythmia with many complications that affect the affected individual's overall health as his/her propensity to stroke and heart failure. This study seeks to investigate the effectiveness of Deep Residual Networks (ResNet) in the categorisation of AF from ECG signals given that this will help improve the AF diagnostic accuracy and speed. The ResNet model is based on deep architecture and uses residual connections to learn various patterns from ECG data. The process includes data preprocessing: filtering, normalization and seg-mentation;model training and testing. The results show a high accuracy of the method, the sensitivity, and specificity, which is higher than in many traditional and some modern techniques. The use of ResNet-based approach suggested for automated AF detections seems to be promising with its clinical application and the consequent effects on patients technology. Improvements for future work may be made to fine-tune the model and the integration of the proposed solution with other diagnostic aids for easier diagnosis of arrhythmias. © 2024 ieee.

关键词： Electrocardiography

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Design and Implementation of a Circumferentially Symmetrical Three-Axis Cargo Rotor UAV 2

Design and Implementation of a Circumferentially Symmetrical...

引用

2nd International conference on Artificial Intelligence and automation Control, AIAC 2024

作者： Li, Jiawei Zhu, Xiaoqing College of Mechanical Engineering University of Science and Technology Beijing Beijing China School of Information Science and Technology Beijing University of Technology Beijing China

ISBN: (纸本)9798331529147

In the context of the national push for the low- altitude economy, to meet the delivery needs between urban express stations, I designed a circular-symmetric, tri-axis rotor cargo drone to address the shortcomings of existing drones, which tend to be large in size, have low cargo capacity, and lack cargo compartments. This drone is characterized by strong maneuverability, compact size, light weight, and low noise, and it is equipped with an intelligent cargo compartment that allows users to view delivery order information on their phones. I conducted simulations using ADMAS to test the takeoff, landing, hovering, and various attitude changes of the circular-symmetric tri-axis rotor cargo drone, as well as experiments along rectangular paths, validating the drone's capabilities for takeoff, landing, hovering, and attitude changes. © 2024 ieee.

关键词： Drones

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An Auto-test Architecture and Use Case Autocompilation Mechanism 8

An Auto-test Architecture and Use Case Autocompilation Mecha...

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8th ieee Information Technology and Mechatronics engineering conference, ITOEC 2025

作者： Yin, Zhenxing Yan, Rong Zhang, Min Liu, Pengfei Jiangsu Automation Research Institute Lianyungang China

ISBN: (纸本)9798331517915

In view of the problems of long time-consumption, high work intensity and unstable result accuracy in the manual function and performance testing of the task control system, we propose an auto-test architecture and automatic preparation mechanism for use cases according to the test requirements of the unmanned task control system, including architecture composition, test process and test case design. In addition, the key algorithm for test case compilation is studied, and the test case is automatically and quickly provided to improve the test efficiency. Finally, compared with the manual testing method, the operation results of the autotest architecture are analyzed, and the results show that the auto-test architecture of the task control system proposed in this paper can meet the requirements of the task control system for test efficiency and test accuracy. In addition, the auto-test case compilation technology can significantly reduce the time and improve the accuracy of test case writing. © 2025 ieee.

关键词： Automatic testing

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Research on path planning optimization of mobile robots empowered by deep reinforcement learning 2

Research on path planning optimization of mobile robots empo...

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2nd ieee International conference on Electrical, automation and Computer engineering, ICEACE 2024

作者： Pai, Chingyuan Liu, Liwei Bao, Jiahua Kang, Wenshan Du, Xuqiang Han, Jiaqi Zhou, Qingli Meng, Shiji Northeastetn University BostonMA United States Zhengzhou Institute of Science and Technology Zhengzhou China Jiangxi Electric Vocational & Technical College College of Electric Power Engineering Nanchang China Troop Urumqi32317 China Troop Urumqi32318 China Shanghai Institute of Technology School of Electrical and Electronic Engineering Shanghai China Guangxi University of Science and Technology Liuzhou China Hangzhou Dianzi University Hangzhou China

ISBN: (纸本)9798350368208

Aiming at the problems of slow convergence speed and poor obstacle avoidance strategy of deep deterministic policy gradient learning (DDPG) path planning algorithm in dynamic environments, an improved optimization scheme of deep deterministic policy gradient learning based on multi-step expected temporal difference method is proposed. Firstly, the n-step expected temporal difference method is used to solve the optimal strategy to solve the problem of poor training decision function. Secondly, when solving the optimal strategy, an expected variable is introduced and a baseline is set to reduce variance operation to enhance the convergence of the algorithm. Then, a continuous reward function is designed to improve the utilization of rewards by the algorithm, and the storage method of priority experience replay is used to optimize the convergence speed. Finally, a ROS simulation environment is built to verify the above method. Experiments show that compared with traditional algorithms, the proposed algorithm improves the convergence speed by 15%;increases the successful number of cumulative arrivals at the target point by 21.6%;and reduces the probability of collision with dynamic obstacles by 11%. © 2024 ieee.

关键词： Deep reinforcement learning

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