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Detection of Brain Tumour By Segmenting the Magnetic Resonance Image Using K-Nearest Neighbour algorithm and Compare the Sensitivity and Accuracy With convolutional neural network algorithm

Detection of Brain Tumour By Segmenting the Magnetic Resonan...

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2023 Intelligent Computing and Control for Engineering and Business Systems, ICCEBS 2023

作者： Sheela, M.Sahaya Nalini, N. Uganya, G. Sathesh, M. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350394580

The main objective of this research is to use the 'K-Nearest Neighbour' ('KNN') algorithm to detect brain tumours in MRI images and to compare its sensitivity and accuracy to that of the 'convolutional neural network' ('CNN')algorithm. Brain tumours were identified in MRI scans using the 'KNN' and 'CNN' algorithm, and the sensitivity and accuracy of the identified images were compared. Group 1 with 20 samples using 'KNN' and Group 2 with 20 samples using 'CNN' were analysed by compiling the entire dataset of 40 samples with 80% of pretest power. Pursuant to the simulation, the 'KNN' algorithm has a 95% sensitivity to detect brain tumours, whereas the 'CNN' approach has a 90% sensitivity to do so. While 'CNN' algorithm only diagnoses brain tumours with 84.10% accuracy, 'KNN' algorithm does so with an accuracy of 98.5%. © 2023 IEEE.

关键词： accuracy convolutional neural network algorithm Image segmentation K-Nearest Neighbour algorithm machine learning sensitivity

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Machine learning-driven simultaneous quantification of Cd(II) and Cu(II) on Co2P/CoP heterostructure: enhanced electrochemical signals via activated Co-P electron bridge

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JOURNAL OF HAZARDOUS MATERIALS 2025年 491卷 138030页

作者： Gao, Zhi-Wei Yu, Yan Chen, Shi-Hua Li, Yong-Yu Liu, Zi-Hao Yang, Meng Li, Pei-Hua Song, Zong-Yin Huang, Xing-Jiu Chinese Acad Sci Inst Solid State Phys Key Lab Environm Opt & Technol HFIPS Hefei 230031 Peoples R China Chinese Acad Sci Inst Solid State Phys Environm Mat & Pollut Control Lab HFIPS Hefei 230031 Peoples R China Univ Sci & Technol China Dept Mat Sci & Engn Hefei 230026 Peoples R China Hefei Comprehens Natl Sci Ctr Inst Environm Hefei 230088 Peoples R China

Simultaneous quantification of multiple heavy metal ions remains a significant challenge in electrochemical methods, as complex high-throughput data from signal interference cannot be accurately analyzed through individual expertise and calibration curves. In this study, machine learning techniques were introduced to co-detect Cd(II) and Cu(II), with their electrochemical interference mechanisms explored on highly active Co2P/ CoP heterostructures. The random forest (RF) model initially identified key feature variables in response currents, which were subsequently input into the convolutional neural network (CNN) to uncover the relationship between electrochemical signals and ion concentrations, demonstrating excellent reliability with R2 values of 0.996 for both Cd(II) and Cu(II). The root mean square error (RMSE) values for Cd(II) and Cu(II) were 0.0177 and 0.0206 mu M, respectively, indicating high predictive accuracy. The experiments and theory calculations revealed that Cu(II) preferentially bonded with P sites over Cd(II). Enhanced electron transfer from Co to P atoms and weakened Cu-P bonds facilitated Cu(II) reduction and desorption from Co2P/CoP, thereby boosting electrochemical signals, while Cd(II) signals were inhibited due to active site loss. Herein, the integration of machine learning provides robust support for simultaneous detection of multiple analytes, accelerating the practical application of electrochemical methods in environmental monitoring.

关键词： Active P sites Competitive adsorption Key feature currents Random forest algorithm convolutional neural network algorithm

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convolutional neural network Based Health Monitoring System with Thermal Scanning Temperature Detection 19

Convolutional Neural Network Based Health Monitoring System ...

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19th Annual International IEEE Conference on System of Systems Engineering (SoSE)

作者： Doctor, Anabella C. Fainsan, Delia S. Espanto, Rhia V. Tipo, Charlotte Mae G. Waga, Hannah Andreah R. Balatbat, Rannex A. Adamson Univ Dept Comp Engn Manila Philippines Philippines Univ Cavite Dept Engn Lyceum Gen Trias Philippines

ISBN: (纸本)9798350365924;9798350365917

This research took place at the Lyceum of the Philippines Cavite Campus and aimed to create a Health Monitoring System for the LPU-Cavite community. The system utilized Face Recognition based on the convolutional neural network (CNN) algorithm along with Thermal Scanning Temperature Detection. By employing a camera for facial recognition, it determined whether the individual was a student or an employee. Additionally, a thermal scanner prototype equipped with an MLX90614 IR temperature sensor was used to detect a person's temperature. The screening data could be accessed by the admin and clinic staff through a website. The research employed a Generic Software Development Life Cycle (SDLC) as its methodology, encompassing various stages including Planning, Analysis, Design, Development, Testing, Integration, and Maintenance. The evaluation of the developed system involved 27 participants who completed survey questionnaires based on ISO/IEC 25010 criteria. The system received a positive assessment, with respondents strongly agreeing with its overall performance. The system overall evaluation is 3.9447 wherein the equivalent remark is "Strongly Agree". This result demonstrates that the system effectively achieves its intended function and objectives. It is recommended to incorporate a graphics processing unit to enhance the system's speed and performance. Additionally, prioritizing user-friendliness in the software design will lead to an improved overall user experience.

关键词： health monitoring system face recognition convolutional neural network algorithm thermal scanning temperature detection

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The Impact of Artificial Intelligence and Deep Learning-Based Family-Centered Care Interventions on the Healing of Chronic Lower Limb Wounds in Children

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IEEE ACCESS 2024年 12卷 125557-125570页

作者： Cao, Yingdong Wang, Yun Sias Univ Sch Med Xinzheng 451150 Henan Peoples R China Shanxi Polytech Coll Dept Artificial Intelligence Taiyuan 030006 Peoples R China

Currently, there are numerous challenges in the nursing and healing of children's chronic lower limb wounds (CLLW), such as prolonged healing times, difficulties in pain management, high risk of infection, and insufficient parental caregiving knowledge. This study aims to investigate the impact of a family-centered care (FCC) system based on artificial intelligence (AI) and convolutional neural network (CNN) algorithms on the healing of CLLW. A FCC for Children's CLLWs (CLLW FCC) system was designed, consisting primarily of four components: an interaction layer, a service layer, a data layer, and a hardware layer. The CNN algorithm was utilized to establish a segmentation method for chronic wound images, which was then applied to the wound management section of the CLLW FCC system, aiming to improve the nursing outcomes of children with CLLW, optimize wound management processes, and enhance the accuracy and efficiency of image processing and analysis. It focused on 92 pediatric patients with CLLW admitted to our hospital from January 2022 to June 2023. The patients were randomly assigned into a control (Con) group and an observation (Obs) group, with 46 cases in each. Patients in the Con group received routine care, while those in the Obs group received FCC system for CLLW. The wound infection rate (WIR), wound healing time (WHT), wound pain scores, parental knowledge scores, and satisfaction with care (SWC) were evaluated for both the Con and Obs groups. The results unveiled that the optimized CNN algorithm achieved mean intersection over union (mIOU) and Kappa values of 0.8965 and 0.8773, respectively, which were higher than those of other algorithms. Patients in the Obs group experienced a shorter WHT and lower wound pain scores in contrast to those in the Con group ( $P\lt 0.05$ ). The parental knowledge scores were higher in the Obs group, showing great differences with those in the Con group ( $P\lt 0.05$ ). The WIR in the Obs group was 2.17%, lower to 10.87%

关键词： Pediatrics FCC Artificial intelligence Medical services Pain convolutional neural networks Medical treatment convolutional neural network algorithm family-centered care system children chronic lower limb wounds healing

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Embedded application of convolutional neural networks on Raspberry Pi for SHM

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ELECTRONICS LETTERS 2018年第11期54卷 680-681页

作者： Monteiro, A. de Oliveira, M. de Oliveira, R. da Silva, T. Mato Grosso Fed Inst Technol Dept Elect & Elect BR-78005200 Cuiaba Brazil

To date, the authors are not aware of an in-depth investigation about embedded applications of the convolutional neural network (CNN) algorithm on small, lightweight, and low-cost hardware (e.g. microcontroller, FPGA, DSP, and Raspberry Pi) applied to detect faults in structural health monitoring (SHM) systems. In this Letter, the authors implement and evaluate both feasibility and performance of an embedded application of the CNN algorithm on the Raspberry Pi 3. The CNN-embedded algorithm quantifies and classifies dissimilarities between the frames representing healthy and damaged structural conditions. In a case study, the CNN-embedded application was experimentally evaluated using three piezoelectric patches glued onto an aluminium plate. The results reveal an impressively effective 100% hit rate. This performance may significantly impact the design and analysis of CNN-based SHM systems where embedded applications are required for identifying structural damage such as those encountered by aerospace structures, rotating machineries, and wind turbines.

关键词： microcontrollers neural nets condition monitoring wind turbines structural engineering plates (structures) convolutional neural networks convolutional neural network algorithm structural health monitoring systems CNN algorithm Raspberry Pi 3 classifies dissimilarities healthy conditions damaged structural conditions CNN-embedded application design SHM systems

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algorithm-Oriented SIMD Computer Mathematical Model and Its Application

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INTERNATIONAL JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGY EDUCATION 2022年第3期18卷 1-18页

作者： Jiang, Yongfeng Li, Yuan Xian Siyuan Univ Dept Basic Courses Xian Peoples R China Shanghai Dianji Univ Shanghai Peoples R China

This paper has designed a professional and practical SIMD computer mathematical model based on the SIMD physical machine model combined with the variable addition method. Furthermore, the model is applied in image collection, processing, and display operations, and a SIMD data parallel image processing system is finally established by absorbing the parallel computing advantages of the mathematical model. In addition, the data-parallel image processing algorithm is introduced and the convolutional neural network algorithm is optimized to promote the significant improvement of the main performance such as the accuracy of the application system. The final experimental results have shown that the highest accuracy of the data-parallel image processing algorithm reaches 93.3% and the lowest error rate reaches 0.11%, which proves the superiority of the SIMD computer mathematical model in image processing applications.

关键词： convolutional neural network algorithm Mathematical Model Data Parallel Image Processing algorithm SIMD Computer SIMD Data Caching Model SIMD Image Parallel Processing System

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Development of quadruped robot for inspection of underground pipelines in nuclear power plants

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ELECTRONICS LETTERS 2022年第6期58卷 234-236页

作者： Jang, Youhyun Seol, Woojin Lee, Kooksun Kim, Kyung-Soo Kim, Soohyun Korea Hydro & Nucl Power Co Ltd Digital Solut Sect Daejeon South Korea KALMAN Corp Gyeongju Si South Korea Korea Adv Inst Sci & Technol Dept Mech Engn Daejeon South Korea Korea Adv Inst Sci & Technol KAIST Robot Program Daejeon South Korea

Structural integrity of seawater pipelines in nuclear power plants is a very important issue. In accordance with the operating technical guidelines, the human operators directly enter the pipe and inspect it at every maintenance test. However, in this regard, safety issues such as narrow space and harmful gas are emerging every year. In response to these needs, a quadruped robot that can inspect underground pipes and assist workers has been developed. The robot has an articulated robotic arm that can receive an impact sound of hammering a pipe wall to test pipe integrity. The state of the pipe was examined using a convolutional neural network algorithm. On the other hand, moving in a plumbing environment requires stable walking ability. To determine the gait sequence, a hierarchical gait controller is proposed. The hybrid controller, which consists of joint impedance and torque control, calculated from Model Predictive Control, can switch the gait modes comparing the reference and the current foot contact condition at each control cycle.

关键词： quadruped robot pipelines maintenance engineering structural integrity articulated robotic arm inspection gait analysis underground pipes underground pipelines operating technical guidelines joint impedance legged locomotion seawater pipelines pipes torque control hierarchical gait controller predictive control seawater pipe integrity pipe wall harmful gas human operators nuclear power plants narrow space convolutional neural network algorithm robot dynamics maintenance test

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Enhancing Accuracy in Crop Yield Estimation Through the Integration of Naive Bayes and convolutional neural networks

Enhancing Accuracy in Crop Yield Estimation Through the Inte...

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2023 Intelligent Computing and Control for Engineering and Business Systems, ICCEBS 2023

作者： Samanth Reddy, P. Surendran, R. Venkatraman, M. Saveetha Institute of Medical and Technical Science Department of Electronics Communication Engineering Saveetha School of Engineering Chennai602105 India Saveetha Institute of Medical and Technical Science Department of Computer Science and Engineering Saveetha School of Engineering Chennai602105 India

ISBN: (纸本)9798350394580

This article improves the accuracy of crop yield forecast by using convolutional neural network and Naive Bayes algorithm. Supplies and Procedures. The study comprised 42 samples, which were grouped into two sets of 21 samples each. While the second group utilised the convolutional neural network technique, the first group used the Naive Bayes technique. The statistical power of the study was established at 80% with an alpha level of 0.05 and a beta level of 0.20, using G power. Results: The Naive Bayes algorithm tends to have a higher accuracy rate, which in this case is around 88%. On the other hand, the convolutional neural network algorithm has a slightly lower accuracy rate of approximately 82%. Independent sample t-test using this paper. It has a significant value of p is.046 (p © 2023 IEEE.

关键词： agricultural convolutional neural network algorithm creative crop yield forecast crop machine learning naive bayes method production Introduction

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The Bayesian CNN-LSTM Mixed Hybrid algorithm Model of the Photovoltaic Short-term Output Forecasting 3

The Bayesian CNN-LSTM Mixed Hybrid Algorithm Model of the Ph...

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3rd International Conference on New Energy and Power Engineering, ICNEPE 2023

作者： Dong, Enchong Guo, Yuxi North China Electric Power University School of Control and Computer Engineering Beijing China Shenzhen College of International Education Shenzhen China

ISBN: (纸本)9798350393224

Due to the volatility, randomness, and intermittency of photovoltaic power generation, it is difficult to accurately forecast its output. This paper proposes a Bayesian-optimized CNN-LSTM mixed neural model for a short-term photovoltaic output prediction model. First of all, the Xinjiang photovoltaic power station dataset is selected and preprocessed in this study. Secondly, the CNN method is applied for data feature extraction. And then the Bayesian optimization algorithm is used to analyze the LSTM super parameter optimization. Finally, the data, after undergoing feature extraction, are input into the Bayes theorem-LSTM model for forecast generation. The results show that the proposed algorithm has realized the accurate prediction of photovoltaic power, getting better than most other single benchmark models. These findings not only highlight the improved efficacy of the proposed algorithm but also affirm its potential applicability and value for practical implementation. © 2023 IEEE.

关键词： Bayesian algorithm convolutional neural network algorithm Long Short-Term Memory algorithm Photovoltaic power output prediction

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Intelligent Parking Management Systems Using IoT and Machine Learning Techniques for Real-Time Space Availability Estimation

Intelligent Parking Management Systems Using IoT and Machine...

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2023 International Conference on Sustainable Communication networks and Application, ICSCNA 2023

作者： Raman, Ramakrishnan Sujatha, V. Bhupeshbhai Thacker, Chintan Bikram, Kirti B Sahaai, Madona Murugan, S. Maharashtra Pune India Shree Sathyam College of Engineering and Technology Department of Electronics and Communication Engineering Salem Tamil Nadu Sankari India Parul Institute of Engineering and Technology Parul University Faculty of Engineering and Technology Gujarat Vadodara India Vimannagar Maharashtra Pune India Department of Electronics and Communication Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350313987

Traffic jams and drivers looking for parking spaces are caused by the increased urbanization and vehicle population. This paper proposes an intelligent parking guidance system that effectively manages spots for parking by using the Internet of Things (IoT) and Machine learning methods. The system uses IoT sensors and cameras positioned in parking lots to track the occupancy status of specific parking spots in real-time. The acquired data is sent to a cloud server for analysis and processing. The convolutional neural network (CNN) algorithm, a deep learning approach, is used to evaluate the camera images and accurately determine if parking spots are occupied. The number of parking spots available, location directions, and expected arrival times may all be accessed by drivers using a user-friendly smartphone application. Advanced features such as requests, payment, and navigation integration may also be added to the system to improve the parking experience. © 2023 IEEE.

关键词： Cloud computing convolutional neural network algorithm Data analysis Internet of Things Parking guidance system

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