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 'Convoluti...
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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 indiv...
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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 convolutionalneuralnetwork (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.
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 Neur...
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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 convolutionalneuralnetwork (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.
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 ins...
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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 convolutionalneuralnetwork (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%
To date, the authors are not aware of an in-depth investigation about embedded applications of the convolutionalneuralnetwork (CNN) algorithm on small, lightweight, and low-cost hardware (e.g. microcontroller, FPGA,...
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To date, the authors are not aware of an in-depth investigation about embedded applications of the convolutionalneuralnetwork (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.
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 ...
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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.
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 shor...
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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 parkin...
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This paper presents a framework leveraging Raspberry Pi for the detection and prevention of plant diseases, employing a convolutionalneuralnetwork (CNN) algorithm for image analysis. With a focus on application in l...
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This article improves the accuracy of crop yield forecast by using convolutionalneuralnetwork and Naive Bayes algorithm. Supplies and Procedures. The study comprised 42 samples, which were grouped into two sets of 2...
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