Recognition of human activity is an active research area. It uses the Internet of Things, Sensory methods, Machine Learning, and Deep Learning techniques to assist various application fields like home monitoring, robo...
详细信息
The kidney is an important organ of humans to purify the *** healthy function of the kidney is always essential to balance the salt,potassium and pH levels in the ***,the failure of kidneys happens easily to human bei...
详细信息
The kidney is an important organ of humans to purify the *** healthy function of the kidney is always essential to balance the salt,potassium and pH levels in the ***,the failure of kidneys happens easily to human beings due to their lifestyle,eating habits and diabetes *** pre-diction of kidney stones is compulsory for timely *** processing-based diagnosis approaches provide a greater success rate than other detection *** this work,proposed a kidney stone classification method based on optimized Transfer Learning(TL).The Deep Convolutional Neural Network(DCNN)models of DenseNet169,MobileNetv2 and GoogleNet applied for clas-sifi*** combined classification results are processed by ensemble learning to increase classification *** hyperparameters of the DCNN model are adjusted by the metaheuristic algorithm of Gorilla Troops Optimizer(GTO).The proposed TL model outperforms in terms of all the parameters compared to other DCNN models.
In the field of autonomous robots,achieving complete precision is challenging,underscoring the need for human intervention,particularly in ensuring *** Autonomy Teaming(HAT)is crucial for promoting safe and efficient ...
详细信息
In the field of autonomous robots,achieving complete precision is challenging,underscoring the need for human intervention,particularly in ensuring *** Autonomy Teaming(HAT)is crucial for promoting safe and efficient human-robot collaboration in dynamic indoor *** paper introduces a framework designed to address these precision gaps,enhancing safety and robotic interactions within such *** to our approach is a hybrid graph system that integrates the Generalized Voronoi Diagram(GVD)with spatio-temporal graphs,effectively combining human feedback,environmental factors,and key *** integral component of this system is the improved Node Selection Algorithm(iNSA),which utilizes the revised Grey Wolf Optimization(rGWO)for better adaptability and ***,an obstacle tracking model is employed to provide predictive data,enhancing the efficiency of the *** insights play a critical role,from supplying initial environmental data and determining key waypoints to intervening during unexpected challenges or dynamic environmental *** simulation and comparison tests confirm the reliability and effectiveness of our proposed model,highlighting its unique advantages in the domain of *** comprehensive approach ensures that the system remains robust and responsive to the complexities of real-world applications.
Partitional clustering techniques such as K-Means(KM),Fuzzy C-Means(FCM),and Rough K-Means(RKM)are very simple and effective techniques for image ***,because their initial cluster centers are randomly determined,it is...
详细信息
Partitional clustering techniques such as K-Means(KM),Fuzzy C-Means(FCM),and Rough K-Means(RKM)are very simple and effective techniques for image ***,because their initial cluster centers are randomly determined,it is often seen that certain clusters converge to local *** addition to that,pathology image segmentation is also problematic due to uneven lighting,stain,and camera settings during the microscopic image capturing ***,this study proposes an Improved Slime Mould Algorithm(ISMA)based on opposition based learning and differential evolution’s mutation strategy to perform illumination-free White Blood Cell(WBC)*** ISMA helps to overcome the local optima trapping problem of the partitional clustering techniques to some *** paper also performs a depth analysis by considering only color components of many well-known color spaces for clustering to find the effect of illumination over color pathology image *** and visual results encourage the utilization of illumination-free or color component-based clustering approaches for image ***-KM and“ab”color channels of CIELab color space provide best results with above-99%accuracy for only nucleus ***,for entire WBC segmentation,ISMA-KM and the“CbCr”color component of YCbCr color space provide the best results with an accuracy of above 99%.Furthermore,ISMA-KM and ISMA-RKM have the lowest and highest execution times,*** the other hand,ISMA provides competitive outcomes over CEC2019 benchmark test functions compared to recent well-established and efficient Nature-Inspired Optimization Algorithms(NIOAs).
This study investigates the relationship between moisture content in cohesive soils and critical slope failure angles during spontaneous liquefaction. We adapted the liquid limit test apparatus, originally developed b...
详细信息
This study aims at investigating the effects on asphalt cement (AC) rheology when adding Nano Clay (NC) and Nano Zinc Oxide (NZnO) compounds. The tested contents of NC were 8%, 10%, and 12%, while those of NZnO were 1...
详细信息
If adversaries were to obtain quantum computers in the future, their massive computing power would likely break existing security schemes. Since security is a continuous process, more substantial security schemes must...
详细信息
Bot detection is considered a crucial security issue that is extensively analysed in various existingapproaches. Machine Learning is an efficient way of botnet attack detection. Bot detectionis the major issue faced b...
详细信息
Bot detection is considered a crucial security issue that is extensively analysed in various existingapproaches. Machine Learning is an efficient way of botnet attack detection. Bot detectionis the major issue faced by the existing system. This research concentrates on adopting a graphbasedfeature learning process to reduce feature dimensionality. The incoming samples arecorrectly classified and optimised using an Adaboost classifier with an improved grey wolfoptimiser (g-AGWO). The proposed IGWO optimisation approach is adopted to fulfil the multiconstraintissues related to bot detection and provide better local and global solutions (to satisfyexploration and exploitation). The extensive results show that the proposed g-AGWO model outperformsexisting approaches to reduce feature dimensionality, under-fitting/over-fitting andexecution time. The error rate prediction shows the feasibility of the given model to work over thechallenging environment. This model also works efficiently towards the unseen data to achievebetter generalization.
Wireless Sensor Network(WSNs)consists of a group of nodes that analyze the information from surrounding *** sensor nodes are responsible for accumulating and exchanging ***,node local-ization is the process of identif...
详细信息
Wireless Sensor Network(WSNs)consists of a group of nodes that analyze the information from surrounding *** sensor nodes are responsible for accumulating and exchanging ***,node local-ization is the process of identifying the target node’s *** this research work,a Received Signal Strength Indicator(RSSI)-based optimal node localization approach is proposed to solve the complexities in the conventional node localization ***,the RSSI value is identified using the Deep Neural Network(DNN).The RSSI is conceded as the range-based method and it does not require special hardware for the node localization process,also it consumes a very minimal amount of cost for localizing the nodes in 3D *** position of the anchor nodes is fixed for detecting the location of the ***,the optimal position of the target node is identified using Hybrid T cell Immune with Lotus Effect Optimization algorithm(HTCI-LEO).During the node localization process,the average localization error is minimized,which is the objective of the optimal node *** the regular and irregular surfaces,this hybrid algorithm effectively performs the localization *** suggested hybrid algorithm converges very fast in the three-dimensional(3D)*** accuracy of the proposed node localization process is 94.25%.
The axial load carrying capacity of rectangular concrete column is acknowledged as one of the key engineering considerations for the construction of such structures. Numerous methods have been used, such as the analyt...
详细信息
The axial load carrying capacity of rectangular concrete column is acknowledged as one of the key engineering considerations for the construction of such structures. Numerous methods have been used, such as the analytical exact solutions and the finite element approach to compute load carrying capacity of concrete column. Few researchers also used ML technique to predict axial load carrying capacity of column. Therefore, this paper uses three machine learning (ML) models namely support vector regression (SVR), random forest (RF) and polynomial regression (PR) to predict the axial load carrying capacity (Pu) of rectangular concrete columns. These three ML models apply their six key input parameters namely the cross section width (B), cross section depth (D), column length (L), characteristics compressive strength of concrete (fck), characteristics yield strength of steel (fy) and percentage of steel (pt) to 300 datasets in order to predict the axial load carrying capacity (Pu). A range of performance indicators including coefficient of determination (R2), Legates and McCabe index of agreement (LMI), a-10 index, root mean square error (RMSE), mean absolute error (MAE) and expanded uncertainty (U95) are used to assess the efficacy of the well-established machine learning models. The result shows that, of the three proposed machine learning models, PR had the best prediction performance based on performance metrics. This was recognized to its maximum R2 = 1.00 and the lowest RMSE = 5.72E-16 during the training phase, as well as R2 = 1.00 and RMSE = 9.93E-15 during the testing phase. Additional tools for evaluating the model's performance include rank analysis, reliability analysis, regression plot, William's plot, Taylor diagram and error matrix plot. The model's reliability index (β) is calculated using the first-order second moment (FOSM) approach, and the result is comp
暂无评论