Fog computing brings computational services near the network edge to meet the latency constraints of cyber-physical System(CPS)*** devices enable limited computational capacity and energy availability that hamper end ...
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Fog computing brings computational services near the network edge to meet the latency constraints of cyber-physical System(CPS)*** devices enable limited computational capacity and energy availability that hamper end user *** designed a novel performance measurement index to gauge a device’s resource *** examination addresses the offloading mechanism issues,where the end user(EU)offloads a part of its workload to a nearby edge server(ES).Sometimes,the ES further offloads the workload to another ES or cloud server to achieve reliable performance because of limited resources(such as storage and computation).The manuscript aims to reduce the service offloading rate by selecting a potential device or server to accomplish a low average latency and service completion time to meet the deadline constraints of sub-divided *** this regard,an adaptive online status predictive model design is significant for prognosticating the asset requirement of arrived services to make float ***,the development of a reinforcement learning-based flexible x-scheduling(RFXS)approach resolves the service offloading issues,where x=service/resource for producing the low latency and high performance of the *** approach to the theoretical bound and computational complexity is derived by formulating the system efficiency.A quadratic restraint mechanism is employed to formulate the service optimization issue according to a set ofmeasurements,as well as the behavioural association rate and adulation *** system managed an average 0.89%of the service offloading rate,with 39 ms of delay over complex scenarios(using three servers with a 50%service arrival rate).The simulation outcomes confirm that the proposed scheme attained a low offloading uncertainty,and is suitable for simulating heterogeneous CPS frameworks.
The U-Net architecture is the focus of this study, which optimizes biomedical picture segmentation. Improving performance in contexts with limited resources is the goal. The methodology uses GradCAM++, k-fold cross-va...
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In order to maintain sustainable agriculture, it is vital to monitor plant health. Since all species of plants are prone to characteristic diseases, it necessitates regular surveillance to search for any symptoms, whi...
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In response to inquiries posed in natural languages, question-answering systems (QASs) produce responses. The capabilities of early QASs are limited because they were designed for certain domains. The current generati...
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We evaluate different Neural Radiance Field(NeRF)techniques for the 3D reconstruction of plants in varied environments,from indoor settings to outdoor *** methods usually fail to capture the complex geometric details ...
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We evaluate different Neural Radiance Field(NeRF)techniques for the 3D reconstruction of plants in varied environments,from indoor settings to outdoor *** methods usually fail to capture the complex geometric details of plants,which is crucial for phenotyping and breeding *** evaluate the reconstruction fidelity of NeRFs in 3 scenarios with increasing complexity and compare the results with the point cloud obtained using light detection and ranging as ground *** the most realistic field scenario,the NeRF models achieve a 74.6％F1 score after 30 min of training on the graphics processing unit,highlighting the efficacy of NeRFs for 3D reconstruction in challenging ***,we propose an early stopping technique for NeRF training that almost halves the training time while achieving only a reduction of 7.4％in the average F1 *** optimization process substantially enhances the speed and efficiency of 3D reconstruction using *** findings demonstrate the potential of NeRFs in detailed and realistic 3D plant reconstruction and suggest practical approaches for enhancing the speed and efficiency of NeRFs in the 3D reconstruction process.
A new stochastic coordinate descent deep learning architectures optimization is proposed for Automated Diabetic Retinopathy Detection and Classification from different data sets and convolution networks. Initially, th...
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Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by challenges in social interaction, communication difficulties, repetitive behaviors, and a range of strengths and differences in...
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Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by challenges in social interaction, communication difficulties, repetitive behaviors, and a range of strengths and differences in cognitive abilities. Early ASD diagnosis using machine learning and deep learning techniques is crucial for preventing its severity and long-term effects. The articles published in this area have only applied different machine learning algorithms, and a notable gap observed is the absence of an in-depth analysis in terms of hyperparameter tuning and the type of dataset used in this context. This study investigated predictive modeling for ASD traits by leveraging two distinct datasets: (i) a raw CSV dataset with tabular data and (ii) an image dataset with facial expression. This study aims to conduct an in-depth analysis of ASD trait prediction in adults and toddlers by doing hyper optimized and interpreting the result through explainable AI. In the CSV dataset, a comprehensive exploration of machine learning and deep learning algorithms, including decision trees, Naive Bayes, random forests, support vector machines (SVM), k-nearest neighbors (KNN), logistic regression, XGBoost, and ANN, was conducted. XGBoost emerged as the most effective machine learning algorithm, achieving an accuracy of 96.13%. The deep learning ANN model outperformed the traditional machine learning algorithms with an accuracy of 99%. Additionally, an ensemble model combining a decision tree, random forest, SVM, KNN, and logistic regression demonstrated superior performance, yielding an accuracy of 96.67%. The XGBoost model, utilized in hyperparameter optimization for CSV data, exhibited a substantial accuracy increase, reaching 98%. For the image dataset, advanced deep learning models, such as ResNet50, VGG16, Boosting, and Bagging, were employed. The bagging model outperformed the others, achieving an impressive accuracy of 99%. Subsequent hyperparameter optimization was conduct
In a growing demand of accurately predicting the stock market and inefficient complex markets the rising accurate relationship prediction is not adequately addressed by the conventional methods. The dynamic and comple...
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Voice-based user interfaces (VUIs) represent a promising avenue for enhancing accessibility in humancomputer interaction (HCI). This research paper investigates the effectiveness of VUIs in addressing accessibility ch...
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Timely estimation of earthquake magnitude plays a crucial role in the early warning systems for earthquakes. Despite the inherent danger associated with earthquake energy, earthquake research necessitates extensive pa...
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