In this article, we focus on solving a class of distributed optimization problems involving n agents with the local objective function at every agent i given by the difference of two convex functions fi and gi (differ...
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This paper presents a low-cost vector based Muometric navigation system for precision location finding applications where GPS does not work. The proposed system obtains the amplitude information from a SiPM photodetec...
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This paper investigates the torque pulsations issue during magnetization in variable flux memory motors for traction applications. The paper proposes an algorithm to mitigate these torque pulsations and their resultan...
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The neurological disease known as autism spectrum disorder (ASD) is characterized by impaired social interaction, communication issues, and constrained and repetitive behavior patterns. For the benefit of early interv...
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ISBN:
(纸本)9798350367461
The neurological disease known as autism spectrum disorder (ASD) is characterized by impaired social interaction, communication issues, and constrained and repetitive behavior patterns. For the benefit of early interventions and support for afflicted persons, timely and accurate ASD prognosis is essential. Deep learning methods have become effective tools for predictive modeling across a range of industries, including healthcare. This study examines the use of deep learning and transfer learning to forecast ASD using a large dataset of clinical and behavioral variables. In this study, the effectiveness of three well-known deep learning architectures VGG16, DenseNet121, and MobileNetv2 in predicting ASDs is compared. A sizable dataset with a variety of ASD-related variables, such as demographic data, medical histories, and behavioral assessments, is used to train the models. To take use of pre-learned weights from models trained on extensive generic image recognition tasks, transfer learning is used. With accuracy rates of 97% apiece, the experimental results show remarkable prediction performance for VGG16 and DenseNet121. These models have significant generalization abilities that make it possible to make reliable predictions for identifying those who are at risk for ASD. In contrast to the other architectures, MobileNetv2 only obtains an accuracy of 73%. The results show that deeper architectures like VGG16 and DenseNet121 capture the rich patterns and fine details of the input data, resulting in more precise predictions. Additionally, thorough investigations are carried out to look into the models' learned representations and pinpoint the primary features that influence ASD prediction. These revelations aid in a better comprehension of the underlying causes and potential biomarkers of ASD. The information gleaned from these studies can direct ongoing research projects and support the creation of individualized interventions and therapies. Overall, the study empha
Accurate detection of the Physical Cell Identity (PCI) is critical for rapid synchronization and connection establishment in 5G New Radio (5G-NR) systems. This paper introduces a deep learning-based approach for PCI c...
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Maritime Authorities face significant challenges in monitoring vast maritime domains and enforcing regulations within their Exclusive Economic Zones (EEZs). Synthetic Aperture Radar (SAR) imaging of Fers an effective ...
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The harmonic and electromagnetic noise issues by switching operations are becoming more serious due to the enhancement of the fast-switching capability of power devices. A modular cascaded linear amplifier (MCLA) has ...
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Coronavirus pandemic, also referred to as COVID-19, broke out in 2019 and caused extensive illnesses. Many experts in the medical field are of the opinion that the epidemic first appeared in Wuhan, China, before it sp...
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In the past few years, image processing has been widely adopted for symptom diagnosis of medical application. To achieve accurate analysis, the medical applications require high quality image for applying to the sympt...
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High precision and reliable wind speed forecasting have become a challenge for *** events,namely,strong winds,thunderstorms,and tornadoes,along with large hail,are natural calamities that disturb daily *** accurate pr...
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High precision and reliable wind speed forecasting have become a challenge for *** events,namely,strong winds,thunderstorms,and tornadoes,along with large hail,are natural calamities that disturb daily *** accurate prediction of wind speed and overcoming its uncertainty of change,several prediction approaches have been presented over the last few *** wind speed series have higher volatility and nonlinearity,it is urgent to present cutting-edge artificial intelligence(AI)*** this aspect,this paper presents an intelligent wind speed prediction using chicken swarm optimization with the hybrid deep learning(IWSP-CSODL)*** presented IWSP-CSODL model estimates the wind speed using a hybrid deep learning and hyperparameter *** the presented IWSP-CSODL model,the prediction process is performed via a convolutional neural network(CNN)based long short-term memory with autoencoder(CBLSTMAE)*** optimally modify the hyperparameters related to the CBLSTMAE model,the chicken swarm optimization(CSO)algorithm is utilized and thereby reduces the mean square error(MSE).The experimental validation of the IWSP-CSODL model is tested using wind series data under three distinct *** comparative study pointed out the better outcomes of the IWSP-CSODL model over other recent wind speed prediction models.
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