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...
详细信息
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
Robotic arms are widely used in the automation industry to package and deliver classified objects. When the products are small objects with very similar shapes, such as screwdriver bits with slightly different threads...
详细信息
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...
详细信息
Numerous autonomous driving systems employ deep learning-based image object detection schemes for their navigation. For developing reliable autonomous driving systems, the training process of the deep image object det...
详细信息
Numerous autonomous driving systems employ deep learning-based image object detection schemes for their navigation. For developing reliable autonomous driving systems, the training process of the deep image object detectors must be performed in a precise manner. Existence of samples with erroneous labels, e.g., erroneous bounding boxes, in the training datasets of autonomous driving systems leads to a reduction in their performance and a decrease in their reliability in real-life situations. Given these explanations, in this paper, we propose a novel erroneous bounding box detection scheme for identifying the bounding boxes in the training datasets that are annotated wrongly, and avoid their use in the training process of autonomous driving systems. Specifically, we employ two efficient techniques, namely, multi-modal information processing and confident learning, in the development of the proposed scheme. In the multi-modal information processing, we first obtain the instance segmentation maps of the images using deep image instance segmentation networks, and then, utilize their interactions with the spatial coordinates of the bounding boxes, along with the spatial coordinates themselves, to generate discriminative sets of features for the task of erroneous bounding box detection. Further, by using the confident learning technique, we leverage the statistical information of the estimated erroneous statuses of the bounding boxes, to further enhance the performance of the task of erroneous bounding box detection. The results of extensive experimentations demonstrate the effectiveness of the proposed erroneous bounding box detection scheme in cleaning the datasets of autonomous driving systems, compared to the state-of-the-art data selection schemes. IEEE
Unmanned Aerial Vehicles (UAVs) have witnessed remarkable significance in diverse sectors, ranging from environmental monitoring, infrastructure inspection, disaster response, wildlife conservation, surveillance, and ...
详细信息
Artificial intelligence (AI) hardware accelerator is an emerging research for several applications and domains. The hardware accelerator's direction is to provide high computational speed with retaining low-cost a...
详细信息
This paper aims to develop a flexible power management approach to interconnect multiple energy resources based on an isolated, monolithic multiport DC-DC power converter. Specifically, a high efficiency, ultra-compac...
详细信息
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...
详细信息
The integration of Mixed Reality(MR)technology into Autonomous Vehicles(AVs)has ushered in a new era for the automotive industry,offering heightened safety,convenience,and passenger ***,the substantial and varied data...
详细信息
The integration of Mixed Reality(MR)technology into Autonomous Vehicles(AVs)has ushered in a new era for the automotive industry,offering heightened safety,convenience,and passenger ***,the substantial and varied data generated by MR-Connected AVs(MR-CAVs),encompassing both highly dynamic and static information,presents formidable challenges for efficient data management and *** this paper,we formulate our indexing problem as a constrained optimization problem,with the aim of maximizing the utility function that represents the overall performance of our indexing *** optimization problem encompasses multiple decision variables and constraints,rendering it mathematically infeasible to solve ***,we propose a heuristic algorithm to address the combinatorial complexity of the *** heuristic indexing algorithm efficiently divides data into highly dynamic and static categories,distributing the index across Roadside Units(RSUs)and optimizing query *** approach takes advantage of the computational capabilities of edge servers or RSUs to perform indexing operations,thereby shifting the burden away from the vehicles *** algorithm strategically places data in the cache,optimizing cache hit rate and space utilization while reducing *** quantitative evaluation demonstrates the superiority of our proposed scheme,with significant reductions in latency(averaging 27%-49.25%),a 30.75%improvement in throughput,a 22.50%enhancement in cache hit rate,and a 32%-50.75%improvement in space utilization compared to baseline schemes.
Cooperative communication through energy harvested relays in Cognitive Internet of Things(CIoT)has been envisioned as a promising solution to support massive connectivity of Cognitive Radio(CR)based IoT devices and to...
详细信息
Cooperative communication through energy harvested relays in Cognitive Internet of Things(CIoT)has been envisioned as a promising solution to support massive connectivity of Cognitive Radio(CR)based IoT devices and to achieve maximal energy and spectral efficiency in upcoming wireless *** this work,a cooperative CIoT system is contemplated,in which a source acts as a satellite,communicating with multiple CIoT devices over numerous *** Aerial Vehicles(UAVs)are used as relays,which are equipped with onboard Energy Harvesting(EH)*** adopted a Power Splitting(PS)method for EH at relays,which are harvested from the Radio frequency(RF)*** conjunction with this,the Decode and Forward(DF)relaying strategy is used at UAV relays to transmit the messages from the satellite source to the CIoT *** developed a Multi-Objective Optimization(MOO)framework for joint optimization of source power allocation,CIoT device selection,UAV relay assignment,and PS ratio *** formulated three objectives:maximizing the sum rate and the number of admitted CIoT in the network and minimizing the carbon dioxide *** MOO formulation is a Mixed-Integer Non-Linear Programming(MINLP)problem,which is challenging to *** address the joint optimization problem for an epsilon optimal solution,an Outer Approximation Algorithm(OAA)is proposed with reduced *** simulation results show that the proposed OAA is superior in terms of CIoT device selection and network utility maximization when compared to those obtained using the Nonlinear Optimization with Mesh Adaptive Direct-search(NOMAD)algorithm.
暂无评论