The integration of chemometric analysis and machine learning techniques to enhance the predictive classification of raisin quality, a vital task for agricultural and food processing industries. The study employs chemo...
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Alzheimer’s disease (AD) is a debilitating neurodegenerative disorder that affects millions worldwide. Early and accurate diagnosis is crucial for timely intervention and management, as it can significantly improve p...
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The usage of swarms of drones is expected to continue growing in the next years, particularly in dangerous scenarios, such as monitoring and rescue missions in hostile and disaster areas. Small-sized Unmanned Aerial V...
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Pervasive Computing has become more personal with the widespread adoption of the Internet of Things(IoT)in our day-to-day *** emerging domain that encompasses devices,sensors,storage,and computing of personal use and ...
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Pervasive Computing has become more personal with the widespread adoption of the Internet of Things(IoT)in our day-to-day *** emerging domain that encompasses devices,sensors,storage,and computing of personal use and surroundings leads to Personal IoT(PIoT).PIoT offers users high levels of personalization,automation,and *** proliferation of PIoT technology has extended into society,social engagement,and the interconnectivity of PIoT objects,resulting in the emergence of the Social Internet of Things(SIoT).The combination of PIoT and SIoT has spurred the need for autonomous learning,comprehension,and understanding of both the physical and social *** research on PIoT is dedicated to enabling seamless communication among devices,striking a balance between observation,sensing,and perceiving the extended physical and social environment,and facilitating information ***,the virtualization of independent learning from the social environment has given rise to Artificial Social Intelligence(ASI)in PIoT ***,autonomous data communication between different nodes within a social setup presents various resource management challenges that require careful *** paper provides a comprehensive review of the evolving domains of PIoT,SIoT,and ***,the paper offers insightful modeling and a case study exploring the role of PIoT in post-COVID *** study contributes to a deeper understanding of the intricacies of PIoT and its various dimensions,paving the way for further advancements in this transformative field.
This paper investigates the input-to-state stabilization of discrete-time Markov jump systems. A quantized control scheme that includes coding and decoding procedures is proposed. The relationship between the error in...
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In recent years, deep learning has significantly advanced skin lesion segmentation. However, annotating medical image data is specialized and costly, while obtaining unlabeled medical data is easier. To address this c...
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The healthcare industry is rapidly adapting to new computing environments and technologies. With academics increasingly committed to developing and enhancing healthcare solutions that combine the Internet of Things (I...
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The healthcare industry is rapidly adapting to new computing environments and technologies. With academics increasingly committed to developing and enhancing healthcare solutions that combine the Internet of Things (IoT) and edge computing, there is a greater need than ever to adequately monitor the data being acquired, shared, processed, and stored. The growth of cloud, IoT, and edge computing models presents severe data privacy concerns, especially in the healthcare sector. However, rigorous research to develop appropriate data privacy solutions in the healthcare sector is still lacking. This paper discusses the current state of privacy-preservation solutions in IoT and edge healthcare applications. It identifies the common strategies often used to include privacy by the intelligent edges and technologies in healthcare systems. Furthermore, the study addresses the technical complexity, efficacy, and sustainability limits of these methods. The study also highlights the privacy issues and current research directions that have driven the IoT and edge healthcare solutions, with which more insightful future applications are encouraged.
Customized keyword spotting needs to adapt quickly to small user *** methods primarily solve the problem under moderate noise *** work increases the level of difficulty in detecting keywords by introducing keyword ***...
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Customized keyword spotting needs to adapt quickly to small user *** methods primarily solve the problem under moderate noise *** work increases the level of difficulty in detecting keywords by introducing keyword ***,the current solution has been explored on large models with many parameters,making it unsuitable for deployment on small *** applying the current solution to lightweight models with minimal training data,the performance degrades compared to the baseline ***,we propose a light-weight multi-task architecture(<9.0×10^(4)parameters)created from integrating the triplet attention module in the ConvMixer networks and a new auxiliary mixed labeling encoding to address the *** results of our experiment show that the proposed model outperforms similar light-weight models for keyword spotting,with accuracy gains ranging from 0.73%to 2.95%for a clean set and from 2.01%to 3.37%for a mixed set under different scales of training ***,our model shows its robustness in different low-resource language datasets while converging faster.
Cancer remains the leading cause of death worldwide, significantly impacting individuals and healthcare systems alike. In recent decades, skin cancer has surged in prevalence compared to other major cancer types. Vari...
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A progressive brain disorder, which eventually destroys memory cells, is termed Alzheimer’s Disease (AD). AD causes memory loss and other regular activities. Due to the variations in cytoarchitecture, the categorical...
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A progressive brain disorder, which eventually destroys memory cells, is termed Alzheimer’s Disease (AD). AD causes memory loss and other regular activities. Due to the variations in cytoarchitecture, the categorical labeling of various tissues presents a difficult task in AD classification. For addressing this challenge, this paper proposes a new GELU and SWISH-based Radial Basis Function Network (GS-RBFN)-centric early prediction and classification of AD. For classifying AD into Mild Cognitive Impairment (MCI), AD, and Control Normal (CN), the proposed model deploys image pre-processing, segmentation, morphological operation, data augmentation, image representation extraction, feature selection, and classification steps. Primarily, images are gathered from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Next, by utilizing normalization, skull removal, and spatial smoothing approaches, the images are pre-processed. Then, by using the Brownian Log Scaling Archimedes Optimization-based Watershed Segmentation (BLSAOWS), significant brain tissues are segmented. After that, using morphological operations, the segmented images are enhanced. Next, for obtaining different formations of the segmented images, a data augmentation process is deployed. Subsequently, the image features are extracted, and the best features are chosen utilizing the Base Switch Rule Infimum and Supremum-centric Rock Hyrax Swarm Optimization (BSRISRHSO) algorithm. Lastly, utilizing a new GS-RBFN classifier, the AD is classified. Through the experimental analysis, the proposed model’s efficiency is determined. Thus, the proposed GS-RBFN proficiently predicts AD individuals with an accuracy, precision, and sensitivity of 98.45%, 98.44%, and 98.44%, respectively. The proposed GS-RBFN achieved a less computation time of 14876 ms. Furthermore, the proposed BSRISRHSO obtained a minimum feature selection time of 24012 ms. The Proposed BLSAOWS acquired a high efficiency of 98%. Also, the pro
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