In the era of rapid technological advancement, the Internet of Things (IoT) has revolutionised healthcare through systems like the Telecare Medicine information System (TMIS), designed to streamline patient-doctor int...
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This paper explores the concept of isomorphism in cellular automata (CAs), focusing on identifying and understanding isomorphic relationships between distinct CAs. A cellular automaton (CA) is said to be isomorphic to...
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Advancements in smart applications highlight the need for increased processing and storage capacity at Smart Devices (SDs). To tackle this, Edge computing (EC) is enabled to offload SD workloads to distant edge server...
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The transmission of medical images via medical agencies raises security concerns, necessitating increased security measures to ensure integrity and security. However, many watermarking algorithms overlook equipoise;th...
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In the current pandemic situation where the coronavirus is spreading very fast that can jump from one human to another. Along with this, there are millions of viruses for example Ebola, SARS, etc. that can spread as f...
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In distributed multi-robot systems, ensuring collision-free motion planning is a complex challenge, especially in dynamic environments where multiple robots are operating simultaneously. Traditional path-planning algo...
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In healthcare,the persistent challenge of arrhythmias,a leading cause of global mortality,has sparked extensive research into the automation of detection using machine learning(ML)***,traditional ML and AutoML approac...
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In healthcare,the persistent challenge of arrhythmias,a leading cause of global mortality,has sparked extensive research into the automation of detection using machine learning(ML)***,traditional ML and AutoML approaches have revealed their limitations,notably regarding feature generalization and automation *** glaring research gap has motivated the development of AutoRhythmAI,an innovative solution that integrates both machine and deep learning to revolutionize the diagnosis of *** approach encompasses two distinct pipelines tailored for binary-class and multi-class arrhythmia detection,effectively bridging the gap between data preprocessing and model *** validate our system,we have rigorously tested AutoRhythmAI using a multimodal dataset,surpassing the accuracy achieved using a single dataset and underscoring the robustness of our *** the first pipeline,we employ signal filtering and ML algorithms for preprocessing,followed by data balancing and split for *** second pipeline is dedicated to feature extraction and classification,utilizing deep learning ***,we introduce the‘RRI-convoluted trans-former model’as a novel addition for binary-class *** ensemble-based approach then amalgamates all models,considering their respective weights,resulting in an optimal model *** our study,the VGGRes Model achieved impressive results in multi-class arrhythmia detection,with an accuracy of 97.39%and firm performance in precision(82.13%),recall(31.91%),and F1-score(82.61%).In the binary-class task,the proposed model achieved an outstanding accuracy of 96.60%.These results highlight the effectiveness of our approach in improving arrhythmia detection,with notably high accuracy and well-balanced performance metrics.
Skin cancer presents in various forms, including squamous cell carcinoma (SCC), basal cell carcinoma (BCC), and melanoma. Established risk factors include ultraviolet (UV) radiation exposure from solar or artificial s...
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With the ever-rising risk of phishing attacks, which capitalize on vulnerable human behavior in the contemporary digital space, requires new cybersecurity methods. This literary work contributes to the solution by nov...
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Parkinson's Disease stands as a widespread cerebral degenerative disorder impacting millions of people across the world. Timely and precise diagnosis of PD is of paramount importance, enabling early interventions ...
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