Industries are embracing information technology and constructing more robust machines known as Cyber-Physical Systems(CPS) to automate processes. CPSs are envisioned to be pervasive, coordinating, and integrating comp...
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Facial Expression Recognition (FER) aims to detect the emotional state of facial images. It is playing an increasingly important role in several application areas, including human–computer interaction (HCI), video tr...
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This paper presents a novel magnetic coupling inverter with a ladder switched-capacitor technique designed for home appliance. The inverter offers an elevated voltage gain while maintaining a minimal shoot-through dut...
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A novel class of achievable rate regions is obtained for the general $K$ -receiver discrete memoryless broadcast channel over which two groupcast messages are to be transmitted, with each message required by an arbitr...
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A novel class of achievable rate regions is obtained for the general $K$ -receiver discrete memoryless broadcast channel over which two groupcast messages are to be transmitted, with each message required by an arbitrary group of receivers. The associated achievability schemes are parameterized by an expansion of the message set which then determines how random coding techniques are employed. These techniques include generalized versions of up-set message-splitting, the generation of possibly multiple auxiliary codebooks for certain compositions of split messages using superposition coding with subset inclusion order, partial interference decoding at all receivers in general, joint unique decoding at receivers that desire both messages, and non-unique or indirect decoding at receivers that desire only one of the two messages. The generality of the proposed class of schemes implies new achievable rate regions for problems previously not considered as well as those that were studied before, with specific members of that class having rate regions that coincide with previously found capacity regions for special classes of broadcast channels with two private or two nested groupcast messages, wherein the group of receivers desiring one message is contained in that desiring the other. Moreover, new capacity results are established for certain partially ordered classes of broadcast channels for a class of two non-nested groupcast messages. To further show the strength of the proposed achievable rate regions we consider the so-called combination network as a test case. When specialized to the combination network, some members of the class of inner bounds are shown, via converse results, to result in the capacity region when the two messages are (a) intended for two distinct sets of $K{-}1$ receivers each and (b) nested, in which one message is intended for one or two (common) receivers and both messages are intended for all other (private) receivers. In the latter two nested
Inpatient falls from beds in hospitals are a common *** falls may result in severe *** problem can be addressed by continuous monitoring of patients using *** advancements in deep learning-based video analytics have m...
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Inpatient falls from beds in hospitals are a common *** falls may result in severe *** problem can be addressed by continuous monitoring of patients using *** advancements in deep learning-based video analytics have made this task of fall detection more effective and *** with fall detection,monitoring of different activities of the patients is also of significant concern to assess the improvement in their *** computation-intensive models are required to monitor every action of the patient *** requirement limits the applicability of such ***,to keep the model lightweight,the already designed fall detection networks can be extended to monitor the general activities of the patients along with the fall *** by the same notion,we propose a novel,lightweight,and efficient patient activity monitoring system that broadly classifies the patients’activities into fall,activity,and rest classes based on their *** whole network comprises three sub-networks,namely a Convolutional Neural Networks(CNN)based video compression network,a Lightweight Pose Network(LPN)and a Residual Network(ResNet)Mixer block-based activity recognition *** compression network compresses the video streams using deep learning networks for efficient storage and retrieval;after that,LPN estimates human ***,the activity recognition network classifies the patients’activities based on their *** proposed system shows an overall accuracy of approx.99.7% over a standard dataset with 99.63% fall detection accuracy and efficiently monitors different events,which may help monitor the falls and improve the inpatients’health.
This research addresses the challenge of identifying and categorizing Bangla regional dialects through the application of sophisticated natural language processing techniques. Automated translation, digital content pe...
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Recently medical image classification plays a vital role in medical image retrieval and computer-aided diagnosis *** deep learning has proved to be superior to previous approaches that depend on handcrafted features;i...
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Recently medical image classification plays a vital role in medical image retrieval and computer-aided diagnosis *** deep learning has proved to be superior to previous approaches that depend on handcrafted features;it remains difficult to implement because of the high intra-class variance and inter-class similarity generated by the wide range of imaging modalities and clinical *** Internet of Things(IoT)in healthcare systems is quickly becoming a viable alternative for delivering high-quality medical treatment in today’s e-healthcare *** recent years,the Internet of Things(IoT)has been identified as one of the most interesting research subjects in the field of health care,notably in the field of medical image *** medical picture analysis,researchers used a combination of machine and deep learning techniques as well as artificial *** newly discovered approaches are employed to determine diseases,which may aid medical specialists in disease diagnosis at an earlier stage,giving precise,reliable,efficient,and timely results,and lowering death *** on this insight,a novel optimal IoT-based improved deep learning model named optimization-driven deep belief neural network(ODBNN)is proposed in this *** context,primarily image quality enhancement procedures like noise removal and contrast normalization are *** the preprocessed image is subjected to feature extraction techniques in which intensity histogram,an average pixel of RGB channels,first-order statistics,Grey Level Co-Occurrence Matrix,Discrete Wavelet Transform,and Local Binary Pattern measures are *** extracting these sets of features,the May Fly optimization technique is adopted to select the most relevant *** selected features are fed into the proposed classification algorithm in terms of classifying similar input images into similar *** proposed model is evaluated in terms of accuracy,precision,recall,and f-
The dynamic field of speaker diarization continues to present significant challenges, despite notable advancements in recent years and the rising focus on complex acoustic scenarios emphasizes the importance of sustai...
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This work focuses on reengineering an HMI implemented in a third-party legacy tool to an IEC 61499 implementation. We propose a method to re-engineer the view for a process system and gather relevant information for t...
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Social media has provided the great opportunity for millions of internet users to express their opinions online. The online reviews have a huge potential to gain rich insight into an individual’s behavior towards an ...
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Social media has provided the great opportunity for millions of internet users to express their opinions online. The online reviews have a huge potential to gain rich insight into an individual’s behavior towards an entity. Sentiment analysis (SA) is a widely accepted Natural Language Processing (NLP) technique that helps in analyzing these reviews. Feature extraction (FE) plays a key role in enhancing the performance of a sentiment classification model. Historically, the most popular techniques employed for FE have been Term Frequency-Inverse Document Frequency (TF-IDF), Word2Vec and GloVe. But these approaches are non-contextual and also domain-specific. The recent research studies have utilized state-of-the-art (SOTA) context-aware Bidirectional Encoder Representations from Transformers (BERT) embeddings for building SA models. However, BERT employs cross-encoder architecture in which it can produce word embeddings only. Sentence embeddings can be derived by averaging word embeddings. But this method is computationally expensive and does not yield optimal sentence embeddings (SEs). To address the shortcomings in the existing FE methods, this paper introduces a novel technique to extract domain-insensitive high-quality SEs directly via Sentence Transformer (ST) and proposes a general-purpose unified framework for SA. In the proposed framework, first we generate semantically rich SEs employing ST and then integrate these embeddings into three different machine learning algorithms including logistic regression, random forest and support vector machine. The proposed method is evaluated on balanced and imbalanced datasets across seven diverse domains on the basis of F1-score, precision, recall, accuracy and AUC values. It has outperformed the existing FE approaches and several SOTA studies. Also, the significant improvement in F1 scores, ranging from 4% to 7% above the SOTA BERT embeddings in case of imbalanced datasets, highlights the efficiency of the proposed metho
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