Obesity is a global health crisis projected to affect one billion people worldwide by 2030. Previous research has emphasized the role of food cues in print media and television as contributing factors to the obesity e...
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Cloud computing is a technology that provides secure storage space for the customer’s massive data and gives them the facility to retrieve and transmit their data efficiently through a secure network in which encrypt...
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Cloud computing is a technology that provides secure storage space for the customer’s massive data and gives them the facility to retrieve and transmit their data efficiently through a secure network in which encryption and decryption algorithms are being *** cloud computation,data processing,storage,and transmission can be done through laptops andmobile *** Storing in cloud facilities is expanding each day and data is the most significant asset of *** important concern with the transmission of information to the cloud is security because there is no perceivability of the client’s *** have to be dependent on cloud service providers for assurance of the platform’s *** security and privacy issues reduce the progression of cloud computing and add ***;most of the data that is stored on cloud servers is in the form of images and photographs,which is a very confidential form of data that requires secured *** this research work,a public key cryptosystem is being implemented to store,retrieve and transmit information in cloud computation through a modified Rivest-Shamir-Adleman(RSA)algorithm for the encryption and decryption of *** implementation of a modified RSA algorithm results guaranteed the security of data in the cloud *** enhance the user data security level,a neural network is used for user authentication and ***;the proposed technique develops the performance of detection as a loss function of the bounding *** Faster Region-Based Convolutional Neural Network(Faster R-CNN)gets trained on images to identify authorized users with an accuracy of 99.9%on training.
The advances from the last few decades in the fields of ML (Machine Learning), DL (Deep Learning), and semantic computing are now changing the shape of the healthcare system. But, unlike physical health problems, diag...
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Researchers have recently achieved significant advances in deep learning techniques, which in turn has substantially advanced other research disciplines, such as natural language processing, image processing, speech r...
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Researchers have recently achieved significant advances in deep learning techniques, which in turn has substantially advanced other research disciplines, such as natural language processing, image processing, speech recognition, and software engineering. Various deep learning techniques have been successfully employed to facilitate software engineering tasks, including code generation, software refactoring, and fault localization. Many studies have also been presented in top conferences and journals, demonstrating the applications of deep learning techniques in resolving various software engineering tasks. However,although several surveys have provided overall pictures of the application of deep learning techniques in software engineering,they focus more on learning techniques, that is, what kind of deep learning techniques are employed and how deep models are trained or fine-tuned for software engineering tasks. We still lack surveys explaining the advances of subareas in software engineering driven by deep learning techniques, as well as challenges and opportunities in each subarea. To this end, in this study, we present the first task-oriented survey on deep learning-based software engineering. It covers twelve major software engineering subareas significantly impacted by deep learning techniques. Such subareas spread out through the whole lifecycle of software development and maintenance, including requirements engineering, software development, testing, maintenance, and developer collaboration. As we believe that deep learning may provide an opportunity to revolutionize the whole discipline of software engineering, providing one survey covering as many subareas as possible in software engineering can help future research push forward the frontier of deep learning-based software engineering more systematically. For each of the selected subareas,we highlight the major advances achieved by applying deep learning techniques with pointers to the available datasets i
In the current era of smart technology, integrating the Internet of Things (IoT) with Artificial Intelligence has revolutionized several fields, including public health and sanitation. The smart lavatory solution prop...
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Time series anomaly detection is an important task in many applications,and deep learning based time series anomaly detection has made great ***,due to complex device interactions,time series exhibit diverse abnormal ...
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Time series anomaly detection is an important task in many applications,and deep learning based time series anomaly detection has made great ***,due to complex device interactions,time series exhibit diverse abnormal signal shapes,subtle anomalies,and imbalanced abnormal instances,which make anomaly detection in time series still a *** and analysis of multivariate time series can help uncover their intrinsic spatio-temporal characteristics,and contribute to the discovery of complex and subtle *** this paper,we propose a novel approach named Multi-scale Convolution Fusion and Memory-augmented Adversarial AutoEncoder(MCFMAAE)for multivariate time series anomaly *** is an encoder-decoder-based framework with four main ***-scale convolution fusion module fuses multi-sensor signals and captures various scales of temporal ***-attention-based encoder adopts the multi-head attention mechanism for sequence modeling to capture global context *** module is introduced to explore the internal structure of normal samples,capturing it into the latent space,and thus remembering the typical ***,the decoder is used to reconstruct the signals,and then a process is coming to calculate the anomaly ***,an additional discriminator is added to the model,which enhances the representation ability of autoencoder and avoids *** on public datasets demonstrate that MCFMAAE improves the performance compared to other state-of-the-art methods,which provides an effective solution for multivariate time series anomaly detection.
Fog computing is an emerging paradigm that provides services near the end-user. The tremendous increase in IoT devices and big data leads to complexity in fog resource allocation. Inefficient resource allocation can l...
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Cancer of the breast popularly known as breast cancer (BC) is the second and third utmost cause of mortality among women in Nigeria and globally, respectively. Biopsy histopathological images (BHI) have gained more at...
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Cancer of the breast popularly known as breast cancer (BC) is the second and third utmost cause of mortality among women in Nigeria and globally, respectively. Biopsy histopathological images (BHI) have gained more attention for the early clinical diagnosis of BC. However, the clinical examination and diagnosis of BC histology images are subject to human error. Consequently, several computer-aided diagnoses (CAD) solutions have been presented to aid histopathologists with the automated classification of cancerous tumor cells on histological images. Deep convolutional neural networks (DCNN) have been utilized to build a sizable portion of the cutting-edge proposed solutions. However, due to the architectural structure of DCNN, which extracts features automatically along with training processes and is coupled with overlapping nucleic features on breast histology images (BHI), the existing solutions suffer from high computational utilization, extensive training time leading to longer convergence times, and reliance on available high-end system resources to build adequate BC classification solutions. In this paper, an enhanced shallow convolutional neural network (ES-CNN) has been proposed for multi-classification of BHI, aimed to improve classification performance and reduce training time across eight BC types and four magnifications in the BreakHis dataset. The research objectives were achieved in three ways. First, we designed the proposed network’s architecture, guided by magnification and patient dependencies. Secondly, we implemented a multi-classification model based on the proposed network, and, finally, two categories of experiments were conducted based on classification accuracy and computational utilization. The experimental results revealed that the proposed methods have minimal computational utilization and improved classification performance compared to the existing work. This research reports a multi-classification accuracy of 96%, 95%, 98%, and 96% acros
Sign language recognition is vital for enhancing communication accessibility among the Deaf and hard-of-hearing *** Japan,approximately 360,000 individualswith hearing and speech disabilities rely on Japanese Sign Lan...
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Sign language recognition is vital for enhancing communication accessibility among the Deaf and hard-of-hearing *** Japan,approximately 360,000 individualswith hearing and speech disabilities rely on Japanese Sign Language(JSL)for ***,existing JSL recognition systems have faced significant performance limitations due to inherent *** response to these challenges,we present a novel JSL recognition system that employs a strategic fusion approach,combining joint skeleton-based handcrafted features and pixel-based deep learning *** system incorporates two distinct streams:the first stream extracts crucial handcrafted features,emphasizing the capture of hand and body movements within JSL ***,a deep learning-based transfer learning stream captures hierarchical representations of JSL gestures in the second ***,we concatenated the critical information of the first stream and the hierarchy of the second stream features to produce the multiple levels of the fusion features,aiming to create a comprehensive representation of the JSL *** reducing the dimensionality of the feature,a feature selection approach and a kernel-based support vector machine(SVM)were used for the *** assess the effectiveness of our approach,we conducted extensive experiments on our Lab JSL dataset and a publicly available Arabic sign language(ArSL)*** results unequivocally demonstrate that our fusion approach significantly enhances JSL recognition accuracy and robustness compared to individual feature sets or traditional recognition methods.
Melanoma is a lethal type of skin cancer that has become very common due to its high metastatic rate. Therefore, accurate and timely diagnosis plays a vital role in a patient’s effective treatment and recovery. Melan...
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Melanoma is a lethal type of skin cancer that has become very common due to its high metastatic rate. Therefore, accurate and timely diagnosis plays a vital role in a patient’s effective treatment and recovery. Melanoma dermoscopic images provide a detailed analysis of pigmented lesions. Traditional manual segmentation by dermatologists has limitations such as inter-observer variability, time consumption, and human error. The deep learning (DL) techniques enhance diagnosis by automating lesion detection and segmentation. In this work, a DL framework for the localization of melanoma lesions using dermoscopic images is presented. The proposed framework utilizes an encoder-decoder architecture inspired by the UNet model. The encoder-decoder architecture enables effective feature extraction and spatial information preservation. The encoder part efficiently captures hierarchical features from the input data. At the same time, the decoder part reconstructs the spatial details, leading to accurate segmentation results. Therefore, the proposed framework takes advantage of the capability of the encoder-decoder architecture and employs it in the depth of 3. Extensive experiments are conducted to determine the optimal set of hyperparameters and architecture. The performance of the proposed framework is assessed on unseen samples via a cross-database validation scenario. The proposed modified UNet framework achieves notable accuracy, with a Jaccard Index and BF Score of 0.95, 0.92, and 0.73, respectively. Subsequently, our proposed framework’s outcomes are visually analyzed using Explainable Artificial Intelligence (XAI) algorithms. It showcases the proposed framework’s ability to accurately segment lesions even in the presence of various artifacts such as hair, clinical swatches, markers, and variations in intensity and size. The performance of the proposed framework is compared with the existing works. The efficacy and robustness of the proposed framework are evident from the
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