CircRNA-disease association(CDA) can provide a new direction for the treatment of diseases. However,traditional biological experiment is time-consuming and expensive, this urges us to propose the reliable computationa...
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CircRNA-disease association(CDA) can provide a new direction for the treatment of diseases. However,traditional biological experiment is time-consuming and expensive, this urges us to propose the reliable computational model to predict the associations between circRNAs and diseases. And there is existing more and more evidence indicates that the combination of multi-biomolecular information can improve the prediction accuracy. We propose a novel computational model for CDA prediction named MBCDA, we collect the multi-biomolecular information including circRNA, disease, miRNA and lncRNA based on 6 databases, and construct three heterogeneous network among them, then the multi-heads graph attention networks are applied to these three networks to extract the features of circRNAs and diseases from different views, the obtained features are put into variational graph auto-encoder(VGAE) network to learn the latent distributions of the nodes, a fully connected neural network is adopted to further process the output of VGAE and uses sigmoid function to obtain the predicted probabilities of circRNA-disease *** a result, MBCDA achieved the values of AUC and AUPR under 5-fold cross-validation of 0.893 and 0.887. MBCDA was applied to the analysis of the top-25 predicted associations between circRNAs and diseases, these experimental results show that our proposed MBCDA is a powerful computational model for CDA prediction.
Speech enhancement is the task of taking a noisy speech input and pro-ducing an enhanced speech *** recent years,the need for speech enhance-ment has been increased due to challenges that occurred in various applicati...
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Speech enhancement is the task of taking a noisy speech input and pro-ducing an enhanced speech *** recent years,the need for speech enhance-ment has been increased due to challenges that occurred in various applications such as hearing aids,Automatic Speech Recognition(ASR),and mobile speech communication *** of the Speech Enhancement research work has been carried out for English,Chinese,and other European *** a few research works involve speech enhancement in Indian regional *** this paper,we propose a two-fold architecture to perform speech enhancement for Tamil speech signal based on convolutional recurrent neural network(CRN)that addresses the speech enhancement in a real-time single channel or track of sound created by the *** thefirst stage mask based long short-term mem-ory(LSTM)is used for noise suppression along with loss function and in the sec-ond stage,Convolutional Encoder-Decoder(CED)is used for speech *** proposed model is evaluated on various speaker and noisy environments like Babble noise,car noise,and white Gaussian *** proposed CRN model improves speech quality by 0.1 points when compared with the LSTM base model and also CRN requires fewer parameters for *** performance of the pro-posed model is outstanding even in low Signal to Noise Ratio(SNR).
Advancements in cloud computing and virtualization technologies have revolutionized Enterprise Application Development with innovative ways to design and develop complex *** Architecture is one of the recent technique...
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Advancements in cloud computing and virtualization technologies have revolutionized Enterprise Application Development with innovative ways to design and develop complex *** Architecture is one of the recent techniques in which Enterprise Systems can be developed as fine-grained smaller components and deployed *** methodology brings numerous benefits like scalability,resilience,flexibility in development,faster time to market,*** the advantages;Microservices bring some challenges *** microservices need to be invoked one by one as a *** most applications,more than one chain of microservices runs in parallel to complete a particular requirement To complete a user’s *** results in competition for resources and the need for more inter-service communication among the services,which increases the overall latency of the application.A new approach has been proposed in this paper to handle a complex chain of microservices and reduce the latency of user requests.A machine learning technique is followed to predict the weighting time of different types of *** communication time among services distributed among different physical machines are estimated based on that and obtained insights are applied to an algorithm to calculate their priorities dynamically and select suitable service instances to minimize the latency based on the shortest queue waiting *** were done for both interactive as well as non interactive workloads to test the effectiveness of the *** approach has been proved to be very effective in reducing latency in the case of long service chains.
Privacy-preserving online disease prediction and diagnosis are critical issues in the emerging edge-cloud-based healthcare *** patient data pro-cessing from remote places may lead to severe privacy ***,the existing cl...
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Privacy-preserving online disease prediction and diagnosis are critical issues in the emerging edge-cloud-based healthcare *** patient data pro-cessing from remote places may lead to severe privacy ***,the existing cloud-based healthcare system takes more latency and energy consumption during diagnosis due to offloading of live patient data to remote cloud *** the privacy *** proposed research introduces the edge-cloud enabled privacy-preserving healthcare system by exploiting additive homomorphic encryption *** can help maintain the privacy preservation and confidentiality of patients’medical data during diagnosis of Parkinson’s *** addition,the energy and delay aware computational offloading scheme is proposed to minimize the uncertainty and energy consumption of end-user *** proposed research maintains the better privacy and robustness of live video data processing during prediction and diagnosis compared to existing health-care systems.
With the recent advent of technology, social networks are accessible 24/7 using mobile devices. During covid-19 pandemic the propagation of misinformation are mostly related to the disease, its cures and prevention. W...
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Alzheimer’s Disease(AD)is a progressive neurological *** diagnosis of this illness using conventional methods is very *** Learning(DL)is one of the finest solutions for improving diagnostic procedures’performance an...
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Alzheimer’s Disease(AD)is a progressive neurological *** diagnosis of this illness using conventional methods is very *** Learning(DL)is one of the finest solutions for improving diagnostic procedures’performance and forecast *** disease’s widespread distribution and elevated mortality rate demonstrate its significance in the older-onset and younger-onset age *** light of research investigations,it is vital to consider age as one of the key criteria when choosing the *** younger subjects are more susceptible to the perishable side than the older *** proposed investigation concentrated on the younger *** research used deep learning models and neuroimages to diagnose and categorize the disease at its early stages *** proposed work is executed in three *** 3D input images must first undergo image pre-processing using Weiner filtering and Contrast Limited Adaptive Histogram Equalization(CLAHE)*** Transfer Learning(TL)models extract features,which are subsequently compressed using cascaded Auto Encoders(AE).The final phase entails using a Deep Neural Network(DNN)to classify the phases of *** model was trained and tested to classify the five stages of *** ensemble ResNet-18 and sparse autoencoder with DNN model achieved an accuracy of 98.54%.The method is compared to state-of-the-art approaches to validate its efficacy and performance.
We consider the online convex optimization (OCO) problem with quadratic and linear switching cost when at time t only gradient information for functions fτ, τ 16(Lµ+5) for the quadratic switching cost, and also...
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We consider the online convex optimization (OCO) problem with quadratic and linear switching cost when at time t only gradient information for functions fτ, τ 16(Lµ+5) for the quadratic switching cost, and also show the bound to be order-wise tight in terms of L, µ. In addition, we show that the competitive ratio of any online algorithm is at least max{Ω(L), Ω(pLµ )} when the switching cost is quadratic. For the linear switching cost, the competitive ratio of the OMGD algorithm is shown to depend on both the path length and the squared path length of the problem instance, in addition to L, µ, and is shown to be order-wise, the best competitive ratio any online algorithm can achieve. Copyright is held by author/owner(s).
Breast cancer is a devastating disease that affects women worldwide, and computer-aided algorithms have shown potential in automating cancer diagnosis. Recently Generative Artificial Intelligence (GenAI) opens new pos...
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Web-services have become most common IT enablers today. Cyber attacks such as the distributed denial of service (DDoS) attacks pose availability concerns which may result into service outages and consequently financia...
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Background: Epilepsy is a neurological disorder that leads to seizures. This occurs due to excessive electrical discharge by the brain cells. An effective seizure prediction model can aid in improving the lifestyle of...
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