A new stochastic coordinate descent deep learning architectures optimization is proposed for Automated Diabetic Retinopathy Detection and Classification from different data sets and convolution networks. Initially, th...
详细信息
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...
详细信息
Unmanned Aerial Vehicles (UAVs) have witnessed remarkable significance in diverse sectors, ranging from environmental monitoring, infrastructure inspection, disaster response, wildlife conservation, surveillance, and ...
详细信息
The need for a personalized user experience brought recommendation systems to the forefront of digital innovation. However, traditional approaches tend to often forget human emotions, which represent a critical driver...
详细信息
In the contemporary world, humanoid robots are likely to play a key role in various fields, including health care, domestic service, hospitality, business, and military and security activities. The robots are employed...
详细信息
Timely estimation of earthquake magnitude plays a crucial role in the early warning systems for earthquakes. Despite the inherent danger associated with earthquake energy, earthquake research necessitates extensive pa...
详细信息
Research on panicle detection is one of the most important aspects of paddy phenotypic analysis.A phenotyping method that uses unmanned aerial vehicles can be an excellent alternative to field-based ***,it entails man...
详细信息
Research on panicle detection is one of the most important aspects of paddy phenotypic analysis.A phenotyping method that uses unmanned aerial vehicles can be an excellent alternative to field-based ***,it entails many other challenges,including different illuminations,panicle sizes,shape distortions,partial occlusions,and complex *** detection algorithms are directly affected by these *** work proposes a model for detecting panicles called Border Sensitive Knowledge Distillation(BSKD).It is designed to prioritize the preservation of knowledge in border areas through the use of feature *** feature-based knowledge distillation method allows us to compress the model without sacrificing its *** imitation mask is used to distinguish panicle-related foreground features from irrelevant background features.A significant improvement in Unmanned Aerial Vehicle(UAV)images is achieved when students imitate the teacher’s *** the UAV rice imagery dataset,the proposed BSKD model shows superior performance with 76.3%mAP,88.3%precision,90.1%recall and 92.6%F1 score.
Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detecti...
详细信息
Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.
Delay tolerant wireless sensor networks(DTWSN)is a class of wireless network that finds its deployment in those application scenarios which demand for high packet delivery ratio while maintaining minimal overhead in o...
详细信息
Delay tolerant wireless sensor networks(DTWSN)is a class of wireless network that finds its deployment in those application scenarios which demand for high packet delivery ratio while maintaining minimal overhead in order to prolong network lifetime;owing to resource-constrained nature of *** fundamental requirement of any network is routing a packet from its source to *** of a routing algorithm depends on the number of network parameters utilized by that routing *** the recent years,various routing protocol has been developed for the delay tolerant networks(DTN).A routing protocol known as spray and wait(SnW)is one of the most widely used routing algorithms for *** this paper,we study the SnW routing protocol and propose a modified version of it referred to as Pentago SnW which is based on pentagonal number *** to binary SnW shows promising results through simulation using real-life scenarios of cars and pedestrians randomly moving on a map.
Drug-target interactions(DTIs) prediction plays an important role in the process of drug *** computational methods treat it as a binary prediction problem, determining whether there are connections between drugs and t...
详细信息
Drug-target interactions(DTIs) prediction plays an important role in the process of drug *** computational methods treat it as a binary prediction problem, determining whether there are connections between drugs and targets while ignoring relational types information. Considering the positive or negative effects of DTIs will facilitate the study on comprehensive mechanisms of multiple drugs on a common target, in this work, we model DTIs on signed heterogeneous networks, through categorizing interaction patterns of DTIs and additionally extracting interactions within drug pairs and target protein pairs. We propose signed heterogeneous graph neural networks(SHGNNs), further put forward an end-to-end framework for signed DTIs prediction, called SHGNN-DTI,which not only adapts to signed bipartite networks, but also could naturally incorporate auxiliary information from drug-drug interactions(DDIs) and protein-protein interactions(PPIs). For the framework, we solve the message passing and aggregation problem on signed DTI networks, and consider different training modes on the whole networks consisting of DTIs, DDIs and PPIs. Experiments are conducted on two datasets extracted from Drug Bank and related databases, under different settings of initial inputs, embedding dimensions and training modes. The prediction results show excellent performance in terms of metric indicators, and the feasibility is further verified by the case study with two drugs on breast cancer.
暂无评论