Aflood is a significant damaging natural calamity that causes loss of life and *** work on the construction offlood prediction models intended to reduce risks,suggest policies,reduce mortality,and limit property damage c...
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Aflood is a significant damaging natural calamity that causes loss of life and *** work on the construction offlood prediction models intended to reduce risks,suggest policies,reduce mortality,and limit property damage caused byfl*** massive amount of data generated by social media platforms such as Twitter opens the door toflood *** of the real-time nature of Twitter data,some government agencies and authorities have used it to track natural catastrophe events in order to build a more rapid rescue ***,due to the shorter duration of Tweets,it is difficult to construct a perfect prediction model for determiningfl*** learning(ML)and deep learning(DL)approaches can be used to statistically developflood prediction *** the same time,the vast amount of Tweets necessitates the use of a big data analytics(BDA)tool forflood *** this regard,this work provides an optimal deep learning-basedflood forecasting model with big data analytics(ODLFF-BDA)based on Twitter *** suggested ODLFF-BDA technique intends to anticipate the existence offloods using tweets in a big data *** ODLFF-BDA technique comprises data pre-processing to convert the input tweets into a usable *** addition,a Bidirectional Encoder Representations from Transformers(BERT)model is used to generate emotive contextual embed-ding from ***,a gated recurrent unit(GRU)with a Multilayer Convolutional Neural Network(MLCNN)is used to extract local data and predict thefl***,an Equilibrium Optimizer(EO)is used tofine-tune the hyper-parameters of the GRU and MLCNN models in order to increase prediction *** memory usage is pull down lesser than 3.5 MB,if its compared with the other algorithm *** ODLFF-BDA technique’s performance was validated using a benchmark Kaggle dataset,and thefindings showed that it outperformed other recent approaches significantly.
The U-Net architecture is the focus of this study, which optimizes biomedical picture segmentation. Improving performance in contexts with limited resources is the goal. The methodology uses GradCAM++, k-fold cross-va...
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With the increase of energy consumption worldwide in several domains such as industry,education,and transportation,several technologies played an influential role in energy conservation such as the Internet of Things(I...
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With the increase of energy consumption worldwide in several domains such as industry,education,and transportation,several technologies played an influential role in energy conservation such as the Internet of Things(IoT).In this article,we describe the design and implementation of an IoT-based energy conser-vation smart classroom system that contributes to energy conservation in the edu-cation *** proposed system not only allows the user to access and control IoT devices(e.g.,lights,projectors,and air conditions)in real-time,it also has the capability to aggregate the estimated energy consumption of an IoT device,the smart classroom,and the building based on the energy consumption and cost model that we ***,the proposed model aggregates the estimated energy cost according to the Saudi Electricity Company(SEC)***,the model aggregates in real-time the estimated energy conservation percentage and estimated money-saving percentage compared to data collected when the system wasn't *** feasibility and benefits of our system have been validated on a real-world scenario which is a classroom in the college of computerscience and engineering,Taibah University,Yanbu *** results of the experimental studies are promising in energy conservation and cost-saving when using our proposed system.
Cloud computing has brought about a significant transformation in the field of Information Technology (IT) by providing the capability to access a shared pool of resources over the internet in a scalable and on-demand...
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The provision of rebate to needy/underprivileged sections of society has been in practice since long in government organizations. The efficacy of such provisions lies in the fact that whether this rebate reaches peopl...
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Intrusion detection is a prominent factor in the cybersecurity domain that prevents the network from malicious attacks. Cloud security is not satisfactory for securing the user’s information because it is based on st...
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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...
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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...
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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.
Voice-based user interfaces (VUIs) represent a promising avenue for enhancing accessibility in humancomputer interaction (HCI). This research paper investigates the effectiveness of VUIs in addressing accessibility ch...
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The requirement for effective data classification on the Dark Web has increased following the rising sophistication and spread of illicit operations over this secretive internet segment. This paper systematically revi...
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ISBN:
(纸本)9798350389128
The requirement for effective data classification on the Dark Web has increased following the rising sophistication and spread of illicit operations over this secretive internet segment. This paper systematically reviews and compares the state-of-the-art Dark Web data classification methods that fall into text-based, image-based, and hybrid approaches. In the review process, this research outlines their strengths, weaknesses, and common challenges while also identifying gaps. The text-based approaches made extensive use of NLP, machine learning algorithms (such as SVM), and deep learning models, most notably, RNN and CNN. These techniques are good at information processing and understanding textual content, but high variability and obfuscation tactics make the communications hard to understand most of the time. Image-based classification leverages state-of-the-art computer vision techniques, for instance, CNN and GAN. These models are effective at detecting and classifying illicit images, such as goods or services that are illegal. However, it faces problems with the low quality or morphed images that are common on the Dark Web. Hybrid approaches incorporate both text and image in the analysis so that the information provided on the Dark Web is considered in an integrated form. These models use multimodal deep learning, which involves the use of CNN for image data and RNN for textual data samples. Hybrid methods combine text and image analysis to provide a more comprehensive understanding of Dark Web data samples. The approaches integrate multimodal deep learning models where CNNs are used for image data and RNNs for text data samples. Hybrid models show promise in improving classification accuracy by capturing the contextual information from both text and image samples. The present work outlines the necessity for developing highly adaptive and robust classification methods for Dark Web data samples. By assessing the strengths and limitations of existing methods, th
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