- Distributed denial-of-service (DDoS) attacks are the major threat that disrupts the services in the computer system and networks using traffic and targeted sources. So, real-world attack detection techniques are con...
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Handwriting is an important skill for children during their academic years. It is the coordination of perceptual-motor and cognitive abilities. Some children have difficulties in handwriting, which is an indication of...
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Load balancing and scheduling are essential components of cloud computing that aim to optimize resource allocation and utilization. In a cloud environment, multiple virtual machines and applications compete for shared...
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Cloud computing has emerged as a viable alternative to traditional computing infrastructures,offering various ***,the adoption of cloud storage poses significant risks to data secrecy and *** article presents an effec...
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Cloud computing has emerged as a viable alternative to traditional computing infrastructures,offering various ***,the adoption of cloud storage poses significant risks to data secrecy and *** article presents an effective mechanism to preserve the secrecy and integrity of data stored on the public cloud by leveraging blockchain technology,smart contracts,and cryptographic *** proposed approach utilizes a Solidity-based smart contract as an auditor for maintaining and verifying the integrity of outsourced *** preserve data secrecy,symmetric encryption systems are employed to encrypt user data before outsourcing *** extensive performance analysis is conducted to illustrate the efficiency of the proposed ***,a rigorous assessment is conducted to ensure that the developed smart contract is free from vulnerabilities and to measure its associated running *** security analysis of the proposed system confirms that our approach can securely maintain the confidentiality and integrity of cloud storage,even in the presence of malicious *** proposed mechanism contributes to enhancing data security in cloud computing environments and can be used as a foundation for developing more secure cloud storage systems.
Skin cancer,a severe health threat,can spread rapidly if ***,early detection can lead to an advanced and efficient diagnosis,thus reducing *** classification techniques analyse extensive skin image datasets,identifyin...
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Skin cancer,a severe health threat,can spread rapidly if ***,early detection can lead to an advanced and efficient diagnosis,thus reducing *** classification techniques analyse extensive skin image datasets,identifying patterns and anomalies without prior labelling,facilitating early detection and effective diagnosis and potentially saving *** this study,the authors aim to explore the potential of unsupervised learning methods in classifying different types of skin lesions in dermatoscopic *** authors aim to bridge the gap in dermatological research by introducing innovative techniques that enhance image quality and improve feature *** achieve this,enhanced super-resolution generative adversarial networks(ESRGAN)was fine-tuned to strengthen the resolution of skin lesion images,making critical features more *** authors extracted histogram features to capture essential colour characteristics and used the Davies-Bouldin index and silhouette score to determine optimal ***-tuned k-means clustering with Euclidean distance in the histogram feature space achieved 87.77% and 90.5% test accuracies on the ISIC2019 and HAM10000 datasets,*** unsupervised approach effectively categorises skin lesions,indicating that unsupervised learning can significantly advance dermatology by enabling early detection and classification without extensive manual annotation.
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.
Early detection of breast cancer is crucial for improving survival rates. Many computer-aided diagnosis (CAD) models have been proposed in the past for the detection of breast cancer. However, analyzing breast cancer ...
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Representation of compound information in a truthful, coarse way forms the layout of the granular computing paradigm. In granular computing, the continuous variables are mapped into intervals to be utilized in the ext...
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Evolutionary machine learning has drawn much attentions on solving data-driven learning problem in the past decades, where classification is a major branch of data-driven learning problem. To improve the quality of ob...
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Evolutionary machine learning has drawn much attentions on solving data-driven learning problem in the past decades, where classification is a major branch of data-driven learning problem. To improve the quality of obtained classifier, ensemble is a simple yet powerful strategy. However, gathering classifiers for ensemble requires multiple runs of learning process which bring additional cost at evaluation on the data. This study proposes an innovative framework for ensemble learning through evolutionary multitasking, i.e., the evolutionary multitasking for ensemble learning (EMTEL). There are four main features in the EMTEL. First, the EMTEL formulates a classification problem as a dynamic multitask optimization problem. Second, the EMTEL utilizes evolutionary multitasking to resolve the dynamic multitask optimization problem for better convergence through the synergy of common properties hidden in the tasks. Third, the EMTEL incorporates evolutionary instance selection for saving the cost at evaluation. Finally, the EMTEL formulates the ensemble learning problem as a numerical optimization problem and proposes an online ensemble aggregation approach to simultaneously select appropriate ensemble candidates from learning history and optimize ensemble weights for aggregating predictions. A case study is investigated by integrating two state-of-the-art methods for evolutionary multitasking and evolutionary instance selection respectively, i.e., the symbiosis in biocoenosis optimization and cooperative evolutionary learning and instance selection. For online ensemble aggregation, this study adopts the well-known covariance matrix adaptation evolution strategy. Experiments validate the effectiveness of the EMTEL over conventional and advanced evolutionary machine learning algorithms, including genetic programming, self-learning gene expression programming, and multi-dimensional genetic programming. Experimental results show that the proposed framework ameliorates state-o
1 Introduction Automatic bug assignment has been well-studied in the past *** textual bug reports usually describe the buggy phenomena and potential causes,engineers highly depend on these reports to fix ***,researche...
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1 Introduction Automatic bug assignment has been well-studied in the past *** textual bug reports usually describe the buggy phenomena and potential causes,engineers highly depend on these reports to fix ***,researchers spend much effort on processing bug reports,aiming for obtaining key information and/or clues for reproducing bugs,analyzing their root causes,assigning them to developers/maintainers,and fixing these ***,our previous research[1]reveals that noises in texts bring adverse impacts to automatic bug assignments unexpectedly,mainly due to insufficiency of classical Natural Language Processing(NLP)techniques.
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