Agriculture is a key component of a nation's economy. In the traditional method, farmers rely on experience to determine the ideal climate and environment for a given crop. technology is advancing, and while the s...
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Emotion recognition is crucial in human-computer interaction and psychological research, utilizing modalities such as facial expressions, voice intonations, and EEG signals. This research investigates AI-driven techni...
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Unlike traditional networks, Software-defined networks (SDNs) provide an overall view and centralized control of all the devices in the network. SDNs enable the network administrator to implement the network policy by...
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Music streaming services are getting increasingly popular as a result of the prevalent need for web and smart gadgets. Melophiles are drawn toward a range of musical genres and create a unique digital footprint. The e...
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Brain tumors are one of the deadliest diseases and require quick and accurate methods of detection. Finding the optimum image for research goals is the first step in optimizing MRI images for pre- and post-processing....
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Partial-label learning(PLL) is a typical problem of weakly supervised learning, where each training instance is annotated with a set of candidate labels. Self-training PLL models achieve state-of-the-art performance b...
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Partial-label learning(PLL) is a typical problem of weakly supervised learning, where each training instance is annotated with a set of candidate labels. Self-training PLL models achieve state-of-the-art performance but suffer from error accumulation problems caused by mistakenly disambiguated instances. Although co-training can alleviate this issue by training two networks simultaneously and allowing them to interact with each other, most existing co-training methods train two structurally identical networks with the same task, i.e., are symmetric, rendering it insufficient for them to correct each other due to their similar limitations. Therefore, in this paper, we propose an asymmetric dual-task co-training PLL model called AsyCo,which forces its two networks, i.e., a disambiguation network and an auxiliary network, to learn from different views explicitly by optimizing distinct tasks. Specifically, the disambiguation network is trained with a self-training PLL task to learn label confidence, while the auxiliary network is trained in a supervised learning paradigm to learn from the noisy pairwise similarity labels that are constructed according to the learned label confidence. Finally, the error accumulation problem is mitigated via information distillation and confidence refinement. Extensive experiments on both uniform and instance-dependent partially labeled datasets demonstrate the effectiveness of AsyCo.
According to the World Health Organization (WHO), lung diseases contribute to millions of fatalities globally each year. Pneumonia stands out as a leading cause, claiming the lives of approximately 2.5 million individ...
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Breast cancer is one of the deadly diseases prevailing in *** detection and diagnosis might prevent the death *** diagnosis of breast cancer remains a significant challenge,and early diagnosis is essential to avoid th...
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Breast cancer is one of the deadly diseases prevailing in *** detection and diagnosis might prevent the death *** diagnosis of breast cancer remains a significant challenge,and early diagnosis is essential to avoid the most severe manifestations of the *** existing systems have computational complexity and classification accuracy problems over various breast cancer *** order to overcome the above-mentioned issues,this work introduces an efficient classification and segmentation ***,there is a requirement for developing a fully automatic methodology for screening the cancer *** paper develops a fully automated method for breast cancer detection and segmenta-tion utilizing Adaptive Neuro Fuzzy Inference System(ANFIS)classification *** proposed technique comprises preprocessing,feature extraction,classifications,and segmentation ***,the wavelet-based enhancement method has been employed as the preprocessing *** texture and statistical features have been extracted from the enhanced ***,the ANFIS classification algorithm is used to classify the mammogram image into normal,benign,and malignant ***,morphological processing is performed on malignant mam-mogram images to segment cancer *** analysis and comparisons are made with conventional *** experimental result proves that the pro-posed ANFIS algorithm provides better classification performance in terms of higher accuracy than the existing algorithms.
The rapid advent in artificial intelligence and big data has revolutionized the dynamic requirement in the demands of the computing resource for executing specific tasks in the cloud environment. The process of achiev...
The rapid advent in artificial intelligence and big data has revolutionized the dynamic requirement in the demands of the computing resource for executing specific tasks in the cloud environment. The process of achieving autonomic resource management is identified to be a herculean task due to its huge distributed and heterogeneous environment. Moreover, the cloud network needs to provide autonomic resource management and deliver potential services to the clients by complying with the requirements of Quality-of-Service(QoS) without impacting the Service Level Agreements(SLAs). However, the existing autonomic cloud resource managing frameworks are not capable in handling the resources of the cloud with its dynamic requirements. In this paper, Coot Bird Behavior Model-based Workload Aware Autonomic Resource Management Scheme(CBBM-WARMS) is proposed for handling the dynamic requirements of cloud resources through the estimation of workload that need to be policed by the cloud *** CBBM-WARMS initially adopted the algorithm of adaptive density peak clustering for workloads clustering of the cloud. Then, it utilized the fuzzy logic during the process of workload scheduling for achieving the determining the availability of cloud resources. It further used CBBM for potential Virtual Machine(VM) deployment that attributes towards the provision of optimal resources. It is proposed with the capability of achieving optimal Qo S with minimized time,energy consumption, SLA cost and SLA *** experimental validation of the proposed CBBMWARMS confirms minimized SLA cost of 19.21% and reduced SLA violation rate of 18.74%, better than the compared autonomic cloud resource managing frameworks.
Skin cancer is one of the most prevalent forms of human cancer. It is recognized mainly visually, beginning with clinical screening and continuing with the dermoscopic examination, histological assessment, and specime...
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