In the context of healthcare and human-computer interaction., this research study provides a thorough analysis of sophisticated computational algorithms for data classification, picture processing, and disease predict...
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One of the fundamental sustainable development goals has been recognized as having access to clean water for drinking purposes. In the Anthropocene era, rapid urbanization put further stress on water resources, and as...
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One of the fundamental sustainable development goals has been recognized as having access to clean water for drinking purposes. In the Anthropocene era, rapid urbanization put further stress on water resources, and associated groundwater contamination expanded into a significant global environmental issue. Natural arsenic and related water pollution have already caused a burden issue on groundwater vulnerability and corresponding health hazard in and around the Ganges delta. A field based hydrogeochemical analysis has been carried out in the elevated arsenic prone areas of moribund Ganges delta, West Bengal, a part of western Ganga-Brahmaputra delta (GBD). New data driven heuristic algorithms are rarely used in groundwater vulnerability studies, spe-cifically not yet used in the elevated arsenic prone areas of Ganges delta, India. Therefore, in the current study, emphasis has been given on integration of heuristic algorithms and random forest (RF) i.e., "RF-particle swarm optimization (PSO) ", "RF-grey wolf optimizer (GWO) " and "RF-grasshopper optimization algorithm (GOA) ", to identify groundwater vulnerable zones on the basis of field based hydrogeochemical parameters. In addition, correspondence health hazard of this area was assessed through human health hazard index. The spatial dis-tribution of groundwater vulnerability revealed that middle-eastern and north-western part of the study area covered by very high and high, whereas central, western and south-western part are covered by very low and low vulnerability zones in outcomes of all the applied models. The evaluation result indicates that RF-GOA (AUC = 0.911) model performed the best considering testing dataset, and thereafter RF-GWO, RF-PSO and RF with AUC value is 0.901, 0.892 and 0.812 respectively. Findings also revealed the groundwater in this study region is quite unfavorable for drinking and irrigation purposes. The suggested models demonstrate their usefulness in fore-telling sustainable groundwate
The recent boom of Machine learning frameworks like Generative Adversarial Networks (GAN), Deep Convolution Generative Adversarial Networks (DCGAN) and the development of high-performance computing for big data analys...
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ISBN:
(纸本)9781450372275
The recent boom of Machine learning frameworks like Generative Adversarial Networks (GAN), Deep Convolution Generative Adversarial Networks (DCGAN) and the development of high-performance computing for big data analysis has the potential to be highly beneficial in many domains and fittingly in the early detection of chronic diseases. The clinical heterogeneity of one such chronic autoimmune disease like Systemic Lupus Erythematosus (SLE), commonly referred to as Lupus, makes it difficult for medical diagnostics. This research employs unsupervised deep learning mechanisms to identify clinical manifestations of lupus from publicly available anonymous pictures of persons who present with cutaneous lesions like the butterfly rash, commonly seen in patients diagnosed with Lupus. We demonstrate the use of artificially generated butterfly rash images generated from GAN to train the discriminator model that differentiates Lupus from its other counter skin diseases using a Neural Network Classifier, as a use-case example. The expected outcomes are to help reduce the time in detection and treatment by gathering insights from its huge heterogeneous data clusters.
The proliferation of sensor devices monitoring human activity generates voluminous amount of temporal sequences needing to be interpreted and categorized. Moreover, complex behavior detection requires the personalizat...
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ISBN:
(纸本)9781509002870
The proliferation of sensor devices monitoring human activity generates voluminous amount of temporal sequences needing to be interpreted and categorized. Moreover, complex behavior detection requires the personalization of multi-sensor fusion algorithms. Conditional random fields (CRFs) are commonly used in structured prediction tasks such as part-of-speech tagging in natural language processing. Conditional probabilities guide the choice of each tag/label in the sequence conflating the structured prediction task with the sequence classification task where different models provide different categorization of the same sequence. The claim of this paper is that CRF models also provide discriminative models to distinguish between types of sequence regardless of the accuracy of the labels obtained if we calibrate the class membership estimate of the sequence. We introduce and compare different neural network based linear-chain CRFs and we present experiments on two complex sequence classification and structured prediction tasks to support this claim.
Aiming at the problems that fuzzy neural network controller has heavy computation and response lag, a T-S fuzzy neural network based on hybridlearning algorithm was proposed. Immune genetic algorithm was used to opti...
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ISBN:
(纸本)9781424421138
Aiming at the problems that fuzzy neural network controller has heavy computation and response lag, a T-S fuzzy neural network based on hybridlearning algorithm was proposed. Immune genetic algorithm was used to optimize the parameters of membership functions off line, and the neural network was used to adjust the parameters of membership functions on fine to enhance the response of the controller. Moreover, the latter network automatically adjusted the fuzzy rules to reduce the computation of the neural network and improve the robustness and adaptability of the controller, so that the controller can work well ever when underwater vehicles work in hostile ocean environment. Finally, simulation experiments were carried on "XX" underwater vehicle The results show that this controller has great improvement in response and overshoot, compared with the traditional controller.
A neural learning-based crowd estimation system for surveillance in complex scenes at the platform of underground stations is presented. Estimation is carried out by extracting a set of significant features from the s...
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A neural learning-based crowd estimation system for surveillance in complex scenes at the platform of underground stations is presented. Estimation is carried out by extracting a set of significant features from the sequences of images. Feature indices are modeled by the neural networks to estimate the crowd density. The learning phase is based on our proposed hybridalgorithms which are capable of providing the global search characteristic and fast convergence speed. Promising experimental results were obtained in terms of estimation accuracy and real-time response capability to alert the operators automatically.
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