Titlebook: Computational Intelligence in Pattern Recognition; Proceedings of CIPR Asit Kumar Das,Janmenjoy Nayak,Danilo Pelusi Conference proceedings

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按等級

A Proposed Gene Selection Approach for Disease Detection,o disease like cancer, where time and experience is very critical, it becomes important to get the right diagnosis done at the right time by experienced and trained system or doctors. If the diagnosis/treatment is provided on time (i.e., within the golden hour), then saving a patient would be an easier and more promising task.

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Evocative

Conference proceedings 2020ng, social network analysis, audio and video data analysis, data mining in dynamic environments, bioinformatics, hybrid computing, big data analytics and deep learning. It also provides innovative solutions to the challenges in these areas and discusses recent developments...?.

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階層

毗鄰

Overdose

Nearest Neighbor-Based Differential Evolution for Reconstructing Gene Regulatory Network,parameter set of RNN obtained from the experiment is used to design the gene regulatory network of our interest. The inferred GRN has been analyzed on the basis of three performance metrics. It is observed that in many performance metrics, the proposed algorithm outperformed the state-of-the-art algorithms.

盲信者

Inference-Based Statistical Analysis for Suspicious Activity Detection Using Facial Analysis,will be developed by integrating following modules facial expression recognition, face detection, speech recognition and object recognition. In this paper, we proposed a novel and cost-effective framework designed for suspicious activity detection using facial expression analysis or emotions detection analysis in law enforcement.

SLING

Deep Recurrent Neural Network (Deep-RNN) for Classification of Nonlinear Data, for a classification task. RNN follows a method for weight updation which is known as Backpropagation Through Time (BPTT) and we have used the concept of Deep-RNN by following the concepts of both forward pass and backward pass. Simulation results are quite impressive as compared to earlier developed machine learning models.