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Based on this successful analysis, a novel approach is proposed where some feasible and robust features are extracted to acquire the emotional variations for various ways of expression. Here, a novel dense-Convolutional Neural Network (CNN) with ResNet (CNN-RN) extracts features from patients\u2019, while for establishing visual modality, deep residual network layers are used. The significance of feature extraction is less sensitive during outlier prediction while modeling the context. To handle these issues, this dense network model is used for training the network in an end-to-end manner by correlating the significance of CNN and RN of every stream and outperforming the overall approach. Here, MATLAB 2020b is used for simulation purposes, and the model outperforms various prevailing methods for consistent prediction. Some performance metrics include detection accuracy, F1-score, recall, MCC, p-value, etc. Based on this evaluation, the experimental results attained are superior to other approaches.<\/jats:p>","DOI":"10.3233\/jhs-222079","type":"journal-article","created":{"date-parts":[[2023,7,14]],"date-time":"2023-07-14T15:42:47Z","timestamp":1689349367000},"page":"279-294","source":"Crossref","is-referenced-by-count":0,"title":["Modelling a stacked dense network model for outlier prediction over medical-based heart prediction data"],"prefix":"10.1177","volume":"29","author":[{"given":"Boddu L.V.\u00a0Siva\u00a0Rama","family":"Krishna","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Annamalai University, Annamalai Nagar, Chidambaram, Tamilnadu, 608002, India"}]},{"given":"V.","family":"Mahalakshmi","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Annamalai University, Annamalai Nagar, Chidambaram, Tamilnadu, 608002, India"}]},{"given":"Gopala\u00a0Krishna\u00a0Murthy","family":"Nookala","sequence":"additional","affiliation":[{"name":"Department of Information Technology, SRKR Engineering College, Bhimavaram, Andhra Pradesh, 534204, India"}]}],"member":"179","reference":[{"issue":"1","key":"10.3233\/JHS-222079_ref1","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1145\/2830544.2830549","article-title":"Theoretical foundations and algorithms for outlier ensembles","volume":"17","author":"Aggarwal","year":"2015","journal-title":"ACM SIGKDD Explore. 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