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6th International Conference on Advances in Artificial intelligence, ICAAI 2022

作者： Sharma, Aniket Singh, Pramod Kumar Prakash, Jay Computational Intelligence and Data Mining Research Laboratory ABV-Indian Institute of Information Technology and Management Gwalior India Department of Computer Science and Engineering National Institute of Technology Calicut India

ISBN: (纸本)9781450396943

A deep understanding of the brain can lead to significant breakthroughs in Artificial intelligence. Many researchers concentrate their efforts on simulating the human mind to comprehend its complexities better. With the intention of better understanding the episodic memory aspect of the human mind, we propose a deep learning model to implement the detection and retrieval properties of human episodic memory, a part of long-term memory. A model based on LSTM and CNN is proposed, which follows the architectural methodology of Rosenblatt's experiential memory model. A comparison of detection efficiency and accuracy and the proposed model's retrieval property with a recently suggested method demonstrate its effectiveness and superiority. © 2022 Association for Computing Machinery.

关键词： Long short-term memory

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An Effective Implementation of Detection and Retrieval Property of Episodic Memory 22

An Effective Implementation of Detection and Retrieval Prope...

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Proceedings of the 6th International Conference on Advances in Artificial intelligence

作者： Aniket Sharma Pramod Kumar Singh Jay Prakash Computational Intelligence and Data Mining Research Laboratory ABV-Indian Institute of Information Technology and Management India Department of Computer Science and Engineering National Institute of Technology India

ISBN: (纸本)9781450396943

A deep understanding of the brain can lead to significant breakthroughs in Artificial intelligence. Many researchers concentrate their efforts on simulating the human mind to comprehend its complexities better. With the intention of better understanding the episodic memory aspect of the human mind, we propose a deep learning model to implement the detection and retrieval properties of human episodic memory, a part of long-term memory. A model based on LSTM and CNN is proposed, which follows the architectural methodology of Rosenblatt’s experiential memory model. A comparison of detection efficiency and accuracy and the proposed model’s retrieval property with a recently suggested method demonstrate its effectiveness and superiority.

关键词： Long Short-Term Memory Rosenblatt’s Experiential Memory Model Episodic Memory Recurrent Convolutional Networks

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ADC@

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Applied intelligence 2021年第2期52卷 2134-2153页

作者： Jain, Ankush Nagar, Surendra Singh, Pramod Kumar Dhar, Joydip Computational Intelligence and Data Mining Laboratory ABV- Indian Institute of Information Technology and Management Gwalior India Modeling and Simulation Laboratory ABV- Indian Institute of Information Technology and Management Gwalior India

In the recommendation systems (RSs), it is imperative to incorporate the hidden contextual meaning of users’ provided ratings in the similarity computation. To draw such contextual meanings, existing models use the fixed categorization of accessible ratings. However, due to the excessive variation in similarly co-rated item pairs, they produce ambiguous contextual meanings that yield inconsistent results for the user pairs similarities. Therefore, to deal with this problem, this paper proposes an adaptive divisional categorization (ADC)-based RS, namely ADC@

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ICSDA: a multi-modal deep learning model to predict breast cancer recurrence and metastasis risk by integrating pathological, clinical and gene expression data

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BRIEFINGS IN BIOINFORMATICS 2022年第6期23卷 bbac448-bbac448页

作者： Yao, Yuhua Lv, Yaping Tong, Ling Liang, Yuebin Xi, Shuxue Ji, Binbin Zhang, Guanglu Li, Ling Tian, Geng Tang, Min Hu, Xiyue Li, Shijun Yang, Jialiang School of Mathematics and Statistics Hainan Normal University Haikou 570100 China Key Laboratory of Data Science and Intelligence Education Ministry of Education Hainan Normal University Haikou China Key Laboratory of Computational Science and Application of Hainan Province Hainan Normal University Haikou China Genies Beijing Co. Ltd. Beijing 100102 China Chifeng Municipal Hospital Chifeng Inner Mongolia 024000 China Qingdao Geneis Institute of Big Data Mining and Precision Medicine Qingdao 266000 China Basic Courses Department Zhejiang Shuren University Hangzhou 310000 China School of Life Sciences Jiangsu University Zhenjiang 212013 China Dept. of Colorectal Surgery National Cancer Center/ Cancer Hospital Chinese Academy of Medical Science 17 Panjiayuan Nanli Chaoyang District Beijing China 100021

Breast cancer patients often have recurrence and metastasis after surgery. Predicting the risk of recurrence and metastasis for a breast cancer patient is essential for the development of precision treatment. In this study, we proposed a novel multi-modal deep learning prediction model by integrating hematoxylin & eosin (H&E)-stained histopathological images, clinical information and gene expression data. Specifically, we segmented tumor regions in H&E into image blocks (256 x 256 pixels) and encoded each image block into a 1D feature vector using a deep neural network. Then, the attention module scored each area of the H&E-stained images and combined image features with clinical and gene expression data to predict the risk of recurrence and metastasis for each patient. To test the model, we downloaded all 196 breast cancer samples from the Cancer Genome Atlas with clinical, gene expression and H&E information simultaneously available. The samples were then divided into the training and testing sets with a ratio of 7: 3, in which the distributions of the samples were kept between the two datasets by hierarchical sampling. The multi-modal model achieved an area-under-the-curve value of 0.75 on the testing set better than those based solely on H&E image, sequencing data and clinical data, respectively. This study might have clinical significance in identifying high-risk breast cancer patients, who may benefit from postoperative adjuvant treatment.

关键词： breast cancer recurrence and metastasis multi-modality residual network attention module

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Semantic and Emotional Dual Channel for Emotion Recognition in Conversation

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IEEE Transactions on Affective Computing 2025年

作者： Yang, Zhenyu Zhang, Zhibo Cheng, Yuhu Zhang, Tong Wang, Xuesong Jinan250353 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China China University of Mining and Technology Engineering Research Center of Intelligent Control for Underground Space Ministry of Education Xuzhou Key Laboratory of Artificial Intelligence and Big Data School of Information and Control Engineering Xuzhou221116 China South China University of Technology Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information School of Computer Science and Engineering Guangzhou510006 China Pazhou Lab Guangzhou510335 China Engineering Research Center Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Guangzhou China

Emotion recognition in conversation (ERC) aims at accurately identifying emotional states expressed in conversational content. Existing ERC methods, although relying on semantic understanding, often encounter challenges when confronted with incomplete or misleading semantic information. In addition, when dealing with the interaction between emotional and semantic information, existing methods are often difficult to effectively distinguish the complex relationship between the two, which affects the accuracy of emotion recognition. To address the problems of semantic misdirection and emotional cross-talk encountered by traditional models when confronted with complex conversational data, we propose a semantic and emotional dual channel (SEDC) strategy for emotion recognition in conversations to process emotional and semantic information independently. Under this strategy, emotion information provides an auxiliary recognition function when the semantics are unclear or lacking, enhancing the accuracy of the model. Our model consists of two modules: the emotion processing module accurately captures the emotional features of each utterance through contrastive learning, and then constructs a dialogue emotion propagation map to simulate the emotional information conveyed in the dialogue;the semantic processing module combines an external knowledge base to enhance the semantic expression of the dialogue through knowledge enhancement strategies. This divide-and-conquer approach allows us to more deeply analyze the emotional and semantic dimensions of complex dialogues. Experimental results on the IEMOCAP, EmoryNLP, MELD, and DailyDialog datasets show that our approach significantly outperforms existing techniques and effectively improves the accuracy of dialogue emotion recognition. Our code is available at https://***/r/SEDC-FCF1. © 2010-2012 IEEE.

关键词： Contrastive Learning

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Hybrid approach for recommendation system 2nd

Hybrid approach for recommendation system

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2nd International Conference on data Engineering and Communication Technology, ICDECT 2017

作者： Passi, Rohan Jain, Surbhi Singh, Pramod Kumar Computational Intelligence and Data Mining Research Laboratory ABV-Indian Institute of Information Technology and Management Gwalior474015 India

ISBN: (纸本)9789811316098

The primary objective of recommendation systems (RSs) is to analyze user’s fondness and taste and recommend similar items to him/her. There exist various methods, e.g., user/item collaborative filtering (CF), content-based filtering (CBF), association rule mining (ARM), hybrid recommender system (HRS), for recommendations. Though these methods possess excellent characteristics, they are inefficient in providing good recommendations in particular situations. For example, item CF produces recommendations for cold-start objects;however, it typically has low accuracy compared to user CF. Conversely, user CF often provides more accurate recommendations;however, it fails to provide recommendations for cold-start objects. The hybrid methods aim to combine different approaches coherently to yield better recommendations. This paper presents an HRS based on user CF, item CF, and adaptive ARM. The proposed HRS employs ARM as a fundamental method;however, it considers only a set of users who are nearest to the target user to generate association rules (ARs) among items. Also, the support levels to mine associations among items are adaptive to the number of rules generated. Results of the study indicate that the proposed HRS provides more personalized and practical suggestions compared to the traditional methods. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Collaborative filtering

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Corrigendum to “GLRM: Logical pattern mining in the case of inconsistent data distribution based on multigranulation strategy” [Int. J. Approx. Reason. 143 (2022) 78–101]

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International Journal of Approximate Reasoning 2022年 151卷 271-271页

作者： Qian Guo Yuhua Qian Xinyan Liang Institute of Big Data Science and Industry Shanxi University Taiyuan 030006 Shanxi China Engineering Research Center for Machine Vision and Data Mining of Shanxi Province Taiyuan 030006 Shanxi China School of Computer and Information Technology Shanxi University Taiyuan 030006 Shanxi China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Taiyuan 030006 Shanxi China

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Multiple data document summarization

Multiple data document summarization

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2017 IEEE International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2017

作者： Kishore, V.V. Krishna Singh, Pramod Kumar Computational Intelligence and Data Mining Research Laboratory ABV-Indian Institute of Information Technology and Management Gwalior Gwalior India

ISBN: (纸本)9781538618660

Document Summarization is the way towards reducing a text report with a program withholding the essential points in the document. Thousands of electronic records are created and made accessible on the web every day. In this paper, we present a method for generating extraction based summaries using a stack based formulation. The stack based method uses a sentence importance score calculator based on various semantic features and a semantic similarity score to select sentences that are the most representative of the document. It uses stack-decoder algorithm as a template and builds on it to produce summaries that are closer to optimal. These approaches are used to generate a 100-word summary for the dataset available as part of DUC 2004. The paper discusses the system developed, algorithms used and their analysis along with improvements. N-gram analysis scores obtained are comparable to other participants in the competition. © 2017 IEEE.

关键词： data mining

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MPPR: Multi Perspective Page Rank for User Influence Estimation

MPPR: Multi Perspective Page Rank for User Influence Estimat...

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International Conference on Big data and Smart Computing (BIGCOMP)

作者： Lifang Wu Bowen Yang Meng Jian Jitao Sang Dai Zhang Lei Zhang Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing China Faculty of Information Technology Beijing University of Technology Beijing China Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing China

ISBN: (纸本)9781538636503

With the rapid development of social networks, it is important to identify users with high influence. In content curation social networks (CCSNs), there are two kinds of user relations. The one is the explicit user relations from user's following behavior. And the other is the content based user relations from user's repin behavior. Based on these observation, we propose multi perspective page rank (MPPR) to estimate user influence. The proposed algorithm integrates both user relations to calculate influence scores of the users automatically. User influences are computed based on the transition matrix of following and repin relations. When the iteration is convergent, every user will get a fixed influence value. Experiments on the dataset containing 11990 users, 920610 following relations and 39321016 repin relations show that the proposed algorithm outperforms the typical PageRank algorithm.

关键词： Conferences Big data

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Empirical study of grey wolf optimizer 5th

Empirical study of grey wolf optimizer

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5th International Conference on Soft Computing for Problem Solving, SocProS 2015

作者： Kishor, Avadh Singh, Pramod Kumar Computational Intelligence and Data Mining Research Laboratory ABV-Indian Institute of Information Technology and Management Gwalior India

ISBN: (纸本)9789811004476

In this paper, the authors empirically investigate performance of the grey wolf optimizer (GWO). A test suite of six non-linear benchmark functions, well studied in the swarm and the evolutionary optimization literature, is selected to highlight the findings. The test suite contains three unimodal and three multimodal functions. The experimental results demonstrate the advantages and weaknesses of the GWO. In case of unimodal problems, initially it hastens towards the optimal solution but soon slows down because of the diversity problem. A similar behaviour is seen in case of multimodal problems with a difference that because of its behaviour it easily sticks to local optima, loses its diversity and stops any further progress. The reason is that it lacks information sharing in the pack. This insight led the authors to propose a modified GWO called the modified grey wolf optimizer (MGWO). An empirical study of the proposed algorithm MGWO shows its promising performance as the obtained results are superior to the GWO for all the test functions. © Springer Science+Business Media Singapore 2016.

关键词： Optimization

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