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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Rajput, Aashita Muskan Yadav, Sachin Chhabra, Megha Agarwal, Arun Prakash School of Engineering and Technology Sharda University Department of Computer Science and Application Greater Noida India

ISBN: (纸本)9789380544472

Breast cancer is the second most common type of cancer due to which females have such a high mortality rate. Several computer-assisted diagnostic systems have recently been proposed to operate with the least amount of data necessary due to the high number of unnecessary breast biopsies. The following review paper discusses the entire process of breast cancer classification along with various kinds of neural networks used to build such models. It further includes the various to-date research done in this domain and the different accuracy obtained by them. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Diseases

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Genetic Algorithm and Naïve Bayes-Based (GANB) Diabetes Mellitus Prediction System 1st

Genetic Algorithm and Naïve Bayes-Based (GANB) Diabetes Mel...

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1st International Conference on Microelectronics, Communication Systems, Machine Learning, and Internet of Things, MCMI 2020

作者： Yadav, Dharmveer Kumar Azad, Chandrashekhar Bala, Kanchan Sharma, Pradeep Kumar Kumar, Sujeet Computer Science and Engineering Katihar Engineering College Katihar India Computer Application Department Computer Science and Engineering NIT Jamshedpur India Sitamarhi Institute of Technology Sitamarhi India Computer Science and Engineering Katihar Engineering College Katihar India

ISBN: (纸本)9789811919053

Today, machine learning plays a significant role in the classification of healthcare conditions. Machine learning is the process of finding, discovering, and modeling massive amounts of data in order to identify unknown relationships and patterns that are useful to the decision makers. Medical data mining has begun to emerge exemplary, with the potential to discover interesting insights from medical domain datasets. These patterns may be used in the making of clinical decisions. In this research work, we proposed genetic algorithm and Naïve Bayes-based (GANB) diabetes mellitus prediction system. In which, genetic algorithm (GA) is used for feature selection and Naïve Bayes is used for prediction. The proposed system is trained using Pima Indian diabetes dataset (PIDD). The dataset is preprocessed using synthetic minority oversampling technique (SMOTE) for solving the issue of class imbalances. The effectiveness of the system is evaluated using classification accuracy (CA) and error. The simulation results show that the proposed GANB system achieves better accuracy as compared to the results of related earlier studies. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Genetic algorithms

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Re-Cognition I: An Inspection of Learner's Cognitive Well-Being, Pursuant to Contemporary Period, Facilitates the Academic Life Cycle 6

Re-Cognition I: An Inspection of Learner's Cognitive Well-Be...

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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Ganguly, Souvik Pati Mandal, Kailash Guha, Debasis Dr. B.C. Roy Engineering College Dept. of Computer Application Durgapur India National Institute of Technology Dept. of Computer Science and Engineering Durgapur India

ISBN: (纸本)9798350306613

Recommender systems enhance the user experience by integrating factual and behavioral practices, enhancing learning strategies, and granting academic opportunities based on learners subjective interests. Cognitive intelligence, a learner's capability to interpret and synthesize cognitive abilities, is crucial to assemble effective recommendations. However, a student encounters diverse adversities in his or her academic life, whether it is his or her academic pacers or outward circumstances. As a result, a learner consequently experienced severe academic setbacks. Thus, this paper proposes a novel approach to identify cognitive deficits of learners in a school or college before recommending any scholastic content or pertaining in any academic activity. We have employed a modified Bi-directional Gated Recurrent Unit (Bi-GRU) method in conjunction with Shuffled Frog Leaping Algorithm (SFLA). Our approach overcomes some of the shortcomings of conventional Bi-GRU method in choosing the right scaling parameters during training. Bi-GRU method is opted for detecting the cognitive deficits of a learner and SFLA is applied over Bi-GRU in order to hyper-Tune the parameters. The proposed method is compared with a few cutting-edge methods, and the experimental result outperforms the other methods in training, validation, and testing accuracies, achieving 99.34%, 98.98%, and 99.19% respectively. © 2024 IEEE.

关键词： Bi-directional GRU Cognitive Deficits Cognitive Equilibrium Learner's Mental State Meta-heuristic Optimization SFLA

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An Analysis Model of Learners’ Online Learning Status Based on Deep Neural Network and Multi-Dimensional Information Fusion

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computer Modeling in engineering & Sciences 2023年第6期135卷 2349-2371页

作者： Mingyong Li Lirong Tang Longfei Ma Honggang Zhao Jinyu Hu Yan Wei College of Computer and Information Science Chongqing Normal UniversityChongqing401331China Chongqing Engineering Research Center of Educational Big Data Intelligent Perception and Application Chongqing401331China

The learning status of learners directly affects the quality of *** with offline teachers,it is difficult for online teachers to capture the learning status of students in the whole class,and it is even more difficult to continue to pay attention to studentswhile ***,this paper proposes an online learning state analysis model based on a convolutional neural network and multi-dimensional information ***,a facial expression recognition model and an eye state recognition model are constructed to detect students’emotions and fatigue,*** integrating the detected data with the homework test score data after online learning,an analysis model of students’online learning status is *** to the PAD model,the learning state is expressed as three dimensions of students’understanding,engagement and interest,and then analyzed from multiple ***,the proposed model is applied to actual teaching,and procedural analysis of 5 different types of online classroom learners is carried out,and the validity of the model is verified by comparing with the results of the manual analysis.

关键词： Deep learning fatigue detection facial expression recognition sentiment analysis information fusion

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Optimal Production Capacity Matching for Blockchain-Enabled Manufacturing Collaboration With the Iterative Double Auction Method

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 550-562页

作者： Ying Chen Feilong Lin Zhongyu Chen Changbing Tang Cailian Chen the Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Normal University the School of Computer Science and Technology Zhejiang Normal University IEEE the College of Physics and Electronic Information Engineering Zhejiang Normal University the Department of Automation Shanghai Jiao Tong University

The increased demand for personalized customization calls for new production modes to enhance collaborations among a wide range of manufacturing practitioners who unnecessarily trust each other. In this article, a blockchain-enabled manufacturing collaboration framework is proposed, with a focus on the production capacity matching problem for blockchainbased peer-to-peer(P2P) collaboration. First, a digital model of production capacity description is built for trustworthy and transparent sharing over the blockchain. Second, an optimization problem is formulated for P2P production capacity matching with objectives to maximize both social welfare and individual benefits of all participants. Third, a feasible solution based on an iterative double auction mechanism is designed to determine the optimal price and quantity for production capacity matching with a lack of personal information. It facilitates automation of the matching process while protecting users' privacy via blockchainbased smart contracts. Finally, simulation results from the Hyperledger Fabric-based prototype show that the proposed approach increases social welfare by 1.4% compared to the Bayesian game-based approach, makes all participants profitable,and achieves 90% fairness of enterprises.

关键词： Blockchain iterative double auction manufacturing collaboration production capacity matching

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Design of a Machine Learning based Data Analytics Engine to Improve Decision Making Processes 5

Design of a Machine Learning based Data Analytics Engine to ...

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5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Pethe, Yoginee Surendra Dandekar, Pranali Rahul Shri Ramdeobaba College of Engineering and Management Department of Computer Application Nagpur India Shri Ramdeobaba College of Engineering and Management Department of Computer Science and Engineering Nagpur India

ISBN: (纸本)9781665474672

Analytical Hierarchy Processing (AHP) assists in estimation of input-specific weights for improving system-level decision making operations. The process initially decides a goal, and then uses different criterion & alternatives to achieve it. But original AHP model is not efficient for large-scale systems, which limits its capability under real-time use cases. To overcome this issue, various Machine Learning based techniques are proposed, and each of them has their own set of limitations. Techniques with higher efficiency also showcase high complexity, while simplistic optimization techniques have limited performance levels. To overcome these limitations, this text proposes design of an efficient AHP model with low complexity via integration of (GWO), and iterative learning (IL) operations. The proposed model initially performs context-based correlation between input parameter sets. These correlation values are used to modify the AHP decisions, which are tuned by GWO process. Results of Grey Wolf optimization this GWO process are optimized via IL based tuning, which incrementally modifies the correlation values for optimum efficiency levels. Due to which the proposed model showcases low complexity, while maintaining higher efficiency when compared with the original AHP process. The proposed model was evaluated on multiple datasets including housing, image processing, and employment scenarios. Based on this evaluation it was observed that the proposed model showcased 8.5% higher accuracy, 6.4% higher precision, with 4.5% 1ower delay when compared with original AHP and standard AHP-optimization techniques. Due to this improvement, the proposed model is capable of deployment for a wide variety of real-time scenarios. © 2023 IEEE.

关键词： Machine learning

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Structured Approach for Relation Extraction in Legal Documents 4

Structured Approach for Relation Extraction in Legal Documen...

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4th IEEE Global Conference for Advancement in Technology, GCAT 2023

作者： Sasidharan, Anjali K. Rahulnath, R. Department of Computer Science and Application Amrita School of Engineering Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350305258

Legal documents contain a wealth of valuable information, making them crucial for knowledge extraction and analysis. Extracting structured relationships between entities in legal texts can significantly enhance our understanding of legal concepts and facilitate the creation of knowledge graphs. In this paper, we propose a structured approach combining relation extraction, graph reasoning, and clustering methods to effectively extract and represent legal relationships. Firstly, we employ state-of-the-art relation extraction techniques to identify relevant entities and their relationships from unstructured legal documents. These techniques leverage natural language processing (NLP) and machine learning algorithms to recognize entity pairs and assign appropriate relationship labels. Next, we leverage graph reasoning to enhance the extracted relationships by incorporating additional contextual information. By representing the legal entities and their relationships as nodes and edges in a knowledge graph, we can apply reasoning algorithms to infer implicit relationships, resolve conflicts, and expand the graph based on the legal domain's rules and regulations. Furthermore, we integrate clustering methods to organize related legal entities and relationships into meaningful clusters within the knowledge graph. By grouping semantically similar entities and relationships, we can uncover patterns, identify common themes, and enable more effective retrieval and analysis of legal information. Our experiments demonstrate that the structured approach, combining relation extraction, graph reasoning, and clustering methods, effectively extracts and represents legal relationships in a knowledge graph. The resulting graph provides a comprehensive and organized representation of legal concepts, facilitating legal research, decision-making, and knowledge discovery in the legal domain. © 2023 IEEE.

关键词： Clustering methods Graph reasoning Knowledge graph creation Legal documents Machine learning Natural language processing Relation extraction

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Modeling and Integration Method of Sys ML Model for Complex Business Scenarios

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系统仿真学报 2024年第12期36卷 2797-2812页

作者： Yu Bing An Baoran Zhao Shicao Institute of Computer Application China Academy of Engineering PhysicsMianyang 621900China

The development process of complex equipment involves multi-stage business processes,multi-level product architecture,and multi-disciplinary physical *** relationship between its system model and various disciplinary models is extremely *** the modeling and integration process,extensive customized development is needed to realize model integration and interoperability in different business ***,the differences in modeling and interaction between different modeling tools make it difficult to support the consistent representation of models in complex *** improve the efficiency of system modeling and integration in complex business scenarios,a system modeling and integration method was *** method took the Sys ML language kernel as the core and system model function integration as the main *** the technical means of model view separation,abstract operation interface,and model view configuration,the model modeling and integration of multi-user,multi-model,multi-view,and different business logic in complex business scenarios were realized.

关键词： SysML model-based system engineering(MBSE) service graphical modeling V-business

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MLTPDFS: Design of Machine Learning Model for Temporal Analysis of Progressive Diseases via Facial Scans 5th

MLTPDFS: Design of Machine Learning Model for Temporal Analy...

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5th International Conference on Information Systems and Management Science, ISMS 2022

作者： Dandekar, Pranali Rahul Pethe, Yoginee Surendra Department of Computer Science and Engineering Shri Ramdeobaba College of Engineering and Management Nagpur India Department of Computer Application Shri Ramdeobaba College of Engineering and Management Nagpur India

ISBN: (纸本)9783031311529

Edge analysis, colour variation analysis, texture change analysis, etc. can identify multiple health conditions in facial scans. Identifying these diseases requires individual image processing models. Temporal facial analysis can identify stress, anxiety, happiness, high blood pressure, diabetes, and other health conditions. Most of these models use independent Machine Learning (ML) components for disease identification, limiting real-time scalability. Some of these models combine ML components, but their clinical prediction and analysis is limited. This text proposes a novel Machine Learning Model for Temporal analysis of progressive diseases via Facial Scans. The proposed model trains CNNs to identify Stress, Anxiety, Heart Conditions, Cancer, and Brain abnormalities. Each disease type requires its own CNN Model. VGGNet-19 predicts stress and anxiety, YoLOV3 identifies brain abnormalities, ResNet-101 predicts heart conditions, and ResNet-152 identifies cancer levels. These models were chosen for their accuracy, precision, and AUC for different diseases. The proposed model combines CNNs using bio-inspired machine learning. The combined model’s hyper-parameters are tuned based on real-time performance metrics. An incremental learning layer improves these metrics by incorporating continuous learning. Due to these operations, the proposed model improves classification accuracy by 5.4%, precision by 6.5%, recall by 8.3%, and AUC by 4.5%. The model performed faster than existing methods due to machine learning, which reduces training and validation delays, making it useful for real-time clinical use cases. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Diseases

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PD-YOLO:Colon Polyp Detection Model Based on Enhanced Small-Target Feature Extraction

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computers, Materials & Continua 2025年第1期82卷 913-928页

作者： Yicong Yu Kaixin Lin Jiajun Hong Rong-Guei Tsai Yuanzhi Huang New Engineering Industry College Putian UniversityPutian351100China Engineering Research Center of Big Data Application in Private Health Medicine(Putian University) Fujian Province UniversityPutian351100China Computer College Guangdong University of Science&TechnologyDongguan523668China

In recent years,the number of patientswith colon disease has increased *** polyps are the precursor lesions of colon *** not diagnosed in time,they can easily develop into colon cancer,posing a serious threat to patients’lives and health.A colonoscopy is an important means of detecting colon ***,in polyp imaging,due to the large differences and diverse types of polyps in size,shape,color,etc.,traditional detection methods face the problem of high false positive rates,which creates problems for doctors during the diagnosis *** order to improve the accuracy and efficiency of colon polyp detection,this question proposes a network model suitable for colon polyp detection(PD-YOLO).This method introduces the self-attention mechanism CBAM(Convolutional Block Attention Module)in the backbone layer based on YOLOv7,allowing themodel to adaptively focus on key information and ignore the unimportant *** help themodel do a better job of polyp localization and bounding box regression,add the SPD-Conv(Symmetric Positive Definite Convolution)module to the neck layer and use deconvolution instead of *** results indicate that the PD-YOLO algorithm demonstrates strong robustness in colon polyp *** to the original YOLOv7,on the Kvasir-SEG dataset,PD-YOLO has shown an increase of 5.44 percentage points in AP@0.5,showcasing significant advantages over other mainstream methods.

关键词： Polyp detection YOLOv7 SPD-Conv CBAM deconvolution

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