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4th international conference on Optimization, learning Algorithms and Applications, OL2A 2024

4th International Conference on Optimization, Learning Algor...

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4th international conference on Optimization, learning Algorithms and Applications, OL2A 2024

ISBN: (纸本)9783031774317

the proceedings contain 41 papers. the special focus in this conference is on Optimization, learning Algorithms and Applications. the topics include: Facial Expression recognition in Virtual Reality Simulations;augmented Reality in Industrial Training: A Comparative Analysis of Teaching Methods;comparison Between Single and Multi-objective Clustering Algorithms: MathE Case Study;learning Algorithms for Breast Cancer Classification and Diagnosis;fuzzy c-Means as a Decision Support Tool for Liver Disease Diagnosis Based on data Analysis;optimization of machine learning Models Applied to Robot Localization in the RobotAtFactory 4.0 Competition;p-Fuzzy System Applied to Population Dynamics: A Case Study;enhancing K-Way Circuit Partitioning: A Deep Reinforcement learning Methodology;optimizing Olive Disease Classification through Hybrid machine learning and Deep learning Techniques;a Neural Network-Based Approach to Identifying Wrinkles and Recommending Cosmetic Products;colorectal Polyp Segmentation: Impact of Combining Different datasets on Deep learning Model Performance;A Comparative Analysis of MATLAB and Python Neural Networks for Diabetes Prediction;cochineal Colony Detection in Cactus Pear: A Deep learning Approach;enhancing Quadruped Robot Performance through Gait Optimization;assessment of Logistics Performance Indicators in Southern European Countries Optimizing the Decision Making;Predicting Retail Store Transaction patterns: A Comparison of ARIMA and machine learning Models;route Optimization for Urban Last-Mile Delivery: Truck vs. Drone Performance;speeding up Line Search for Composite Objective Function with a Linear Inside Part;the Infodemic Issue: Numerical Modelling;a Multi-objective Approach for Solving Distributed Job Shop Scheduling Problems.

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recognition of MFR based motif discovery 4

Recognition of MFR based motif discovery

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4th IEEE international conference on Electronic Information and Communication Technology, ICEICT 2021

作者： Wang, Pengcheng Liu, Weisong Wang, Chao Zhang, Pengyu Wen, Tailai Liu, Zheng College of Electronic Science and Technology National University of Defense Technology Changsha China

ISBN: (纸本)9781665432030

Multi-function radar (MFR) has pulse-level parameter agility, and under the scheduling of complex procedures, it can adjust resource allocation in an optimal way to perform multiple tasks in different directions. the agility of the parameters makes it difficult to describe MFR statistically, and traditional statistical description methods based on signal characteristics are difficult to support the needs of MFR recognition tasks. Another potential MFR description method is using the high-dimensional temporal pattern hidden in pulse stream, which is represented by the pulse repetition interval (PRI) switch rule and value range. this paper uses motif mining algorithm to find high-dimensional repeated patterns in MFR pulse stream, and the key PRI sequence of MFR is obtained through further screening. then, multiple key sample sequence matchers are integrated into a matching degree generator, which is used to output the matching degree between the MFR to be identified and the reference MFR. Simulation results show that compared with the deep learning method, it has better performance and stronger interpretability. © 2021 IEEE

关键词： Deep learning

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Deep Adaptively Feature Extracting Network for Cervical Squamous Lesion Cell Detection 4th

Deep Adaptively Feature Extracting Network for Cervical Squ...

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4th international conference on machine learning for Cyber Security, ML4CS 2022

作者： Peng, Zhonghua Hu, Rong Wang, Fuen Fan, Haoyi Eng, Yee Wei Li, Zuoyong Zhou, Liwei Fujian Provincial Key Laboratory of Big Data Mining and Applications School of Computer Science and Mathematics Fujian University of Technology Fujian Fuzhou350118 China School of Computer and Artificial Intelligence Zhengzhou University Henan Zhengzhou450001 China School of Computer Sciences Universiti Sains Malaysia Penang11800 Malaysia Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fujian Fuzhou350121 China Department of Nutrition The First Affiliated Hospital of Zhengzhou University Henan Zhengzhou450052 China

ISBN: (纸本)9783031200984

Cervical cancer is one of the most widespread malignancies affecting women’s health worldwide today. However, the task of detection is particularly difficult due to the complex background of the cervical smear, where cells are previously stacked in clusters. To address this problem, we utilize the YOLOv5 as the baseline and build on YOLOv5 by using the simple Transformer Block which only combines with multi-head self attention layers and MLP to better extract cell features as well as to obtain global information. In addition, we allow the model to refine features adaptively to assist with detection by using Convolutional Block Attention Module (CBAM), an attention module being simple and effective for feed-forward convolutional neural networks, in the complex background information. Finally, we compare the model with YOLOv5 as baseline. In CDetector dataset, our model obtains 52.5% mAP@.5, which is 6% better than baseline. In transfer learning, it is 62.2%, which outperforms baseline by 3.2%. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cells

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Design and Implementation of Financial Bill Anti Counterfeiting and recognition System based on Computer Vision

Design and Implementation of Financial Bill Anti Counterfeit...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE international conference on

作者： Xin Zhang College of Digital Business Liaoning Vocational University of Technology Jinzhou China

ISBN: (数字)9798331533663

ISBN: (纸本)9798331533670

In the context of rapid digitalization, financial bills play a critical role in economic activities, yet traditional anti-counterfeiting methods face challenges due to increasingly sophisticated forgery techniques such as high-precision printing and image manipulation software. this paper presents a computer vision-based financial bill anti-counterfeiting and recognition system, integrating advanced image preprocessing (including grayscale conversion, median-Gaussian denoising, and edge detection for cropping), feature extraction using local binary patterns (LBP) and scale-invariant feature transform (SIFT), and deep learning models to detect security features (watermarks, microtext, encrypted QR codes) and extract key information (amounts, dates). the modular system architecture includes image acquisition, preprocessing, feature extraction, authenticity verification, information recognition, and result output. Experimental validation on 100 samples demonstrates the system's effectiveness: 98% accuracy in anti-counterfeiting detection, 100% security and confidentiality via encrypted storage and blockchain integration, and over 99% precision in recognizing critical bill data. this innovative approach addresses limitations of conventional methods, offering a reliable solution for financial management. Future work will focus on enhancing low-quality image preprocessing and exploring blockchain integration for full lifecycle traceability, further improving system robustness and security.

关键词： Deep learning Computer vision Accuracy Noise reduction Feature extraction Robustness Blockchains Image preprocessing data mining Counterfeiting

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pattern recognition of Control Chart Based on Fuzzy c-Means Clustering Algorithm

Pattern Recognition of Control Chart Based on Fuzzy c-Means ...

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international conference on Artificial Intelligence and Advanced Manufacturing (AIAM)

作者： Renping Gong School of Management Tianjin University of Technology Tianjin China

the pattern recognition of the control chart can distinguish the Normal factors and unnatural factors in the manufacturing process, improve the quality of products in the manufacturing process, reduce costs and improve benefits in the environment of industrial big data. Due to the fact that manufacturing process data are becoming more and more complicated under the environment of big data, the fundamental assumptions of traditional statistical process control methods are no longer invalid, which makes them unreliable to recognize the control chart patterns effectively. In this paper, the Monte Carlo method is used to generate samples. One-dimensional discrete wavelet transform is used to process the original data. the fuzzy c-means clustering algorithm is used to recognize the control chart pattern. the recognition accuracy was 99.43%, and its standard deviation was 0.0028. It shows that the method based on the control chart pattern recognition has high accuracy, good stability, and is simple and efficient compared with the existing control chart pattern recognition methods.

关键词： Training Manufacturing processes machine learning algorithms Clustering algorithms Process control Control charts Production

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Predictive Clustering learning Algorithms for Stroke Patients Discharge Planning 14

Predictive Clustering Learning Algorithms for Stroke Patient...

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14th international Joint conference on Biomedical Engineering Systems and Technologies (BIOSTEC) / 14th Int Conf on Bio-inspired Systems and Signal Processing (BIOSIGNALS) / 14th Int Conf on Biomedical Electronics and Devices (BIODEVICES)

作者： Lella, Luigi Gentile, Luana Pristipino, Christian Toni, Danilo ASUR Marche Via Oberdan 2 Ancona Italy Sapienza Univ Dept Human Neurosci Rome Italy ASL1 Roma San Filippo Neri Hosp Rome Italy

ISBN: (纸本)9789897584909

Stroke patients discharge planning is a complex task that could be carried out by the use of a suitable decision support system. Such a platform should be based on unsupervised machine learning algorithms to reach the best results. More specifically, in this kind of prediction task clustering learning algorithms seem to perform better than the other unsupervised models. these algorithms are able to independently subdivide the treated clinical cases into groups, and they can serve to discover interesting correlations among the clinical variables taken into account and to improve the prediction accuracy of the treatment outcome. this work aims to compare the prediction accuracy of a particular clustering learning algorithm, the Growing Neural Gas, with the prediction accuracy of other supervised and unsupervised algorithms used in stroke patients discharge planning. this machine learning model is also able to accurately identify the input space topology. In other words it is characterized by the ability to independently select a subset of attributes to be taken into consideration in order to correctly perform any predictive task.

关键词： pattern recognition and machine learning Big data in Healthcare data mining and data Analysis Decision Support Systems

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Chronic Disease Symptom Checker with NLP

Chronic Disease Symptom Checker with NLP

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Sentiment Analysis and Deep learning (ICSADL), international conference on

作者： Vinoth Kumar P Arul Selvam N Soundharajan P Artificial Intelligence and Data Science St.Joseph's Institue Of Technology Chennai India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Chronic disease remains a critical global health challenge due to lifestyle and environmental changes, leading to increased morbidity and mortality. this paper presents an intelligent symptom checker utilizing Natural Language Processing (NLP) to assess probable chronic diseases based on user-reported symptoms. By analyzing free-text symptom descriptions, the system maps them to potential medical conditions using Named Entity recognition (NER)and a rule-based approach. the methodology involves extensive medical data collection, pre-processing, and training of machine learning models to recognize symptom-disease patterns. the checker's backend integrates an NLP model with a user-friendly interface, providing real-time assessments of probable diseases with confidence levels. the system's accuracy is validated through preclinical tests and user studies, comparing outputs with professional diagnoses. Ethical considerations, including patient privacy and diagnostic reliability, are also addressed. With potential applications in early detection and healthcare support, this system aims to enhance disease management and reduce healthcare burdens. the key challenges include NLP accuracy, data privacy, and integration with existing healthcare systems, which are addressed through robust preprocessing, security measures, and model optimization techniques

关键词： Training data privacy Accuracy Terminology Named entity recognition Natural language processing Real-time systems data models Medical diagnostic imaging Diseases

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Advanced machine learning Frameworks for Cancer Classification Using Genomic Information

Advanced Machine Learning Frameworks for Cancer Classificati...

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international conference on ICT in Business Industry & Government (ICTBIG)

作者： Vivek Deshpande A. Sivanantham Nittin Sharma Maheshwari B Shreya Ghosal Shalini Department of Computer Engineering Vishwakarma Institute of Technology Pune India Department of Mechanical Engineering Karpagam Academy of Higher Education Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of EEE Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Mangement PG Management JAIN (Deemed to be University) Bangalore Karnataka India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

Computational advancements have improved patient survival rates and treatment of severe illnesses. Cancer is a hereditary illness caused by mutations and variations in the patient's DNA. Cancer therapy is challenging due to the complexity of the tumor's microenvironment. Patients experiencing the same cancer kind have varying responses to targeted therapy. Clinical trials and conventional drug discovery are complicated and laborious. thus, researchers strive to develop effective treatments for severe disorders. the abundance of internet data sources in oncology and pharmacogenomics has encouraged research in this discipline. Recent advances in data mining including machine learning are enhancing analysis based on data. the following section introduces a cancer classification method that uses multi-objective meta-heuristics and machine learning techniques to predict novel and relevant markers. HMOSHSSA, a hybrid choice of genes method, uses MOSHO and Salp Swarm method. the proposed composite algorithm combines SSA and MOSHO characteristics for mining and extraction. the suggested strategy is compared to a few state-of-the-art methods. the precision of classification and gene count are used to assess the proposed approach. Comparing findings shows that the recommended strategy outperforms competitors. the SVM and KNN classification method for leukemia dataset identifies four genes with 100% accuracy. Existing leukemia dataset prediction methods are less accurate.

关键词： Support vector machines Accuracy machine learning algorithms Leukemia DNA Nearest neighbor methods Prediction algorithms Classification algorithms data mining Tumors

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Healthcare Management System to Improve data Analytics Using Support Vector machine Algorithm

Healthcare Management System to Improve Data Analytics Using...

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Innovative Practices in Technology and Management (ICIPTM), international conference on

作者： A. Edwin Raj V. Bini Marin C. Balakrishnan K. Elangovan R. Meenakshi M. Muthulekshmi Department of Mathematics Hindustan Institute of Technology and Science Chennai Tamil Nadu India Faculty of Management Studies SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering S.A.Engineering College Chennai Tamil Nadu India Department of Mechanical Engineering Dhanalakshmi Srinivasan Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350307757

ISBN: (纸本)9798350307764

Knowledge discovery in databases (KDD) is concerned with progress in data analysis tools. data mining is a crucial parts of the KDD process. data mining is a method for discovering and extracting patterns from huge medical datasets. there are a number of benefits to using the Support Vector machine (SVM) algorithm in healthcare systems. SVM is a strong and flexible machine learning algorithm that may excel at certain healthcare analytics applications. SVM is preferred in healthcare systems because of their adaptability, ability to manage imbalanced datasets, feature interpretability, handling of high-dimensional data, handling of classification, predictive modeling tasks, handling of non-linear relationships, resistance to overfitting and provision of a distinct margin of separation. It is essential to carefully consider the unique needs and features of the healthcare application when choosing an algorithm. Healthcare systems that use SVM algorithms do so with the intention of capitalizing on SVM's capabilities to tackle domain-specific problems and tasks. In light of this development, the relevant literature on this topic with regard to approach, SVM algorithm, and outcomes are surveyed. the publications evaluated in this study have been categorized according to their relevance to the disciplines, models, tasks, and approaches discussed. data mining is the practice of sifting through massive amounts of unstructured data for meaningful patterns and insights. Results show 90% accuracy and 98% precision and F measures of the given medical dataset.

关键词： Support vector machines machine learning algorithms databases Medical services Prediction algorithms Classification algorithms data mining

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data Enhancement Strategy Using Reinforcement learning And Xgboost With BlockChain Security

Data Enhancement Strategy Using Reinforcement Learning And X...

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Intelligent Technologies (CONIT), international conference on

作者： S Selvapriya N Manvizhi S Ajay V K Kavya Rosario Gilmary Dept. of Information Technology IFET College of Engineering Villupuram Tamilnadu India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

the enormous volumes of data generated by Next Generation Sequencing (NGS) technology have transformed genomics and made efficient data analysis techniques necessary. To create data analytics applications, the extraction, transformation, and loading (ETL) process is necessary. In understanding data features, representation, storage, I/O operations, intermediate findings, in-memory analysis, and total processing time, the data format is crucial, as this study emphasizes. data conformance is required for effective analysis since different machine learning and data mining algorithms require different types and forms of data. In this paper, we explore data format-centric optimization of the ETL process for NGS data analysis. We investigate how computing efficiency and analytical correctness are affected by the choice of data type. By using distributed computing and parallel processing, we evaluate the effectiveness of several data formats in NGS data analysis processes, such as FASTQ, BAM, and VCF. Our study offers suggestions for developing data analytics applications in the genomics field by clarifying the relationship between data format, ETL, and algorithmic needs. We also look at the trade-offs between resource usage and data format choice. In the end, our study contributes to more accurate and efficient genomics research by addressing the urgent need for well-informed data format options in NGS data analysis.

关键词： Sequential analysis data analysis Loading Genomics Reinforcement learning Parallel processing data mining

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