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Analysis of machine learning Methods for Water Quality Evaluation of Penaeus Vannamei 23

Analysis of Machine Learning Methods for Water Quality Evalu...

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Proceedings of the 2023 4th international conference on machine learning and Computer Application

作者： Xiaohong Peng Zixin Li Zebin Ma Ying Zhang School of Mathematics and Computer Science Guangdong Ocean University China

ISBN: (纸本)9798400709449

In global aquaculture, Penaeus vannamei stands out due to its immense economic importance. Water quality, being pivotal for its successful cultivation, demands precise evaluation techniques. this research undertook a meticulous systematic review and, leveraging data mining, crafted a rich dataset of 50,000 water quality samples pertinent to Penaeus vannamei. Diving deep into machine learning, we assessed four key algorithms: decision tree, Bayesian, k-nearest neighbor, and support vector machine, each tailored for intricate multi-feature multi-classification challenges. Of these, the Gaussian Parsimonious Bayes-based model was distinguished by its superior accuracy and efficiency. this study successfully applied machine learning techniques to develop a reliable and efficient water quality evaluation model for Penaeus vannamei farming, offering a scientific tool for the aquaculture industry and facilitating more efficient, scientifically informed farming management. this research contributes an innovative scientific approach and theoretical foundation for the sustainable growth of the aquaculture industry.

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A Symptom-based Disease Diagnosis: A Pre-Classification ML Approach

A Symptom-based Disease Diagnosis: A Pre-Classification ML A...

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Pervasive Computing and Social Networking (ICPCSN), international conference on

作者： Suram Rohith K. Kalaiselvi K. Jawanth Reddy V.J. Venkata Rami Reddy Department of Networking and Communications SRM Institute of Science and Technology Chennai India Department of Networking and Communications Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798350386349

ISBN: (纸本)9798350386356

In emergency hospitals, the hospital conveyance framework is seen as an important component of the clinical, financial, and executive well-being areas. this study incorporates Artificial Intelligence (AI) techniques to assess and investigate cases of various ailments that occur in low-income families. the proposed system utilizes unstructured and newly presented hospital data by applying the machine learning Certificate Tree computation. mining anticipates hospitalization this study proposes a data-driven, scalable alternative to conventional diagnostic techniques by addressing the issues like unbalanced data and guaranteeing model interpretability.

关键词： Pervasive computing Hospitals Social networking (online) Computational modeling Sociology data models Medical diagnosis

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Enhancing the Performance of Multi-Objective Regression for Pelvic Organ Prolapse Prediction via data Augmentation

Enhancing the Performance of Multi-Objective Regression for ...

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Big data & Artificial Intelligence & Software Engineering (ICBASE), international conference on

作者： Yanlin Mi Sabin Tabirca A O’Reilly School of Computer Science and Information Technology University College Cork Cork IE SFI Centre for Research Training in Artificial Intelligence University College Cork Cork IE Faculty of Mathematics and Informatics Transylvania University of Brasov Brasov RO Department of Obstetrics and Gynecology Cork University Maternity Hospital Cork IE

In healthcare, machine learning has been increasingly applied to predictive models, but the efficacy of these models is often compromised due to limitations in data quality, diversity, and metrics. In other domains, such as image recognition and natural language processing, data augmentation techniques have been successfully applied to mitigate these challenges, but in healthcare such strategies have not been widely applied. therefore, our research actively explores how these data augmentation techniques can be applied to machine learning models for predicting the outcome of pelvic organ prolapse surgery. We first performed in-depth data preprocessing and then tried innovative data enhancement techniques such as noise injection and self-sampling. the results show that the application of data enhancement techniques significantly improves the performance of predictive models and effectively addresses data scarcity and quality issues, which opens up new possibilities for wider application of data enhancement techniques in the medical field in the future.

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Explainable machine-learning for identifying the genetic biomarker MGMT in brain tumors using magnetic resonance imaging radiomics

Explainable Machine-Learning for identifying the genetic bio...

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pattern recognition Systems (ICPRS), international conference on

作者： Sebastian Ponce Steren Chabert Leondry Mayeta Pamela Franco Francisco Plaza-Vega Marvin Querales Rodrigo Salas Universidad de Valparaíso Valparaíso Chile Center of Interdisciplinary Biomedical and Engineering Research for Health Millennium Institute for Intelligent Healthcare Engineering (iHEALTH) School of Biomedical Engineering Universidad de Valparaíso Valparaíso Chile Center of Interdisciplinary Biomedical and Engineering Research for Health Departamento de Matemática y Ciencia de la Computación Universidad de Santiago de Chile Santiago Chile School of Medical Technology Universidad de Valparaíso Valparaíso Chile

ISBN: (数字)9798350375657

ISBN: (纸本)9798350375664

Brain tumors often feature the genetic biomarker O6-Methylguanine-DNA-Methyltransferase (MGMT) associated with a favorable response to chemotherapy and an improved prognosis. Currently, detecting MGMT presence relies on invasive brain biopsy procedures. thus, this study aims to develop a data mining-based radiomics methodology for non-invasive identifying and evaluating brain tumor genetic biomarkers using radiomics in magnetic resonance images (MRIs). MRIs with segmentation masks were used to extract variables and employ feature selection techniques. Several machine learning models were trained, where Logistic Regression, employing LASSO selection, emerged as the best-performing model, achieving 61% accuracy. Additionally, an explainability module utilizing Shap values identified three significant variables: a T1CE sequence variable related to texture, a FLAIR sequence variable of first-order statistics, and a T1 sequence variable of first-order statistics. this radiomic methodology, with its performance and explainable nature, could offer diagnostic support to clinicians in tumor management.

关键词： Chemotherapy Biological system modeling Magnetic resonance imaging machine learning Predictive models Genetics Feature extraction

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YOLO-Based Obstacle Detection Under Few-Shot learning

YOLO-Based Obstacle Detection Under Few-Shot Learning

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data Science and Computer Application (ICDSCA), IEEE international conference on

作者： Chengyu Liu Yuan Yang School of Electronic and Information Engineering Beijing Jiaotong University Beijing China

ISBN: (数字)9798350368239

ISBN: (纸本)9798350368246

the obstacle detection industry has been developing rapidly in recent years. Obstacle detection technology has grown by leaps and bounds. Few-shot obstacle detection has a profound significance, which can help us to solve the problem of small samples under high speed conditions in target detection. Due to the limitation of the shooting environment, the sample size about the obstacles is small today. the goal of small sample learning is not for the machine to recognize images in the training set and generalize to the test set; the goal of small sample learning is for the machine to learn to learn on its own. the purpose of learning is for the model to understand the similarities and differences between things and learn to distinguish between them. In this paper, we help with this process by adding a parameter reweighting module. the models will fall into overfitting when a small number of samples are available. In this paper, we propose a small-sample detection method based on improved YOLO-v8. For the problem of small sample size, we improve the generalization ability of the model by adding an attention mechanism at the detection header, and for the problem of model overfitting, we update the model parameters by adding a feature reweighting module to improve the model effect. Finally, the proposed method is validated based on the track area dataset, and the experimental results show that the model can realize the accurate recognition of small sample obstacles.

关键词： Training Industries Attention mechanisms Accuracy Target tracking Target recognition Object detection Predictive models Feature extraction Overfitting

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theoretical Evaluation of Ensemble machine learning Techniques

Theoretical Evaluation of Ensemble Machine Learning Techniqu...

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international conference on Smart Systems and Inventive Technology (ICSSIT)

作者： Milind Shah Kinjal Gandhi Kinjal A Patel Harsh Kantawala Rohini Patel Ankita Kothari Department of Computer Science & Engineering Krishna School of Emerging Technology & Applied Research –Drs. Kiran & Pallavi Patel Global University (KPGU) Vadodara Gujarat India Faculty of Computer Applications & Information Technology Gujarat Law Society University Ahmedabad Gujarat India Department of Computer Engineering G H Patel College of Engineering & Technology Charutar Vidhya Mandal University (CVM) Vallabh - Vidhyanagar Gujarat India

the use of ensemble techniques is widely recognized as the most advanced approach to solving a variety of problems in machine learning. these strategies train many models and combine the results from all of those models in order to enhance the predictive performance of a single model. During the period of the last several years, the disciplines of artificial intelligence, pattern recognition, machine learning, neural networks, and data mining have all given a considerable consideration to the concept of ensemble learning. Ensemble learning has shown both effectiveness and usefulness across a broad range of problem domains and in significant real-world applications. Ensemble learning is a technique thatinvolves the construction of many classifiers or a group of base learneis and the merging of their respective outputs in order to decrease the total variance. When compared to using only one classifier or one base learner at a time, the accuracy of the results achieved by combining numerous dassifiers or the set of base learners is greatly improved. It has been shown that the use of ensemble methods may increase the predicted accuracy of machine learning models for a range of tasks, including classification, regression, and the identification of outliers. this study will discuss about ensemble machine learning techniques and its various methods such as bagging, boosting, and stacking. finally, all the factors involved in bagging, boosting, and stacking are compared.

关键词： Stacking Neural networks Merging Predictive models Boosting Ensemble learning data mining

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the DeepHealth Toolkit: A Unified Framework to Boost Biomedical Applications 25

The DeepHealth Toolkit: A Unified Framework to Boost Biomedi...

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25th international conference on pattern recognition (ICPR)

作者： Cancilla, Michele Canalini, Laura Bolelli, Federico Allegretti, Stefano Carrion, Salvador Paredes, Roberto Gomez, Jon A. Leo, Simone Piras, Marco Enrico Pireddu, Luca Badouh, Asaf Marco-Sola, Santiago Alvarez, Lluc Moreto, Miquel Grana, Costantino Univ Modena & Reggio Emilia Dipartimento Ingn Enzo Ferrari Modena Italy Univ Politecn Valencia PRHLT Res Ctr Valencia Spain CRS4 Data Intens Comp Grp Pula Italy Barcelona Supercomp Ctr Barcelona Spain Univ Autonoma Barcelona Barcelona Spain Univ Politecn Cataluna Barcelona Spain

ISBN: (纸本)9781728188089

Given the overwhelming impact of machine learning on the last decade, several libraries and frameworks have been developed in recent years to simplify the design and training of neural networks, providing array-based programming, automatic differentiation and user-friendly access to hardware accelerators. None of those tools, however, was designed with native and transparent support for Cloud Computing or heterogeneous High-Performance Computing (HPC). the DeepHealth Toolkit is an open source Deep learning toolkit aimed at boosting productivity of data scientists operating in the medical field by providing a unified framework for the distributed training of neural networks, which is able to leverage hybrid HPC and cloud environments in a transparent way for the user. the toolkit is composed of a Computer Vision library, a Deep learning library, and a front-end for non-expert users;all of the components are focused on the medical domain, but they are general purpose and can be applied to any other field. In this paper, the principles driving the design of the DeepHealth libraries are described, along with details about the implementation and the interaction between the different elements composing the toolkit. Finally, experiments on common benchmarks prove the efficiency of each separate component and of the DeepHealth Toolkit overall.

关键词： Libraries

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Research on Heart Sound Classification Based on SVM Model

Research on Heart Sound Classification Based on SVM Model

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Electronic Information Engineering and Computer Communication (EIECC), international conference on

作者： Qingmei Chen Yizhang Zou Dan Chen Yuting Yan Wenshu Zhao The School of Computer Science Jiangxi University of Chinese Medicine Nanchang China

ISBN: (数字)9798331534622

ISBN: (纸本)9798331534639

Heart sound is an important physiological signal that is of great significance for evaluating heart function and discovering heart diseases. this article selects 610 sets of heart sound audio from the publicly available datasets of PhysioNet/Cinc Challenge 2016 and PASCAL Challenge. through wavelet transform denoising and Shannon theory extraction of envelopes, binary processing is performed to obtain key information of heart sounds and extract six characteristic indicators of heart sounds. Finally, these indicators were used as input parameters for machine learning, and SVM algorithm was used to classify heart sound signals, resulting in a total of ten cases with recognition accuracy of over 81.67%. the accuracy, recall, and F1 score of the SVM model are all above 0.7. the results indicate that wavelet transform and Shannon envelope extraction can ensure the quality of heart sound signals and the acquisition of effective features. the SVM model can effectively classify heart sound signals, providing a way to assist clinical decision-making.

关键词： Heart Support vector machines Wavelet transforms Accuracy Wavelet domain Decision making Feature extraction Wavelet analysis Vectors data mining

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Semi-supervised learning Based Entity recognition Method for Power Dispatch Operation Statements

Semi-supervised Learning Based Entity Recognition Method for...

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Signal Processing, Communications and Engineering Management conference (ISPCEM), international

作者： Qiang Chen Xin Liu Zongyuan Zhang Qiangren Zheng Cong Si Tonglong He Xiaohua Zhang Yunfeng Bai State Grid Jilin Electric Power Company Yanbian Power Supply Company Yanji China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

With the continuous development of the power grid, power grid dispatching faces the problems of complicated business data, inefficient information retrieval and time-consuming operation ticket preparation. the existing operation ticket generation method has been difficult to meet the safe operation of the power grid, and the time-consuming retrieval of the paper dispatch manual has led to the difficulty of efficient retrieval of the dispatch response programme and the inefficiency of the operation ticket preparation. In order to shorten the time consuming operation ticket generation and improve the efficiency of dispatchers, it is necessary to accurately identify the entities of key operation statements such as power dispatch operation steps, equipment, operation time, etc. in the operation ticket document to improve the retrieval efficiency. therefore, this paper proposes a deep entity recognition model based on scheduling operation statements, which firstly employs a pre-training model, ERNIE, to capture entity-level semantic information, and secondly explores the entity recognition knowledge in the unlabelled data through the proposed semi-supervised training architecture and the loss function scheduling mechanism, so as to improve the performance of the power scheduling entity recognition. In order to verify the effectiveness of this approach, experiments are conducted in this paper using the power dispatch operation text dataset, and the experimental results show that the proposed semi-supervised power dispatch entity recognition method improves the entity recognition precision, recall and F1 score of power dispatch operation statements by 4.43%, 2.06% and 3.24%, respectively, under the full amount of annotated data, as compared to the previous optimal baseline based on fully supervised learning. 3.24%. In particular, with only 10% of labelled data, the F1 score is improved by 15.92% compared to the optimal baseline. these results fully demonstrate the effectiv

关键词： Training Analytical models Text recognition Semantics Supervised learning Semisupervised learning Signal processing data models Power grids data mining

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Deep learning with Conditional Generative Adversarial Network Based Intrusion Detection System on Balanced data

Deep Learning with Conditional Generative Adversarial Networ...

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Electronics and Sustainable Communication Systems (ICESC), 2020 international conference on

作者： K. Hemavathi R. Latha Dept. of Computer Science St. Peter’s Institute of Higher Education and Research Chennai

Networks have an important role to play in modern life, and cyber security is an active research area. An Intrusion Detection System (IDS) becomes a crucial cyber security method that monitored the state of hardware and software running in the network. IDS can find attacks in available environments. the machine learning (ML) method is one among the emerging approaches that have better performance in the situation they have encountered already, and enjoy a wide variety of applications in outlier analysis speech recognition, pattern detection, and so on. With unbalanced data, the predictive model established utilizing ML method might produce an unacceptable classifier that affects the accuracy of predicting intrusion. Conventionally, researcher workers applied oversampling and undersampling to balance data in the dataset for overcoming these problems. therefore, this article presents a Deep learning with Conditional Generative Adversarial Network-based Intrusion Detection System (DLCGAN-IDS) technique on Balanced data. the goal of the DLCGAN-IDS technique lies in the proper balancing of the network samples and identify the intrusions accurately. To accomplish this, the presented DLCGAN-IDS approach primarily normalizes the input data by employing min-max normalization. For imbalanced data handling, the CGAN approach is used for balancing the sample numbers in the dataset. Finally, DL based Long Short-Term Memory (LS TM) method is enforced for detecting and classifying intrusions in the network. the results of the DLCGAN-IDS method execute on the IDS dataset. the comprehensive outcomes pointed out the superior achievement of the DLCGAN-IDS model over other current algorithms.

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