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An approach to human motor skill training for uniform group performance

INTERNATIONAL JOURNAL OF INDUSTRIAL ERGONOMICS 2020年 75卷 102894-102894页

作者： Ma, Wenqi Kaber, David Zahabi, Maryam Yanfeng Automot Interiors Holland MI USA Univ Florida Ind & Syst Engn Dept Gainesville FL USA Texas A&M Univ Ind & Syst Engn Dept College Stn TX USA

Prior research has focused on designing training approaches for novice operators to support maximum motor skill development. However, in production operations, workers must be trained to uniform performance levels to prevent 'bottlenecks' or work-in-process inventory accumulation. This study introduces a new approach to support assignment of training protocols to operators to achieve comparable levels of motor performance. Thirty-six participants performed a computer-based motor test. Based on performance classification results, each participant was assigned to a specific haptic virtual reality training condition. Results revealed participants identified as 'medium' or 'low' performers achieved levels of motor performance comparable to 'high' performers through 1-h training. Relevance to Industry: Findings can be applied to operator training in manual assembly operations, promoting a group of novice workers to achieve uniform performance levels and mitigating production bottlenecks.

关键词： Motor skill performance Training classification algorithm Assembly operations Virtual reality

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Study and implementation of automated system for detection of PCOS from ultrasound scan images using artificial intelligence

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IMAGING SCIENCE JOURNAL 2024年第7期72卷 828-839页

作者： Sumathi, M. Chitra, P. Sheela, S. Ishwarya, C. Sathyabama Inst Sci & Technol Chennai India Sathyabama Inst Sci & Technol Jeppiaar NagarRajiv Gandhi Salai 119 Chennai 600119 India

Artificial Intelligence (AI), is a field of science and engineering that deals with intelligent behaviour which has the potential of improved access and the cost of healthcare *** Ovarian Syndrome (PCOS) is characterised by a protracted menstrual cycle and frequent excess androgen levels, which often affect many women of reproductive age. There are now no trustworthy objective tests that might fully confirm the diagnosis and comprehension of PCOS. Effective image processing steps have been utilized in this work for the automation of PCOS diagnosis and the classification algorithms used are DarkNet-19, AlexNet, SqueezeNet, and SVM. As a result of the classifier's accelerated PCOS diagnosis and improved performance analysis, there will be fewer instances of potentially deadly consequences that can arise from a delayed diagnosis. The system produced metrics demonstrating improved performance, such as accuracy to provide a comprehensive ultrasound picture diagnosis using the classifier DarkNet-19 with 99%.

关键词： AI classification algorithm detection of PCOS ovary of ultrasound scan images performance metrics segmentation feature extraction graphical user interface >

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COMPUTER HYBRID SYSTEM OF HEMORRHAGE (HES) DETECTION USED FOR AIDED DIAGNOSIS OF DIABETIC RETINOPATHY

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JOURNAL OF MECHANICS IN MEDICINE AND BIOLOGY 2021年第6期21卷 2150044-2150044页

作者： Feroui, A. Messadi, M. Lazouni, A. Bessaid, A. Univ Tlemcen Fac Technol Dept Elect & Elect Biomed Lab Tilimsen 13000 Algeria

Diabetes cause's metabolic and physiological abnormalities in the retina and the changes suggest a role for inflammation in the development of diabetic retinopathy. Abnormal blood vessels can form in the back of the eye of a person with diabetes. These new blood vessels are weaker and prone to breaking and causing hemorrhage (HEs). Diabetic retinopathy (DR) accounts for 31.5-54% of all cases of vitreous hemorrhage in adults in the world. Therefore, detection of HEs is still a challenging factor task for computer-aided diagnostics of DR. Many researchers have developed advanced algorithms of hemorrhages detection using fundus images. In this paper, a robust and computationally efficient approach for HEs with different shape and size detection and classification is presented. First, brightness correction and contrast enhancement are applied to fundus images. Second, candidate hemorrhages are extracted by using an unsupervised classification algorithm. Third, an approach based on mathematical morphology is carried out for vascular network and macula segmentation. Finally, a total of 13 HEs features are considered in this study and selected for classification. The proposed method is evaluated on 419 fundus images of DIARETDB0, DIARETDB1 and MESSIDOR databases. Experimental results show that overall average sensitivity, specificity, predictive value and accuracy for hemorrhage in lesion level are 98.90%, 99.66%, 97.63% and 99.56%, respectively. The results show that the proposed method outperforms other state-of-the-art methods in detection of hemorrhages. These results indicate that this new method may improve the performance of diagnosis of DR system.

关键词： Diabetes diabetic retinopathy fundus image red lesion hemorrhage image processing segmentation mathematical morphology classification algorithm FP reduction

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Prediction model for recurrence of hepatocellular carcinoma after resection by using neighbor2vec based algorithms

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WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY 2021年第2期11卷 e1390-e1390页

作者： Cao, Yuankui Fan, Junqing Cao, Hong Chen, Yunliang Li, Jie Li, Jianxin Zhang, Simin China Univ Geosci Sch Comp Sci Wuhan Peoples R China Shandong Univ Shandong Prov Hosp Affiliated Jinan Shandong Peoples R China Deakin Univ Sch Informat Technol Melbourne Vic Australia

Liver cancer has become the third cause that leads to the cancer death. For hepatocellular carcinoma (HCC), as the highly malignant type of liver cancer, its recurrence rate after operation is still very high because there is no reliable clinical data to provide better advice for patients after operation. To solve the challenging issue, in this work, we design a novel prediction model for recurrence of HCC using neighbor2vec based algorithm. It consists of three stages: (a) In the preparation stage, the Pearson correlation coefficient was used to explore the independent predictors of HCC recurrence, (b) due to the low correlation between individual dimension and prediction target, K-nearest neighbors (KNN) were found as a K-vectors list for each patient (neighbor2vec), (c) all vectors lists were applied as the input of machine learning methods such as logistic regression, KNN, decision tree, naive Bayes (NB), and deep neural network to establish the neighbor2vec based prediction model. From the experimental results on the real data from Shandong Provincial Hospital in China, the proposed neighbor2vec based prediction model outperforms all the other models. Especially, the NB model with neighbor2vec achieves up to 83.02, 82.86, 77.6%, in terms of accuracy, recall rates, and precision. This article is categorized under: Technologies > Data Preprocessing Technologies > classification Technologies > Machine Learning

关键词： classification algorithm hepatocellular carcinoma prediction model neighbor2vec

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RHSOFS: Feature Selection Using the Rock Hyrax Swarm Optimization algorithm for Credit Card Fraud Detection System

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SENSORS 2022年第23期22卷 9321-9321页

作者： Padhi, Bharat Kumar Chakravarty, Sujata Naik, Bighnaraj Pattanayak, Radha Mohan Das, Himansu Centurion Univ Technol & Management Dept Comp Sci & Engn Bhubaneswar 761211 Odisha India VSSUT Dept Comp Applicat Burla 768018 Odisha India VIT AP Univ Sch Comp Sci & Engn Amravati 522237 Andhra Pradesh India KIIT Deemed be Univ Sch Comp Engn Bhubaneswar 751024 Odisha India

In recent years, detecting credit card fraud transactions has been a difficult task due to the high dimensions and imbalanced datasets. Selecting a subset of important features from a high-dimensional dataset has proven to be the most prominent approach for solving high-dimensional dataset issues, and the selection of features is critical for improving classification performance, such as the fraud transaction identification process. To contribute to the field, this paper proposes a novel feature selection (FS) approach based on a metaheuristic algorithm called Rock Hyrax Swarm Optimization Feature Selection (RHSOFS), inspired by the actions of rock hyrax swarms in nature, and implements supervised machine learning techniques to improve credit card fraud transaction identification approaches. This approach is used to select a subset of optimal relevant features from a high-dimensional dataset. In a comparative efficiency analysis, RHSOFS is compared with Differential Evolutionary Feature Selection (DEFS), Genetic algorithm Feature Selection (GAFS), Particle Swarm Optimization Feature Selection (PSOFS), and Ant Colony Optimization Feature Selection (ACOFS) in a comparative efficiency analysis. The proposed RHSOFS outperforms existing approaches, such as DEFS, GAFS, PSOFS, and ACOFS, according to the experimental results. Various statistical tests have been used to validate the statistical significance of the proposed model.

关键词： credit card fraud risk feature selection classification algorithm rock hyrax algorithm

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A machine learning algorithm for stock picking built on information based outliers

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 184卷 115497-115497页

作者： Barucci, Emilio Bonollo, Michele Poli, Federico Rroji, Edit Politecn Milan Dept Math Milan Italy Univ Milano Bicocca Dept Stat & Quantitat Methods Milan Italy Lason Italy SRL Milan Italy

Stock picking based on regularities in time series is one of the most studied topics in the financial industry. Various machine learning techniques have been employed for this task. We build a trading strategy algorithm that receives as input indicators defined through outliers in the time series of stocks (return, volume, volatility, bid-ask spread). The procedure identifies the most relevant subset of indicators for the prediction of stock returns by combining an heuristic search strategy, guided from the Information Gain Criterium, with the Naive-Bayes classification algorithm. We apply the methodology to two sets of stocks belonging respectively to the EUROSTOXX50 and the DOW JONES index. Performance is smoother than in the Buy&Hold strategy and yields a higher risk-adjusted return, in particular in a turbulent period. However, outperformance vanishes when transaction costs (5-15 basis points) are inserted. Asset return and return/volume serial correlation turn out to be the most relevant indicators to build the trading algorithm.

关键词： Finance Stock picking Technical analysis Private information classification algorithm

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Application and Development of EEG Acquisition and Feedback Technology: A Review

BIOSENSORS-BASEL

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BIOSENSORS-BASEL 2023年第10期13卷 930页

作者： Qin, Yong Zhang, Yanpeng Zhang, Yan Liu, Sheng Guo, Xiaogang Beijing Inst Technol Inst Adv Struct Technol Beijing 100081 Peoples R China Beijing Perfect Protect Technol Co Ltd Beijing 101601 Peoples R China

This review focuses on electroencephalogram (EEG) acquisition and feedback technology and its core elements, including the composition and principles of the acquisition devices, a wide range of applications, and commonly used EEG signal classification algorithms. First, we describe the construction of EEG acquisition and feedback devices encompassing EEG electrodes, signal processing, and control and feedback systems, which collaborate to measure faint EEG signals from the scalp, convert them into interpretable data, and accomplish practical applications using control feedback systems. Subsequently, we examine the diverse applications of EEG acquisition and feedback across various domains. In the medical field, EEG signals are employed for epilepsy diagnosis, brain injury monitoring, and sleep disorder research. EEG acquisition has revealed associations between brain functionality, cognition, and emotions, providing essential insights for psychologists and neuroscientists. Brain-computer interface technology utilizes EEG signals for human-computer interaction, driving innovation in the medical, engineering, and rehabilitation domains. Finally, we introduce commonly used EEG signal classification algorithms. These classification tasks can identify different cognitive states, emotional states, brain disorders, and brain-computer interface control and promote further development and application of EEG technology. In conclusion, EEG acquisition technology can deepen the understanding of EEG signals while simultaneously promoting developments across multiple domains, such as medicine, science, and engineering.

关键词： EEG acquisition devices electrodes classification algorithm

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Landscape functional zoning at a county level based on ecosystem services bundle: Methods comparison and management indication

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2019年 249卷 109315-000页

作者： Liu, Yanxu Li, Tong Zhao, Wenwu Wang, Shuai Fu, Bojie Beijing Normal Univ Fac Geog Sci State Key Lab Earth Surface Proc & Resource Ecol Beijing 100875 Peoples R China

The perspective of ecosystem services bundle is virtually a spatial clustering on landscape to mapping the relationship between ecosystem services and support the spatial strategy of landscape management. However, the efficiency of various clustering algorithms for geographically different regions are still in obscurity. In this study, we provided landscape functional zoning as a planning tool based on the ecosystem services bundles formed by carbon sequestration, soil retention and water yield. Then we used four landscape pattern indices to evaluate the performance of six clustering algorithms on landscape functional zoning. The case counties include Lankao, Jinggangshan and Luquan in China. The results showed the Natural Breaks (Jenks) scheme should be the most reasonable zone because of its high aggregated distribution and low diversity. This scheme was adjusted using some other schemes and has been employed as the final 7 kinds of zoning types. There were 5 types appeared in Lankao and Jinggangshan, and 6 types appeared in Luquan. We discussed that landscape functional zone can be a nexus connecting landscape planning and social policy. Rural reconstructing process on landscape was depicted, and landscape functional zone was proposed a practical planning tool bridged human wellbeing. The task of landscape functional zoning with the management indications may provide interdisciplinary support to decision-makers and natural resource users on landscape management.

关键词： Landscape functional zone Ecosystem services classification algorithm County level Rural restructuring

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A Pruning Optimized Fast Learn plus plus NSE algorithm

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IEEE ACCESS 2021年 9卷 150733-150743页

作者： Chen, Yong Zhu, Yuquan Chen, Haifeng Shen, Yan Xu, Zhao Jiangsu Univ Sch Comp Sci & Commun Engn Zhenjiang 212013 Jiangsu Peoples R China China Unicom Internet Things Co Ltd Nanjing 210014 Jiangsu Peoples R China Jiangsu Univ Dept Informat Management & Informat Syst Zhenjiang 212013 Jiangsu Peoples R China Nanjing Longyuan Microelect Technol Co Ltd Nanjing 211106 Jiangsu Peoples R China

Due to the large number of typical applications, it is very important and urgent to study the fast classification learning of accumulated big data in nonstationary environments. The newly proposed algorithm, named Learn++.NSE, is one of the important research results in this research field. And a pruning version, named Learn++.NSE-Error-based, was given for accumulated big data to improve the learning efficiency. However, the studies have found that the Learn++.NSE-Error-based algorithm often encounters a situation that the newly generated base classifier is pruned in the next integration, which reduces the accuracy of the ensemble classifier. The newly generated base classifier is very important in the next ensemble learning and should be retained. Therefore, the two latest base classifiers are reserved without being pruned, and a new pruning algorithm named NewLearn++.NSE-Error-based was proposed. The experimental results on the generated dataset and the real-world dataset show that NewLearn++.NSE-Error-based can further improve the accuracy of the ensemble classifier under the premise of obtaining the same time complexity as Learn++.NSE algorithm. It is suitable for fast classification learning of long-term accumulated big data.

关键词： classification algorithms Big Data Error analysis Detection algorithms Data models Batch production systems Probability distribution Ensemble learning nonstationary environment classification algorithm big data mining

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Filtrování paketů v počítačových sítích

Filtrování paketů v počítačových sítích

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作者： Faron, Jan Brno University of Technology

Tato bakalářská práce se zabývá problematikou klasifikace paketů v počítačových sítích. V úvodu jsou popsány některé oblasti využívající klas... 详细信息

Tato bakalářská práce se zabývá problematikou klasifikace paketů v počítačových sítích. V úvodu jsou popsány některé oblasti využívající klasifikaci paketů. Dále je uvedena potřebná teorie spolu s požadavky na klasifikační algoritmus. Jsou popsány čtyři vysokoúrovňové přístupy ke klasifikaci paketů. Ke každému přístupu jsou popsány principy několika algoritmů. Pro detailnější rozbor a implementaci je vybrán algoritmus EffiCuts. Tento klasifikační algoritmus je porovnán s jinými klasifikačními algoritmy z knihovny NetBench.

关键词： klasifikace paketů efficuts klasifikační algoritmus packet classification efficuts classification algorithm Text

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