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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Pu, Li'e Ru, Huasuo Tang, Yufang Yunnan Land and Resources Vocational College Yunnan Kunming650500 China

ISBN: (纸本)9798350319125

Digital media object classification plays an important role in digital media, but there is a problem of low classification accuracy. Deep learning algorithms cannot improve the accuracy of digital media object classification. Therefore, this paper proposes a large-interval distribution learning algorithm to classify digital media objects. Digital media objects are analyzed through distributed learning, and indicators are divided according to classification requirements. The large interval distribution learning algorithm forms the digital media object classification scheme, and the formed classification scheme is comprehensively analyzed. The simulation results show that the classification accuracy and classification efficiency of the proposed algorithm are better than those of the deep learning algorithm. © 2023 IEEE.

关键词： arge interval distribution learning classification algorithm digital media object classification

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A Comparison of classification algorithms Based on The Number of Features 39

A Comparison of Classification Algorithms Based on The Numbe...

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39th Chinese Control Conference (CCC)

作者： Xiong, Peichen Ping, Wei Xian Jiaotong Liverpool Univ Comp Sci & Software Engn Suzhou 215123 Peoples R China Ningbo Univ Coll Sci & Technol Sch Informat Engn Ningbo 315300 Peoples R China

ISBN: (纸本)9789881563903

As the most widely known data milling algorithm, classification algorithms have attracted wide attention. K-Nearest Neighbor (KNN) algorithm and decision tree algorithm are the two widely known algorithms in classification algorithms. Sometimes, people not sure how to choose the suitable to solve the classification problems. In this paper, we establish KNN algorithm model and decision tree ID3 algorithm model to analyze the accuracy of the two algorithms in the same data set with different number of features. Through the learning curve and cross validation, we find ID3 algorithm is better than KNN algorithm, and when the number of feature increased the accuracy of KNN is increasing while ID3 is decreased.

关键词： KNN Decision Tree ID3 classification algorithm

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The Anatomy of Sorting-Evidence From Danish Data

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ECONOMETRICA 2023年第6期91卷 2409-2455页

作者： Lentz, Rasmus Piyapromdee, Suphanit Robin, Jean-Marc Univ Wisconsin Dept Econ Madison WI 53715 USA Univ Aarhus Dale T Mortensen Ctr Aarhus Denmark UCL Dept Econ London England Sci Po Sci Paris France

In this paper, we formulate and estimate a flexible model of job mobility and wages with two-sided heterogeneity. The analysis extends the finite mixture approach of Bonhomme, Lamadon, and Manresa (2019) and Abowd, McKinney, and Schmutte (2019) to develop a new classification Expectation-Maximization algorithm that ensures both worker and firm latent-type identification using wage and mobility variations in the data. Workers receive job offers in worker-type segmented labor markets. Offers are accepted according to a logit form that compares the value of the current job with that of the new job. In combination with flexibly estimated layoff and job finding rates, the analysis quantifies the four different sources of sorting: job preferences, segmentation, layoffs, and job finding. Job preferences are identified through job-to-job moves in a revealed preference argument. They are in the model structurally independent of the identified job wages, possibly as a reflection of the presence of amenities. We find evidence of a strong pecuniary motive in job preferences. While the correlation between preferences and current job wages is positive, the net present value of the future earnings stream given the current job correlates much more strongly with preferences for it. This is more so for short- than long-tenure workers. In the analysis, we distinguish between type sorting and wage sorting. Type sorting is quantified by means of the mutual information index. Wage sorting is captured through correlation between identified wage types. While layoffs are less important than the other channels, we find all channels to contribute substantially to sorting. As workers age, job arrival processes are the key determinant of wage sorting, whereas the role of job preferences dictate type sorting. Over the life cycle, job preferences intensify, type sorting increases, and pecuniary considerations wane.

关键词： Heterogeneity wage distributions employment and job mobility mutual information finite mixtures EM algorithm classification algorithm sorting decomposition of wage inequality

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Method of Physiological State Abnormality Discrimination Based on Improved KNN algorithm 10

Method of Physiological State Abnormality Discrimination Bas...

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10th International Conference on Big Data and Information Analytics

作者： Miao Yongfei Li Xiaolong Wu Shikai Zhao Xiaoqing Yang Yufeng Yu Yue Air Force Commun NCO Acad Dept Ground Air Nav Dalian Peoples R China

ISBN: (数字)9798350354621

ISBN: (纸本)9798350354638;9798350354621

Physiological state abnormality due to genetic diseases, excessive exercise, etc. is becoming a fatal killer endangering people's life and safety because of its hidden characteristics. K-Nearest Neighbor(KNN) algorithm is widely used in various fields due to its simple implementation, but when the sample capacity is too large or the feature attributes are too many, the classification efficiency decreases significantly. This paper proposed an improved KNN(IKNN) algorithm based on clustering by hierarchically clustering the data in data pre-processing, which reduced the search space of the algorithm and effectively improved the search efficiency. When the improved KNN algorithm was applied in the physiological state abnormality discrimination field, which better improved the efficiency and accuracy of physiological abnormality discrimination. Results show that this could provide an effective guarantee for the early discovery of physiological parameter abnormality symptom, the timely adoption of dispositive measures, and the maintenance of people's life safety.

关键词： Physiological Abnormality classification algorithm Hierarchical Clustering KNN algorithm

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Analysis of Student Performance to Predict Career Specialization using Random Forest Data Mining Technique 24

Analysis of Student Performance to Predict Career Specializa...

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9th International Conference on Intelligent Information Technology (ICIIT)

作者： Hermogenes, Mary Grace G. Repaso, Jennifer Anne A. Perez, Joann G. Bulacan State Univ Malolos Philippines

ISBN: (纸本)9798400716713

Career specialization has emerged as a critical factor for students to attain success in today's highly competitive job market. Therefore, analyzing students' academic performance data to predict their career specialization can be a valuable tool for educational institutions. This study uses Random Forest to forecast graduates' job specialties. The study employed 360 records of IT graduates and the related grades from their technical/major subjects. The conducted tracer research was used to determine the graduates' present occupations. The model made use of 16 attributes in total. An accuracy rate of 87.19% was obtained from the prediction, which is quite good. The collected data also revealed that while students who perform fairly well in most of the major topics may have another relevant vocation, those who achieve very well will likely obtain a job as a developer.

关键词： Career Prediction classification algorithm Learning Analytics Prediction Random Forest

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Brain Microstructural Changes in Patients with Amnestic mild Cognitive Impairment Detected by Neurite Orientation Dispersion and Density Imaging (NODDI) Combined with Machine Learning

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CLINICAL NEURORADIOLOGY 2023年第2期33卷 445-453页

作者： Fu, Xiuwei Wang, Xiaonan Zhang, Yu Li, Tongtong Tan, Zixuan Chen, Yuanyuan Zhang, Xianchang Ni, Hongyan Tianjin Med Univ Gen Hosp Dept Radiol Tianjin Peoples R China Tianjin Med Univ Cent Clin Inst 1 Dept Radiol Tianjin Peoples R China Tianjin Med Univ Inst Med Imaging Tianjin Peoples R China Tianjin Univ Inst Med Engn & Translat Med Tianjin Peoples R China Siemens Healthineers Ltd MR Collaborat Beijing Peoples R China Tianjin First Cent Hosp Dept Radiol 24 Fukang Rd Tianjin 300192 Peoples R China

Purpose This study investigated brain microstructural changes in patients with amnestic mild cognitive impairment (aMCI) by retrospectively analyzing neurite orientation dispersion and density imaging (NODDI) data with machine learning *** A total of 26 aMCI patients and 24 healthy controls (HC) underwent NODDI magnetic resonance imaging (MRI) examinations. The NODDI parameters including neurite density index (NDI), orientation dispersion index (ODI), and volume fraction of isotropic water molecules (Viso) were estimated. Machine learning algorithms such as K-nearest neighbor (KNN), logistic regression (LR), random forest (RF), and support vector machine (SVM) were used to evaluate the diagnostic efficacy of NODDI parameters in predicting aMCI. The differences in the NODDI parameter values between the aMCI and HC groups were analyzed using the independent sample t-test, False discovery rate (FDR) correction was used for multiple testing. After adjusting for age, sex, and educational years, partial correlation analysis was used to evaluate the relationship between NODDI parameters and clinical cognitive status of aMCI *** The NDI, ODI, and Viso values of white matter (WM) and gray matter (GM) structure templates combined with the KNN, LR, RF and SVM machine learning algorithms accomplished the discrimination between aMCI and HC groups. The NDI and ODI values decreased (p value range, < 0.001-0.042) and Viso values increased (p value range, < 0.001-0.043) in the aMCI group compared to the HCs. The NDI, ODI, and Viso values of the WM and GM structure templates with significant differences were significantly correlated with mini-mental state examination (MMSE) and Montreal cognitive assessment (MoCA) *** NODDI combined with machine learning algorithms is a promising strategy for early diagnosis of aMCI. Moreover, NODDI parameters correlated with the clinical cognitive status of aMCI patients.

关键词： Cognitive impairment classification algorithm Diffusion weighted imaging Neurite density Orientation dispersion

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Quantum Entanglement Inspired Correlation Learning for classification 26th

Quantum Entanglement Inspired Correlation Learning for Class...

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26th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

作者： Zhang, Junwei Li, Zhao Wang, Juan Wang, Yinghui Hu, Shichang Xiao, Jie Li, Zhaolin Tianjin Univ Coll Intelligence & Comp1 Tianjin Peoples R China Zhejiang Univ Hangzhou Zhejiang Peoples R China Alibaba Grp Hangzhou Zhejiang Peoples R China Hangzhou Yugu Technol Co Ltd Hangzhou Peoples R China Tianjin Xiniu Huaan Technol Co Ltd Tianjin Peoples R China

ISBN: (纸本)9783031059360;9783031059353

Correlation is an important information resource, which is often used as a fundamental quantity for modeling tasks in machine learning. Since correlation between quantum entangled systems often surpasses that between classical systems, quantum information processing methods show superiority that classical methods do not possess. In this paper, we study the virtue of entangled systems and propose a novel classification algorithm called Quantum Entanglement inspired the classification algorithm (QECA). Particularly, we use the joint probability derived from entangled systems to model correlation between features and categories, that is, Quantum Correlation (QC), and leverage it to develop a novel QC-induced Multi-layer Perceptron framework for classification tasks. Experimental results on four datasets from diverse domains show that QECA is significantly better than the baseline methods, which demonstrates that QC revealed by entangled systems can improve the classification performance of traditional algorithms.

关键词： Quantum correlation Quantum-inspired algorithms classification algorithm

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Emotion classification of Songs Using Deep Learning

Emotion Classification of Songs Using Deep Learning

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International Conference on Green Energy, Computing and Sustainable Technology (GECOST)

作者： Mate, Nikita Akre, Durva Patil, Gaurav Sakarkar, Gopal Basuki, Thomas Anung GH Raisoni Coll Engn Dept Artificial Intelligence Nagpur Maharashtra India DY Patil Inst Master Comp Applicat & Management Pune Maharashtra India Univ Southampton Dept Comp Sci Iskandar Puteri Johor Malaysia

ISBN: (纸本)9781665486637

The sentiment prediction of songs will have a vast quantity of applications within the current era, like the Music recommendation system for request, choosing the music for public gatherings like procession or restaurants to enhance the emotional standing of a personal or cluster, seemingly folks and customers. A song is one way to say something indirectly and one of the most adventurous parts is that the one song could express more than one emotion. We can use songs to apologize, congratulate, express happiness or unhappiness, etc. Emotion is a vital part of daily life: it affects choice-making, conception, human interdependence, and human understanding. There are units of positive emotions and negative emotions;positive emotions are units a lot related to human health also as work potency, whereas negative emotions could cause health issues. In this research paper, we are going to use Deep Learning Frameworks, which are mostly used for classification techniques like Multilayer Perceptron (MLP), Gated Recurrent Units(GRU), Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM). In this paper, the dataset operated is the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS)[1] for the enactment of audio categorization. As a result, we obtained graphs for each Deep Learning framework that is for training and validation loss, training and validation accuracy. After implementation, we got the highest accuracy for CNN that is 92.71% and the least accuracy for MLP that is 52.60% and for LSTM and GRU it lies between the range of 63% - 66%.

关键词： Emotion classification Deep Learning algorithms classification algorithm

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Data Mining Applied to the HFC Network to Analyze the Availability of Telecommunication Services 4th

Data Mining Applied to the HFC Network to Analyze the Availa...

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4th International Research and Innovation Congress - Smart Technologies and Systems (CI3)

作者： Alarcon-Loza, Shirley Estacio-Corozo, Karen Inst Super Tecnol ARGOS Guayaquil Ecuador

ISBN: (纸本)9783031634338;9783031634345

The failures that affect the telecommunications service in hybrid fiber-coaxial networks (HFC) can be anticipated, through data analysis, to minimize the impact on service availability and users. This work seeks to analyze the causes that affect the performance of HFC networks in Ecuador, through a predictive model for their timely detection and application of anticipated works. The base studied included the components and causes that affected the availability of television and telephony service in a HFC network in 2020, from a sample of 17707 records. For the research, the dependent variable CATEGORY CAUSE is considered, and the objective was to analyze the causes that affect the telecommunications service. The KDD methodology was used and, from the use of WEKA software, classification algorithms were established to determine suitable predictive features, being the causes, the type of event, and the network components, the most significant. The results showed that, for the television service, the best classification algorithm was J48, with a precision value of 68.668% and an area under the curve (ROC Area) of 0.913. For the telephony service, the best classification algorithm was Random Tree, with a precision of 73.666% and an area under the curve of 0.969. The conducted research demonstrated the importance of data mining in the process of analyzing the causes of the impact on television and telephone services.

关键词： HFC network Hybrid fiber-coaxial networks Data Mining classification algorithm

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Oxides classification with Random Forests 18th

Oxides Classification with Random Forests

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18th International Conference on Intelligent Computing (ICIC)

作者： Xiao, Kai Chen, Baitong Bao, Wenzheng Cheng, Honglin Xuzhou Univ Technol Sch Informat Engn Xuzhou 221018 Jiangsu Peoples R China Xuzhou First Peoples Hosp Xuzhou 221000 Jiangsu Peoples R China

ISBN: (纸本)9783031138294;9783031138287

Oxides are divided into binary and ternary oxides according to different components. Binary oxides are widely used in industrial catalysis, and the development of new energy field of ternary oxides is also very broad. This paper mainly studies the oxide classification method based on machine learning. In order to facilitate calculation, the method of describing oxides is redefined. The prediction results are evaluated according to the accuracy of evaluation index, F1 score, accuracy and recall rate, and the best algorithm is selected. The results show that the random forest has a good classification effect and is of great significance to the identification of oxides.

关键词： Oxide classification algorithm Machine learning Random forests

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