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Semi-overlap functions and novel fuzzy reasoning algorithms with applications

INFORMATION SCIENCES 2022年 614卷 104-122页

作者： Zhang, Xiaohong Wang, Mei Bedregal, Benjamin Li, Mengyuan Liang, Rong Shaanxi Univ Sci & Technol Sch Math & Data Sci Xian 710021 Shaanxi Peoples R China Shaanxi Univ Sci & Technol Sch Elect & Control Engn Xian 710021 Shaanxi Peoples R China Univ Fed Rio Grande do Norte Dept Informat & Matemat Aplicada Campus Univ S-N BR-59072970 Natal Brazil

In order to get a more general result related on fuzzy implications that induced by aggre-gation functions, we relax the definition of general overlap functions, more precisely, removing its right-continuous, and introduce a new kind of aggregation function, which called semi-overlap function. Subsequently, we explore some of their related algebraic properties and its corresponding residual implications. Moreover, serval scholars have pro-vided kinds of methods for fuzzy modus ponens (FMP, for short) problems, such as Zadeh's compositional rule of inference (CRI, for short), Wang's triple I method (TIM, for short) and quintuple implication principle (QIP, for short). Compared with CRI and TIM, QIP has some advantages in solving FMP problems. Based on the above theory foundation of semi -overlap functions and their residual implications, we further consider the QIP for FMP problems. Finally, we propose a new classification algorithm that based on semi-overlap functions and QIP, which called SO5I-FRC algorithm. Through the comparative tests, the average accuracy of SO5I-FRC algorithm is higher than FARC-HD algorithm. The experi-mental results indicate that semi-overlap functions and QIP have certain advantages and a wide range of applications in classification problems.(c) 2022 Elsevier Inc. All rights reserved.

关键词： Overlap function Semi-overlap function Fuzzy reasoning Quintuple implication principle classification algorithm

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A Novel Machine Learning-based Framework for Detecting Religious Arabic Hatred Speech in Social Networks

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2022年第9期13卷 767-776页

作者： Masadeh, Mahmoud Davanager, Hanumanthappa Jayappa Muaad, Abdullah Y. Yarmouk Univ Comp Engn Dept Irbid 21163 Jordan Mysore Univ Dept Studies Comp Sci Mysore 570006 Manasagangothri India Sanaa Community Coll Sanaa 5695 Yemen

Social media platforms generate a huge amount of data every day. However, liberty of speech through these networks could easily help in spreading hatred. Hate speech is a severe concern endangering the cohesion and structure of civil societies. With the increase in hate and sarcasm among the people who contact others over the internet in this era, there is a dire need for utilizing artificial intelligence (AI) technology innovation that would face this problem. The rampant spread of hate can dangerously break society and severely damage marginalized people or groups. Thus, the identification of hate speech is essential and becoming more challenging, where the recognition of hate speech on time is crucial in stopping its dissemination. The capacity of the Arabic morphology and the scarcity of resources for the Arabic language makes the task of distinguishing hate speech even more demanding. For fast identification of Arabic hate speech in social network comments, this work presents a comprehensive framework with eight machine learning (ML) and deep learning (DL) algorithms, namely Gradient Boosting (GB), K-Nearest Neighbor (K-NN), Logistic Regression (LR), Naive Bayes (NB), Passive Aggressive Classifier (PAC), Support Vector Machine (SVM), Ara-BERT, and BERT-AJGT are implemented. Two representation techniques have been used in the proposed framework in order to extract features: a bag of words followed by BERT-based context text representations. Based on the result and discussion part, context text representation techniques with Ara-BERT and BERT-AJGT outperform all other ML models and related work with accuracy equal to 79% for both models.

关键词： Machine learning Arabic language hatred detection social network classification algorithm

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Impedance technique combined with supervised algorithms-based internal degradation state classification and its economic analysis for safety in retired battery pack

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JOURNAL OF ENERGY STORAGE 2023年 73卷

作者： Lee, Miyoung Han, Dongho Yoo, Kisoo Kim, Jonghoon Chungnam Natl Univ Dept Elect Engn Energy Storage & Convers Lab Daejeon 34134 South Korea Yeungnam Univ Sch Mech Engn Gyoungsan 38541 South Korea

As the growth of lithium-ion batteries (LIBs) applications accelerates, future problems for waste LIB disposal are also appeared. When LIB reaches 80 % of the initial capacity, it is defined as the End-of-life (EOL). Since power and capacity of the LIB abruptly decreases at this point, it is determined that the LIB can't be used. However, in the case of the battery pack, even if it reaches 80 % of its initial capacity, it can be reused with secondary applications such as energy storage devices and uninterruptible power supplies (UPS). Therefore, in order to reduce environmental problems, such a battery pack reuse technique is required. However, as the internal degradation state of the LIB differs depending on the operating environment, research is needed to select the LIB in the same state through classification algorithm and configure the waste battery pack. Since the internal degradation of LIB cannot be evaluated qualitatively with existing technology, like capacity and internal resistance, so a new qualitative evaluation factor is required. This research classified an internal degradation state according to LIB environment using an impedance technique and supervised algorithms (Decision tree, Support vector machine, K-nearest neighbor) that could qualitatively represent LIB degradation mechanism. And it is validated that new parameters are good at classifying operating environment compared with existing parameters. Also, Economic analysis was performed for comparison with existing methods, and as a result, it was proved that it showed superior performance compared to existing techniques in both classification accuracy and economic analysis.

关键词： Electrochemical impedance spectroscopy (EIS) classification algorithm Support vector machine (SVM) Decision tree algorithm K-nearest neighbor algorithm (KNN) Battery operating environment classification

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Predicting Employee Turnover in IT Industries using Correlation and Chi-Square Visualization

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2022年第12期13卷 71-75页

作者： Priambodo, Bagus Jumaryadi, Yuwan Rahayu, Sarwati Ani, Nur Ratnasari, Anita Salamah, Umniy Putra, Zico Pratama Otong, Muhamad Univ Mercu Buana Fac Comp Sci Jakarta Indonesia Univ Nusa Mandiri Fac Informat Technol Jakarta Indonesia Univ Sultan Ageng Tirtayasa Dept Elect Engn Serang Indonesia

Employee turnover in the IT industry is among the highest compared to other industries. Knowing factors that influence the turnover may help reduce this issue in future. One of these factors is job satisfaction, which are composed of two important factors, status and seniority. In this study, the correlation and chi-square visualization are utilized to determine the factors that affect employee turnover. The experiment was carried out to predict turnover using a private IT consultant dataset comparing three classification algorithms (decision tree, Naive Bayes, and Random Forest). The result shows that job duration and positioning are factors that influence employee turnover in a software company.

关键词： Employee turnover turnover factors chi-square classification algorithm

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Identification of Human Protein Subcellular Location with Multiple Networks

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CURRENT PROTEOMICS 2022年第4期19卷 344-356页

作者： Wang, Rui Chen, Lei Shanghai Maritime Univ Coll Informat Engn Shanghai 201306 Peoples R China

Background: Protein function is closely related to its location within the cell. Determination of protein subcellular location is helpful in uncovering its functions. However, traditional biological experiments to determine the subcellular location are of high cost and low efficiency, which cannot meet today's needs. In recent years, many computational models have been set up to identify the subcellular location of proteins. Most models use features derived from protein sequences. Recently, features extracted from the protein-protein interaction (PPI) network have become popular in studying various protein-related problems. Objective: A novel model with features derived from multiple PPI networks was proposed to predict protein subcellular location. Methods: Protein features were obtained by a newly designed network embedding algorithm, Mnode2vec, which is a generalized version of the classic Node2vec algorithm. Two classic classification algorithms: support vector machine and random forest, were employed to build the model. Results: Such model provided good performance and was superior to the model with features extracted by Node2vec. Also, this model outperformed some classic models. Furthermore, Mnode2vec was found to produce powerful features when the path length was small. Conclusion: The proposed model can be a powerful tool to determine protein subcellular location, and Mnode2vec can efficiently extract informative features from multiple networks.

关键词： Protein subcellular location multiple networks network embedding algorithm Mnode2vec node2vec classification algorithm

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Design and Implementation of a Multi-layered Employee Profile System for Power Enterprises Based on LSTM and Mini-batch K-means 5

Design and Implementation of a Multi-layered Employee Profil...

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5th International Conference on Frontiers Technology of Information and Computer, ICFTIC 2023

作者： Wang, Ziheng Li, Xiaozhen Meng, Yu Zhang, Qian State Grid Information and Communication Industry Group Co. LTD. Beijing China

ISBN: (纸本)9798350309034

Abstract-To facilitate more efficient and convenient online office work, major enterprises have launched mobile portal applications, integrating various office micro-applications to provide employees with a unified entry point. However, employees in different positions have varying application demands and there is a lack of a personalized recommendation mechanism. Accurately profiling employees has thus become a research focus for many scholars. In response to this issue, this paper proposes a multi-layered employee profile system for power enterprises. We designed a data collection model suitable for processing massive logs, and used LSTM and mini-batch K-means algorithms to cluster employees at two levels: application usage and provincial distribution. Employee labels were established through statistical characteristics and professional analysis, and a classification model was trained for employee profiling. To verify the effectiveness of the system proposed in this paper, real data was collected to conduct experiments. The results were compared with traditional clustering algorithms and other outstanding profile algorithms from previous studies. The findings demonstrate that the profile system proposed in this paper achieves superior performance in profiling employees of power enterprises compared to other algorithms. © 2023 IEEE.

关键词： classification algorithm Clustering algorithm LSTM Mini-batch K-means User Profiling

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Data mining: will first-year results predict the likelihood of completing subsequent units in accounting programs?

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ACCOUNTING EDUCATION 2023年第4期32卷 409-444页

作者： Sithole, Seedwell T. M. Ran, Guang de Lange, Paul Tharapos, Meredith O'Connell, Brendan Beatson, Nicola Univ Tasmania Tasmanian Sch Business & Econ Accounting Hobart Tas Australia RMIT Univ Sch Accounting Informat Syst & Supply Chain Melbourne Vic Australia Univ Otago Dept Accountancy & Finance Dunedin New Zealand

This study introduces data mining methods to accounting education scholarship to explore the relationship between accounting students' current academic performance (grades), demographic information, pre-university entrance scores and predicted academic performance. It adopts a C4.5 classification algorithm based on decision-tree analysis to examine 640 accounting students enrolled in an undergraduate accounting program at an Australian university. A significant contribution of this study is improved prediction of academic performance and identification of characteristics of students deemed to be at risk. By partitioning students into sub-groups based on tertiary entrance scores and employing clustering of study units, this study facilitates a more nuanced understanding of predictor attributes. Key findings were the dominance of a cluster of second year units in predicting students' later academic performance;that gender did not influence performance;and that performance in first year at university, rather than secondary school grades, was the most important predictor of subsequent academic performance.

关键词： Academic performance accounting students classification algorithm educational data mining performance prediction

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Binary Hermitian Lattices over Number Fields

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EXPERIMENTAL MATHEMATICS 2022年第1期31卷 280-301页

作者： Kirschmer, Markus Nebe, Gabriele Rhein Westfal TH Aachen Math D D-52056 Aachen Germany

In a previous paper the authors developed an algorithm to classify certain quaternary quadratic lattices over totally real fields. The present article applies this algorithm to the classification of binary Hermitian lattices over totally imaginary fields. We use it in particular to classify the 48-dimensional extremal even unimodular lattices over the integers that admit a semilarge automorphism.

关键词： binary Hermitian forms Hermitian lattices over CM-fields genera of lattices orders in quaternion algebras class numbers classification algorithm

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Crop Advancement with Machine Learning 7

Crop Advancement with Machine Learning

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2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Pawar, Rajendra Bisht, Vishnu Pratap Pandey, Ritika Lodhe, Urvik Pawar, Aryan Chouksey, Aaryan Jatale, Sarthak School of Computer Engineering & Technology Mit World Peace University Maharashtra Pune India

ISBN: (纸本)9798350304268

Crop prediction is a crucial task in agriculture that can help farmers and agricultural researchers decide which crops to grow based on environmental variables such as soil type, temperature, rainfall, and humidity. In this project, we propose a data-driven approach for crop prediction using K-means clustering and classification algorithms. We use K-means clustering to group the data into different clusters based on similarities in the environmental variables, and then we use a classification algorithm such as decision trees or support vector machines to predict the best crop to grow in a particular location. This approach can help farmers and agricultural researchers make more informed decisions about crop selection and improve crop yields. We validate our model by comparing historical data on crop yields to our predictions and conducting experiments by growing different crops in different locations. Overall, this project demonstrates the potential of machine learning algorithms for crop prediction in agriculture. © 2023 IEEE.

关键词： Agriculture classification algorithm Crop prediction Environmental variables K-means clustering

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Screening gene signatures for clinical response subtypes of lung transplantation

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MOLECULAR GENETICS AND GENOMICS 2022年第5期297卷 1301-1313页

作者： Zhang, Yu-Hang Li, Zhan Dong Zeng, Tao Chen, Lei Huang, Tao Cai, Yu-Dong Shanghai Univ Sch Life Sci Shanghai 200444 Peoples R China Harvard Med Sch Brigham & Womens Hosp Channing Div Network Med Boston MA 02115 USA Jilin Engn Normal Univ Coll Food Engn Changchun 130052 Peoples R China Chinese Acad Sci Univ Chinese Acad Sci Biomed Big Data Ctr Shanghai Inst Nutr & HlthCAS Key Lab Computat Bi Shanghai 200031 Peoples R China Shanghai Maritime Univ Coll Informat Engn Shanghai 201306 Peoples R China Chinese Acad Sci Univ Chinese Acad Sci Shanghai Inst Nutr & Hlth CAS Key Lab Tissue Microenvironm & Tumor Shanghai Peoples R China

Lung is the most important organ in the human respiratory system, whose normal functions are quite essential for human beings. Under certain pathological conditions, the normal lung functions could no longer be maintained in patients, and lung transplantation is generally applied to ease patients' breathing and prolong their lives. However, several risk factors exist during and after lung transplantation, including bleeding, infection, and transplant rejections. In particular, transplant rejections are difficult to predict or prevent, leading to the most dangerous complications and severe status in patients undergoing lung transplantation. Given that most common monitoring and validation methods for lung transplantation rejections may take quite a long time and have low reproducibility, new technologies and methods are required to improve the efficacy and accuracy of rejection monitoring after lung transplantation. Recently, one previous study set up the gene expression profiles of patients who underwent lung transplantation. However, it did not provide a tool to predict lung transplantation responses. Here, a further deep investigation was conducted on such profiling data. A computational framework, incorporating several machine learning algorithms, such as feature selection methods and classification algorithms, was built to establish an effective prediction model distinguishing patient into different clinical subgroups, corresponding to different rejection responses after lung transplantation. Furthermore, the framework also screened essential genes with functional enrichments and create quantitative rules for the distinction of patients with different rejection responses to lung transplantation. The outcome of this contribution could provide guidelines for clinical treatment of each rejection subtype and contribute to the revealing of complicated rejection mechanisms of lung transplantation.

关键词： Lung transplantation Gene signatures classification algorithm Feature selection

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