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LSTM, VADER and TF-IDF based Hybrid Sentiment Analysis Model

INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2021年第7期12卷 265-275页

作者： Chiny, Mohamed Chihab, Marouane Chihab, Younes Bencharef, Omar Ibn Tofail Univ Lab Comp Sci Kenitra Morocco Cadi Ayyad Univ Dept Comp Sci Marrakech Morocco

Most sentiment analysis models that use supervised learning algorithms consume a lot of labeled data in the training phase in order to give satisfactory results. This is usually expensive and leads to high labor costs in real-world applications. This work consists in proposing a hybrid sentiment analysis model based on a Long Short-Term Memory network, a rule-based sentiment analysis lexicon and the Term Frequency-Inverse Document Frequency weighting method. These three (input) models are combined in a binary classification model. In the latter, each of these algorithms has been implemented: Logistic Regression, k-Nearest Neighbors, Random Forest, Support Vector Machine and Naive Bayes. Then, the model has been trained on a limited amount of data from the IMDB dataset. The results of the evaluation on the IMDB data show a significant improvement in the Accuracy and Fl score compared to the best scores recorded by the three input models separately. On the other hand, the proposed model was able to transfer the knowledge gained on the IMDB dataset to better handle a new data from Twitter US Airlines Sentiments dataset.

关键词： Sentiment analysis hybrid model long short-term memory (LSTM) Valence Aware Dictionary and sEntiment Reasoner (VADER) term frequency-inverse document frequency (TF-IDF) classification algorithm

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Network embedding framework for driver gene discovery by combining functional and structural information

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BMC GENOMICS 2023年第1期24卷 426-426页

作者： Chu, Xin Guan, Boxin Dai, Lingyun Liu, Jin-xing Li, Feng Shang, Junliang Qufu Normal Univ Sch Comp Sci Rizhao 27826 Peoples R China

Comprehensive analysis of multiple data sets can identify potential driver genes for various cancers. In recent years, driver gene discovery based on massive mutation data and gene interaction networks has attracted increasing attention, but there is still a need to explore combining functional and structural information of genes in protein interaction networks to identify driver genes. Therefore, we propose a network embedding framework combining functional and structural information to identify driver genes. Firstly, we combine the mutation data and gene interaction networks to construct mutation integration network using network propagation algorithm. Secondly, the struc2vec model is used for extracting gene features from the mutation integration network, which contains both gene's functional and structural information. Finally, machine learning algorithms are utilized to identify the driver genes. Compared with the previous four excellent methods, our method can find gene pairs that are distant from each other through structural similarities and has better performance in identifying driver genes for 12 cancers in the cancer genome atlas. At the same time, we also conduct a comparative analysis of three gene interaction networks, three gene standard sets, and five machine learning algorithms. Our framework provides a new perspective for feature selection to identify novel driver genes.

关键词： Driver gene Gene interaction network Network embedding Mutation data classification algorithm

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Machine learning-based processing of unbalanced data sets for computer algorithms

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OPEN COMPUTER SCIENCE 2023年第1期13卷

作者： Zhou, Qingwei Qi, Yongjun Tang, Hailin Wu, Peng Guangdong Baiyun Univ Fac Megadata & Comp Guangzhou 510450 Guangdong Peoples R China Mongolian Univ Sci & Technol Sch Informat & Commun Technol Ulan Bator 13341 Mongolia Sichuan Tourism Univ Sch Informat & Engn Chengdu 610000 Sichuan Peoples R China

The rapid development of technology allows people to obtain a large amount of data, which contains important information and various noises. How to obtain useful knowledge from data is the most important thing at this stage of machine learning (ML). The problem of unbalanced classification is currently an important topic in the field of data mining and ML. At present, this problem has attracted more and more attention and is a relatively new challenge for academia and industry. The problem of unbalanced classification involves classifying data when there is insufficient data or severe category distribution deviations. Due to the inherent complexity of unbalanced data sets, more new algorithms and tools are needed to effectively convert a large amount of raw data into useful information and knowledge. Unbalanced data set is a special case of classification problem, in which the distribution between classes is uneven, and it is difficult to classify data accurately. This article mainly introduces the research on the processing method of computer algorithms based on the processing method of unbalanced data sets based on ML, aiming to provide some ideas and directions for the processing of computer algorithms based on unbalanced data sets based on ML. This article proposes a research strategy for processing unbalanced data sets based on ML, including data preprocessing, decision tree data classification algorithm, and C4.5 algorithm, which are used to conduct research experiments on processing methods for unbalanced data sets based on ML. The experimental results in this article show that the accuracy rate of the decision tree C4.5 algorithm based on ML is 94.80%, which can be better used for processing unbalanced data sets based on ML.

关键词： machine learning unbalanced data sets classification algorithm data processing computer algorithm decision tree classification

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Role of different types of RNA molecules in the severity prediction of SARS-CoV-2 patients

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PATHOLOGY RESEARCH AND PRACTICE 2023年 242卷 154311页

作者： Jeyananthan, Pratheeba Univ Jaffna Fac Engn Jaffna Sri Lanka

SARS-CoV-2 pandemic is the current threat of the world with enormous number of deceases. As most of the countries have constraints on resources, particularly for intensive care and oxygen, severity prediction with high accuracy is crucial. This prediction will help the medical society in the selection of patients with the need for these constrained resources. Literature shows that using clinical data in this study is the common trend and molecular data is rarely utilized in this prediction. As molecular data carry more disease related information, in this study, three different types of RNA molecules (lncRNA, miRNA and mRNA) of SARS-COV-2 patients are used to predict the severity stage and treatment stage of those patients. Using seven different machine learning al-gorithms along with several feature selection techniques shows that in both phenotypes, feature importance selected features provides the best accuracy along with random forest classifier. Further to this, it shows that in the severity stage prediction miRNA and lncRNA give the best performance, and lncRNA data gives the best in treatment stage prediction. As most of the studies related to molecular data uses mRNA data, this is an interesting finding.

关键词： Severity prediction classification algorithm lncRNA miRNA and mRNA COVID-19 molecular data Feature selection Treatment stage

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Antenna classification Using Gaussian Mixture Models (GMM) and Machine Learning

IEEE OPEN JOURNAL OF ANTENNAS AND PROPAGATION

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IEEE OPEN JOURNAL OF ANTENNAS AND PROPAGATION 2020年第1期1卷 320-328页

作者： Ma, Yihan Hao, Yang Queen Mary Univ London Elect Engn Dept London E1 4NS England

Radio frequency fingerprinting (RFF) is the concept arising from classification of wireless emitters due to their unique radio frequency features. RFF has been further extended to applications including both RF devices classification and wireless signal identification. In this paper, we adopt Gaussian Mixture Models (GMM) technique as feature extraction approach and firstly apply it to extract RFF of antennas. 9 classical antennas with 3 different load conditions (open, short, match) were studied in our experiment. Moreover, we also made a theoretical analysis about the reason scattered signal has the unique features. Specifically, we adopt the Random Noise Radar (RNR) technique to obtain reflected RF signals of antenna under test (AUT) and apply the GMM technique to fit RF signals and then extract the RFF of AUT. A support vector machine (SVM) is proposed to recognize the RFF at different signal-to-noise ratio (SNR) environment. Compared with the conventional feature extraction approaches, for example, from variance, skewness and kurtosis (VSK) values, our method demonstrates better performance on large datasets with classification accuracy above 88% using a SVM classifier. Moreover, the accuracy remains higher than 75% even when the Signal to Noise Ratio (SNR) is equal to 0dB, indicating that the proposed approach has the strong capability of noise immunity.

关键词： Antenna Gaussian mixture models radar signal processing classification algorithm machine learning

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Machine Learning for Explosive Detection from Electronic Nose Datasets 13

Machine Learning for Explosive Detection from Electronic Nos...

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13th International Conference on Knowledge and Smart Technology (KST)

作者： Wongwattanaporn, Supawit Phienthrakul, Tanasanee Mahidol Univ Fac Engn Dept Comp Engn Nakhon Pathom Thailand

ISBN: (纸本)9781728176024

An electronic nose has been applied in many areas such as the food industry, the environmental area, and this technology can be used to detect some explosives. Many classification machine learning techniques are applied for creating the model which manipulates data into a defined group to be used for customer grouping, marketing, anomaly detection, and medical analysis. The purpose of this research is to find a suitable classification technique to be applied in an electronic nose to imitate the ability of sniffer dogs to detect the chemical substances. This research compares the accuracy of eight different classification techniques, which are Logistic Regression, Support Vector Machine (SVM), Decision Tree, Random Forest (RF), Adaptive Boosting, K-Nearest Neighbors, Gaussian Naive Bayes, and Multilayer Perceptron in both binary and multi-class gas sensor array open source datasets. Experimental results show the top algorithms are RF, and SVM models, which give average score as 99.66 and 98.93, respectively.

关键词： electronic nose classification algorithm gas sensor array explosive detection

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Prediction Based on the Solution of the Set of classification Problems of Supervised Learning and Degrees of Membership

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PATTERN RECOGNITION AND IMAGE ANALYSIS 2020年第1期30卷 63-69页

作者： Lukanin, A. A. Ryazanov, V. V. Kiselyova, N. N. Natl Res Univ Moscow Inst Phys & Technol Moscow 115184 Russia Russian Acad Sci Fed Res Ctr Comp Sci & Control Moscow 119333 Russia Russian Acad Sci Baikov Inst Met & Mat Sci Moscow 119991 Russia

It is proposed to use the degrees of membership of objects to each class in the process of recognition in the linear corrector model to solve the problem of restoring dependences from precedent samples. Two models of the algorithm for calculating estimates are used as classifiers. The work of the proposed model is compared with the original method and with the well-known data analysis methods. The dependence of the work of the linear corrector on its parameters is studied.

关键词： regression dependence feature classification algorithm linear corrector algorithm for calculating estimates degree of membership

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Design and Analysis of Electronic Head Protector for Taekwondo Sports

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SENSORS 2022年第4期22卷 1415页

作者： Qureshi, Fayez Krishnan, Sri Ryerson Univ Dept Elect Comp & Biomed Engn Toronto ON M5B 2K3 Canada

Electronic point scoring systems (PSS) for vests are heavily relied upon in taekwondo. However, no classification and assessment of legal and illegal taekwondo techniques exist. This is also referred to as hit-validation and the objective of this research is to create an electronic helmet (eHelmet) for hit-validation. Three main studies were performed to achieve this objective: Robustness Testing, Sensor Placement and classification of Impacts to the head. The first two studies are preliminary to the main classification of Impacts study. This is needed as no data sets using an IMU are currently available for taekwondo. Robustness Testing: proved that IMU can in-fact be used in the inherently harsh environments of taekwondo with a linear response. The calculated response for the IMU is: f(x) = mx + b, where m is 0.2947 and b is 1.499 (accelerometer) and f(x) = mx + b, where m is 28.33 and b is 84.8 (gyroscope). Sensor Placement: Qualitatively and quantitatively concluded the ideal location for the sensor and electronics is indeed the back of the head, based on durability, cost, human factors, and signal quality. classification of Impacts: IMU classified real-world impacts with 90% accuracy. The two classes were roundhouse kick (legal) and punch (illegal). An eHelmet using an IMU is capable of classifying impacts with high accuracy. The benefit of our system includes low cost, lightweight algorithm for on-device computing (edge computing), and real-time classification. Furthermore, it possesses all the safety requirements of current protective headgear.

关键词： signal analysis classification algorithm IMU point scoring system taekwondo

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Classifying log del Pezzo surfaces with torus action

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Revista Matematica Complutense 2025年 1-74页

作者： Hättig, Daniel Hausen, Jürgen Springer, Justus Mathematisches Institut Universität Tübingen Auf der Morgenstelle 10 Tübingen 72076 Germany

We consider log del Pezzo surfaces coming with a non-trivial torus action. Such a surface is 1/k-log canonical if it allows a resolution of singularities with discrepanies all greater than or equal to 1/k-1. We provide a concrete classification algorithm for fixed k and give explicit results for k=1, 2 and 3. This comprises in particular the cases of Gorenstein index 1, 2 and 3. © The Author(s) 2025.

关键词： classification algorithm combinatorial minimality Log del Pezzo surfaces torus action

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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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