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Translator Data Pre-processing Gram Feature Algorithmic Model (TDGA) for Enhancing Classifier Accuracy in the Healthcare Domain

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SN Computer Science 2023年第5期4卷 542页

作者： Sathya, A. Mythili, M.S. Department of Computer Science Bishop Heber College (Autonomous) (Affiliated to Bharathidasan University) Tamil Nadu Trichy 620017 India Department of Computer Applications Bishop Heber College (Autonomous) (Affiliated to Bharathidasan University) Tamil Nadu Trichy 620017 India

Coronavirus COVID-19 has been spreading like wildfire all over the world since the year 2019. Nowadays, everyone is interacting with social media on a regular basis. In this study, the primary objective is to examine how Twitter users feel about COVID-19’s social life and to conclude their opinions. The polarity of feelings is determined by machine-learning classifiers that use popular words such as Coronavirus and COVID-19 to identify them. In addition, viruses such as BA-4 and BA-5 types of omicrons are spreading widely all over the globe. In order to prevent themselves from these types of viruses, the public needs to know the exact sentiment of the current social life problem. Using the newly reported topics/themes/issues and the associated sentiments from various factors, the COVID-19 pandemic can be better understood. A large dataset of tweets conveying information regarding COVID-19 is analyzed in this article, in particular, the credibility of the information shared on Twitter. It was evaluated using unigrams, bigrams, and trigrams with different parameters such as f1 score, precision, recall and compared against accuracy using machine-learning techniques such as Support Vector machine (SVM), Naive Bayes (NB), and Logistic Regression (LR). The model TDGA (Translator Data pre-processing Gram feature Algorithmic model) performs well in individuals’ assessments of COVID-19 and benchmark COVID datasets, with a maximum efficiency of 86%. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Language translation method machine-learning algorithms Pre-processing techniques Sentiment analysis Social media

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The Cognitive Cycle

The Cognitive Cycle

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3rd International Conference on Innovative Network Systems and Applications (iNetSApp) held in conjunction with Federated Conference on Computer Science and Information Systems (FedCSIS)

作者： Sowa, John F.

ISBN: (纸本)9788360810668

In the twenty years from first grade to a PhD, students never learn any subject by the methods for which machine-learning algorithms have been designed. Those algorithms are useful for analyzing large volumes of data. But they don't enable a computer system to learn a language as quickly and accurately as a three-year-old child. They're not even as effective as a mother raccoon teaching her babies how to find the best garbage cans. For all animals, learning is integrated with the cognitive cycle from perception to purposeful action. Many algorithms are needed to support that cycle. But an intelligent system must be more than a collection of algorithms. It must integrate them in a cognitive cycle of perception, learning, reasoning, and action. That cycle is key to designing intelligent systems.

关键词： cognition data analysis learning (artificial intelligence) cognitive cycle computer system intelligent system large data volume analysis machine-learning algorithms Games Hidden Markov models machine learning Mathematical model Psychology Solid modeling

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Sentence-based Plot Classification for Online Review Comments

Sentence-based Plot Classification for Online Review Comment...

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IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (WI-IAT)

作者： Iwai, Hidenari Hijikata, Yoshinori Ikeda, Kaori Nishida, Shogo Osaka Univ Grad Sch Engn Sci Suita Osaka 565 Japan

ISBN: (纸本)9781479941438

Many shopping sites provide functions to submit a user review for a purchased item. Reviews of items including stories such as novels and movies sometimes contain spoilers (undesired and revealing plot descriptions) along with the opinions of the review author. In this paper, we propose a system that helps users see reviews without seeing such plot descriptions. This system classifies each sentence in a user review as plot-related or non-plot-related and hides plot descriptions from user reviews. We tested five common machine-learning algorithms to ascertain the appropriate algorithm to address this problem. We also proposed a method of generalizing people's names, which we think is strongly related to the plot description. We verified its contribution to the classification results. Finally, we implemented a display interface of user reviews in which users can control the level of plot hiding.

关键词： customer satisfaction learning (artificial intelligence) pattern classification retail data processing user interfaces classilication results display interface machine-learning algorithms online review comments plot descriptions plot hiding purchased item review author sentence-based plot classification shopping sites user review Classification algorithms DVD Educational institutions Logistics machine learning algorithms Motion pictures Support vector machines bag of words machine learning plot classification sentiment analysis text mining machine learning algorithms Cinema business data processing Sorting algorithm Educational institutions text mining User Interface Device Component Pattern recognition Consumer Satisfaction Logistics Support Vector Network Films Digital versatile discs DIGITAL VIDEO DISC Consumers

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Modeling the properties of terminal blend crumb rubber modified bitumen with crosslinking additives

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CONSTRUCTION AND BUILDING MATERIALS 2024年 444卷

作者： Jegatheesan, N. Ibrahim, Mohd Rasdan Ahmed, Ali Najah Koting, Suhana El-Shafie, Ahmed Katman, Herda Yati Binti Univ Malaya Fac Engn Dept Civil Engn Kuala Lumpur 50603 Malaysia Univ Malaya Fac Engn Ctr Transportat Res Dept Civil Engn Kuala Lumpur 50603 Malaysia Sunway Univ Sch Engn & Technol Dept Engn Jalan Univ 5 Bandar Sunway 47500 Selangor Darul Malaysia United Arab Emirate Univ Natl Water & Energy Ctr POB 15551 Al Ain U Arab Emirates Univ Tenaga Nas Inst Energy Infrastruct IEI Putrajaya CampusJalan IKRAM UNITEN Kajang 43000 Selangor Malaysia

This study aimed to develop models assessing 26 machine-learning algorithms in regression analysis to predict the properties of terminal blend crumb rubber-modified bitumen (TB-CRMB) made with crosslinking additives. During the data collection, the properties of the modified binders prepared at 6, 10 and 14% of crumb rubber (CR), considering three types of modifications and eighteen blending scenarios with different interaction factors, were assessed in terms of penetration, softening point, rotational viscosity, storage stability, rheological parameters, and rutting and fatigue factors. Results showed that the Matern 5/2 Gaussian Process Regression (GPR) model demonstrated efficient performance in predicting physical, viscoelastic, rutting, and fatigue properties whereas wide artificial neural networks exhibited enhanced accuracy in predicting storage stability and rotational viscosity. The results also suggest that it is feasible to implement a single type of model developed using the Matern 5/2 GPR algorithm for accurately predicting all the TB-CRMB properties considered. The best models demonstrated that crosslinking additives significantly influenced TBCRMB production and performance. In TB-CRMB production, sulfur as a crosslinking additive showed better compatibility than trans-polyoctenamer-rubber and significantly reduced interaction temperatures at lower CR content, leading to energy savings compared to the traditional TB production.

关键词： machine-learning algorithms Prediction models Terminal blend-crumb rubber modified bitumen Crosslinking additive Composite modification High interaction parameters

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Development of a Universal Validation Protocol and an Open-Source Database for Multi-Contextual Facial Expression Recognition

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SENSORS 2023年第20期23卷 8376页

作者： La Monica, Ludovica Cenerini, Costanza Vollero, Luca Pennazza, Giorgio Santonico, Marco Keller, Flavio Univ Campus Biomed Roma Dept Engn Unit Computat Syst & Bioinformat I-00128 Rome Italy Univ Campus Biomed Roma Dept Engn Unit Elect Sensor Syst I-00128 Rome Italy Univ Campus Biomed Roma Unit Elect Sensor Syst Dept Sci & Technol Sustainable Dev & Hlth 1 I-00128 Rome Italy Univ Campus Biomed Roma Unit Dev Neurosci Dept Med I-00128 Rome Italy Inst Cognit Sci & Technol Natl Res Council I-00185 Rome Italy

Facial expression recognition (FER) poses a complex challenge due to diverse factors such as facial morphology variations, lighting conditions, and cultural nuances in emotion representation. To address these hurdles, specific FER algorithms leverage advanced data analysis for inferring emotional states from facial expressions. In this study, we introduce a universal validation methodology assessing any FER algorithm's performance through a web application where subjects respond to emotive images. We present the labelled data database, FeelPix, generated from facial landmark coordinates during FER algorithm validation. FeelPix is available to train and test generic FER algorithms, accurately identifying users' facial expressions. A testing algorithm classifies emotions based on FeelPix data, ensuring its reliability. Designed as a computationally lightweight solution, it finds applications in online systems. Our contribution improves facial expression recognition, enabling the identification and interpretation of emotions associated with facial expressions, offering profound insights into individuals' emotional reactions. This contribution has implications for healthcare, security, human-computer interaction, and entertainment.

关键词： facial expression recognition labelled data database facial landmarks machine-learning algorithms affective computing

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Using machine-learning-driven approaches to boost hot-spot's knowledge

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WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2022年第5期12卷 e1602-e1602页

作者： Rosario-Ferreira, Nicia Bonvin, Alexandre M. J. J. Moreira, Irina S. Univ Coimbra Fac Sci & Technol CQC Coimbra Chem Ctr Chem Dept Coimbra Portugal Univ Coimbra CIBB Ctr Innovat Biomed & Biotechnol Coimbra Portugal Univ Utrecht Sci Fac Chem Bijvoet Ctr Biomol Res Utrecht Netherlands Univ Coimbra Dept Life Sci Calcada Martim de Freitas P-3000456 Coimbra Portugal Univ Coimbra CNC Ctr Neurosci & Cell Biol CIBB Coimbra Portugal

Understanding protein-protein interactions (PPIs) is fundamental to describe and to characterize the formation of biomolecular assemblies, and to establish the energetic principles underlying biological networks. One key aspect of these interfaces is the existence and prevalence of hot-spots (HS) residues that, upon mutation to alanine, negatively impact the formation of such protein-protein complexes. HS have been widely considered in research, both in case studies and in a few large-scale predictive approaches. This review aims to present the current knowledge on PPIs, providing a detailed understanding of the microspecifications of the residues involved in those interactions and the characteristics of those defined as HS through a thorough assessment of related field-specific methodologies. We explore recent accurate artificial intelligence-based techniques, which are progressively replacing well-established classical energy-based methodologies. This article is categorized under: Data Science > Databases and Expert Systems Structure and Mechanism > Computational Biochemistry and Biophysics Molecular and Statistical Mechanics > Molecular Interactions

关键词： binding hot-spots computational alanine scanning mutagenesis interaction energetics machine-learning algorithms protein-protein interactions

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Detection of Android Malware in the Internet of Things through the K-Nearest Neighbor Algorithm

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SENSORS 2023年第16期23卷 7256-7256页

作者： Babbar, Himanshi Rani, Shalli Sah, Dipak Kumar Alqahtani, Salman A. Bashir, Ali Kashif Chitkara Univ Chitkara Univ Inst Engn & Technol Rajpura 140401 Punjab India GLA Univ Dept Comp Engn & Applicat Mathura 281406 Uttar Pradesh India King Saud Univ Coll Comp & Informat Sci Dept Comp Engn POB 51178 Riyadh 11543 Saudi Arabia Manchester Metropolitan Univ Dept Comp & Math Manchester M15 6BH England

Predicting attacks in Android malware devices using machine learning for recommender systems-based IoT can be a challenging task. However, it is possible to use various machine-learning techniques to achieve this goal. An internet-based framework is used to predict and recommend Android malware on IoT devices. As the prevalence of Android devices grows, the malware creates new viruses on a regular basis, posing a threat to the central system's security and the privacy of the users. The suggested system uses static analysis to predict the malware in Android apps used by consumer devices. The training of the presented system is used to predict and recommend malicious devices to block them from transmitting the data to the cloud server. By taking into account various machine-learning methods, feature selection is performed and the K-Nearest Neighbor (KNN) machine-learning model is proposed. Testing was carried out on more than 10,000 Android applications to check malicious nodes and recommend that the cloud server block them. The developed model contemplated all four machine-learning algorithms in parallel, i.e., naive Bayes, decision tree, support vector machine, and the K-Nearest Neighbor approach and static analysis as a feature subset selection algorithm, and it achieved the highest prediction rate of 93% to predict the malware in real-world applications of consumer devices to minimize the utilization of energy. The experimental results show that KNN achieves 93%, 95%, 90%, and 92% accuracy, precision, recall and f1 measures, respectively.

关键词： android malware recommender system Internet of Things static analysis machine-learning algorithms K-nearest neighbor

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Performance Prediction for a Marine Diesel Engine Waste Heat Absorption Refrigeration System

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ENERGIES 2022年第19期15卷 7070页

作者： Sun, Yongchao Sun, Pengyuan Zhang, Zhixiang Zhang, Shuchao Zhao, Jian Mei, Ning Ocean Univ China Coll Engn Qingdao 266100 Peoples R China Xiamen Univ Coll Energy Xiamen 361005 Peoples R China Dezhou State Owned Sports Ind Dev Ltd Dezhou 253300 Peoples R China Qingdao City Univ Coll Mech & Elect Engn Qingdao 266106 Peoples R China

The output of the absorption refrigeration system driven by exhaust gas is unstable and the efficiency is low. Therefore, it is necessary to keep the performance of absorption refrigeration systems in a stable state. This will help predict the dynamic parameters of the system and thus control the output of the system. This paper presents a machine-learning algorithm for predicting the key parameters of an ammonia-water absorption refrigeration system. Three new machine-learning algorithms, Elman, BP neural network (BPNN), and extreme learning machine (ELM), are tested to predict the system parameters. The key control parameters of the system are predicted according to the exhaust gas parameters, and the cooling system is adjusted according to the predicted values to achieve the goal of stable cooling output. After comparison, the ELM algorithm has a fast learning speed, good generalization performance, and small test set error sum, so it is selected as the final optimal prediction algorithm.

关键词： exhaust gas heat recovery ammonia-water-based absorption refrigeration quantitative control of refrigeration output machine-learning algorithms prediction

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Extracting Information on Rocky Desertification from Satellite Images: A Comparative Study

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REMOTE SENSING 2021年第13期13卷 2497-2497页

作者： Pu, Junwei Zhao, Xiaoqing Dong, Pinliang Wang, Qian Yue, Qifa Yunnan Univ Sch Earth Sci Kunming 650500 Yunnan Peoples R China Yunnan Univ Inst Int Rivers & Ecosecur Kunming 650500 Yunnan Peoples R China Univ North Texas Dept Geog & Environm Denton TX 76203 USA

Rocky desertification occurs in many karst terrains of the world and poses major challenges for regional sustainable development. Remotely sensed data can provide important information on rocky desertification. In this study, three common open-access satellite image datasets (Sentinel-2B, Landsat-8, and Gaofen-6) were used for extracting information on rocky desertification in a typical karst region (Guangnan County, Yunnan) of southwest China, using three machine-learning algorithms implemented in the Python programming language: random forest (RF), bagged decision tree (BDT), and extremely randomized trees (ERT). Comparative analyses of the three data sources and three algorithms show that: (1) The Sentinel-2B image has the best capability for extracting rocky desertification information, with an overall accuracy (OA) of 85.21% using the ERT method. This can be attributed to the higher spatial resolution of the Sentinel-2B image than that of Landsat-8 and Gaofen-6 images and Gaofen-6's lack of the shortwave infrared (SWIR) bands suitable for mapping carbonate rocks. (2) The ERT method has the best classification results of rocky desertification. Compared with the RF and BDT methods, the ERT method has stronger randomness in modeling and can effectively identify important feature factors for extracting information on rocky desertification. (3) The combination of the Sentinel-2B images and the ERT method provides an effective, efficient, and free approach to information extraction for mapping rocky desertification. The study can provide a useful reference for effective mapping of rocky desertification in similar karst environments of the world, in terms of both satellite image sources and classification algorithms. It also provides important information on the total area and spatial distribution of different levels of rocky desertification in the study area to support decision making by local governments for sustainable development.

关键词： rocky desertification open-access satellite image information extraction machine-learning algorithms southwest China

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A Classification Model Using Personal Biometric Characteristics to Identify Individuals Vulnerable to an Extremely Hot Environment

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JOURNAL OF MANAGEMENT IN ENGINEERING 2024年第2期40卷

作者： Choi, Yujin Seo, Seungwon Hong, Taehoon Koo, Choongwan Incheon Natl Univ Div Architecture & Urban Design Incheon 22012 South Korea Yonsei Univ Dept Architecture & Architectural Engn Seoul 03722 South Korea

The rise in heatwaves due to climate change is becoming a significant concern for outdoor workers, particularly leading to an increasing number of heat-related illnesses. To address the challenge, this study aimed to propose, as a process-based approach, a classification model using personal biometric characteristics to identify individuals who are vulnerable to extremely hot environments (i.e., high-risk groups). To this end, an experimental study was conducted, and experimental conditions were set in an environmental chamber by considering the extremely hot summer weather in Korea. With the data collected from a total of 70 people who voluntarily participated in the experiment, the classification model was developed by adopting multiple methodologies such as time-series clustering, independent samples t-test, and machine-learning algorithms. Consequently, it was found that the classification performance was the best with the multilayer perceptron algorithm, resulting in 0.800 in terms of the area under the receiver operating characteristic (AUROC) and 0.811 in terms of the area under the precision-recall curve (AUPRC). This study creates new ground in identifying individuals vulnerable to extremely hot environments in the domain of management in engineering by employing machine-learning-based classification algorithms with personal biometric characteristics. The proposed approach can be realized by utilizing a simple and low-cost bioelectrical impedance method for estimating human body composition (such as body fat mass and skeletal muscle mass) before they are put into the field. It is expected to aid in providing a more systematic and individualized management system for proactively preventing personal heat-related illnesses.

关键词： Heat strain Extremely hot environment High-risk group Classification Personal biometric characteristics machine-learning algorithms

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