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Bright, periodic, compacton and bell-shape soliton solutions of the extended QZK and (3+1)-dimensional ZK equations

Communications in Theoretical Physics 2021年第10期73卷 23-35页

作者： M Ali Akbar Md Abdul Kayum M S Osman Department of Applied Mathematics University of RajshahiBangladesh Department of Computer Science&Engineering North Bengal International UniversityRajshahiBangladesh Department of Mathematics Faculty of ScienceCairo UniversityGiza 12613Egypt

The(3+1)-dimensional Zakharov–Kuznetsov(ZK) and the new extended quantum ZK equations are functional to decipher the dense quantum plasma, ion-acoustic waves, electron thermal energy,ion plasma, quantum acoustic waves, and quantum Langmuir waves. The enhanced modified simple equation(EMSE) method is a substantial approach to determine competent solutions and in this article, we have constructed standard, illustrative, rich structured and further comprehensive soliton solutions via this method. The solutions are ascertained as the integration of exponential, hyperbolic,trigonometric and rational functions and formulate the bright solitons, periodic, compacton, bellshape, parabolic shape, singular periodic, plane shape and some new type of solitons. It is worth noting that the wave profile varies as the physical and subsidiary parameters change. The standard and advanced soliton solutions may be useful to assist in describing the physical phenomena previously mentioned. To open out the inward structure of the tangible incidents, we have portrayed the three-dimensional, contour plot, and two-dimensional graphs for different parametric values. The attained results demonstrate the EMSE technique for extracting soliton solutions to nonlinear evolution equations is efficient, compatible and reliable in nonlinear science and engineering.

关键词： (3+1)-dimensional ZK the extended QZK equation enhanced modified simple equation method soliton solutions NLEEs

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Unmasking Hate: Telugu Language Hate Speech Detection Using Transformers

SSRN

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SSRN 2023年

作者： Khanduja, Namit Kumar, Nishant Chauhan, Arun Department of Computer Science & Engineering Faculty of Engineering & Technology Gurukula Kangri Deemed to be University Uttarakhand Haridwar India Department of Computer Science & Engineering Graphic Era Deemed to be University Uttarakhand Dehradun India

In today's digital era, social media has become a new tool of communication and sharing information, which tends to reach masses much faster as compared with earlier methods of communication and information sharing. Advancement in the proliferation of abusive language and hate speech has become a growing concern, particularly on social media platforms. While efforts have been made to curb such behaviour in high resource languages like English, speakers of low-resource languages such as Telugu continue to engage in abusive language against others. Our primary objective is to combat the rampant usage of abusive language in Telugu and contribute towards a safer online environment. By leveraging advanced natural language processing techniques, we aim to pave the way for effective measures in reducing the prevalence of hate speech in low-resource languages. The study focuses on hate speech classification in the Telugu language, a widely spoken Dravidian language in India, using state-of-the-art transformer models. In this evaluation study we present an in-depth analysis of the performance of several pre-trained transformer models fine-tuned for hate speech detection on a custom Telugu dataset. Curated dataset has been developed using a snscrape tool, by utilizing the vast expanse of Twitter as primary data source. The models evaluated in this study include mBERT, DistilBERT, IndicBERT, NLLB, Muril, RNN+LSTM, XLM-RoBERTa, and Indic-Bart. Through extensive experimentation and evaluation, study compares the effectiveness of various transformer models in identifying hate speech in Telugu text. © 2023, The Authors. All rights reserved.

关键词： Speech recognition

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IoT-Based Healthcare Monitoring System

IoT-Based Healthcare Monitoring System

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Emerging Technologies for Dependable Internet of Things (ICETI), International Conference on

作者： Marwa Abdulrahman Al-Hadi Ghaleb Hamoud Al-Gaphari Ibrahim Ahmed Al-Baltah Francisco Bacallao Julian Ahmed Abdulrahman Al-Hadi Department of Computer Science Faculty of Computer Science and IT Sana’a University Sana’a Yemen Department of Information Technology Faculty of Computer Science and IT Sana’a University Faculty of Science and Engineering Al-Hikma University Sana’a Yemen Consultant Cardiovascular Surgeon in Cardiac-Center Military Hospital Yemen Consultant Cardiovascular Surgeon in Cardiac-Center Military Hospital Spain Department of Science and Technologies Faculty of Engineering and Technology Mohamed Boudiaf University M'sila Algeria Department of Industrial Electronic and Automatic Engineering Faculty of Higher Polytechnic Technology Burgos University Burgos Spain

ISBN: (数字)9798331533557

ISBN: (纸本)9798331533564

In an era when healthcare was becoming increasingly crucial, many developing nations, including Yemen, struggled to provide basic medical services. Nearly half of Yemen's population lacked access to adequate healthcare, with the situation being even more dire in rural and remote areas. While sectors such as industry, agriculture, and environmental science rapidly embraced technological advancements, healthcare systems in these regions lagged, with limited research addressing affordable and accessible solutions. This gap was further highlighted by the global healthcare crisis and the urgent need for innovative, cost-effective approaches. In response, this study outlined the creation and deployment of a cost-effective health monitoring system utilizing Internet of Things (IoT) technology to address these challenges. The system was designed to monitor vital signs, specifically heart rate (HR), using an Arduino Uno and ECG sensor, at a meager total cost of $27.25. It provided real-time monitoring for situations requiring immediate intervention. Power consumption tests demonstrated that the system operated efficiently, consuming between 1.061 and 1.35 watts, making it practical and affordable. Additionally, the potential integration of deep learning techniques promises to enhance the system's accuracy and efficiency. Although this study focused on IoT-based health monitoring, its potential extended beyond this, offering broader implications for future healthcare technology solutions.

关键词： Heart rate Deep learning Power demand Costs Medical services Electrocardiography Real-time systems Internet of Things Biomedical monitoring Monitoring

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A Code-Switched Arabic-English Sentiment Analysis Approach Based on Deep-Learning

A Code-Switched Arabic-English Sentiment Analysis Approach B...

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Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

作者： Tasneem S. Almasah Gamal A. Ebrahim Marwa A. Abdelaal Electrical Engineering Department Faculty of Engineering The British University in Egypt Cairo Egypt Computer and Systems Engineering Department Faculty of Engineering Ain Shams University Cairo Egypt Computer Science Department Faculty of Computers and Information Sadat Academy for Management Sciences Cairo Egypt

Sentiment analysis within Online Social Networks (OSNs) becomes a major challenge. Mainly, because of the large amount of data on social networks and the mix of different languages that can be used in these environments. Due to the lack of existing research and datasets on code-switched Arabic-English sentiment analysis, this study creates a new dataset collected from various online social networks. The data is subjected to several preprocessing steps before being analyzed using various deep learning architectures. Moreover, a thorough evaluation of the preprocessing techniques and the performance of the deep learning models have been conducted. This is achieved using comprehensive Twitter datasets in Arabic and English. The study reveals that individual deep learning architectures, specifically, Convolutional Neural Networks (CNNs), Bidirectional Long Short Term Memory (Bi-LSTM), and LSTM demonstrated superior results compared to their hybrid counterparts. Notably, CNN emerged as the most accurate model with an accuracy of 83%, followed closely by Bi-LSTM at 82.3%. Additionally, the paper presents the deployment of an automated machine learning tool, which is Auto-Keras. Furthermore, it designs an optimized sentiment analysis model that aims to enhance accuracy. This approach leads to an increase in the model accuracy to 86%.

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Comparing Time-Series Analysis Approaches Utilized in Research Papers to Forecast COVID-19 Cases in Africa: A Literature Review

arXiv

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arXiv 2023年

作者： Ebadi, Ali Sahafizadeh, Ebrahim Department of Computer Engineering Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr Iran

This literature review aimed to compare various time-series analysis approaches utilized in forecasting COVID-19 cases in Africa. The study involved a methodical search for English-language research papers published between January 2020 and July 2023, focusing specifically on papers that utilized time-series analysis approaches on COVID-19 datasets in Africa. A variety of databases including PubMed, Google Scholar, Scopus, and Web of science were utilized for this process. The research papers underwent an evaluation process to extract relevant information regarding the implementation and performance of the time-series analysis models. The study highlighted the different methodologies employed, evaluating their effectiveness and limitations in forecasting the spread of the virus. The result of this review could contribute deeper insights into the field, and future research should consider these insights to improve time series analysis models and explore the integration of different approaches for enhanced public health decision-making. © 2023, CC BY-NC-ND.

关键词： Coronavirus

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Application of Artificial Intelligence Technology in Big Data Nining 6th

Application of Artificial Intelligence Technology in Big Dat...

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6th International Conference on Innovative Computing, IC 2023

作者： Zhou, Xueyun Qi, Yongjun Tang, Hailin Department of Computer Science and Engineering Guangzhou College of Technology and Business Guangdong510850 China Faculty of Metadata and Computing Guangdong Baiyun University Guangdong510450 China Mongolian University of Science and Technology Bayanzurkh District Ulaanbaatar13341 Mongolia

ISBN: (纸本)9789819920914

Data mining is the process of expressing knowledge through data collection and processing. This is an important technique for efficiently extracting batch data. Artificial intelligence (AI) is a simulation technology of the human brain. It makes full use of computer technology and other subject technologies for intelligent learning and machine control. Both are advanced data management technologies, and both have broad application prospects. This article mainly introduces the clustering algorithm and TextRank algorithm. This paper uses AI technology to conduct research and application in big data mining (BDM), and establishes a mathematical model of the underlying TextRank algorithm. The model is solved by the TextRank algorithm, and the status of research and application of AI technology in BDM is assessed,. Experimental results show that the algorithm improves the research and application of embedded big data intelligent technology by 55% and reduces false positive rate and false positive percentage. Finally, by comparing the research and application analysis of artificial intelligence technology in large data storage. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data mining

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Creep response of fiber-reinforced composites: a review

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Discover Mechanical engineering 2022年第1期1卷 1-31页

作者： Katouzian, Mostafa Vlase, Sorin Marin, Marin Öchsner, Andreas Department of Mechanical Engineering Transilvania University of Brasov Brasov 500036 Romania Romanian Academy of Technical Science Bucharest Romania Department of Mathematics and Computer Science Transylvania University of Brasov Brasov 500036 Romania Faculty of Mechanical and Systems Engineering Esslingen University Esslingen 73728 Germany

The paper presents the results obtained in modeling the creep phenomenon of unidirectional composites reinforced with fibers. Thus, several models that have proven their validity and results obtained with their help are discussed. Analyzing a multitude of models described in the paper presented in references the authors describe the most used by the researchers. The micromechanical model, the homogenization method, the finite element method and the Mori–Tanaka formalism are described. These methods are most used in engineering applications studies in the last time. Following the analysis of each method, the advantages and disadvantages are presented and discussed. The results obtained are compared with experimental determinations. The application of the methods is done to composite materials reinforced with aligned carbon fiber. The methods are, sure, valid for every type of composite reinforced with fibers. Since the creep of materials is a frequently encountered phenomenon in practice, the knowledge of material properties and the existence of convenient calculation models become important for designers, which is why the paper presents the most used calculation methods to model this behavior. © The Author(s) 2022.

关键词： Carbon fiber Constitutive low Creep behavior Fiber reinforced composite Mechanical constants Viscoelastic model

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FireFly Project: UAV Development for Distributed Sensing of Forest Fires

FireFly Project: UAV Development for Distributed Sensing of ...

引用

International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Pargorn Puttapirat Kampol Woradit Henrik Hesse Dinesh Bhatia Department of Computer Engineering Faculty of Engineering Chiang Mai University Chiang Mai Thailand School of Engineering University of Glasgow Glasgow UK School Science Engineering & Technology RMIT University Vietnam

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

Forest fires pose significant threats to ecosystems and communities globally. This paper explores the development of an automated system for forest fire monitoring and prevention, particularly focusing on regions around Northern Thailand. Despite recent applications of Unmanned Aerial Vehicles (UAVs) for fire detection, existing technologies face challenges in detecting early onset of fires in dense forests due to limited resolution and sensitivity of thermal cameras. To address this, this paper proposes a novel approach combining UAV-based surveying with ground-based Internet of Things (IoT) sensors to enable early detection of forest fires, even when obscured by tree canopies. The low-cost IoT sensors measure temperature, humidity and air quality at forest ground level. To overcome limitations in 4G communication for the IoT sensors, our system leverages UAVs as communication hubs to collect data from IoT sensors and survey the area for smoke and fire. The proposed system, part of the FireFly Project in collaboration with Chiang Mai University and the University of Glasgow, aims to overcome limitations of existing technologies and provide effective forest fire monitoring and prevention solutions. Experimental results presented in this paper demonstrate the performance of the distributed UAV-IoT system in detecting and communicating potential forest fires, paving the way for enhanced wildfire management strategies in fire-prone areas.

关键词： Temperature sensors Temperature measurement Protocols Forestry Infrared image sensors Autonomous aerial vehicles Sensor systems

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Development of Automotive-mobility Training in the TRIREME project

Development of Automotive-mobility Training in the TRIREME p...

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International Symposium for Design and Technology of Electronics Packages (SIITME)

作者： Bálint Medgyes Csaba Farkas Marek Spanyik Stefano Danieli Elena Cantiani Jakub Stolfa Department of Electronics Technology Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary Department of Computer Science Faculty of Electrical Engineering and Computer Science VSB - Technical University of Ostrava Ostrava Czechia Mylia – Adecco Formazione Milan Italy

ISBN: (数字)9798331539511

ISBN: (纸本)9798331539528

To achieve climate neutrality by 2050, the consistent transformation of the mobility sector is one of the most important pillars of the European Green deal. As a result of green and sustainable approaches, novel automotive concepts and massive digitalisation in the mobility ecosystem is undergoing a continuous development towards eco-friendly solutions and novel business models, and the changing landscape of automotive industry comes with a significant social impact as well. The overall horizontal collaboration on skills agenda in the Automotive-Mobility Ecosystem is assured throughout the Automotive Skills Alliance (ASA) [1], which is the Large-scale Pact for Skills Partnership. The latest strategic project of the partnership to support its activities is the Digital & Green Skills Towards Future of the Mobility Ecosystem (TRIREME) which promotes a collaboration between industry stakeholders and training providers including Vocational education Trainings (VETs), Higher Educations (HEs) and universities), moreover, also emphasizing the widespread public representation of different project segments and evaluating the social impacts and regional impact of the changes in the Automotive-Mobility Ecosystem. In parallel to the development of sectoral skills strategy, our department (department of Electronics Technology - ETT) is contributing to deliver specific training materials and courses within Work Package 4 (WP4), which is based on the gathered and processed intelligence from previous desk research, survey activity and participation in professional events. The methodology of course development relies on the concept of modularity, our professional competences in the field of Automotive-mobility, and the fulfillment of requirements to cover the needed skills, the competence and the occupational profile subsets.

关键词： Training Industries Surveys Ecosystems Green products Collaboration Tutorials Stakeholders Automotive engineering Meteorology

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Safety Index Calculating Method for Technological Process State Assessing

Safety Index Calculating Method for Technological Process St...

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2023 International Conference on Industrial engineering, Applications and Manufacturing, ICIEAM 2023

作者： Toichkin, Nikolay Bogatikov, Valery Sanaeva, Galina Murmansk Arctic State University Department of Computer Science and Computer Engineering Apatity Russia Tver State Technical University Information Technology Department Tver Russia Novomoskovsk Institute of D. Mendeleev University of Chemical Technology of Russia Faculty of Cybernetics Novomoskovsk Russia

ISBN: (纸本)9781665475952

The fuzzy nature of the selection criteria when carrying out procedures for diagnosing the technological processes states leads to the need using expert assessments, which often turn out the only information basis for decision-making. As a result, there is a need to develop methods that allow effectively receiving and processing fuzzy expert information. One of such methods is the state separation method, which allows to analyze the system states under conditions of information uncertainty about the diagnosed object. The paper considers the development of this method in the field of the technological processes states safety assessing based on the fuzzy sets theory. A technique for calculating the safety index for assessing the state of a technological process is given in the context of developing a system for diagnosing the state and managing technological safety. A computational experiment is presented for calculating the safety index for a four-pan evaporator plant, which is part of the chlorine production evaporator shop. The software implementation of the computational experiment was made in the Phyton programming language using the Scikit-Fuzzy computing library. © 2023 IEEE.

关键词： Membership functions

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