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13th IEEE International Conference on System Engineering and Technology, ICSET 2023

作者： Issa, Bayan Jasser, Muhammed Basheer Chua, Hui Na Hamzah, Muzaffar University of Aleppo Faculty of Informatics Engineering Syria Sunway University Department of Computing and Information Systems Selangor Sunway City Malaysia Universiti Malaysia Sabah Faculty of Computing and Informatics Sabah Kota Kinabalu88450 Malaysia

ISBN: (纸本)9798350340891

KeyBERT is a method for keywords/keyphrases extraction, which has three steps. The first step is selecting candidate keywords from a text using sklearn library, the second step is the embedding operation of the text and its candidate keywords;this operation is done by BERT to get a numerical representation that represents the meanings. The third step is calculating the cosine similarity between individual keywords vectors and document vector. In this paper, we focus on the second step of KeyBERT (embedding step). Although KeyBERT has a lot of supported models for the embedding operation, there are no extensive previous comparative studies to analyze and study the effect of using different supported models in KeyBERT. We introduce a comparative study of two commonly used groups of models;the first group is sentence-transformers pretrained models, supported via the sentence-transformers library, and the second group includes the Longformer model, supported via the Hugginface Transformers library. We conduct the comparative study of models on benchmark datasets, which contain English text documents of multi-domains with different text lengths. Based on the study, we found that the Paraphrase-mpnet-base-v2 model provides the best results among all other models in keyword extraction in terms of effectiveness (f1-score, recall, precision, MAP) on all datasets, with higher efficiency (time) on short text compared with using it on long text;accordingly, we recommend using it in that context. On the other hand, the Longformer model is the most efficient/fastest in keyword extraction among all other models on all datasets and this superiority has been evident, especially in long text;accordingly, we recommend using it in that context. © 2023 IEEE.

关键词： Embeddings

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The Concept of Cognition as Categorization in the Development of New Metaheuristics and Algorithms Inspired by Nature 46

The Concept of Cognition as Categorization in the Developmen...

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46th ICT and Electronics Convention, MIPRO 2023

作者： Tulicic, Domagoj Ivkovic, Nikola College for Information Technologies Department of Computer Systems and Networks Zagreb Croatia Faculty of Organization and Informatics Department of Computing and Technology Varaždin Croatia

ISBN: (纸本)9789532331042

The sensorimotor system is a crucial mechanism that enables organisms to categorize information in their environment, a skill necessary for survival. According to cognitive scientist Steven Harnad's 'To Cognize is to Categorize: Cognition is Categorization all living things are essentially sensorimotor systems. This approach is proposed to serve as a novel concept for the development of algorithms inspired by swarm intelligence. Many authors of such algorithms have drawn inspiration from animal species, attempting to emulate their abilities to interact with and navigate their environment. However, these abilities are essentially rooted in the organisms' categorization skills, which are a fundamental characteristic of all sensorimotor systems. Therefore, when designing new algorithms and metaheuristics, it is unnecessary to seek out new species with specific behaviors;instead, the entire sensorimotor system can be observed, and its sensor capabilities can be incorporated even if they do not exist in nature. In order to justify this, abstraction was used as a way to demonstrate the creation of concepts. The paper offers a new perspective on the development of swarm intelligence algorithms and promotes a holistic approach to algorithm design based on Steven Harnad's viewpoint of sensory motor systems. © 2023 MIPRO Croatian Society.

关键词： Swarm intelligence

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Content Validity Assessment of Metaverse Characteristics Instrument for High School Teachers' Adoption 10

Content Validity Assessment of Metaverse Characteristics Ins...

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10th International Conference on Smart computing and Communication, ICSCC 2024

作者： Cassandra, Cadelina Masrek, Mohamad Noorman Aman, Fadhilah School of Information Science College of Computing Informatics and Media University Teknologi MARA Selangor Shah Alam Malaysia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia

ISBN: (纸本)9798350363104

This study's aim is to investigate the content validity of an instrument designed to evaluate the Metaverse characteristic assessment for high school teachers' adoption. The content validity of the instrument was explored based on the perspectives of five experts who were chosen based on their expertise and experiences. The Content Validity use content validity index (I-CVI) and scale content validity index (S-CVI). In the pre-testing process, two contexts were tested: the relevant context and clarity context. In the Relevant context, the mean I-CVI ranged from 0.9 to 1, the S-CVI/UA ranged from 0.5 to 1, and the S-CVI/AVE ranged from 0.9 to 1 for the construct of Interactivity, Persistency and Corporeity, the result shows three items need to be revised. In the Clarity context, the mean I-CVI ranged from 0.95 to 1, the S-CVI/UA ranged from 0.75 to 1, and the S-CVI/AVE ranged from 0.95 to 1 for the construct of Interactivity, Persistency and Corporeity, the result show one items need to be revised. The revised items are also proposed in this study for better instruments. © 2024 IEEE.

关键词： Teaching

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ROS Integration and Evaluation for Robot Navigation in a Narrow-Path Indoor Environment 14

ROS Integration and Evaluation for Robot Navigation in a Nar...

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14th IEEE International Conference on Control System, computing and Engineering, ICCSCE 2024

作者： Ung, Lilian Wei Fang Wong, Richard T.K. Eu, Kok Seng Jasser, Muhammed Basheer Issa, Bayan Sunway University Department of Computing and Information Systems Selangor Sunway City Malaysia Taylor's University Selangor Malaysia University of Aleppo Faculty of Informatics Engineering Syria

ISBN: (纸本)9798350364507

This project explores the implementation of ROS for robot navigation in an indoor environment with narrow pathways typically found in a grocery shop or library. The study discusses the design of the integration of different components including hardware such as sensors and actuators, and algorithms in ROS. The system uses LiDAR to first map the real-world environment to determine the static components of the environment. The purpose of the project is to determine the optimal system integration to achieve obstacle detection and avoidance specifically on dynamic obstacles in environments with narrow pathways. The results show 100% collision-free navigation either through avoiding obstacles or re-routing to an obstacle-free course. © 2024 IEEE.

关键词： Integration

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Drone-Based Public Surveillance Using 3D Point Clouds and Neuro-Fuzzy Classifier

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Computers, Materials & Continua 2025年第3期82卷 4759-4776页

作者： Yawar Abbas Aisha Ahmed Alarfaj Ebtisam Abdullah Alabdulqader Asaad Algarni Ahmad Jalal Hui Liu Faculty of Computing and AI Air UniversityIslamabad44000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Technology College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Computer Science and Engineering College of InformaticsKorea UniversitySeoul02841Republic of Korea Cognitive Systems Lab University of BremenBremen28359Germany

Human Activity Recognition(HAR)in drone-captured videos has become popular because of the interest in various fields such as video surveillance,sports analysis,and human-robot ***,recognizing actions from such videos poses the following challenges:variations of human motion,the complexity of backdrops,motion blurs,occlusions,and restricted camera *** research presents a human activity recognition system to address these challenges by working with drones’red-green-blue(RGB)*** first step in the proposed system involves partitioning videos into frames and then using bilateral filtering to improve the quality of object foregrounds while reducing background interference before converting from RGB to grayscale *** YOLO(You Only Look Once)algorithm detects and extracts humans from each frame,obtaining their skeletons for further *** joint angles,displacement and velocity,histogram of oriented gradients(HOG),3D points,and geodesic Distance are *** features are optimized using Quadratic Discriminant Analysis(QDA)and utilized in a Neuro-Fuzzy Classifier(NFC)for activity ***-world evaluations on the Drone-Action,Unmanned Aerial Vehicle(UAV)-Gesture,and Okutama-Action datasets substantiate the proposed system’s superiority in accuracy rates over existing *** particular,the system obtains recognition rates of 93%for drone action,97%for UAV gestures,and 81%for Okutama-action,demonstrating the system’s reliability and ability to learn human activity from drone videos.

关键词： Activity recognition geodesic distance pattern recognition neuro fuzzy classifier

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Cyberbullying Sexism Harassment Identification by Metaheurustics-Tuned eXtreme Gradient Boosting

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Computers, Materials & Continua 2024年第9期80卷 4997-5027页

作者： Milos Dobrojevic Luka Jovanovic Lepa Babic Miroslav Cajic Tamara Zivkovic Miodrag Zivkovic Suresh Muthusamy Milos Antonijevic Nebojsa Bacanin Technical Faculty Singidunum UniversityBelgrade11000Serbia Informatics and Computing Singidunum UniversityBelgrade11000Serbia Business Economics Singidunum UniversityBelgrade11000Serbia Computing and Informatics Sinergija UniversityBijeljina76300Bosnia and Herzegovina Department for Information Systems and Technologies University“Union Nikola Tesla”Cara DusanaBelgrade11080Serbia Department for Computer Science and Informatics School of Electrical EngineeringUniversity of BelgradeBelgrade11000Serbia Department of Electrical and Electronics Engineering Kongu Engineering College(Autonomous)PerunduraiErode638060India Department of Mathematics Saveetha School of Engineering(Deemed to be University)SIMATS ThandalamChennai602105India MEU Research Unit Middle East UniversityAmman11831Jordan

Cyberbullying is a form of harassment or bullying that takes place online or through digital devices like smartphones,computers,or *** can occur through various channels,such as social media,text messages,online forums,or gaming *** involves using technology to intentionally harm,harass,or intimidate others and may take different forms,including exclusion,doxing,impersonation,harassment,and ***,due to the rapid growth of malicious internet users,this social phenomenon is becoming more frequent,and there is a huge need to address this ***,the main goal of the research proposed in this manuscript is to tackle this emerging challenge.A dataset of sexist harassment on Twitter,containing tweets about the harassment of people on a sexual basis,for natural language processing(NLP),is used for this *** algorithms are used to transform the text into a meaningful representation of numbers for machine learning(ML)input:Term frequency inverse document frequency(TF-IDF)and Bidirectional encoder representations from transformers(BERT).The well-known eXtreme gradient boosting(XGBoost)ML model is employed to classify whether certain tweets fall into the category of sexual-based harassment or ***,with the goal of reaching better performance,several XGBoost models were devised conducting hyperparameter tuning by *** this purpose,the recently emerging Coyote optimization algorithm(COA)was modified and adjusted to optimize the XGBoost ***,other cutting-edge metaheuristics approach for this challenge were also implemented,and rigid comparative analysis of the captured classification metrics(accuracy,Cohen kappa score,precision,recall,and F1-score)was ***,the best-generated model was interpreted by Shapley additive explanations(SHAP),and useful insights were gained about the behavioral patterns of people who perform social harassment.

关键词： Coyote optimization algorithm NLP TF-IDF BERT XGBoost online harassment and cyberbullying metaheuristics

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Framework for Quality Assurance and Quality Control in DL systems

Framework for Quality Assurance and Quality Control in DL Sy...

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2023 IEEE International Conference on Electrical, Computer and Energy Technologies, ICECET 2023

作者： Oliveira, Domingos F. Brito, Miguel A. University Mandume Ya Ndemufayo Department of Informatics and Computing Lubango Angola Centro Algoritmi University of Minho Department of Information Systems Guimarães Portugal

ISBN: (纸本)9798350327816

The use of deep learning systems represents a significant challenge for the industry, as the quality assurance and control of these systems still need to be resolved, for the simple fact that these systems suffer from defects and vulnerabilities, which can lead to severe tragedies, mainly when applied to safety-critical applications in the real world, as they present a programming paradigm that differs in the representation of decision logic, as well as their development practices are unclear and centre on the data-driven paradigm, so how to ensure and control the quality of this system is unclear. This article discusses the development of a framework that will help specialists implement quality assurance and quality control in developing these systems. The framework combines two widely used tools, namely PDCA, applied to the CRISP-DM set of activities to enable quality assurance and control in developing DL systems, specifically in DS. In order to be implemented in the industry, it needs to be evaluated and validated. To this end, case studies and expert evaluation will be used to assess and validate the framework, as this stage will be addressed in future work. © 2023 IEEE.

关键词： deep learning systems development processes Quality assurance quality assurance tools Quality control quality control tools

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A LoRa-Based Emotion Estimation Scheme for Smart Home Automated Actions Using ELMs 8

A LoRa-Based Emotion Estimation Scheme for Smart Home Automa...

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8th IEEE International Smart Cities Conference, ISC2 2022

作者： Karras, Christos Karras, Aristeidis Drakopoulos, Georgios Tsolis, Dimitrios Mylonas, Phivos Sioutas, Spyros Decentralized Systems Computing Group University of Patras Computer Engineering and Informatics Department Greece Ionian University Humanistic and Social Informatics Laboratory Department of Informatics Greece

ISBN: (数字)9781665485616

ISBN: (纸本)9781665485616

Emotion detection is crucial in many IoT deployments from an operational perspective with examples ranging from digital health to smart cities. This is particularly true in smart homes where the interaction between the local IoT ecosystem and the inhabitants are continuous, pervasive, and nuanced. More specifically, emotion estimation from human speech attributes is an integral architectural component of such ecosystems. In this work, we survey the emerging LPWAN technologies and after selecting the most optimal for our use-case, we propose an emotion estimation scheme based on LoRa wireless technology for automated actions in smart home environments. In particular, a voice recognition module coupled with a transmitter are installed in a car. Then, depending on the estimation outcome, the smart home may undertake one or more preemptive actions according to its configuration as the car passenger approaches. The prototype LoRa system has been tested with the widely-used TESS dataset with encouraging results as expressed in the emotion confusion matrix obtained from a extreme learning machine with various kernels. The preceding paves the way for adaptive smart homes tailored to the needs of their inhabitants. © 2022 IEEE.

关键词： Automation

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The Continuous Use of a Highway Traffic Management System (HTMS) Services on Mobile Application 10

The Continuous Use of a Highway Traffic Management System (H...

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10th International HCI and UX Conference in Indonesia, CHIuXiD 2024

作者： Anwar, Norizan Majid, Masni Aida Ahmad, Norzuraiza Rina Kurniawan, Yohannes Universiti Teknologi Mara College of Computing Informatics and Mathematics Selangor Malaysia Sdn. Bhd. Testing & Commissioning Department Selangor Malaysia Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia

ISBN: (纸本)9798350380439

Mobile applications are used across various fields and for different purposes. M-commerce, social media, education, and navigation are just a few examples of applications available on mobile platforms. These applications enable users to access them anytime and anywhere. However, there is a limited number of mobile applications that specifically provide information about traffic, emergency services (SOS), toll fares, amenities, and more. As one of the largest highway concessionaires in Malaysia, XYZ has also developed a mobile application to disseminate information to highway users. Thus, the purpose of this study is to examine the factors that influence the continued use of the XYZ mobile application. The results show that user experience factors - such as Effort Use (EU), Social Influence (SI), Accuracy (ACC), Completeness (COM), Pragmatic Quality (PQ), Stimulation Quality (SQ), Identity Quality (IQ), and Attractiveness (ATT) - positively affect the continued use of the XYZ mobile application. © 2024 IEEE.

关键词： Highway administration

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Intelligence COVID-19 Monitoring Framework Based on Deep Learning and Smart Wearable IoT Sensors

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Computers, Materials & Continua 2023年第10期77卷 583-599页

作者： Fadhil Mukhlif Norafida Ithnin Roobaea Alroobaea Sultan Algarni Wael Y.Alghamdi Ibrahim Hashem Information Assurance and Security Research Group(IASRG) Faculty of ComputingUniversiti Teknologi Malaysia(UTM)Johor BahruJohor81310Malaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia College of Computing and Informatics Department of Computer ScienceUniversity of SharjahSharjah27272United Arab Emirates

The World Health Organization(WHO)refers to the 2019 new coronavirus epidemic as COVID-19,and it has caused an unprecedented global crisis for several *** every country around the globe is now very concerned about the effects of the COVID-19 outbreaks,which were previously only experienced by Chinese *** of these nations are now under a partial or complete state of lockdown due to the lack of resources needed to combat the COVID-19 epidemic and the concern about overstretched healthcare *** time the pandemic surprises them by providing new values for various parameters,all the connected research groups strive to understand the behavior of the pandemic to determine when it will *** prediction models in this research were created using deep neural networks and Decision Trees(DT).DT employs the support vector machine method,which predicts the transition from an initial dataset to actual figures using a function trained on a *** short-term memory networks(LSTMs)are a special sort of recurrent neural network(RNN)that can pick up on long-term *** an added bonus,it is helpful when the neural network can both recall current events and recall past events,resulting in an accurate prediction for *** provided a solid foundation for intelligent healthcare by devising an intelligence COVID-19 monitoring *** developed a data analysis methodology,including data preparation and dataset *** examine two popular algorithms,LSTM and Decision tree on the official ***,we have analysed the effectiveness of deep learning and machine learning methods to predict the scale of the *** issues and challenges are discussed for future *** is expected that the results these methods provide for the Health Scenario would be reliable and credible.

关键词： Healthcare framework AI COVID-19 machine&deep learning LSTM RNN decision tree

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