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21st IEEE/ACIS International Conference on software engineering Research, Management and Applications, SERA 2023

作者： Khan, Sajid Ali Bin Abdulrahman Alawairdhi, Mohammed Al-Akhras, Mousa Foundation University Islamabad Department of Software Engineering Islamabad Pakistan Saudi Electronic University College of Computing and Informatics Saudi Arabia The University of Jordan Amman King Abdullah II School for Information Technology Computer Information Systems Department Jordan

ISBN: (纸本)9798350345889

Facial expressions are the most effective way to characterize people's motives, emotions, and feelings. Several new methods are proposed each year;however, the accuracy of facial expression recognition still needs to be improved especially in uncontrolled conditions. In this paper, we propose a hybrid facial expression model that considers both texture and orientation features to classify expressions. Two types of descriptors namely Local binary pattern and Weber local descriptor are used to preserve the local intensity information and orientation of edges. In the next step, computing the Histograms of oriented gradients (HOG) features from the Local binary pattern and Weber local descriptor images to capture micro-expressions. Then, the AdaBoost feature selection algorithm is utilized to choose the best features from the combined HOG features. The results of the experiments demonstrate that the method proposed in this study performs better than existing methods. © 2023 IEEE.

关键词： Local binary pattern

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Sinhala Sign Language Detection Approach for Deaf People Using Human Pose Estimation

Sinhala Sign Language Detection Approach for Deaf People Usi...

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International Research Conference on Smart computing and systems engineering (SCSE)

作者： S. J. M. J. Nadeesha W. V. S. K. Wasalthilaka Department of Computing and Information Systems Faculty of Computing Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka Department of Software Engineering Faculty of Computing Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka

ISBN: (数字)9798350375688

ISBN: (纸本)9798350375695

People with disabilities have physical, sensory, cognitive, or behavioral health problems that limit their participation in activities and interaction with the environment. Deafness is one of the most common types of disability in society. Since verbal communication is impossible for the deaf, sign language is one of the most often used forms of communication among those disable people. However, a third party who is fluent in sign language is necessary for efficient communication. This research study proposed a sign language detector for Sinhala using Logistic Regression, Ridge Classifier, Random Forest, and Gradient Boosting algorithms with human pose estimation. The method used upper-body stance landmarks with 100 human pose videos and 40 widely used signals to extract features for body language classification. With an overall 86.75% success rate, this study is a major step toward improving deaf people's communication in Sri Lanka.

关键词： Training Sign language Logistic regression Pose estimation Detectors Feature extraction systems engineering and theory

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Uvlhub: A Feature Model Data Repository Using Uvl and Open Science Principles

SSRN

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SSRN 2023年

作者： Romero-Organvidez, David Galindo, José A. Sundermann, Chico Horcas, Jose-Miguel Benavides, David Department of Computer Languages and Systems University of Seville Spain Institute of Software Engineering and Programming Languages Ulm University Germany CAOSD ITIS Software University of Málaga Spain

Feature models are the de facto standard for modelling variabilities and commonalities. Concrete models in different domains exist;however, many are in private or sparse repositories or belong to discontinued projects. The dispersion of knowledge of feature models hinders the study and reuse of these artefacts in different studies. The Universal Variability Language (UVL) is a community effort textual feature model language that promotes a common way of serialising feature models independently of concrete tools. Open science principles promote transparency, accessibility, and collaboration in scientific research. Although some attempts exist to promote feature model sharing, the existing solutions lack open science principles by design. In addition, existing and public feature models are described using formats not always supported by current tools. This paper presents UVLHUB, a repository of feature models in UVL format. UVLHUB provides a front end that facilitates the search, upload, storage, and management of feature model datasets, improving the capabilities of discontinued proposals. Furthermore, the tool communicates with Zenodo providing a permanent save of datasets and following open science principles. UVLHUB includes existing datasets and is readily available to include new data and functionalities in the future. It is maintained by three active universities in variability modelling. © 2023, The Authors. All rights reserved.

关键词： Concretes

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Unveiling Public Perception of ChatGPT Through Twitter Data and Machine Learning 4

Unveiling Public Perception of ChatGPT Through Twitter Data ...

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4th International Conference on Data Analytics for Business and Industry, ICDABI 2023

作者： Abishethvarman, Vadivel Banujan, Kuhaneswaran Wijeratne, Ashansa Nirubikaa, Ravikumar Kumara, Samantha Prasanth, Senthan Sabaragamuwa University of Sri Lanka Department of Computing and Information Systems Belihuloya Sri Lanka Sabaragamuwa University of Sri Lanka Department of Software Engineering Belihuloya Sri Lanka Sabaragamuwa University of Sri Lanka Department of Physical Sciences and Technology Belihuloya Sri Lanka

ISBN: (纸本)9798350369786

The study comprehensively analysed public sentiment towards ChatGPT, a language processing AI tool, using approximately 135,000 tweets from an initial dataset of 236,275 entries. After cleaning and pre-processing the data using basic NLP techniques, Machine Learning (ML) models - Long Short-Term Memory (LSTM), Decision Tree (DT), and Support Vector Machine (SVM) - were applied to classify the tweets into positive, negative, and neutral categories. The LSTM model demonstrated superior performance across all evaluation metrics - accuracy, precision, recall, and F1 score - for all tweet categories. Word clouds generated for each category revealed that the most frequent words in positive tweets included 'student,' 'education,' 'research,' and 'teacher,' indicating a strong association of ChatGPT with educational and research contexts. Similarly, 'AI,' 'student,' 'research,' 'course,' and 'education' dominated neutral and negative tweets, highlighting prevalent discussion topics. However, the study had limitations, including dependency on a single dataset, potential loss of contextual information during pre-processing, and a focus on English tweets only. Despite these limitations, the study provides valuable insights into the public's perception of ChatGPT, underscoring its significant association with education and research and the effectiveness of the LSTM model in classifying tweets. The findings suggest that future research should incorporate diverse data sources and languages better to understand global sentiment towards ChatGPT and similar AI tools. © 2023 IEEE.

关键词： Tweets

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VisioSLR: A Vision Data-Driven Framework for Sign Language Video Recognition and Performance Evaluation on Fine-Tuned YOLO Models

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Procedia Computer Science 2025年 257卷 85-92页

作者： Leila Ismail Nada Shahin Henok Tesfaye Alain Hennebelle Intelligent Distributed Computing and Systems (INDUCE) Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates Independent Researcher

With the emergence of AI for good, there has been an increasing interest in building computer vision data-driven deep learning inclusive AI solutions. Sign language Recognition (SLR) has gained attention recently. It is an essential component of a sign-to-text translation system to support the deaf and hard-of-hearing population. This paper presents a computer VISIOn data-driven deep learning framework for Sign Language video Recognition (VisoSLR). VisioSLR provides a precise measurement of translating signs for developing an end-to-end computational translation system. Considering the scarcity of sign language datasets, which hinders the development of an accurate recognition model, we evaluate the performance of our framework by fine-tuning the very well-known YOLO models, which are built from a signs-unrelated collection of images and videos, using a small-sized sign language dataset. Gathering a sign language dataset for signs training would involve an enormous amount of time to collect and annotate videos in different environmental setups and multiple signers, in addition to the training time of a model. Numerical evaluations of VisioSLR show that our framework recognizes signs with a mean average precision of 97.4%, 97.1%, and 95.5% and 11, 12, and 12 milliseconds of recognition time on YOLOv8m, YOLOv9m, and YOLOv11m, respectively.

关键词： Artificial intelligence Computer vision Deep learning Fine-tuning Machine translation Neural machine translation Sign language Sign language recognition Transfer learning YOLO

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Fingerprint Agreement Using Enhanced Kerberos Authentication Protocol on M-Health

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Computer systems Science & engineering 2022年第11期43卷 833-847页

作者： A.S.Anakath S.Ambika S.Rajakumar R.Kannadasan K.S.Sendhil Kumar School of Computing E.G.S.Pillay Engineering CollegeNagapattinam611002India Department of Computer Science and Engineering University College of Engineering AriyalurAriyalur621705India Department of Mathematics University College of Engineering AriyalurAriyalur621705India Department of Software Systems School of Computer Science and Engineering(SCOPE)Vellore Institute of Technology UniversityVellore632014India Department of IoT School of Computer Science and Engineering(SCOPE)Vellore Institute of Technology UniversityVellore632014India

Cloud computing becomes an important application development platform for processing user data with high *** providers are accustomed to providing storage centers outside the trusted location preferred by the data ***,ensuring the security and confidentiality of the data while processing in the centralized network is very *** secured key transmission between the sender and the receiver in the network is a huge challenge in managing most of the sensitive data transmission among the cloud *** are very active over the network like real authenticated user to hack the personal sensitive data,such as bank balance,health data,personal data,and confidential documents over the cloud *** this research,a secured key agreement between the sender and the receiver using Kerberos authentication protocol with fingerprint is proposed to ensure security in *** of patients are monitored using wireless sensor devices and are then transferred to the *** protocol helps in avoiding unnecessary communication of authenticated data over the cloud *** security process is a procedure with the best security in most of the authentication *** node is responsible in carrying data packets from the sender to the receiver in the cloud *** Kerberos protocol is used in trust node to ensure *** communication between the local health center and the healthcare server is ensured by using a fingerprint feature called minutiae form,which refers to the fingerprint image of both sender and *** computational and communicational cost of the proposed system is lesser when compared with other existing authentication methods.

关键词： Protocol security m-health cloud computing biometric fingerprint kerberos protocol

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Model for Recognition of Sign Language and Hand Gesture by Hard Hearing People

Model for Recognition of Sign Language and Hand Gesture by H...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Manjula, R. Jagadeesan, S. Ramalingam, Venkadeshan Vetriselvi, T. Johnpeter, T. Srm Institute of Science and Technology College of Engineering and Technology Department of Computing Technologies Chennai India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Tamilnadu Vellore India Information Technology Department University of Technology and Applied Sciences-Shinas Branch Sultanate of Oman Oman Vellore Institute of Technology School of Computer Science and Engineering Department of Iot Tamilnadu Vellore India K.Ramakrishnan College of Engineering Department of Computer Science and Engineering Trichy India

ISBN: (纸本)9798350313987

Hard hearing and deaf people are communicating with other people using the sign language. This is the only way of communication of such people to convey Since this is the only means by which these individuals can communicate, it is imperative that others be able to understand their language. Sign language recognition by computer begins with the acquisition of sign gestures and ends with recognition and conversion to text or voice. Static and dynamic are the two ways of sign gestures used. This study aims at focusing on the dynamic gesture recognition and the steps for recognizing sign language are discussed. The collected data are preprocessed and feature are extracted for classification. Finally the results are all investigated and developed for single user. © 2023 IEEE.

关键词： Gesture analysis Hand gesture recognition Hand tracking Sign language

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Proposed intelligence systems based on digital Forensics: Review paper

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Materials Today: Proceedings 2023年 80卷 2647-2651页

作者： Ismael Sahib, Hussein Qahatan AlSudani, Mustafa Hasan Ali, Mohammed Qassim Abbas, Haydar Moorthy, Kohbalan Mundher Adnan, Myasar Faculty of Computer Science and Information Technology UniversitiTun Hussein Onn Malaysia Computer Techniques Engineering Department Faculty of Information Technology Imam Ja'afar Al-sadiq University Najaf Iraq Faculty of Computer Systems & Software Engineering Universiti Malaysia Pahang Kuantan Malaysia Islamic University Najaf Iraq Faculty of Engineering School of Computing Universiti Teknologi Malaysia 81310 Johor Bahru Malaysia

The field of information security, in general, has seen shifts a traditional approach to an intelligence system. Moreover, an increasing of researchers to focus on propose intelligence systems and framework based on the forensic case studies because of the limitations of traditional methods such as analysis intensive data manually, intelligence visualization to make the evidence more understandable and intelligence system for store data. However, most of these intelligence systems are still facing different limitations. Furthermore, the primary goal of this work analysis popular intelligence system that was used based on forensic. Moreover, propose new algorithms and hybrid model which it's achieved good results in dif-ferent other fields to develop the forensic systems in the future. © 2021

关键词： Digital forensics

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Robust speech perception and classification-driven deep convolutional neural network with natural language processing

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Alexandria engineering Journal 2025年 123卷 358-368页

作者： Alshammari, Alya Alzaidi, Muhammad Swaileh A. Alrusaini, Othman Alshuhail, Asma Allafi, Randa Yafoz, Ayman Alsini, Raed Khadidos, Alaa O. Department of Applied Linguistics College of Languages Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of English Language College of Language Sciences King Saud University P. O. Box 145111 Riyadh Saudi Arabia Department of Engineering and Applied Sciences Applied College Umm Al-Qura University Makkah Saudi Arabia Department of Information Systems College of Computer Sciences & Information Technology King Faisal University Saudi Arabia Department of Computer Science College of Sciences Northern Border University Arar Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

Speech recognition systems use computer algorithms to interpret and process spoken words and translate them into text. Speech-to-text, or Speech detection, is the proficiency of a program or machine to find spoken words aloud and translate them into plain text. Speech detection systems are the most common complex systems that use wide-ranging research in computer engineering, linguistics, and computer science. In today's significant data era, several machine learning (ML) approaches for processing raw, unlabelled speech data still need to be improved. Deep learning (DL) models, on the other hand, with their powerful modelling ability for extensive data, are capable of directly processing unlabelled data and have become a crucial area of research in the domain of automated speech recognition. The DL model has recently gained popularity for voice disorders and automated speech detection. DL is an ML that exploits artificial neural networks (ANN) to gain meaningful insights from data. The DL approach demonstrated great success and achieved significant attention in different fields comprising speech processing, computer vision (CV), and natural language processing (NLP). Therefore, this article develops a Deep Learning with Natural Language Processing based Robust Speech Detection (DLNLP-RSD) technique. The DLNLP-RSD technique derives features from the mel-frequency cepstral coefficient (MFCC) technique. Followed by the DLNLP-RSD technique employs a one-dimensional deep convolutional neural network (CNN) approach for the detection and classification of speech. Moreover, the parameter tuning model uses the grasshopper fractals optimization algorithm (GOA) approach. The performance evaluation of the proposed technique highlights its significant performance over other models concerning distinct measures. An extensive simulation analysis is executed to examine the improved results of the DLNLP-RSD model. The experimental validation of the DLNLP-RSD technique portrayed a sup

关键词： Convolutional neural networks

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Comparing Fairness of Generative Mobility Models

arXiv

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arXiv 2024年

作者： Wang, Daniel McFarland, Jack Mashhadi, Afra Ugurel, Ekin Computing and Software Systems University of Washington Bothell United States Department of Civil & Environmental Engineering University of Washington Seattle United States

Crowd flow data serves as a foundation for predictive modeling, aiding in understanding of city structure [3], epidemics [12], and visitation patterns [9]. Extensive mobility studies have applied variety of models in this area, however there has been little work in quantifying the fairness of them in terms of the equity of their performance across geographical space. Indeed, as recent evidence from the broader machine learning domain has shown, the systematic discrimination in making decisions against different groups has been shifted from people to autonomous algorithms [6]. In many applications, discrimination may be defined by different protected attributes, such as race, gender, ethnicity, and religion, that directly prevent favorable outcomes for a minority group in societal resource allocation, education equality, employment opportunity, etc [8]. Similarly, in the context of spatiotemporal data, mobility demand prediction algorithms have been shown to offer higher service quality to neighborhoods with a larger Caucasian population [4]. However, in such contexts, only a handful of recent studies exist that examine the fairness of location-based systems [13], and to the best of our knowledge, no effort has yet been made in measuring the fairness of generative mobility models and their resulting synthetic traces. In this paper, we seek to measure and compare these models based on fairness performance across multiple datasets. We do so by introducing metrics for evaluating both the utility of a generative model and the quality and fairness of their resulting traces, where accuracy is measured in terms of Common Part of Commuters (CPC). This metric is based on the Sørensen-Dice index and measures the similarity between real flows and generated flows (Equation 1) (Equation presented). Where ŷ is the generated flow, and y is the real flow between location i and j. It is worth noting that, CPC is always positive and contained in the closed interval [0,1] where 1 is a

关键词： Resource allocation

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