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8th International Congress on information and Communication technology, ICICT 2023

作者： Stupavský, Igor Dakić, Pavle Faculty of Informatics and Information Technologies Institute of Informatics Information Systems and Software Engineering Slovak University of Technology in Bratislava Bratislava Slovakia Faculty of Informatics and Computing Singidunum University Belgrade Serbia

ISBN: (纸本)9789819932351

The aim of this review is to explore the relationship between the architecture of microservices and antisocial behavior. Microservice architecture is a software design approach that involves decomposing a system into small, independent services that can offer benefits such as flexibility and scalability. Expected result is to analyze dataset of statuses, which defines the length of time the status displays on the user’s device. Because people spend time on social networks at every opportunity, even for hours. When combined with the time-valued, we try to gain a deeper understanding of social networks and users behavior. They are forced into this behavior through the feedback loop mechanism of the neurotransmitter Dopamine. Main goal is to understand how to minimize the impact of antisocial behavior on social networks and what exactly makes users spend a disproportionate amount of time on these platforms. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： software design

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Bridging Machine Learning and Formal Concept Analysis for Effective Crowd Detection 25

Bridging Machine Learning and Formal Concept Analysis for Ef...

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25th International Arab Conference on information technology, ACIT 2024

作者： Al-Oraiqat, Anas M. Drieiev, Oleksandr Samara, Ghassan Almatarneh, Sattam Al-Oraiqat, Karim A. Alrawashdeh, Hazim Drieieva, Hanna Elrashidi, Ali Hasan, Yassin M. Y. Zarqa University Faculty of Information Technology Department of Computer Science Jordan Central Ukrainian National Technical University Department of Cybersecurity and Software Kropyvnytskyi Ukraine Zarqa University Faculty of Information Technology Department of Data Science and Artificial Intelligence Jordan Zarqa University Faculty of Information Technology Department of Software Engineering Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Assiut University Electrical Engineering Department Egypt

ISBN: (纸本)9798331540012

Crowd detection and prevention systems have become essential for managing densely populated areas. Modern systems leverage the combined power of machine learning, data mining, and image processing to extract and analyse features from crowded zones, enabling the identification of behavioural patterns and anomalies. However, most current solutions primarily focus on detection and lack robust decision-making and recommendation mechanisms for selecting appropriate crowd-prevention strategies. To address this gap, we integrate the predictive capabilities of machine learning models with the analytical and clustering strengths of Fuzzy Formal Concept Analysis (fuzzy FCA). Machine learning is employed to extract valuable insights from images of the area, with a neural network used to identify human figures, track individuals' positions, and predict crowd levels. This data is fed into an FFCA-based decision system, where crowd information is structured and clustered using lattice theory. This latter helped exclude low-crowd zones and cluster the rest of the monitored areas based on their crowd features. Additionally, we define bottom-up parsing algorithms to recommend suitable crowd-prevention plans based on crowd density levels within fuzzy formal concepts. Experiments confirmed not only the ability of fuzzy FCA to "exclude"low-crowd zones thanks to a used crowd threshold but also the efficient "feature-based clustering"of crowded zones into hierarchical formal concepts and these latter's "bottom-up parsing"to finally identify the dense zones. © 2024 IEEE.

关键词： Adversarial machine learning

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Fusion of speech and handwritten signatures biometrics for person identification

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International Journal of Speech technology 2023年第4期26卷 833-850页

作者： Abushariah, Ahmad A. M. Abushariah, Mohammad A. M. Gunawan, Teddy Surya Chebil, J. Alqudah, Assal A. M. Ting, Hua-Nong Mustafa, Mumtaz Begum Peer Faculty of Engineering University of Malaya Kuala Lumpur50603 Malaysia Department of Computer Information Systems King Abdullah II School of Information Technology The University of Jordan Amman Jordan ECE Department Faculty of Engineering International Islamic University Malaysia Kuala Lumpur53100 Malaysia Department of Computer Science Faculty of Science and Information Technology Al-Zaytoonah University of Jordan Amman Jordan Department of Biomedical Engineering Faculty of Engineering University of Malaya Kuala Lumpur50603 Malaysia Department of Software Engineering Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia

Automatic person identification (API) using human biometrics is essential and highly demanded compared to traditional API methods, where a person is automatically identified using his/her distinct characteristics including speech, fingerprint, iris, handwritten signatures, and others. The fusion of more than one human biometric produces bimodal and multimodal API systems that normally outperform single modality systems. This paper presents our work towards fusing speech and handwritten signatures for developing a bimodal API system, where fusion was conducted at the decision level due to the differences in the type and format of the features extracted. A data set is created that contains recordings of usernames and handwritten signatures of 100 persons (50 males and 50 females), where each person recorded his/her username 30 times and provided his/her handwritten signature 30 times. Consequently, a total of 3000 utterances and 3000 handwritten signatures were collected. The speech API used Mel-Frequency Cepstral Coefficients (MFCC) technique for features extraction and Vector Quantization (VQ) for features training and classification. On the other hand, the handwritten signatures API used global features for reflecting the structure of the hand signature image such as image area, pure height, pure width and signature height and the Multi-Layer Perceptron (MLP) architecture of Artificial Neural Network for features training and classification. Once the best matches for both the speech and the handwritten signatures API are produced, the fusion process takes place at decision level. It computes the difference between the two best matches for each modality and selects the modality of the maximum difference. Based on our experimental results, the bimodal API obtained an average recognition rate of 96.40%, whereas the speech API and the handwritten signatures API obtained average recognition rates of 92.60% and 75.20%, respectively. Therefore, the bimodal API system is a

关键词： Biometrics

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Enhancing Classification Algorithm Recommendation in Automated Machine Learning: A Meta-Learning Approach Using Multivariate Sparse Group Lasso

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Computer Modeling in engineering & Sciences 2025年第2期142卷 1611-1636页

作者： Irfan Khan Xianchao Zhang Ramesh Kumar Ayyasamy Saadat M.Alhashmi Azizur Rahim School of Software Dalian University of TechnologyDalian116620China Faculty of Information and Communication Technology Universiti Tunku Abdul RahmanKampar31900Malaysia College of Computing and Informatics University of SharjahSharjah27272United Arab Emirates Department of Computer Systems Engineering University of Engineering and Applied SciencesSwat19200Pakistan

The rapid growth of machine learning(ML)across fields has intensified the challenge of selecting the right algorithm for specific tasks,known as the Algorithm Selection Problem(ASP).Traditional trial-and-error methods have become impractical due to their resource *** Machine Learning(AutoML)systems automate this process,but often neglect the group structures and sparsity in meta-features,leading to inefficiencies in algorithm recommendations for classification *** paper proposes a meta-learning approach using Multivariate Sparse Group Lasso(MSGL)to address these *** method models both within-group and across-group sparsity among meta-features to manage high-dimensional data and reduce multicollinearity across eight meta-feature *** Fast Iterative Shrinkage-Thresholding Algorithm(FISTA)with adaptive restart efficiently solves the non-smooth optimization *** validation on 145 classification datasets with 17 classification algorithms shows that our meta-learning method outperforms four state-of-the-art approaches,achieving 77.18%classification accuracy,86.07%recommendation accuracy and 88.83%normalized discounted cumulative gain.

关键词： Meta-learning machine learning automated machine learning classification meta-features

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TOST: School Bus Monitoring and Management System for Schools and Parents

TOST: School Bus Monitoring and Management System for School...

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2023 IEEE International Conference on Computing, ICOCO 2023

作者： Thakur, Fahad Aqeel Hafiz, Muhammad Alani, Maath Saadoon, Muntadher Kian Chiew, Chiew Thiam University of Malaya Faculty of Computer Science & Information Technology Kuala Lumpur Malaysia Hillool Software Engineering Department Queensland Australia Universiti Malaya Faculty of Computer Science and Information Technology Department of Software Engineering Kuala Lumpur50603 Malaysia

ISBN: (纸本)9798350302684

School bus contributes positively to reducing the number of cars on the road, thereby reducing the environmental impact of cars. It also dramatically relieves working parents, who do not have to pick up and drop off their children separately in their private vehicles. However, the irregularity of the bus arrival times often leads to an extended waiting time for students at the bus stops, potentially compromising the students' safety. The communication barrier between schools, parents and bus drivers is also a significant challenge, mainly when a delay occurs during the transit of the students. This paper presents TOST (to-school-together), a school bus monitoring and management system for schools and parents. The school administrators, parents, and bus drivers are connected in one comprehensive school bus system. The proposed system provides realtime updates on the bus status, allowing peace of mind and easing the burden of parents, school administrators, and bus drivers. The design of TOST is based on feedback from the system stakeholders collected via a survey. We draw a complete understanding of the existing issues by analyzing the feedback. Finally, we have implemented the system using state-of-the-art technologies to address the identified issues and fulfil the system requirements. © 2023 IEEE.

关键词： Buses

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Fake News Classification: Past, Current, and Future

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Computers, Materials & Continua 2023年第11期77卷 2225-2249页

作者： Muhammad Usman Ghani Khan Abid Mehmood Mourad Elhadef Shehzad Ashraf Chaudhry Department of Computer Science University of Engineering and TechnologyLahore54890Pakistan Department of Computer Science&Information Technology Abu Dhabi UniversityAbu Dhabi59911United Arab Emirates Department of Software Engineering Faculty of Engineering and ArchitectureNisantasi UniversityIstanbulTurkey

The proliferation of deluding data such as fake news and phony audits on news web journals,online publications,and internet business apps has been aided by the availability of the web,cell phones,and social *** can quickly fabricate comments and news on social *** most difficult challenge is determining which news is real or ***,tracking down programmed techniques to recognize fake news online is *** an emphasis on false news,this study presents the evolution of artificial intelligence techniques for detecting spurious social media *** study shows past,current,and possible methods that can be used in the future for fake news *** different publicly available datasets containing political news are utilized for performing *** supervised learning algorithms are used,and their results show that conventional Machine Learning(ML)algorithms that were used in the past perform better on shorter text *** contrast,the currently used Recurrent Neural Network(RNN)and transformer-based algorithms perform better on longer ***,a brief comparison of all these techniques is provided,and it concluded that transformers have the potential to revolutionize Natural Language Processing(NLP)methods in the near future.

关键词： Supervised learning algorithms fake news classification online disinformation transformers recurrent neural network(RNN)disinformation transformers recurrent neural network(RNN)

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Decoding Diabetes: Unconventional Indicators for Diabetic Diagnosis 10

Decoding Diabetes: Unconventional Indicators for Diabetic Di...

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10th International Conference on engineering and Emerging Technologies, ICEET 2024

作者： Sangavi, G. Kajanan, T. Methiny, S. Tissera, Wishalya Rajendran, Karthiga Sri Lanka Institute of Information Technology Faculty of Computing Malabe Colombo Sri Lanka Sri Lanka Institute of Information Technology Department of Software Engineering Malabe Colombo Sri Lanka

ISBN: (纸本)9798331532895

Diabetes diagnosis has always involved glucose tests;however, it is possible to recognize diabetic complications by simply looking at skin and nails. This work presents a new approach for identifying diabetes-related complications, including acanthosis nigricans, foot ulcers, and nail infections, utilizing and TensorFlow for deep learning. Both conditions are associated with visual signals and pass through a strictly trained dataset, marked up for maximum accuracy. From the methodologies, including image resizing, data augmentation, and hyperparameter tuning. The findings of accuracy confirm that the platform should register specific characteristics of the problem area and has the essential precision to maintain close attention to possible risks of diabetes and intervene early to reduce the likelihood of patients' deteriorating health. © 2024 IEEE.

关键词： Diagnosis

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Analyzing Data Format Interoperability in API Ecosystems Using Big Data Architecture 11

Analyzing Data Format Interoperability in API Ecosystems Usi...

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11th International Conference on information technology, ICIT 2023

作者： Alzyadat, Wael Al-Showarah, Suleyman University of Jordan Faculty of Science and IT Alzaytoonah Department of Software Engineering Amman Jordan Mutah University Faculity of Information Technology Dept of Software Engineering Jordan

ISBN: (纸本)9798350320060

The API is a key player in cut edge technology for interact and exchange information across platforms whatever internally or external where the transfer information needs to be adjusted by several issues during request/response *** traditional concept of API was handle it by data mapping functions for this point uncapitalizable with big data architecture especially of variety of data which data able to presents in different kinds of structured data that aimed to fnd out approach to solve it, for this point the XML play an important role for variety data by dynamic behavior with effective © 2023 IEEE.

关键词： Big data

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KidSwatch: Children Development Observation System 5

KidSwatch: Children Development Observation System

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Oshadhini, P.A.L.N. Siriwardana, K.A.D.H.O. Peshala Ranmini, H.K. Sandeepa, S.R. Weerasinghe, Lokesha Kelegama, Thamali Sri Lanka Institute of Information Technology Faculty of Computing Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

The kidSwatch is a mobile application designed specifically for grade 1 children aged six years. It serves as a comprehensive framework for observing and evaluating children's developmental progress. The application focuses on the main components of children's development: Cognitive development, Emotional development, Language development, and Learning development. By analyzing the results obtained from observing these four components, parents can gain valuable insights into their children's development within a matter of minutes. The kidSwatch employs a range of observational techniques, including direct observation, structured interviews, and standardized tests, to gather information on a child's talents, skills, and behaviors. Trained observers proficient in the kidSwatch framework carefully record and analyze the child's interactions, activities, problem-solving abilities, emotional expressions, and social interactions. Ultimately, the kidSwatch empowers parents with a quick and effective means of understanding and assessing their child's development. To satisfy the above features there are a Hand number detection algorithm, letter recognition algorithm, Emotion recognition algorithm, and voice-based answering algorithm They perform an accuracy between 75%-90% on CNN. © 2023 IEEE.

关键词： Emotion Recognition

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Sinhala Sign Language Translation through Immersive 3D Avatars and Adaptive Learning 5

Sinhala Sign Language Translation through Immersive 3D Avata...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Gamage, B.G.J. Paranagama, R.P.S.D. Ranaweera, R.M.S.H. Dilshan, A.V.R. Weerathunga, Ishara Kasthurirathna, Dharshana Sri Lanka Institute of Information Technology Faculty of Computing Department of Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

This research paper addresses the challenge of bridging the communication gap between sign language users and non-users through a comprehensive study on sign language translation. The focus is on Sinhala Sign Language (SSL), and the research problem encompasses various innovative components. These include the conversion of Sinhala Sign Language (SSL) into text, emotion recognition and representation, personalized sign language learning, and accurate identification and translation of sign language gestures. By leveraging machine learning, deep learning, 3D modeling, and image/video processing techniques, the system enhances accessibility, communication, and education for the deaf and hard-of-hearing community. The outcomes of this research encompass an accurate and visually appealing sign language translation system, personalized learning interfaces, and the integration of emotions into sign language communication. The research contributes by providing insights into SSL grammar and lexicon, developing a tailored sign language learning curriculum, implementing reinforcement learning algorithms for personalized feedback, utilizing 3D components for enhanced visual quality, and optimizing deployments and models for improved performance. The proposed system has significant implications for improved accessibility, education, and inclusivity, fostering effective communication and understanding between sign language users and non-users. © 2023 IEEE.

关键词： Emotion Recognition

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