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14th International Conference on Advanced computer Information Technologies, ACIT 2024

作者： Henseruk, Halyna Martyniuk, Serhii Vasylenko, Oksana Henseruk, Yuliia Henseruk, Viktor Habrusiev, Valerii Ternopil Volodymyr Hnatiuk National Pedagogical University Department of Computer Science and Teaching Methods Ternopil Ukraine Anhalt University of Applied Sciences Department of Theory and Practice of Translation Köthen Germany Ternopil Volodymyr Hnatiuk National Pedagogical University Department of English Philology and Methods of Teaching English Ternopil Ukraine Ternopil Volodymyr Hnatiuk National Pedagogical University Department of Pedagogy and Management of Education Ternopil Ukraine

ISBN: (纸本)9798350350036

In a digital society, professionals who are professionally competent and have a variety of skills are increasingly in demand. Digital skills are important and form the basis of the digital competence of specialists. The purpose of the study is to analyse regulatory documents in the context of the development of digital competence of specialists, to develop, substantiate and implement the trajectory of its development in a higher education institution. A questionnaire for self-assessment by bachelor's and master's students and practitioners of their own level of digital competence has been developed, which contains 12 descriptors and their detailed description. Based on the analysis of regulatory documents and the survey results, the author outlines the need to include disciplines in the training programmes for specialists that would contribute to the development of their digital competence. The authors have developed a technology for the system of development of digital competence of specialists. The design stage is substantiated, which includes three blocks: academic disciplines, self-education and professional self-development. The role of professional disciplines in digital technologies in the development of digital competence of specialists is determined. The study emphasises that continuity and continuity in the training of specialists in the context of the use of digital technologies in various aspects of activity is important in this development. © 2024 IEEE.

关键词： Students

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Leveraging Machine Learning for Crime Intent Detection in Social Media Posts 1st

Leveraging Machine Learning for Crime Intent Detection in ...

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1st International Conference on AI-generated Content, AIGC 2023

作者： Bokolo, Biodoumoye George Onyehanere, Praise Ogegbene-Ise, Ebikela Olufemi, Itunu Tettey, Josiah Nii Armah Department of Computer Science Sam Houston State University HuntsvilleTX77340 United States Department of Architecture Technology and Engineering University of Brighton BrightonBN2 4AT United Kingdom Department of Computer and Software Engineering Kennesaw State University KennesawGA United States Department of Computer Science and Quantitative Methods Austin Peay State University ClarksvilleTN37044 United States Department of Computer Science and Engineering Wright State University DaytonOH45435 United States

ISBN: (纸本)9789819975860

Detecting crime intent from user-generated content on social media platforms has become increasingly important for law enforcement and crime prevention. This paper presents a comprehensive approach for crime intent detection from user tweets using machine learning techniques. The study utilizes a dataset of about 400,000 tweets and applies data preprocessing, feature selection, and model training with logistic regression, ridge regression classifier, Stochastic Gradient Descent (SGD) classifier, Random Forests, and support vector machine models. Evaluation metrics such as accuracy, precision, recall, and F1 score are employed to assess the models’ performance. The results reveal that the logistic regression model achieves the highest accuracy ratio of 0.981 in detecting crime intent from tweets. This research showcases the effectiveness of machine learning and advanced transformer-based models in leveraging social media data for crime analysis. The findings provide valuable insights into the potential for early detection and monitoring of crime intent using online platforms, contributing to the field of crime prevention and law enforcement. The utilization of machine learning techniques offers new avenues for understanding and analyzing crime-related sentiments expressed by social media users. By accurately detecting crime intent from user-generated content, law enforcement agencies can enhance their proactive measures, monitor public sentiment towards crime, and shape policies and interventions to address public concerns effectively. The research highlights the significance of leveraging social media data for crime detection and emphasizes the potential impact of advanced machine learning models in improving public safety and crime prevention efforts. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sentiment analysis

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teaching schoolchildren to program in python environment in online communities 1

Teaching schoolchildren to program in python environment in ...

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1st International Conference on Technology Enhanced Learning in Higher Education, TELE 2021

作者： Kasyanov, Sergey Komissarova, Svetlana Volgograd State Socio-Pedagogical University The Department of Computer Science and Methods of Teaching Computer Science Volgograd Russia

The article deals with the issues of teaching the schoolchildren of the online communities of computer programming in Python environment with the use of the systems of Learning Management Systems and the services of t... 详细信息

ISBN: (纸本)9781665443968

关键词： Python

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Enhancing Timely Graduations: An Explainable AI Approach to Predict Academic Risks in South African Students

Enhancing Timely Graduations: An Explainable AI Approach to ...

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

作者： Bokgoshi, Lenny Sixhaxa, Khanyisile Jadhav, Ashwini Nyamane, Seipati Ajoodha, Ritesh School of Computer Science and Applied Mathematics University of Witwatersrand Johannesburg South Africa University of Witwatersrand Science Teaching and Learning Unit Faculty of Science Johannesburg South Africa Sol Plaatje University Department of Computer Science and Information Technology Kimberley South Africa

ISBN: (纸本)9798350327816

With rising dropout rates and extended degree completion times in South African institutions, there's a pressing need to better understand and address the hurdles faced by students during their academic journey. This research harnesses the power of explainable AI to predict and classify students based on their likelihood of not completing their degrees on time. Utilizing synthetic data generated via a Bayesian network, we used predictive models that categorize students into four distinct risk profiles. This clarity in prediction not only illuminates the underlying causes of academic delays but also empowers faculty, advisors, and student support services with actionable insights. The goal of this research is to facilitate timely interventions, tailored support, and seamless transitions for students transferring between universities, ensuring more students can realize their academic aspirations within expected time frames. © 2023 IEEE.

关键词： academic performance at-risk students classification models Machine learning student attrition

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A High-Capacity Image Steganography Based on LSB Inversion and Summing of Bit Pairs 6

A High-Capacity Image Steganography Based on LSB Inversion a...

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6th IEEE Novel Intelligent and Leading Emerging sciences Conference, NILES 2024

作者： Mamdouh, Loay Bekhet, Saddam South Valley University Faculty of Computers and Artificial Intelligence Department of Computer Science Hurghada Egypt South Valley University Faculty of Commerce Department of Quantitative Methods Qena Egypt

ISBN: (纸本)9798350378511

Nowadays, computer users face a challenge in maintaining the security of their information. There are many techniques for transferring information safely over networks. Image steganography, one of these techniques, is an effective way to conceal important information. The Least Significant Bit (LSB) technique is a famous method for data hiding. This technique exchanges the LSBs of the cover image pixel with the mystery message bits. However, it was always the challenging problem of increasing the number of bits utilized for embedding that led to a decreased quality of the stego image. Hence, this research targets optimizing the stego image quality by inverting the LSBs of image pixels to reduce the changes that occur in pixels. This paper proposes a new method based on summing the pairs and inverting the LSBs of the cover image pixels. The secret data are arranged in pairs, and then the sum of every two successive pairs is computed. After that, the secret bits are embedded by inverting the pixel bits depending on that sum. Finally, for more enhancement of the results, some of the inverted bits will be inverted again. Rigorous experiments using benchmark datasets confirm that the proposed scheme yields superior results compared to the substitution LSB scheme. Also, shows the effectiveness of the proposed approach in achieving a high payload with acceptable visual quality for stego image that cannot be noticed by human eyes when compared with state-of-the-art steganography techniques. © 2024 IEEE.

关键词： Image quality

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Automatic essay scoring for discussion forum in online learning based on semantic and keyword similarities

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Asian Association of Open Universities Journal 2023年第3期18卷 262-278页

作者： Dhini, Bachriah Fatwa Girsang, Abba Suganda Sufandi, Unggul Utan Kurniawati, Heny Department of Multimedia Teaching Material Production Center Universitas Terbuka Tangerang Selatan Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Sains and Technology Universitas Terbuka Tangerang Selatan Indonesia

Purpose: The authors constructed an automatic essay scoring (AES) model in a discussion forum where the result was compared with scores given by human evaluators. This research proposes essay scoring, which is conducted through two parameters, semantic and keyword similarities, using a SentenceTransformers pre-trained model that can construct the highest vector embedding. Combining these models is used to optimize the model with increasing accuracy. Design/methodology/approach: The development of the model in the study is divided into seven stages: (1) data collection, (2) pre-processing data, (3) selected pre-trained SentenceTransformers model, (4) semantic similarity (sentence pair), (5) keyword similarity, (6) calculate final score and (7) evaluating model. Findings: The multilingual paraphrase-multilingual-MiniLM-L12-v2 and distilbert-base-multilingual-cased-v1 models got the highest scores from comparisons of 11 pre-trained multilingual models of SentenceTransformers with Indonesian data (Dhini and Girsang, 2023). Both multilingual models were adopted in this study. A combination of two parameters is obtained by comparing the response of the keyword extraction responses with the rubric keywords. Based on the experimental results, proposing a combination can increase the evaluation results by 0.2. Originality/value: This study uses discussion forum data from the general biology course in online learning at the open university for the 2020.2 and 2021.2 semesters. Forum discussion ratings are still manual. In this survey, the authors created a model that automatically calculates the value of discussion forums, which are essays based on the lecturer's answers moreover rubrics. © 2023, Bachriah Fatwa Dhini, Abba Suganda Girsang, Unggul Utan Sufandi and Heny Kurniawati.

关键词： Discussion forum Essay scoring Keyword similarity Semantic similarity

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On the Issue of Developing Financial Literacy of Future Digital Consultants in the Context of the Digitalisation of Society

On the Issue of Developing Financial Literacy of Future Digi...

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Technology Enhanced Learning in Higher Education (TELE), International Conference on

作者： Sergey Kasyanov Katerina Fadeeva The Department of Computer Science and Methods of Teaching Computer Science Volgograd State Socio-Pedagogical University Volgograd Russia The Department of Economics and Entrepreneurship Volgograd State Technical University Volgograd Russia

This study delves into the unique aspects of the formation of digital financial literacy among future digital consultants., particularly focusing on the modules of the training program and the structure of the module ... 详细信息

ISBN: (数字)9798350353532

ISBN: (纸本)9798350353549

关键词： Training Sociology Focusing Organizations Security Statistics Investment

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MUTUAL COVERINGS OF CONES OF MONOTONE FUNCTIONS AND ESTIMATES OF THEIR INTEGRAL MAJORANTS

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Journal of Mathematical sciences (United States) 2023年第6期271卷 714-732页

作者： Bakhtigareeva, E.G. Goldman, M.L. Karshygina, G. Zh. Department of Function Theory Steklov Mathematical Institute of Russian Academy of Sciences 8 Gubkina Street Moscow 119991 Russian Federation Department of Methods of Teaching Mathematics and Computer Science Karaganda Buketov University 28 University Street Karaganda 100024 Kazakhstan

Questions of mutual covering and equivalence of cones of monotone functions and the corresponding estimates of their majorants are studied. Two types of coverings are considered: pointwise and integral. They correspond to pointwise and integral estimates of the majorants on these cones. The results obtained are used to derive formulas that allow estimating pointwise and integral majorants of decreasing rearrangements for generalized Riesz and Bessel potentials. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Cones of decreasing rearrangements of potentials Cones of monotone functions Generalized Riesz potentials Integral majorants on cones Pointwise and integral coverings

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Chaos-Based Novel Watermarked Satellite Image Encryption Scheme

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computer Modeling in Engineering & sciences 2025年第4期143卷 1049-1070页

作者： Mohamed Medani Yahia Said Nashwan Adnan Othman Farrukh Yuldashev Mohamed Kchaou Faisal Khaled Aldawood Bacha Rehman Applied College of Muhayil Aseer King Khalid UniversityAbha62529Saudi Arabia Center for Scientific Research and Entrepreneurship Northern Border UniversityArar73213Saudi Arabia Department of Computer Engineering College of EngineeringKnowledge UniversityErbil44001Iraq Department of Computer Engineering Al-Kitab UniversityAltun Kupri36001Iraq Department of Informatics and Its Teaching Methods Tashkent State Pedagogical UniversityTashkent100070Uzbekistan Department of Industrial Engineering College of EngineeringUniversity of BishaBishaP.O.Box 001Saudi Arabia Department of Computer Science Solent UniversitySouthamptonSO140YNUK

Satellite images are widely used for remote sensing and defence applications,however,they are subject to a variety of *** ensure the security and privacy of these images,theymust be watermarked and encrypted before ***,this paper proposes a novel watermarked satellite image encryption scheme based on chaos,Deoxyribonucleic Acid(DNA)sequence,and hash *** watermark image,DNA sequence,and plaintext image are passed through the Secure Hash Algorithm(SHA-512)to compute the initial condition(keys)for the Tangent-Delay Ellipse Reflecting Cavity Map(TD-ERCS),Henon,and Duffing chaotic maps,*** bitwise XOR and substitution,the TD-ERCS map encrypts the watermark *** ciphered watermark image is embedded in the plaintext *** embedded plaintext image is permuted row-wise and column-wise using the Henon chaotic *** permuted image is then bitwise XORed with the values obtained from the Duffing *** additional security,the XORed image is substituted through a dynamic *** evaluate the efficiency and performance of the proposed algorithm,several tests are performed which prove its resistance to various types of attacks such as brute-force and statistical attacks.

关键词： DNA sequence TD-ERCS chaoticmap henon chaoticmap duffing chaotic system SHA-512 encryption technique watermark embedding

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Arabic Sign Language Gesture Classification Using Deer Hunting Optimization with Machine Learning Model

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computers, Materials & Continua 2023年第5期75卷 3413-3429页

作者： Badriyya B.Al-onazi Mohamed K.Nour Hussain Alshahran Mohamed Ahmed Elfaki Mrim M.Alnfiai Radwa Marzouk Mahmoud Othman Mahir M.Sharif Abdelwahed Motwakel Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science Faculty of Computer Science and Information TechnologyOmdurman Islamic UniversityOmdurman14415Sudan Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Sign language includes the motion of the arms and hands to communicate with people with hearing *** models have been available in the literature for sign language detection and classification for enhanced *** the latest advancements in computer vision enable us to perform signs/gesture recognition using deep neural *** paper introduces an Arabic Sign Language Gesture Classification using Deer Hunting Optimization with Machine Learning(ASLGC-DHOML)*** presented ASLGC-DHOML technique mainly concentrates on recognising and classifying sign language *** presented ASLGC-DHOML model primarily pre-processes the input gesture images and generates feature vectors using the densely connected network(DenseNet169)*** gesture recognition and classification,a multilayer perceptron(MLP)classifier is exploited to recognize and classify the existence of sign language ***,the DHO algorithm is utilized for parameter optimization of the MLP *** experimental results of the ASLGC-DHOML model are tested and the outcomes are inspected under distinct *** comparison analysis highlighted that the ASLGC-DHOML method has resulted in enhanced gesture classification results than other techniques with maximum accuracy of 92.88%.

关键词： Machine learning sign language recognition multilayer perceptron deer hunting optimization densenet

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