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The design and implementation of interactive systems and Human-Machine Interfaces (HMI) use different techniques from both software engineering and ergonomics. To improve the productivity and quality of software, automating the development process is an important factor. User interfaces are nonfunctional but complex software components that play a vital role in the development of interactive applications. We propose in this paper an approach for the automatic production of Human-Machine Interfaces (HMI) for the development of interactive applications according to the Model-Driven Engineering (MDE) approach. A source Meta-Model called "DD_IHM" ("Description Diagram for Human-Machine Interfaces"), a target Meta-Model specific to the PHP language called "CGFP" (Context Grammar for PEAR) for the construction of HMIs and, a set of generic rules for transforming a model conforms to the source meta-Model into a model conforms to the target Meta-Model, written in the QVT language are develop. We apply this approach to the creation of a simple online registration platform.

Interactive Systems, Ergonomie, Models Transformation, Context-free Grammar, QVT Language, Software productivity.

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Thierry Noulamo, Bernard Fotsing Talla, Jean Pierre Lienou, Alain Djimeli-Tsajio, "An Approach to Build the Ergonomics of Interactive Software based on MDE," International Journal of Computer Sciences and Engineering, Vol.11, Issue.8, pp.1-8, 2023.

Cryptocurrencies, such as Bitcoin and Ethereum, have experienced significant price volatility over the years, and investors and traders often look for ways to predict future price movements to make informed investment decisions. However, predicting the prices of cryptocurrencies is a challenging task due to the highly unpredictable nature of the market and the lack of a centralized authority to regulate it. Overall, smart consensus algorithms play a crucial role in maintaining the security and reliability of decentralized systems by enabling all nodes to agree on the state of the network without the need for a centralized authority. Because of the problem of making predictions on the prices of cryptocurrencies, this system proposed a Bi-Directional Long Short-Memory algorithm for the prediction of bitcoin prices. This system uses stock market data starting from 2014 to 2022. The dataset was pre-processed so that it will be suitable for training a robust model. The model was trained using Bi-LSTM. The result of the model is promising with a Mean Absolute error of 0.012% and a predicting accuracy of 99.9%. The proposed system was compared with other existing models, and the result shows that the model outperforms the existing model. The proposed system model was also saved and deployed to the web so that users can make use of it in making a future prediction of the prices of cryptocurrencies.

Crypto Currency, Bi-LSTM, Stock Market, Bitcoin

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J.B. Bekele, O.E. Taylor, "Forecasting the Price of Cryptocurrency using an Integrated Consensus Mining System," International Journal of Computer Sciences and Engineering, Vol.11, Issue.8, pp.9-14, 2023.

This study explores the impact of AI-driven personalization on learners` performance. Through quantitative and qualitative analysis, the research demonstrates a positive correlation between personalized AI-based adaptive learning and improved academic achievement, engagement, and satisfaction. The findings highlight the potential of AI-driven personalization to enhance learners` performance and transform education practices.

Learners` performance, Education technology, Artificial intelligence, learning outcomes, Learning Analytics, Engagements, Personalized Learning

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Amit Das, Sanjeev Malaviya, Manpreet Singh, "The Impact of AI-Driven Personalization on Learners` Performance," International Journal of Computer Sciences and Engineering, Vol.11, Issue.8, pp.15-22, 2023.

Artificial Intelligence (AI) has emerged as a powerful tool for measuring the psychophysiological behavior of students, enabling a deeper understanding of their cognitive and emotional states. By leveraging AI algorithms and data analytics, researchers can analyze various data sources such as facial expressions, voice tone, eye movements, and physiological signals to infer students` engagement levels, attention spans, stress levels, and overall emotional well-being. This technology has applications in educational settings, where AI can be utilized to develop intelligent tutoring systems that adapt to students’ individual needs, providing personalized feedback and interventions. Yoga and have gained significant popularity in recent years due to their potential positive effects on physical, mental, and emotional well-being. This research paper explores the effects of yoga on the psychological and physiological well-being of university students, using an Artificial Neural Network (ANN) model. The study aims to analyze the relationship between regular yoga practice and various indicators of well-being among students. The ANN model is employed to uncover complex patterns and interactions within the dataset, providing insights into the potential benefits of these practices. The findings of this research have implications for promoting holistic well-being among university students. A sigmoid axon was used as a transfer function for input and output layers.

Artificial Intelligence (AI), Machine Learning, Artificial Neural Network, Physiological Parameters, Psychological Parameters, Yoga.

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Shubham Chahar, J.K. Arora, Utkarsh Kumar, "AI-based Model for Physio-Psycho Behavior of University Students," International Journal of Computer Sciences and Engineering, Vol.11, Issue.8, pp.23-28, 2023.

The COVID-19 pandemic has significantly impacted various industries, leading to the adoption of advanced technologies to address the challenges faced during and after the crisis. Industry 4.0 technologies have played a crucial role in reshaping business operations and enhancing resilience. This research paper focuses on the progress of Industry 4.0 technologies, with a specific emphasis on image processing AI, and explores their applications in the post-COVID-19 era. The paper presents an overview of Industry 4.0 technologies, highlights the role of image processing AI, discusses its relevance in the context of the pandemic, and provides insights into the implementation and future potential of these technologies.

COVID-19, Coronavirus, image processing AI, Learning technologies, Industry 4.0

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Makund Arora, "Progress of Industry 4.0 Technologies and Their Applications in Post-COVID-19 Pandemic: A Study on Image Processing AI," International Journal of Computer Sciences and Engineering, Vol.11, Issue.8, pp.29-39, 2023.

"Scalable Prediction of Heart Disease using a Hybrid Model: A Machine Learning Perspective" presents a approach to predict heart disease using a hybrid machine learning model. The proposed model combines different machine learning algorithms to improve the prediction accuracy and scalability. The dataset used in the study contains various clinical and demographic features of patients, which were pre-processed and feature-selected to reduce noise and improve the model`s performance. Heart disease is a leading cause of mortality worldwide, and early diagnosis and treatment can significantly improve patient outcomes. Machine learning algorithms have shown promising results in predicting heart disease using clinical and demographic data. The performance of the model was evaluated using several evaluation metrics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve. The results show that the proposed hybrid model outperformed other state-of-the-art machine learning models in terms of prediction accuracy and scalability. The dataset was preprocessed and feature-selected to reduce noise and improve the model`s performance. The training process was parallelized using distributed computing to reduce the training time and improve the scalability of the model. the study provides a valuable contribution to the field of machine learning in healthcare and highlights the potential of using advanced algorithms to improve the diagnosis and treatment of cardiovascular diseases.

machine learning, heart disease, feature learning, hybrid approach, prediction accuracy, ensemble learning, performance measures

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Pooja Rani, Aruna Bhatia, "Scalable Prediction of Heart Disease using a Hybrid Model: A Machine Learning Perspective," International Journal of Computer Sciences and Engineering, Vol.11, Issue.8, pp.40-47, 2023.

This paper presents a novel approach to optimization using genetic algorithms (GAs) for the autonomous design of digital integrated circuits using CMOS technology. The genetic algorithm, implemented through a user-friendly Graphical User Interface (GUI) in MATLAB, optimizes transistor dimensions while considering the trade-offs between power consumption, delay, and speed. By executing the GA program multiple times, optimal values for n-type and p-type MOSFET dimensions (Wn and Wp), layout area, power consumption, high-to-low propagation delay (Tplh), and low-to-high propagation delay (Tphl) are stored in a matrix. The algorithm then identifies the chromosome associated with the minimum power consumption and displays the corresponding values of Wp, Wn, Tplh, and Tphl in the GUI. Furthermore, the accuracy of the algorithm is confirmed through circuit simulation in HSPICE software, demonstrating close agreement between the simulated results and those obtained through the genetic algorithm in MATLAB. This comprehensive approach offers an effective solution for optimizing digital integrated circuits in CMOS technology.

Genetic Algorithm; Automatic Design; CMOS Digital Integrated Circuits; Full-Adder; VLSI

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Ali Mohammadi, Safoura Mehdizadeh, "A Library for Designing Automatic CMOS Digital Integrated Circuits Using Genetic Algorithms," International Journal of Computer Sciences and Engineering, Vol.11, Issue.8, pp.48-55, 2023.

This project is focused on building a sophisticated chatbot that makes use of cutting-edge Natural Language Processing (NLP) methods to create seamless communication between users and healthcare practitioners. The main objective is to close the communication gap between medical professionals and those looking for quick answers to their health-related questions. The chatbot effectively understands user inputs by analyzing complex language correlations contained in their inquiries by utilizing NLP. Beyond solving the present drawbacks in remote healthcare encounters, this cutting-edge chatbot demonstrates the possibility for predictive diagnosis by spotting patterns in the symptoms that are regularly reported. This idea greatly improves the quality of remote medical consultations by fusing cutting-edge technology with healthcare. Improved patient care and outcomes are made possible by the prompt and accurate responses that are provided.

Chat bot, Health care, Natural language processing (NLP), Medical expertise

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[7] B. R. Ranoliya, N. Raghuwanshi and S. Singh, "Chatbot for university related FAQs," 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Udupi, India, pp.1525-1530, 2017. Doi: 10.1109/ICACCI.2017.8126057.
[8] M. Omar, S. Choi, D. Nyang and D. Mohaisen, "Robust Natural Language Processing: Recent Advances, Challenges, and Future Directions," in IEEE Access, Vol.10, pp.86038-86056, 2022. Doi: 10.1109/ACCESS.2022.3197769.
[9] A. Goel, Satyam and S. Sharma, "Artificial Intelligence based Healthcare Chat Bot System," 2023 8th International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India, pp.1362-1366, 2023. Doi: 10.1109/ICCES57224.2023.10192727.
[10] T. A. H. Shaikh and M. Mhetre, "Autonomous AI Chat Bot Therapy for Patient with Insomnia," 2022 IEEE 7th International conference for Convergence in Technology (I2CT), Mumbai, India, pp.1-5, 2022. Doi: 10.1109/I2CT54291.2022.9825008.
[11] A. K. Singh, "Conversational Interfaces for Health Consultation," 2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART), Moradabad, India, pp.35-38, 2022. Doi: 10.1109/SMART55829.2022.10046984.
[12] A. Wahal, M. Aggarwal and T. Poongodi, "IoT based Chatbots using NLP and SVM Algorithms," 2022 3rd International Conference on Intelligent Engineering and Management (ICIEM), London, United Kingdom, pp.484-489, 2022. Doi: 10.1109/ICIEM54221.2022.9853095.
[13] R. Goel, R. P. Goswami, S. Totlani, P. Arora, R. Bansal and D. Vij, "Machine Learning Based Healthcare Chatbot," 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), Greater Noida, India, pp.188-192, 2022. Doi: 10.1109/ICACITE53722.2022.9823901.
[14] Vanshika and N. Gupta, "Machine Learning Applications in Healthcare," 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), Noida, India, pp.1-6, 2022. Doi: 10.1109/ICRITO56286.2022.9964865.

S. Nithish Kumar, S.Sujatha, "Medical Chatbot Using Sequence Modelling in Machine Learning," International Journal of Computer Sciences and Engineering, Vol.11, Issue.8, pp.56-64, 2023.

Cloud computing and mobile computing have increasing its performance with rapid manner through numerous area of applications, these are extending such as digital payments, storage and confidential information accessing. Current technology offers several internet applications by using cloud based electronic payment methods, therefore security and confidentiality is necessary. According to national herald in India 42% frauds are identified in various fields from 1990 to 2020. Like “no fraud” agency in USA identified around 30% frauds since 1990, every year these frauds are increases with high ratios. Frauds did not have particular patterns, also change their behavior at every time. These frauds are most probably recognized at cloud based e-commerce and trade business websites. A real and precise fraud detection system must be developed in order to reduce this fraud ratio. In this exploration with the assistance of profound and AI improvement strategies has been utilized to recognize the cloud based fakes. So many, existed works settle this issue yet precision, F-score, review and precession are exceptionally less. Due to this impediment, in this work is introduced deep learning mechanisms like fully Edited Nearest Neighbor (ENN) and deep neural network (DNN). The DNN with ENN is best technique for credit card fraud prediction and achieve good accuracy.

Credit Card, Deep Learning, ENN, DNN, Accuracy

[1] Tesfahun Berhane , Tamiru Melese, Assaye Walelign, and Abdu Mohammed, “A Hybrid Convolutional Neural Network and Support Vector Machine-Based Credit Card Fraud Detection Model”, Mathematical Problems in Engineering (SCI), Hindawi, pp.1-10, 2023.
[2] Ibomoiye Domor Mienye and Yanxia Sun, “A Machine Learning Method with Hybrid Feature Selection for Improved Credit Card Fraud Detection”, Applied Science, 2023.
[3] Vipul Jain, H Kavitha and S Mohana Kumar, “Credit Card Fraud Detection Web Application using Streamlit and Machine Learning”, International Conference on Data Science and Information System (ICDSIS), IEEE 2022.
[4] Yathartha Singh, Kiran Singh and Vivek Singh Chauhan, “Fraud Detection Techniques for Credit Card Transactions”, 3rd International Conference on Intelligent Engineering and Management (ICIEM), IEEE 2022.
[5] J. Leach and U. Tayasivam, “Optimizing data evaluation metrics for fraud detection using machine learning,” International Journal of Mathematics and Computer Science, Vol.16, Issue.8, pp.52–59, 2022.
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[7] V. Plakandaras, P. Gogas, T. Papadimitriou, and I. Tsamardinos, “Credit card fraud detection with automated machine learning systems,” Applied Artifcial Intelligence, vol. 36, no. 1, Article ID 2086354, 2022.
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[9] S. Al-Faqir and O. Ouda, “Credit card frauds scoring model based on deep learning ensemble,” Journal of Teoretical and Applied Information Technology, Vol.100, Issue.14, 2022.
[10] F. K. Alarfaj, I. Malik, H. U. Khan, N. Almusallam, M. Ramzan, and M. Ahmed, “Credit card fraud detection using state-of-the-art machine learning and deep learning algorithms,” IEEE Access, Vol.10, pp.39700–39715, 2022.
[11] F. Itoo and S. Singh "Comparison and analysis of logistic regression Naïve Bayes and KNN machine learning algorithms for credit card fraud detection" International Journal of Information Technology Vol.13, Issue.4, pp.1503-1511 2021.
[12] E. S. C. R. S K Saddam Hussain "Fraud Detection in Credit Card Transactions Using SVM and Random Forest Algorithms" IEEE Xplore 2021.
[13] E. Ileberi Y. Sun and Z. Wang "Performance Evaluation of Machine Learning Methods for Credit Card Fraud Detection Using SMOTE and AdaBoost" IEEE Vol.9, Issue.5, pp.165286-165294 2021.
[14] D. Tanouz R. R. Subramanian and D. Eswar "Credit Card Fraud Detection Using Machine Learning" IEEE 2021.
[15] S. Khatri A. Arora and A. P. Agrawal "Supervised Machine Learning Algorithms for Credit Card Fraud Detection: A Comparison" IEEE 2020.

Kajol Khan, Poornima Dwivedi, "Improved Credit Card Fraud Prediction using Edited Nearest Neighbors Learning Technique," International Journal of Computer Sciences and Engineering, Vol.11, Issue.8, pp.65-70, 2023.