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2023 International Conference on Recent Advances in science and Engineering Technology, ICRASET 2023

作者： Dabwan, Basel A. Jadhav, Mukti E. Abosaq, Hamad A. Olayah, Fekry A. Al Yami, Mohammed Abdelrahman, Yahya A. AlBaydha University Department of Information System AlBaydha Yemen Shri Shivaji Science & Art College Department of Computer Science India Najran University Computer Science Dep. Saudi Arabia Najran University Kingdom of Saudi Arabia Department of Information System Saudi Arabia Najran University Faculty of Computer Science and Information Systems Department of Information Systems Saudi Arabia Najran University College of Computer Science and Information Systems Department of Information System Saudi Arabia

ISBN: (纸本)9798350306927

Sign language serves as a vital medium of communication for the Deaf and dumb people. We present a real-time system to translate ASL into text, addressing the communication gap between deaf and dumb people and normal people. Our system leverages computer vision and machine learning techniques to accurately recognize ASL gestures. In this study, we introduce a convolutional neural network (CNN) and (LSTM) approaches designed to identify hand gestures associated with human actions as captured through images from cameras. By utilizing the hand's spatial positioning and orientation, we construct the training and testing datasets for the CNN-LSTM model, we were used 4680 skeleton images of the ASL alphabet, from A to Z as a dataset. The hand image undergoes an initial filtering process followed by classification, where a classifier determines the classification of the hand gesture. Subsequently, the refined images are harnessed to train the CNN-LSTM model. Our real-time system provides accurate and reliable results in real-time 99.9%, in addition to generating new datasets that are processed and can be trained on any model. © 2023 IEEE.

关键词： Real time systems

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Multi-Criteria Decision-Making Model to Achieve Sustainable Developmental Goals in Industry 4.0 for Smart City Infrastructure

HighTech and Innovation Journal

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HighTech and Innovation Journal 2024年第4期5卷 1135-1153页

作者： Akila, D. Pal, Souvik Sarkar, Bikramjit Jayalaksshmi, S. Muthaiyah, Saravanan Anbananthen, Kalaiarasi Sonai Muthu Department of Computer Applications Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences India Department of Management Information Systems Saveethas College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences India Department of Computer Science and Engineering JIS College of Engineering Kalyani India Department of Information Technology Vels Institute of Science Technology and Advanced Studies India School of Business and Technology International Medical University Kuala Lumpur57000 Malaysia Faculty of Information Science and Technology Multimedia University Melaka75450 Malaysia

Due to a shortage of funding and other market challenges, Small and Medium-sized Enterprises (SMEs) face difficulties in adopting new technologies. Numerous technological obstacles negatively impact the long-term commercial achievement of SMEs. The deployment of Industry 4.0hopes to resolve these technological challenges. A sustainable city is a complex structure where economic, societal, and ecological components interact and compete. There is a scarcity of l methodologies for measuring interactions in this complex structure. Industry 4.0 aims to obtain higher performance effectiveness, profitability, and automation. The main goal is to develop a reliable method of evaluating small and medium-sized enterprises (SMEs) adopting Industry 4.0 technologies, particularly concerning smart city applications. This paper aims to determine the influence of Industry 4.0 in fostering economic efficiency and sustainability amongst these SMEs. The study introduces a multi-criteria decision-making (SC-MCDM) system designed to test an SME’s achievement of their targeted sustainable developmental goals. A technique for computing the interaction between various standards, i.e., (static interactions and dynamical pattern resemblance), as well as the weightage of variables of every indicator generated by the connection, is included within SC-MCDM. Furthermore, applying the suggested technique is validated by assessing the sustainable development goals of twelve Chinese cities within the Triple Bottom Line (TBL) paradigm. From a geographic-temporal viewpoint, spatial variations in city sustainability reveal regional sustainable inequalities. Indicator scores suggest that the most significant factors for most communities are the lack of research spending, falling financing in stationary assets, shortage of financial development, and inadequate shared transit. Furthermore, the growth of tertiary industries, improvement of energy performance, expansion of green areas, and reduction of poll

关键词： Sustainable city

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Leveraging Machine Learning to Predict Asthma following Vaccination 5

Leveraging Machine Learning to Predict Asthma following Vacc...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Gull, Seema C. NaveenKumar, C.G. Gull, Karuna C. Sanskruti, V.B. Laddi, Mahantesh Karnataka State Open University Department of Studies in Computer Science Mysuru India S.G.Balekundri Institute of Technology Department of Computer Science & Business Systems Belagavi India M.S. Sheshgiri College Engineering and Technology Department of Biomedical Engineering Belagavi India Bharatesh Institute of Technology Department of Computer Science and Engineering Belagavi India

ISBN: (纸本)9798350376685

This project seeks to employ more sophisticated approaches to machine learning to determine asthma occurrences after vaccine administration. As vaccine distribution becomes more widespread around the world, it is important to monitor and prevent issues such as asthma. The goals for future research include the creation of risk-adjusted models that may predict the risk of asthma following vaccination, depending on patients' demographic information and the characteristics of the vaccines. The research uses several sophisticated techniques such as Artificial-Neural-Networks (ANN), Random-Forests (RF), Naïve-Bayes (NB), and logistic regression (LR) to improve the ability to predict and identify prevention measures and healthcare management. Thus, a significant innovation of this work is the comprehensive assessment of these algorithms when exposed to noise, which is a common issue in many medical data sets. The study also showed that Random-Forest and ANN models were significantly more accurate and yielded larger ROC AUC scores than other models;moreover, they were much less sensitive to noise with noise values ranging from 0 to 0.5. This places RF and ANN on a better pedestal to offer a better prediction of post-vaccination asthma than the other models, especially in a scenario where there is the possibility of data contamination. The present findings can thus help create better and more resistant models of prediction that are useful in better health management and treatment of asthma over and after vaccination. © 2024 IEEE.

关键词： Logistic regression

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How transformations of representation for input data can affect the properties of induced decision reducts and rules 27

How transformations of representation for input data can aff...

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27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023

作者： Stanczyk, Urszula Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland

Decision reducts and rules belong to forms used for the representation of knowledge learnt from input data while using a rough set approach in the exploration stage. As with any patterns that capture properties of data, the size is considered as the most important indicator of their quality, as a more general and concise description allows for improved understanding and simplified interpretation. The size of a reduct is given by its cardinality, and for a rule it is a number of included conditions, both referred to as lengths. These lengths undergo transformations along with data transformations, so such processing as discretisation leads to changed characteristics of existing patterns and through them also reducts and rules. The paper presents research with contributions in the observations focused on the influence of supervised and unsupervised discretisation on properties of attributes, induced decision reducts, and rules, offering a deeper insight into relations among them. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Data exploration decision reduct decision rule discretisation knowledge representation rough set theory transformation

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Machine Learning Approaches for Early Detection of Heart Diseases 25

Machine Learning Approaches for Early Detection of Heart Dis...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Al-Sheyab, Asalla M. Kousa, Msallam Hamad, Hanadi Bani Almomani, Adai Elrashidi, Ali Philadelphia Private University College of Information Technology Department of Computer Science Irbid25113 Jordan Isfahan University Faculty of Computer Engineering Department of Artificial Intelligence Isfahan Iran Zarqa Private University College of Information Technology Department of Computer Science Irbid25113 Jordan Zarqa University Department of Computer Science Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

The main objective of the study is to predict the probability of developing heart disease using a Decision Tree Classifier, K-nearest neighbors, and Support Vector Machine, Logistic Regression. Age, sexual pressure, quantity, and levels are among the variables included in the Kaggle dataset. A combination of accuracy, sensitivity, and specificity was used to train and evaluate the algorithms. Based on the accuracy of participants' predictions, the results suggest that learning algorithms could help treat heart *** "Heart Disease Prediction"project contains all project information and results. Contributing to the use of multiple algorithms improves patient outcomes to enhance the detection and treatment of heart diseases. Logistic Regression has achieved an accuracy of 86.41%. A module capable of predicting heart disease in a patient has been implemented. A variety of techniques are employed to assess models, including F-measure, ROC, precision, recall, and accuracy. The bagging set learning method using logistic regression provides the best performance, according to the data. © 2024 IEEE.

关键词： Support vector regression

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Effective Visualization and Trend Forecasting of Crime Data Through Big Data Analytics and Mining 3

Effective Visualization and Trend Forecasting of Crime Data ...

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3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Mahendhiran, Pd Apsara, S.K. Vasudevan, Harshitha Nithish, S. Sri Krishna College of Engineering and Technology Department of Computer Science and Business Systems Tamil Nadu Coimbatore India Sri Krishna College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350322842

A crime is an act that merits public condemnation and penalties, which is usually in the form of a fine or jail. It is a widespread social issue that affects a country's life style, economic progress, and credibility. People are at a worrisome point in terms of reducing criminal activity at a greater rate, as criminality rates have risen dramatically. If people can evaluate and predict the crime pattern and implement preventive steps ahead of time, crime rates can be lowered. In this research, the author has present Crime Cast, a system that analyses crime reports and anticipates trends and crime statistics based on a specified data set using deep learning techniques such as LSTM and DNN at a faster rate and with greater accuracy. Within a huge percentage of crime data, Big Data Analytics is utilized to analyze and detect different crime patterns, their relationships, and trends. The information contains cases of kidnapping, murder, rape, stealing, and other crimes. With the help of the LSTM model and DNN several operations performed on the data. The expected outcomes will aid police and law enforcement agencies in clearer grasp crime trends, allowing them to track incidences and improve their decision-making processes. These results will allow the law enforcement and regulatory agencies to further immediately spot inappropriate behavior issues and provide information that will allow them to monitor activities and predict the likelihood of limiting the number of events, transporting property securely, and improving the dynamic cycle. © 2023 IEEE.

关键词： Crime

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A Way to Design Data Transfer System Modelling to Secure and Transmit Data with the Help of Fuzzy Based Logic in Healthcare System 4

A Way to Design Data Transfer System Modelling to Secure and...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Raman, Ramakrishnan Kumar, Vikram Pillai, Biju G. Verma, Apurv Rastogi, Shailesh Meenakshi, R. India Srv Media Pvt. Limited India Sri Balaji University Faculty of Management It & Business Analytics Pune India Parul Institute of Technology Parul University Computer Science and Engineering India Chennai Institute of Technology Computer Science and Engineering India

ISBN: (纸本)9798350360165

Healthcare utilization of data plays a key part in cancer types and disease prediction. Bringing fuzzy linear regression algorithms into dynamic analysis improves decision-making in medical business, especially for dynamic analyses of data. To handle issues linked to computing intricacy and processing time and energy, a convex hull method is suggested, focused on using the Beneath-Beyond algorithm. This gradual method lowers computing speed and complexity, reducing current limits. Using a real fuzzy medicinal products dataset compared to the industry, the study includes creating an offering of fuzzy numerical optimization alongside applying the convex hull method to obtain coordinate points in hyper-rectangles circles. This combo results in a new convex hull-based flexible linear equation model designed for dynamic curved health care data. This improved program aims to quicken processes of decision-making, especially among critical healthcare areas, giving a useful addition to the field. © 2024 IEEE.

关键词： Electronic health record

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Developing a Differential Evolution with Adaptive Evaluation Function to Treat Real Optimization Problems with Black-box Constraints 14

Developing a Differential Evolution with Adaptive Evaluation...

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14th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2023

作者： Shiono, Yuma Hori, Takato Uchitane, Takeshi Ito, Nobuhiro Iwata, Kazunori Aichi Institute of Technology Graduate School of Business Administration and Computer Science Toyota Japan Aichi Institute of Technology Faculty of Infomation Science Toyota Japan Aichi University Faculty of Business Administration Nagoya Japan

ISBN: (纸本)9798350324228

In recent years, much attention has been paid to the development of evolutionary computation algorithms that can perform well not only on benchmark problems but also on real optimization problems. Wind Turbine Design optimization Problem (WTDOP) is a real optimization problem in Evolutionary Computation Competition in Japan. WTDOP has 22 constraints and they are described as black-box constraint functions. From previous studies, it was found that WTDOP has a multi-modal landscape fitness function. Hori et al. performed optimization by established a new evaluation function which includes objective function value and a penalty value of constraint violations and by using iDEaSm which is an evolutionary computation algorithm. However there were two issues. One is a fixed penalty which was added to the objective value of solution. Another is that the variety of solutions is not ensured. In order to overcome such issues, in this paper, two improvements are proposed and examined. One of the improvements is the adaptive evaluation function. By changing the penalty depending on the search depth, it is possible to search the boundary between the infeasible and feasible regions without eliminating the infeasible solutions in the early or middle stage of the search. Another is an additional mutation of solution candidates. The additional mutations ensure diversity stochastically and allow the search to continue after the initial convergence. From the comparison between proposed and previous algorithms, our proposed algorithm could find better solutions. Therefore, our proposed algorithm may have better possibility to solve other real optimization problems. © 2023 IEEE.

关键词： Evolutionary algorithms

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A Continual Learning System with Self Domain Shift Adaptation for Fake News Detection 10

A Continual Learning System with Self Domain Shift Adaptatio...

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10th IEEE International Conference on Data science and Advanced Analytics, DSAA 2023

作者： Basterrech, Sebastian Kasprzak, Andrzej Platos, Jan Wozniak, Michal Technical University of Denmark Department of Applied Mathematics and Computer Science Kongens Lyngby Denmark Wroclaw University of Science and Technology Department of Systems and Computer Networks Wroclaw Poland VŠB-Technical University of Ostrava Faculty of Electrical Engineering and Computer Science Ostrava Czech Republic

ISBN: (纸本)9798350345032

Detecting fake news is currently one of the critical challenges facing modern societies. The problem is particularly relevant, as disinformation is readily used for political warfare but can also cause significant harm to the health of citizens, such as by promoting false data on the harmfulness of selected therapies. One way to combat disinformation is to treat fake news detection as a machine learning task. This paper presents such an approach, which additionally addresses an important problem related to the non-stationarity characteristics of the fake news. We elaborated a stream data with the simulation of domain shift based on two popular benchmark datasets dedicated to the fake news classification problem (Kaggle Fake News and Constraint@AAAI2021-COVID19 Fake News Detection). The proposed learning system works in a Continual Learning (CL) framework and integrates a self domain shift adaptation in a machine learning scheme. The method was built following state-of-the-art techniques, that includes Word2Vec as a feature extractor and the LSTM model as a classifier. The performance of the approach has been evaluated over the generated data stream. The convenience of our approach is showed in the results, where the accuracy gain with respect to a CL approach without domain adaptation is observed to be significant. © 2023 IEEE.

关键词： Long short-term memory

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Report on the 12th International Workshop on Quantitative Approaches to Software Quality (QuASoQ 2024) 12

Report on the 12th International Workshop on Quantitative Ap...

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12th International Workshop on Quantitative Approaches to Software Quality, QuASoQ 2024

作者： Lichter, Horst Wild, Nils Sunetnanta, Thanwadee Anwar, Toni Research Group Software Construction RWTH Aachen University Aachen Germany Computer Science Academic Group Faculty of Information And Communication Technology Mahidol University Bangkok Thailand Faculty of Science and Information Technology Chair Computer & Information Systems Universiti Teknologi Petronas Bandar Seri Iskandar Perak Malaysia

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