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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Singh, Swati Nanda, R.T. Sathya, S. Banchhor, Chitrakant O. Pandey, Pratik Rampal, Sourav Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Basic Science Karnataka Ramnagar India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350319125

The emergence of system mastering has enabled actual-time information analysis to come to be extra accurate, efficient, and comfy. ML can rapidly examine and classify excessive-volume streams of raw records, allowing companies to reply to quickly changing environments. This technology has had a considerable impact on the way groups and developers manner complicated datasets and make decisions in realtime. It has revolutionized how agencies use and combine information to gain new insights and understand trends. ML performs a vital position in helping businesses efficiently technique large quantities of data while reducing human errors. At the same time, it provides security and integrity of the data, lowering the capacity for change and loss. In this paper, we discover the effect of ML on actual-time information evaluation and talk about its diverse applications. We additionally offer an overview of different methods of ML and their advantages and disadvantages. ultimately, we speak of future traits for mixing ML with actual-time records analytics and the consequences of this integration. © 2023 IEEE.

关键词： disadvantages environments organizations revolutionized understand

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Periodicity and bifurcation of single pulse and soliton molecules

Periodicity and bifurcation of single pulse and soliton mole...

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2024 Asia Communications and Photonics Conference, ACP 2024 and International Conference on information Photonics and Optical Communications, IPOC 2024

作者： Ban, Xiaoqiang Li, Xiaohui Li, Qian Zhong, Ming School of Electronic and Computer Engineering Peking University Shenzhen China School of Physics and Information Technology Shaanxi Normal University Xi'an China Faculty of Education Shenzhen University Shenzhen China

Period bifurcation of single pulse and soliton molecule are studied in fiber lasers, where period-timing bifurcation reveals the abundant nonlinear pulse dynamics and may be applied to optical switch. © 2024 IEEE.

ISBN: (纸本)9798350379266

关键词： Fiber lasers

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Exploring the Use of Machine Learning Techniques for Enhancing Real-Time Data Access 3

Exploring the Use of Machine Learning Techniques for Enhanci...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Rampal, Sourav Shreenidhi, H.S. Shrivastava, Manish Singh, Akhilendra Pratap Reena, R. Kumar, Santosh D. Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science and Engineering Faculty of Engineering and Technology Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India

ISBN: (纸本)9798350319125

Actual-time statistics to date has updated, increasingly vital as the quantity of virtual information maintains updated growth. Effective choice-making depends on the capability to update access and analyze records. Device up-to-date techniques provide the capacity up-to-date enhance actual-time statistics up-to-date by using offering insights from disparate pieces of information. This paper addresses the potential benefits of using a system with date knowledge and updated, up-to-date strategies to broaden extra clever statistics up to date updated systems. It summarizes the contemporary nation of the art and highlights studies in machine learning and up-to-date imaginative and prescient real-time information up-to-date. It additionally discusses present-day challenges and shows viable instructions for future studies. The studies network up-to-date recall how gadgets are up to date know that updated strategies may be used to facilitate real-time get entry updated the maximum precious pieces of facts. © 2023 IEEE.

关键词： Machine learning

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Towards personalized control of things using Arabic voice commands for elderly and with disabilities people

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International Journal of information technology (Singapore) 2023年 1-22页

作者： Salah, Abdelrahman Adel, Ghada Mohamed, Hussein Baghdady, Youssef Moussa, Sherin M. Department of Software Engineering and Information Technology Faculty of Engineering and Technology Egyptian Chinese University Cairo 11351 Egypt Department of Information Systems Faculty of Computer and Information Sciences Ain Shams University Cairo 11566 Egypt Laboratoire Interdisciplinaire de l’Université Française d’Égypte (UFEID Lab) Université Française d’Égypte Cairo 11837 Egypt

Considering recent smart home solutions that automate home appliances through voice commands, elderly people and/or with special needs prefer using their mother-tongue. However, most of current systems adopt the English language, making it harder for non-native English speakers, while personalization is not heavily considered. In this paper, we propose the Arabic Personalized Control of Things (APCoT) system that achieves inhabitant-tailored home automation through Arabic voice commands based on the user’s preferences to control a home device. A new dataset is created for a wider range of Arabic voice commands to interact with home appliances rather than just turning them on/off. Internet of Things, voice and speech recognition are utilized to attain personalization according to the inhabitants' voiceprints, while infrared is adopted to control non-smart devices supporting infrared signals. As a Proof-of-Concept, three home appliances are adopted: TV, Lights and Bluetooth speaker. Machine learning algorithms are customized for Arabic voice recognition, integrating face and hand gestures recognition to provide an additional facility for inhabitant’s identification and sending control commands respectively. The experimental results indicate that the best average accuracies obtained for Arabic voice recognition were 99.63% for a single speaker and 95.4% for multi-speakers, with average response times 0.141s and 14.29s respectively. For speech recognition, Google online API and a customized Vosk offline pretrained model achieved average accuracies of 95% and 90%, average response times 0.7553s and 0.3933s respectively. The face and hand gestures recognition achieved average accuracies of 90% and 98%, average response times 0.5934s and 15.62049s respectively. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Arabic commands Elderly people Home automation People with disabilities Smart home Speech and voice recognition

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Fingerprint Liveness Detection with Voting Ensemble Classifier 6

Fingerprint Liveness Detection with Voting Ensemble Classifi...

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6th International Conference on information technology, InCIT 2022

作者： Sittirit, Napahatai Mongkolwat, Pattanasak Thaipisutikul, Tipajin Supratak, Akara Chen, Zhi-Sheng Wang, Jia-Ching Mahidol University Faculty of Information and Communication Technology Thailand Providence University Department of Computer Science and Information Engineering Taiwan National Central University Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9781665489126

Detecting a user's fingerprint is a common verification process in many daily products such as smartphones and laptops. The convenience makes it popular, but this method is vulnerable to a presentation attack. Any fingerprint can be copied onto materials such as wood glue and gelatin, using only a few simple steps. Therefore, detecting whether the fingerprint comes from a live person is essential. In this paper, we proposed a method that employs a voting ensemble classification model to aggregate predictions from multiple individually trained machine learning models to determine whether an input fingerprint image is a live or a fake one. The input image is first pre-processed with a wavelet denoising algorithm, then Local Binary Pattern (LBP) and Local Phase Quantization (LPQ) are used for feature extraction. Next, we train a Voting Ensemble Classifying Model, utilizing predictions from several trained models, to find the majority vote for fingerprint liveness detection. According to the performance on the public LivDet 2015 dataset, the proposed method achieved better accuracy classification error (ACE) compared to the state-of-the-art models on three out of four sensor types: Greenbit (ACE=0.95%), Digital Persona (ACE=3.71%), and Hi Scan (ACE=1.39%). © 2022 IEEE.

关键词： Local binary pattern

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Text Prediction Zero Probability Problem Handling with N-gram Model and Laplace Smoothing

Text Prediction Zero Probability Problem Handling with N-gra...

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2022 International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2022

作者： Mimi, Raonak Jahan Masud, Md Abdul Rahman, Rifat Dina, Nusrat Sultana Patuakhali Science and Technology University Faculty of Computer Science and Engineering Bangladesh Patuakhali Science and Technology University Department of Computer Science and Information Technology Bangladesh

ISBN: (数字)9781665469449

ISBN: (纸本)9781665469449

In Natural Language Processing, text prediction represents the process of predicting the word with the highest probability through a predictive language model from a series of text corpus. The N-gram model is familiar and considered the handiest and most computationally cost-effective model for text processing. Additionally, higher N-gram models, especially the 5-gram ones, give the best text prediction. Interestingly, these better prediction results were obtained only on the training dataset. In contrast, the highest N-gram model imploded badly on the evaluation dataset. This paper proposes an approach where the N-gram model, especially the bi-gram model, and the fine tuning with Laplace Smoothing, provide the best prediction results at the evaluation stage. © 2022 IEEE.

关键词： Text processing

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AI and Big Data: Achieving Optimal Data Retrieval Solutions 15

AI and Big Data: Achieving Optimal Data Retrieval Solutions

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Singh, Akhilendra Pratap Sood, Gourav Venkadeshwaran, K. Shrimal, Gajendra Santhosh Kumar, C. Dhabliya, Dharmesh Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Chitkara University Institute of Engineering and technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Faculty of Engineering and Technology Department of Mechanical Engineering Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Civil Engineering Chennai127 India Vishwakarma Institute of Technology Department of Information Technology Maharashtra Pune India

ISBN: (纸本)9798350370249

Huge records and AI generation have the capacity to revolutionize the way records is retrieved and used, enabling greater green and powerful retrieval approaches. AI algorithms are increasingly getting used to optimize the retrieval of information from large databases, enabling extra correct decisions and quicker seek effects. AI algorithms can be hired to use filters, visualize information, and cluster information into extraordinary corporations, thereby improving get entry to applicable facts. Additionally, AI algorithms can be used to pick out patterns within the information that permit for better forecasting and prediction. Huge information analytics similarly empower AI algorithms to effectively analyze massive datasets and develop insights and correlations in an automated way. These insights and correlations may be used to higher apprehend and utilize statistics in a greater powerful way. © 2024 IEEE.

关键词： Algorithms Applicable Databases Enabling information

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Cancellable template generation for speaker recognition based on spectrogram patch selection and deep convolutional neural networks

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International Journal of Speech technology 2022年第3期25卷 689-696页

作者： El-Moneim, Samia A. Nassar, M.A. Dessouky, Moawad I. Ismail, Nabil A. El-Fishawy, Adel S. El-Samie, Fathi E. Abd Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt

Nowadays, biometric systems have replaced password-or token-based authentication systems in many fields to improve the security level. However, biometric systems are also vulnerable to security threats. Unlike passwords, biometric templates cannot be replaced if lost or compromised. To deal with issue of compromising biometric templates, template protection schemes have evolved to make it possible to replace the biometric templates. A cancellable biometric scheme is such a template protection scheme that can replace a biometric template, when it is stolen or lost. The biometric used here is speech. It is important to preserve user confidentiality. Cancellable biometrics is a new notion addressed for this problem. This paper presents a scheme for cancellable speaker recognition based on spectrogram patch selection. The simulation results reveal that the suggested approach is practical, and it satisfies the desired criteria such as renewability, security and performance. The accuracy of the proposed cancellable speaker recognition scheme reaches 98.75% with a Convolutional Neural Network (CNN) composed of three layers. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature.

关键词： Biometrics

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Satellite-Derived NO2 Data to Predict Hospital Admissions: A Machine Learning-Based Approach

Satellite-Derived NO2 Data to Predict Hospital Admissions: A...

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Emerging Technologies and Innovation for Sustainability (EmergIN), International Conference on

作者： Bhushan Pawar Lalit Garg Vijay Prakash Ryan Stevenson Matthew Amaira Daria Soloducha Adam Kraus Computer Information Systems Faculty of Information & Communication Technology University of Malta Msida Malta Communications & Computer Engineering Faculty of Information & Communication Technology University of Malta Msida Malta

ISBN: (数字)9798350391268

ISBN: (纸本)9798350391275

Rising Levels of air pollution and changing environmental conditions are the global concern. There is substantial evidence linking short-term and long-term exposure to NO 2 (nitrogen dioxide) with multiple disorders such as asthma, Bronchitis, Chronic Obstructive Pulmonary Disease (COPD), and Respiratory Infections in humans. However, multiple researchers have mainly depended on time-based exposure data, possibly overlooking fluctuations in NO 2 levels across different locations. We aim to analyze the relationship between NO 2 fluctuations and emergency department hospital admissions. We adopted machine learning (ML) based regression approaches to predict hospital admissions, we obtained emergency department hospital admissions from 2018–2019, while NO 2 tropospheric concentration was collected from the Sentinel 5P satellite. The results are evaluated using various evaluation matrices such as MSE (Mean Square Error), RMSE (Root Mean Square Error), R2 score, etc. Our study demonstrated that Random Forest and ExtraTreesRegressor models gained substantial predictive accuracy compared to other applied regression models. ExtraTreesRegressor proves its significance with R2 score of 67.11. These results deliver significant insights for hospitals and healthcare authorities by offering resource management through anticipating fluctuating NO 2 concentrations. Moreover, the findings of our study highlight the potential of extending this research to include several air pollutants and meteorological factors.

关键词： Technological innovation Hospitals Atmospheric modeling Predictive models Air pollution Resource management Nitrogen Forecasting Regression tree analysis Random forests

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End-to-End Congestion Avoidance Scheme for Underwater High Speed Communication Networks 3

End-to-End Congestion Avoidance Scheme for Underwater High S...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Shivank Jain, Romil Reena, R. Kadam, Prema S. Das, Sunanda Singh, Swati Vivekananda Global University Department of Computer Science & Engineering Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Computer Science and Engineering Faculty of Engineering and Technology Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350319125

The cease-to-give up Congestion Avoidance Scheme for Underwater high pace verbal exchange Networks, commonly known as E2E-UCAN, is a critical technique for lowering plausible throughput by using efficiently warding off congestion inside a community. E2E-UCAN is a disbursed congestion control mechanism which uses statistics obtained from cease-to-end feedbacks to regulate sending prices on the give up systems. By means of monitoring the differences among expected network overall performance and actual performance, E2E-UCAN is able to pick out congestion stages inside the device, and is therefore able to regulate to prevent congestion before it happens. This enables to improve the stableness of the community and to increase the throughput of a network. Moreover, E2E-UCAN is likewise capable of efficaciously coping with queuing, avoiding packet loss in non-uniform networks, and in preferred, permitting applications to maximize performance. © 2023 IEEE.

关键词： Community Congestion Lowering Mechanism Performance

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