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Facilitating Data Use: Achieving Maximum Efficiency with AI-Enhanced Information Capture and Retrieval 3

Facilitating Data Use: Achieving Maximum Efficiency with AI-...

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

作者： Dhabliya, Dharmesh Vashisht, Nitish Singh, Swati Mahesh, T.R. Kumar, Ravindra Mahalakshmi, V. Vishwakarma Institute of Information Technology Department of Information Technology Maharashtra Pune India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science and Engineering India Vivekananda Global University Department of Civil Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Chennai127 India

ISBN: (纸本)9798350319125

The extraordinary boom of data keeps to undertaking the conventional methods to information usage. the use of synthetic intelligence (AI) and gadget studying (ML) is reworking how information is captured and used, as businesses increasingly more undertake a zero-believe approach to garage and retrieval of records. this article explores the ability of AI-enhanced data capture and retrieval to assist groups unleashes the price of their facts with the intention to maximize performance. It covers the basics of AIbased facts capture and retrieval, inclusive of photo popularity, speech reputation, optical character recognition, and herbal language processing (NLP). It additionally examines the effective implications of AI-improved statistics use, in addition to capacity troubles which can prevent or put off its adoption. sooner or later, the thing affords insights into how organizations can equip themselves with the essential AI-based skills to optimize their facts use and selection-making processes. © 2023 IEEE.

关键词： essential examines performance prevent retrieval

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Estimation of Bandwidth Requirement for Underwater Fiber Optic Networks

Estimation of Bandwidth Requirement for Underwater Fiber Opt...

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2023 IEEE International Conference on Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Choudhary, Sanjay Sahoo, Girija Shankar Rajesh, A. Khurana, Jatin Yuvaraj, S. Shewale, Chaitali R. Vivekananda Global University Department of Mechanical Engineering Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore 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 Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Information Technology Pune India

ISBN: (纸本)9798350329773

This paper affords an estimation of the bandwidth requirement for underwater fiber optic networks by thinking about transmission c together with signal attenuation, dispersion, and nonlinear effects (NLEs). The authors present an evaluation of the signal power level and the propagation of the signal in an underwater environment. From this evaluation, they obtain empirical expressions for the desired signal-to-noise ratio (SNR) to achieve a given bit-errors charge (BER) for different sorts of fiber optics. The impact of NLEs at the sign excellent is also investigated by means of modeling these effects with polynomial expressions under high electricity excitation of the optical fiber. The authors suggest numerous methods for estimating the bit fee required for a given data rate and BER, inclusive of the p-endurance method, which is based totally on an assessment of the energy of the received optical sign. The results acquired from this observation suggest that NLEs can extensively affect the overall performance of optical communications, leading to extra design concerns and extended machine requirements for underwater fiber optic networks. © 2023 IEEE.

关键词： Signal to noise ratio

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Predicting academic performance of undergraduate students in blended learning

Predicting academic performance of undergraduate students in...

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IEEE Colombian Communications Conference (COLCOM)

作者： Wilson Chango Geovanny Silva Hugo Sanchez Santiago Logrono Computer Science and Electronics Information Technology ESPOCH Riobamba Ecuador Environmental engineering Faculty of Sciences ESPOCH Riobamba Ecuador

ISBN: (数字)9798331504724

ISBN: (纸本)9798331504731

Abstract—In this paper, we propose to predict the academic performance of university students from multiple-source data in multimodal and mixed learning environments using data fusion and extraction. We have collected data from 135 university students and different variables from four different sources. First, we applied data fusion and preprocessing to create a set of summary data in numerical and categorical format. We then applied different white box classification algorithms provided by Weka’s data mining tool to select the best algorithm. The PART algorithm shows the best performance of the observed quality metrics, (ROC Area =0.917). for the next test we applied attribute selection algorithms being ClassifierSubsetEval with J48 the best with a value of (ROC Area =0.9380), then we used two automatic learning algorithms Voting and Stacking given being the best voting result with the Jrip algorithm (ROC Area =0.9330) Finally, we show the best prediction model that is a hybrid between the classification and automatic learning algorithms with a value (ROC Area =0.9420) to help the instructor to take corrective actions with the students at risk of abandonment or failure.

关键词： Education Hybrid learning Predictive models Multisensory integration Algorithm design and analysis Data integration Performance evaluation Glass box Classification algorithms Statistical analysis Learning systems

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Leveraging Reinforcement Learning to Improve Content Retrieval 3

Leveraging Reinforcement Learning to Improve Content Retriev...

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

作者： Indira, G. Shelke, Ganesh C. Sharma, Bhuvnesh Singh, Vaishali Mahesh, T.R. Reddy, B. Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Chennai127 India Department of Information Technology Vishwakarma Institute of Information Technology Pune India Vivekananda Global University Department of Mechanical Engineering Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Computer Science Engineering Faculty of Engineering and Technology Karnataka Bangalore India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350319125

Reinforcement studying (RL) is an artificial intelligence (AI) paradigm that can be used to decorate content material retrieval systems, which frequently rent supervised gaining knowledge of strategies. It explains how traditional methods of content retrieval often struggle with providing accurate and relevant search results, and how reinforcement learning offers a potential solution by continuously learning and adapting to user behavior and preferences. The abstract also highlights the benefits and challenges of using this approach, as well as the potential impact it could have on the field of information retrieval. It could permit greater scalability and advanced user experience. The proposed method is predicted to surpass existing content material retrieval structures in terms of content material accuracy and efficiency. © 2023 IEEE.

关键词： Artificial Intelligence Data Mining Information Retrieval Optimization Personalization Search Algorithms User Experience

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Ad-hoc Vehicular Network to Optimize Vehicular Mobility

Ad-hoc Vehicular Network to Optimize Vehicular Mobility

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IEEE Colombian Communications Conference (COLCOM)

作者： Wilson Chango Geovanny Silva Nestor Estrada Hugo Sanchez Computer Science and Electronics Information Technology ESPOCH Riobamba Ecuador Environmental engineering Faculty of Sciences ESPOCH Riobamba Ecuador

ISBN: (数字)9798331504724

ISBN: (纸本)9798331504731

In Ecuador, land transportation is mainly carried out by vehicles circulating on roads. The growth of the transportation sector is linked to other phenomena, such as traffic accidents, with a consequent increase in fatalities and injuries. According to data from the National Transit Agency (ANT), in 2022 there was a slight increase of 2% in traffic accidents, 8% in injuries and 3% in fatalities, compared to *** proposed solution involves implementing VANET (Vehicular Ad-Hoc Network) technology to improve vehicular traffic management. This technology enables intercommunication between vehicles and road infrastructure, improving traffic efficiency and safety. Advanced simulation tools such as SUMO, OMNeT++ and Veins were used to model different traffic scenarios and test vehicle management strategies before their actual *** simulation showed that vehicles equipped with OBUs could receive real-time information about traffic jams and accidents, allowing them to adjust their routes to avoid congestion. The implementation of RSUs provided the necessary communication infrastructure to support the vehicular ad-hoc network.

关键词： Vehicular ad hoc networks Traffic control Vehicle safety Traffic congestion Accidents Statistical analysis Safety management Modeling Management Communication networks

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Harnessing Machine Learning for Increased Efficiency in Real-Time Data Analysis 3

Harnessing Machine Learning for Increased Efficiency in Real...

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

作者： Singh, Dhiraj Kumar Umamaheswari, B. Junnarkar, Aparna A. Singh, Vaishali Singh, Manpreet Savitha, R. Vivekananda Global University Department of Electrical Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Electrical and Electronics Engineering Faculty of Engineering and Technology Kanataka Bangalore India

ISBN: (纸本)9798350319125

The goal of this research is to analyze the ability of device mastering (ML) to increase the efficiency of real-time records evaluation. Notably, the focus is on leveraging ML techniques to correctly pick out, examine, and categorize information streams as a way to offer more green selection-making possibilities. This study will explore present ML strategies, which include supervised and unsupervised learning, deep studying, and reinforcement learning, and their software for real-time statistics evaluation. Additionally, the studies will remember the feasibility of combining ML strategies with present information science strategies and technology, including streaming data infrastructure and facts engineering. Sooner or later, the research will compare the potential prices and benefits of enforcing the sort of device in phrases of computational requirements, statistics handling, and accuracy. Via presenting insight into the feasibility of device mastering for actual-time records analysis, this research aims to cope with the issue of scalability in present-day techniques and help tell future selections in statistics engineering and analytics. © 2023 IEEE.

关键词： Efficiency

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Design and Implementation of Task Synchronization Method in BLE Mesh Network for Educational Robot 19

Design and Implementation of Task Synchronization Method in ...

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19th International Conference on Electrical engineering/electronics, computer, Telecommunications and Information technology, ECTI-CON 2022

作者： Jirakiat, Jirayus Kwankeo, Natavut Kaemarungsi, Kamol Tsuyoshi, Isshiki Kasetsart University Faculty of Engineering Department of Electrical Engineering Bangkok Thailand National Electronics and Computer Pathum Thani Thailand Tokyo Institute of Technology Department of Information and Communications Engineering Tokyo Japan

ISBN: (纸本)9781665485845

Many well-known wireless networks are currently being used to develop educational robots. Bluetooth Low Energy (BLE) is one of the well-suited for educational robotic systems because of its simplicity, low power consumption, low cost, and robustness. To operate several robots simultaneously in a limited space like a classroom, a mesh network topology is necessary to communicate amongst robots. However, the BLE mesh network has a managed flooding protocol, which is inefficient for educational robotic systems since it is appropriate for small networks. Thus, a task synchronization is required to ensures the accuracy and stability of the system. This paper proposes task synchronization method in BLE mesh network to operate educational robots simultaneously and quickly as possible, but not in real-time without a central control system. The results demonstrate that the proposed method operate the task simultaneously and faster than a typical broadcasting in BLE. © 2022 IEEE.

关键词： Bluetooth

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Investigation of Dynamic Spectrum Allocation Algorithms for Ultra-High Capacity Optical Networks 3

Investigation of Dynamic Spectrum Allocation Algorithms for ...

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

作者： Balakumar, P. Sakhare, Sachin R. Singar, Mahendra Kumar Singh, Shweta Mamatha, G.N. Sidhu, Amritpal Prince Shri Venkateshwara Padmavathy Engineering College Department of Information Technology Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Vivekananda Global University Department of Civil Engineering Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Jain Electronics and Communication Engineering Faculty of Engineering and Technology Karnataka Bangalore 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 rapid evolution of generation has enabled the development of extremely-excessive capability optical networks that can aid multi-Gigabit/s records transmissions among nodes. to utilize this increased capability, green spectrum allocation algorithms need to be developed. This summary will discuss the continued studies efforts to develop dynamic spectrum allocation algorithms for ultra-excessive capacity optical networks. studies is performed the use of algorithmic tactics and numerical simulations to decide the most efficient scheme for useful resource allocation inside the network. The algorithms should make sure green usage of the spectrum whilst also accounting for changes in optical sign strength through the years. furthermore, the take a look at evaluates network overall performance, inclusive of capacity, latency and packet loss rate. those overall performance parameters are then used to assess the effectiveness of the chosen algorithms. The goal of these studies is to identify efficient spectrum allocation algorithms that could maximize the usage of available sources and make sure superior network performance. © 2023 IEEE.

关键词： Allocation Capacity Network Sources Superior

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Automating Real-Time Data Analysis with Machine Learning Algorithms 3

Automating Real-Time Data Analysis with Machine Learning Alg...

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

作者： Bhoite, Sonali P. Singh, Satendra Naval, Preeti Panduranga Rao, M.V. Bhushan, Bharat Indira, G. Vishwakarma Institute of Information Technology Department of Information Technology Pune India Vivekananda Global University Department of Mechanical Engineering Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Chennai127 India

ISBN: (纸本)9798350319125

Device getting-to-know algorithms enable the automation of actual-time information analysis, permitting agencies to behave on well-timed insights and optimize selection-making. By leveraging predictive models, companies can increase customized services, optimize operations, and obtain a competitive area. This paper discusses how device learning algorithms can automate real-time facts evaluation and the ways such algorithms can be used to uncover developments, locate anomalies, and enhance models. Moreover, the paper explores how applying device mastering algorithms can boost accuracy and efficiency in data evaluation, in addition to providing businesses with new opportunities for innovation. © 2023 IEEE.

关键词： Information analysis

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Estimation Analysis of Edge and Line Detection Methods in Digital Image Processing 3

Estimation Analysis of Edge and Line Detection Methods in Di...

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

作者： Garg, Prateek Yadav, Rakesh Kumar Devaraj Verma, C. Nirmala, D. Sable, Nilesh P. Murari, Krishn Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Ramnagar562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Vivekananda Global University Department of Civil Engineering Jaipur India

ISBN: (纸本)9798350319125

This paper provides an estimation evaluation of the area and line detection methods in virtual photo processing. This analysis evaluates the performance of numerous facet and line detection algorithms in phrases of facet/line first-rate, accuracy, computational complexity, and robustness to noise in the context of artificial and actualinternational snapshots. Mainly, it discusses the overall performance of 4 edge detection methods: Roberts, Prewitt, Sobel, and Canny;and 3 line detection techniques: Hough remodel, Linear Estimation, and Probabilistic Hough remodel. as compared to the classical algorithms, the Probabilistic Hough rework algorithm is determined to have the excellent accuracy and robustness to noise. Furthermore, a contrast of computational complexities shows that the Hough transform has the bottom complexity and computational time, even as the Linear Estimation algorithm has the best complexity and computational time. The primary outcome of this study is that the brink/line first-rate, accuracy, computational complexity, and robustness are very dependent on the sort of entered snapshots and the respective selected parameters. © 2023 IEEE.

关键词： comparison computational performance Probabilistic Transform

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