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Adaptive AI-Based Personalized Learning for Accelerated Vocabulary and Syntax Mastery in Young English Learners

International Journal of Advanced computer science and Applications 2025年第4期16卷 678-687页

作者： Aravind, Angalakuduru Durairaj, M. Chitkara, Preeti Baker El-Ebiary, Yousef A. Muniyandy, Elangovan Ushasree, Linginedi Ammar, Mohamed Ben Kodad Telangana 508206 India Dept.of English Panimalar Engineering College Poonamallee Chennai600123 India Head PR & International Relations Department of Applied Sciences KIET Group of Institutions Delhi-NCR Ghaziabad India Faculty of Informatics and Computing UniSZA University Malaysia Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602 105 India Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur Dist. 522302 India Center for Scientific Research and Entrepreneurship Northern Border University Arar73213 Saudi Arabia

Language acquisition is an integral part of early schooling, but young English language learners struggle to learn vocabulary and syntax since they are not provided with specialized instruction. Conventional teaching may vary according to different learning speeds and it leads to unbalanced levels of proficiency among students and possibly leading to disengagement among slow learners. The present computer-assisted learning aids provide practice interactively but without real-time adaptation and personalized feedback, limiting their capacity to address learners’ unique problems. To overcome these constraints, this study suggests an Artificial Intelligence based personalized learning system that supports vocabulary and syntax learning via adaptive learning models, NLP-based chatbots and gamified interactive lessons. The system dynamically adapts content according to students’ most recent performance in real time to enable a personalized learning experience, which results in efficient Learning. The research has experimental study design, and two groups are considered, an AI-supported learning group and a traditional learning group. Pre-test and post-test design measures the effects of the system on vocabulary recall and syntax correctness. Other learner engagement rates like survey results and qualitative feedback inform learner experience and learning efficacy. Initial results indicate that learners working with the Artificial Intelligence powered learning system gained 25percent in recalling vocabulary and 30percent in syntax accuracy over the control group. Further, learner engagement rates are elevated because of real-time feedback and gamification components. These results emphasize the promise of AI-based personalized learning to boost language acquisition and lay the basis for further effective innovations in adaptive education technologies. © (2025), (science and Information Organization). All rights reserved.

关键词： Students

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Secure and Efficient Data Transmission for IoT Devices in Untrusted Environments

Secure and Efficient Data Transmission for IoT Devices in Un...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Jain, Upma Smitha, K. Battula, R.S.S. Raju Sarkar, Arindam Martin, Natasha Natrayan, L. Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Adichunchanagiri University Bgs Institute of Technology Department of Artificial Intelligence and Machine Learning India Aditya University Department of Computer Applications India Heritage Institute of Technology Department of Applied Electronics and Instrumentation Engineering Kolkata India Department of Law Symbiosis Law Pune India School of Engineering Simats Department of Mechanical Engineering Saveetha India

ISBN: (纸本)9798350375442

One technological trend that will not go away in the modern world is the Internet of Things (IoT). With the development of the Internet of Things, the world is becoming smarter. The efficacy, flexibility, and simplicity of deployment of IoT make it the preferred choice for most real-time applications. But energy efficiency and security are the big obstacles for the Internet of Things. Presented here are safe, energy-efficient solutions for the Internet of Things (IoT), a problem whose significance this study acknowledges. Since routing is the primary energy consumer, this study presents secure routing strategies that are both energy efficient and reliable. Three distinct safe data transmission techniques for the Internet of Things (IoT) are presented in this paper, all of which aim to reduce energy usage during routing. The first part of this study lays out a clustering-and secure route selection-based architecture for safe and energy-efficient data transmission with the Internet of Things (IoT). The W-HHO technique, which is based on Whale Optimization, is used to identify the secure routes, and fuzzy-based secure clustering is used for that purpose. Encryption protocols that work in tandem with cypher block chaining ensure the safety of sensitive information. Using industry-standard metrics like packet delivery rate, throughput, energy efficiency, network longevity, etc., we assess how well each solution performs. When compared to the current methods, the outcomes produced by the presented works demonstrate promising performance. © 2024 IEEE.

关键词： clustering data transmission energy efficiency fuzzy clustering harris hawks optimization (HHO) internet of things (IoT) secure routing security whale optimization algorithm

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Research Challenges and Future Facet of Cellular Computing 2

Research Challenges and Future Facet of Cellular Computing

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Alnawayseh, Saif E. A. Khan, Tahir Abbas Farooq, Umer Zulfiqar, Sidra Khan, Shan Al-Kassem, Amer Hani Mutah Univesity Faculty of Engineering Electrical Engineering Department Jordan NCBA&E School of Computer Science Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan LGU Department of Computer Science Lahore Pakistan College of Business Management Sharjah United Arab Emirates

ISBN: (纸本)9798350335644

cellular computing is promising and exciting domain. As the field continues to evolve, several future facets are expected to emerge. One potential facet is the development of more sophisticated and efficient cellular-based computing systems that can perform complex calculations and data processing tasks. This could lead to the creation of new tools and technologies in various fields, including medicine, biotechnology, and environmental monitoring. Moreover, technology has changed the way of communication, sharing and exchanging data. Mobile computing has grown to be very vital as the number of cellular devices have elevated fairly in the preceding years. In this paper, we have mentioned cellular computing, aspects of cell computing, working of cell computing. Also, we have discussed that what challenges are most common in the development of cellular computing. In last we discussed merits, demerits, application, and its future. © 2023 IEEE.

关键词： Mobile computing

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Integrating Deep Learning and Machine Translation for Understanding Unrefined Languages

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computers, Materials & Continua 2022年第1期70卷 669-678页

作者： Hong Geun Ji Soyoung Oh Jina Kim Seong Choi Eunil Park Department of Applied Artificial Intelligence Sungkyunkwan UniversitySeoul03063Korea Raon Data Seoul03073Korea Department of Computer Science and Engineering University of MinnesotaMinneapolis55455MNUSA

In the field of natural language processing(NLP),the advancement of neural machine translation has paved the way for cross-lingual ***,most studies in NLP have evaluated the proposed language models on well-refined *** investigatewhether amachine translation approach is suitable for multilingual analysis of unrefined datasets,particularly,chat messages in *** order to address it,we collected the dataset,which included 7,066,854 and 3,365,569 chat messages from English and Korean streams,*** employed several machine learning classifiers and neural networks with two different types of embedding:word-sequence embedding and the final layer of a pre-trained language *** results of the employed models indicate that the accuracy difference between English,and English to Korean was relatively high,ranging from 3%to 12%.For Korean data(Korean,and Korean to English),it ranged from 0%to 2%.Therefore,the results imply that translation from a low-resource language(e.g.,Korean)into a high-resource language(e.g.,English)shows higher performance,in contrast to vice *** implications and limitations of the presented results are also *** instance,we suggest the feasibility of translation from resource-poor languages for using the tools of resource-rich languages in further analysis.

关键词： Twitch multilingual machine translation machine learning

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Real-Time Enhanced GAN-Powered Intrusion Prevention System: Safeguarding Networks with Advanced AI 2

Real-Time Enhanced GAN-Powered Intrusion Prevention System: ...

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2nd International Conference on Computing and Data Analytics, ICCDA 2024

作者： Sutherlin Subitha, G. Seldev Christopher, C. Wilfred Blessing, N.R. Sheeja Kumari, V. Stella Mary's College of Engineering Department of Computer Science and Engineering Tamil Nadu Kanyakumari India St. Xavier's Catholic College of Engineering Department of Computer Science and Engineering Tamil Nadu Nagercoil India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Saveetha School of Engineering Simats Univeristy Department of Computational Intellegence Chennai600025 India

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542573

In an era of escalating cyber threats and evolving networks, the demand for robust intrusion prevention is paramount. Introducing the Real-Time Enhanced GAN-Powered Intrusion Prevention System (RT-EGAN-IPS), a state-of-the-art solution fortified with advanced AI. RT-EGAN-IPS harnesses the power of Generative Adversarial Networks (GANs) to revolutionize real-time intrusion detection. GANs, renowned for their data synthesis capabilities, excel at uncovering subtle anomalies and emerging threats within network data. Its real-time adaptability enables it to swiftly identify novel attack patterns by continuously learning from incoming network data. Deep reinforcement learning and predictive analytics empower RT-EGAN-IPS to not only detect intrusions but autonomously respond, bolstering network protection. Versatile in handling multi-modal data streams, from text logs to multimedia content, RT-EGAN-IPS thrives on a collaborative approach. Security experts collaborate with the system for real-time validation and feedback. Dynamic thresholding ensures responsiveness to evolving threats, supported by low-latency responses and an ensemble approach. This abstract unveils RT-EGAN-IPS, a holistic AI-driven network security paradigm, promising a new level of protection in the ever-changing realm of cyber threats. © 2024 IEEE.

关键词： Network intrusion

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Characterization of Vertical Wind Speed Profiles Based on Ward's Agglomerative Clustering Algorithm

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Journal of Modern Power Systems and Clean Energy 2023年第5期11卷 1437-1449页

作者： M.C.Bueso A.Molina-García A.P.Ramallo-González A.Fernández-Guillamón Department of Applied Mathematics and Statistics Universidad Politécnica de Cartagena30202 CartagenaSpain Department of Automatics Electrical Engineering and Electronic TechnologyUniversidad Politécnica de Cartagena30202 CartagenaSpain Department of Information and Communications Engineering Computer Science FacultyUniversity of Murcia30100 MurciaSpain Universidad de Castilla La-Mancha Campus Universitario02071 AlbaceteSpain

Wind turbine blades have been constantly increasing since wind energy becomes a popular renewable energy source to generate electricity. Therefore, the wind sector requires a more efficient and representative characterization of vertical wind speed profiles to assess the potential for a wind power plant site. This paper proposes an alternative characterization of vertical wind speed profiles based on Ward's agglomerative clustering algorithm, including both wind speed module and direction data. This approach gives a more accurate incoming wind speed variation around the rotor swept area, and subsequently, provides a more realistic and complete wind speed vector characterization for vertical profiles. Real wind database collected for 2018 in the Forschungsplattformen in Nordund Ostsee(FINO) research platform is used to assess the methodology. A preliminary pre-processing stage is proposed to select the appropriated number of heights and remove missing or incomplete data. Finally, two locations and four heights are selected, and 561588 wind data are characterized. Results and discussion are also included in this paper. The methodology can be applied to other wind database and locations to characterize vertical wind speed profiles and identify the most likely wind data vector patterns.

关键词： Clustering algorithm wind speed data management power generation

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A Deep Learning-based 6DoF Video Synthesizing Method Using Instant-NGPs

A Deep Learning-based 6DoF Video Synthesizing Method Using I...

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2023 IEEE International Conference on Visual Communications and Image Processing, VCIP 2023

作者： Choi, Jaeyeol Jeong, Jong-Beom Park, Junhyeong Ryu, Eun-Seok Department of Applied Artificial Intelligence Seoul Korea Republic of Department of Computer Science Education Seoul Korea Republic of Department of Immersive Media Engineering Seoul Korea Republic of

ISBN: (纸本)9798350359855

This paper introduces a new method for synthesizing six degree of freedom (6DoF) videos using neural radiance fields, which allows training from plain 2D images to render 3D scene at arbitrary viewports. Neural network model representing a previous timepoint is fine-Tuned to train models for subsequent timepoints. Additionally, instant neural graphics primitives (Instant-NGP) is applied for speed improvement. The proposed method achieved both improved objective quality for same number of training iterations and enhanced consistency between frames. Furthermore, it shows superiority over other methods for generating 6DoF videos, in terms of quality and time efficiency. © 2023 IEEE.

关键词： Virtual reality

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Novelty of Different Distance Approach for Multi-Criteria Decision-Making Challenges Using q-Rung Vague Sets

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computer Modeling in engineering & sciences 2024年第6期139卷 3353-3385页

作者： Murugan Palanikumar Nasreen Kausar Dragan Pamucar Seifedine Kadry Chomyong Kim Yunyoung Nam Saveetha School of Engineering Saveetha Institute of Medical and Technical SciencesChennai602105India Department of Mathematics Faculty of Arts and ScienceYildiz Technical UniversityEsenlerIstanbul34220Turkey Department of Operations Research and Statistics Faculty of Organizational SciencesUniversity of BelgradeBelgrade11000Serbia College of Engineering Yuan Ze UniversityTaoyuan City320315TaiwanChina Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman346United Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblos1102-2801Lebanon ICT Convergence Research Center Soonchunhyang UniversityAsan31538South Korea Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538South Korea

In this article,multiple attribute decision-making problems are solved using the vague normal set(VNS).It is possible to generalize the vague set(VS)and q-rung fuzzy set(FS)into the q-rung vague set(VS).A log q-rung normal vague weighted averaging(log q-rung NVWA),a log q-rung normal vague weighted geometric(log q-rung NVWG),a log generalized q-rung normal vague weighted averaging(log Gq-rung NVWA),and a log generalized q-rungnormal vagueweightedgeometric(logGq-rungNVWG)operator are discussed in this *** is provided of the scoring function,accuracy function and operational laws of the log q-rung *** algorithms underlying these functions are also described.A numerical example is provided to extend the Euclidean distance and the Humming ***,idempotency,boundedness,commutativity,and monotonicity of the log q-rung VS are examined as they facilitate recognizing the optimal alternative more quickly and help clarify *** chose five anemia patients with four types of symptoms including seizures,emotional shock or hysteria,brain cause,and high fever,who had either retrograde amnesia,anterograde amnesia,transient global amnesia,post-traumatic amnesia,or infantile *** numbers q are used to express the results of the *** demonstrate the effectiveness and accuracy of the models we are investigating,we compare several existing models with those that have been developed.

关键词： Vague set aggregating operators euclidean distance hamming distance decision making

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Enhanced Security in Payment Gateways Through Face Detection: An Advanced Approach Using DenseNet 121- BiLSTM Models 1

Enhanced Security in Payment Gateways Through Face Detection...

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1st IEEE International Conference on Electronics, Computing, Communication and Control Technology, ICECCC 2024

作者： Gowsic, K. Sowmiya, M. Nirmala Devi, M. Chauhan, Amit Arul Mary Rexy, V. Singh, Dharmendra Department of Computer Science and Engineering Namakkal India Department of Life Sciences Karnataka Bengaluru India St. Martin's Engineering College Department of English Secunderabad India Saveetha College of Liberal Arts and Sciences SIMATS Department of Commerce Kuthambakkam India School of Management Sciences Department of Applied Science Lucknow India

ISBN: (纸本)9798350371802

Because it is one of the most promising applications of image analysis, face recognition has been the subject of intense research and development for many decades. Many modern identification and verification requirements have found a potential new home with the introduction of face recognition (FR) technology. Facial recognition is just one of numerous uses for biometric pattern recognition algorithms. Sequencing is essential for many tasks, including as feature extraction, model training, and preprocessing. Eliminating background noise and obtaining dense vertical edges are part of the preprocessing procedures. Facial feature extraction will be employed to extract features after feature extraction. Use attributes cautiously when training a Desnet121-BiLSTM model. In every respect, the suggested method outperforms two state-of-the-art algorithms, Desnet121 and BiLSTM. An accuracy rating of 97.19% was indicative of a considerable improvement in the figures. © 2024 IEEE.

关键词： Face recognition

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Robust speech perception and classification-driven deep convolutional neural network with natural language processing

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Alexandria engineering Journal 2025年 123卷 358-368页

作者： Alshammari, Alya Alzaidi, Muhammad Swaileh A. Alrusaini, Othman Alshuhail, Asma Allafi, Randa Yafoz, Ayman Alsini, Raed Khadidos, Alaa O. Department of Applied Linguistics College of Languages Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of English Language College of Language Sciences King Saud University P. O. Box 145111 Riyadh Saudi Arabia Department of Engineering and Applied Sciences Applied College Umm Al-Qura University Makkah Saudi Arabia Department of Information Systems College of Computer Sciences & Information Technology King Faisal University Saudi Arabia Department of Computer Science College of Sciences Northern Border University Arar Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

Speech recognition systems use computer algorithms to interpret and process spoken words and translate them into text. Speech-to-text, or Speech detection, is the proficiency of a program or machine to find spoken words aloud and translate them into plain text. Speech detection systems are the most common complex systems that use wide-ranging research in computer engineering, linguistics, and computer science. In today's significant data era, several machine learning (ML) approaches for processing raw, unlabelled speech data still need to be improved. Deep learning (DL) models, on the other hand, with their powerful modelling ability for extensive data, are capable of directly processing unlabelled data and have become a crucial area of research in the domain of automated speech recognition. The DL model has recently gained popularity for voice disorders and automated speech detection. DL is an ML that exploits artificial neural networks (ANN) to gain meaningful insights from data. The DL approach demonstrated great success and achieved significant attention in different fields comprising speech processing, computer vision (CV), and natural language processing (NLP). Therefore, this article develops a Deep Learning with Natural Language Processing based Robust Speech Detection (DLNLP-RSD) technique. The DLNLP-RSD technique derives features from the mel-frequency cepstral coefficient (MFCC) technique. Followed by the DLNLP-RSD technique employs a one-dimensional deep convolutional neural network (CNN) approach for the detection and classification of speech. Moreover, the parameter tuning model uses the grasshopper fractals optimization algorithm (GOA) approach. The performance evaluation of the proposed technique highlights its significant performance over other models concerning distinct measures. An extensive simulation analysis is executed to examine the improved results of the DLNLP-RSD model. The experimental validation of the DLNLP-RSD technique portrayed a sup

关键词： Convolutional neural networks

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