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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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Image Enhancement for Detection of Underwater Moulted Crabs in Greenhouse Soft-Shell Crab Farming Using Deep Learning

SSRN

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SSRN 2025年

作者： Aini, Mohammad Affaiq Bin Leong, Siow Hoo Yong, Yueh Tiam Lee, Beng Yong Zhao, Xiaomin Manaf, Sharifah Raina Abdullah, Firdaus Khong, Heng Yen College of Computing Informatics and Mathematics Universiti Teknologi MARA Sarawak Kota Samarahan94300 Malaysia College of Automotive Engineering Guangdong Polytechnic of Industry and Commerce Guangzhou510000 China Faculty of Plantation and Agrotechnology Universiti Teknologi MARA Sarawak Mukah96400 Malaysia Faculty of Business and Management Universiti Teknologi MARA Selangor42300 Malaysia Faculty of Applied Sciences Universiti Teknologi MARA Sarawak Kota Samarahan94300 Malaysia

In soft-shell crab farming, collecting the moulted crabs within a narrow window of one hour is crucial to meet the quality of high-graded paper shell crab. This paper incorporates IoT-based computer vision technology and deep learning techniques in greenhouse soft-shell crab farming to detect moulted crabs. The challenges arise in accurately detecting underwater crabs due to wide-angle distortion and reflections in images captured by cameras installed on rails within the greenhouse. Image enhancement methods are proposed to address these challenges and improve the performance of crab detection using the YOLOv7 deep-learning algorithm. Specifically, the performance of two methods for rectifying wide-angle distortion and two methods for removing reflections are compared, respectively. Data augmentation techniques are also applied to overcome the limitation of annotated underwater crab targets for deep-learning training. Results show that the fisheye flattening method outperforms the stretch flattening method for rectifying wide-angle distortion, while 2D-image flat field correction yields better results than the non-local mean filter for reducing reflections. Furthermore, data augmentation significantly enhances crab detection performance. Application of the best image enhancement methods and data augmentation techniques shows a successful rate of 92% in detecting the moulted crabs in greenhouse soft-shell crab farming.. © 2025, The Authors. All rights reserved.

关键词： Shellfish

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Robust Attacks Detection Model for Internet of Flying Things based on Generative Adversarial Network (GAN) and Adversarial Training

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IEEE Internet of Things Journal 2025年

作者： Gaber, Tarek Ali, Tarek Nicho, Mathew Torky, Mohamed Engineering and Environment University of Salford School of Science Manchester United Kingdom Suez Canal University Faculty of Computers and Informatics Ismailia Egypt Manchester Metropolitan University Department of Computing and Mathematics Manchester United Kingdom Rabdan Academy Research and Innovation Centre Abu Dhabi United Arab Emirates University of Queensland School of Electrical Engineering and Computer Science Australia Egyptian Russian University-Badr City Faculty of Artificial Intelligence Cairo Egypt

The Internet of Flying Things (IoFT) holds significant promise in fields like disaster management and surveillance. However, it is increasingly vulnerable to cyberattacks that can compromise the confidentiality, integrity, and availability (CIA) of sensitive data. Despite the growing interest in proposing Intrusion Detection Systems (IDSs) for IoFT networks, current literature faces key limitations, particularly the shortage of publicly available IoFT datasets with diverse attacks, and the fact that existing IDSs lack robustness against sophisticated adversarial machine learning attacks. This paper is the first study to address these limitations by proposing a more resilient and accurate IDS tailored for IoFT networks (RIDS-IoFT). We introduce a novel IDS that leverages Generative Adversarial Networks (GANs) to generate a hybrid dataset that combines real IoFT traffic data with GAN-generated adversarial attacks, addressing the dataset diversity issue. Additionally, we introduce an innovative adversarial training method to enhance the system’s defense against evolving threats, such as Fast Gradient Sign Method (FGSM), Basic Iterative Method (BIM), and Carlini & Wagner (C&W) attacks. The proposed RIDS-IoFT was evaluated using four machine learning models, Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), and Logistic Regression (LR), on two datasets: ECU-IoFT and CICIDS2018. The IDS’s performance was assessed based on its ability to detect both traditional and adversarial attacks. The results show that the Random Forest model achieved the highest detection accuracy, up to 96.5%, demonstrating superior performance across both real and hybrid datasets. The proposed RIDS-IoFT not only enhances detection accuracy but also strengthens resilience against adversarial threats, making it suitable for resource-constrained IoFT environments. In conclusion, this study presents a comprehensive approach to securing IoFT networks by combining real and synthetic d

关键词： Generative adversarial networks

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Enhanced Speech Emotion and Gender Recognition Using Hybrid CNN-LSTM Models

Enhanced Speech Emotion and Gender Recognition Using Hybrid ...

引用

Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： S Priyadharshini G. Naveen Sundar D. Narmadha Senthil Kumar Thangavel Adeyemi Abel Ajibesin Selvanayaki Kolandapalayam Shanmugam Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Computer Science and Engineering Amrita School of Computing Coimbatore India Department of Information Technology Faculty of Informatics and Design Cape Peninsula University of Technology South Africa Department of Mathematics and Computer Science Ashland University USA

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

The current paper proposes a new approach for peripheral speech emotion analysis and gender estimation incorporating the best machine learning architectures such as CNNs and LSTMs. Its correct depiction of emotions and the variation of the gendered speech to indicate manifold signal-to-noise ratio make the approach accommodate the difficulty inherent in recognizing emotions from the recording of voices as well as predicting the gender of a speaker. It is then able to ally with preprocessing and collection of a vast range of different speech samples, feature extraction of pitch, tone and spectral data. These features serve as input to the CNNs and LSTMs: LSTMs work well at temporal regular features and the CNNs work well in feature extraction by location (spatio feature). The approach is tested on the RAVDESS and IEMOCAP datasets and establishes a near perfect match to prior approaches with reference to recognition accuracy of emotion analysis and gender prediction. Thus, applying the identified dual method approach, which could be described by such characteristics as highly accurate data pre-preprocessing, clear indication of the features of interest, a different CNN-LSTM model joint training, it offers a reasonably good platform to gain certain insights into such characteristics of the speech as our experiment demonstrates. This work offers satisfactory configurations on concerns such as human computer relation, customer relation and mental health surveillance to suggest that other applications could be achieved in future.

关键词： Training Emotion recognition Accuracy Computational modeling Speech recognition Speech enhancement Predictive models Feature extraction Data models Convolutional neural networks

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Boltzmann framework for polyatomic gases: review on well-posedness, higher integrability and physical relevance

arXiv

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arXiv 2025年

作者： Alonso, Ricardo Čolić, Milana Texas A&M Division of arts & sciences Education City Doha Qatar Department of Mathematics and Informatics Faculty of Sciences University of Novi Sad Trg Dositeja Obradovića 4 Novi Sad21000 Serbia Institute for Mathematics and Scientific Computing University of Graz Heinrichstr. 36 Graz8010 Austria

This paper reviews results on the scalar Boltzmann equation for a single-component polyatomic gas with continuous internal energy. For the space homogeneous problem, L1-theory is established, for solutions with initial strictly positive mass and bounded energy, which enables to solve the Cauchy problem for initial data with L12+ -moments using the comparison principle for ODEs. Then, deriving entropy-based estimates, Lp-integrability properties of the solution are explored, p ∈ (1, ∞]. All these analytical results hold under a specific assumption on the collision kernel corresponding to cut-off and hard-potentials type. A mean to verify physical applicability of the model is to evaluate the corresponding Boltzmann collision operator and to derive models for transport coefficients in terms of the collision kernel parameters. Comparison with experimental data for polytropic gases determines values of these parameters showing the physical relevance of the collision kernel. © 2025, CC BY.

关键词： Boltzmann equation

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An effective security-aware side channel attack detection framework using RA-GRU and TPCC

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Australian Journal of Electrical and Electronics Engineering 2025年

作者： Yalla, Rama Krishna Mani Kanta Devarajan, Mohanarangan Veerappermal Ganesan, Thirusubramanian Yallamelli, Akhil Raj Gaius Mamidala, Vijaykumar Sambas, Aceng Amazon Web Services SeattleWA United States Sacramento United States Cognizant Technology Solutions TX United States Amazon Web Services Inc Seattle United States BroomfieldCO United States Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Kuala Malaysia Department of Mechanical Engineering Universitas Muhammadiyah Tasikmalaya Tasikmalaya Indonesia

Side-channel attack refers to the leakage of information from a physical system. However, the existing SCA detection systems didn’t have centralised verification and detection. Therefore, this paper presents a security-aware SCA detection framework using RA-GRU and TPCC. Primarily, the data transmission is done from the source to the destination. Here, keys are generated by Twi strass Prime Curve Cryptography (TPCC). Then, the data is encrypted based on TPCC and given to a MAC verification-based SCA detection system. In the meantime, the device setup is done. Afterwards, the signal traces are extracted, time stamp-based UUID is generated, and both are fused. Next, the fused traces are stored in IPFS, which generates a hash code. Subsequently, MAC creation and matching are performed. If both MAC values are matched, the traces are given to the SCA detection system;otherwise, the traces are blocked. Here, the SCA detection system performs pre-processing, feature extraction and selection, and classification. Eventually, the RA-GRU classifier detects whether the traces are attacked or non-attacked. Subsequently, the non-attacked traces are decrypted for a successful transaction. The outcomes proved that the proposed system achieved a high accuracy of 98.15%, which outperformed prevailing techniques. © 2025 Engineers Australia.

关键词： Discrete wavelet transforms

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Electrohysterogram Data Augmentation Using Generative Adversarial Network for Pregnancy Outcome Prediction

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IEEE Access 2025年

作者： Cheema, Muhammad Omar Din, Zia Mohy Ud Imran, Azhar Khan, Fawad Salam Al-Rawi, Mahmood Basil A. El-Meligy, Mohammed Gul, Jahan Zeb Air University Department of Biomedical Engineering Islamabad44000 Pakistan Air University Faculty of Computing & AI Department of Creative Technologies Islamabad44000 Pakistan King Saud University College of Applied Medical Sciences Department of Optometry Riyadh Saudi Arabia Amman Arab University College of Computer Science and Informatics Amman Jordan Maynooth University Department of Electronic Engineering Ireland

There are many difficulties in managing and detecting preterm pregnancies, especially in the early stages. Analyzing electrohysterogram data, which show the electrical activity of uterine muscles, is a promising non-invasive method for classifying term and preterm pregnancies. However, the effectiveness of machine learning classifiers is hampered by the inherent class imbalance in datasets. In order to overcome this constraint, this study uses a Generative Adversarial Network to produce high-quality synthetic EHG data while maintaining the statistical properties of actual signals. By successfully reducing class imbalance, the enhanced dataset makes it possible for machine learning models to be trained with resilience. Real and synthetic features did not differ significantly, according to statistical validation using a t-test (p > 0.05). With this method, the Random Forest classifier obtained a ROC-AUC of 0.99 and a 10-fold cross-validation accuracy of 98%. The suggested GAN-based method’s simplicity and efficacy demonstrate its promise for resolving class imbalance and developing reliable classification systems for diagnostic applications. © 2013 IEEE.

关键词： Generative adversarial networks

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Moment estimates for polyatomic Boltzmann equation with frozen collisions

arXiv

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arXiv 2025年

作者： Alonso, Ricardo Čolić, Milana Division of Arts & Sciences Texas A&M University at Qatar Education City Doha Qatar Department of Mathematics and Informatics Faculty of Sciences University of Novi Sad Trg Dositeja Obradovića 4 Novi Sad21 000 Serbia Department of Mathematics and Scientific Computing University of Graz Heinrichstraße 36 Graz8010 Austria

In this paper, a polyatomic gas with continuous internal energy is considered, allowing for frozen collisions, in which the kinetic energy of the colliding particle pair is conserved, and the internal energy of each particle remains unchanged. A priori moment estimates are derived for solutions of the space-homogeneous Boltzmann equation with a collision kernel of the hard potentials type with cut-off. The model with frozen collisions is first analyzed, followed by a review of general collisions—referred to as pure polyatomic—which preserve the total kinetic and internal energy. By combining existing results for pure polyatomic collisions with the newly derived estimates for frozen collisions, moment estimates are established for the Boltzmann equation with a collision operator that convexly combines both types of collisions. In particular, the moment generation property is shown to be driven by the rate of the pure polyatomic operator, and the moment propagation property holds. © 2025, CC BY.

关键词： Boltzmann equation

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Reconfigurable Intelligent Surface-Enabled Physical-Layer Network Coding for Higher Order M-QAM Signals

arXiv

引用

arXiv 2025年

作者： Doost, Ehsan Atefat Saghezchi, Firooz B. Fondo-Ferreiro, Pablo Gil-Castiñeira, Felipe Papaioannou, Maria Vardakas, John Surattanagul, Jinwara Rodriguez, Jonathan Instituto de Telecomunicações Universidede de Aveiro Aveiro Portugal Information Technologies Group AtlanTTic Research Center University of Vigo Vigo Spain Faculty of Engineering and Science University of Greenwich Chatham Maritime United Kingdom Iquadrat Informatica S.L Barcelona Spain Dept. of Informatics UOWM Greece Faculty of Computing Engineering and Science University of South Wales Pontypridd United Kingdom

Physical-Layer Network Coding (PNC) is an effective technique to improve the throughput and latency in wireless networks. However, there are two major challenges for PNC, especially when using higher order modulations: 1) phase synchronization and power control at the paired User Equipments (UEs);and 2) the ambiguity removal of the PNC mapping at the relay node. To address these challenges, in this paper, we apply power control at transmitting UEs and exploit Reconfigurable Intelligent Surfaces (RISs) to synchronize the phase of the transmitted signals and ensure that they arrive at the relay with the same power and phase rotation. Then, we employ modular addition for an unambiguous PNC mapping for M-ary Quadrature Amplitude Modulations (M-QAM). We evaluate the performance of the system in the framework of Orthogonal Frequency Division Multiplexing (OFDM)-PNC for different RIS sizes and modulation orders. Furthermore, we study the sensitivity of PNC systems for Channel Estimation Error (CEE). The results reveal that 1) PNC systems show quite higher sensitivity to CEE compared with RIS-assisted one-way relay channel systems;2) when the CEE is low, RIS can considerably enhance the Signal-to-Noise Ratio (SNR) of the PNC system, e.g., for a Bit Error Rate (BER) of 10-3 (without channel coding), increasing the RIS size from one to 256 elements in 28 GHz band leads to 200% improvement in SNR. Copyright © 2025, The Authors. All rights reserved.

关键词： Quadrature amplitude modulation

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Comparison of half and full-leaf shape feature extraction for Malaysian medicinal leaf identification and classification

引用

Revista Tecnica de la Facultad de Ingenieria Universidad del Zulia 2016年第9期39卷 31-39页

作者： Sainin, Mohd Shamrie Ahmad, Faudziah Alfred, Rayner School of Computing Universiti Utara Malaysia Malaysia Faculty of Computing and Informatics Universiti Malaysia Sabah Malaysia

Shape is the main information for leaf feature that most of the current literatures in leaf identification utilize the whole leaf for feature extraction and to be used in the leaf identification process. In this paper, study of half-leaf features extraction for leaf identification is carried out and the results are compared with the results obtained from the leaf identification based on a full-leaf features extraction. Identification and classification is based on shape features that are represented as cosines and sinus angles. Ten classifiers obtained from WEKA and seven ensemble methods are used to compare their performance accuracies over this data. The classifiers were trained using 65 leaves in order to classify 5 different species of preliminary collection of Malaysian medicinal plants. The result shows that half-leaf features extraction can be used for leaf identification without decreasing the predictive accuracy.

关键词： Classification (of information)

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