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Telugu Language Analysis with XLM-RoBERTa: Enhancing Parts of Speech Tagging for Effective Natural Language Processing

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SN Computer Science 2025年第2期6卷 1-13页

作者： Bharathi Mohan, G. Prasanna Kumar, R. Krishna Jayanth, K. Doss, Srinath Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Faculty of Engineering and Technology Botho University Gaborone Botswana

This research investigates the efficacy of XLM-RoBERTa, a potent deep learning architecture rooted in transformer networks, for Part-of-Speech (POS) tagging—a foundational task in Natural Language Processing (NLP). The study narrows its focus to address the formidable UCD Telugu dataset, renowned for its intricate morphological characteristics that pose challenges to accurate tagging. Through meticulous fine-tuning of the pre-trained XLM-RoBERTa model. This study adeptly discerns and exploit intricate syntactic and semantic patterns underpinning the interplay between lexical elements and their respective POS tags. The rigorous evaluation regimen, complemented by a comprehensive statistical comparison with the Multilingual BERT model, conclusively establishes XLM-RoBERTa’s superiority. This advanced model attains a remarkable accuracy score of 91%, decisively surpassing the Multilingual BERT model, which achieves an accuracy of 88%. These findings not only underscore XLM-RoBERTa’s prowess in precise POS tagging, especially in the context of morphologically rich languages like Telugu, but also carry significant implications for the broader NLP community. This research unlocks valuable insights and positions XLM-RoBERTa as an exemplar of reliability and robustness in POS tagging. Furthermore, it extends an invitation for further exploration and innovation in deep learning architectures within the NLP domain. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Bert Multilingual Bert NLP Parts-of-speech tagging Telugu XLM-RoBERTa

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New Antenna Array Beamforming Techniques Based on Hybrid Convolution/Genetic Algorithm for 5G and Beyond Communications

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Computer Modeling in engineering & Sciences 2024年第3期138卷 2749-2767页

作者： Shimaa M.Amer Ashraf A.M.Khalaf Amr H.Hussein Salman A.Alqahtani Mostafa H.Dahshan Hossam M.Kassem Electronics and Communications Engineering Department Higher Institute of Engineering and TechnologyKafr ElsheikhEgypt Electronics and Electrical Communications Engineering Department Faculty of EngineeringMinia UniversityMiniaEgypt Electronics and Electrical Communications Engineering Department Faculty of EngineeringTanta UniversityTantaEgypt Electronics and Electrical Communications Engineering Department Faculty of EngineeringHorus University EgyptNew DamiettaEgypt Department of Computer Engineering College of computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia School of Computing Mathematics and EngineeringCharles Sturt UniversityBathurstNSW2795Australia

Side lobe level reduction(SLL)of antenna arrays significantly enhances the signal-to-interference ratio and improves the quality of service(QOS)in recent and future wireless communication systems starting from 5G up to ***,it improves the array gain and directivity,increasing the detection range and angular resolution of radar *** study proposes two highly efficient SLL reduction *** techniques are based on the hybridization between either the single convolution or the double convolution algorithms and the genetic algorithm(GA)to develop the Conv/GA andDConv/GA,*** convolution process determines the element’s excitations while the GA optimizes the element *** M elements linear antenna array(LAA),the convolution of the excitation coefficients vector by itself provides a new vector of excitations of length N=(2M−1).This new vector is divided into three different sets of excitations including the odd excitations,even excitations,and middle excitations of lengths M,M−1,andM,*** the same element spacing as the original LAA is used,it is noticed that the odd and even excitations provide a much lower SLL than that of the LAA but with amuch wider half-power beamwidth(HPBW).While the middle excitations give the same HPBWas the original LAA with a relatively higher *** the increased HPBWof the odd and even excitations,the element spacing is optimized using the ***,the synthesized arrays have the same HPBW as the original LAA with a two-fold reduction in the ***,for extreme SLL reduction,the DConv/GA is *** this technique,the same procedure of the aforementioned Conv/GA technique is performed on the resultant even and odd excitation *** provides a relatively wider HPBWthan the original LAA with about quad-fold reduction in the SLL.

关键词： Array synthesis convolution process genetic algorithm(GA) half power beamwidth(HPBW) linear antenna array(LAA) side lobe level(SLL) quality of service(QOS)

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HealthScan AI-Deep Learning-Based Multi-Disease Diagnosis from Medical Imaging

HealthScan AI-Deep Learning-Based Multi-Disease Diagnosis fr...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Ganesh, Prudhvi Sai Kiriti, Chakali Murthy Gari Keerthi Rama, P. Computing Technologies College of Engineering and Technology SRM Institute of Science and Technology Faculty of Engineering and Technology TN Kattankulathur603203 India College of Engineering and Technology SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies TN Kattankulathur603203 India

ISBN: (纸本)9798350375237

The HealthScan AI is a comprehensive health diagnosis AI system which utilizes deep learning algorithms and the Keras library to determine if a person has certain diseases or not using their chest x-rays and other scans. It utilizes six distinct CNN models for the identification of COVID-19 and pneumonia using the chest X-rays, Glaucoma from internal eye scans. Our methodology involves rigorous training and evaluation processes, resulting in a highly accurate and efficient model. The integration of these models into a user-friendly interface, developed with Streamlit, allows seamless interaction. This interface empowers users to upload medical images, facilitating real-time diagnostic feedback on their health status. By providing a flexible and effective tool for the identification of a range of disorders, this novel technique advances the field of medicine and has the potential to completely transform healthcare procedures. © 2024 IEEE.

关键词： COVID-19

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Artificial intelligence-inspired comprehensive framework for desertification prediction

International Journal of Cognitive Computing in Engineering

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International Journal of Cognitive computing in engineering 2025年 6卷 462-476页

作者： Alsubai, Shtwai Mosleh, Mogeeb A.A. Al-Hadhrami, Suheer A. Bhatia, Munish College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Alkharj Saudi Arabia Faculty of Engineering & Computing University of Science & Technology Aden Yemen Faculty of Engineering and Information Technology Taiz Univeristy Taiz Yemen Department of Computer Engineering College of Engineering Al Mukalla Hadhramout Yemen Department of Computer Applications National Institute of Technology Kurukshetra India

Desertification greatly affects land deterioration, farming efficiency, economic growth, and health, especially in Gulf nations. Climate change has worsened desertification, making developmental issues in the area even more difficult. This research presents an enhanced framework utilizing the Internet of Things (IoT) for ongoing monitoring, data gathering, and analysis to evaluate desertification patterns. The framework utilizes Bayesian Belief Networks (BBN) to categorize IoT data, while a low-latency processing method on edge computing platforms enables effective detection of desertification trends. The classified data is subsequently analyzed using an Artificial Neural Network (ANN) optimized with a Genetic Algorithm (GA) for forecasting decisions. Using cloud computing infrastructure, the ANN-GA model examines intricate data connections to forecast desertification risk elements. Moreover, the Autoregressive Integrated Moving Average (ARIMA) model is employed to predict desertification over varied time intervals. Experimental simulations illustrate the effectiveness of the suggested framework, attaining enhanced performance in essential metrics: Temporal Delay (103.68 s), Classification Efficacy—Sensitivity (96.44 %), Precision (95.56 %), Specificity (96.97 %), and F-Measure (96.69 %)—Predictive Efficiency—Accuracy (97.76 %) and Root Mean Square Error (RMSE) (1.95 %)—along with Reliability (93.73 %) and Stability (75 %). The results of classification effectiveness and prediction performance emphasize the framework's ability to detect high-risk zones and predict the severity of desertification. This innovative method improves the comprehension of desertification processes and encourages sustainable land management practices, reducing the socio-economic impacts of desertification and bolstering at-risk ecosystems. The results of the study hold considerable importance for enhancing regional efforts in combating desertification, ensuring food security, and formulatin

关键词： Life on land Climate action Desertification Internet of things Artificial neural network

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An approach in Applying Software Quality Assurance in Academic application: Case study of Student Information System - International Student Module (ISM)

An approach in Applying Software Quality Assurance in Academ...

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2023 International Conference on Mathematical and Statistical Physics, Computational Science, Education and Communication, ICMSCE 2023

作者： Suradi, Nur Razia Mohd Shukor, Nur Syufiza Ahmad Nar, Nik Nordiana Adnan, Zuraidy Hassan, Wan Azlan Wan Abdullah, Khairul Annuar Amil, Mohd Azril Selangor University Computing Department Faculty Communication Visual Art and Computing Malaysia Selangor University Faculty of Engineering and Life Sciences Malaysia

ISBN: (纸本)9781510671768

Software Quality Assurance (SQA) is a way to verify quality in the software. It is the set of activities which make certain processes, procedures as well as standards fit for the project and implemented appropriately. SQA is a process which works parallel to the development of software. It concentrates on improving the software development process so problems can be avoided. SQA is an activity that is applied throughout the software process, which will bring value to the project by saving money and time, implementing stable, competitive, and safe product, building constant process, helping to meet client expectations, and will create good developer’s reputation among the users. SQA can give significant impact to the software project in an academic environment with high defect removal efficiency which decreases the cost of software development and satisfy the users and target customers with respective to their expectations. Based on the implementation of SQA in International Student Module (ISM), the result showed that the error could be minimized beside reduced time effort in developing the software. © 2023 SPIE.

关键词： Acceptance tests

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An Effective Hybrid Metaheuristic Algorithm for Solving Global Optimization Algorithms

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Multimedia Tools and Applications 2024年第37期83卷 85103-85138页

作者： Seyyedabbasi, Amir Tareq Tareq, Wadhah Zeyad Bacanin, Nebojsa Computer Engineering Deptartment Faculty of Engineering and Natural Sciences Istinye University Istanbul Turkey Department of Mathematics Saveetha School of Engineering SIMATS Thandalam Tamilnadu India Faculty of Informatics and Computing Singidunum University Belgrade Serbia MEU Research Unit Middle East University Amman Jordan

Recently, the Honey Badger Algorithm (HBA) was proposed as a metaheuristic algorithm. Honey badger hunting behaviour inspired the development of this algorithm. In the exploitation phase, HBA performs poorly and stagnates at the local best solution. On the other hand, the sand cat swarm optimization (SCSO) is a very competitive algorithm compared to other common metaheuristic algorithms since it has outstanding performance in the exploitation phase. Hence, the purpose of this paper is to hybridize HBA with SCSO so that the SCSO can overcome deficiencies of the HBA to improve the quality of the solution. The SCSO can effectively exploit optimal solutions. For the research conducted in this paper, a hybrid metaheuristic algorithm called HBASCSO was developed. The proposed approach was evaluated against challenging CEC benchmark instances taken from CEC2015, CEC2017, and CEC2019 benchmark suites The HBASCSO is also evaluated concerning the original HBA, SCSO, as well as several other recently proposed algorithms. To demonstrate that the proposed method performs significantly better than other competitive algorithms, 30 independent runs of each algorithm were evaluated to determine the best, worst, mean, and standard deviation of fitness functions. In addition, the Wilcoxon rank-sum test is used as a non-parametric comparison, and it has been found that the proposed algorithm outperforms other algorithms. Hence, the HBASCSO achieves an optimum solution that is better than the original algorithms. © The Author(s) 2024.

关键词： Benchmarking

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Liberated Arabic Handwritten Text Recognition using Convolutional Recurrent Neural Networks

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for Computer Science and Mathematics 2025年第2期6卷 72-81页

作者： AlRababah, Ahmad AbdulQadir Aljahdali, Mohammed Khalid Al jahdali, Abdulrahim Abdulhamid AlGhanmi, Mohammed Saleh Al Barazanchi, Israa Ibraheem Department of Computer Science Faculty of Computing and Information Technology in Rabigh King Abdulaziz University Saudi Arabia College of Engineering University of Warith Al-Anbiyaa Karbala Iraq

Arabic script is exhibited in a cursive style, which is a departure from the norm in many common languages, and the shapes of letters are contingent on their positions within words. The form of the first letter is influenced by the subsequent letter, middle letters are shaped by both preceding and succeeding letters, and the shape of the final letter is determined by the preceding letter. Additionally, certain letters are found to have strikingly similar shapes, making Arabic text recognition a formidable challenge in computer vision. The challenge of detecting and recognizing Arabic handwritten text is addressed in this paper by proposing a novel system that integrates two Deep Learning models without the constraints of a predefined dictionary or language model. Object Detection techniques are employed in the first model to accurately identify lines, words, and punctuation marks, providing a robust foundation for text recognition. In the second model, a powerful recognition system is developed, trained on the IFN/ENIT dataset to predict character sequences from handwritten text images. The architecture is comprised of a convolutional neural network (CNN) with residual connections, followed by a Bi-directional Long Short-Term Memory (BLSTM) layer, and culminates in a fully connected layer. State-of-the-art results in unconstrained Arabic text recognition tasks are achieved by this approach. © 2025 The Author(s).

关键词： Long short-term memory

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Job Resumes Recommendation using Integration of Fuzzy Discernibility Matrix Feature Selection and Convolutional Neural Network Multi-label Text Classification

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第13期49卷 49-58页

作者： Hayami, Regiolina Yusoff, Nooraini Daud, Kauthar Mohd Fatma, Yulia Department of Informatics Engineering Faculty Computer Science Universitas Muhammadiyah Riau Indonesia Faculty of Data Science and Computing Universiti Malaysia Kelantan 16100 Malaysia Center for Artificial Intelligence Technology Faculty of Information Science and Technology Universiti Kebangsaan Malaysia 43600 Malaysia

The manual analysis of job resumes poses specific challenges, including the time-intensive process and the high likelihood of human error, emphasizing the need for automation in content-based recommendations. Recent advancements in deep learning, particularly Convolutional Neural Networks (CNN) for Multi-label Text Classification (MLTC), offer a promising solution for addressing these challenges through artificial intelligence. While CNN is renowned for its robust feature extraction capabilities, it faces specific challenges such as managing high-dimensional data and poor data interpretability. To address these limitations, this study employs the Fuzzy Discernibility Matrix (FDM) feature selection technique to determine the relevance of skills for each job vacancy. FDM assigns weights to the features of each job category and ranks their relevance based on the highest scores, which are then utilized in the MLTC CNN model. The integration of FDM and MLTC CNN serves as the foundation for generating content-based recommendations derived from key features in job resumes. This study produces a content-based job recommendation system that displays the top three job categories, accompanied by explanations of the skills supporting each selected category. These recommendations also consider cosine similarity values between analyzed items and the integrated results of FDM and MLTC CNN. The application of FDM for feature weighting has been proven to enhance multi-label text classification outcomes by providing better insights during the feature selection process. With a recall of 97.26%, precision of 94.81%, and accuracy of 98.58%, the MLTC model integrating FDM feature selection and CNN demonstrates robust performance characteristics in content-based job recommendation tasks. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Convolutional neural networks

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Fair k-Center Problem with Outliers on Massive Data

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Tsinghua Science and Technology 2023年第6期28卷 1072-1084页

作者： Fan Yuan Luhong Diao Donglei Du Lei Liu Beijing Institute for Scientific and Engineering Computing Faculty of ScienceBeijing University of TechnologyBeijing 100124China Faculty of Management University of New BrunswickFrederictonE3B 5A3Canada

The clustering problem of big data in the era of artificial intelligence has been widely *** of the huge amount of data,distributed algorithms are often used to deal with big data *** distributed computing model has an attractive feature:it can handle massive datasets that cannot be put into the main *** the other hand,since many decisions are made automatically by machines in today’s society,algorithm fairness is also an important research area of machine *** this paper,we study two fair clustering problems:the centralized fair k-center problem with outliers and the distributed fair k-center problem with *** these two problems,we have designed corresponding constant approximation ratio *** theoretical proof and analysis of the approximation ratio,and the running space of the algorithm are given.

关键词： machine learning distributed algorithm fairness constraints outlier constraints k-center problem

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Comparative Analysis of Real-Clinical MRI and BraTS Datasets for Brain Tumor Segmentation 26

Comparative Analysis of Real-Clinical MRI and BraTS Datasets...

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26th Irish Machine Vision and Image Processing Conference, IMVIP 2024

作者： Ranjbarzadeh, Ramin Keles, Ayse Crane, Martin Bendechache, Malika Department of Artificial Intelligence Engineering Faculty of Engineering and Natural Sciences Ankara Medipol University Ankara Turkey School of Computing Faculty of Engineering and Computing Dublin City University Ireland ADAPT Research Centre School of Computer Science University of Galway Ireland

ISBN: (纸本)9781837242672

This study compares the BraTS 2020 dataset with a Real-Clinical dataset from Ankara Bilkent City Hospital for brain tumor segmentation. We analyzed histogram attributes and image dimensions, revealing that the Real-Clinical dataset has a more diverse and skewed intensity distribution compared to the uniformity of the BraTS dataset. This inconsistency suggests potential challenges for algorithms trained on BraTS data when applied in clinical settings, which exhibit greater image variation. Additionally, the higher resolution and inclusion of the entire skull in the clinical dataset complicate processing and segmentation, necessitating more robust algorithms. Our research underscores the importance of developing advanced machine-learning tools that can handle the complexity and variability of clinical MRI scans, enhancing diagnostic accuracy and clinical applicability. This study lays the groundwork for improving medical imaging algorithms to ensure their effectiveness in real-world clinical environments. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/)

关键词： Image segmentation

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