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Artificial intelligence-inspired comprehensive framework for desertification prediction

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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NL2Code: A Hybrid NLP and Model-Driven Framework for Automated Code Generation from Natural Language and UML

NL2Code: A Hybrid NLP and Model-Driven Framework for Automat...

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2025 IEEE International Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Nair, Reshma P Thushara, M.G. Amrita School of Computing Amrita Department of Computer Science & Engineering Kerala Vishwa Vidyapeetham Amritapuri Kollam India Amrita School of Computing Amrita Department of Computer Science & Applications Kerala Vishwa Vidyapeetham Amritapuri Kollam India

ISBN: (纸本)9798331529833

The process of converting natural language requirements and visual models into executable software code remains an ongoing challenge in software engineering. We developed an intelligent system that adopts Natural Language Processing (NLP) and Model-Driven engineering (MDE) to generate code from natural language requirements and UML diagrams. It then applies advanced NLP techniques for natural language specification, such as tokenization, POS tagging, semantic analysis, and information extraction. This information can be transformed into computer language construct and design patterns with the help of MDE concepts. It generates code templates, skeleton code, and real code and incorporates key principles of software engineering and technological specializations. Case studies explain how the system can improve the software development lifecycle for a development company by automatically translating the natural language and the visual models into code. The system synthesized code with 92-95 % accuracy and 92-93 % completeness in three particular cases of use, proving the efficiency of the translation process of natural language requirements and UML diagrams into executable code. The present work enriches software engineering, NLP, as well as the integration of MDE, and code generation, reducing time consumption, manual efforts, and fast communication in the Software Development Process. © 2025 IEEE.

关键词： Semantics

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Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detection

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Neural computing and applications 2025年第6期37卷 4567-4583页

作者： Sayem, Faizul Rakib Islam, Md. Sakib Bin Naznine, Mansura Nashbat, Mohammad Hasan-Zia, Mazhar Kunju, Ali K Ansaruddin Khandakar, Amith Ashraf, Azad Majid, Molla Ehsanul Kashem, Saad Bin Abul Chowdhury, Muhammad E. H. Department of Electrical and Electronic Engineering University of Dhaka Dhaka Bangladesh Department of Biomedical Engineering Military Institute of Science and Technology Dhaka Bangladesh Department of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Chemical Engineering Department University of Doha for Science and Technology Doha Qatar Department of Electrical Engineering Qatar University Doha2713 Qatar Computer Applications Department Academic Bridge Program Qatar Foundation Doha Qatar Department of Computing Science AFG College With the University of Aberdeen Doha Qatar

Given the severity of waste pollution as a major environmental concern, intelligent and sustainable waste management is becoming increasingly crucial in both developed and developing countries. The material composition and volume of urban solid waste are key considerations in processing, managing, and utilizing city waste. Deep learning technologies have emerged as viable solutions to address waste management issues by reducing labor costs and automating complex tasks. However, the limited number of trash image categories and the inadequacy of existing datasets have constrained the proper evaluation of machine learning model performance across a large number of waste classes. In this paper, we present robust waste image classification and object detection studies using deep learning models, utilizing 28 distinct recyclable categories of waste images comprising a total of 10,406 images. For the waste classification task, we proposed a novel dual-stream network that outperformed several state-of-the-art models, achieving an overall classification accuracy of 83.11%. Additionally, we introduced the GELAN-E (generalized efficient layer aggregation network) model for waste object detection tasks, obtaining a mean average precision (mAP50) of 63%, surpassing other state-of-the-art detection models. These advancements demonstrate significant progress in the field of intelligent waste management, paving the way for more efficient and effective solutions. © The Author(s) 2024.

关键词： Deep learning for waste management Intelligent waste management Object detection in waste management Sustainable waste processing Urban solid waste Waste image classification

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A novel soft computing based efficient feature selection approach for timely identification of COVID-19 infection using chest computed tomography images: a human centered intelligent clinical decision support system

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Multimedia Tools and applications 2025年第14期84卷 13717-13785页

作者： Singh, Law Kumar Khanna, Munish Garg, Hitendra Singh, Rekha Department of Computer Engineering and Applications GLA University Mathura281406 India School of Computing Science and Engineering Galgotias University Plot No.2 Sector 17-A Yamuna Expressway Gautam Buddh Nagar Greater Noida203201 India Department of Physics Uttar Pradesh Rajarshi Tandon Open University Uttar Pradesh Prayagraj India

The SARS-CoV-2 coronavirus strain’s introduction in December 2019 resulted in the development of the new coronavirus disease, COVID-19. Following its first appearance, the virus quickly spread throughout the world and is now considered to be a pandemic. There were 6,885,962 recorded deaths and 689,853,908 confirmed cases as of April 4, 2023. It is essential to put in place a thorough testing strategy in order to stop the disease from spreading. Nonetheless, a number of testing approaches are presently under consideration due to a restricted inventory and a limited supply of testing equipment. Recent expert analysis suggests that images from chest computed tomography (CT) scans could reveal important information about COVID-19, which is why we are using this modality as a focus of our investigation. Moreover, numerous recent studies have demonstrated that selecting the most informative features from the subject images improves the classification models’ efficiency and shortens the time needed for training and testing. All of this encourages us to present a study that suggests a novel, efficient, and fast feature selection system as the core of the proposed highly competent clinical decision support system for COVID-19 infection prediction. Using a publicly accessible CT image dataset, this four-phase system divides the images into two categories: "COVID-19-infected human" and "healthy human". Pre-processing is the first step in the study, after which features from different categories in the images are extracted in the next phase. In the third phase, the most influential features are then selected using three algorithms: the Teaching Learning-Based Optimization Algorithm (TLBO), the Cuckoo Search Optimization Algorithm (CSO), and a proposed hybrid of these two. To the best of the authors’ knowledge, these algorithms have rarely been applied to feature selection for COVID-19 infection prediction, which highlights the originality and inventiveness of the work. Followin

关键词： COVID-19

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A Reinforcement Learning Approach for Routing in Marine Communication Network of Fishing Vessels

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SN Computer Science 2025年第1期6卷 1-12页

作者： Surendran, Simi Montresor, Alberto Vinodini Ramesh, Maneesha Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri Kollam India Department of Information Engineering and Computer Science University of Trento Trento Italy Amrita Center for Wireless Networks & Applications (AmritaWNA) Amrita School of Engineering Amrita Vishwa Vidyapeetham Amritapuri Kollam India

The lack of affordable communication facilities to the shore remains a fundamental problem for fishermen engaged in deep-sea fishing. The Offshore Communication Network (OCN) is a wireless network of fishing vessels, whose goal is to provide Internet over the ocean. However, the dynamic nature of OCNs characterized by extreme weather, the difficulty of deploying additional infrastructure, wave-induced vessel movements, and high mobility causes significant challenges for traditional routing protocols. This paper proposes OCN-AR, a Q-learning-based adaptive routing strategy for ocean networks. The quality of the learning process relies on the reward function, which has been carefully designed to incorporate the most important features, including real-time forecasts of connectivity quality, path probability, link availability duration, and distance to the destination. The routing performance is evaluated through extensive simulations conducted under diverse conditions, including varying mobility scenarios, transmission rates, vessel rocking intensities, and node densities, and is compared against traditional protocols. The results demonstrate that OCN-AR significantly outperforms existing routing approaches, making it a reliable solution for maritime communication. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Fishing vessel network Marine communication network Reinforcement learning Routing protocol

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Enhanced satellite imagery analysis for post-disaster building damage assessment using integrated ResNet-U-Net model

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Multimedia Tools and applications 2025年第5期84卷 2689-2714页

作者： Bhardwaj, Diwakar Nagabhooshanam, N. Singh, Ajeet Selvalakshmi, B. Angadi, Sanjeevkumar Shargunam, S. Guha, Tapas Singh, Gurkirpal Rajaram, A. Computer Engineering and Applications GLA University Chaumuhan India Department of Research Analytics Saveetha Dental College and Hospitals Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Aditya University Surampalem Andhra Pradesh India VIT Bhopal University Madhya Pradesh Bhopal466114 India Department of Computing Science and Engineering Tagore Engineering College Rathinamangalam Tamil Nadu Chennai600127 India Department of Computing Science and Engineering Nutan College of Engineering and Research Talegaon Dabhade Maharashtra 410507 India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Tamil Nadu Srivilliputhur626126 India Department of CSE Techno International New Town West Bengal Kolkata700156 India Department of Architecture PTU Punjab Mohali140307 India Department of Electronics and Communication Engineering E.G.S. Pillay Engineering College Tamil Nadu Nagapattinam611002 India

In the aftermath of natural disasters, timely and accurate building damage assessment is crucial for effective disaster response. This research introduces an innovative integrated ResNet-U-Net ensemble model that significantly enhances the accuracy and efficiency of damage detection from satellite imagery. The model combines the deep feature extraction capabilities of ResNet with the precise localization advantages of U-Net, supplemented by attention mechanisms to focus on critical damage indicators. Methodologically, the research employs a dataset of high-resolution satellite images from post-Hurricane Iota, processed through normalization and augmentation techniques. The model demonstrates exceptional accuracy, achieving 96.91% on an unbalanced dataset representative of real-world conditions, and 89.35% on a balanced dataset. This performance indicates a substantial improvement over existing approaches, offering a rapid and reliable tool for aiding disaster relief efforts. The proposed model's computational efficiency and generalizability underscore its potential to transform disaster management practices. Future work will explore the model's application to various disaster types and integration with real-time data processing systems. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Satellite imagery

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Named Entity Recognition for Telugu and Kannada on Naamapadam Dataset Using Various Machine Learning and Deep Learning Algorithms

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Procedia Computer Science 2025年 258卷 3381-3392页

作者： Kuruva Divya Sree Gunti Geethika Aarathi Rajagopalan Nair Deepa Gupta G Veena Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

Named Entity Recognition is a crucial task in Natural Language Processing. It involves detecting and categorizing named entities such as individuals, places, and organizations within a text. This process is vital for transforming un-structured text into structured information, which is useful for various applications like information retrieval, question answering, and text summarization. The proposed study investigates bilingual NER for Telugu and Kannada languages using pre-trained language models and embeddings for contextual vectorization. Utilizing the Naamapadam dataset, the research employs IndicFT, MuRIL and IndicBERT embeddings along with machine learning and deep learning algorithms. Notably, utilized only 10% of the considered dataset and achieved results that surpass those reported in the base paper. Among the models tested, MuRIL combined with XGBoost achieved the best performance, with an F1 score of 89.00 for Telugu and 87.00 for Kannada. Performance evaluation demonstrates varying effectiveness across techniques, shedding light on optimal strategies for NER in under resourced languages. These findings contribute to advancing NLP techniques for Indian languages, facilitating broader accessibility and usability in diverse linguistic contexts.

关键词： Natural Language Processing Named Entity Recognition Naamapadam Da-taset Contextual Vectorization Techniques Machine learning models Deep learning models

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Comprehensive Integration of Machine Learning, Deep Learning, and Fine-Tuned Large Language Models for Malayalam News Categorization

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Procedia Computer Science 2025年 258卷 3357-3368页

作者： C V Anushka Ardra K John Aarathi Rajagopalan Nair Deepa Gupta G Veena Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

The comprehension of news is substantially improved when it is conveyed in an individual’s native language. The increasing volume of news articles, driven by global events, necessitates the effective organization and categorization of vast amounts of information. Dravidian languages, such as Malayalam, characterized by their intricate morphology and diacritical features, pose distinctive challenges in this domain. This research embarks on an innovative initiative to classify Malayalam news articles into four principal categories: business, entertainment, sports, and technology. A synthesis of Deep Learning and Machine Learning methodologies, alongside diverse embedding techniques, is utilized to assess the precision and effectiveness of the categorization endeavor. The ensemble classifiers XGBoost and Stacking, when integrated with the XLM RoBERTa word embedding, attained the highest accuracy rate of 0.91. Similarly, XLM RoBERTa and FastText word embedding techniques, attained a performance with an accuracy rate of 0.93 for almost all the deep learning model. Furthermore, large language models were leveraged, investigating both zero-shot learning and fine-tuning strategies. The fine-tuned BLOOM model reached an accuracy level of 0.89 The research additionally presents a comprehensive examination of the importance of large language models in facilitating the categorization process. Furthermore, the research provides a comprehensive examination of the role of large language models in enhancing the categorization process. By advancing the discipline of Natural Language Processing, this investigation provides significant insights into the utilization of sophisticated technologies for regional news analysis, underscoring their potential to enhance information management and accessibility.

关键词： Natural Language Processing Large Language Models Embeddings Zero-shot learning Malayalam News categorization

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Extracting Triplets from Domain-specific Texts using Fine-Tuned LLaMA Models: A Comparative Study

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Procedia Computer Science 2025年 258卷 3750-3759页

作者： Veena G Aparna Rajendran Deepa Gupta Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Dept of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

This research centers on extracting triplets (Subject, Verb, Object) from domain-specific texts, particularly focusing on the film industry. We compiled a corpus of 4,300 sentences from 500 well-known movie articles and Wikipedia entries. The study targeted five key relations: Composed by, Written by, Released on, Made f rom, and Released in , along with their respective entities. Dependency parsing was employed to extract triplets from this dataset. These extracted triplets were then used for supervised fine- tuning of the LLaMa model. We assessed the evaluation loss across different versions of the LLaMA models and found that LLaMA 3 outperformed the others, showing the best performance for this task. For testing, the fine-tuned LLaMA model is used to extract triplets from unseen sentences. The extracted triplets can be used to create knowledge graphs, offering structured representations of information by mapping relationships between entities.

关键词： LLM LLaMA NLP Finetuning Zero-shot

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Enhancing Stock Price Prediction Through Integration of Astrological and Astronomical Data using XGBoost

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Procedia Computer Science 2025年 258卷 3970-3979页

作者： Rekha R Nair Tina Babu Kishore S Vishal S Sheila Mahapatra Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India Department of Computer Applications Nitte Deemed to be university Nitte Institute of Professional Education Mangalore Karnataka India Department of Computer Applications School of Engineering Dayananda Sagar University Bengaluru India Department of Electrical and Electronics Engineering Alliance University Bangalore India

Predicting stock prices continues to be a significant challenge in the financial industry, prompting researchers to investigate various methods to improve accuracy. Hence proposed a novel approach by incorporating astrological and astronomical data into XGBoost models for stock price classification and prediction, utilizing deep neural networks (DNN). The dataset is collected from NASA JPL Horizons and Jaganath Hora software. The raw data is preprocessed with feature engineering, data cleaning, []and normalization to obtain a better forecasting stock prices and classification results. The proposed data executed with machine learning models, such as logistic regression, decision trees, random forest, SVM, XGB Classifier, LSTM, RNN, and CNN other than XGB Regressor for the forcasting. The proposed research is evaluated for classification with accuracy, precision, recall, F1 score, and forecasting with RMSE, MAE, R 2 , AIC, and BIC. The XGBoost classifier achieved 99% accuracy in predicting stock market movements, surpassing other models in determining whether the next day’s stock price would be higher or lower than the previous day’s price, while the DNN performed the best among all algorithms for overall stock price prediction. This approach could influence investment strategies, potentially affecting wealth distribution and financial market stability.

关键词： Independent Component Analysis Principal Component Analysis Probable Neural Network Expectation Maximization algorithm Natural language processing

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