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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Ramadevi, M. Ramkumar, M.O. Jayakumar, D. Rajmohan, R. Arunkumar, A. Ifet college of Engineering Villupuram India Srm Institute of Science and Technology Department of Computing Technology Kattankulathur campus Tamilnadu India

ISBN: (纸本)9798331509828

Around the world., lung diseases like Pneumonia., lung nodule infiltration., pneumothorax are the main cause of death and severe health problems. In this digital world., many new technologies were developed for early and accurate detection of lung disease but many of the technologies are not able to predict the lung disease accurately and required large amount of labelled data. This paper describes a new approach by combining bidirectional deep learning and masked autoencoders for lung disease detection and classification from medical images. The masked autoencoder learns meaningful representations by reconstructing masked regions in lung images., while bidirectional deep learning model context from both directions., improving disease detection accuracy This algorithm doesn't required large labelled data it can learns from unlabelled data. The main purpose of this algorithm is used to learn effective representation of medical images by masking parts of input data and reconstructing them. which are crucial for accurate disease detection and classification. The proposed Bidirectional deep learning framework combines forward & Backward propagation process to enchance the learning efficiency and robustness of the lung images. This methodology detect and classify the disease like pneumonia., lung nodule., pneumothorax., infiltration and other lung diseases. Experimental results show significant improvements in sensitivity and specificity., especially in cases with subtle abnormalities in the lung tissue. © 2025 IEEE.

关键词： Lung cancer

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High-Efficiency Asymmetrically Designed Three-Phase Virtual 48-pulse Power Supply for Electrolytic Hydrogen

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IEEE Transactions on Power Electronics 2025年第8期40卷 10335-10342页

作者： Tian, Hanlei Cui, Haoran Han, Wei Sathik M, Jagabar Liang, Guozhuang Chen, Maolin Mekhilef, Saad Sustainable Energy and Environment Thrust The Hong Kong University of Science and Technology (GZ) Guangzhou China Electrical Engineering School of Hebei University of Science and Technology Shijiazhuang China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India College of Electrical Engineering Sichuan University Chengdu China School of Science Computing and Engineering Technologies Swinburne University of Technology Melbourne VIC Australia

To address the low-voltage, high-current requirements in hydrogen production applications, a virtual 48-pulse three-phase rectifier is proposed, and achieves the equivalent performance of four parallel 12-pulse rectifiers (12-PR). The proposed approach builds upon the conventional 12-PR design and incorporates novel Asymmetric Current Injection Units (ACIU). The ACIU is installed on the DC side of the AC-DC converter, generating a specific current at the DC link to modulate and enhance the operating modes of the 12-PR. Consequently, the 12-pulse is extended to 48, resulting in an almost sinusoidal input current with a Total Harmonic Distortion (THD) of 3.2% and an output current with a ripple rate (RR) of 2.3%, thus significantly improving power quality. Furthermore, the asymmetric design of the current injection units eliminates the need for a phase-shifting transformer. The efficacy of the proposed rectifier for electrolytic hydrogen production is validated using a 2.2 kW experimental prototype. © 1986-2012 IEEE.

关键词： Voltage Hydrogen Rectifiers Production Windings Circuits Inductors Transformers Harmonic analysis Electrochemical processes

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Optimized deep transfer learning techniques for spine fracture detection using CT scan images

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Multimedia Tools and Applications 2025年 1-34页

作者： Kanna, G. Prabu Kumar, Jagadeesh Parthasarathi, P. Bhardwaj, Priya Kumar, Yogesh VIT Bhopal University Bhopal India Department of Computer Science and Engineering Kathir College of Engineering Coimbatore India Department of CS Bannari Amman Institute of Technology Sathyamangalam India School of Computing DIT University Dehradun India Department of CSE School of Technology Pandit Deendayal Energy University Gujarat Gandhinagar India

Spine fractures pose an important health concern that requires a quick diagnosis to protect people from any long-term complications. This paper aims to propose an automatic prediction system that uses deep transfer learning techniques for spine fractures and minimizes the time required for classification compared to traditional techniques. The CT scan images of the cervical spine have been collected in the form of JPEG format and subsequently segmented using several phases such as mask exploring, detecting, zooming, and cropping the affected area at 6 different angles followed by the computation of contour features to analyze their geometric as well as intensity-based parameters. Ten advanced deep learning models along with ADAM optimizer are trained where InceptionResNetV2 obtained the highest accuracy of 99.70% while the lowest loss, as well as root mean error square score, has been obtained by ResNet50V2 with 0.0493 and 0.2220.36 respectively. In the case of precision, recall, and F1 score, DenseNet169 computed the highest precision and recall values of 99.99% and 98.495% respectively while Xception did well in terms of F1 score with 98.32%. These models perform better because InceptionResNetV2’s architecture is well-suited for capturing intricate spatial hierarchies, DenseNet169 benefits from dense connectivity for feature reuse, and ResNet50V2’s optimized residual blocks contribute to minimizing error rates. Apart from this, less diversity and class imbalance are also seen within the dataset which affects model performance. These limitations highlight the need for future work to expand the dataset and ensure a balanced distribution of classes to enhance the robustness and clinical relevance of the proposed AI-based system. However, despite these challenges, the results underscore the transformative potential of deep learning in improving clinical diagnosis of spine fractures. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part

关键词： Computerized tomography

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Generative Adversarial Network Based Advanced Framework for High Resolution Multi-Style Cartoon Image Synthesis in Animation and Game Development 1

Generative Adversarial Network Based Advanced Framework for ...

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1st International Conference on AIML-Applications for engineering and technology, ICAET 2025

作者： Kathiravan, M. Ramesh, S. Buvanesvari, R. Sreesubha, S. Irumporai, A. Venkataramana, R. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of CSE Chennai India School of Computing College of Engineering and Technology Department of Computing Technologies Chennai India Rajalakshmi Engineering College Department of CSE Chennai India ICT Trainer Datec India QIS College of Engineering & Technology Department of CSE Andhra Pradesh Ongole India

ISBN: (纸本)9798350355611

Industries like entertainment, education, and advertisements require cartoon image generation as a most powerful business tool now a days. Classic systems such as texture synthesis and edge detection were largely unable to generate more natural images with an arbitrary artistic style. This research overcomes these limitations by presenting a Generative Adversarial Network (GAN)-based framework for high-resolution, style transferable cartoon picture generation. The use of GAN stems from the generator's creation of images and the discriminator's perception of which images it created, both of which aim to minimize and maximize costs, thereby increasing the diversity of outputs over time. The model was first trained with 40,000 images that were classified according to the anime-GAN dataset and then tested on 10,000 different anime images. Proposed a novel loss function that consists of both adversarial and content loss, allowing the generated images to preserve their structural relationships while providing better quality and style variety. Evaluation on Fréchet Inception Distance (FID) revealed that our model outperforms existing methods with a large margin both in image quality and adaptive diversity. It is fast, efficient, and versatile;it can generate cartoon images in a wide range of styles with no need for retraining. The proposed approach has numerous implications in animation and game development as a tool to generate cartoon images of high diversity and quality quickly, alleviating some limitations of existing traditional methods and past machine learning models. © 2025 IEEE.

关键词： Generative adversarial networks

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A Novel CAPTCHA Recognition System Based on Refined Visual Attention

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Computers, Materials & Continua 2025年第4期83卷 115-136页

作者： Zaid Derea Beiji Zou Xiaoyan Kui Monir Abdullah Alaa Thobhani Amr Abdussalam School of Computer Science and Engineering Central South UniversityChangsha410083China College of Computer Science and Information Technology Wasit UniversityWasit52001Iraq Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha67714Saudi Arabia Electronic Engineering and Information Science Department University of Science and Technology of ChinaHefei230026China

Improving website security to prevent malicious online activities is crucial,and CAPTCHA(Completely Automated Public Turing test to tell Computers and Humans Apart)has emerged as a key strategy for distinguishing human users from automated ***-based CAPTCHAs,designed to be easily decipherable by humans yet challenging for machines,are a common form of this ***,advancements in deep learning have facilitated the creation of models adept at recognizing these text-based CAPTCHAs with surprising *** our comprehensive investigation into CAPTCHA recognition,we have tailored the renowned UpDown image captioning model specifically for this *** approach innovatively combines an encoder to extract both global and local features,significantly boosting the model’s capability to identify complex details within CAPTCHA *** the decoding phase,we have adopted a refined attention mechanism,integrating enhanced visual attention with dual layers of Long Short-Term Memory(LSTM)networks to elevate CAPTCHA recognition *** rigorous testing across four varied datasets,including those from Weibo,BoC,Gregwar,and Captcha 0.3,demonstrates the versatility and effectiveness of our *** results not only highlight the efficiency of our approach but also offer profound insights into its applicability across different CAPTCHA types,contributing to a deeper understanding of CAPTCHA recognition technology.

关键词： Text-based CAPTCHA recognition refined visual attention web security computer vision

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Multi-lingual news stack based ensemble classification for deep news feed analysis

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Neural computing and Applications 2025年 1-12页

作者： Sakthivel, Rakesh Kumar Nagasubramanian, Gayathri Sankayya, Muthuramalingam Kadry, Seifedine Rocha, Alvaro Karnataka Bengaluru India Department of Information Technology Thiagarajar College of Engineering Madurai India Faculty of Applied Computing and Technology Noroff University College Kristiansand Norway Alvaro Rocha ISEG University of Lisbon Lisbon Portugal

Analyzing and extracting effective words of news feeds are critical tasks. News making sources may feed their news articles in different manners based on their own perspective. In this situation, finding likelihood samples from the vast news feeds is an essential task. Moreover, acquiring optimal word deviations, news varieties, sentiment metrics, and multi-lingual analysis are major tasks of this proposed system. To achieve this massive news feed analysis task, the proposed system uses specially designed multi-classifier units and news stack manipulation procedures. The proposed system integrates different classifiers and dynamic news stacks to handle datasets of multi-lingual attributes. The results show that the proposed method performs with better accuracy rate than existing techniques. This gives 10 to 15% higher accuracy rate than existing works. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Data accuracy

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AFuNet: an attention-based fusion network to classify texts in a resource-constrained language

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Neural computing and Applications 2025年第9期37卷 6725-6748页

作者： Hossain, Md. Rajib Hoque, Mohammed Moshiul Dewan, M. Ali Akber Hoque, Enamul Siddique, Nazmul Department of Computer Science & Engineering Chittagong University of Engineering & Technology Chittagong4349 Bangladesh School of Computing and Information Systems Faculty of Science and Technology Athabasca University AthabascaABT9S 3A3 Canada School of Information Technology York University TorontoONM3J 1P3 Canada School of Computing Engineering and Intelligent Systems Ulster University Coleraine United Kingdom

In the era of widespread Internet use and extensive social media interaction, the digital realm is accumulating vast amounts of unstructured text data. This unstructured data often contain undesirable information, necessitating time-consuming manual classification efforts. An intelligent text classification system capable of automatically categorizing digitized texts based on semantic meaning is crucial. However, this task is particularly challenging for low-resource languages like Bengali due to a shortage of annotated corpora, issues with out-of-vocabulary words, lack of domain-specific hyperparameter tuning, limited ability to extract generalized text features, and class imbalances within the corpus. AFuNet: an attention-based fusion network to classify texts in a resource-constrained language. AFuNet undergoes a comprehensive four-phase experimental process, including baseline model evaluation and hyperparameter tuning, late fusion and model selection, attention-based early fusion and model identification, and an ablation study with impact analysis. Fine-tuned based on five Bengali text classification corpora, AFuNet achieves impressive accuracies: 96.60 ± 0.2 (BTCC11), 85.37 ± 0.2 (OSBC), 97.35 ± 0.2 (BARD), 93.74 ± 0.2 (IndicNLP), and 96.51 ± 0.2 (ProthomAlo). In comparison with previous state-of-the-art models on these corpora, AFuNet demonstrates significant accuracy improvements ranging from 0.54% to 4.49%, showcasing its effectiveness in advancing text classification capabilities for the Bengali language. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Semantics

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Advancing Air Quality Monitoring: TinyML-Based Real-Time Ozone Prediction with Cost-Effective Edge Devices 2nd

Advancing Air Quality Monitoring: TinyML-Based Real-Time Ozo...

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Ken, Huam Ming Behjati, Mehran Rafsanjani, Ahmad Sahban Aslam, Saad Meng, Yap Kian Majeed, Anwar P. P. Abdul Zheng, Yufan Department of Computing and Information Systems School of Engineering and Technology Sunway University Selangor Bandar Sunway47500 Malaysia Department of Computing and Information Systems School of Engineering and Technology Sunway University Selangor Bandar Sunway47500 Malaysia Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

The escalation of urban air pollution necessitates innovative solutions for real-time air quality monitoring and prediction. This paper introduces a novel TinyML-based system designed to predict ozone concentration in real-time. The system employs an Arduino Nano 33 BLE Sense microcontroller equipped with an MQ7 sensor for carbon monoxide (CO) detection and built-in sensors for temperature and pressure measurements. The data, sourced from a Kaggle dataset on air quality parameters from India, underwent thorough cleaning and preprocessing. Model training and evaluation were performed using Edge Impulse, considering various combinations of input parameters (CO, temperature, and pressure). The optimal model, incorporating all three variables, achieved a mean squared error (MSE) of 0.03 and an R-squared value of 0.95, indicating high predictive accuracy. The regression model was deployed on the microcontroller via the Arduino IDE, showcasing robust real-time performance. Sensitivity analysis identified CO levels as the most critical predictor of ozone concentration, followed by pressure and temperature. The system’s low-cost and low-power design makes it suitable for widespread implementation, particularly in resource-constrained settings. This TinyML approach provides precise real-time predictions of ozone levels, enabling prompt responses to pollution events and enhancing public health protection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Air quality

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Psychological Impact of Internet Blackouts: A Case Study With Machine Learning-Based Stress Analysis

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IEEE Access 2025年 13卷 83505-83527页

作者： Islam, Mohammad Ariful Islam, Tahidul Hossain, Gahangir Mofazzal Hossain, M. The University of Melbourne Department of Computing and Information Systems MelbourneVIC3010 Australia Bangladesh University of Engineering and Technology Department of Computer Science and Engineering Dhaka1000 Bangladesh University of North Texas Department of Data Science DentonTX76205 United States Southeast University Department of Electrical and Electronic Engineering Dhaka1208 Bangladesh

Access to the internet has become a vital part of modern life, especially for communication and essential services. However, during politically sensitive times, internet blackouts can disrupt daily routines, leading to significant psychological impacts. This study explores the mental health effects of the internet shutdown imposed during the Bangladesh Quota Movement in July 2024, when the government cut off access to control information flow. The blackout hindered communication, financial transactions, and critical services, amplifying stress, feelings of isolation, and emotional distress. A survey of 2,085 participants was conducted to assess the behavioral, emotional, and psychological consequences, particularly in academic, work, and social settings. Stress levels among respondents varied from minimal to extreme, reflecting widespread mental distress. To classify these stress levels, machine learning models;Decision Tree (DT), Random Forest (RF), Bernoulli Naive Bayes (BNB), Logistic Regression (LR), eXtreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM) were applied. The SVM model outperformed others, achieving high precision (99.33%), recall (99.00%), F1-score (99.33%), and accuracy (99.49%). This study underscores the urgent need for mental health support during such crises, particularly in low- and middle-income countries like Bangladesh, where mental health care is often neglected. These findings are aligned with Sustainable Development Goal (SDG 3), "Good Health and Well-Being," stressing the importance of mental health interventions in fostering resilience, well-being, and social stability during crises. © 2013 IEEE.

关键词： Support vector machines

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Optimizing Household Energy Consumption: A Web-Based Tool for Real-Time Carbon Footprint Calculation and Sustainable Benchmarking 1

Optimizing Household Energy Consumption: A Web-Based Tool fo...

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1st International Conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Raja, Harsh Kishore, Jayaditya Kalaivani, J. Angayarkanni, V. Rama, P. Srm Institute Of Science And Technology Faculty Of Engineering And Technology Department Of Computing Technologies Kattankulathur Chengalpattu District Tamil Nadu 603203 India

ISBN: (纸本)9798331518578

This is an innovative project from an energy-saving perspective. This project ensures that users become in control of their energy consumption and able to take an actively reducing their carbon footprints. It provides users with an interface to input details about household appliances and usage patterns, through which a correct calculation of the carbon footprint could be done. Compared against standard norms, it would help the users identify certain areas where improvement is possible and adopt more sustainable use of energy. Apart from personalized energy assessments, it has now become the central source that connects users to local schemes and initiatives on sustainable energy, including information on resources and programs into waste energy and carbon emission reduction. This integration, therefore, promotes more engagement and involvement within the community as users are nudged to join in the efforts that are being made for the goal of energy conservation. The platform educates its users about energy consumption and aids them in making green choices in data;however, with a holistic approach in carbon footprint management and education pertaining to sustainable long-term behavior, the process takes place through integrated accurate energy tracking, fine recommendations, and community-driven action. © 2025 IEEE.

关键词： Carbon sequestration

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