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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Devi, Takhellambam Babylon Jamatia, Anupam Rudrapal, Dwijen Chakma, Kunal National Institute of Technology Agartala Computer Science & Engineering Department Agartala India

ISBN: (纸本)9798331523893

In today's digital landscape, the rapid spread of fake news presents significant challenges, particularly in multilingual nations like India. Manipuri, the primary language of Manipur in Northeast India, has seen a surge in online usage and digital adaptation, making it easier for Manipuri-speaking communities to engage with each other. However, despite this growth, there is a lack of resources for identifying fake news in Manipuri, especially in its Romanized form. This study focuses on developing a dataset to detect fake news in Romanized Manipuri, especially in the sports domain. Since there were no existing datasets for this language, the dataset was compiled by collecting articles from credible Manipuri news sources, transliterating them with contributors, and labelling each as either 'fake' or 'real' to aid in effective detection. Every step, from collecting data to labelling it, was done carefully by hand to ensure accuracy. Statistical tests were also performed to find important language patterns that help distinguish between real and fake news. Our findings show the unique language features of Romanized Manipuri and provide a solid foundation for future work in developing machine-learning models for detecting misinformation. By developing this dataset, we aim to improve the availability of reliable information for Manipuri-speaking communities. © 2025 IEEE.

关键词： Transfer learning

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High-Frequency Performance with Microwave and Millimeter-Wave Methods 1

High-Frequency Performance with Microwave and Millimeter-Wav...

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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Poornima, D. Jany Shabu, S.L. Refonaa, J. Karunya, K. Bobby, M.P. Sathyabama Institute of Science & Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350359756

Modern wireless communication, radar systems, and the realization of new technologies like 5G and beyond all rely heavily on high-frequency performance. The use of microwave and millimeter-wave techniques (MMWT) could provide revolutionary new uses for vast amounts of data at extremely low latency. High-frequency performance optimization is still a challenging and essential problem. Interference Mitigation, Antenna Design, Signal Processing, and Regulatory Compliance are all considered to be sizeable obstacles towards accomplishing High-Frequency Performance. The motive of the research is to advocate the use of Regulatory Compliance Frequency Band Analysis (RCFBA) for the purpose of figuring out target frequencies in accordance with utility necessities and acting spectrum scanning and evaluation with the intention to assess regulatory constraints and available channels. Maximizing signal energy even as keeping off interference via cautiously choosing and positioning antennas. Enabling higher data throughput, decrease latency, and a wider variety of applications for high-frequency systems by way of easing their adoption and deployment in fields like telecommunications, radar systems, satellite communique, and 5G networks. High-frequency performance can be modelled and evaluated with the help of present day modeling tools, that may then be used to optimize gadget parameters, propagation, and antenna designs. It equips users with the way for thorough simulation evaluation, optimization, and fulfillment in excessive-frequency packages, taking into account the deployment of excessive-frequency structures that healthy the desires of contemporary packages and regulatory standards. © 2023 IEEE.

关键词： Regulatory compliance

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Biomedical Lay Summarization: Research Progress and Challenges 3

Biomedical Lay Summarization: Research Progress and Challeng...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Bhowmik, Souramita Jamatia, Anupam Rudrapal, Dwijen Chakma, Kunal National Institute of Technology Agartala Computer Science & Engineering Department Agartala India

ISBN: (纸本)9798331523893

Biomedical lay summarization focuses on transforming complex scientific concepts into clear, concise, and accessible language, avoiding specialized technical terminology. This approach enables audiences without specialized knowledge to understand intricate biomedical topics. As research in biomedical lay summarization progresses, it aims to deliver accurate and user-friendly health information. Lay summaries not only condense content but also simplify language, enhancing both accessibility and readability. This review provides an extensive analysis of the field, highlighting key methods, models, evaluation systems, and challenges. The study surveys recent advancements in summarization quality and underscores the potential of biomedical lay summarization to improve patient engagement, public health communication, and informed decision-making. This thorough review encourages further innovation and emphasizes the need for multimodal approaches in biomedical summarization. © 2025 IEEE.

关键词： Biomedical Biomedical Lay summarization Lay-friendly Language Machine learning Natural Language Processing Summarization

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IoT-Enhanced Machine Learning Approach for Early Detection and Prediction of Urinary Tract Infection 2

IoT-Enhanced Machine Learning Approach for Early Detection a...

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2nd International Conference on Advances in Computation, Communication and Information technology, ICAICCIT 2024

作者： Anisha Sabharwal, Munish Tripathi, Rohit Department of Computer Science and Engineering Galgotias Univerisity Greater Noida India MRIIRS Faridabad India Department of Computer Science and Engineering IILM University Greater Noida India Department of Electronics Engineering JC Bose University of Science & Technology India

ISBN: (纸本)9798331541217

Millions of people worldwide suffer from urinary tract infections (UTIs), which are a common and recurring health problem. In order to limit possible health problems and stop the spread of disease, early identification and prompt management are essential. In order to overcome this issue, the Internet of Things and machine learning techniques are combined in the novel approach presented in this study. The suggested method continually monitors real-time vital signs and urine biomarkers using IoT-enabled wearable and smart sensors. Principle Component Analysis feature selection approaches are used to optimize the pertinent data inputs in order to improve the accuracy of UTI prediction. A centralized ML-driven processing unit is used to perform complex ML algorithms, such as the Ensemble Bagging Decision Tree Classifier classification model, to analyses and decipher the patterns of the gathered data. The technology reliably recognizes and anticipates UTIs, sending early warning signals to patients and medical professionals. In order to improve patient care and public health outcomes, our ML-IoT fusion in UTI detection offers a non-intrusive and proactive technique. The suggested EBDTC technique outperforms the existing approaches with an accuracy rate of 96.25%. The results of the study demonstrate how disruptive technology has the ability to successfully treat common health issues, expanding the area of healthcare diagnostics. © 2024 IEEE.

关键词： Feature Selection

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Spatio-Temporal NDVI Prediction for Rice Crop

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SN computer science 2025年第3期6卷 1-13页

作者： Dey, Anamika Sarkar, Somrita Mondal, Arijit Mitra, Pabitra Centre for Computational and Data Sciences Indian Institute of Technology Kharagpur West Bengal Kharagpur 721302 India Department of Computer Science and Engineering Indian Institute of Technology Patna Bihar Bihta 801106 India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur West Bengal Kharagpur 721302 India

Accurately predicting the Normalized Difference Vegetation Index (NDVI) is crucial for effective agricultural planning and decision-making. Despite much literature on NDVI prediction, most of these methods do not consider spatial-temporal variations. Generating a dataset for predicting NDVI is another major bottleneck, as such data needs to be collected over an extensive period. In this work, we develop a semi-automated framework to generate a comprehensive time-series dataset after necessary preprocessing using the information from Sentinel-2, POWER Data Access Viewer, and the Google Earth Engine (GEE). We propose a deep learning-based model to predict NDVI based on its historical time series and meteorological and spatial information for the rice crop in India. Here, we use a Long Short-Term Memory (LSTM)-based model augmented with spatial information to predict the NDVI of the near-about region for which the data is unavailable. We compared the proposed approach with a method that uses a weighted average of nearest neighbors prediction. Also, we compare the proposed method with existing statistical techniques such as the Autoregressive (AR), AutoRegressive Integrated Moving Average (ARIMA), and Seasonal AutoRegressive Integrated Moving Average (SARIMA). Experimental results show that our methodology outperforms other techniques by a margin of 4% to 8%. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Autoregressive integrated moving average Long short-term memory Normalized difference vegetation index Seasonal autoregressive integrated moving average Sentinel-2 Weighted average of nearest neighbors prediction

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Dempster-shafer deep capsule attention model (DDCAM)

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Multimedia Tools and Applications 2025年 1-29页

作者： Mehravaran, Zahra Ghanizadeh, Ahmad Navid Hamidzadeh, Javad Harati, Ahad Mashhad Iran Department of Computer Science Saarland University Saarbrücken Germany Faculty of Computer Engineering and Information Technology Sadjad University Mashhad Iran

A capsule neural network faces significant challenges in achieving high accuracy on complex datasets due to its high computational complexity and limited ability to represent features. To overcome these limitations, this paper proposes a novel framework, Dempster-Shafer Deep Capsule Attention Model (DDCAM), that combines 3D convolution with attention mechanisms to improve feature representation. This model utilizes the Dempster-Shafer Theory to manage uncertainty and optimize feature selection, resulting in more reliable and accurate learning. In this architecture, dense capsule blocks are used to improve gradient flow and mitigate the effects of vanishing gradients. The DDCAM consistently outperforms state-of-the-art capsule networks on a wide range of datasets, especially complex datasets such as MNIST, CIFAR10, SVHN, and Fashion MNIST. DDCAM improves accuracy by 1.29% on CIFAR10 and 0.76% on SVHN compared to existing models, while achieving precision improvements of 1.22% and 1.08% on CIFAR10 and SVHN, respectively. Additionally, experiments conducted on the AffNIST dataset demonstrate the robustness of DDCAM in handling affine transformations, where it achieved significantly higher accuracy compared to existing models. These results illustrate the model’s enhanced ability to capture complex data relationships and improve both accuracy and precision, advancing capsule neural network performance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Affine transforms

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Cardiovascular Disease Prediction using ML Techniques to Find the Best Accurate Model 8

Cardiovascular Disease Prediction using ML Techniques to Fin...

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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Reddy, Riya Kumari, Shweta Sardarmaran, P. Sathyabama Insitutte of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350396638

Heart disease and cardiovascular disease are both illnesses that damage the heart. Correct functioning of the heart is very important for physical health. There are numerous types of cardio disease includes myocardial ischemia, cardiac arrest, congenital heart disease, myocardial infarction, peripheral heart disease, coronary heart disease, HCD. These types of diseases can be life threatening. It has been diagnosed that men experience more HCD compared to women. Also, it has been said that men diagnosed quick heart attacks than in women. It is the need to be early diagnosis of these diseases more efficiently and accurately. The goal of the study is to create and evaluate several ML algorithms for reliably predicting if a patient will have a cardiovascular ailment. The primary contribution is the evaluation of three well-known ML algorithms, namely Random Forest, Naive Bayes and Support Vector Machines, for predicting cardiovascular disease. When compared with all the results of various algorithms, Random Forest has the highest accuracy (97.94%). © 2023 IEEE.

关键词： Support vector machines

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Advancing Abstractive Bangla Text Summarization: A Deep Learning Approach Using Seq2seq Encoder- Decoder Model and T5 Transformer 5

Advancing Abstractive Bangla Text Summarization: A Deep Lear...

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5th International Conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Tasnimul Hasan, S.M. Rahman, Md Ashfaqur Hasan, Md Mahamudul Hasan, Mohammad Rakibul Hoque, Md Moinul Ahsanullah University of Science and Technology Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350394290

Text summarization plays a vital role in distilling essential information from large volumes of text. While significant progress has been made in English text summarization using deep learning techniques, the research on Bangla text summarization remains limited, with most existing methods relying on non-deep learning approaches. This study addresses the scarcity of abstractive Bangla text summarization methods by conducting a comprehensive analysis using two deep learning models: Sequence-to-sequence (se q2se q) encoder-decoder model and T5 transformer. Through evaluation using BERT and ROUGE scores on the AUST NLP Research dataset and standard BNLPC datasets, this study reveals the Seq2seq model's superior performance in generating coherent abstractive summaries. According to the investigation's findings, the average F1-score is 0.8551, with average precision and recall of 0.8527 and 0.8578, respectively, determined through BERT score evaluations. © 2023 IEEE.

关键词： Deep learning

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Unmasking Deepfake Faces from Videos Using An Explainable Cost-Sensitive Deep Learning Approach 26

Unmasking Deepfake Faces from Videos Using An Explainable Co...

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26th International Conference on computer and Information technology, ICCIT 2023

作者： Mahmud, Faysal Abdullah, Yusha Islam, Minhajul Aziz, Tahsin Ahsanullah University of Science and Technology Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350359015

Deepfake technology is widely used, which has led to serious worries about the authenticity of digital media, making the need for trustworthy deepfake face recognition techniques more urgent than ever. This study employs a resource-effective and transparent cost-sensitive deep learning method to effectively detect deepfake faces in videos. To create a reliable deepfake detection system, four pre-trained Convolutional Neural Network (CNN) models: XceptionNet, InceptionResNetV2, EfficientNetV2S, and EfficientNetV2M were used. FaceForensics++ and CelebDf-V2 as benchmark datasets were used to assess the performance of our method. To efficiently process video data, key frame extraction was used as a feature extraction technique. Our main contribution is to show the model's adaptability and effectiveness in correctly identifying deepfake faces in videos. Furthermore, a cost-sensitive neural network method was applied to solve the dataset imbalance issue that arises frequently in deepfake detection. The XceptionNet model on the CelebDf-V2 dataset gave the proposed methodology a 98% accuracy, which was the highest possible whereas, the InceptionResNetV2 model, achieves an accuracy of 94% on the FaceForensics++ dataset. Source Code: https://***/Faysal-MD/Unmasking-Deepfake-Faces-from-Videos-An-Explainable-Cost-Sensitive-Deep-Learning-Approach-IEEE2023 © 2023 IEEE.

关键词： CelebDf CNN Cost-sensitive Deepfake video Explainable AI (XAI) Face Detection FaceForensics++ Keyframe

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Data-driven strategies for digital native market segmentation using clustering

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 178-191页

作者： Uddin, Md Ashraf Talukder, Md. Alamin Ahmed, Md. Redwan Khraisat, Ansam Alazab, Ammar Islam, Md. Manowarul Aryal, Sunil Jibon, Ferdaus Anam School of Information Technology Deakin University Waurn Ponds Campus Geelong Australia Department of Information Technology Crown Institute of Higher Education 116 Pacific Highway North SydneyNSW2060 Australia Department of Computer Science and Engineering International University of Business Agriculture and Technology Dhaka Bangladesh Department of Computer Science and Engineering Jagannath University Dhaka Bangladesh Centre for Artificial Intelligence and Optimization Torrens University Australia

The rapid growth of internet users and social networking sites presents significant challenges for entrepreneurs and marketers. Understanding the evolving behavioral and psychological patterns across consumer demographics is crucial for adapting business models effectively. Particularly, the emergence of new firms targeting adolescents and future generations underscores the importance of comprehending online consumer behavior and communication dynamics. To tackle these challenges, we introduce a Machine Learning-based Digital Native Market Segmentation designed to cater specifically to the interests of digital natives. Leveraging an open-access prototype dataset from social networking sites (SNS), our study employs a variety of clustering techniques, including Kmeans, MiniBatch Kmeans, AGNES, and Fuzzy C-means, to uncover hidden interests of teenage consumers from SNS data. Through rigorous evaluation of these clustering approaches by default parameters, we identify the optimal number of clusters and group consumers with similar tastes effectively. Our findings provide actionable insights into business impact and critical patterns driving future marketing growth. In our experiment, we systematically evaluate various clustering techniques, and notably, the Kmeans cluster outperforms others, demonstrating strong segmentation ability in the digital market. Specifically, it achieves silhouette scores of 63.90% and 58.06% for 2 and 3 clusters, respectively, highlighting its effectiveness in segmenting the digital market. © 2024 The Authors

关键词： Cluster analysis

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