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27th International Conference on Pattern Recognition, ICPR 2024

作者： Das, Debashis Maji, Suman Kumar Department of Computer Science and Engineering Indian Institute of Technology Bihar Patna801106 India

ISBN: (纸本)9783031781247

In the realm of computer vision, image denoising remains a formidable challenge with profound implications for fields like medical imaging, remote sensing, and photography. Despite notable advancements in deep learning, there are enduring challenges: current convolutional neural networks (CNNs) frequently struggle with training complexities due to their emphasis on increased network depth. At the same time, these networks often fail to adequately consider the crucial role of gradient information in the denoising process. Furthermore, there is a distinct gap in leveraging transform domain analysis in image denoising. This study addresses these limitations with MDFIDNet, a novel triple-phase attentive fusion network tailored for image denoising. MDFIDNet integrates three independent feature extraction pipelines: a frequency domain processing pipeline (FDP) enhanced by a multi-scale convolutional attention Block (MSCAB), a spatial domain processing pipeline (SDP) focusing on detail feature preservation, and a gradient-domain processing pipeline (GDP) driven by multidirectional gradient information. Experimental validation demonstrates that MDFIDNet surpasses existing benchmarks, exhibiting robust performance across diverse datasets. Comprehensive ablation studies underscore the individual contributions of each network component, elucidating the novel advancements that underpin MDFIDNet’s superior denoising efficacy. The source code and further details are available in the https://***/debashis15/MDFIDNet. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Comparison Between Dependent and Independent Ranked Set Sampling Designs for Parametric Estimation with Applications

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Annals of Data science 2023年第1期10卷 167-182页

作者： Shaaban, M. Yahya, M. The High Institute for Tourism Hotels and Computer El-Seyouf Alexandria Egypt Modern Academy for Computer Science and Management Technology Cairo Egypt

This paper is concerned with the estimation problem using maximum likelihood method of estimation for the unknown parameters of exponetiated gumbel distribution based on neoteric ranked set sampling (NRSS) as a new modification of the usual ranked set sampling (RSS) technique. Numerical study is conducted to compare NRSS as a dependent ranked set sampling technique, with RSS, and median ranked set sampling as independent sampling techniques, and then the performance of RSS and its modifications will be compared with simple random sampling based on their mean square errors and efficiencies. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Neoteric ranked set sampling Median ranked set sampling Ranked set sampling Maximum likelihood estimation Gumbel distribution Maximum likelihood estimation Estimation theory Parametric statistics Simple random sample Estimation

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Diagnosis of Coronavirus Disease From Chest X-Ray Images Using DenseNet-169 Architecture

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SN computer science 2023年第3期4卷 214页

作者： Dalvi, Pooja Pradeep Edla, Damodar Reddy Purushothama, B.R. Department of Computer Science and Engineering National Institute of Technology Goa India

The coronavirus disease (COVID-19) is a very contagious and dangerous disease that affects the human respiratory system. Early detection of this disease is very crucial to contain the further spread of the virus. In this paper, we proposed a methodology using DenseNet-169 architecture for diagnosing the disease from chest X-ray images of the patients. We used a pretrained neural network and then utilised the transfer learning method for training on our dataset. We also used Nearest-Neighbour interpolation technique for data preprocessing and Adam Optimizer at the end for optimization. Our methodology achieved 96.37 % accuracy which was better than that obtained using other deep learning models like AlexNet, ResNet-50, VGG-16, and VGG-19. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： AlexNet Chest X-rays COVID-19 DenseNet-169 ResNet-50 VGG-16 VGG-19

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Federated Learning for Cybersecurity: A Promising Frontier 2

Federated Learning for Cybersecurity: A Promising Frontier

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2nd IEEE International Conference on IoT, Communication and Automation technology, ICICAT 2024

作者： Ananya, G. Bindu Madhavi, V. Murthy, Monisha Guruprasad, N. Global Academy of Technology Dept. of Computer Science and Engineering Karnataka Bengaluru India Buddha Institute of Technology Computer Science and Engineering Uttar Pradesh Gorakhpur India

ISBN: (纸本)9798350368109

In today's world, Artificial Intelligence and Machine Learning are transforming many industries, but they rely on huge amounts of data leading to privacy issues. A better alternative is Federated learning. Federated Learning involves training the model from multiple sources using the decentralization technique, meaning each device trains the model on its local data thereby reducing the strain on the single server. This is useful in cases where the data is too large to be sent and maintained on a central server or in handling privacy and security concerns. Cybersecurity is a prime domain where vulnerable attacks and breaches can be prevented using this technique. Other domains like finance, healthcare, IoT etc. have transformed the idea of data-driven decisions. This paper gives an insight into the concept of Federated learning and why it is a better choice. It includes the various algorithms that can be implemented in multiple applications depicted through a comparative analysis. The choice of algorithm depends upon its efficiency and the desired cybersecurity application. © 2024 IEEE.

关键词： Federated learning

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Efficient Collision-Free Motion Planning in Distributed Multi-Robot Systems 17

Efficient Collision-Free Motion Planning in Distributed Mult...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Sharma, Om Chakraborty, Nilotpal India Institute of Information Technology Department of Computer Science and Engineering Assam Guwahati India

ISBN: (纸本)9798331531195

In distributed multi-robot systems, ensuring collision-free motion planning is a complex challenge, especially in dynamic environments where multiple robots are operating simultaneously. Traditional path-planning algorithms are effective for individual robot navigation but struggle when dealing with the unpredictable movements of other robots in the system. To overcome these limitations, this research focuses on advanced motion planning techniques that ensure safe and efficient robot coordination. We model the overall motion planning problem into a two-dimensional grid and our objective is to minimize both the time and the energy taken by the robots to reach their destinations from their respective sources. We enhance standard path-finding algorithms such as A∗ search, along with Deep Reinforcement Learning (DRL) with suitable modifications to adapt to real-time changes. DRL allows robots to learn from their environment and make optimal decisions, improving their ability to avoid collisions and navigate efficiently in uncertain conditions. By integrating these algorithms and conducting a comparative analysis, we propose a framework that enables robots to dynamically adjust their paths, ensuring collision-free movement and optimal coordination. © 2025 IEEE.

关键词： Robot programming

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CNLP-NITS-PP at GenAI Detection Task 3: Cross-Domain Machine-Generated Text Detection Using DistilBERT Techniques 1

CNLP-NITS-PP at GenAI Detection Task 3: Cross-Domain Machine...

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1st Workshop on GenAI Content Detection, GenAIDetect 2025

作者： Teja, L.D.M.S. Sai Yadagiri, Annepaka Vardhan, M. Srikar Pakray, Partha Department of Computer Science & Engineering National Institute of Technology Assam Silchar788010 India

ISBN: (纸本)9798891762053

This paper presents a Cross-domain Machine-Generated Text Detection model developed for the COLING 2025 Workshop on Detecting AI-generated Content (DAIGenC). As large language models evolve, detecting machine-generated text becomes increasingly challenging, particularly in contexts like misinformation and academic integrity. While current detectors perform well on unseen data, they remain vulnerable to adversarial strategies, including paraphrasing, homoglyphs, misspellings, synonyms, whitespace manipulations, etc. We introduce a framework to address these adversarial tactics designed to bypass detection systems by adversarial training. Our team DistilBERT-NITS detector placed 7th in the Non-Adversarial Attacks category, and Adversarial-submission-3 achieved 17th in the Adversarial Attacks category. © 2025 International Conference on Computational Linguistics.

关键词： Adversarial machine learning

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Heart Disease Prediction Using ML and DL Approaches 1

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6th International Conference on Communications and Cyber-Physical Engineering, ICCCE 2023

作者： Anusha, K. Archana, Manne Janardhan, G. Department of Computer Science and Engineering Vignan Institute of Technology and Science Telangana Deshmukhi508284 India

ISBN: (数字)9789819971374

ISBN: (纸本)9789819971367

As known that heart disease (HD) is one among the most deadly diseases that hamper lives of many of the people across the globe. Life loss will be prevented when the heart disease is detected early. The cardiac hubs and hospitals are hugely depending on the ECG as a common tool to assess and diagnose the heat failure disease at early stages. Early detection of heart disease is one of the most vital issues in HCS (Health Care Services). This paper presents various Machine Learning (ML) and Deep Learning (DL) technologies based heart disease prediction systems in brief analysis. The SVM (Support Vector Machine), NB (Naïve Bayes), XGBoost, Enhanced Deep Convolutional Neutral Network (EDCNN), Deep Neutral Network (DNN) and K-Nearest Neighbour (KNN) are the used classifiers in this study. These classifiers use the data of heart disease patients from different datasets. Various performance parameters are used for the performance evaluation of individual classifiers and these are Accuracy, Precision, recall and f1-measure. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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Recommendation System using Emotion Detection 3

Recommendation System using Emotion Detection

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Susindra, S. Sweta, B. Keerthikha Shri, R. Brindha, K. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798331527549

The need for a personalized user experience brought recommendation systems to the forefront of digital innovation. However, traditional approaches tend to often forget human emotions, which represent a critical driver in decision making. The paper proposes a new framework in which the detection of emotions becomes part of the recommendation system through advanced facial expression analysis and sentiment evaluation. The paper discusses a hybrid architecture combining emotion recognition via CNN and a collaborative filtering technique for dynamic recommendations. The study outcome demonstrated an enormous improvement in user engagement and satisfaction. It further underlines the transformative potential of emotion-aware systems. © 2025 IEEE.

关键词： Emotion Recognition

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PulseSync BP: A Neural Network Framework for Non-Contact Blood Pressure Estimation

PulseSync BP: A Neural Network Framework for Non-Contact Blo...

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2024 International Conference on Advances in Data Engineering and Intelligent computing Systems, ADICS 2024

作者： Ghosh, Tanumay Singal, Omisha Lodh, Namit Suchitra, S. School Of Computing Srm Institute Of Science And Technology Department Of Data Science And Business Systems Tamil Nadu Kattankulathur India

ISBN: (纸本)9798350364828

Using a cutting-edge neural network framework, 'PulseSync BP' estimates blood pressure without contact. Using photoplethysmogram (PPG) signals and other physiological data, this novel model uses advanced signal processing. The suggested framework changes blood pressure monitoring by eliminating cuff-based measures. PulseSync BP's RNN architecture is carefully designed for optimal prediction accuracy. The algorithm effectively estimates blood pressure through extensive experimentation, demonstrating its potential for continuous healthcare applications. This framework's capacity to use various physiological cues shows its adaptability and versatility. PulseSync BP allows real-time, non-intrusive blood pressure monitoring, advancing individualized health tracking solutions. Cuffless blood pressure estimate is advanced here, demonstrating how artificial intelligence could improve healthcare accessibility and patient-centric monitoring. PulseSync BP and other revolutionary technologies can transform blood pressure monitoring in healthcare. © 2024 IEEE.

关键词： Blood pressure

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Multi-model Summarization on Extending T5 Transformer for Text, Audio, and Images 5th

Multi-model Summarization on Extending T5 Transformer for Te...

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5th EAI International Conference on Cognitive computing and Cyber Physical Systems, IC4S 2024

作者： Kiran, Kompella Bhargava Marumudi, ManojKumar Mudunuri, Aniketh Varma Kondaveeti, Sainadh Kondapalli, Rohith Pradeep, Kali I. Sai Baba, B. Ch. S. N. L. S. Department of Computer Science and Engineering Vishnu Institute of Technology Andhra Pradesh Bhimavaram India

ISBN: (纸本)9783031770807

This research proposes an integrated approach to text summarization, extending the T5 transformer model’s capabilities to include not only textual data but also audio and images. Leveraging the T5 transformer’s versatility in natural language processing, we fine-tune the model on a substantial corpus of text documents for effective text summarization. Additionally, we integrate audio and image processing modules into the system, employing speech-to-text conversion for audio and feature extraction techniques for images. Our unified summarization framework seamlessly handles multi-modal data, allowing users to input text, audio, or images, and generates cohesive and informative summaries. This innovative approach enhances the T5 transformer’s capabilities and addresses the growing demand for summarization in diverse data formats. © ICST institute for computer sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Natural language processing systems

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