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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Valarmathi, B. Gupta, N. Srinivasa Karthick, V. Chellatamilan, T. Santhi, K. Chalicheemala, Dhanush Department of Software and Systems Engineering School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Manufacturing School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Assistant Systems Engineer Tata Consultancy Services Chennai India Department of Analytics School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Information Technology School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

A key method used in the study of Natural Language Processing (NLP) is sentiment analysis, or emotion analysis, plays a pivotal role in text analysis. Its primary function is to discern and categorize the underlying emotions within textual content, classifying them as positive, neutral, or negative sentiments. A variety of textual inputs can be used with this process, including entire documents, individual sentences, and more. Understanding the sentiments expressed by individuals is of paramount importance to organizations in today's digital age. Clients and customers now have the means to express their thoughts and emotions with greater ease and immediacy than ever before. This wealth of sentiment data can offer valuable insights that organizations can leverage to enhance their products, services, and overall customer experience. The present study focuses on the sentiment analysis of Twitter data related to the COVID-19 pandemic, employing the Long Short-Term Memory (LSTM) algorithm. To improve the accuracy of sentiment analysis, a pre-processing procedure is introduced, which involves the use of the Neat Text module in Python to clean the tweets. In this research endeavor, a dataset comprising 1,79,107 COVID-19-related tweets is subjected to sentiment analysis using the proposed pre-processing module and LSTM. The results demonstrate an impressive accuracy rate of 96% in accurately determining the sentiments conveyed in these COVID-19 tweets. In contrast, the existing algorithm, based on Artificial Neural Network (ANN), achieved a significantly lower accuracy rate of 76%. This research not only showcases the effectiveness of LSTM and pre-processing in sentiment analysis but also highlights the significance of sentiment analysis in gaining valuable insights from vast amounts of textual data, especially when important events like the COVID-19 pandemic are involved. © 2024 Elsevier B.V.. All rights reserved.

关键词： COVID-19

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Integrated Leaf Disease Recognition Across Diverse Crops through Transfer Learning 5

Integrated Leaf Disease Recognition Across Diverse Crops thr...

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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Tambe, Utkarsh Yashwant Shanthini, A. Hsiung, Pao-Ann Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chennai603203 India Department of Computer Science and Information Engineering National Chung Cheng University Chiayi621301 Taiwan

Global food security is seriously threatened by plant diseases and can cause severe economic losses to farmers. Automated detection of plant diseases using computer vision and machine learning techniques has become a popular research area due to its potential to provide faster and more accurate results than traditional methods. In this research, we propose a plant disease detection model based on transfer learning using the MobileNetV2. We evaluate the suggested method on a dataset consisting of 38 various kinds of diseases across 14 different plants. Our experimental findings indicate that the proposed method has a typical accuracy of 91.98% and outperforms additional cutting-edge CNN models for plant disease detection. The experimental findings support the suggested approach's validity and show that it effectively detects plant diseases. We also have put forth the evaluation metrics to investigate the model. The suggested method has potential applications in real-world scenarios and can help farmers detect diseases in their crops at an early stage, allowing them to take timely action to minimize crop losses. © 2024 The Authors. Published by Elsevier B.V.

关键词： Losses

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Alzheimer Disease Detection Studies: Perspective on Multi-Modal Data

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Yearbook of Medical Informatics 2025年第1期33卷 266-276页

作者： Dehghani, Farzaneh Derafshi, Reihaneh Lin, Joanna Bayat, Sayeh Bento, Mariana Biomedical Engineering Department University of Calgary Canada Computer Science Department University of Calgary Canada Geomatics Engineering Department University of Calgary Canada Hotchkiss Brain Institute Cumming School of Medicine University of Calgary Canada Electrical and Software Engineering Department University of Calgary Canada

OBJECTIVES: Alzheimer's Disease (AD) is one of the most common neurodegenerative diseases, resulting in progressive cognitive decline, and so accurate and timely AD diagnosis is of critical importance. To this end, various medical technologies and computer-aided diagnosis (CAD), ranging from biosensors and raw signals to medical imaging, have been used to provide information about the state of AD. In this survey, we aim to provide a review on CAD systems for automated AD detection, focusing on different data types: namely, signals and sensors, medical imaging, and electronic medical records (EMR). METHODS: We explored the literature on automated AD detection from 2022-2023. Specifically, we focused on various data resources and reviewed several preprocessing and learning methodologies applied to each data type, as well as evaluation metrics for model performance evaluation. Further, we focused on challenges, future perspectives, and recommendations regarding automated AD diagnosis. RESULTS: Compared to other modalities, medical imaging was the most common data type. The prominent modality was Magnetic Resonance Imaging (MRI). In contrast, studies based on EMR data type were marginal because EMR is mostly used for AD prediction rather than detection. Several challenges were identified: data scarcity and bias, imbalanced datasets, missing information, anonymization, lack of standardization, and explainability. CONCLUSION: Despite recent developments in automated AD detection, improving the trustworthiness and performance of these models, and combining different data types will improve usability and reliability of CAD tools for early AD detection in the clinical practice. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://***/licenses/by/4.0/).

关键词： Alzheimer’s Disease computer-aided Diagnosis Electronic Medical Records Medical Imaging Signals and Sensors

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OSN Rumor Control Model Based on Community Immunization 22

OSN Rumor Control Model Based on Community Immunization

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Dong, Ming Sun, Zhiqiang Tan, Zhenhua Zhang, Bin Northeastern University School of Computer Science and Engineering Shenyang China Northeastern University Software College Shenyang China Northeastern University National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang China Ministry of Education China

ISBN: (纸本)9798331509712

This paper proposes a rumor control model based on community immunization. Based on the community division and the trust network inference algorithm, the model redefines the standard to measure the importance of nodes in the network. First, the model uses the Louvain clustering algorithm based on the Ochiai coefficient to discover the network community and then presents the trust network inference algorithm. By analyzing the key factors that affect trust transfer between nodes, the trust evaluation between unfamiliar nodes is inferred, and important nodes with a high degree of trust in the network community are calculated. Finally, combined with the characteristics of inner degree and outer degree centrality of nodes in the network community, five types of important nodes in the network are screened out. To avoid repeated selection of nodes, this paper identifies a group of key nodes in the network community for local immunization by means of deduplication and taking intersection, so as to realize effective control of rumors in the network. © 2024 IEEE.

关键词： Clustering algorithms

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Self-Guided Autoencoders for Unsupervised Change Detection in Heterogeneous Remote Sensing Images

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2458-2471页

作者： Shi, Jiao Wu, Tiancheng Kai Qin, Alex Lei, Yu Jeon, Gwanggil Northwestern Polytechnical University School of Electronics and Information Xi'an710129 China Northwestern Polytechnical University in Shenzhen Research and Development Institute Shenzhen518057 China Chongqing Innovation Center of Northwestern Polytechnical University Chongqing401135 China Swinburne University of Technology Department of Computer Science and Software Engineering HawthornVIC3122 Australia Incheon National University Department of Embedded Systems Engineering Incheon22012 Korea Republic of

To address the problem of enormous differences in two heterogeneous images, the traditional unsupervised frameworks are most normally realized by converting two images into a common domain with various auxiliary strategies, such as transformation and alignment, which requires extensive calculation and has difficulty balancing the training tasks. To achieve a concise framework, this article proposes self-guided autoencoders (SGAE) for unsupervised change detection (CD) in heterogeneous remote sensing images (RSIs). Unlike traditional methods that aim to narrow the differences of heterogeneous images to highlight the changed information, SGAE forces the flow of identification in formation generated in unlabeled data through self-guided iterations. First, initial unsupervised networks output an elementary change map that will be screened to obtain reliable pseudolabels. The selected pseudolabeled samples will be used as the input of a supervised network to obtain another change map. Then, multiple change maps will be fused to refine the confidence of pseudolabels again, obtaining new fused pseudolabeled samples for the self-guided network, which will be trained with pseudolabeled samples and unlabeled samples. Finally, the above operations will be repeated to continuously optimize the net, which helps itself to extract the discriminative features for classification in self-guided iterations. Experiments compared with several algorithms on four datasets demonstrate the effectiveness and robustness of our method, which can help unsupervised models improve discriminative feature extraction and classification performance with a more flexible learning method. © 2020 IEEE.

关键词： Feature extraction

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A Robust Key Exchange and Tamper-Resistant Protocol for HAN and NAN Networks in Smart Grids

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IEEE Internet of Things Journal 2025年第12期12卷 21394-21403页

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Shamshad, Salman Das, Ashok Kumar Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University College of Informatics Department of Computer Science and Engineering Anam-ro Seoul145 Korea Republic of Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China

Smart Grids (SG) rely on Home Area Networks (HAN) and Neighborhood Area Networks (NAN) to ensure efficient power distribution, real-time monitoring, and seamless communication between smart devices. Despite these advantages, the use of public communication channels in HAN and NAN networks introduces critical challenges, such as vulnerability to impersonation, physical tampering, and scalability issues in resource-constrained environments. These issues compromise the stability, reliability, and security of SG environments. Existing protocols often fail to adequately address these challenges, particularly in ensuring resistance to physical tampering of supply lines and impersonation attacks. Additionally, they struggle to minimize the computation, communication, and energy costs associated with securing a resource-constrained SG environment. Therefore, we propose a robust key exchange and tamper-resistant protocol for HAN and NAN networks to address these limitations. The proposed protocol leverages the Physical Unclonable Function (PUF) mechanism to offer physical tampering resistance to smart meters. We validate our protocol formally using the Random or Real (RoR) model, which confirms its security robustness. Additionally, our informal security analysis highlights the protocol's resilience against impersonation, physical tampering attacks, etc. We analyze and compare the performance of the proposed protocol, which further demonstrates our protocol's effectiveness and security compared to competing protocols. Moreover, the proposed protocol achieves resource efficiency with 45.99% and 58.85% substantial reductions in computation overhead and energy overhead, respectively, compared to competing protocols. These results showcase the protocol's enhanced security and practicality for resource-constrained SG environments. © 2014 IEEE.

关键词： Authentication Protocol

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Oct Image Blind Despeckling Based on Gradient Guided Filter with Speckle Statistical Prior 48

Oct Image Blind Despeckling Based on Gradient Guided Filter ...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Li, Sanqian Xiong, Muxing Yang, Bing Zhang, Xiaoqing Higashita, Risa Liu, Jiang Southern University of Science and Technology Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Shenzhen China Tomey Corporation Japan

ISBN: (纸本)9781728163277

Optical coherence tomography (OCT) imaging technique has been widely used for ocular disease diagnosis. However, speckles occur in OCT images due to the property of coherent imaging, inevitably affecting the visual quality and clinical analysis. To alleviate this problem, we propose a novel gradient-guided speckle image filtering method (GGSF) with structure enhancement for directly removing speckles in OCT images. Specifically, the multiplicative characteristic of speckle noise is incorporated into the guided filtering processing for modeling raw OCT images. To avoid getting trapped in image distortions, we further employ gradient regularization to integrate the structure prior information into the guided speckle image filtering procedure. Additionally, we introduce the statistical property of speckle noise obeying a gamma distribution into the least square method solver for the resulting non-convex GGSF model. Experimental results on the AS-OCT dataset demonstrate the effectiveness of GGSF for OCT image despeckling compared with competitive methods. Furthermore, we validate the benefits of GGSF for subsequent clinical analysis with the CM-OCT dataset. © 2023 IEEE.

关键词： Optical tomography

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A Hybrid Blockchain Framework for Secure, Scalable, and Energy-Efficient Digital Identity Management with Token-based Selective Disclosure 3

A Hybrid Blockchain Framework for Secure, Scalable, and Ener...

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3rd International Conference on Electronics and Renewable systems, ICEARS 2025

作者： Velmurugan, M. Shinzeer, C.K. Maya, G. Ramya, R. Kanimozhi, R. Lathif, Syed Ismail Abdul Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science And Technology Department Of Computer Science & Engineering Chennai600 062 India Lovely Professional University Department of School of Computer Applications Punjab Phagwara144411 India Srm Institute of Science and Technology Faculty of Management Kattankulathur603 203 India K.Ramakrishnan College of Engineering Department of Electronics and Communication Engineering Samayapuram621112 India Muthayammal Engineering College Computer Science and Engineering Rasipuram637408 India Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai603203 India

ISBN: (纸本)9798331509675

Data breaches, identity theft, and the lack of user control within traditional digital identity management systems, it begs a more secure and decentralized alternative. In this study we propose a blockchain based digital identity management framework that implements privacy, security and transparency by employing distributed ledger technology. To provide tamper proof identity storage and automated verification, the proposed model uses cryptographic hashing and smart contracts. The use of a novel token-based access mechanism enables users to safely share their identity data with third parties, while keeping sensitive information out of sight. Security metrics are enhanced by 30% for data integrity with 45% reduction in unauthorized access attempts all through simulations. The framework also achieves improved efficiency reducing identity verification time by 40 percent compared to traditional systems. It offers a promising approach to address important vulnerabilities in centralized identity management in line with the development of marketable and user-friendly digital identity solutions. © 2025 IEEE.

关键词： Data integrity

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I $^{2}$ RNN: An Incremental and Interpretable Recurrent Neural Network for Encrypted Traffic Classification

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IEEE Transactions on Dependable and Secure Computing 2023年 1-14页

作者： Song, Zhuoxue Zhao, Ziming Zhang, Fan Xiong, Gang Cheng, Guang Zhao, Xinjie Guo, Shize Chen, Binbin College of Computer Science and Technology Zhejiang University Hangzhou China Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Science and Engineering Southeast University Nanjing China Information Systems Technology and Design Singapore University of Technology and Design Singapore

Traffic classification occupies a significant role in cybersecurity and network management. The widespread of encryption transmission protocols such as SSL/TLS has led to the dominance of deep learning based approaches. In cybersecurity, strong adversaries often complicate their strategies by constantly developing emerging attacks. Meanwhile, security practitioners desire to grasp the reasons for inference results. However, existing deep learning approaches lack efficient adaptation for incremental traffic types and often have less interpretability. In this paper, we propose I $^{2}$ RNN, an Incremental and Interpretable Recurrent Neural Network for encrypted traffic classification. The I $^{2}$ RNN proposes a novel propagation process to extract the sequence fingerprints from sessions with local robustness. Meanwhile, this proposal provides interpretability including time-series feature attribution and inter-class similarity portrait. Moreover, we design I $^{2}$ RNN in an incremental manner to adapt to emerging traffic types. The I $^{2}$ RNN only needs to train an additional set of parameters for the newly added traffic type rather than retraining the whole model with the entire dataset. Extensive experimental results show that our I $^{2}$ RNN can achieve remarkable performance in traffic classification, incremental learning, and model interpretability. Compared with other local interpretability methods, our I $^{2}$ RNN exhibits excellent stability, robustness, and effectiveness in the interpretation of network traffic data. IEEE

关键词： Cryptography

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Middle School Architect: Programmatic Modeling and Construction via a 3D Graphics Platform in Elm 2

Middle School Architect: Programmatic Modeling and Construct...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Parcha, Anurag Mishra, Sanjana Anand, Christopher Kumar Pushparaj, Jagalingam Simon Fraser University BurnabyBC Canada McMaster University Computing and Software HamiltonON Canada School of Computer Science and Engineering Vellore Institute of Technology TN Vellore India

ISBN: (纸本)9798350328202

Today, school students are first introduced to geom-etry and advanced mathematical concepts by plotting a graph in 2D and they struggle with 3D concepts since current technologies are either too difficult to grasp or too simple with only drag-drop functionalities disconnected from coordinates. This article presents a 3D graphics library and an interactive, educational system for visualization of a 3D environment where planks of Cross Laminated Timber (CLT) can be manipulated by geometric and mathematical functions to produce a 3D building. Throughout, there is a focus on functions, 3D geometry and other underlying mathematics, and links are made with the world of computing, specifically computer graphics, to increase students' interest and confidence. Activities built on this library simultaneously meet multiple curricular objectives: mathematics (including 3D geometry), programming (in Elm, a web program-ming language used commercially), and sustainable development (using a negative embodied carbon building material). © 2024 IEEE.

关键词： Sustainable development

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