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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Chen, Yingyu Yang, Ziyuan Xiong, Deng Zhang, Yi College of Computer Science Sichuan University Chengdu China Computer Science Department Stevens Institute of Technology HobokenNJ United States School of Cyber Science and Engineering Sichuan University Chengdu China

ISBN: (纸本)9798350368741

Multi-modality (MM) semi-supervised learning (SSL) based medical image segmentation has recently gained increasing attention due to its ability to utilize MM data and low dependency on labeled images. However, current MM-SSL methods face two major challenges: (1) Complex network designs make it difficult to apply these methods to scenarios involving more than two modalities. (2) The use of generative methods to leverage unlabeled data may not be reliable for SSL learning. To address these challenges, we propose Modality Modulation Dual Consistency, dubbed MM-DC. Specifically, we design a modality all-in-one network to process data from all modalities, with learnable plug-in Modality Modulation Layers (MML) to gradually modulate features from different modalities into a modality-invariant feature space, enabling unified segmentation. Additionally, we propose a dual-consistency strategy that enforces consistency at both the image and feature levels, which eliminates the requirements for generative methods. Extensive experiments demonstrate that MM-DC outperforms other state-of-the-art methods on open-source datasets with 2- and 4-modalities. The code is available. © 2025 IEEE.

关键词： Medical image segmentation multi-modality learning semi-supervised learning

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Neem Leaf Disease Detection based on Enhanced Leaky Capacitor-Fired Neuron (LCFN) Model 9

Neem Leaf Disease Detection based on Enhanced Leaky Capacito...

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9th International Conference on Science, technology, engineering and Mathematics, ICONSTEM 2024

作者： Kathiresan, Anandhi Shetty, Sahana Ramesh, S.M. Arun, T. Praveen Kumar, R. Glory, E. Karpagam College of Engineering Electronics &telecommunication Engineering Coimbatore India School of Cse FET-Jain University CSE-AIML And Cyber Security India Kpr Institute of Engineering And Technology Dept of Electronics And Communication Engineering Coimbatore Arasur India Christ The King Engineering College Department of Electronics And Communication Engineering Tamilnadu Coimbatore641104 India Hindusthan College of Engineering And Technology Department of Information Technology India Saveetha School of Engineering Simats Department of Biosciences India

ISBN: (纸本)9798350365092

In herbal products manufacturing field, Neem plays a vital role in India. Abiotic and biotic agents affects the leaves and growth of Neem tree. Viruses, fungus and bacteria are the well known biotic agents and humidity, temperature and water are the well know abiotic agents. High quality herbal product production and plant growth are reduced due to biotic active ingredients. Bacteria affects the neem leaves. Neem leaf disease symptoms can be easily identified by farmers. Laboratory tests like polymerase chain reaction (PCR) is also used for the identification and it is a costly one. In this work, Leaky Capacitor-Fired Neuron (LCFN) model is proposed to identify the neem diseases. One dimensional time sequences are extracted by LCFN from neem leaves. By combining pixels of neighborhood images, a continuous image sequence is created by LCFN. Optimized features from neem leaves are extracted using LCFN. An ensemble classifier is used to classify the leaves based on extracted features. Better performance are exhibited by LCFN as shown in experimental results. © 2024 IEEE.

关键词： Bacteria

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Another Lattice Attack Against ECDSA with the wNAF to Recover More Bits per Signature 1

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18th EAI International Conference on security and Privacy in Communication Networks, SecureComm 2022

作者： Ma, Ziqiang Li, Shuaigang Lin, Jingqiang Cai, Quanwei Fan, Shuqin Zhang, Fan Luo, Bo School of Information Engineering Ningxia University Yinchuan China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Science and Technology of China Hefei China State Key Laboratory of Cryptology Beijing China School of Cyber Science and Technology College of Computer Science and Technology Zhejiang University Hangzhou310027 China Key Laboratory of Blockchain and Cyberspace Governance of Zhejiang Province Hangzhou310027 China Department of Electrical Engineering and Computer Science University of Kansas Lawrence United States

ISBN: (数字)9783031255380

ISBN: (纸本)9783031255373

In the resource-constrained environment such as the Internet of Things, the windowed Non-Adjacent-Form (wNAF) representation is usually used to improve the calculation speed of the scalar multiplication of ECDSA. This paper presents a practical cache side channel attack on ECDSA implementations which use wNAF representation. Compared with existing works, our method exploits more information from the cache side channels, which is then efficiently used to construct lattice attacks in the ECDSA private key recovery. First, we additionally monitor the invert function which is related to the sign of the wNAF digits, and obtain a Double-Add-Invert chain through the Flush+Flush cache side channel. Then, we develop effective methods extracting 154.2 bits information of the ephemeral key per signature for 256-bit ECDSA from this chain, much more than the best known result which extracts 105.8 bits per signature. Finally, to efficiently use the extracted information, we convert the problem of recovering the private key to the Hidden Number Problem (HNP) and the Extended Hidden Number Problem (EHNP) respectively, which are solved by lattice reduction algorithms. We applied the attack on ECDSA with the secp256k1 curve in OpenSSL 1.1.0h. The experimental results show that only 3 signatures are enough to recover the private key. To the best of our knowledge, this work exploits the signs of the wNAF representation, along with the Double-Add chain against ECDSA, to recover the private key with the least number of signatures. © 2023, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Side channel attack

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FNOC: A Superficial Learning Methodology to Predict Digital Image Forgeries Using Fuzzy Enabled Neural Optimization Classifier

FNOC: A Superficial Learning Methodology to Predict Digital ...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Franklin Thamil Selvi, M.S. Malar, R. Jeya Vasuki, A. Vinotha Pavithra, N. Adkane, R.V. Odeh, M. Sathyabama Institute of Science and Technology Department of Mathematics Chennai600119 India Kings Engineering College Department of Cse Chennai India J.P. College of Engineering Department of Cse Tenkasi India Jeppiaar Institute of Technology Department of Information Technology Sunguvarchatram Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Zarqa University Faculty of Information Technology Department of Cyber Security Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

There has been an explosion in the production and dissemination of altered and false images in recent years, thanks to the widespread availability and user-friendliness of image editing programs. Many methods have been suggested for determining whether a image is genuine and, in certain instances, for identifying where the image has been changed or falsified. In order to assess the efficacy of the suggested system, this study introduces a novel deep learning model named Fuzzy enabled Neural Optimization Classifier (FNOC) and uses it in conjunction with the more traditional learning technique known as Support Vector Machine (SVM). In order to identify fakes, the suggested method employs a FNOC to glean characteristics based on residual noise from images. In order to differentiate between genuine and altered images, this method entails discovering the pattern of noise that the counterfeit process leaves behind. The capacity to handle undetected forgeries is a major advantage of employing a FNOC for image forgery detection. Traditional methods of detecting image forgeries can be challenged by more advanced technology. Yet, FNOCs can pick up on patterns that aren't established explicitly, so they can spot forgeries that weren't there before. The use of FNOCs to the identification of image forgeries has enormous promise in the fight against the issue of image manipulation. Digital images used in contexts ranging from medical reports to crime scene investigations might benefit from this technology's increased dependability and trustworthiness with more development. © 2024 IEEE.

关键词： FNOC Forgeries Detection Fuzzy Logic Image Forgery Neural Optimization Classifier Superficial Learning Support Vector Machine SVM

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A Novel Fast Video Fragment Matching Algorithm for Copyright Protection

A Novel Fast Video Fragment Matching Algorithm for Copyright...

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2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, 2023 International Conference on Pervasive Intelligence and Computing, 2023 International Conference on Cloud and Big Data Computing, 2023 International Conference on cyber Science and technology Congress, DASC/PiCom/CBDCom/cyberSciTech 2023

作者： Chen, Yan Yang, Zhongliao Dong, Chen Lin, Hui Chen, Zhenyi College of Computer and Data Science Fuzhou University Fuzhou350108 China College of Computer and Cyber Security Fujian Normal University Fuzhou350007 China University of South Florida Department of Computer Science and Engineering TampaFL33620 United States

ISBN: (纸本)9798350304602

The rise of the short-form video industry marks a revolutionary change in the digital media landscape. This has opened up new opportunities for content distribution and creation but has also brought about several challenges, one of which is the difficulty of effectively protecting video copyrights. In the industrial sector, for example, the industrial digital design, manufacturing, and production process often generate a large amount of video data. If these videos are stolen, copied, or tampered with by unauthorized third parties, infringement issues can arise. To effectively monitor and protect the video copyrights of users or enterprises, this paper proposes a lightweight video clip fast matching (KMP-FVFM) algorithm. The algorithm utilizes the idea of Knuth-Morris-Pratt (KMP) algorithm, which takes into account the inter-frame fragment relationship of the query video to improve the video matching speed. At the same time, the duplicate segment locations are pinpointed in the original video. This paper also implements a plain method for video fragment matching, and the experimental results show that KMP-FVFM can effectively improve the efficiency of video fragment matching. © 2023 IEEE.

关键词： Digital storage

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Application of Machine Learning Predicting Injuries in Traffic Accidents through the Application of Random Forest

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Recent Patents on engineering 2025年第2期19卷 108-121页

作者： Singh, Veer Bhadra Pratap Hemamalini, V. Muttipati, Appala Srinuvasu Raju, S. S. S. V. Gopala Shatil, Abu Hena Md Sharma, Abhishek Department of Cyber Security School of CSIT Symbiosis Skills and Professional University Kiwale Pune India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai India Bangalore India Department of Civil Engineering Rajiv Gandhi University of Knowledge Technologies Nuzvid Campus Andhra Pradesh 521202 India Dhaka Bangladesh Shri Vaishnav Vidyapeeth Vishwavidyalaya Madhya Pradesh Indore India

Background: The objective of this work is to analyze and predict the harmfulness in traffic accidents. Method: Several Random Forest statistical models are created, in which the predictable variable (response/ output variable) is the harmfulness of the accident, while the input variables are the various characteristics of the accident. In addition, these generated models will allow estimating the influence or importance of each of the factors studied (input variables) concerning the harmfulness of road accidents so that it is possible to know in which aspects it is more profitable to work with the objective of reducing mortality from traffic accidents [1]. Results: In this regard, the predictive algorithm has an out-of-bag error of 26.55% and an overall accuracy of 74.1%. Meanwhile, the local accuracy of the mildly wounded class is 66.1% compared to 81.4% of the dead and severely wounded class, which, as mentioned, has higher prediction reliability. Conclusion: Finally, it is worth noting the enormous usefulness of the Random Forest machine learning technique, which provides very useful information for possible research or studies that may be carried out. In the specific case of this patent work, through the use of the R programming language, which in turn presents a wide range of freely accessible utilities and functions with which it may be interesting working, it has generated results of great value for this area of activity, important to society as road safety. © 2025 Bentham Science Publishers.

关键词： Highway accidents

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Adapting to Evasive Tactics through Resilient Adversarial Machine Learning for Malware Detection

Adapting to Evasive Tactics through Resilient Adversarial Ma...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： G Bala Krishna G Sravan Kumar Mummadi Ramachandra K Sampurnima Pattem D Sandhya Rani Geeta Kakarla Department of Computer Science Engg. CVR College of Engineering Ibrahimpatnam Rangareddy India Department of Computer Science Engg. Cyber Security CVR College of Engineering Ibrahimpatnam Rangareddy India Department of Computer Science Engg. Sreenidhi Institute of Science and Technology Hyderabad India Department of Computer Science Engg. Cyber Security Sreenidhi Institute of Science and Technology Hyderabad India

This paper presents the Adaptive Resilience-based Convolutional Network (ARCNet), a sophisticated machine learning framework specifically designed to detect advanced, evasive malware. ARCNet combines convolutional and recurrent neural networks, making it highly adaptable to changing cyber threats. Its core components, the Adversarial Learning Module (ALM), Predictive Analysis Engine (PAE), and Dynamic Adaptation System (DAS), significantly boost its detection power. Tests using a synthetic dataset show ARCNet’s superiority over traditional models like the Support Vector Machine (SVM). It achieved 95.2% accuracy under normal conditions (compared to SVM’s 89.4%) and maintained 92.5% accuracy even during adversarial attacks (against SVM’s 80.3%). Notably, ARCNet’s detection rates improved from 78.5% to 86.7% in five months after integrating the DAS. These results confirm ARCNet’s efficiency in tackling complex malware challenges, contributing greatly to cybersecurity. The study underscores the importance of evolving and enhancing machine learning methods to keep pace with the rapidly changing landscape of cyber threats.

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Balancing accuracy and interpretability: AI-driven predictive modeling of construction schedule performance in India

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Asian Journal of Civil engineering 2025年第7期26卷 3083-3098页

作者： Maurya, Sudhanshu Lakkimsetty, Nageswara Rao Manjunath, T.C. Shukla, Anoop Kumar Sethy, Barada Prasad Behera, Rashmi Rekha Symbiosis Institute of Technology Symbiosis International (Deemed University) Nagpur Campus Nagpur India Department of Chemical and Petroleum Engineering School of Engineering American University of Ras Al Khaimah (AURAK) Ras al Khaimah United Arab Emirates Ras al Khaimah United Arab Emirates Department of Computer Science & Engineering Cyber Security & Blockchain Technology Rajarajeswari College of Engineering Bengaluru India Manipal School of Architecture and Planning Manipal Academy of Higher Education Manipal India NIST University Berhampur India Centurion University of Technology and Management Gajapati India

Construction schedule delays are a persistent challenge in India’s infrastructure sector, leading to significant cost overruns, stakeholder dissatisfaction, and inefficient resource utilization. In this context, the integration of Artificial Intelligence (AI) into project forecasting offers a promising solution. This study presents an AI-driven predictive modeling framework aimed at estimating construction schedule adherence, with a dual focus on predictive accuracy and model interpretability. Three machine learning models—multiple linear regression (MLR), artificial neural networks (ANN), and gradient boosting machine (GBM)—were developed and evaluated using real-world data from 150 construction projects across India. The models were assessed based on standard performance metrics (MAE, RMSE, R²), computational efficiency, and transparency. While MLR offered high interpretability, it lacked accuracy in handling nonlinear interactions. ANN showed high accuracy but limited explainability. GBM outperformed both, achieving the best predictive performance (R² = 0.94) and a favorable trade-off between accuracy and interpretability. Feature importance and sensitivity analyses identified equipment utilization, material availability, and labor productivity as key influencers. Case study validation further reinforced the practical value of GBM in diverse project settings. The findings advocate for the strategic adoption of interpretable AI tools like GBM in construction management to enhance scheduling precision and decision-making efficiency. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Artificial intelligence Construction schedule prediction Gradient boosting machine (GBM) Interpretability in machine learning Project performance analytics

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Evaluation of Three Different Operators for Object Highlighting in Medical RGB Images: Canny, Roberts, and LoG in Independent Color Spaces 25

Evaluation of Three Different Operators for Object Highlight...

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25th International Arab Conference on Information technology, ACIT 2024

作者： Ababneh, Jafar Abu-Jassar, Amer Abuowaida, Suhaila Liubchenko, Valentyn Lyashenko, Vyacheslav Zarqa University Faculty of Information Technology Cyber Security Department Zarqa Jordan University of Business and Technology College of Engineering Jeddah21448 Saudi Arabia Amman Arab University College of of Computer Sciences and Information Amman Jordan Al Al-Bayt University Faculty of Information Technology Department of Computer Science Al-Mafraq Jordan Kharkiv National University of Radio Electronics Department of Informatics Kharkiv Ukraine Kharkiv National University of Radio Electronics Department of Media Systems and Technology Kharkiv Ukraine

ISBN: (纸本)9798331540012

The research presents a comparative analysis of the selection of objects of interest in color medical cytology images. For these purposes, the individual edge detection operators studied are Canny, Roberts, and LoG. The comparison contrasts the standard method of assessing color photographs with a technique that incorporates supplementary information. Data from individual color channels, derived from the breakdown of a color image in RGB format, serve as supplementary information. The following individual cases for potential examination are emphasized: Image processing with a complete level of reuse of edge detection operators;with a partial level of reuse of edge detection operators. The results are displayed through an array of tables, graphs, and images. Metrics of visual perception of images are utilized for comparative analysis. Compared with traditional edge detection methods, the proposed approach yields better results, which can exceed the estimates of the classical approach by 20%. © 2024 IEEE.

关键词： Color photography

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Prompt Based Tri-Channel Graph Convolution Neural Network for Aspect Sentiment Triplet Extraction

Prompt Based Tri-Channel Graph Convolution Neural Network fo...

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2024 SIAM International Conference on Data Mining, SDM 2024

作者： Peng, Kun Jiang, Lei Peng, Hao Liu, Rui Yu, Zhengtao Ren, Jiaqian Hao, Zhifeng Philip, S.Yu. Institute of Information Engineering Chinese Academy of Sciences China School of Cyberspace Security University of Chinese Academy of Sciences China School of Cyber Science and Technology Beihang University China Faculty of Information Engineering and Automation Kunming University of Science and Technology China College of Science University of Shantou China University of Illinois at Chicago Department of Computer Science United States

ISBN: (纸本)9781611978032

Aspect Sentiment Triplet Extraction (ASTE) is an emerging task to extract a given sentence's triplets, which consist of aspects, opinions, and sentiments. Recent studies tend to address this task with a table-filling paradigm, wherein word relations are encoded in a two-dimensional table, and the process involves clarifying all the individual cells to extract triples. However, these studies ignore the deep interaction between neighbor cells, which we find quite helpful for accurate extraction. To this end, we propose a novel model for the ASTE task, called Prompt-based Tri-Channel Graph Convolution Neural Network (PT-GCN), which converts the relation table into a graph to explore more comprehensive relational information. Specifically, we treat the original table cells as nodes and utilize a prompt attention score computation module to determine the edges' weights. This enables us to construct a target-aware grid-like graph to enhance the overall extraction process. After that, a triple-channel convolution module is conducted to extract precise sentiment knowledge. Extensive experiments on the benchmark datasets show that our model achieves state-of-the-art performance. The code is available at https://***/KunPunCN/PT-GCN. Copyright © 2024 by SIAM.

关键词： Extraction

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