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A temporal-spatial feature coordination network for change detection of VHR images 3

A temporal-spatial feature coordination network for change d...

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3rd international conference on Geographic Information and Remote Sensing Technology, GIRST 2024

作者： Zeng, Yuxian Zhang, Xinxin College of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen University of Technology Xiamen361024 China

ISBN: (纸本)9781510689077

Deep learning techniques have significantly improved the accuracy and efficiency of change detection of very high resolution (VHR) images. However, many current models ignore the inherent heterogeneity of bi-temporal remote sensing images, thus making it difficult to distinguish between target changes and background variations. To address this problem, we propose a deep learning (DL) framework, namely temporal-spatial feature coordination network (TSFCN), to improve the accuracy of detection by enhancing the differentiation of change pixel features and improving the extraction and processing of change information. Further comparison and ablation experiments on the SVCD dataset show that the proposed model, compared with other models, improves the F1 scores and IoU by 2.09% and 3.81%, respectively. The results demonstrate that our model can achieve better accuracy and performance improvement. © 2025 SPIE.

关键词： Change detection

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A Succinct Study on the DDoS Attack Detection Methods 3rd

A Succinct Study on the DDoS Attack Detection Methods

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3rd international conference on machine Vision and Augmented intelligence, MAI 2023

作者： Shylla, Dapynhunlang Nandan, Durgesh Symbiosis International Symbiosis Institute of Technology Deemed University Pune411042 India

ISBN: (纸本)9789819743582

The growth of "distributed denial-of-service" attacks has become one of the most serious threats to Internet security in recent years. They are classified as serious and have the potential to disrupt network operations. In a short length of time, a "Distributed Denial of Service (DDoS)" assault can overwhelm a victim's computer and communication resources. Many defense systems have been evolved due to the seriousness of the situation. For years, network availability has been harmed by "denial-of-service" attacks, often known as DDoS. Despite multiple warnings, the attack continues to be quite successful. In this paper, we'll look at approaches for detecting "distributed denial of service" attacks in SDN, however, there are two novel DDoS detection algorithms to look for. The first method employs the degree of the attack in order to detect it. The "K-Nearest Neighbors algorithm" is used in the other technique to detect it. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Software defined networking

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23rd China National conference on Computational Linguistics, CCL 2024

23rd China National Conference on Computational Linguistics,...

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23rd China National conference on Computational Linguistics, CCL 2024

ISBN: (纸本)9789819783663

The proceedings contain 34 papers. The special focus in this conference is on Computational Linguistics. The topics include: Joint Similarity Guidance Hash Coding Based on Adaptive Weight Mixing Strategy For Cross-Modal Retrieval;generate-then-Revise: An Effective Synthetic Training Data Generation Framework for Event Detection;e3: Optimizing Language Model Training for Translation via Enhancing Efficiency and Effectiveness;multi-features Enhanced Multi-task Learning for Vietnamese Treebank Conversion;SimCLNMT: A Simple Contrastive Learning Method for Enhancing Neural machine Translation Quality;translate-and-Revise: Boosting Large Language Models for Constrained Translation;a Multi-task Biomedical Named Entity recognition Method Based on Data Augmentation;biomedical Event Causal Relation Extraction by Reasoning Optimal Entity Relation Path;joint Entity and Relation Extraction Based on Bidirectional Update and Long-Term Memory Gate Mechanism;MFE-NER: Multi-feature Fusion Embedding for Chinese Named Entity recognition;UDAA: An Unsupervised Domain Adaptation Adversarial Learning Framework for Zero-Resource Cross-Domain Named Entity recognition;triple-view Event Hierarchy Model for Biomedical Event Representation;dialectMoE: An End-to-End Multi-dialect Speech recognition Model with Mixture-of-Experts;distinguishing Neural Speech Synthesis Models Through Fingerprints in Speech Waveforms;knowledge Graph-Enhanced Recommendation with Box Embeddings;Readability-Guided Idiom-Aware Sentence Simplification (RISS) for Chinese;a Tone-Based Hierarchical Structure of Chinese Prosody;Linguistic Guidance for Sequence-to-Sequence AMR Parsing;Automatic Construction of the English Sentence pattern Structure Treebank for Chinese ESL Learners;cost-Efficient Crowdsourcing for Span-Based Sequence Labeling: Worker Selection and Data Augmentation;DLUE: Benchmarking Document Language Understanding;do Large Language Models Understand Conversational Implicature – A Case Study with a Chinese Sit

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Optimizing Blood Bank Management with Cloud-Hosted Long Short-Term Memory Models for Inventory Forecasting and Utilization 3

Optimizing Blood Bank Management with Cloud-Hosted Long Shor...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Devi, R. Aiyshwariya Sree Southry, S. Rajanarayanan, S. Sekaran, S. Chandra Sivaraman, Audithan Srinivasan, S. R.M.K. College of Engineering and Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Sona College of Technology Department of Electronics and Communication Engineering Tamil Nadu Salem India Department of Computer Science and Engineering Tamil Nadu Salem India P.S.V College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Krishnagiri India Department of Information Technology Jafferpur New Delhi India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509675

This research presents an innovative method for blood bank management using Cloud-based Long Short-Term Memory (LSTM) models for precise inventory forecasting and optimization. The objective of this research is to increase blood bank efficiency by utilizing LSTM models to accurately estimate demand, optimize inventory levels, and enhance overall resource utilization. The system uses cloud computing to provide real-time demand forecasting, minimizing blood waste and assuring a reliable supply for healthcare requirements. LSTM models use historical data to identify long-term trends and variations in blood demand, facilitating accurate inventory forecasting. This predictive system optimizes the allocation process, resulting in improve resource utilization and increased operational efficiency. In compared to traditional techniques, the proposed system provides a more scalable and flexible solution by using cloud technology to provide continuous monitoring and dynamic modifications of inventory levels. It provides a robust framework for management of blood supply and enhancing patient care for improved resource management. Future enhancements may include broadening the system to incorporate real-time multi-source data integration and investigating hybrid machine learning models to better optimize demand forecasts. © 2025 IEEE.

关键词： Efficiency

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CNN-KNN Model for Assessing Hair Health in Telogen Effluvium 3

CNN-KNN Model for Assessing Hair Health in Telogen Effluvium

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Kumar, Gotte Ranjith Shubham Banerjee, Deepak School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331527549

The abstract provides a concise overview of a study that focuses on predicting hair breakage levels using a CNN-KNN hybrid model. The study evaluates the model's performance across five Breakage Degrees (BDs) by utilizing precision, recall, and F1-score metrics. Among these degrees, BD2 stands out with precision, recall, and F1-score values of 94.20%, indicating the model's ability to accurately classify hair damage levels between 21% and 40%. The support values, which indicate the number and proportion of instances in each BD, demonstrate a well-balanced distribution across all categories, ensuring the model's robust training. Researchers used a dataset of 10,050 images to reach an impressive 94.02% accuracy in predicting hair breakage levels caused by Telogen Effluvium in the research. Moreover, the model achieves an impressive overall accuracy of 98%, showcasing its effectiveness in distinguishing between various degrees of hair breakage severity. This high accuracy level highlights the model's potential application in clinical settings for dermatological diagnostics and personalized treatment planning in hair care management. The abstract underscores the CNN-KNN hybrid approach as a promising methodology for automated hair health assessment, providing healthcare professionals with a dependable tool to improve diagnostic accuracy and treatment efficacy. By incorporating machine learning techniques, specifically convolutional and k-nearest neighbor algorithms, the study contributes to the advancement of computational methods in dermatology and cosmetology. The results suggest practical implications for enhancing patient care through early detection and intervention of hair-related conditions, ultimately supporting better health outcomes and quality of life for individuals affected by hair breakage concerns. © 2025 IEEE.

关键词： Nearest neighbor search

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Anomaly-Based Intrusion Detection Systems by using machine Learning based Stacking Ensemble Model 3

Anomaly-Based Intrusion Detection Systems by using Machine L...

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3rd international conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Buvaneswari, P.R. Madhura, G.K. Alasady, Heba Alagarraja, K. Soni, Mukesh School of Electrical and Electronics Engineering Vit Bhopal University Bhopal India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq New Prince Shri Bhavani College of Engineering and Technology Department of Mechanical Engineering Chennai India University Centre for Research & Development Chandigarh University Department of Computer Science and Engineering Mohali India

ISBN: (纸本)9798331508456

In recent years, network intrusion refers to unauthorized access to systems or networks by external attackers or insiders aimed at stealing information or deploy malicious software. Traditional approaches for anomaly-based intrusion detection systems have faced several challenges which include high computational overhead and limited capability in detecting zero-day attacks due to reliance on predefined attack signatures or handcrafted features. To address these limitations, this research proposes a machine Learning-based Stacking Ensemble Model (ML-SEM) for anomaly-based intrusion detection, effectively reducing computational overhead and enhancing detection of previously unseen attacks. Initially, data is collected from University of Nevada, Reno Intrusion Detection Dataset (UNR-IDD) which consists of network traffic data and various types of attacks. Then, the collected data is preprocessed by using standard scaler normalization which ensures that all features have a mean of zero and a standard deviation of one. After that, the features are selected using Principal Component Analysis (PCA) which reduces dimensionality and enhances model efficiency by eliminating redundant and less significant features. Finally, anomaly-based intrusion detection is done by using proposed ML-SEM. The proposed ML-SEM achieved better results in terms of accuracy (98.6%), precision (95.5%) and recall (98.3%) when compared with existing Random Forest (RF). © 2025 IEEE.

关键词： Dimensionality reduction

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Role of Explainable Artificial intelligence Approaches in Cybersecurity 3rd

Role of Explainable Artificial Intelligence Approaches in Cy...

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3rd international conference on machine Vision and Augmented intelligence, MAI 2023

作者： Bawane, Nishant Nandan, Durgesh Pune411042 India School of CS & AI S R University Warangal India

ISBN: (纸本)9789819743582

The increasing use of Deep Learning (DL) algorithms in cybersecurity, for example in identifying dangerous source code or Android malware, has made Artificial intelligence (AI) and machine Learning (ML) more crucial. AI-based cybersecurity solutions, like those in other DL application domains like Computer Vision (CV) and Natural Language Processing (NLP), are not able to clearly communicate to people the implications (which might range from detection and prediction to reasoning and decision-making). Explainable AI, or XAI, has emerged as a key area of research to address the related problems of providing human users with an interpretable or comprehensible AI model. Security operators in the cybersecurity industry are overworked due to the tens of thousands of security notifications that are received every day, the majority of which are false positives. They might be better able to recognize potential threats and have less alert weariness thanks to XAI. We do a thorough literature assessment on the relationship between cybersecurity and XAI. The security of XAI and its applications to cybersecurity (such as intrusion detection and malware classification) are especially examined from two angles in the current literature (e.g., attacks on XAI pipelines, and potential countermeasures). To characterize the security of XAI, we make use of a variety of security traits that have been researched in the literature. We list open issues that have not been resolved or have been addressed properly while also highlighting potential study opportunities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cybersecurity

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Fault Detection Using ReliefF Algorithm with Canonical Correlation Analysis Based on Improved Support Vector machine 3

Fault Detection Using ReliefF Algorithm with Canonical Corre...

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3rd international conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Patchamatla, Pavan Srikanth Subbaraju Ramesh, M. Abas, Hayder Muhamed Pareek, Piyush Kumar Sundaram, N. Kalyana RKInfo-Tech Llc India MKumarasamy College of Engineering Department of Electrical and Electronics Engineering Namakkal India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Al Diwaniyah Iraq Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India New Prince Shri Bhavani College of Engineering and Technology Department of Information Technology Chennai India

ISBN: (纸本)9798331508456

Recently, the increasing time-complexity of electronic circuit has made fault detection and diagnosis a challenging task. The existing Deep Belief Networks (DBN) has been used for fault detection and circuit diagnosis however they possess a significant challenge due to their complex behavior and sensitivity to component variations. Hence, this research proposes ReliefF algorithm to optimize Canonical Correlation Analysis combined with Improved Support Vector machine (RCCA-ISVM) for accurate nonlinear circuit diagnosis and fault detection by learning patterns as well as anomalies in circuit behavior. Initially, the input data is collected from the Sensor Fault Detection dataset. Further, fault characteristics are extracted using the Wavelet Packet Transform (WPT) from the time-domain, statistical and frequency-domain features. Canonical Correlation Analysis (CCA) algorithm is applied to enhance the correlation between the features based on their respective weights, improving the relationship between the selected features as informative for fault diagnosis. Then the fused features undergo dimensionality reduction using Principal Component Analysis (PCA). Finally, an ISVM classifier is employed to perform the fault diagnosis based on the reduced set of features. From the results, the proposed achieved outstanding results in terms of accuracy (99.30%) when compared with the existing RCCA-SVM. © 2025 IEEE.

关键词： Wavelet analysis

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Tokenization's Potential for Santhali Language Processing 3rd

Tokenization's Potential for Santhali Language Processing

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3rd international conference on machine Vision and Augmented intelligence, MAI 2023

作者： Ohm, Anand Kumar Panday, Aman Kumar Singh, Koushlendra Kumar Machine Vision and Intelligence Lab Department of CSE National Institute of Technology Jamshedpur Jharkhand India

ISBN: (纸本)9789819743582

Santhali is one of the popular local languages and is mainly spoken by tribal people of States like Jharkhand, Odisha, and West Bengal, as well as a few other parts of the country. Tokenization is one of the major challenges in Natural Language Processing (NLP). Tokenization reduces the length of sentences and paragraphs. There are various methods in use to tokenize different languages depending on the writing and structure of the language. Tokenization is important in many tasks like machine translation, text summarization, chatbot creation, and spam detection. In Natural Language Processing (NLP) most of the Indian spoken languages have high resource availability and have improved technologically in various fields. Still, a low-resource language like SANTHALI does not make much of its existence in technology. The present work is based on the tokenization of Santhali. All available tokenizers to tokenize in the Santhali language have been explored. Ol-Chiki font has been used for the present study. A detailed comparison of tokenization has been represented in the manuscript in the present study. It has been observed that for Santhali a writing system is alphabetic and most of the characters are made of real-world symbols. spaCy model performs better than other available tokenizer models based on the mechanism of the Santhali language. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Natural language processing systems

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Auction-Based Optimization with machine Learning for Cyber Threat Detection and Classification Model 3rd

Auction-Based Optimization with Machine Learning for Cyber T...

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3rd international conference on Computing and Communication Networks, ICCCN 2023

作者： Alqahtani, Hamed Department of Information Systems College of Computer Science Center of Artificial Intelligence Unit of Cybersecurity King Khalid University Abha Saudi Arabia

ISBN: (纸本)9789819726707

The detection of cyber threats and cybersecurity can be benefitted greatly from the advances in artificial intelligence (AI). Cyber threat detection is the main focus of cybersecurity. It has used various tools and techniques to find unauthorized access to systems, attacks, effective security incidents or applications, and networks. Owing to data flow presence over networks and extensive devices, the possibility of intrusion detection and cyberattacks has increased. Monitorization of this immense volume of data traffic is found to be tough, though machine learning (ML) techniques effectively support this task. This study develops an auction-based optimization with machine learning for cyber threat detection and classification (ABOML-CTDC) technique. The presented ABOML-CTDC technique concentrates on the identification and classification of cyber threats in accomplishing cybersecurity. The proposed ABOML-CTDC technique makes use of the ABO-based feature subset selection (ABO-FSS) technique to elect optimal features. For cyber threat detection, the ABOML-CTDC technique utilizes a variational autoencoder (VAE) model. Finally, the moth flame optimization (MFO) algorithm is exploited for the parameter selection of the VAE method del to improve the detection rate. The experimental validation of the ABOML-CTDC approach is tested on benchmark datasets and the results pointed out the promising results on detecting threats over recent state-of-the-art methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cyber attacks

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