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共 3588 条记录，以下是121-130 订阅

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Handwritten Character Recognition with Convolution Neural Network 6th

Handwritten Character Recognition with Convolution Neural Ne...

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6th international conference on intelligent Communication, Control and Devices, ICICCD 2024

作者： Soni, Vishnu Kant Chouksey, Praveen Tandan, S.R. Miri, Rohit Nema, Neetesh Kumar Naik, Muskan Dr. C. V. Raman University C.G. Bilaspur India Department of Computer Science and Engineering Dr. C. V. Raman University C.G. Bilaspur India Department of Computer Science Government R. V. R. S. Kanya Mahavidyalaya C.G. Kawardha India Department of Computer Science and Engineering UTD CSVTU Bhilai C.G. Bhilai India Department of Computer Science and Engineering Lakhmi Chand Institute of Technology C.G. Bilaspur India

ISBN: (纸本)9789819783281

In the realm of pattern recognition, the automated detection of handwritten text or symbols poses intricate challenges in the field of handwriting recognition. the paper introduces a novel approach that considers the batch_size to calculate the accuracy of the machine learning method to attain precise and effective recognition of handwritten characters. the suggested technique employs artificial neural networks, particularly convolutional neural networks (CNNs), to train a model skilled of precisely identifying and categorizing handwritten characters. Experimental result indicates that the proposed model achieves a notable accuracy percentage of 99.10% on a widely used data set (NIST). the attained high level of accuracy accentuates the effectiveness and viability of machine learning algorithms in research works related to handwriting recognition. the achievement also paves the way for numerous potential applications in fields like document analysis, optical character recognition, and interfaces based on handwriting. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Decision Systems for Adaptive Cybersecurity Incident Response 12th

Decision Systems for Adaptive Cybersecurity Incident Respons...

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12th international conference on Frontiers of intelligent Computing: theory and Applications, FICTA 2024

作者： Goyal, Dinesh Gandhi, Yatin Dongre, Deepika Shailesh Bhagat, Gorakh P. Pawar, Rohit Department of Computer Engineering Poornima Institute of Engineering and Technology Rajasthan Jaipur India Competent Softwares Maharashtra Pune India Artificial Intelligence and Data Science VIIT Pune India Electronics and Communication Engineering Arya College of Engineering Jaipur India Department of Mechanical Engineering Dr. D. Y. Patil Institute of Technology Pimpri Maharashtra Pune India Shri Ramdeobaba College of Engineering and Management Maharashtra Nagpur India

ISBN: (纸本)9789819601462

Traditional methods of responding to cybersecurity incidents often use set playbooks or routines that don’t change, which might not work well with the constantly changing nature of modern cyber dangers. the idea behind this paper is to create and use decision systems that are specifically designed for adapting how to handle hacking incidents. Advanced artificial intelligence (AI) methods, such as machine learning and natural language processing, are used by these decision systems to analyze security events in real time and help people make quick, well-informed decisions. these systems improve the speed and efficiency of hacking reaction by constantly learning from past events and responding to new threats. Automatic danger recognition, risk assessment, and reaction planning are important parts of the suggested decision systems. automated systems that look for threats use AI algorithms to look for signs of bad behavior in network traffic, system logs, and other relevant data sources. the risk assessment tools look at the dangers and how bad they could be, considering things like how sensitive the data is, how important the system is, and the rules that need to be followed. Decision systems actively plan responses based on danger recognition and risk assessment results. these responses may include separating affected systems, blocking suspicious IP addresses, or sending events to human experts for further study. Over time, these systems get better at making decisions by using adaptive learning methods and constant feedback loops. this makes it easier for them to spot and stop new cyber threats. the creating and using decision systems for adaptive cybersecurity incident reaction is a big step toward making organizations’ defenses more resilient and effective in the face of changing cyber dangers. By using AI and machine learning, these systems stop threats before they happen, giving cybersecurity teams the tools they need to stay ahead of attackers in the ongoing battl

关键词： Cyber attacks

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HyPRETo: Hybrid Pre-trained Ontology Approach for Contextual Relation Classification on Mosquito Vector Biocontrol Agents 8th

HyPRETo: Hybrid Pre-trained Ontology Approach for Contextual...

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8th IFIP TC 12 international conference on Computer, Communication and Signal Processing, ICCCSP 2024

作者： Jeyakodi, G. Bala, P. Shanthi Department of Computer Science School of Engineering and Technology Pondicherry University Puducherry India

ISBN: (纸本)9783031736162

Pre-trained Language Model facilitates contextual relation classification by capturing contextual information, addressing word ambiguity, encoding global sentence context, enabling transfer learning, handling out-of-vocabulary words, and improving performance with limited labelled data. Existing pre-training approaches suffer in size, bias, interpretability, generalization, and the lack of domain specificity. To address this, HyPRETo, the hybrid model that combines the strength of token replacement and dynamic masking is proposed to achieve upgraded performance to increase classification accuracy. the Mosquito Vector Biocontrol Agents data is used for implementing the model for a contextual relation classification task. HyPRETo uses ontology to provide structured knowledge. HyPRETo is pre-trained by ELECTRA and fine-tuned by RoBERTa models. Feedforward and softmax activation function is used for classification. the Natural Language Processing technique and SQL database are used to develop an automated question-answering system. the HyPRETo was evaluated with state-of-art models and achieved 98.42% accuracy. As a contribution, the manually annotated input dataset on the mosquito vector control agent is prepared for the classification task. Subsequently, the enhanced model is developed. the interface for an automated question-answering system for mosquito vector biocontrol agents is developed to assist public health applications such as mosquito vector control, disease control, ecosystem management, environmental conservation, and so on. © IFIP international Federation for Information Processing 2025.

关键词： Vectors

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EBUD: Evolving Disaster Burst Detection over Social Streams 25th

EBUD: Evolving Disaster Burst Detection over Social Streams

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25th international conference on Web Information Systems engineering, WISE 2024

作者： Qiao, Xiyu Zhou, Xiangmin Zhou, Changjun Wang, Hua Zhang, Yanchun Institute for Sustainable Industries and Liveable Cities Victoria University MelbourneVIC3011 Australia School of Computing Technologies RMIT University MelbourneVIC3000 Australia School of Computer Science and Technology Zhejiang Normal University Jinhua321004 China

ISBN: (纸本)9789819605668

the rapid rise of social media platforms such as Twitter and Weibo has heightened the growing significance of crisis warning and situational awareness. the nature of bursts or events on these platforms has become more complex, as information spreads rapidly and unpredictably, often involving changes in time and location. Current research is insufficient in detecting these complex, evolving bursts, especially during the early stages of incidents. To address these challenges, we propose an Evolving Disaster Burst Detection (EBUD) framework over social streams, which enables early identification and tracking of bursts, facilitating quick response and decision-making. Specifically, we propose a novel Adaptive Entropy Distance based active learning strategy for user to label the most representative data, while the model is tasked with labelling the rest of the data, effectively improving the accuracy of data labelling. Finally, a two-stage sketch algorithm is proposed to monitor the frequency of each label in real-time, enabling swift detection of sudden surges that may signify emerging events. Comprehensive evaluations on social data streams have been carried out to show the effectiveness and efficiency of our proposed method. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Active learning Burst Detection Social Networks

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intelligent Multi-Agent Systems for UAV-Robot Path Optimization via Reflective Evolution 25th

Intelligent Multi-Agent Systems for UAV-Robot Path Optimiza...

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25th international conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2024

作者： Chen, Yijie Zhu, Kexin Chen, Zhide Feng, Chen Zhuang, Zhengjie College of Computer and Cyberspace Security Fujian Normal University Fuzhou350007 China Department of Information Engineering Sun Yat-Sen University of Taiwan Kaohsiung804201 China

ISBN: (纸本)9789819642069

With the rise of precision agriculture, efficiently identifying fruit tree locations and optimizing operational paths have become increasingly challenging. To address these issues, we developed an integrated path automation system that leverages the collaboration between unmanned aerial vehicles (UAVs) and ground robots. the system captures multi-angle images of fruit trees using UAVs, which are then processed by the Multi-MiniGPT-v2 model, which is an intelligent multi-agent system incorporating MiniGPT-v2 and MetaGPT, to determine tree coordinates and assess fruit ripeness from both top-down and side views. this coordinate data is input into the Adaptive Reflective Evolution (AREvo) model, which utilizes the large model and Reflective Evolution algorithm for automatic path planning and generates optimized robot paths. Experimental results indicate that this method significantly enhances the operational efficiency of orchard robots, improves the accuracy of fruit tree status detection, and reduces costs. this technology alleviates manual labor, boosts agricultural productivity, and optimizes resource utilization, providing strong support for advancing precision agriculture. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Fruits

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A Comparative Study of Log-Based Anomaly Detection Methods in Real-World System Logs

A Comparative Study of Log-Based Anomaly Detection Methods i...

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10th international conference on Internet of things, Big data and Security, IoTBDS 2025

作者： Nipa, Nadira Anjum Bouguila, Nizar Patterson, Zachary Concordia Institute for Information and Systems Engineering Concordia University MontrealQC Canada

ISBN: (纸本)9789897587504

the reliability and security of today’s smart and autonomous systems increasingly rely on effective anomaly detection capabilities. Logs generated by intelligent devices during runtime offer valuable insights for monitoring and troubleshooting. Nonetheless, the enormous quantity and complexity of logs produced by contemporary systems render manual anomaly inspection impractical, error-prone, and laborious. In response to this, a variety of automated methods for log-based anomaly detection have been developed. However, many current methods are evaluated in controlled environments with set assumptions and frequently depend on publicly available datasets. In contrast, real-world system logs present greater complexity, lack of labels, and noise, creating substantial challenges when applying these methods directly in industrial settings. this work explores and adapts existing machine learning and deep learning techniques for anomaly detection to function on real-world system logs produced by an intelligent autonomous display device. We conduct a comparative analysis of these methods, evaluating their effectiveness in detecting anomalies through various metrics and efficiency measures. Our findings emphasize the most efficient approach for detecting anomalies within this specific system, enabling proactive maintenance and enhancing overall system reliability. Our work provides valuable insights and directions for adopting log-based anomaly detection models in future research, particularly in industrial applications. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Industrial research

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An intelligent Optimization Technique Of Automatic Speech Recognition For Smart Homes 13

An Intelligent Optimization Technique Of Automatic Speech Re...

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13th international conference on Research in ComputIng at Feminine, RIF 2024

作者： Aly, Rabab Hamed M. Hussein, Aziza I. The Higher Institute for Management and Information Technology Minya Egypt Electrical and Computer Engineering Department College of Engineering Effat University Jeddah Saudi Arabia Computer Science Department Faculty of Computer Science Nahda University Beni Suef Egypt

the creation of new approaches to the design and configuration of smart buildings relies heavily on AI tools and Machine learning (ML) algorithms, particularly optimization techniques. the widespread use of electronic devices has sparked a strong desire to incorporate the Internet of things (IoT) into houses, leading to the development of smart homes. As networked gadgets proliferate rapidly, this phenomenon is characterized by rapid proliferation. In smart buildings, smart cities, smart grids, and smart homes, interconnected electronic devices are becoming more popular. the objective of this paper is to enhance the functionality of home automation systems through the performance of speech recognition using the Bat-Salp Swarm Optimization (BSSO). this paper investigates the notion of (BSSO), a data analysis methodology that facilitates the automated construction of analytical models. the implementation of BSSO provides an enhancement to the feature selection process in speech recognition, providing an approximation solution that improves the accuracy of system decisions. the use of the BSSO technique improves the precision of the voice recognition system and also incorporates an Artificial Neural Network (ANN) for the classification part. the findings substantiated the efficacy of the employed methodology. © 2024 Copyright for this paper by its authors.

关键词： Bat Optimization Algorithm Salp Optimization Algorithm Smart homes Speech recognition

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Enhancing Open-Set Recognition with Global Feature Representation 25th

Enhancing Open-Set Recognition with Global Feature Represen...

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25th international conference on Web Information Systems engineering, WISE 2024

作者： Ping, GuoLou School of Software Tsinghua University Beijing China

ISBN: (纸本)9789819605750

Open-set recognition (OSR) is crucial for classifying known classes while identifying unknown data in real-world applications. Existing methods often rely on discriminative features from deep models but struggle with recognizing new data with non-discriminative differences from known classes and are susceptible to adversarial attacks. To address this limitation, we redefine the OSR problem by incorporating the unknown space with non-discriminative differences. Specifically, we introduce global feature representations that encapsulate both discriminative and non-discriminative features. Unlike discriminative features alone, global representations capture variations between arbitrary samples, thereby covering unknown spaces that deviate from known samples in various feature aspects. As an exploratory solution, we propose the Open Calibrated K-Nearest Neighbor (OpenCKNN) classifier based on global feature representation. OpenKNN, an open version of the traditional closed KNN, preserves all global features without information loss, enabling the recognition of all unknown classes. Moreover, KNN’s local neighbor learning and average nearest neighbor discrimination effectively handle challenges posed by irregular data distributions and atypical points. We also employ nearest-neighbor distance calibration using a pseudo-extreme value machine to mitigate inconsistencies across different class clusters. Extensive experiments on benchmark vision and intrusion detection datasets demonstrate that our approach significantly enhances open-set recognition and resistance to adversarial attacks. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Generative Adversarial Networks for Imputing Sparse learning Performance 27th

Generative Adversarial Networks for Imputing Sparse Learnin...

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27th international conference on Pattern Recognition, ICPR 2024

作者： Zhang, Liang Yeasin, Mohammed Lin, Jionghao Havugimana, Felix Hu, Xiangen Institute for Intelligent Systems University of Memphis MemphisTN38152 United States Department of Electrical and Computer Engineering University of Memphis MemphisTN38152 United States Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA15213 United States Centre for Learning Analytics Monash University MelbourneVIC3800 Australia Department of Applied Social Sciences Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9783031781711

learning performance data, such as correct or incorrect responses to questions in intelligent Tutoring Systems (ITSs) is crucial for tracking and assessing the learners’ progress and mastery of knowledge. However, the issue of data sparsity, characterized by unexplored questions and missing attempts, hampers accurate assessment and the provision of tailored, personalized instruction within ITSs. this paper proposes using the Generative Adversarial Imputation Networks (GAIN) framework to impute sparse learning performance data, reconstructed into a three-dimensional (3D) tensor representation across the dimensions of learners, questions and attempts. Our customized GAIN-based method computational process imputes sparse data in a 3D tensor space, significantly enhanced by convolutional neural networks for its input and output layers. this adaptation also includes the use of a least squares loss function for optimization and aligns the shapes of the input and output with the dimensions of the questions-attempts matrices along the learners’ dimension. through extensive experiments on six datasets from various ITSs, including AutoTutor, ASSISTments and MAthia, we demonstrate that the GAIN approach generally outperforms existing methods such as tensor factorization and other generative adversarial network (GAN) based approaches in terms of imputation accuracy. this finding enhances comprehensive learning data modeling and analytics in AI-based education. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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25th international conference on Web Information Systems engineering, WISE 2024

作者： Tu, Longchuan Meng, Shunmei Liu, Xiao Liu, Guanfeng Beheshti, Amin Zhang, Xuyun School of Computer Science and Engineering Nanjing University of Science and Technology Nanjin China State Key Laboratory for Novel Software Technology Nanjing University Nanjin China School of Computing Macquarie University Sydney Australia

ISBN: (纸本)9789819605699

Recently, graph neural networks(GNNs) have played a key crucial in many recommendation situations. In particular, contrastive learning-based hypergraph neural networks (HGNNs) are gradually becoming a research focus for addressing issues of data sparsity and noise. Despite many studies proving their outstanding performance, there are still some shortcomings: i) Most contrastive learning-based HGNNs primarily rely on cross-view contrastive learning, while neglecting contrastive learning on the interaction graph. ii) Utilizing node embedding for hypergraph structure learning is susceptible to the influence of low-quality representations, thereby constraining the learning capability of HGNNs. To address these issues, we offer a Semantic Similarity-based Graph Contrastive learning framework (SSGCL), which aims to jointly learn representations with rich semantic information through within-view and cross-view contrastive learning. Specifically, we first introduce consistency contrastive learning, which captures self-supervised signals through semantic similarity between nodes and their neighborhoods. It better captures the unique features of nodes through the connections between nodes and their neighborhoods. then, we utilize interaction graph learning hypergraph structure, promoting it to extract potential node connections and thus improving its ability to describe homogeneous node relationships. Experimental evaluations on three actual datasets show that SSGCL performs much better than the current baseline models. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive learning

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