检索结果-内蒙古大学图书馆

2nd International Conference on Cyber Intelligence and Information Retrieval, CIIR 2023

作者： Muloor, Kiran Hemanthraj Sahu, Somesh Samanta, Debabrata Karuppiah, Marimuthu Bhattacharya, Abhishek Dutta, Soumi LTIMindtree Limited Karnataka Bangalore India Department of Computer Science CHRIST University Karnataka Bangalore India Department of Computing and Information Technologies Rochester Institute of Technology Pristina Kosovo School of Computer Science and Engineering and Information Science Presidency University Karnataka Bengaluru560064 India Sister Nivedita University Kolkata India

ISBN: (纸本)9789819776023

Today, we are constantly surrounded by vast amounts of data, a trend that is expected to grow significantly over the next decade. The abundance of data presents challenges for thorough analysis and extraction of valuable insights buried within unstructured information. Advanced tools like data mining are crucial in uncovering this useful information and making full use of it. In light of the increasing number of security threats in networks, there is a need for robust security solutions. While traditional network security measures have been primarily managed locally, concerns about internet-based security have grown due to heightened computer usage leading to cybercriminal activities previously limited to physical intrusions. A threat intelligence program aims to enhance analytical and preventive capabilities by acquiring knowledge about potential or existing threats based on evidence. As most devices are interconnected with the Internet, many organizations prioritize cybersecurity as they acknowledge the vulnerabilities arising from this connectivity—providing opportunities for cyber-attacks. Effective threat intelligence concerning network traffic necessitates a comprehensive understanding supported by thoughtful representation techniques. This paper proposes an extensive exploration of various machine learning methods aimed at identifying weaknesses in detecting invasive activity using different approaches and evaluating their performance against the KDD 99 benchmark dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network intrusion

来源：评论

学校读者我要写书评

暂无评论

An 81.7M Ω-lnput-lmpedance 179.5dB-FOMSNDR 1.8Vpp-lnput-Range Noise-Shaping-SAR-Based Sensing Frontend with Dynamic Input-Impedance Boosting and Prediction-Assisted Mismatch-Shaping-DEM

An 81.7M Ω-lnput-lmpedance 179.5dB-FOMSNDR 1.8Vpp-lnput-Ran...

引用

Custom Integrated Circuits Conference (CICC)

作者： Yiming Han Linran Zhao Tzuping Huang Alper Bozkurt Yaoyao Jia Chandra Family Department of Electrical and Computer Engineering The University of Texas at Austin USA Department of Electrical and Computer Engineering North Carolina State University USA

ISBN: (数字)9798331517458

ISBN: (纸本)9798331517465

Sensing body vital signals, such as ECG, EEG, EMG, and EOG (ExG), is crucial for human health monitoring. To achieve accurate signal acquisition, sensing frontends are expected to have high input impedance $(Z_{1\mathrm{N}} > 10\mathrm{M}\Omega)$ to reduce signal attention, wide input linear range $(IR > 1 V_{PP})$ to better tolerate large artifacts and interferences, and low power consumption (<10µW) to prolong battery life. Recently, direct digitizing sending frontends with high resolution and low power consumption have been well developed, demonstrating their potential to meet these requirements. However, the existing state-of-the-art still faces challenges with either limited Z IN or limited IR.

关键词： Electrooculography Power demand Electrocardiography Electromyography Electroencephalography Sensors Impedance Signal resolution Monitoring Faces

来源：评论

学校读者我要写书评

暂无评论

Reinforcement Learning-based Hybrid Routing Algorithms for Vehicular Ad Hoc Networks

引用

Procedia computer science 2025年 257卷 623-630页

作者： Narges Haghighati Boroujeni Suprakash Datta Pouya Firouzmakan Telus Corp Toronto Canada Electrical Engineering and Computer Science Dept York University Toronto Canada

This paper proposes a new hybrid routing algorithm, ReLeVR, for vehicular ad hoc networks (VANETs). Our algorithm aims to efficiently solve the problem of routing messages from vehicles to specific geographical locations over VANETs. We assume that a VANET consists of vehicles and roadside units (RSUs). First, we propose a simple strategy to find the optimal locations for RSUs with the objective of minimizing their number. Our strategy computes RSU locations using available prior traffic information. Then we use Q-learning, a reinforcement learning algorithm, to learn from traffic flow patterns and compute a routing policy for the vehicles. This policy determines which grid the message should be sent to in the next step along the way for each location in the city. The routing policy is broadcast to all of the vehicles in the VANET. We demonstrate through simulations on real traffic data that our algorithm outperforms an older algorithm GPSR and a recent Q-learning-based algorithm, QGrid G in terms of metrics like the delivery ratio and delay. We observe that the number of RSUs used by our algorithm is significantly lower than that of a recent algorithm, QTAR.

关键词： Vehicular Ad-hoc Network Routing Position-based Routing Q-learning

来源：评论

学校读者我要写书评

暂无评论

Efficient Policy Iteration and Value Iteration via State Space Clustering

Efficient Policy Iteration and Value Iteration via State Spa...

引用

2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Paidi, Avinash Mitra, Pabitra Indian Institute of Technology Department of Artificial Intelligence Kharagpur India Indian Institute of Technology Department of Computer Science and Engineering Kharagpur India

ISBN: (纸本)9798331504847

This paper presents a novel state-space clustering technique specifically designed for Reinforcement Learning (RL) problems with discrete state and action spaces. Building on this clustering methodology, we propose a Hierarchical Reinforcement Learning (HRL) framework to improve the efficiency of policy iteration (PI) and value iteration (VI) algorithms. Our approach partitions the state space into well-defined, homogeneous clusters and identifies representative prototype states for each cluster. Transitioning between clusters is facilitated through macro-actions, which are derived using the Depth First Search (DFS) algorithm. By focusing value function updates exclusively on these prototype states during PI and VI operations, the proposed method substantially reduces computational overhead without compromising performance. Experimental evaluations demonstrate that the proposed technique generates highly cohesive clusters, enhances policy learning quality, and accelerates the convergence of the PI and VI algorithms. © 2025 IEEE.

关键词： Hierarchical clustering

来源：评论

学校读者我要写书评

暂无评论

A Study of Contemporary ML Algorithm on Intrusion Detection Using IIoT Datasets

引用

Procedia computer science 2025年 258卷 3682-3692页

作者： Sunday A. Ajagbe T. Ananth Kumar Rajendrane Rajmohan Emmanuel O. Balogun Oluwafemi O. Olawoyind Morolake O. Lawrence Matthew O. Adigun Computer Science Department University of Zululand Kwadlangezwa 3886 South Africa Computer Engineering Department Abiola Ajimobi Technical University Ibadan 200255 Nigeria Computer Science and Engineering Department IFET College of Engineering India Computing Technologies Department SRM Institute of Science and Technology India ICT Unit Ajayi Crowther University Oyo Oyo Nigeria Computer Science Department Baze University Abuja Nigeria

The global digitization of most activities of corporate and non-corporate bodies has come to stay. The interconnection of physical object "things" embedded with different technologies such as sensors, softwares and other technologies to connect and allow for data exchange with different systems and devices over the internet is known as the Internet of Things (IoT). To guarantee lower expenses and increased operational effectiveness, Data-driven insights for prompt decision-making, complete, remote asset and resource management, Prescriptive, predictive, and real-time insights along with enhanced end-user satisfaction Industrial Internet of Things (IIoT) became a given. Implementing machine learning (ML) algorithms for intrusion detection in IIoT systems entails numerous hurdles, such as data imbalance and adversarial attacks. And different ML algorithms handle data imbalance and adversarial attacks for intrusion detection differently. Therefore, there is a need for comparative studies on ML algorithms to be used as a tool to help prevent unauthorised intrusion into various networks. In this research, an experimental approach has been used to compare contemporary ML algorithms such as Linear SVC, random forest RF, XGBoost, decision tree (DT), logistic regression (LR) on IIoT datasets to detect intrusion. Data exploration, Feature engineering, selection and data partition were done at a rate of 80-20 % ratio for the train set and test set respectively. Performance evaluation was carried out using accuracy, recall, precision and F1-score and a confusion matrix was plotted. The performance evaluation shows the accuracy of 100%, 99.99%, 99.35%, 97.49% and 97.27% for RF, XGBoost, DT, Linear SVC and LR algorithms respectively the test set evaluation. RF outperformed the other algorithm that was experimented.

关键词： Machine learning Deep learning IIoT dataset Intusion detection Data security Random forest

来源：评论

学校读者我要写书评

暂无评论

Optimized deep transfer learning techniques for spine fracture detection using CT scan images

引用

Multimedia Tools and Applications 2025年 1-34页

作者： Kanna, G. Prabu Kumar, Jagadeesh Parthasarathi, P. Bhardwaj, Priya Kumar, Yogesh VIT Bhopal University Bhopal India Department of Computer Science and Engineering Kathir College of Engineering Coimbatore India Department of CS Bannari Amman Institute of Technology Sathyamangalam India School of Computing DIT University Dehradun India Department of CSE School of Technology Pandit Deendayal Energy University Gujarat Gandhinagar India

Spine fractures pose an important health concern that requires a quick diagnosis to protect people from any long-term complications. This paper aims to propose an automatic prediction system that uses deep transfer learning techniques for spine fractures and minimizes the time required for classification compared to traditional techniques. The CT scan images of the cervical spine have been collected in the form of JPEG format and subsequently segmented using several phases such as mask exploring, detecting, zooming, and cropping the affected area at 6 different angles followed by the computation of contour features to analyze their geometric as well as intensity-based parameters. Ten advanced deep learning models along with ADAM optimizer are trained where InceptionResNetV2 obtained the highest accuracy of 99.70% while the lowest loss, as well as root mean error square score, has been obtained by ResNet50V2 with 0.0493 and 0.2220.36 respectively. In the case of precision, recall, and F1 score, DenseNet169 computed the highest precision and recall values of 99.99% and 98.495% respectively while Xception did well in terms of F1 score with 98.32%. These models perform better because InceptionResNetV2’s architecture is well-suited for capturing intricate spatial hierarchies, DenseNet169 benefits from dense connectivity for feature reuse, and ResNet50V2’s optimized residual blocks contribute to minimizing error rates. Apart from this, less diversity and class imbalance are also seen within the dataset which affects model performance. These limitations highlight the need for future work to expand the dataset and ensure a balanced distribution of classes to enhance the robustness and clinical relevance of the proposed AI-based system. However, despite these challenges, the results underscore the transformative potential of deep learning in improving clinical diagnosis of spine fractures. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part

关键词： computerized tomography

来源：评论

学校读者我要写书评

暂无评论

Infants Sucking Pattern Identification Using Machine-Learned Computational Modeling

引用

Journal of engineering and science in Medical Diagnostics and Therapy 2025年第3期8卷 031003页

作者： Olapojoye, Abdullahi Singh, Abhishek Nishi, Eri Fei, Baowei Nostratinia, Aria Hassanipour, Fatemeh Department of Mechanical Engineering University of Texas at Dallas RichardsonTX75080 United States Department of Electrical Engineering Setsunan University Osaka Prefecture Neyagawa572-8508 Japan Department of Bioengineering University of Texas at Dallas RichardsonTX75080 United States Department of Radiology UT Southwestern Medical Center DallasTX75390 United States Department of Electrical and Computer Engineering University of Texas at Dallas RichardsonTX75080 United States

Breastfeeding involves a complex coordination of swallowing, breathing, and sucking, with the infant's sucking proficiency being crucial for adequate nutrient intake. However, real-time assessment of milk intake is difficult, and issues with sucking often become apparent after the infant shows signs of nutrient deficiency. Traditional assessments by clinicians rely on the expertise and subjective judgment of healthcare professionals, enabling personalized evaluations. In this research, we introduce a novel approach to identifying sucking patterns by leveraging data collected from infants during breastfeeding sessions. This method utilizes artificial nipple-based sensors to capture the tongue forces exerted by infants, generating valuable clinical data. In the analysis of the collected time-series data, we applied machine-learned computational modeling (MLCM) algorithms to extract pertinent features and identify distinctive sucking patterns. The best-performing model demonstrated an accuracy of 90%, an 80% recall score, a perfect 100% precision score, a 0.90 f1-score, and an area under the curve (AUC) of 0.80. The proposed classification system has the potential to serve as a reliable decision-support tool for clinicians, offering valuable insights into infants' sucking behaviors. By integrating machine learning (ML)-based computational modeling into clinical practice, we aim to enhance the early identification of unhealthy sucking patterns, allowing for timely interventions and pro-active healthcare management. © 2025 by ASME.

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

Intermediate-Task Transfer Learning: Leveraging Sarcasm Detection for Stance Detection

arXiv

引用

arXiv 2025年

作者： Nkhata, Gibson Gauch, Susan Department of Electrical Engineering & Computer Science University of Arkansas FayettevilleAR72701 United States

Stance Detection (SD) in the context of social media has emerged as a prominent area of interest with implications for social, business, and political applications, thereby garnering escalating research attention within the realm of Natural Language Processing (NLP). The inherent subtlety, nuance, and complexity of texts procured from online platforms via crowd-sourcing pose challenges for SD algorithms in accurately discerning the author’s stance. Particularly, the inclusion of sarcastic and figurative language drastically impacts the performance of SD models. This paper addresses this challenge by employing sarcasm detection intermediate-task transfer learning tailored for SD. The proposed methodology involves the fine-tuning of BERT and RoBERTa and the sequential concatenation of convolutional, bidirectional LSTM, and dense layers. Rigorous experiments are conducted on publicly available benchmark datasets to evaluate our transfer-learning framework. The performance of the approach is assessed against various State-Of-The-Art (SOTA) baselines for SD, providing empirical evidence of its effectiveness. Notably, our model outperforms the best SOTA models, achieving average F1-score gaps of 0.038 and 0.053 on the SemEval 2016 Task 6A Dataset (SemEval) and Multi-Perspective Consumer Health Query Data (MPCHI), respectively, even prior to sarcasm-detection pre-training. The integration of sarcasm knowledge into the model proves instrumental in mitigating misclassifications of sarcastic textual elements in SD. Our model accurately predicts 85% of texts that were previously misclassified by the model without sarcasm-detection pre-training, thereby amplifying the average F1-score of the model. Furthermore, our experiments revealed that the success of the transfer-learning framework is contingent upon the correlation of lexical attributes between the intermediate task (sarcasm detection) and the target task (SD). This study represents the first exploration of sarcasm detect

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Intrusion Detection in Cybersecurity: A Machine Learning Classifier with Optimized Feature Selection 3

Intrusion Detection in Cybersecurity: A Machine Learning Cla...

引用

3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Ahamed, S. Niyas Thayanandeswari, C. S. Sree Vasanth, R. Durai Subharathna, N. Malik, M. Abdul Tamizharasi, A. Anna University Department of Electronics and Communication Engineering Chennai India Pet Engineering College Department of Electronics and Communication Engineering Tirunelveli India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Bannari Amman Institute of Technology Department of Electronics and Communication Engineering Erode India R.M.D Engineering College Department of Computer Science and Engineering Thiruvallur India

ISBN: (纸本)9798331508456

Concerns about security and privacy were heightened by the increasing dependence on interconnected networks, underscoring the vital role intrusion detection systems (IDS) play in protecting private data. As essential components of modern cybersecurity frameworks, IDS are designed to detect and prevent malicious activities. However, traditional systems face challenges such as handling high-dimensional data, addressing class imbalance, and detecting unknown threats due to the rising sophistication of cyberattacks and growing network traffic. This study proposes a Selective Features Optimized Machine Learning (ML) Classifier that improves IDS performance by utilizing Support Vector Machine (SVM) classification and Genetic Algorithm (GA) based feature selection. The model achieves impressive results, with accuracy of 96%, precision of 95.4%, and recall of 94%, demonstrating its reliability and effectiveness in identifying malicious network traffic. By integrating GA driven feature selection with SVM classification, the proposed system significantly improves threat detection, offering robust solution for real world cybersecurity challenges. © 2025 IEEE.

关键词： Network intrusion

来源：评论

学校读者我要写书评

暂无评论

Leveraging Feature engineering and Machine Learning for Internet of Things (IoT) Cyberattack Detection: A Scalable Approach

Leveraging Feature Engineering and Machine Learning for Inte...

引用

International Symposium on Digital Forensic and Security (ISDFS)

作者： Arafat Asim Peter J. Hawrylak School of Cyber Studies College of Engineering and Computer Science The University of Tulsa Tulsa OK USA Electrical & Computer Engineering College of Engineering and Computer Science The University of Tulsa Tulsa OK USA

ISBN: (数字)9798331509934

ISBN: (纸本)9798331509941

Internet of Things (IoT) devices are a fast-growing market, but they also possess a great challenge in terms of cyber-security, and therefore need a strong intrusion detection system (IDS). We present a machine learning-based IDS that improves attack classification through effective Feature engineering and optimized Model Selection. Whereas traditional methods rely on static and well-known features, our approach offers novel ones tcp. payload and tcp. options to improve your detection of injection and vulnerability attacks. Our results demonstrate that Random Forest outperforms the convolutional models while achieving the highest accuracy of 99.07% (6-class) and 98.53% (I5-class) in five models evaluated on the Edge-IIoTset dataset. Using SMOTE to handle class im-balance and incorporating network features related to behavior, the method is able to better detect minority class general attacks. Building upon that foundation, we utilized behavioral feature extraction, data balancing, and deep learning optimization techniques that top centralized IDS models use to improve accuracy and resilience across our framework.

关键词： Deep learning Accuracy Image edge detection Intrusion detection Feature extraction Internet of Things Security Random forests Optimization Payloads

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：