检索结果-内蒙古大学图书馆

Enhanced Cybercrime Detection on Twitter Using Aho-Corasick Algorithm and Machine Learning Techniques

Informatica (Slovenia) 2024年第18期48卷 97-108页

作者： Rawat, Romil Raj, A. Samson Arun Chakrawarti, Rajesh Kumar Sankaran, Krishnan Sakthidasan Sarangi, Sanjaya Kumar Rawat, Hitesh Rawat, Anjali Department of Computer Science Engineering Shri Vaishnav Institute of Information Technology Shri Vaishnav Vidyapeeth Vishwavidyalaya Indore India Division of Computer Science and Engineering School of Computer Science and Technology Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore India MP Indore India Department od Electronics and Communication Engeneering Hindustan Institute of Technology and Science Chennai India Department of Computer Science and Engineering Utkal University Bhubaneswar India Department of Business Management and Economics University of Extremadura Spain Department of Computer and Communication Technology University of Extremadura Spain

The proposed work objective is to adapt Online social networking (OSN) is a type of interactive computer-mediated technology that allows people to share information through virtual networks. The microblogging feature of Twitter makes cyberspace prominent (usually accessed via the dark web). The work used the datasets and considered the Scrape Twitter Data (Tweets) in Python using the SN-Scrape module and Twitter 4j API in JAVA to extract social data based on hashtags, which is used to select and access tweets for dataset design from a profile on the Twitter platform based on locations, keywords, and hashtags. The experiments contain two datasets. The first dataset has over 1700 tweets with a focus on location as a keypoint (hacking-for-fun data, cyber-violence data, and vulnerability injector data), whereas the second dataset only comprises 370 tweets with a focus on reposting of tweet status as a keypoint. The method used is focused on a new system model for analysing Twitter data and detecting terrorist attacks. The weights of susceptible keywords are found using a ternary search by the Aho-Corasick algorithm (ACA) for conducting signature and pattern matching. The result represents the ACA used to perform signature matching for assigning weights to extracted words of tweet. ML is used to evaluate Twitter data for classifying patterns and determining the behaviour to identify if a person is a terrorist. SVM (Support Vector Machine) proved to be a more accurate classifier for predicting terrorist attacks compared to other classifiers (KNN- K-Nearest Neighbour and NB-Naïve Bayes). The 1st dataset shows the KNN-Acc. -98.38% and SVM Accuracy as 98.85%, whereas the 2nd dataset shows the KNN-Acc. -91.68% and SVM Accuracy as 93.97%. The proposed work concludes that the generated weights are classified (cyber-violence, vulnerability injector, and hacking-for-fun) for further feature classification. Machine learning (ML) [KNN and SVM] is used to predict the occurrence and

关键词： Tweets

来源：评论

学校读者我要写书评

暂无评论

Neural network-guided motion blur estimation and patch representation method for face hallucination

引用

Neural Computing and Applications 2025年 1-28页

作者： Kumar, Banti Rajput, Shyam Singh Department of Computer Science and Engineering National Institute of Technology Patna Bihar Patna India

Face hallucination (or super-resolution) is a technique of generating high-resolution face images from low-resolution (LR) inputs. The current face hallucination algorithms struggle with motion blur, a common issue in captured images due to various factors, including camera defocusing, objects in motion, etc. To address this problem, a new motion blur robust face hallucination algorithm via neural network-guided motion blur estimation and patch representation (NNMEPR) is proposed in this paper. The NNMEPR algorithm first estimates the motion blur kernels i.e., length (λ) and angle (α) from a motion-blurred LR test faces using a neural network fitting approach with scale conjugate gradient (SCG) learning algorithm. Then, the estimated factor is embedded in the LR dictionary face images to make them compatible with the test images and mitigate the impact of motion blur from the reconstruction process. Moreover, the proposed algorithm employs a neighboring position patch representation in the correlation coefficient calculation process to sustain the sharp edges and texture details in the resulting HR face images. Experiments on standard datasets and locally captured real-world faces demonstrate the superior performance of the proposed NNMEPR algorithm over the existing state-of-the-art techniques. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Motion estimation

来源：评论

学校读者我要写书评

暂无评论

Premature deposition of lithium polysulfide in lithium-sulfur batteries

引用

Journal of Energy Chemistry 2023年第7期82卷 507-512,I0011页

作者： Zi-Xian Chen Yu-Tong Zhang Chen-Xi Bi Meng Zhao Rui Zhang Bo-Quan Li Jia-Qi Huang School of Materials Science and Engineering Beijing Institute of TechnologyBeijing 100081China Advanced Research Institute of Multidisciplinary Science Beijing Institute of TechnologyBeijing 100081China

Lithium-sulfur(Li-S)batteries have attracted extensive attention due to ultrahigh theoretical energy density of 2600 Wh kg^(-1).Liquid-solid deposition from dissolved lithium polysulfides(LiPSs)to solid lithium sulfide(Li_(2)S)largely determines the actual battery ***,a premature liquidsolid deposition process of LiPSs is revealed at higher thermodynamic potential than Li_(2)S deposition in Li-S *** premature solid deposit exhibits higher chemical state and hemispherical morphology in comparison with Li_(2)S,and the premature deposition process is slower in kinetics and higher in deposition ***,a supersaturation deposition mechanism is proposed to rationalize the above findings based on thermodynamic *** work demonstrates a unique premature liquid-solid deposition process of Li-S batteries.

关键词： Lithium-sulfur batteries Lithium polysulfides Liquid-solid deposition Supersaturation

来源：评论

学校读者我要写书评

暂无评论

HSALC: hard sample aware label correction for medical image classification

引用

Multimedia Tools and Applications 2024年 1-21页

作者： Wang, Yangtao Ye, Yicheng Xie, Yanzhao Tang, Maobin Fan, Lisheng Metaverse Research Institute School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China

Medical image automatic classification has always been a research hotspot, but the existing methods suffer from the label noise problem, which either discards those samples with noisy labels or produces wrong label correction, seriously preventing the medical image classification performance improvement. To address the above problems, in this paper, we propose a hard sample aware label correction (termed as HSALC) method for medical image classification. Our HSALC mainly consists of a sample division module, a clean·hard·noisy (termed as CHN) detection module and a label noise correction module. First, in the sample division module, we design a sample division criterion based on the training difficulty and training losses to divide all samples into three preliminary subsets: clean samples, hard samples and noisy samples. Second, in the CHN detection module, we add noise to the above clean samples and repeatedly adopt the sample division criterion to effectively detect all data, which helps obtain highly reliable clean samples, hard samples and noisy samples. Finally, in the label noise correction module, in order to make full use of each available sample, we train a correction model to purify and correct the wrong labels of noisy samples as much as possible, which brings a highly purified dataset. We conduct extensive experiments on five image datasets including three medical image datasets and two natural image datasets. Experimental results demonstrate that HSALC can greatly promote classification performance on noisily labeled datasets, especially with high noise ratios. The source code of this paper is publicly available at GitHub: https://***/YYC117/HSALC. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

Leveraging EfficientNetB3 in a Deep Learning Framework for High-Accuracy MRI Tumor Classification

引用

computers, Materials & Continua 2024年第10期81卷 867-883页

作者： Mahesh Thyluru Ramakrishna Kuppusamy Pothanaicker Padma Selvaraj Surbhi Bhatia Khan Vinoth Kumar Venkatesan Saeed Alzahrani Mohammad Alojail Department of Computer Science&Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bengaluru562112India School of Computer Science and Engineering VIT-AP UniversityAmaravati522241India Department of Computer Science&Technology Madanapalle Institute of Technology&ScienceMadanapalle517325India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK School of Computer Science Engineering&Information Systems(SCORE) Vellore Institute of Technology(VIT)Vellore632014India Management Information System Department College of Business AdministrationKing Saud UniversityRiyadh11531Saudi Arabia Adjunct Research Faculty Centre for Research Impact&OutcomeChitkara UniversityRajpuraPunjab140401India

Brain tumor is a global issue due to which several people suffer,and its early diagnosis can help in the treatment in a more efficient *** different types of brain tumors,including gliomas,meningiomas,pituitary tumors,as well as confirming the absence of tumors,poses a significant challenge using MRI *** approaches predominantly rely on traditional machine learning and basic deep learning methods for image *** methods often rely on manual feature extraction and basic convolutional neural networks(CNNs).The limitations include inadequate accuracy,poor generalization of new data,and limited ability to manage the high variability in MRI *** the EfficientNetB3 architecture,this study presents a groundbreaking approach in the computational engineering domain,enhancing MRI-based brain tumor *** approach highlights a major advancement in employing sophisticated machine learning techniques within computer science and engineering,showcasing a highly accurate framework with significant potential for healthcare *** model achieves an outstanding 99%accuracy,exhibiting balanced precision,recall,and F1-scores across all tumor types,as detailed in the classification *** successful implementation demonstrates the model’s potential as an essential tool for diagnosing and classifying brain tumors,marking a notable improvement over current *** integration of such advanced computational techniques in medical diagnostics can significantly enhance accuracy and efficiency,paving the way for wider *** research highlights the revolutionary impact of deep learning technologies in improving diagnostic processes and patient outcomes in neuro-oncology.

关键词： Deep learning MRI brain tumor cassification EfficientNetB3 computational engineering healthcare technology artificial intelligence in medical imaging tumor segmentation neuro-oncology

来源：评论

学校读者我要写书评

暂无评论

Heat Load Prediction of Building Rooms Using Only the Whole Building Data via Heat Allocation Approach

引用

Journal of Thermal science 2025年第3期34卷 953-969页

作者： TAN Xin WANG Yahui SUN Guoxin WU Linfeng YU Qihui YU Yongheng School of Mechanical Engineering Inner Mongolia University of Science and Technology New Energy and Equipment Laboratory Inner Mongolia University of Science and Technology Ordos Energy Research Institute Peking University

The next-generation heating systems,crucial for rational heat distribution and refined management,rely heavily on accurate zone-specific heat load *** paper introduces a method for rapid zone-specific heat load prediction based on heat consumption allocation and data-driven *** approach involves predicting the overall heat load of the building and then redistributing the total heat according to a heat consumption *** eliminates the need for real-time data collection from each room,resulting in cost savings on hardware and improved computational *** overall building heat load data is obtained through a data-driven algorithm,while the heat consumption matrix is constructed through energy software simulation *** Building 2 in the Baotou Industrial Park,China,as a case study,the paper analyzes the differences between actual measurements and room *** results indicate an average error of 7.02% for the proposed estimation *** not achieving high precision(>95%) in heat load prediction,this level of accuracy is deemed sufficient to meet the requirements of feedforward control.

关键词： building energy consumption simulation heat allocation heat load forecasting separate rooms

来源：评论

学校读者我要写书评

暂无评论

Accelerating Lithium Deposition Kinetics Via Lithiophilic Ag-Decorated Graphitic Carbon Nitride Spheres for Stable Lithium Metal Anode

引用

Energy & Environmental Materials 2025年第2期8卷 68-75页

作者： Hyojun Lim Minsu Choi Haeun Kang Wonchang Choi Energy Storage Research Center Korea Institute of Science and Technology(KIST)Seongbuk-guSeoul02792RepublicofKorea Department of Nuclear Science and Engineering and Department of Material Science and Engineering Massachusetts Institute of Technology(MIT)Cambridge 02139MassachusettsUSA Department of Energy Engineering Konkuk UniversityRepublic of Korea

This study presents a novel Li metal host material with a unique hollow nano-spherical structure that incorporates Ag nano-seeds into a graphitic carbon nitride(g-C_(3)N_(4))shell layer,referred to as g-C_(3)N_(4)@Ag hollow *** g-C_(3)N_(4)@Ag spheres provide a managed internal site for Li metal encapsulation and promote stable Li *** g-C_(3)N_(4) spheres are uniformly coated using polydopamine,which has an adhesive nature,to enhance lithium plating/stripping *** strategic presence of Ag nano-seeds eliminates the nucleation barrier,properly directing Li growth within the hollow *** design facilitates highly reversible and consistent lithium deposition,offering a promising direction for the production of high-performance lithium metal *** well-designed g-C_(3)N_(4)@Ag hollow spheres ensure stable Li plating/stripping kinetics over more than 500 cycles with a high coulombic efficiency of over 97%.Furthermore,a full cell made using LiNi_(0.90)Co_(0.07)Mn_(0.03)O_(2) and Li-g-C_(3)N_(4)@Ag host electrodes demonstrated highly competitive performance over 200 cycles,providing a guide for the implementation of this technology in advanced lithium metal batteries.

关键词： hollow sphere lithiophilic site lithium deposition kinetics lithium metal anode lithium-ion conductor

来源：评论

学校读者我要写书评

暂无评论

Identifying the crystal structure of T 1 precipitates in Al-Li-Cu alloys by ab initio calculations and HAADF-STEM imaging

引用

Journal of Materials science & technology 2023年第2期133卷 41-57页

作者： Shuo Wang Xianghai Yang Junsheng Wang Chi Zhang Chengpeng Xue School of Materials Science and Engineering Beijing Institute of TechnologyBeijing 100081China Advanced Research Institute of Multidisciplinary Science Beijing Institute of TechnologyBeijing 100081China

Controversial experimental reports on the crystal structure of T 1 precipitates in Al-Li-Cu alloys have ex-isted for a long time,and all of them can be classified into five *** clarify its ground-state atomic structure,herein,we have combined high-throughput first-principles calculations and CALPHAD,as well as aberration-corrected HAADF-STEM *** the special quasi-random structure(SQS)and supercell approximation(SPA)methods to simulate the local disorder on Al-Cu sub-lattices,we find that none of the present models can satisfy the phase stability in Al-Li-Cu ternary system based on temperature-dependent convex hull *** the cluster expansion(CE)formulas,structural predic-tions derived from the five-frame models were ***,by introducing the vibrational contribution to the free energy at aging temperatures,we proposed a novel ground-state T 1 structure that maintains a coherent relationship with Al-matrix at the<112>Al *** underlying phase transition between the variants of T 1 precipitates was further *** means of ab initio molecular dynamics(AIMD)simulations,we resolved the controversy regarding the number of atomic layers con-stituting the T 1 phase and acknowledged the existence of Al-Li corrugated *** root cause of this structural distortion is triggered by atomic forces and *** work can have an positive impact on the novel fourth generation of Al-Cu-Li alloy designs by engineering the T 1 strengthening phase.

关键词： Al-Cu-Li alloys T 1 precipitates Cluster expansion First-principle calculations ab initio molecular dynamics HAADF-STEM

来源：评论

学校读者我要写书评

暂无评论

Deep transfer learning technique to detect white blood cell classification in regular clinical practice using histopathological images

引用

Multimedia Tools and Applications 2025年第9期84卷 5699-5723页

作者： Davamani, K. Anita Jawahar, Malathy Anbarasi, L. Jani Ravi, Vinayakumar Al Mazroa, Alanoud Robin, C. R. Rene Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Leather Process Technology Division CSIR-Central Leather Research Institute Chennai India School of Computing Science and Engineering Vellore Institute of Technology Vellore India Computer Science and Engineering Sri Sairam Engineering College Sirukalathur India Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh11671 Saudi Arabia

Leukemia, a malignant disease characterized by the rapid proliferation of specific types of white blood cells (WBC), has prompted increased interest in leveraging automatic WBC classification system. This study presents the design of a robust WBC classification system, integrating deep learning with microscopic image analysis. The proposed classification model is designed to categorize blood cells into four distinct groups: eosinophil, lymphocyte, monocyte, and neutrophil. The study employs a Kaggle image dataset for comprehensive learning. The dataset comprises 12,500 microscopic images used to categorize the four blood cell groups. To enhance performance and reduce the likelihood of overfitting, transfer learning techniques are employed. Six deep transfer learning models namely VGG16, ResNet50, AlexNet, MobileNet, GoogLeNet, and EfficientNetBO are used for feature extraction and classification. The results of the experiments show that VGG16 and MobileNet demonstrated enhancements compared to other pre-trained models when evaluated on both augmented and original datasets. A diverse set of evaluation criteria was employed to thoroughly assess the effectiveness of medical image classification. Notably, the VGG16 and MobileNet models accurately predicted Lymphocytes and Monocytes with a 100% accuracy rate, and Eosinophil and Neutrophil with rates of 99.35% and 99.81%, respectively. This research work empower medical practitioners, particularly in the field of pathology and hematology by providing advanced tools for accurate blood cell classification. The proposed VGG16 and MobileNet models hold promise for simplifying WBC classification, improving decision-making processes, and ultimately advancing patient care in the medical domain. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Adaptive Attention Mechanisms Based Multicriteria Recommender Systems for Improved User Preference Discovery

引用

SN computer science 2025年第4期6卷 1-16页

作者： Rajput, Ishwari Singh Tewari, Anand Shanker Tiwari, Arvind Kumar Department of Computer Science & Engineering National Institute of Technology Patna Bihar Patna India Department of Computer Science & Engineering Kamla Nehru Institute of Technology Sultanpur (UP) India

Personalized recommender systems are becoming more popular to reduce the issue of information overload. It is also observed that the recommendations provided by multi-criteria recommender system (MCRS) are more accurate and precise in comparison to conventional single-criteria recommender systems. MCRS incorporates customers’ preferences based on multiple criteria while recommending any item. Recent research has shown that MCRS can efficiently use implicit and explicit input across various dimensions to make exact recommendations across multiple domains. Due to use of multiple criteria, existing MCRS methods often struggle with effectively aggregating and interpreting these criteria, making it hard to accurately determine user preferences. In this article, we introduce a novel adaptive attention mechanism that dynamically assigns weights to each criterion based on user preferences. Each criterion is initially weighted based on its importance to a user. An attention mechanism is used to understand the exact user’s preference for each criterion. Later, the nearest-neighbor method finds users with similar interest to predict unknown ratings which is further used for Top-N recommendations. The performance of the proposed approach (AM-MCRS) is validated on Yahoo! movies multi-criteria dataset having 1716 unique users and 965 different movies and a comparative analysis is performed with existing single and multi-criteria recommender systems. The results clearly show that the AM-MCRS methodology outperform baseline methods, achieving the highest precision (0.9412), F-score (0.9343), accuracy (0.929), and coverage (0.9407), while also observing a significant reduction in MAE (0.7623) and RMSE (0.9145). These findings highlight the capability of AM-MCRS to improve recommendation accuracy and dependability across various domains. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Attention mechanism Deep learning Dynamic user preference Multi-criteria recommender system Nearest-neighbor

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：