检索结果-内蒙古大学图书馆

1st International Conference on Software, Systems and Information technology, SSITCON 2024

作者： Nanthini, S. Jain, Upma Arora, Renuka Bhatia, Rishu Sutaria, Kruti Patil, Harshal Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Jagannath University Department of Computer Science and Engineering Haryana India Ganga Institute of Technology and Management Department of Electronics and Communication Engineering Haryana India Parul Institute of Engineering and Technology Faculty of Engineering and Technology Gujarat India School of Computer Science and Engineering Uttar Pradesh India

ISBN: (纸本)9798350352931

The Internet has grown in importance and impact over the years, causing people to become more reliant on it. The Internet has evolved into a major vector for cybercrime because to its ever-increasing user base. Over the last decade, the number of these computing systems - including desktops, laptops, smartphones, and the Internet of Things (IoT) - has skyrocketed. Among them, cell phones are practically integral to modern life. The popularity of web-based assaults has skyrocketed with the exponential growth in the number of individuals using the Internet. These web-based assaults are increasingly being combatted by security corporations. Unfortunately, new forms of these assaults are appearing all the time, making it hard for older security measures to stay up. Artificial intelligence (AI) is a source of optimism in the current cybersecurity landscape, offering a potential solution to the ever-changing digital dangers. The fast development of AI over the last decade has given rise to this optimism, because it is now impacting the expansion of every industry. With AI bringing so many advantages in every field, online security is one sector that just cannot afford to ignore it. This planned effort's work represents an advance in that direction. Critical online security issues have been the focus of this proposed work's study, which aims to address these issues using AI. Web security issues for desktop and mobile devices have been addressed in the proposed work. The planned work's contributions to online security are as follows: The 'MalCrawler' web crawler is a targeted tool for finding and exploring the web. This crawler makes it easy to gather websites, particularly ones that are harmful. It does a better job of collecting dangerous websites than a typical crawler. Additionally, it is built to circumvent the evasion strategies used by rogue websites. The crawler's ability to gather webpages - particularly dangerous ones - in order to provide datasets for ML-based an

关键词： Web crawler

来源：评论

学校读者我要写书评

暂无评论

Incorporation of Two-Fold Trust in Group Recommender System to Handle Popularity Bias

引用

SN computer science 2024年第2期5卷 1-15页

作者： Richa Bedi, Punam Gupta, Shivani Kumar, Ranjeet Department of Computer Science and Engineering Birla Institute of Technology Mesra Jharkhand Ranchi India Department of Computer Science University of Delhi Delhi New Delhi India School of Computer Science Engineering and Technology Vellore Institute of Technology Tamilnadu Chennai India School of Electronics Engineering Vellore Institute of Technology Tamilnadu Chennai India

In recent years, there has been a rise in work which focuses on various issues of recommender systems other than accuracy. Popularity bias is one factor that causes the list of recommendations to deviate from the user’s expectation and causes biased list to generate. This paper proposed an approach that handles the popularity bias in trust aware group recommender system using trust propagation. The proposed work considers two factors, novelty and serendipity, to evaluate the presence of bias. By incorporating trust, along with novelty, diversity, and unexpectedness, the diverse list of recommendation that maintains accuracy should be generated. Trust propagation combines trustworthy user’s opinion indirectly involves enhancing the accuracy. The trust propagation and randomness in trust propagation ensures the trade-off between the accuracy and bias of the recommendations. A prototype is developed using the tourism dataset and compared using traditional approach of recommendation using various accuracy measures and coverage. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Diversity Novelty Popularity bias Recommender system Relevancy

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of Machine Learning, Ensemble Learning and Deep Learning Classifiers for Parkinson’s Disease Detection

引用

SN computer science 2024年第1期5卷 1-14页

作者： Goyal, Palak Rani, Rinkle Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala India

A progressive neurodegenerative ailment called Parkinson's disease (PD) is marked by the death of dopamine-producing cells in the substantia nigra area of the brain. The exact etiology of PD remains elusive, but it is believed to involve the presence of Lewy bodies, abnormal protein aggregates, in affected brain regions, leading to the mobile symptoms of PD. Hence, as the management of PD continues to evolve, there is a growing demand for the establishment of a descriptive system that enables the early detection of PD. In this study, we conducted an extensive analysis using machine learning, ensemble learning, and deep learning models with different hyperparameters to develop accurate classification models for PD prediction. To enhance classifier performance and address overfitting, we employed principal component analysis (PCA) for feature selection along with various preprocessing techniques. The dataset used consisted of voice samples, comprising 188 PD patients and 64 normal individuals. Our results demonstrated that the Random Forest (RF) model with accuracy of 82.37% outperformed the other base classifiers Among the ensemble classifiers, the LGBM model exhibited the highest accuracy of 85.90% when compared to both base and ensemble classifiers. Notably, the deep learning model has 91.33% training accuracy and 85.02% testing accuracy, suggesting that deep learning models perform comparably equivalent on small datasets compared to machine learning classifiers. Overall, our findings underscore the effectiveness of machine learning, ensemble techniques and deep learning models in accurately predicting PD. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Deep learning Hyperparameters LGBM Machine learning Parkinson’s disease Random forest

来源：评论

学校读者我要写书评

暂无评论

Intelligent gold nanocluster for effective treatment of malignant tumor via tumor-specific photothermal–chemodynamic therapy with AIE guidance

引用

National science Review 2024年第5期11卷 326-342页

作者： Feng Liu Tianfeng Yang Xiaowei Chang Li Chen Cheng Cheng Xiuhong Peng Haihu Liu Yanmin Zhang Xin Chen School of Energy and Power Engineering Xi'an Jiaotong University Department of Chemical Engineering Shaanxi Key Laboratory of Energy Chemical Process IntensificationInstitute of Polymer Science in Chemical Engineering School of Chemical Engineering and Technology Xi'an Jiaotong University School of Pharmacy Health Science Center Xi'an Jiaotong University

Precise and efficient therapy of malignant tumors is always a challenge. Herein, gold nanoclusters co-modified by aggregation-induced-emission（AIE） molecules, copper ion chelator（acylthiourea） and tumor-targeting agent（folic acid） were fabricated to perform AIE-guided and tumor-specific synergistic therapy with great spatio-temporal controllability for the targeted elimination and metastasis inhibition of malignant tumors. During therapy, the functional gold nanoclusters（Au NTF） would rapidly accumulate in the tumor tissue due to the enhanced permeability and retention effect as well as folic acid-mediated tumor targeting, which was followed by endocytosis by tumor cells. After that, the overexpressed copper ions in the tumor cells would trigger the aggregation of these intracellular Au NTF via a chelation process that not only generated the photothermal agent in situ to perform the tumor-specific photothermal therapy damaging the primary tumor, but also led to the copper deficiency of tumor cells to inhibit its ***, the copper ions were reduced to cuprous ions along with the chelation, which further catalysed the excess H2O2in the tumor cells to produce cytotoxic reactive oxygen species, resulting in additional chemodynamic therapy for enhanced antitumor efficiency. The aggregation of Au NTF also activated the AIE molecules to present fluorescence, which not only imaged the therapeutic area for real-time monitoring of this tumor-specific synergistic therapy, but also allowed us to perform near-infrared radiation at the correct time point and location to achieve optimal photothermal therapy. Both in vitro and in vivo results revealed the strong tumor elimination, effective metastasis inhibition and high survival rate of tumor-bearing mice after treatment using the Au NTF nanoclusters, indicating that this AIE-guided and tumor-specific synergistic strategy could offer a promising approach for tumor therapy.

关键词： AIE-guided precise tumor treatment copper-dependent photothermal therapy copper-triggered chemodynamic therapy primary tumor elimination copper-plunder-induced metastasis inhibition

来源：评论

学校读者我要写书评

暂无评论

Securing healthcare big data in industry 4.0: cryptography encryption with hybrid optimization algorithm for IoT applications

引用

Optical and Quantum Electronics 2024年第3期56卷 1-29页

作者： Goswami, Chandrashekhar Tamil Selvi, P. Sreenivas, Velagapudi Seetha, J. Kiran, Ajmeera Talasila, Vamsidhar Maithili, K. Department of Computer Science and Engineering School of Computing MIT ADT University Maharashtra Pune India School of Computing Science and Engineering Rajalakshmi Engineering College Mevalurkuppam India Department of Computer Science and Engineering SRK Institute of Technology Vijayawada (A.P) India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering MLR Institute of Technology Telangana Dundigal Hyderabad India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur Dist(A.P) India Department of CSE KG Reddy College of Engineering and Technology Telangana Chilukuru Village India

In recent years, the Internet of Things (IoT) has grown at an exponential rate, transforming the healthcare business and perhaps leading to the creation of healthcare big data. As a result, there is a requirement to safeguard data from being attacked in order to ensure secure data transfer through the network. Cryptography has been discovered to be a simple and efficient method for safeguarding healthcare big data. At the same time, in cryptography, the best key generation process is viewed as an optimization issue that may be addressed with meta-heuristic algorithms. As a result, the main focus of this research is on the investigation of health care data security in IoT using the ASS-JFO-DHEA model, which combines an innovative hybrid Artificial Shuffle Shepherd Integrated Jellyfish optimization (ASS-JFO) algorithm with Digital Homomorphism Elgamal Algorithm (DHEA) encryption for data security. MATLAB software is used to carry out the execution and experiments for this research. On different benchmark images from a healthcare dataset, the ASS-JFO-DHEA model is experimentally validated. The peak signal to noise ratio, root mean square error, encryption time, mean square error, and other metrics are used to assess the *** findings are compared and contrasted as a consequence of this execution, and a variety of encryption algorithms with their optimization techniques from the literature are recognized as having the most intense PSNR values, i.e., 74 dB, generated by the suggested approach. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Cryptography Healthcare Internet of things and cloud Optimization Security

来源：评论

学校读者我要写书评

暂无评论

AI-Generated Content-as-a-Service in IoMT-Based Smart Homes: Personalizing Patient Care With Human Digital Twins

引用

IEEE Transactions on Consumer Electronics 2024年第1期71卷 1-1页

作者： Akram, Junaid Aamir, Misha Raut, Roshani Anaissi, Ali Jhaveri, Rutvij H. Akram, Awais School of Computer Science The University of Sydney Camperdown NSW Australia Department of Information Technology Pimpri Chinchwad College of Engineering Savitribai Phule Pune University Pune India Department of Computer Science and Engineering School of Technology Pandit Deendayal Energy University India COMSATS University Islamabad Vehari Pakistan

We introduce the AI-Generated Optimal Decision (AIGOD) algorithm and the Deep Diffusion Soft Actor-Critic (DDSAC) framework, marking a significant advancement in integrating Human Digital Twins (HDTs) with AI-Generated Content (AIGC) within IoMT-based smart homes. Our innovative AI-Generated Content-as-a-Service (AIGCaaS) architecture, optimized for IoMT environments, leverages network edge servers to enhance the selection of AI-Generated Content Service Providers (AISPs) tailored to the unique characteristics of individual HDTs. Extensive experiments demonstrate DDSAC’s HDT-centric approach outperforms traditional Deep Reinforcement Learning algorithms, offering optimal AIGC services for diverse healthcare needs. Specifically, DDSAC achieved a 20% improvement in task completion rates and a 15% increase in overall utility compared to existing methods. These findings highlight the potential of HDTs in personalized healthcare by simulating and predicting patient-specific medical outcomes, leading to proactive and timely interventions. This integration facilitates personalized healthcare, establishing a new standard for patient-centric care in smart home environments. By leveraging cutting-edge AI techniques, our research significantly contributes to the fields of IoMT and AIGC, paving the way for smarter and more responsive healthcare services. IEEE

关键词： Real time systems

来源：评论

学校读者我要写书评

暂无评论

MOALF-UAV-MEC: Adaptive Multi-Objective Optimization for UAV-Assisted Mobile Edge Computing in Dynamic IoT Environments

引用

IEEE Internet of Things Journal 2025年第12期12卷 20736-20756页

作者： Al-Bakhrani, Ali A. Li, Mingchu Obaidat, Mohammad S. Amran, Gehad Abdullah Dalian University of Technology School of Software Dalian116024 China Albaydha University Al Bayda Yemen Jiangxi Normal University School of Computer and Information Engineering Jiangxi China School of Software Technology Dalian University of Technology Nanchang Dalian China University of Science and Technology School of Computer and Communication Engineering Beijing100083 China SRM University School of Computing Department of Computational Intelligence TN Kattankulathur603203 India The Amity University School of Engineering UP Noida201301 India Dalian University of Technology Department of Management Science and Engineering Dalian116024 China

The proliferation of Internet of Things (IoT) devices and computation-intensive applications has led to unprecedented demands on network resources and computing capabilities. This paper presents MOALF-UAV-MEC, a novel Multi-Objective Adaptive Learning Framework for UAV-Assisted Mobile Edge Computing (MEC) tailored for dynamic IoT environments. The framework integrates Multi-Objective Reinforcement Learning (MORL), Model Predictive Control (MPC), Adaptive Particle Swarm Optimization (APSO), and Lyapunov Optimization to optimize UAV trajectories, dynamic resource allocation, and system stability. MOALF-UAV-MEC addresses critical challenges in UAV-assisted MEC, including multi-objective optimization, adaptive resource allocation, energy efficiency, scalability, and quality of service guarantees. Our approach employs a unique burst mode feature for UAVs, enabling temporary performance boosts in high-demand situations. Extensive simulations demonstrate the framework's efficiency in enhancing task completion rates, energy efficiency, and long-term system sustainability. Results show a task completion rate of 94.50%, significantly outperforming existing approaches, with an average of 1,890 completed tasks per UAV and a load balancing efficiency of 96%. The framework exhibits robust adaptive behavior, achieving a 38% reduction in UAV route optimization and a 55% increase in task completion during high-load periods. This research contributes to the advancement of edge computing in IoT environments, offering a scalable and adaptive solution for deploying computational resources in areas with limited infrastructure, during temporary events, or in emergency situations. © 2014 IEEE.

关键词： Model predictive control

来源：评论

学校读者我要写书评

暂无评论

A Highly Accurate Dysphonia Detection System Using Linear Discriminant Analysis

引用

computer Systems science & engineering 2023年第3期44卷 1921-1938页

作者： Anas Basalamah Mahedi Hasan Shovan Bhowmik Shaikh Akib Shahriyar Department of Computer Engineering Umm Al-Qura UniversityMakkahSaudi Arabia Department of Computer Science and Engineering Khulna University of Engineering&TechnologyKhulna9203Bangladesh

The recognition of pathological voice is considered a difficult task for speech ***,otolaryngologists needed to rely on oral communication with patients to discover traces of voice pathologies like dysphonia that are caused by voice alteration of vocal folds and their accuracy is between 60%–70%.To enhance detection accuracy and reduce processing speed of dysphonia detection,a novel approach is proposed in this *** have leveraged Linear Discriminant Analysis(LDA)to train multiple Machine Learning(ML)models for dysphonia *** ML models are utilized like Support Vector Machine(SVM),Logistic Regression,and K-nearest neighbor(K-NN)to predict the voice pathologies based on features like Mel-Frequency Cepstral Coefficients(MFCC),Fundamental Frequency(F0),Shimmer(%),Jitter(%),and Harmonic to Noise Ratio(HNR).The experiments were performed using Saarbrucken Voice Data-base(SVD)and a privately collected *** K-fold cross-validation approach was incorporated to increase the robustness and stability of the ML *** to the experimental results,our proposed approach has a 70%increase in processing speed over Principal Component Analysis(PCA)and performs remarkably well with a recognition accuracy of 95.24%on the SVD dataset surpassing the previous best accuracy of 82.37%.In the case of the private dataset,our proposed method achieved an accuracy rate of 93.37%.It can be an effective non-invasive method to detect dysphonia.

关键词： Dimensionality reduction dysphonia detection linear discriminant analysis logistic regression speech feature extraction support vector machine

来源：评论

学校读者我要写书评

暂无评论

Vision-based Robotic Arm Control for Screwdriver Bit Placement Tasks

引用

Sensors and Materials 2024年第3期36卷 1003-1018页

作者： Lin, Cheng-Jian Lin, Pei-Jung Shih, Chi-Huang Department of Computer Science and Information Engineering National Chin-Yi University of Technology Taichung411 Taiwan Department of Information Engineering and Computer Science Feng Chia University Taichung407 Taiwan

Robotic arms are widely used in the automation industry to package and deliver classified objects. When the products are small objects with very similar shapes, such as screwdriver bits with slightly different threads, pointed tips, and thicknesses, object selection and assembly often lead to misjudgment. We have developed a practical robotic arm control system based on vision detection techniques for screwdriver bits' placement. In addition to effectiveness, easy deployment and high flexibility in the field are also taken into account. The vision-based system consists of four processing stages in the following order: world coordinate conversion from image pixel coordinates, object detection, edge detection, and object orientation. In the first stage, a manual two-point marking method is proposed to easily configure the coordinate conversion for robot operating system (ROS)-based manipulators. For the following stages, we focus on the fine integration of state-of-the-art methods for the technical feasibility of the screwdriver bit placement. Such integration includes the selection between object detection methods and the data flow control among system stages. The experimental results show that (1) in detecting screwdriver bits, You Only Look Once (YOLO) v4 outperforms YOLOv7 and Single Shot MultiBox Detector at an accuracy rate of 99.51%;(2) in the edge detection, the object detection output can better illustrate the object contour than a whole image, achieving a mean absolute error of 0.86% in estimating the object angle;and (3) a successful real-time replacement rate of 96% is achieved for 12 screwdriver bits randomly scattered on a conveyor belt. © 2024 M Y U Scientific Publishing Division. All rights reserved.

关键词： Robotic arms

来源：评论

学校读者我要写书评

暂无评论

Analysis of community question-answering issues via machine learning and deep learning:State-of-the-art review

引用

CAAI Transactions on Intelligence technology 2023年第1期8卷 95-117页

作者： Pradeep Kumar Roy Sunil Saumya Jyoti Prakash Singh Snehasish Banerjee Adnan Gutub Department of Computer Science and Engineering Indian Institute of Information TechnologySuratIndia Indian Institute of Information Technology DharwadIndia Department of Computer Science and Engineering National Institute of TechnologyPatnaIndia The York Management School University of YorkYorkUK Computer Engineering Department College of Computer&Information SystemsUmm Al-Qura UniversityMakkahSaudi Arabia

Over the last couple of decades,community question-answering sites(CQAs)have been a topic of much academic *** have often leveraged traditional machine learning(ML)and deep learning(DL)to explore the ever-growing volume of content that CQAs *** clarify the current state of the CQA literature that has used ML and DL,this paper reports a systematic literature *** goal is to summarise and synthesise the major themes of CQA research related to(i)questions,(ii)answers and(iii)*** final review included 133 *** research themes include question quality,answer quality,and expert *** terms of dataset,some of the most widely studied platforms include Yahoo!Answers,Stack Exchange and Stack *** scope of most articles was confined to just one platform with few cross-platform *** with ML outnumber those with ***,the use of DL in CQA research is on an upward trajectory.A number of research directions are proposed.

关键词： answer quality community question answering deep learning expert user machine learning question quality

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：