检索结果-内蒙古大学图书馆

Person identification based on voice biometric using deep neural network

International Journal of information technology (Singapore) 2023年第2期15卷 789-795页

作者： AL-Shakarchy, Noor D. Obayes, Hadab Khalid Abdullah, Zahraa Najm Department of Computer Science Faculty of Computer Science and Information Technology Kerbala University Kerbala Iraq College of Education for Humanities Studies University of Babylon Babylon Iraq

Nowadays in all everyday transactions, technological progress has become an intrinsic characteristic that depends on such electronic applications as financial and banking transfers, health care, project management, and other crucial life aspects. The core of these applications is person Identification and/or verification steps which can be considered one of the complicated limitations. Accordingly, the employment of biometric attributes can yield promising outcomes in these fields. A One’s voice is a unique bio-feature whereby people can be authenticated and precludes others from assuming a one’s identity without their previous knowing or assent. This work proposes a model with a new architecture to identify the person by exploiting the unique individual characteristics available in one’s voice based on deep learning. An augmentation method is utilized to increase the samples in the available dataset. The available temporal information at an input audio file is analysed then feature maps from this information are extracted which represent the salient temporal feature (time-domain features). The decision is made based on tracking these voice features over time. Successful and promising results are achieved through this work, the accuracy is close to 99.81% (± 1.78%) and the values of loss function are close to 0.009 over VoxCeleb1 dataset for identifying 40 subjects. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Batch normalization layer (BN) One Dimensional Convolutional Neural Networks (1DCNN) Person identification PI Pooling layer (Pool) Rectified linear units (ReLU) Voice biometric

来源：评论

学校读者我要写书评

暂无评论

The Design of Portrait Photography Guiding system Using VGG16 Model 14th

The Design of Portrait Photography Guiding System Using VGG1...

引用

14th International Conference on Frontier Computing, FC 2024

作者： Chiu, Chuan-Feng Huang, Chun-Hong Tseng, Hsiang-Chieh Wu, Pei-Jung Lin, Wei-Sheng Peng, Hsiang-Min Department of Information Management Minghsin University of Science and Technology Hsinchu Taiwan Department of Computer Information and Network Engineering Lunghwa University of Science and Technology Taoyuan City Taiwan

ISBN: (纸本)9789819623945

Because of the population of photography camera, human being could have photography equipment to take a picture became an easy task. However, to have a good photography is not an easy task. The basic of a good photo is the composition of the photography. A better photography composition could lead better visual effect. So, in this paper we intent to design a photography guiding system framework for users to have good photos especially for portrait photography. In our work, we design a system framework using VGG16 model based on Rule of Thirds and luminance distribution to guide user to select better photography composition. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Photographic equipment

来源：评论

学校读者我要写书评

暂无评论

Learning-Based Artificial Algae Algorithm with Optimal Machine Learning Enabled Malware Detection

引用

computer systems science & Engineering 2023年第9期46卷 3103-3119页

作者： Khaled M.Alalayah Fatma S.Alrayes Mohamed K.Nour Khadija M.Alaidarous Ibrahim M.Alwayle Heba Mohsen Ibrahim Abdulrab Ahmed Mesfer Al Duhayyim Department of Computer Science College of Science and ArtsNajran UniversitySharurahSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Computer Department Applied CollegeNajran UniversityNajran66462Saudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

Malware is a‘malicious software program that performs multiple cyberattacks on the Internet,involving fraud,scams,nation-state cyberwar,and *** malicious software programs come under different classifications,namely Trojans,viruses,spyware,worms,ransomware,Rootkit,botnet malware,*** is a kind of malware that holds the victim’s data hostage by encrypting the information on the user’s computer to make it inaccessible to users and only decrypting it;then,the user pays a ransom procedure of a sum of *** prevent detection,various forms of ransomware utilize more than one mechanism in their attack flow in conjunction with Machine Learning(ML)*** study focuses on designing a Learning-Based Artificial Algae Algorithm with Optimal Machine Learning Enabled Malware Detection(LBAAA-OMLMD)approach in computer *** presented LBAAA-OMLMDmodelmainly aims to detect and classify the existence of ransomware and goodware in the *** accomplish this,the LBAAA-OMLMD model initially derives a Learning-Based Artificial Algae Algorithm based Feature Selection(LBAAA-FS)model to reduce the curse of dimensionality ***,the Flower Pollination Algorithm(FPA)with Echo State Network(ESN)Classification model is *** FPA model helps to appropriately adjust the parameters related to the ESN model to accomplish enhanced classifier *** experimental validation of the LBAAA-OMLMD model is tested using a benchmark dataset,and the outcomes are inspected in distinct *** comprehensive comparative examination demonstrated the betterment of the LBAAAOMLMD model over recent algorithms.

关键词： computer networks machine learning security malware detection feature selection ransomware

来源：评论

学校读者我要写书评

暂无评论

A Self-Labeling Method for Adaptive Machine Learning by Interactive Causality

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第5期5卷 2093-2102页

作者： Ren, Yutian Yen, Aaron Haohua Li, Guann-Pyng University of California Irvine Department of Electrical Engineering and Computer Science The California Institute for Telecommunications and Information Technology IrvineCA92697 United States

Learning from unlabeled data or self-learning, can substantially reduce the complexity of machine learning (ML) utilization in real-time deployment. While the development of un/semisupervised algorithms shows promising results in learning with reduced labels, the fundamental assumption of data smoothness restricts its scope of application, especially with nonstationary data distributions in different domains. Leveraging transdomain invariant causal relationships, causality has recently been employed to foster robust ML. In this article, we have developed a generic method for self-labeling data that relies on known causality among interactive objects and learned temporal relations among causal events for identifying and associating labels and input data. The causal time-interval between asynchronous cause and effect events is studied to achieve self-labeling. We utilize dynamical system theory in a 1-d setting to demonstrate that our proposed method outperforms traditional feature similarity-based semisupervised learning. A computer simulation experiment was conducted to generate high-dimensional data, and the comprehensive results reveal the potential of learning adaptation in dynamic environments to improve ML robustness against shifts in data distribution. © 2023 IEEE.

关键词： Timing circuits

来源：评论

学校读者我要写书评

暂无评论

IoT-Supported Indoor Navigation system Using Machine Learning Technique 12th

IoT-Supported Indoor Navigation System Using Machine Learnin...

引用

12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Christy Jeba Malar, A. Deva Priya, M. Bhuvana, S. Vidhya, K. Sudharsan, S. Suhita, S. Department of Information Technology Sri Krishna College of Technology Tamilnadu Coimbatore India Department of Computer Science and Engineering Karpagam College of Engineering Tamilnadu Coimbatore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9789819788354

Indoor Navigation system (INS) supports seamless movement of objects within confined spaces in smart environments. In this paper, a novel INS that relies on ESP32-based Received Signal Strength Indication (RSSI) measurements for precise estimation of distance using K-Nearest Neighbour (K-NN) algorithm is proposed. The system architecture includes three ESP32 nodes functioning as clients, and ESP8266 combined with ESP32 acting as a server. To overcome the limitations of Global Positioning system (GPS) within an indoor environment, Machine Learning (ML)-based prediction models are employed to estimate distance between nodes and predict user locations along with the distance between source and destination. This approach not only addresses the challenges posed by GPS in indoor settings, but also shows the potential of ESP32 devices and predictive algorithms for enhancing navigation accuracy and reliability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Global positioning system

来源：评论

学校读者我要写书评

暂无评论

Event-Triggered Cooperative Tracking Control of Multiagent systems with a Dynamic Leader via Approximate Dynamic Programming

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2752-2765页

作者： Fu, Hao He, Haodong Chen, Yang Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System The School of Computer Science and Technology Wuhan430081 China Wuhan University of Science and Technology Eng. Res. Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan430081 China

This article investigates the optimal cooperative tracking control problem for unknown nonlinear multiagent systems with a real dynamic leader and uncertainties. Its main difficulty lies in eliminating effect of the unmatched uncertainty and noncooperation in the event-triggered manner. To overcome this difficulty, a distributed robust event-triggered control algorithm is developed with the aid of a disturbance observer and robust approximate dynamic programming (ADP). Specifically, this problem is converted into a cooperative multiplayer game for the distributed auxiliary systems employing observer-based feedforward compensation, which contributes to reducing conservatism;via an adaptive event-triggered mechanism and a new weight tuning law, a distributed robust ADP approach is proposed with hope to obviate judging the existence of the saddle point beforehand. Moreover, the local neighbor tracking error and the weight estimation error are demonstrated to be uniformly ultimately bounded by means of Lyapunov stability theory. We also prove that Zeno behavior is excluded. A comparative simulation example for multiquadrotor systems is carried out to verify the effectiveness and superiority of our algorithm. © 2020 IEEE.

关键词： Dynamic programming

来源：评论

学校读者我要写书评

暂无评论

Integrated modular approach to provide optimized VLE for learners’ engagement

引用

Multimedia Tools and Applications 2024年 1-18页

作者： Laeeq, Kashif Memon, Zulfiqar A. Abbasi, Muhammad Asad Awan, Shafique Ahmed Khan, Abdullah Ayub Federal Urdu University of Arts Sciences & Technology Karachi Pakistan National University of Computer & amp Emerging Sciences NUCES-FAST Karachi Pakistan Department of Computer Science and Information Technology Benazir Bhutto Shaheed University Lyari Karachi Pakistan

Higher education is severely damaged by the COVID-19 epidemic, which necessitated a switch from traditional chalk-and-talk instruction to online learning. The COVID-19 pandemic's needs are not fully met by virtual learning environments (VLEs), despite the fact that VLEs have been designed to offer a platform for conducting e-learning with the necessary resources. According to recent studies, students are no longer as enthusiastic, involved, or engaged in online learning. This article proposes IMPROVE (Integrated Modular Approach to Provide Optimized VLE for Learners' Engagement), a modular-based strategy to enhance VLEs for the Covid-19 pandemic scenario. The four cutting-edge modules that comprise IMPROVE are PLEM (Personal Learning Environment Module), FILM (Fellows-Inspired-Learning Module), LMGM (Like-Minded Group Module), and VBM (Virtual Bot Module). The MCP (Master Control Panel) may be used to manage these modules. The suggested method is created and incorporated into Moodle VLE. To evaluate the viability of the suggested strategy, several usability and engagement tests were conducted. A selection of university students was made to help with the assessment of the suggested system. The evaluation findings showed that the suggested plan greatly increased the learners' constructive engagement during online learning and had a good effect on learners' performance and VLE usability. The suggested system's plan is innovative and noteworthy, and it will greatly enhance the field of knowledge. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

DNBP-CCA:A Novel Approach to Enhancing Heterogeneous Data Traffic and Reliable Data Transmission for Body Area Network

引用

computers, Materials & Continua 2024年第5期79卷 2851-2878页

作者： Abdulwadood Alawadhi Mohd.Hasbullah Omar Abdullah Almogahed Noradila Nordin Salman A.Alqahtani Atif M.Alamri InterNetWorks Research Laboratory School of ComputingUniversiti Utara MalaysiaSintok06010Malaysia Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn MalaysiaParit RajaJohor86400Malaysia School of Games&Creative Technology University for the Creative ArtsFarnhamGU97DSUK Computer Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

The increased adoption of Internet of Medical Things (IoMT) technologies has resulted in the widespread use ofBody Area Networks (BANs) in medical and non-medical domains. However, the performance of IEEE 802.15.4-based BANs is impacted by challenges related to heterogeneous data traffic requirements among nodes, includingcontention during finite backoff periods, association delays, and traffic channel access through clear channelassessment (CCA) algorithms. These challenges lead to increased packet collisions, queuing delays, retransmissions,and the neglect of critical traffic, thereby hindering performance indicators such as throughput, packet deliveryratio, packet drop rate, and packet delay. Therefore, we propose Dynamic Next Backoff Period and Clear ChannelAssessment (DNBP-CCA) schemes to address these issues. The DNBP-CCA schemes leverage a combination ofthe Dynamic Next Backoff Period (DNBP) scheme and the Dynamic Next Clear Channel Assessment (DNCCA)scheme. The DNBP scheme employs a fuzzy Takagi, Sugeno, and Kang (TSK) model’s inference system toquantitatively analyze backoff exponent, channel clearance, collision ratio, and data rate as input parameters. Onthe other hand, the DNCCA scheme dynamically adapts the CCA process based on requested data transmission tothe coordinator, considering input parameters such as buffer status ratio and acknowledgement ratio. As a result,simulations demonstrate that our proposed schemes are better than some existing representative approaches andenhance data transmission, reduce node collisions, improve average throughput, and packet delivery ratio, anddecrease average packet drop rate and packet delay.

关键词： Internet of Medical Things body area networks backoff period tsk fuzzy model clear channel assessment media access control

来源：评论

学校读者我要写书评

暂无评论

DBNet: A Curve-based Dynamic Association Framework for Lane Detection

IEEE Transactions on Intelligent Vehicles

引用

IEEE Transactions on Intelligent Vehicles 2024年 1-10页

作者： Dai, Xinguang Xie, Jun Zhang, Guoxin Chang, Kenglun Chen, Feng Wang, Zhepeng Tang, Chunming Artificial intelligence Tiangong University Tianjin China Lenovo Research Beijing China Information Science and Engineering Hebei University of Science and Technology Shijiazhuang China Department of Computer Science and Technology Tsinghua University Beijing China

Lane detection plays an important role in the autonomous driving system and attracts widespread interest in recent years. Despite the advantages of conventional work, such as segmentation-based and point-based methods, these models either practically require a large sum of anchors or only leverage the connection message of discrete points in the lane. Different from this, curve-based methods enable one to naturally learn the holistic representation of lanes based on the geometric semantics of curves and conduct end-to-end optimization conveniently, thus serving a promising direction. However, the existing curve-based methods encounter draws in: 1) modeling some complicated curves mathematically. 2) Simultaneously capturing geometric semantics within and between lane curves. 3) The endpoints of lane lines are susceptible to occlusion, a factor that is crucial for curve modeling. To tackle these issues, we revisit the curve-based methods and propose a novel model Dynamic NURBS Network (DBNet). Specifically, we introduce the NURBS curve to model lanes, enabling to theoretically fit various complicated curves and guarantee the robustness of local and global optimization. Based on this, we introduce three key modules to address lane detection challenges. Firstly, we propose Local Dynamic-Interaction (LDI) that adaptively exploits the geometric message of local at the feature level. Secondly, a Curve Fitting Enhancement (CFE) module to enhance the feature of both ends of the lane instance. Lastly, a Global Association-Sharing (GAS) module captures the global features of lanes, which can together promote semantic association and tackle the occlusion problem inside and outside the lane. Moreover, the proposed method achieves a new state-of-the-art performance on three public benchmarks and especially achieves 77.98% of the F1 score on the curve scenario in the CULane dataset. IEEE

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Harnessing ML and NLP for Enhanced Cybersecurity: A Comprehensive Approach for Phishing Email Detection 25

Harnessing ML and NLP for Enhanced Cybersecurity: A Comprehe...

引用

25th International Arab Conference on information technology, ACIT 2024

作者： Aljamal, Mahmoud Alquran, Rabee Aljaidi, Mohammad Aljamal, Osama S Alsarhan, Ayoub Al-Aiash, Issa Samara, Ghassan Banisalman, Mohammad Khouj, Mohammad Irbid National University Faculty of Science and Information Technology Department of Cybersecurity Irbid21110 Jordan The Hashemite University Faculty of Prince Al-Hussien bin Abdullah for It Department of Information Technology Zarqa Jordan Zarqa University Faculty of Information Technology Department of Computer Science Zarqa13110 Jordan Al Hussein Technical University Department of Computer Science Amman Jordan Irbid Jordan Zarqa University Department of Computer Science Zarqa13110 Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

Emails stand as a predominant mode of online communication, facilitating the exchange of information. With the ubiquity of emails, there has been a marked rise in unsolicited messages, particularly phishing emails. These deceptive emails aim to mislead recipients, often masquerading as trustworthy entities, to extract confidential information. Embedded with dubious offers, advertisements, malicious links, malware, and trojans, perpetrators deploy these emails with malicious intent, primarily to commit fraud. Consequently, distinguishing between phishing attempts and genuine emails becomes paramount. This study constructs machine learning-based models to detect phishing emails, ensuring accurate discernment between genuine and fraudulent messages. Leveraging two datasets, the Fraud Email Dataset and the Phishing Email Dataset, we developed and evaluated classifiers, including Logistic Regression, Decision Tree, Random Forest, and SVM. The Random Forest model emerged as the most effective, achieving accuracies of 98.72% on the Fraud Email Dataset and 99.15% on the Phishing Email Dataset, demonstrating strong generalizability across diverse phishing patterns. These results underscore the efficacy of ensemble methods for enhancing cybersecurity by reliably detecting phishing emails. © 2024 IEEE.

关键词： Phishing

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：