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共 16713 条记录，以下是291-300 订阅

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Research on Detection of Compromised Campus Accounts Based on Features of Logging into Different Business Systems from Different networks 13

Research on Detection of Compromised Campus Accounts Based o...

引用

13th International conference on Information Technology in Medicine and Education, ITME 2023

作者： Zhong, Wenfeng Wang, Bo Chen, HuaiChu Tsinghua University Information Technology Center Beijing China Peking University Computer Center Beijing China

ISBN: (纸本)9798350319156

Detecting compromised campus accounts is of great significance to protecting the information security of colleges and universities. this paper proposes a method to use a machine learning model to analyze account sign-in logs to detect compromised campus accounts. the proposed method detects the compromised campus accounts mainly by analyzing the number and frequency of user accounts logging into different campus business systems in off-campus and on-campus network environments. this paper uses the proposed method to extract the features of campus users from the authentication logs of a university for three consecutive months, and then train the machine learning model, and then verify the prediction effect. the results show that the proposed method achieves a detection rate of up to 87.08% with a false-positive rate of 0.08% in identifying compromised campus accounts. © 2023 IEEE.

关键词： Machine learning

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ChronicNet: Randome Forest Classifier-based Chronic Heart Failure Detection with CNN Feature Analysis 13

ChronicNet: Randome Forest Classifier-based Chronic Heart Fa...

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13th IEEE International conference on Communication Systems and Network Technologies, CSNT 2024

作者： Malleswari, Dronadula Vazram, Bolla Jhansi Shaik, Rafi Narasaraopeta Engineering College Department of Computer Science & Engineering Andhra Pradesh Narasaraopet India

ISBN: (纸本)9798350305463

Chronic heart failure (CHF) is a common illness that affects the heart. the conventional machine learning and deep learning algorithms failed to identify CHF in the early stages. So, this work implemented the ChronicNet by combining the properties of both deep learning and machine learning. this study is all about creating a network for finding out CHF from sound recordings of the heart. these are called phonocardiogram data, or PCG. the first step, which is noise removal, signal boosting, and dividing parts, makes sure that heartbeat sounds are of good quality. We use Mel and pitchbased coefficients (MFCC) to analyze the frequency changes in a heartbeat signal. this helps pick out features that show what makes CHF heart disease more special. the convolutional neural network (CNN) feature extraction, using deep learning, helps the MFCC automatically find out special features on its own. the Random Forest classifier (RFC) is used to build a model that can predict CHF faster. In groups with problems to classify, the RFC uses a set of decision trees. this gives benefits such as holding up, fitting to big sizes, and being highly accurate. the proposed system uses CNN features to find CHF from PCG data and correctly identify it using RFC. the simulation results show that the proposed ChronicNet outperformed traditional approaches with 98.84% accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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Scene Text Recognition using Deep Learning Techniques 4

Scene Text Recognition using Deep Learning Techniques

引用

4th International conference on Emerging Trends in networks and computer Communications, ETNCC 2024

作者： Ying, Xin Murugesan, Raja Kumar Raja Sindiramutty, Siva Wei, Goh Wei Balakrishnan, Sumathi Kumar, Devender Verma, Sahil School of Computer Science Taylor's University Subang Jaya Malaysia Lovely Professional University Division of Research and Development Punjab Phagwara144411 India KKR and KSR Institute of Technology and Sciences Andhra Pradesh Guntur India Chandigarh Group of Colleges Jhanjeri Department of Computer Science and Engineering Punjab Mohali140307 India

ISBN: (纸本)9798350353266

In the discipline of computer vision, which aims to automatically identify text regions from photos and transform them into editable text data, text recognition in natural contexts presents a difficult task. Deep learning-based methods for text recognition in natural settings have advanced significantly in recent years. the feature extraction-based CRNN technique, which was tested in this paper, performed better. It is possible to accurately portray text properties in real-world environments by using the local entropy property, which is used to describe the information distribution and texture of images. In the CRNN approach based on local entropy feature extraction, the local entropy features from the picture are first extracted using the convolutional neural network. When these features are fed into the recurrent neural network for sequence modeling, the text output is subsequently generated. the CRNN technique based on feature extraction from local entropy may be able to recognize text in real-world situations because local entropy can correctly characterize the texture and information distribution in images. the SCUT-FORU dataset experiment results demonstrate the efficacy of the local entropy feature extraction based CRNN technique for text recognition in realistic settings. there are several fonts, sizes, colors, lighting, backdrops, and other elements in the SCUT-FORU data collection. the CRNN technique based on local entropy feature extraction provides improved accuracy and can perform the text recognition task more quickly, according to experiments on this dataset. In conclusion, the local feature extraction-based CRNN method is a potent text recognition method that may be applied in a range of real-world application scenarios. the continued improvement of deep learning technology will enable future developments and innovations in the field of character recognition in natural settings. © 2024 IEEE.

关键词： Convolutional neural networks

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Designing a Digital Repository for the Cultural and Natural Heritage of the Island of Zakynthos 9

Designing a Digital Repository for the Cultural and Natural ...

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9th South-East Europe Design Automation, computer Engineering, computer networks and Social Media conference, SEEDA-CECNSM 2024

作者： Palamas, Stergios Kabassi, Katerina Mylonas, Phivos Ionian University Department of Informatics Corfu Greece Ionian University Department of Environment Zakynthos Greece University of West Attica Department of Informatics and Computer Engineering Athens Greece

ISBN: (纸本)9798350342482

the present study discusses the design of a digital archive for the preservation and dissemination of the Cultural and Natural Heritage of Zakynthos Island. the proposed architecture suggests a Content Management System (CMS) to provide the core functionality, while a web application, interacting with the CMS through a RESTFul API, will provide an alternative front-end targeting mainly on-site end-users using mobile devices. Authors and contributors can submit multimedia content to the repository, which is reviewed and approved by system administrators before publishing, ensuring this way the information quality and *** system will also provide a RESTful API, allowing integration with 3rd party systems and web applications, thereby expanding the repository's reach and capabilities. Content delivery is personalized based on user profiles, location, and preferences, enhancing engagement and usability. By integrating these features, the repository effectively preserves and makes accessible the unique cultural and natural heritage of Zakynthos to both local and global audiences. © 2024 IEEE.

关键词： Augmented Reality Biodiversity Content Management Systems Cultural Heritage Multimedia Digital Repositories Natural Heritage Web Applications

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Applying Project-based Learning to Improve computer networks Courses: An Experience Report 13

Applying Project-based Learning to Improve Computer Networks...

引用

13th IEEE Global Engineering Education conference (IEEE EDUCON)

作者： Song, Zheng Shah, Nidhi Guo, Jinhua Zhu, Qiang Univ Michigan Comp & Informat Sci Dept Dearborn MI 48128 USA

ISBN: (纸本)9781665444347

Project-based learning (PjBL) has been increasingly adopted in computer science courses to improve students' engagement and learning outcomes. Although a computer networks course is in great need of a PjBL course module, no such module is available due to the huge gap between PjBL's design requirements and the current structure and content of the course. this paper introduces a novel PjBL module for a computer networks course, which challenges the students with a real world problem of developing the communication system for a smart lock. Following the PjBL design principles, we devise several scaffolding activities and assignments, which can be integrated into a semester-long computer networks course. We test ran the PjBL module in both undergraduate- and graduate-level computer networks courses. Our preliminary evaluation results show that the proposed PjBL module is well received by the students and helps improve their learning outcomes.

关键词： computer science conferences computer networks Engineering education

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Detecting Textual Deception in Courtroom Testimonies for Romanian Language Using Neural Network Methods 20

Detecting Textual Deception in Courtroom Testimonies for Rom...

引用

20th IEEE International conference on Intelligent computer Communication and Processing conference, ICCP 2024

作者： Crudu, Mâlina Lupea, Mihaiela Babes-Bolyai University Faculty of Mathematics and Computer Science Cluj-Napoca Romania Babes-Bolyai University Computer Science Department Cluj-Napoca Romania

ISBN: (纸本)9798331539979

Detecting deception through automated tools involves computational techniques to identify falsehoods in text, which is crucial for evaluating the truthfulness of testimonies in the legal field. the present research explores deception detection in Romanian texts using various methods. Initially, ANNs with different text representations, such as TF-IDF, LSA, and LIWC features were employed. these approaches were compared with BERT-based models on augmented data, assessing improvements in classification performance. Additionally, CNNs with embedding representations were used to examine the impact of context on detection accuracy. To gain deeper insights, LIME (local Interpretable Model-agnostic Explanations) was applied to visualise the importance of specific words and phrases in court statements. the present work is particularly novel for Romanian, as research on textual perjury detection in this language is limited. the obtained results are competitive with existing literature, highlighting the potential of the proposed approaches and impacting the fields of forensic linguistics and natural language processing, especially given the lack of research in non-English languages. © 2024 IEEE.

关键词： Convolutional neural networks

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Research on Genetic Algorithm of Network Search under computer Artificial Intelligence Big Data Technology 4

Research on Genetic Algorithm of Network Search under Comput...

引用

4th Asia-Pacific conference on Communications Technology and computer Science, ACCTCS 2024

作者： Li, Yuan Yu, Xin Modern Finance Industry School Shandong Institute of Commerce and Technology Shandong Jinan China

ISBN: (纸本)9798350359985

In this paper, an improved genetic simulated annealing algorithm (SAGA) for wireless sensor networks is studied and designed. the aim of this project is to better study the optimal transmission path in wireless sensor networks (WSN s) based on inter-node communication and reduce node energy consumption. Firstly, the model is built based on the optimal control index. then the fitness function, cross operation and variation operation in the evolutionary process are studied. the routing calculation and genetic evolution calculation are carried out at the same time until the approximate optimal path is found. this method can solve the traditional local optimization problem well. then the Metropolis criterion is given to make the jump variable in the simulated annealing algorithm have some regularity. Simulation results show that the proposed algorithm can effectively solve the problem of wireless sensor path optimization. Compared with the existing methods, the proposed method not only saves a lot of energy, but also saves a lot of calculation time. © 2024 IEEE.

关键词： Optimization algorithms

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Proposal of a Computational Vision Model for the Pre-diagnosis of Anemia Based on the Image of the Ocular Conjunctiva 13th

Proposal of a Computational Vision Model for the Pre-diagnos...

引用

13th computer Science Online conference

作者： Ildefonso, Alejandro Ticona, Wilfredo Univ ESAN Lima Peru Univ Tecnol Peru Lima Peru

ISBN: (纸本)9783031705175;9783031705182

Anemia is a persistent public health problem in Peru, with significant repercussions on individual quality of life and socio-economic progress at the national level. Although the hemogram is considered the reference method for diagnosing anemia, its need for time and a laboratory setting to be performed can represent a considerable limitation, especially in remote or less developed areas. the aim of the present research is to implement a computer vision model for the pre-diagnosis of anemia from the image of the ocular conjunctiva. the applied methodology was based on 5 phases: obtaining dataset, preprocessing, modeling and classification, feature extraction and implementation of CNN architectures. Several models were run with the classifiers: SVM, RF, MLP, and RNN and feature extraction techniques: SIFT, SURF, ORB and HOG. the model with RF and HOG extractor obtained the highest accuracy with 79%. Finally, deep learning models were explored, adjusting parameters such as the number of neurons, epochs, and samples in each model. Although initially the custom model obtained the highest accuracy of 93.18%, the Inception-ResNet-v2 model, supported by existing studies, was finally chosen and demonstrated a robust accuracy of 90.69% and loss of 0.2788, which is better than the loss of 0.3563 obtained with the comparison model.

关键词： computer Vision Anemia Machine Learning Predictive Accuracy Convolutional Neural Network Architectures Ocular Physiology Pre-diagnosis

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15th International conference on Ambient Systems, networks and Technologies networks, ANT 2024 / the 7th International conference on Emerging Data and Industry 4.0, EDI40 2024

15th International Conference on Ambient Systems, Networks a...

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15th International conference on Ambient Systems, networks and Technologies networks, ANT 2024 / the 7th International conference on Emerging Data and Industry 4.0, EDI40 2024

the proceedings contain 144 papers. the topics discussed include: towards continuous development for quantum programming in decentralized IoT environments;automated extension-based penetration testing for web vulnerabilities;addressing imbalanced data in predicting injury severity after traffic crashes: a comparative analysis of machine learning models;short-term glucose prediction in type 1 diabetes;an implementation, evaluation and validation of a dynamic fire and conflagration risk indicator for wooden homes;navigating the local transport gap: a comprehensive development proposal for a real-time location service application;addressing local and regional recharging demand: allocation of charging stations through iterative route analysis;wearable solution for health monitoring of car drivers;and simulating incident management team response and performance.

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Cyber Security Posterior Distribution 13

Cyber Security Posterior Distribution

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13th IEEE International conference on Communication Systems and Network Technologies, CSNT 2024

作者： Andrews, Leo John Baptist Venkatachary, Sampath Kumar Sarathkumar, D. Alagappan, Annamalai Fathima, M. Raj, Raymon Antony Rajeshkanna, R. Botho University Faculty of Engineering and Technology Department of Information Technology Gaborone Botswana Kongu Engineering College Department of Electrical and Electronics Engineering Erode district Tamilnadu 638 060 India Grant Thornton Gaborone Botswana Botho University Faculty of Engineering and Technology Department of Network and Infrastructure Management Gaborone Botswana Thamirabarani Engineering College Department of Eee Tamil Nadu Tirunelveli627 358 India Sri Krishna College of Engineering and Technology Department of Computer Science and Business Systems Tamilnadu Coimbatore India

ISBN: (纸本)9798350305463

Currently, the vast majority of international interactions and activities related to trade, government, culture, social interaction, and economics take place online. these interactions involve people, non-governmental organizations, governments, and government agencies at all levels. Recently, a sizeable number of commercial firms and government organizations all over the world have been presented with the challenge offered by cyberattacks as well as the danger represented by wireless communication technologies. A very little amount of time has passed since this had place. We were able to develop a model for the risk of cyber security by making use of Bayesian networks. this model has the capability of locating the point of cyber security that is weak and merging two different cyber analysis models. Among these is the activityquality model, which shows how individuals and/or groups adhere to the legal requirements for cyber security. Evidence describing the critical component of cyber security for a research reactor's RPS may be produced via the cyber security risk model. An further model that shows the probability of a cyberattack against RPS architecture is the cyber security risk model. © 2024 IEEE.

关键词： Bayesian networks

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