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检索条件"机构=Computer Technology and Application"

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Anonymous and Practical Multi-factor Authentication for Mobile Devices Using Two-Server Architecture 5th

Anonymous and Practical Multi-factor Authentication for Mo...

引用

5th International Conference on Security and Privacy in New Computing Environments, SPNCE 2022

作者： Cao, Haiyan Xie, Yong Department of Computer Technology and Application Qinghai University Xining China

ISBN: (纸本)9783031306228

At present, password authentication technology using single-server architecture has been widely used in practice. However, it cannot resist internal privilege attack, dictionary guessing attack, and other attacks. To solve the above problems, this paper proposes an anonymous and practical multi-factor authentication protocol for mobile devices using two-server architecture with honeywords. The protocol is more secure than the existing single-factor authentication protocols, and can solve serious security problems such as internal privilege attack and direct leakage of private data after the server is compromised using a single server architecture. The strict security analysis proves that the protocol is secure. Compared to similar protocols, our protocol needs lower computation and communication costs, and can better meet the practical application requirements. © 2023, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Network architecture

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Zero-Shot Object Detection Using YOLO

Zero-Shot Object Detection Using YOLO

引用

2024 IEEE International Conference on Information technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Anushiya, R. France, K. Hindustan Institute of Technology and Science Department of Computer Application Chennai India Hindustan Institute of Technology and Science Department of Computer Applications Chennai India

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382693

Zero-shot object detection is an emerging approach that aims to detect unseen object categories by leveraging the knowledge from known categories. In this study, we propose a novel zero-shot object detection framework based on the popular YOLO (You Only Look Once) architecture. Our approach addresses the challenge of detecting objects that have not been seen during training by learning a mapping between the visual features of unseen categories and their textual descriptions. We introduce a semantic embedding space that captures the relationship between visual and textual representations and use it to generate visual features for the unlabeled object instances. By aligning these visual features with the textual descriptions of unseen categories, we enable the detection of previously unseen object classes. Our experimental results demonstrate the effectiveness of our approach, achieving competitive performance for both unseen and seen object categories on kaggle datasets. Moreover, our framework's incremental learning capability allows for the continuous incorporation of new unseen object categories without the need for retraining the entire system. The proposed zero-shot object detection method opens up new opportunities for various applications, such as autonomous vehicles, surveillance systems, and robotics, where the ability to recognize novel objects is crucial with the high level of Accuracy-98.2%, Precision-97.9%, Recall-97.8%, F1 score-96.8% using You Only Look Once (YOLO) in machine learning algorithm. © 2024 IEEE.

关键词： Zero-shot learning

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Air Quality Prediction Using Supervised Machine Learning Techniques 6th

Air Quality Prediction Using Supervised Machine Learning Tec...

引用

6th International Conference on Information and Communication technology for Intelligent Systems, ICTIS 2021

作者： Shrivastava, Atul Lal Dwivedi, Rajendra Kumar Department of Information Technology and Computer Application MMMUT Gorakhpur India

ISBN: (纸本)9789811935701

To maintain good air quality, the observation of pollution levels framework estimates a variety of air contaminants in various locations. In the current circumstances, it is the most desirable concern. The release of hazardous gases into the environment by businesses, vehicles, and other sources pollutes the air. These days, the level of pollution in the air has surpassed fundamental levels, and the amount of contamination in several major urban areas has exceeded the public authority’s air quality list criteria. It has a tremendous impact on a person’s health. With advances in machine learning technology, it is now possible to forecast the future of toxins based on historical data. In this paper, we described a device that can collect the current toxic gases. With the help of the previous records of toxic gases, we executed a computation using machine learning techniques to detect the contamination in the air. It would be a good idea to use machine learning to forecast the future contamination information. The information gathered is saved in an Excel document for further analysis. To collect contamination data, the respective sensors are used on Arduino. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Contamination

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Detecting Deceptive News in Social Media Using Supervised Machine Learning Techniques 6th

Detecting Deceptive News in Social Media Using Supervised Ma...

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6th International Conference on Information and Communication technology for Intelligent Systems, ICTIS 2021

作者： Malviya, Anshita Dwivedi, Rajendra Kumar Department of Information Technology & Computer Application MMMUT Gorakhpur India

ISBN: (纸本)9789811935749

Our society has witnessed numerous incidents concerning fake *** media has always played a significant role in its *** with notorious mindsets often are the generators and spreaders of such *** mischievous people spread the fake news without even realizing the effect it has on naive *** believe on the fake news and start behaving *** news appeals to our *** plays with our feelings, and it can make us angry, happy, or ***, fake news can lead to hatred or anger toward a specific ***, people easily befool each other using social media as a tool to spread the fake *** this paper, we proposed a machine learning-based model for detection of such deceptive news which creates disturbances in our *** model is implemented in *** used machine learning algorithms, *** regression, multinomial Naive Bayes, passive-aggressive classifier, and multinomial classifier with *** vectorization techniques have been used for evaluation of *** show that the passive-aggressive classifier using Tf-idf vectorization outperforms others in terms of accuracy to detect the fake news. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Social networking (online)

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Segment Anything Model (SAM) to Segment lymphocyte from Blood Smear Images 3

Segment Anything Model (SAM) to Segment lymphocyte from Bloo...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Patil, Ashwini P. Hiremath, Manjunatha Department of Computer Science India Cmr Institute of Technology Department of Computer Application Bengaluru India Department of Computer Science Bengaluru India

ISBN: (纸本)9798350384598

Automated lymphocyte segmentation from smear images plays an important role in disease diagnosis and monitoring, aiding in the assessment of immune system function and pathology detection. This study proposes an approach for lymphocyte segmentation utilizing Segment Anything Model (SAM) which is a deep learning model. Our method leverages a pre trained SAM architecture and fine-tunes it on a custom dataset comprising smear images containing lymphocytes. The pretrained model's ability of versatile segmentation combined with fine-tuning on the specific dataset enhances its performance in accurately identifying lymphocyte boundaries. We evaluate the proposed approach on a diverse set of smear images, demonstrating its effectiveness in segmenting lymphocytes with impressive IOU score and Dice Score. SAM deep learning model, fine-tuned on custom datasets, holds promise for robust and efficient lymphocyte segmentation from blood smear images. © 2024 IEEE.

关键词： Image segmentation

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Simulation and Evaluation of Mental Sand Table based on Virtual Reality 3

Simulation and Evaluation of Mental Sand Table based on Virt...

引用

3rd International Conference on Consumer Electronics and computer Engineering, ICCECE 2023

作者： Xie, Sijia Liu, Zhiqiang Tang, Chuanqian Chen, Yanruyu Qinghai University Computer Technology and Application Department Xining China

ISBN: (纸本)9798350331578

In view of the low popularity rate of traditional mental sand table, insufficient site resources can not meet the problems of multiple people at the same time, based on virtual reality technology to achieve the real simulation of functional characteristics of mental sand table, promote the development and use of mental sand table. According to the functional characteristics of the traditional psychological sand table, the three-dimensional simulation construction of scene style, atmosphere characteristics, sand types and quantities is realized. The ray range is obtained based on the window matrix and the 3D perspective, and the 3D model boundary is detected and recolor is carried out to enhance the user's interactive visual feedback. The sand model and user interaction data were serialized and deserialized, and the horizontal and vertical offsets of model grid vertices were recorded to realize the storage and recovery of sand table data. Combined with the self-rating anxiety scale, the user experiment results show that VR mental sand table has positive significance in relieving anxiety and popularizing mental sand table. © 2023 IEEE.

关键词： Virtual reality

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"Nyctophy": Assessing the effectiveness of serious game for nyctophobia based on virtual reality by usability and heart rate monitoring 9th

"Nyctophy": Assessing the effectiveness of serious game for ...

引用

9th International Conference on computer Science and Computational Intelligence, ICCSCI 2024

作者： Ardiyanto, Elshad Ryan Alexander, Patrick Putra, Edyth Novian Ramdhan, Dimas Game Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

Nyctophobia is a phobia of the dark and is common among children but also found in adults. While the phobia itself is commonly known, the diversity of its treatment is still minimal. As technology has reached its highest point, numerous inventions and innovations have emerged in the medical sector. This study aimed to examine the usability of Virtual Reality and Keyboard and Mouse as an interface using the Extended Reality Game Usability Questionnaire (XR-GUQ) in Nyctophobia therapy and to analyze the heart rate response, impact, and implications using t-test Paired/Wilcoxon Signed Rank. Thirty-four Bina Nusantara University students from various backgrounds completed the test using interfaces and a smartwatch. They were divided into two groups, high- and low fear, using the modified Acrophobia Questionnaire (AQ), which resulted in a significant difference. The XR-GUQ result was insignificant in all variables, which indicates similar usability scores on both interfaces. The t-test Paired/Wilcoxon Signed Rank test revealed a significant difference between the high- and low-fear groups when using the Virtual Reality interface compared to the Keyboard and Mouse interface. This significant difference was found within each group regardless of the interface used. This research shows similar scores in terms of usability but a significant difference in heart rate response with the help of a serious game to give a new sensation of phobia therapy. © 2024 The Authors.

关键词： Serious games

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Machine Learning-Driven Economic Modeling for Enhanced Unemployment Rate Prediction and Analysis 16

Machine Learning-Driven Economic Modeling for Enhanced Unemp...

引用

16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Arora, Rakhi Dixit, Nitin Sihare, Muskan Kinger, Shreshtha Shivhare, Sneha Kulshreshtha, Komal Institute of Technology & Management Computer Science and Engineering Gwalior India Masters of Computer Application Vikrant University Gwalior India

ISBN: (纸本)9798331505264

This study presents a comprehensive analysis of economic modeling using Python, specifically focusing on the prediction of unemployment rates. In response to the growing significance of data-driven decision-making in economics, our research explores the application of logistic regression and random forest classification models to comprehend and forecast unemployment status. The dataset utilized in this analysis encompasses historical unemployment rate data, serving as a valuable source for our modeling endeavors. Our methodology begins with logistic regression, establishing a baseline model to scrutinize the relationship between diverse economic indicators and the binary classification of unemployment status (above or below 10%). We evaluate the model's efficacy in capturing trends over time and identifying influential factors in determining unemployment status. Subsequently, we advance our approach by employing random forest classification, a sophisticated machine learning technique adept at capturing non-linear relationships and interactions within the data. Our investigation shows that both random forest classification and logistic regression are good at predicting jobless status. We conduct a comparative evaluation of these models, shedding light on their respective strengths and limitations. Additionally, we delve into the significance of feature selection, hyperparameter tuning, and model evaluation within the realm of economic modeling using Python. © 2024 IEEE.

关键词： Logistic regression

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Multi-class Classification Approach for Smart Intrusion Attack Detection Using AI and ML Classifiers 8th

Multi-class Classification Approach for Smart Intrusion Atta...

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8th International Conference on Information and Communication technology for Intelligent Systems, ICTIS 2024

作者： Rathod, Dushyantsinh B. Patel, Yesha Jethava, Archana Gohel, Namrata Suthar, Dhruvi Prajapati, Ramesh Rathod, Ghanshyam Computer Engineering Department Ahmedabad Institute of Technology Gujarat Ahmedabad India Department of CSE Faculty of Engineering and Technology GLS University Gujarat Ahmedabad India Computer Engineering Department Shree Swaminarayan Institute of Technology Gujarat Gandhinagar India Department of Computer Application Parul Institute of Computer Application Baroda India

ISBN: (纸本)9789819766772

Due to increased network traffic and security concerns, intrusion detection system (IDS) research has received a lot of attention in computer science. Advanced intrusion detection systems have arbitrary incursion categories and demand a high processing power usage. Notwithstanding the fact that there is a wealth of literature on IDS issues, our objective is to provide a more thorough review. We offer the taxonomy to differentiate between modern IDSs based on the extensive survey and intricate organization. In addition, the architecture we provided in the content aids the reader in grasping the whole idea of IDSs with ease. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Network intrusion

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Using Machine Learning to Identify Autism Spectrum Disorder Across All Ages 2

Using Machine Learning to Identify Autism Spectrum Disorder ...

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2nd IEEE International Conference on IoT, Communication and Automation technology, ICICAT 2024

作者： Kumar, Anil Jaiswal, Umesh Chandra Madan Mohan Malaviya University of Technology Information Technology & Computer Application U.P Gorakhpur India

ISBN: (纸本)9798350368109

Autism spectrum disorder (ASD) is a neurodevelopmental condition affecting children's societal and cognitive skills, leading to recurring behaviors, limited interests, communiqué challenges, and difficulties in societal interaction. Early detection of ASD can mitigate its sternness and long-term impact. Federated learning (FL) is a cutting-edge technique useful for precise ASD diagnoses in the early stages or preventing prolonged effects. This article applies FL technique uniquely to detect autism. It trains support vector machine (SVM) and local logistic regression (LR) classifiers for ASD classification in both children and adults. Results from classifiers affected by FL are sent to a central server, where a meta-classifier is trained to identify the most accurate approach for detecting ASD in both children and adults. Four distinct datasets related to ASD patients, each comprising over 700 records of affected individuals, were acquired from various repositories for feature extraction. The suggested model accurately predicted ASD in children with a 97% accuracy rate and in adults with an 85% accuracy rate. © 2024 IEEE.

关键词： Federated learning

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