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Exploring Students' Attitudes Towards Robotics Technology in STEM Education: A Machine Learning-Based Prediction Analysis 4

Exploring Students' Attitudes Towards Robotics Technology in...

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4th IEEE International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2024

作者： El Aziz, Mahmoud I. Abd Elshewey, Ahmed M. Tarek, Zahraa El-Kenawy, El-Sayed M. Ghazy, Ahmed M. Bhatnagar, Roheet Shams, Mahmoud Y. Kafrelsheikh University Faculty of Education Department of Curriculum Instruction and Educational Technology Kafrelsheikh33511 Egypt Suez University Faculty of Computers and Information Computer Science Department Suez Egypt Mansoura University Faculty of Computers and Information Computer Science Department Mansoura35561 Egypt Delta Higher Institute of Engineering and Technology Dept. of Communications and Electronics Mansoura35111 Egypt Manipal University Department of Computer Science and Engineering Rajasthan Jaipur India Kafrelsheiku University Faculty of Artificial Intelligence Dept. of Machine Learning 33516 Egypt

ISBN: (纸本)9798350387063

The aim of this paper is to predict the extent to which students will accept robotics technology in future teaching plans. The study involves students engaging in a two-week project-based learning program on robotics technology, after which they will decide whether it is suitable for them. To achieve this, the paper uses several prediction algorithms based on Machine Learning (ML), including Linear Regression (LR), Ridge Regression (RR), Elastic Net (EN) regression, and K-Nearest Neighbor (KNN) models. The sample group consists of 35 students from STEM high schools in Egypt. The research findings indicate that the students have gained confidence and satisfaction in robotics technology and believe it will have significant value in the future. The prediction results obtained from the ML regression models support these findings. The dataset used in the study includes seven input features and one target output representing the total prediction values of accepting and rejecting robotics technology in STEM schools. The regression models LR, RR, EN, and KNN achieve determination coefficients (R2) of 99.99%, 98.80%,99.20%, and 85.60%, respectively. © 2024 IEEE.

关键词： Prediction models

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A Innovative Network Security Regulations Dependent on Improved Support Vector Machine from the Outlook of Modern Cities

A Innovative Network Security Regulations Dependent on Impro...

引用

International Conference on computer and Communications Technologies (ICCCT)

作者： Vasanthan P S. Srinivasan Dept. of Information Technology Sri Sairam Engineering College Dept. of Computer Science and Engineering R.M.D Engineering College

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

The concerns about the security created by the PC have gotten more advanced and complicated. Interruption detection is a pragmatic subject in the area of PC security whose essential target is to identify uncommon assault or attacks and to guarantee the safety of inside frameworks. This paper likewise suggests a semi-class interruption recognition strategy that joins various classifiers to mastermind exemptions and regular activities in a PC framework. In the consideration preference tree learning-iterative dichotomy 3, the maltreatment recognition method is developed and is gathered by using the cumulative knowledge based on the peculiarity detection system performed by one class- uphold vector machine. As of late, individuals have paid more thoughtfulness regarding ID/interruption avoidance framework, which is firmly identified with the insurance and use of framework the executives. A couple of AI principles including neural framework, genetic programming, and progressed uphold vector machines, Bayesian framework, multivariate adaptable backslide splines, feathery deduction systems and other analogical frameworks has been scrutinized for the layout of interruption identification framework. In this article, we suggest a combination strategy dependent on DTL-ID3 and OC-SVM assess the presentation of the extended procedure by utilizing a particular dataset and a hybrid technique to upgrade the precision of IDS/IPS when stood out from a solitary help vector machine.

关键词： Surveys Support vector machines Urban areas Vectors Data models Regulation Safety Registers Splines (mathematics) Pragmatics

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Prioritized Semi-supervised Deep Embedded Clustering

Prioritized Semi-supervised Deep Embedded Clustering

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2022 International Conference on Innovative Trends in information Technology, ICITIIT 2022

作者： Saladi, Pranita Guntupalli, Rishi Manudeep Puppala, Sudheer Kumar Pulabaigari, Viswanath Indian Institute of Information Technology Sri City Dept. Computer Science Engineering Sri City India

ISBN: (纸本)9781665406703

Clustering, to group similar objects, is an important problem. Recently deep learning-based methods like Deep Embedded Clustering (DEC) [6] and its semi-supervised version called Semi-supervised Deep Embedded Clustering (SDEC) [12], where partially labeled data or data with constraints is available, are shown to give promising results. Both DEC and SDEC learn a latent space where similar objects are closer and dissimilar are away. While promising results are shown, the information present in constraints or a labeled subset of the data is not fully utilized. This paper proposes to use priorities for constraints so that important constraints are given more weightage than unimportant ones. Those constraints with points that are far away, but should be clustered into a group, gets more weight than other labeled points. Similarly, those in different groups which are very close get more weightage. The appropriate loss function is used in the learning process. The proposed method is called Prioritized Semi-supervised Deep Embedded Clustering (PSDEC). The results are compared using a few standard data sets against recent and classical similar methods. PSDEC is found to achieve a better result than un-prioritized constraints. © 2022 IEEE.

关键词： Interpolation

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Recognition of Identifying Malicious Cyber Attack Intention in Cyber Physical System

Recognition of Identifying Malicious Cyber Attack Intention ...

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International Conference on computer and Communications Technologies (ICCCT)

作者： P Vasanthan S. Srinivasan Dept. of Information Technology Sri Sairam Engineering College Dept. of Computer Science and Engineering R.M.D Engineering College

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

As the threat over the cyber-attack is predominantly increasing in every aspects such as based on network traffic, spoofing, phishing attacks the intrusion detection system is used. The network layer is maliciously attacked by the intruder. The intruder tries to capture network packets of the connection between client and server. Based upon the encryption standard, the intruder tries to comprise the user system. Intrusion cannot be easily detected since it is a manual process. In this novel approach, deep learning reinforcement technique is implemented. The bidirectional end to end communication along with the Bayesian inverse reinforcement learning is used to detect the malicious cyber intention in cyber physical system. Inverse Bayesian reinforcement learning technique is initiated to identify the behavior of the pattern recognition and to resample the dataset. This deals with the risk factor and determines whether the connection is comprised. In this work, the novel application is introduced with deep reinforcement learning to identify the intruder. The intrusion detection is labeled in dataset. These dataset implements reward functions to detect the intrusion over the cps.

关键词： Uncertainty Phishing Intrusion detection Telecommunication traffic Probability distribution Bayes methods Pattern recognition Servers Cyberattack Standards

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PotholeGuard: A Pothole Detection Approach by Point Cloud Semantic Segmentation

PotholeGuard: A Pothole Detection Approach by Point Cloud Se...

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2023 International Conference on Modeling and E-information Research, Artificial Learning and Digital Applications, ICMERALDA 2023

作者： Nawale, Sahil Khut, Dhruv Dave, Daksh Sawhney, Gauransh Aggrawal, Pushkar Devadkar, Kailas Information Technology Sardar Patel Institute of Technology Mumbai India Bits Pilani Dept. of Electrical Electronics Pilani India Sardar Patel Institute of Technology Dept. of Computer Science and Engineering Mumbai India

ISBN: (纸本)9798350369359

Pothole detection is crucial for road safety and maintenance, traditionally relying on 2D image segmentation. However, existing 3D Semantic Pothole Segmentation research often overlooks point cloud sparsity, leading to suboptimal local feature capture and segmentation accuracy. Our research presents an innovative point cloud-based pothole segmentation architecture. Our model efficiently identifies hidden features and uses a feedback mechanism to enhance local characteristics, improving feature presentation. We introduce a local relationship learning module to understand local shape relationships, enhancing structural insights. Additionally, we propose a lightweight adaptive structure for refining local point features using the K-nearest neighbor algorithm, addressing point cloud density differences and domain selection. Shared MLP Pooling is integrated to learn deep aggregation features, facilitating semantic data exploration and segmentation guidance. Extensive experiments on three public datasets confirm PotholeGuard's superior performance over state-of-the-art methods. Our approach offers a promising solution for robust and accurate 3D pothole segmentation, with applications in road maintenance and safety. © 2023 IEEE.

关键词： computer vision

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Advancing Wi-Fi Intrusion Detection Systems with Machine Learning Techniques for Enhanced Classification of Wireless Attacks

Advancing Wi-Fi Intrusion Detection Systems with Machine Lea...

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International Conference on Advancements in Computational sciences (ICACS)

作者： Hasseeb Ahmad Bhutta Muhammad Talha Jahangir Areeb Ahmad Bhutta Computer Science Dept. National University of Sciences and Technology Islamabad Pakistan Computer Science Dept. MNS University of Engineering and Technology Multan Pakistan Computer Science Dept. Information Technology University Lahore Pakistan

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

WiFi usability and scalability have contributed to an improvement in human living standards. However, this has also led to increased vulnerabilities and attack vectors within WiFi networks. Authentic users can face network failures and denial of service caused by attacks on wireless networks by some third-party. Wireless Intrusion Detection System (IDS) is needed to detect wireless attacks. Main of objective of wireless IDS is to monitor network traffic and classify if it is an attack or normal traffic. Modern anomaly based wireless intrusion detection systems use machine learning (ML) to learn from previous attacks in the dataset to learn patterns of the attack. Although this method is effective, but there is also downside to this approach. The time required to train and test these ML models are exceedingly high along with high computational costs. Big data is emerging technologies that is getting advanced day by day, due to which, network technology is increasing rapidly. For that reason, we need to discuss the issue, that is high computational costs. In our proposed solution, we used our custom oversampling technique BBOT (Balanced Boost Oversampling Technique) to rectify class imbalance, generating artificial samples for the classes, enhancing model performance. The results of the experiments show that the combination of feature selection, BBOT, and Decision Tree outperformed all other classifiers with reduced computational overhead, providing a practical and efficient solution for real-time intrusion detection in WiFi networks. When compared to XGBoost, the provided solution achieves a similar level of accuracy, but with significantly reduced training time.

关键词： Wireless communication Computational modeling Wireless networks Intrusion detection Machine learning Big Data Feature extraction Vectors Computational efficiency Wireless fidelity

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Integrating Social Platforms with IoT: Security Challenges, Applications, and Trust Issues 5

Integrating Social Platforms with IoT: Security Challenges, ...

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5th IEEE India Council International Subsections Conference, INDISCON 2024

作者： Mohanta, Bhabendu Kumar Dehury, Mohan Kumar Tulasi, Desetti Das, Sunayana United Arab Emirates University College of Information Technology Al AinP.O. Box 15551 United Arab Emirates Amity Institute of Information Technology Amity University Jharkhand Ranchi India Koneru Lakshmaiah Education Foundation Dept. of Computer Science and Engineering Vaddeswaram Guntur India Driems University Tangi Dept. of Computer Scinece and Engineering Odisha Cuttack754 022 India

ISBN: (纸本)9798350376753

The term Social Internet of Things (Social IoT) refers to an expansion of the term Internet of Things (IoT) that integrates social networking capabilities and concepts into IoT applications. The number of connected devices has already crossed the total world population. With the development of other technologies like Blockchain, Artificial Intelligence, Machine Learning, and Data science, IoT become more usable in many applications. With the rapid use of digital devices, social media like Facebook and others are part of the human lifestyle. The integration of IoT with a social platform for different services become an emerging research field in recent times. In this paper, the authors investigated the impact of social services in IoT and its corresponding decentralized architecture. The paper also identified the security challenges associated with the Social IoT and trust issues. Finally, the paper addresses some of the emerging technologies which are capable of addressing the security challenges in Social IoT. © 2024 IEEE.

关键词： Decentralization IoT Privacy Secure Communication Security Social IoT Trust Management

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The Impact of Middle and High School Students Participation in the Innovation Nation Competition on Their Education 24

The Impact of Middle and High School Students Participation ...

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24th International Arab Conference on information Technology, ACIT 2023

作者： Mustafa, Dheya Zriqat, Samah Mustafa, Intisar Ghazi Althebyan, Qutaibah College of Engineering The Hashemite University Dept. of Computer Engineering Zarqa13133 Jordan Yarmouk University Dept. of Information Technology and Computer Science Irbid Jordan College of Education Qatar University Dept. of Educational Sciences Doha Qatar Al Ain University Cybersecurity Program Al Ain United Arab Emirates Jordan University of Science and Technology Irbid Jordan

ISBN: (纸本)9798350384307

The Innovative Nation competition is a Jordanian initiative that uses workshops, training camps, and competitions to improve students' general and soft skills, problem-solving, creativity, and invention. The curriculum is divided into three parts, each of which has the potential to increase interest in science, Technology, engineering, and Mathematics (STEM). The goal of this research is to determine how much the STEM competition has influenced students' education and decision to pursue jobs in science and technology. Personal interviews with students and mentors were employed in this study to assess their skill sets, prospective career paths, and interests. The collected data was reviewed, and it was found that participation in the Innovation Nation competition increased students' interest in STEM vocations. © 2023 IEEE.

关键词： Artificial intelligence education Innovation non-curricular learning PictoBlox STEM

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A Wise Matrix Factorization Model for Image Representation

A Wise Matrix Factorization Model for Image Representation

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2022 International Conference on Multimedia Analysis and Pattern Recognition, MAPR 2022

作者： Duong, Viet Hang Bui, Manh Quan Wang, Jia Ching University of Information Technology VNU-HCM Dept. of Computer Science Ho Chi Minh Viet Nam VietNam Aviation Academy Dept. of Information Technology Ho Chi Minh Viet Nam National Central University Dept. of Computer Science and Information Engineering Chungli Taiwan

ISBN: (数字)9781665474108

ISBN: (纸本)9781665474108

The paper presents matrix factorization frameworks on the complex domain to get both intuitive features and high performance in image representation tasks. The real data matrix is transformed to a complex number first which allows exploiting a robust dissimilarity measure based on the Euler representation of complex numbers. Wirtinger's calculus is used to compute the derivative of the cost function. The gradient descent method is utilized to solve complex optimization problems. We show that these frameworks provide feature extraction ability to face recognition models for enhanced performance. Experiments on two scenarios for face recognition including holistic face, and key points occluded face demonstrate that the proposed method of complex matrix factorization provides more faithful basis factors and consistently better recognition results as compared to standard real matrix factorization models. © 2022 IEEE.

关键词： Face recognition

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A/B Testing and Audience Creation for Effective Digital Marketing: Evidences from Facebook Analytics 6

A/B Testing and Audience Creation for Effective Digital Mark...

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6th International Conference on information Systems and computer Networks, ISCON 2023

作者： Singh, Nripendra Jaiswal, Anand Singh, Prakhar Sharma, Dilip Kr. PennWest University Dept. of Management and Marketing Clarion United States New Delhi Institute of Management Dept. of Operations and Scm Delhi India PennWest University Computer Information Science Department Clarion United States GLA University Dept. of Computer Engineering and Applications Mathura India

ISBN: (纸本)9798350346961

Digital marketers use a number of strategies, including custom audiences, lookalike audiences, ad creative, and A/B testing, to organize and enhance how they present advertisements to consumers. The purpose of this research is to show an effective method and process for running advertisements on Facebook more efficiently. The study has taken a case study of a company- Great Deal Tires and analyzed the Facebook analytics data and insights with A/B testing and audience creation approach to assess the digital marketing impact. The study has implemented three test strategies for designing and placing Facebook ads for Great Deal Tires in order to assess the marketing impact. In the first test, a custom audience was chosen, and an identical Facebook video ad's impact was evaluated based on the duration of the ad. In the second test, A/B testing was used to test Facebook ads, and the results of the tests were compared across three different audience groups to determine how well the ads performed. The third test evaluated on the audience conversion in the terms of subscribing to the Page, in which three different ads were shown to three distinct audience groups in order to compare the effects of the various ads on the diverse audiences. The study's findings provided important information about Facebook ad placement, its effects on various audience segments, and digital marketing strategies to increase audience outreach. The study is important in demonstrating that Facebook advertisements are an effective marketing medium and a digital marketer can achieve good outreach with a modest investment of funds.. © 2023 IEEE.

关键词： Marketing

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