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Gamification of Educational Content by Using Virtual Teacher in Online learning Environment 26th

Gamification of Educational Content by Using Virtual Teacher...

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26th international conference on Interactive Collaborative learning (ICL) - Towards a Hybrid, Flexible and Socially Engaged Higher Education / 52nd IGIP international conference on engineering Pedagogy

作者： Bondarenko, Tetiana Kovalenko, Denys Khotchenko, Iryna Zelenin, Hennadii Bozhko, Viktor Ukrainian Engn Pedag Acad Kharkiv Ukraine Kharkov Natl Univ Kharkiv Ukraine

ISBN: (纸本)9783031530210;9783031530227

A new impetus for the development of information technology in the educational process was given by the COVID-19 coronavirus pandemic and the quarantine imposed to prevent the spread of the disease. Due to the quarantine and the war in Ukraine, the educational process has moved to the online space, which has created conditions for remote learning using media content. Media education is designed to prepare students for life in the new information environment, to teach them to fully perceive different types of information, to understand and master the means of communication based on interactive forms of communication. It also improves the quality of information perception, students' engagement in interactive classes and the engagement of all participants in the learning process in the online environment. Thus, today's realities require new approaches and teaching tools that should be implemented by modern higher education institutions in the era of Education 4.0.

关键词： Digital Technologies Gamification Virtual Teacher Interactivity

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Face Recognition-Based Automated Attendance System 5

Face Recognition-Based Automated Attendance System

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5th IEEE international conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Tripathi, Arunendra Mani Rai, Ashish Kumar Pandey, Divyanshu Galgotias University School of Computing Science and Engineering Greater Noida India

ISBN: (纸本)9798350383522

The attendance system is an essential part of any organization, and the traditional manual attendance system can be very prone to errors and very time-consuming. This paper introduces a sophisticated automated attendance system that combines Principal Component Analysis (PCA) algorithm and Artificial Intelligence (AI) for enhanced accuracy and efficiency. In contrast to error-prone and time-consuming manual attendance processes, our system leverages PCA for robust facial feature analysis, allowing precise identification of students or workers. The integration of AI further refines the recognition process, adapting and learning from diverse facial characteristics to improve system performance over time. © 2024 IEEE.

关键词： AI eigenfaces eigenvectors Facial Recognition FLTK OpenCV PCA

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Leveraging Ensemble learning for Dry Beans Classification

Leveraging Ensemble Learning for Dry Beans Classification

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2024 international conference on engineering and computing, ICECT 2024

作者： Jahanzaib, Muhammad Zaheer, Qasim Rehman, Attique Ur Javed, Sabeen Ali, Tahir Muhammad Mir, Azka University of Sialkot Department of Software Engineering Sialkot Pakistan GULF University for Sciences and Technology Department of Computer Science Mubarak Al-Abdullah Kuwait

ISBN: (纸本)9798350349719

For dietary and commercial purposes, dry beans are significant. Dry beans offer many advantages, such as energy and protein. It is crucial to the fight against malnutrition and ensuring food security. The most crucial element in the manufacturing of dry beans is the condition of the seed. As the quality of the seed is so important, seed verification is crucial for dry bean farmers and marketplaces. Choosing high-quality seeds is essential to achieving optimal yield. This research uses a variety of machine-learning techniques to classify different varieties of dry beans. For this research work, a dataset from the UCI machine learning repository is used. The outcomes of model evaluation have been compared, and various classifiers have been trained. In the process, the k-fold cross-validation technique is applied with k = 10 as a parameter. This research aims to differentiate between the seven different types of dry beans by using classification algorithms. An accuracy of 82.01%, 93.46%, 94.67%, and 94.64% has been achieved with the k-nearest neighbor, decision tree, naïve Bayes, and random forest classifiers respectively. Also, the voting ensemble classifier has an accuracy of 95.46%. After the experiments, it was observed that the voting ensemble classifier has the highest accuracy. Future research can examine the performance of new algorithms for better results. Additionally, the dataset can be increased to improve the model's accuracy. © 2024 IEEE.

关键词： Decision trees

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AdvBinSD: Poisoning the Binary Code Similarity Detector via Isolated Instruction Sequences 21

AdvBinSD: Poisoning the Binary Code Similarity Detector via ...

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21st IEEE international Symposium on Parallel and Distributed Processing with Applications, 13th IEEE international conference on Big Data and Cloud computing, 16th IEEE international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Yi, Xiaoyu Li, Gaolei Ding, Ao Zheng, Yan Li, Yuqing Li, Jianhua Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai China China University of Political Science and Law School of Information Management for Law Beijing China Wuhan University School of Cyber Science and Engineering Wuhan China

ISBN: (纸本)9798350329223

Binary code similarity detection (BinSD) systems trend to utilize deep learning to identify semantic features of assembly code and exhibits superior performance, gaining increasing popularity against traditional methods. However, it has been observed that existing deep learning models are susceptible to data poisoning attacks, posing a latent threat to the robustness and reliability of BinSD. Existing data poisoning strategies in BinSD are easily detectable for the generated triggers will destroy code functions. Moreover, selecting trigger injection location needs repeated exploration and verification, increasing the attack cost. To address this issue, we propose a novel adversarial scheme, named as AdvBinSD, which can poison the deep learning-based binary code similarity detector and make it sensitive to isolated instruction sequences. In AdvBinSD, the isolated instruction sequences generally refer to those instructions that have no data dependencies with other instructions and do not affect the function of original binary code, and also it is difficult to discovery those isolated instruction sequences by verifying syntactic validity and semantic integrity. Different from existing data poisoning strategies, AdvBinSD first estimates a code fragment that has the greatest impact on software functionality as the poisoning location, and then add isolated instruction sequences into this location to synthesize effective poisoned samples. This location estimation is achieved by maximizing the similarity between function-level feature vectors and instruction-level feature vectors, ensuring that the modified assembly code can execute correctly. Furthermore, to improve the efficiency of feature vector similarity computing process, a k-order greedy feature comparison (k-GFC) algorithm is also designated. Extensive experiments demonstrate that our proposed AdvBinSD can successfully poison the state-of-the-art deep learning-based binary code similarity detectors. © 2023 IEEE.

关键词： Binary codes

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EVAL IQ: Intelligent Educational Assessment and Recommendation Platform 4

EVAL IQ: Intelligent Educational Assessment and Recommendati...

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4th IEEE international conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Vandana, C.P. Poornachandra, Poornachandra Shetty, Rohan New Horizon College of Engineering Department of Information Science and Engineering Bangalore India

ISBN: (纸本)9798331518981

EVAL IQ is an innovative educational assessment platform designed to simplify the evaluation process for both educators and learners. It offers separate portals tailored to each group's needs. Teachers can manage student data, create, and organize courses, design questions, and assess performance using automated scoring and in-depth analytics. The platform also includes a feedback system to facilitate continuous improvement. For students, EVAL IQ provides an easy-to-use interface for completing assessments, meeting deadlines, and providing feedback. Built with modern technology, EVAL IQ ensures efficient, transparent, and effective educational assessments, enhancing the overall teaching and learning experience. © 2024 IEEE.

关键词： Contrastive learning

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Anomalous Human Action Recognition with Deep learning Technique 18

Anomalous Human Action Recognition with Deep Learning Techni...

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18th INDIAcom;11th international conference on computing for Sustainable Global Development, INDIACom 2024

作者： Pallewar, Megha G. Pawar, Vijaya R. Gaikwad, Arun N. Zeal College of Engineering & Research Dept. of E&tc Engineering Pune India Savitribai Phule Pune University Dept. of E&tc Engineering Pune India

ISBN: (纸本)9789380544519

Detecting and comprehending human actions poses a significant challenge, especially in the context of enhancing human security. This area of study has garnered attention recently, and machine learning-based methods have emerged as the forefront approach for identifying unusual activities. In this research, the main focus is on spotting abnormal activities in videos, utilizing the UCF crime database as the primary source of data. The proposed system uses a range of pre-processing methods on the video data from this collection. The system employs deep learning techniques with Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) algorithms. The frames extracted from the videos are fed into CNN, which processes and extracts important features. The LSTM module is responsible for categorizing the activities portrayed in the input video. By combining the strengths of CNN and LSTM, system successfully identifies six distinct anomalous activities: Abuse, Arson, Assault, Burglary, Fight and Robbery. For training and evaluating the system, divide the dataset into an 80% training set, a 10% testing set, and a 10% validation set, including cross-validation steps. The resulting model demonstrates an impressive accuracy rate of approximately 97.8%. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Abnormal activity detection Anomaly Convolutional neural networks Human action detection Long short time memory

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Application of Network Course Platform Based on Cloud computing in Physical Education 2nd

Application of Network Course Platform Based on Cloud Comput...

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2nd EAI international conference on Application of Big Data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2022

作者： Zhang, Jianbin Basic Courses Department JiangXi Technical College Of Manufacturing Jiangxi Nanchang330095 China

ISBN: (纸本)9783031239496

On the basis of traditional teaching, through the combination and application of current network technology, we can finally break through the restrictions of space and time in the traditional classroom. This is the so-called network course teaching, which can meet the needs of modern education more effectively. With the continuous development of network course teaching, it has attracted the attention of many scholars, and the research on network course teaching has become more and more extensive and in-depth. Nevertheless, there are some problems in the process of the continuous development of network course teaching, and the introduction of cloud computing in network course teaching can effectively improve these problems. This paper introduces the application of network course platform based on cloud computing in physical education teaching for your reference. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Cloud computing

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Next-Gen Parking Facilities: Deep learning for Face and License Plate based Access 18

Next-Gen Parking Facilities: Deep Learning for Face and Lice...

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18th INDIAcom;11th international conference on computing for Sustainable Global Development, INDIACom 2024

作者： Balasubramanian, K. Kannan, N. Sivakami, S. Senthil, S. Immaculate Rexi Jenifer, P. Murali, N. E. G. S. Pillay Engineering College Department of Computer Science and Engineering Nagapattinam India K. Ramakrishnan College of Technology Department of Computer Science and Engineering Trichy India School of Computing SASTRA Department of Computer Science and Engineering India

ISBN: (纸本)9789380544519

Parking congestion has become a major problem in today's metropolitan environments, resulting in lost time, higher emissions, and irritated drivers. There is a severe shortage of parking places in metropolitan areas because of the ever-growing number of the vehicles and the ever-growing urban population. This shortage has exacerbated traffic congestion and environmental pollution in addition to making it difficult and time-consuming to find a parking space. Conventional parking management systems are finding it harder and harder to handle these issues;they frequently depend on physical barriers or manual ticketing. Innovative approaches that incorporate automation and technology are becoming more prevalent to address this problem. Conventional parking management systems frequently depend on human intervention and physical infrastructure, which leads to inefficiencies and constrained scalability. In contrast, new opportunities for a more intelligent, effective, and user-friendly experience are made possible by the incorporation of deep learning methods and cutting-edge computer vision technology into parking management. This system makes use of two essential elements: automatically recognizing license plate numbers for vehicle identification and facial recognition technology for person identification. Through the smooth integration of these technologies, the system not only makes parking easier, but it also improves security and maximizes parking space usage. We will examine the essential elements, advantages, and possible effects of the Face and Number Plate-Based Smart Parking System in this survey. According to experimental findings, employing a face- and number-plate verification method can increase the efficiency of smart parking systems. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Deep learning Face recognition system Features based matching Slot allocation Smart parking system

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Block chain and Deep learning Based Secure Communication using SAE-LSTM &salp Swarm Optimizer for Multivariate Industrial IoT-oriented Infrastructure 15

Block chain and Deep Learning Based Secure Communication usi...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： MathalaiRaj, J. Sivaranjani, S. Rajalakshmi, J. Jayachandran, T. Maheswari, M. Kavin Kumar, K. Chandrasekaran, Gokul Nadar Saraswathi College of Engineering and Technology Department of Computer Science and Engineering Theni India Sethu Institute of Technology Department of Computer Science and Engineering Virudhnagar India Sethu Institute of Technology Department of Electronics and Communication Engineering Virudhunagar India Nandha Engineering College Department of Electronics and Communication Engineering Erode India Kongu Engineering College Department of Electronics and Communication Engineering Erode Perundurai638060 India Karpagam Institute of Technology Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798350370249

The rapid development of the Industrial Internet of Things (IIoT) imposes the needs for digitization of industrial procedures so as to enhance the efficiency of the network. Cyber-Physical System (CPS) has important role in applications of industries and infrastructure towards IoT. There are several challenges in IIoT environment such as centralization, scalability, privacy, security and communication latency. To overcome these issues, Deep learning based Sparse Autoencoder-Long Short-Term Memory (SAE-LSTM) with Salp swarm optimizer SSA based IoT-oriented infrastructure is used to detect the attacks. The major objective of this work is to propose a deep learning and blockchain based smart contract technique for a secure communication in smart city using Salp Swarm Optimizer algorithm for determining data privacy and security for the IIoT's application service. Blockchain & data aggregation with Proof-of-Work (PoW) generates a distributed network for communication phase of CPS. The protocols to forward the data in the network is established by Software-Defined Networking (SDN). Smart contract is employed to execute the transaction. The suggested technique is implemented and the performance is carried out by estimating Accuracy, Precision, Recall and F1 Score. © 2024 IEEE.

关键词： Blockchain and data aggregation with Proof-of-Work (PoW) Decision Sparse Autoencoder-Long Short-Term Memory (DSAE-LSTM) Industrial IoT Salp swarm optimizer SSA Smart contract

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EEG-Based Emotion Recognition Using Deep learning Models 2

EEG-Based Emotion Recognition Using Deep Learning Models

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2nd international conference on Recent Trends in Microelectronics, Automation, computing, and Communications Systems, ICMACC 2024

作者： Meghana, Alokam BilakantiVanshika, V. VedaSamhitha, Karnam Murali, Keerthana Singh, Rimjhim Padam Palaniswamy, Suja Amrita School of Computing Department of Computer Science & Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350366570

Emotion recognition is an emerging field of research and modalities like images, videos, speech, text, physiological signals, and EEG are used for the same. In this work, EEG based emotion recognition has been proposed using deep learning models. The recognition of emotion from EEG has attracted a lot of interest and has been applied in numerous fields, particularly in health, technology, and computing. In this work, two methods are proposed for emotion recognition from EEG signals, viz., a signal-based approach and an image conversion approach. the accuracy achieved by the models CNN+GRU, 2D- CNN and LSTM are 96.54%, 90.58%, and 87%, respectively. This work also addresses some of the issues related to model deployment, how to deal with logical and causal relations, and how to implement the outcomes in a real-time environment. © 2024 IEEE.

关键词： Emotion Recognition

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