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Serious games in science education:a systematic literature review

Virtual Reality & Intelligent Hardware 2022年第3期4卷 189-209页

作者： Mohib ULLAH Sareer Ul AMIN Muhammad MUNSIF Muhammad Mudassar YAMIN Utkurbek SAFAEV Habib KHAN Salman KHAN Habib ULLAH Department of Computer Science Norwegian University of Science and Technology2815 GjøvikNorway Department of Computer Science Islamia College University Peshawar25000 Pakistan Department of Information Security and Communication Norwegian University of Science and Technology2815 GjøvikNorway Faculty of Science and Technology Norwegian University of Life Sciences1430ÅsNorway

Teaching science through computer games,simulations,and artificial intelligence(AI)is an increasingly active research *** this end,we conducted a systematic literature review on serious games for science education to reveal research trends and *** discussed the role of virtual reality(VR),AI,and augmented reality(AR)games in teaching science subjects like ***,we covered the research spanning between 2011 and 2021,investigated country-wise concentration and most common evaluation methods,and discussed the positive and negative aspects of serious games in science education in particular and attitudes towards the use of serious games in education in general.

关键词： Serious games Simulations,Artificial intelligence,Virtual reality,Augmented reality Games in education

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ATFS: A deep learning framework for angle transformation and face swapping of face de-identification

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Multimedia Tools and Applications 2024年第12期83卷 36797-36822页

作者： Tsai, Chwei-Shyong Wu, Hsien-Chu Chen, Wei-Ting Ying, Josh Jia-Ching Department of Management Information Systems National Chung Hsing University Taichung402 Taiwan Language Center National Chin-Yi University of Technology Taichung411 Taiwan Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung404 Taiwan

This paper proposes an effective framework for improving angle transformation and face replacement. This ATFS framework aims to solve the image distortion caused by angled face swapping in face de-identification. In the ATFS framework, TransNet and SWGAN were proposed based on generative adversarial networks. When TransNet uses neural networks to reconstruct images, it combines the predicted facial points. As a result, Transnet can locate the target’s facial features and generate multi-angle transformed images to achieve angle transformation. The SWGAN applies a complicated neural network that combines residual operations and self-attention modules to extract the input image features and replace the face region of the input image with the target image to achieve face de-identification. Both TransNet and SWGAN adopt discriminators and various loss functions to train the neural network and accurately accelerate the training process. The experimental results show that the proposed method can maintain high-quality images and avoid image distortion during face swapping for image angle transformation compared to previously proposed methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Generative adversarial networks

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A causal multiomics study discriminates the early immune features of Ad5-vectored Ebola vaccine recipients

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The Innovation 2024年第3期5卷 77-87页

作者： Zhe Zhang Wenjing Yang Zhengshan Chen Haoang Chi Shipo Wu Wanru Zheng Ruochun Jin Busen Wang Yudong Wang Nan Huo Jinlong Zhang Xiaohong Song Liyang Xu Jun Zhang Lihua Hou Wei Chen Institute of Biotechnology Academy of Military Medical SciencesBeijing 100071China Department of Intelligent Data Science College of Computer Science and TechnologyNational University of Defense TechnologyChangsha 410073China Intelligent Game and Decision Lab Academy of Military ScienceBeijing 100091China

The vaccine-induced innate immune response is essential for the generation of an antibody *** date,how Ad5-vectored vaccines are influenced by preexisting anti-Ad5 antibodies during activation of the early immune response remains ***,we investigated the specific alterations in GP1,2-specific IgG-related elements of the early immune response at the genetic,molecular,and cellular levels on days 0,1,3,and 7 after Ad5-EBOV *** a causal multiomics analysis,distinct early immune responses associated with GP1,2-specific IgG were observed in Ad5-EBOV recipients with a low level of preexisting anti-Ad5 *** study revealed the correlates of the Ad5-EBOV-induced IgG response and provided mechanistic evidence for overcoming preexisting Ad5 immunity during the administration of Ad5-vectored vaccines.

关键词： vaccine immunity alterations

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Improved Secure Identification-Based Multilevel Structure of Data Sharing in Cloud Environments

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computer Systems science & Engineering 2022年第11期43卷 785-801页

作者： Saraswathi Shunmuganathan Sridharan Kannan T.V.Madhusudhana Rao K.Ambika T.Jayasankar Department of Computer Science and Engineering SSN College of EngineeringChennaiTamil Nadu603110India Department of Computer Science and Engineering J.K.K.Munirajah College of TechnologyErodeTamil Nadu638506India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Engineering University College of EngineeringBIT CampusAnna UniversityTiruchirappalliTamil Nadu620024India Department of Electronics and Communication Engineering University College of EngineeringBIT CampusAnna UniversityTiruchirappalli620024India

The Cloud Computing Environment(CCE)developed for using the dynamic cloud is the ability of software and services likely to grow with any *** has transformed the methodology for storing the enterprise data,accessing the data,and Data Sharing(DS).Big data frame a constant way of uploading and sharing the cloud data in a hierarchical architecture with different kinds of separate privileges to access the *** the requirement of vast volumes of storage area in the CCEs,capturing a secured data access framework is an important *** paper proposes an Improved Secure Identification-based Multilevel Structure of Data Sharing(ISIMSDS)to hold the DS of big data in *** complex file partitioning technique is proposed to verify the access privilege context for sharing data in complex *** access control Encryption Method(EM)is used to improve the *** Complexity is measured to increase the authentication *** active attack is protected using this ISIMSDS *** proposed ISIMSDS method assists in diminishing the Complexity whenever the user’s population is increasing *** security analysis proves that the proposed ISIMSDS methodology is more secure against the chosen-PlainText(PT)attack and provides more efficient computation and storage space than the related *** performance of the proposed ISIMSDS methodology provides more efficiency in communication costs such as encryption,decryption,and retrieval of the data.

关键词： Data sharing cloud environments big data chosen-plaintext attack security

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Twin attention based multi-task convolutional bidirectional long short term memory for facial expression recognition

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Multimedia Tools and Applications 2025年第10期84卷 8037-8070页

作者： Sreenivas, Velagapudi Sivaneasan, B. Vani, K. Suvarna Chakrabarti, Prasun Singapore Institute of Technology Singapore138683 Singapore Engineering Cluster Singapore Institute of Technology Singapore138683 Singapore Department of Computer Science and Engineering AI/ML Research Group Head VR Siddhartha Engineering College Andhra Pradesh Vijayawada520007 India Department of Computer Science and Engineering Sir Padampat Singhania University Rajasthan Udaipur313601 India

Facial Expression Recognition (FER) aims to detect the emotional state of facial images. It is playing an increasingly important role in several application areas, including human–computer interaction (HCI), video transcriptions, and social communications. This article provides an adequate attention-based multi-task deep learning method for facial expression recognition. First, the input videos are collected from the RAVDESS and MELD datasets. Then, the input videos are converted using a threshold-based keyframe extraction algorithm. Next, the input data is pre-processed using the Adaptive Pixel Density Median Filtering (A-PDMF) method. Key features such as shape, color and texture are extracted from the pre-processed images. Finally, the facial expressions are recognized by proposing a novel twin attention-based multi-task convolutional bidirectional long-short-term memory method (TA-MC-BiLSTM). In addition, the classification parameters are optimally tuned using the EX-AHA method (Extended Artificial Hummingbird Algorithm). The proposed model reduces the size of facial features while accurately identifying the wide range of facial expressions. For simulation, the proposed method prefers python tool and results are analyzed using RAVDESS and MELD datasets. The simulation results show that the proposed model provides better results than other existing models in terms of accuracy of 99.6% for RAVEDESS and 99.4% for MELD. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Pixels

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An effective mechanism to mitigate packet dropping attack from manets using chaotic map based authentication technique

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Recent Patents on Engineering 2024年第3期18卷 66-76页

作者： Atheeq Ahmed, C. Altaf, C. Mohammad, Mohammad Mohammed, Aleem Department of CSE School of Technology GITAM University Hyderabad India Department of ECE Lords Institute of Science and Technology Osmania University Hyderabad India Department of Information Technology Melbourne Institute of Technology Sydney Australia Australian Computer Society Inc Sydney Australia

Background: MANET is a self-organized wireless network with no infrastructure. Especially data transfer from one system to another system needs to be done in a secure way. In order to provide data integrity, authentication plays an important role in data communication. RSA and ECC are widely used algorithms in the real world, but authentication using these algorithms is time-consuming. Towards this, various algorithms came into existence with different security primitives. However, it is important to design an effective key agreement process with reduced computational cost among these security mechanisms. We have designed an effective mechanism to mitigate packet dropping attacks to secure end-to-end communication in MANET. Objective: The proposed light weight authentication method is based on chaotic maps that uses Chebyshev polynomials as primitive operation. Methods: The methodology includes semi group property of Chebyshev polynomials and also the discrete logarithmic problem based chaotic maps that takes less time than these existing algorithms and evaluates with respect to attack Resilience, packet deliverya fraction, delay, throughput, and overhead. Results: Simulation produces the result of the proposed mechanism and give better performance in terms of packet delivery ratio, overhead and computational cost. Conclusion: Authentication based on developed mechanism consumes less time as shown in statistical analysis and mitigates packet dropping attacks effectively than that of traditional methods like RSA and ECC with respect to key generation mechanism. © 2024 Bentham science Publishers.

关键词： Authentication

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FMHADP:Design of an Efficient Pre-Forensic Layer for Mitigating Hybrid Attacks via Deep Learning Pattern Analysis

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Journal of Harbin Institute of technology(New Series) 2024年第5期31卷 55-67页

作者： Meesala Sravani B Kiran Kumar M Rekha Sundari D Tejaswi Department of Computer Science and Engineering GMR Institute of TechnologyRajam 532127Andhra PradeshIndia Information Technology Anil Neerukonda Institute of Technology&Science(Autonomous)Sangivalasa 531162BheemunipatnamIndia

Network attack detection and mitigation require packet collection,pre-processing,feature analysis,classification,and *** for these tasks sometimes become complex or inefficient when applied to real-time data *** mitigate hybrid assaults,this study designs an efficient forensic layer employing deep learning pattern analysis and multidomain feature *** this paper,we provide a novel multidomain feature extraction method using Fourier,Z,Laplace,Discrete Cosine Transform(DCT),1D Haar Wavelet,Gabor,and Convolutional *** method dragon fly optimisation reduces feature dimensionality and improves feature selection *** selected features are fed into VGGNet and GoogLeNet models using binary cascaded neural networks to analyse network traffic patterns,detect anomalies,and warn network *** suggested model tackles the inadequacies of existing approaches to hybrid threats,which are growing more common and challenge conventional security *** model integrates multidomain feature extraction,deep learning pattern analysis,and the forensic layer to improve intrusion detection and prevention *** diverse attack scenarios,our technique has 3.5% higher accuracy,4.3% higher precision,8.5% higher recall,and 2.9% lower delay than previous models.

关键词： digital replay attack perceptual hashing content authentication content identification Differential Luminance Block Means(DLBM) normalization shifts

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Stock Price Prediction Using Fuzzy-RNN and Portfolio Management with Genetic Algorithm 8th

Stock Price Prediction Using Fuzzy-RNN and Portfolio Manage...

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8th International Conference on Intelligent Technologies, ICIT 2023

作者： Jaiswal, Hrishikesh Anjali Arya, K.V. Department of Information Technology ABV-Indian Institute of Information Technology and Management Gwalior474015 India Department of Computer Science and Engineering ABV-Indian Institute of Information Technology and Management Gwalior474015 India

ISBN: (纸本)9789819738588

Stock prediction and portfolio management are vital in the financial industry, aiding investors in informed decisions. This study introduces a novel approach termed the Stock Prediction and Portfolio Management Model, which amalgamates advanced machine learning algorithms with portfolio optimization techniques. The Stock Prediction and Portfolio Management Model framework substantially enhances the precision of stock price predictions, leading to an elevated performance of investment portfolios while concurrently mitigating inherent risks. The initial phase of the model focuses on predicting stock prices by harnessing historical data and employing technical indicators like the Relative Strength Index (RSI) and Moving Average Convergence Divergence (MACD). In the subsequent phase, the SPPMM system meticulously constructs optimal portfolios utilizing genetic algorithms, further solidifying its efficacy in aiding investors in making informed decisions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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Heart disease prediction using machine learning, deep Learning and optimization techniques-A semantic review

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Multimedia Tools and Applications 2024年第39期83卷 86895-86922页

作者： Bhavekar, Girish Shrikrushnarao Das Goswami, Agam Vasantrao, Chafle Pratiksha Gaikwad, Amit K. Zade, Amol V. Vyawahare, Harsha Department of Artificial Intelligence & Data Science CSMSS Chh. Shahu College of Engineering Aurangabad431136 India Department of SENSE VIT-AP University Andhra Pradesh 522237 India Department of AI & DS CSMSS Chh. Shahu College of Engineering Maharashtra Aurangabad431136 India Department of Computer Science & Engineering GH Raisoni University Maharashtra Amravati444701 India Department of Computer Science & Engineering Sipna College of Engineering & Technology Maharashtra Amravati444701 India

Cardiovascular disease holds the position of being the foremost cause of death worldwide. Heart Disease Prediction (HDP) is a difficult task as it needs advanced knowledge with better experience. Moreover, it encounters numerous significant challenges in clinical data analysis. While many researchers have focused on predicting heart disease, the performance metric, namely prediction accuracy, remains suboptimal. The accurate HDP can help the person to prevent himself from life threats and at the same time, inaccurate prediction can prove to be fatal. To solve these issues, in this review work several Deep Learning (DL), Machine Learning (ML) and optimization based HDP techniques are discussed. In recent times, many researchers have been utilizing different DL and ML algorithms to help the professionals and health care industry for the prediction of heart disease. Further, it discussed about various optimization-based algorithms and its performance analysis. Therefore, this review paper suggests that the optimization-based HDP algorithm could assist doctors in predicting the occurrence of heart disease in advance and offering suitable treatment. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Forecasting

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Sustainable Diffusion-Based Incentive Mechanism for Generative AI-Driven Digital Twins in Industrial Cyber-Physical Systems

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 139-149页

作者： Wen, Jinbo Kang, Jiawen Niyato, Dusit Zhang, Yang Mao, Shiwen Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing210016 China Guangdong University of Technology School of Automation Guangzhou510006 China Nanyang Technological University College of Computing and Data Science Singapore639798 Singapore Auburn University Department of Electrical and Computer Engineering AuburnAL36849 United States

Industrial Cyber-Physical Systems (ICPSs) are an integral component of modern manufacturing and industries. By digitizing data throughout product life cycles, Digital Twins (DTs) in ICPSs enable a shift from current industrial infrastructures to intelligent and adaptive infrastructures. Thanks to data process capability, Generative Artificial Intelligence (GenAI) can drive the construction and update of DTs to improve predictive accuracy and prepare for diverse smart manufacturing. However, mechanisms that leverage Industrial Internet of Things (IIoT) devices to share sensing data for DT construction are susceptible to adverse selection problems. In this paper, we first develop a GenAI-driven DT architecture in ICPSs. To address the adverse selection problem caused by information asymmetry, we propose a contract theory model and develop a sustainable diffusion-based soft actor-critic algorithm to identify the optimal feasible contract. Specifically, we leverage dynamic structured pruning techniques to reduce parameter numbers of actor networks, allowing sustainability and efficient implementation of the proposed algorithm. Numerical results demonstrate the effectiveness of the proposed scheme and the algorithm, enabling efficient DT construction and updates to monitor and manage ICPSs. © 2023 IEEE.

关键词： Smart manufacturing

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