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International Conference on Management of Large-Scale System Development (MLSD)

作者： Viktor Medennikov Larisa Bogatyreva Information and Computing Systems Department Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences Moscow Russia Laboratory of modular data processing and control systems V.A. Trapeznikov Institute of Control Sciences of the Russian Academy of Sciences Moscow Russia

ISBN: (数字)9798350375718

ISBN: (纸本)9798350375725

The paper deals with complementary changes in scientific research and real economy (exemplified by the farming industry) taking place when using the holistic approach to the industry”s digital transformation resulting in the appearance of precise agrarian technologies that require a comprehensive., system-based combination of research and production resources that are able to ensure increased productivity in the industry. We demonstrate that among these new technologies, the artificial intelligence methods have a special significance. They have to, however, go through integration transformations to become standards for the proposed common digital farming management platform.

关键词： Productivity Digital transformation Large-scale systems Artificial intelligence Standards Farming

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Retinal disorder diagnosis based on hybrid deep learning models

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Multimedia Tools and Applications 2025年 1-25页

作者： Sedik, Ahmed El-Shafai, Walid El-Hag, Noha A. El-Banby, Ghada M. Abd El-Samie, Fathi E. Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafrelsheikh University Kafr el-Sheikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt The Higher Institute of Commercial Sciences Algarbia Al Mahalla Al Kubra31951 Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Anomaly detection from medical images is badly needed for automated diagnosis. For example, medical images obtained with several modalities, such as magnetic resonance (MR) and confocal microscopy, need to be classified for anomaly detection. The MR images may contain tumors that need to be detected with anomaly detection techniques. In MR imaging, brain tumors are represented as areas composed of abnormal cells that need to be detected. On the other hand, confocal microscopy can be used for retinal imaging. Hence, there is a need to apply classification techniques for diagnosing diseases of the eye, such as Diabetic Retinopathy (DR), which leads to exudates in different regions of the retina. The detection of exudates is necessary for the diagnosis of DR. In this paper, two models are presented for the classification of MR and retinal images. Much research has been done in this area, but it did not achieve high accuracy of classification in an acceptable time. In addition, the previous models did not consider both types of image modalities considered in this paper. The suggested models are Convolutional Neural Network (CNN) and Convolutional Long Short-Term Memory (Conv-LSTM). They are used to classify normal and abnormal cases of both brain tumor and DR diseases. The CNN model is composed of six convolutional layers and six max-pooling layers. A soft-max activation function takes the classification decision. The proposed Conv-LSTM model is composed of three stages: pre-processing, feature extraction, and classification. The deep learning model performance is better than those of traditional networks. The CNN gives an accuracy of 94.26% for DR classification and 95.8% for brain tumor classification, while the Conv-LSTM gives the maximum accuracy of 96.41% for DR classification and 97.4% for brain tumor classification. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Confocal microscopy

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A Systematic Literature Review of Gamification in Massive Online Open Course 5

A Systematic Literature Review of Gamification in Massive On...

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5th IEEE International Conference on information Technology, information systems and Electrical Engineering, ICITISEE 2021

作者： Bangkit Saputra, Jeffri Prayitno Nizar Hidayanto, Achmad Prabowo, Harjanto Meyliana Universitas Amikom Purwokerto Doctor Of Computer Science Graduate Program Bina Nusantara University Faculty Of Computer Science Jakarta Indonesia Universitas Indonesia Faculty Of Computer Science Depok Indonesia Bina Nusantara University Computer Science Graduate Program Jakarta Indonesia Bina Nusantara University School Of Information Systems Information Systems Department Jakarta Indonesia

ISBN: (纸本)9781665401968

The development of information and communication technology makes it easy for anyone to be able to take part in online-based learning. Currently, online-based learning has been offered by many institutions or commercial industries. Gamification is one of the strategies to increase students' learning motivation. In this study, we focus on examining the role of gamification and game elements are contained in gamification-based learning. From the results of our research, it shows that gamification in the online-based learning process is growing. Gamification is proven to have a positive impact on students' learning motivation, although it is still dependent on various factors and conditions. © 2021 IEEE.

关键词： Motivation

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Troi: Towards Understanding Users Perspectives to Mobile Automatic Emotion Recognition System in Their Natural Setting

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Proceedings of the ACM on Human-computer Interaction 2022年第MHCI期6卷 1–22页

作者： Dissanayake, Vipula Tang, Vanessa Elvitigala, Don Samitha Wen, Elliott Wu, Michelle Nanayakkara, Suranga The University of Auckland New Zealand Augmented Human Lab ABI The University of Auckland New Zealand HCI BoDi Lab School of Computer Science and Engineering University of New South Wales Australia Augmented Human Lab Department of Information Systems & Analytics School of Computing National University of Singapore Singapore

Emotional Self-Awareness (ESA) plays a vital role in physical and mental well-being. Recent advancements in artificial intelligence technologies have shown promising emotion recognition results, opening new opportunities to build systems to support ESA. However, little research has been done to understand users’ perspectives on artificial-intelligence-based emotion recognition systems. We introduce Troi, an automatic emotion recognition mobile app using wearable signals. With Troi, we ran a multi-day user study with 12 users to understand user preference parameters, such as perceived accuracy, confidence, preferred emotion representations, effect of self-awareness of emotions, and real-time use cases. Further, we extend our study to evaluate the machine learning model in-the-wild to understand behaviours in-the-wild. We found that users perceived accuracy of the emotion recognition model is higher than the actual model prediction accuracy;there was no strong preference for one specific emotion representation, and users’ self-awareness of emotions improved over time. © 2022 Association for Computing Machinery.

关键词： Emotion Recognition

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Deep Learning Forecast of Cognitive Workload Using fNIRS Data

Deep Learning Forecast of Cognitive Workload Using fNIRS Dat...

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IEEE International Conference on Human-Machine systems

作者： Nicolas Grimaldi Yunmei Liu Ryan McKendrick Jaime Ruiz David Kaber Industrial and Systems Engineering University of Florida Gainesville FL USA Industrial Engineering University of Louisville Louisville KY USA Mission Systems Northrop Grumman Falls Church VA USA Computer & Information Science & Engineering University of Florida Gainesville FL USA

ISBN: (数字)9798350315790

ISBN: (纸本)9798350315806

Introduction: In the domain of helicopter piloting, the pilot’s performance is driven by many cognitive processes, demanding substantial cognitive resources. The pilot must maintain situation awareness and perform rapid decision-making. An objective of integrated helicopter technologies is to predict and effectively manage pilot cognitive workload to ensure safety and efficiency throughout flight. Methods: In this study, we collected data on seven participants, including three experienced pilots, using a UH-60V cockpit simulator to perform 46 distinct trials under various flight conditions. fNIRS neuroimaging was used to collect high-resolution neurophysiological data for exploring and forecasting cognitive workload using a collection of deep learning models. Model implementation: Three deep learning architectures are detailed in this work: a stacked LSTM model, a CNN-LSTM hybrid, and a transformer model. Results: An evaluation of three Seq2Seq models, each with two distinct forecasting lengths (10s and 30s), revealed LSTM-based architectures as superior performers for 10s forecasting tasks. Discussion: The LSTM-based models’ superior performance suggested potential limitations with the transformer’s self-attention mechanisms for our specific application. Surprisingly, the CNN-LSTM architecture did not surpass the stacked LSTM model’s performance during forecasting tasks. Conclusion: Future research directions include exploring diverse time-series Seq2Seq methods and forecasting cognitive workload as ordinal measures, offering insights into shifting cognitive demands.

关键词： Deep learning Analytical models Predictive models Transformers Data models Real-time systems Safety

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The Effect of Imbalanced Data and Parameter Selection via Genetic Algorithm Long Short-Term Memory (LSTM) for Financial Distress Prediction

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IAENG International Journal of Applied Mathematics 2023年第3期53卷 25页

作者： Adisa, Juliana Adeola Ojo, Samuel Owolawi, Pius Adewale Pretorius, Agnieta Ojo, Sunday Olusegun Tshwane University of Technology Department of Computer Systems Engineering Soshanguve Campus South Africa Department of Computer Systems Engineering Tshwane University of Technology Soshanguve Campus South Africa Faculty of ICT Tshwane University of Technology Soshanguve Campus South Africa Computer Science Department of Information Technology Durban University of Technology Durban South Africa

Financial companies are grappling with a burning issue about bankruptcy prediction. There are many methods for bankruptcy prediction, including statistical models and machine learning. Real-life datasets are often imbalanced with high dimensionality. Therefore, it is challenging to train a robust model to predict bankruptcy. Thus, we first applied an oversampling technique known as the Synthetic Minority Oversampling Technique (SMOTE) to reduce the skewness of the data. The balanced data was trained with the baseline models, the ensemble classifiers using different combination methods and the long short-term memory (LSTM) model. In addition, we employed an optimization technique called a genetic algorithm (GA) to optimize and determine the learning parameters of an LSTM network. We further determine the effects of using different training/testing ratios on the developed models. An autoencoder long short-term memory (LSTM) model was developed to extract the best feature representation of the input data. A comparative analysis was carried out between the LSTM-GA and autoencoder-LSTM. The results show that the improved LSTM-GA model with an accuracy of 98.11% performs better than other models. Overall, the research work concluded that all models and LSTM have good performances, while the optimized LSTM model via genetic algorithm outperforms the classical machine learning models. © (2023), (International Association of Engineers). All Rights Reserved.

关键词： Genetic algorithms

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Wind Driven Optimization-Based Medical Image Encryption for Blockchain-Enabled Internet of Things Environment

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computers, Materials & Continua 2022年第11期73卷 3219-3233页

作者： C.S.S.Anupama Raed Alsini N.Supriya E.Laxmi Lydia Seifedine Kadry Sang-Soo Yeo Yongsung Kim Department of Electronics&Instrumentation Engineering V.R.Siddhartha Engineering CollegeVijayawada520007India Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Engineering Malla Reddy Engineering College(A)Hyderabad500100India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University College4612KristiansandNorway Department of Computer Engineering Mokwon UniversityDaejeon35349Korea Department of Technology Education Chungnam National University 99Daehak-roYuseong-guDaejeon34134Korea

Internet of Things(IoT)and blockchain receive significant interest owing to their applicability in different application areas such as healthcare,finance,transportation,*** image security and privacy become a critical part of the healthcare sector where digital images and related patient details are communicated over the public *** paper presents a new wind driven optimization algorithm based medical image encryption(WDOA-MIE)technique for blockchain enabled IoT *** WDOA-MIE model involves three major processes namely data collection,image encryption,optimal key generation,and data ***,the medical images were captured from the patient using IoT ***,the captured images are encrypted using signcryption *** addition,for improving the performance of the signcryption technique,the optimal key generation procedure was applied by WDOA *** goal of the WDOA-MIE algorithm is to derive a fitness function dependent upon peak signal to noise ratio(PSNR).Upon successful encryption of images,the IoT devices transmit to the closest server for storing it in the blockchain *** performance of the presented method was analyzed utilizing the benchmark medical image *** security and the performance analysis determine that the presented technique offers better security with maximum PSNR of 60.7036 dB.

关键词： Internet of things image security medical images encryption optimal key generation blockchain

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Empowering Cybersecurity: Unveiling the Art of Identifying and Thwarting Malicious Tactics Within the Sandbox Environment

Empowering Cybersecurity: Unveiling the Art of Identifying a...

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International Conference on Electrical Engineering/Electronics, computer, Telecommunications and information Technology, ECTI-CON

作者： Tharaniyawarma Kumaralingam Sanika K. Wijayasekara Department of Computer Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Sri Lanka CIET - SLIIT Discovery Lab Faculty of Computing Sri Lanka Institute of Information Technology Sri Lanka

ISBN: (数字)9798350381559

ISBN: (纸本)9798350381566

In order to combat typical cyber-security concerns, malware analysis is essential. As highly sophisticated malware develops, it calls for a technique to install and study behavioral software properties. In order to lessen the impact on current infrastructure, environments that are isolated from production systems have historically been created using virtual machines. Malware developers usually create evasion tactics to avoid detection in sandbox environments since they are aware of this. This research delved into examining anti-evasion mechanisms to assess their effectiveness in detecting malware attempting to hide within a conventional sandbox setting. In the same way as ticking time bombs count down the seconds until they delete data or get new commands from internet, for example, worms, viruses, and other malware may do the same. Here a novel virtual-machine-based analytic technique for automatically establishing a malware piece is described, or when events take place, so that rest of the analysis may identify the events risk. When responding to quickly spreading malware, this information may be very helpful, and self-driven identification can give more correct data with shorter wait time considered to painstaking manual investigation. A challenging research issue is developing an automated system that constructs a malware's chronology. In this study, implementation of the detection of timers without assuming the integrity of the kernel of the compromised system, which is a crucial part of such a system is provided.

关键词： Production systems computer viruses Weapons Manuals Malware Telecommunications Kernel

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${\sf GoCrowd}$GoCrowd: Obliviously Aggregating Crowd Wisdom With Quality Awareness in Crowdsourcing

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IEEE Transactions on Dependable and Secure Computing 2024年第1期22卷 710-722页

作者： Xiaoning Liu Yifeng Zheng Xingliang Yuan Xun Yi School of Computing Technologies RMIT University Melbourne VIC Australia School of Computer Science and Technology Harbin Institute of Technology Shenzhen Guangdong China School of Computing and Information Systems University of Melbourne Melbourne VIC Australia

Organizations these days capitalize on crowdsourcing to learn collective wisdom from a population of individuals. Vast amounts of data have been gathered, making the crowdsourcing platforms a lucrative target to steal data from and thus raising severe privacy concerns. Data contributed by workers may carry sensitive individual information. Meanwhile, organizations deem the aggregate statistics as intellectual property. In this paper, we propose, design, and evaluate GoCrowd, a system framework for obliviously aggregating wisdom with quality assurance in crowdsourcing. At its core, we propose constructions for two procedures. The starting point is a gold-standard based private worker quality control procedure that provides privacy-friendly worker quality assurance under the widely popular gold-standard mechanism. The subsequent procedure is an oblivious wisdom aggregation procedure that obliviously learns aggregate statistics over workers’ data while considering their quality. We securely realize these procedures with only lightweight secret sharing techniques. Our system is utterly oblivious to the service provider, and ensures that only the requester can learn the aggregate quality-aware statistics but nothing more. Extensive evaluations show that GoCrowd can produce quality statistics over data from 500 workers for 200 16-choice questions within 1 s.

关键词： Crowdsourcing Cryptography Quality control Servers Task analysis Aggregates Data privacy

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Next Generation Memristor Reservoir Computing

Next Generation Memristor Reservoir Computing

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Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), IEEE International Conference on

作者： K. Nikiruy T. Ivanov M. Ziegler D. Rossetti F. Corinto A. Ascoli R. Tetzlaff A. S. Demirkol N. Schmitt Department of Electrical Engineering and Information Technology Micro-and Nanoelectronic Systems TU Ilmenau Ilmenau Germany Department of Electronics and Telecommunications (DET) Politecnico di Torino Turin Italy Faculty of Electrical and Computer Engineering Institute of Circuits and Systems TU Dresden Dresden Germany

ISBN: (数字)9798350378009

ISBN: (纸本)9798350378016

The nonlinear transformation used in reservoir computing can be effectively replaced by nonlinear vector autoregression (NVAR) for data prediction. In such a method, also known as next generation reservoir computing (NGRC), the input signal consists of a linear part, including several previous data points, and their nonlinear combinations. Here we show that the application of this method to a network with memristive weights (memristors) can be used to predict signals, depending on the nature of the nonlinear functions and the number of memristors. The network allows an accurate prediction of chaotic time series of Mackey-Glass and Duffing oscillators.

关键词： Performance evaluation Accuracy Time series analysis Memristors Reservoir computing Neural engineering Ordinary differential equations Vectors Next generation networking Oscillators

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