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14th International Conference on Information and Communication Systems, ICICS 2023

作者： Taqieddin, Eyad Mustafa, Dheya Shatnawi, Ahmed Al-Khaleel, Osama Papachristou, Christos Jordan University of Science and Tech. Network Eng. and Security Department Irbid22110 Jordan The Hashemite University Faculty of Engineering Dept. of Computer Engineering Zarqa13133 Jordan Jordan University of Science and Tech. Software Engineering Department Irbid22110 Jordan Jordan University of Science and Tech. Computer Engineering Department Irbid22110 Jordan Case Western Reserve University Ecse Department ClevelandOH44106 United States

ISBN: (纸本)9798350307870

Due to the ever increasing usage of multimedia content sharing and the inherent nature of the insecure links used for transmission, it is quite necessary to provide a fast and secure encryption algorithm. Current symmetric ciphers possess properties that may reveal some content of the plaintext images, especially when a block cipher is used without any chaining. Chaotic encryption has been proposed to allow for hiding inherent properties of the images and to generate almost uniform histograms of the cipher image (i.e.;hiding any visual properties). To that end, we propose the use of hardware to implement chaos-based ciphers for faster execution time. The results of this work show that such an implementation is feasible, and even favorable, when compared to software-based image encryption. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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On the Impact of Noisy Labels on Supervised Classification Models 23rd

On the Impact of Noisy Labels on Supervised Classificatio...

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23rd International Conference on Computational Science, ICCS 2023

作者： Dubel, Rafal Wijata, Agata M. Nalepa, Jakub Faculty of Automatic Control Electronics and Computer Science Department of Algorithmics and Software Silesian University of Technology Akademicka 16 Gliwice44-100 Poland Faculty of Biomedical Engineering Silesian University of Technology Roosevelta 40 Zabrze41-800 Poland KP Labs Konarskiego 18C Gliwice44-100 Poland

ISBN: (纸本)9783031360206

The amount of data generated daily grows tremendously in virtually all domains of science and industry, and its efficient storage, processing and analysis pose significant practical challenges nowadays. To automate the process of extracting useful insights from raw data, numerous supervised machine learning algorithms have been researched so far. They benefit from annotated training sets which are fed to the training routine which elaborates a model that is further deployed for a specific task. The process of capturing real-world data may lead to acquring noisy observations, ultimately affecting the models trained from such data. The impact of the label noise is, however, under-researched, and the robustness of classic learners against such noise remains unclear. We tackle this research gap and not only thoroughly investigate the classification capabilities of an array of widely-adopted machine learning models over a variety of contamination scenarios, but also suggest new metrics that could be utilized to quantify such models’ robustness. Our extensive computational experiments shed more light on the impact of training set contamination on the operational behavior of supervised learners. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Digital storage

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Accident detection and road monitoring in real time using deep learning and lane detection algorithms 21

Accident detection and road monitoring in real time using de...

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4th International Conference on Machine Learning and Machine Intelligence, MLMI 2021

作者： Ayman Mounir, Shehab Eldeen Hussein, Walid H. Karam, Omar Software Engineering Faculty of Informatics and Computer Science The British University in Egypt Egypt Computer Science Faculty of Informatics and Computer Science The British University in Egypt Egypt Information Systems Faculty of Informatics and Computer Science The British University in Egypt Egypt

ISBN: (纸本)9781450384247

Automating the monitoring of the roads would mean safer roads for both car drivers and pedestrians. The objectives of the system were to build a real time surveillance system for intelligent roads of the future. The system should be able to detect lane lines, cars, pedestrians and other moving vehicles on the road. In case a car goes off its course, the system should report it as an accident. Relying on machine learning technology, a real time detection based neural network was constructed to be trained on picking up objects in any given scene like cars, buses, pedestrians, bikes and motor bikes. Also, an algorithm was designed to detect lane lines and highlight them on the screen using image processing techniques and edge detection. The system achieved its main required target which is being able to detect objects in almost real time. © 2021 ACM.

关键词： Accidents

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Electromagnetic Sensor Behavior Prediction Using LSTM Neural Network for Enhanced Accuracy and Reliable Performance

Electromagnetic Sensor Behavior Prediction Using LSTM Neural...

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Electrical Machines and Drives (ICEMD), International Conference on

作者： Ahmad Sasani Mehrdad Ghafari Mostafa Eftekharizadeh Faculty of Electrical and Computer Engineering Semnan University Semnan Iran Chief Technical Officer Javaneh Inc. Tehran Iran Software Engineering Javaneh Inc. Tehran Iran

ISBN: (数字)9798331507510

ISBN: (纸本)9798331507527

Accurate sensors are essential in industries for system monitoring and control. High-precision sensor data can enhance predictions and prevent costly errors, especially in noisy environments that require detailed analysis. In this paper, we simulate the Long Short-Term Memory (LSTM) neural network model to predict the real magnetic signal from noisy data. The LSTM algorithm effectively removes Gaussian and random noise from the received data, providing accurate predictions of the real magnetic signals. Compared to previous works that used simpler methods, such as classical filters or basic models, our proposed algorithm demonstrates significantly higher accuracy. By using LSTM to remove noise and improve predictions, errors have been minimized, resulting in enhanced prediction accuracy. The simulation results indicate that the model can simulate the real behavior of the system with 97.67% accuracy, with minimal difference between the predicted and actual signals. This level of accuracy allows our model to significantly outperform previous methods in predicting magnetic signals in noisy environments and proves highly effective for industrial applications requiring precise accuracy.

关键词： Accuracy Magnetic sensors Noise Neural networks Predictive models Prediction algorithms Data models Sensor systems Noise measurement Long short term memory

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Linear Random Early Detection for Congestion Control at the Router Buffer

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Informatica (Slovenia) 2022年第5期46卷 105-114页

作者： Abu-Shareha, Ahmad Adel Al-Kasasbeh, Basil Shambour, Qusai Y. Abualhaj, Mosleh M. Alsharaiah, Mohmmad Abdalla Al-Khatib, Sumaya N. Department of Computer Science Faculty of Information Technology Al-Ahliyya Amman University Amman Jordan Riyadh Saudi Arabia Department of Software Engineering Faculty of Information Technology Al-Ahliyya Amman University Amman Jordan Department of Networks and Information Security Faculty of Information Technology Al-Ahliyya Amman University Amman Jordan Department of Data Science and Artificial Intelligence Faculty of Information Technology Al-Ahliyya Amman University Amman Jordan

Active Queue Management (AQM) methods control the router's buffer to maintain high network performance and control congestion at the router buffer. Random Early Detection (RED) method is the most well-known and the most utilized AQM. RED suffers from a high dropping rate, which motivates the later AQM methods to use more complex processes, which reach the limits of using fuzzy systems as a processing technique. Yet, high computational cost affects the router's performance specifically and the network as a whole, with so-called processing delay. In this paper, a linear version of RED (LRED) is presented to reduce the computational cost of the original RED and maintain the network performance in terms of throughput, delay, dropping, and loss. LRED is built based on two distinctive features, simplifying the congestion indicator calculation and reducing the operations in calculating the dropping probability. The experimental results showed that the proposed method reduces the delay and the processing time while maintaining the throughput and loss of the RED method. © 2022 Slovene Society Informatika. All rights reserved.

关键词： Network performance

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Architecting Digital Twins for Intelligent Transportation Systems

arXiv

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arXiv 2025年

作者： Bhatt, Hiya Sahil Vaidhyanathan, Karthik Biju, Rahul Gangadharan, Deepak Trestian, Ramona Shah, Purav Software Engineering Research Centre IIIT Hyderabad India Computer Systems Group IIIT Hyderabad India Faculty of Science and Technology Middlesex University London United Kingdom

Modern transportation systems face growing challenges in managing traffic flow, ensuring safety, and maintaining operational efficiency amid dynamic traffic patterns. Addressing these challenges requires intelligent solutions capable of real-time monitoring, predictive analytics, and adaptive control. This paper proposes an architecture for DigIT, a Digital Twin (DT) platform for Intelligent Transportation Systems (ITS), designed to overcome the limitations of existing frameworks by offering a modular and scalable solution for traffic management. Built on a Domain Concept Model (DCM), the architecture systematically models key ITS components enabling seamless integration of predictive modeling and simulations. The architecture leverages machine learning models to forecast traffic patterns based on historical and real-time data. To adapt to evolving traffic patterns, the architecture incorporates adaptive Machine Learning Operations (MLOps), automating the deployment and lifecycle management of predictive models. Evaluation results highlight the effectiveness of the architecture in delivering accurate predictions and computational efficiency. © 2025, CC BY.

关键词： Adaptive control systems

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Virtual Reality Semiconductor Laboratory: Usability Testing to Improve User Experience (UX) in Virtual Environments 3

Virtual Reality Semiconductor Laboratory: Usability Testing ...

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3rd IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer engineering, MI-STA 2023

作者： Ahmed Alnagrat, Ahmed Jamah Ismail Alkhallas, Muhannad Imhmed Mohamed, Abdalslam S Che Ismail, Rizalafande Syed Idrus, Syed Zulkarnain Salem Saed, Halifah Department of Computer Science and Information Technology Wadi al-Shati Fezzan Libya Perlis Kangar01000 Malaysia Faculty of Engineering Technology in Zuwarah Department of Software Engineering Zuwarah Libya Aljufra University Faculty of Information Technology Aljufra Libya Arau02600 Malaysia Department of Communication Perlis Kangar01000 Malaysia Higher Institute of Science and Technology Wadi Al-Shati Department of Computer Science and Information Technology Wadi al-Shati Fezzan Libya

ISBN: (纸本)9798350319897

The Virtual Reality (VR) Semiconductor Laboratory, a computer-based virtual lab designed for training engineering students in semiconductor concepts, was evaluated to enhance user experience (UX) in virtual environments. A sample of 97 participants, including undergraduate, Master's, and PhD students, participated in the study. The User Experience Questionnaire (UEQ) was developed to collect data from users to evaluate their experience with the VR lab. The results of the study showed that the participants had a positive experience with the VR learning environment. They found the experience to be enjoyable, easy to understand, and helpful in completing learning tasks. The majority of participants rated the experience as very easy or easy in terms of learning and understanding the material, enjoyment, ease of using the interface, comfort with body movement during learning, and assistance with tasks. Additionally, most participants rated the experience as very easy in terms of task completion and comfort with the VR experiment. The study found high reliability in terms of usability, indicating that the UEQ is a valid and reliable tool for assessing virtual laboratory usability. These findings suggest that virtual laboratories can be effective learning and training tools in various fields, such as science and engineering. This research highlights the potential of VR technology in improving the training and learning experience for students. © 2023 IEEE.

关键词： Virtual reality

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PyTime IoT: A Bootcamp to Motivate High School Students to Choose STEM Careers

PyTime IoT: A Bootcamp to Motivate High School Students to C...

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IEEE Education engineering (EDUCON)

作者： Adriana Collaguazo Monica Villavicencio Alain Abran Carmen Vaca-Ruiz Faculty of Electrical and Computer Engineering FIEC Escuela Superior Politécnica del Litoral ESPOL Guayaquil Ecuador Department of Software and IT Engineering École de Technologie Supérieure (ETS) Montreal Canadá

ISBN: (数字)9798331539498

ISBN: (纸本)9798331539504

In the rapidly evolving landscape of modern education, Science, Technology, engineering and Mathematics (STEM) disciplines stand out for their role in equipping students with the skills necessary to address complex real-world problems. Despite the critical importance of STEM careers, disparities persist, particularly among young women and students from public schools. This motivated the creation of an IoT bootcamp - referred to as PyTime IoT, a 2-day course aimed at high school students. This bootcamp integrates hands-on activities in Python programming and IoT systems that demonstrate the tangible application of theoretical STEM concepts through real-world scenarios. Our approach enhances participants' understanding of IoT and programming and also serves as a stepping-stone for high school students contemplating STEM careers. Preliminary results from the first two bootcamp editions indicate a positive shift in participants' skills and a marked increase in STEM career interest, suggesting that targeted bootcamps like PyTime IoT are effective in bridging the educational gap and inspiring the next generation of STEM professionals.

关键词： Industries engineering profession Data handling Organizations Programming profession Next generation networking Monitoring STEM Python Gender issues

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From Data to Diagnosis: Investigating Approaches in Mental Illness Detection

From Data to Diagnosis: Investigating Approaches in Mental I...

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Intelligent Methods, Systems, and Applications (IMSA)

作者： Reem Kadry Montasser Sherif A. Mazen Iman M. A. Helal Software Engineering Department Faculty of Computer Science October University for Modern Sciences and Arts Giza Egypt Information Systems Department Faculty of Computers and Artificial Intelligence Cairo University Giza Egypt

ISBN: (数字)9798350362633

ISBN: (纸本)9798350362640

Mental illness is a considerable global public health problem, impacting both individual well-being and society's health. The growing popularity of social media and the increase of other data sources led to more research interest in detecting mental illness with a particular focus on utilizing and fusing emotional information. Artificial intelligence techniques like natural language processing and others, have shown encouraging improvements in recognizing complex relationships in a wide range of textual data, including clinical notes, social media posts, and interviews. In this paper, we assess the different branches of artificial intelligence based on previous research in mental illness detection, discussing methods, trends, challenges, and future research directions.

关键词： Deep learning Machine learning algorithms Accuracy Social networking (online) Soft sensors Mental health Feature extraction

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The research of human interaction recognition based on fusion features of key frame feature library

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International Journal of Information and Communication Technology 2021年第1期18卷 57-69页

作者： Zhang, Haiyan Gao, Shangbing Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huai'an China Nanjing Key Laboratory of Trusted Cloud Computing and Big Data Analysis Nanjing Xiaozhuang University Nanjing China

Some issues such as computational complexity and low recognition accuracy still exist in human interaction recognition. In order to solve the problem, the paper has proposed innovative and effective method based on fixed features of key frame feature library. Firstly, GIST feature and HOG feature were extracted from the pre-processed videos. Secondly, training videos with different kinds of actions were clustered by the K-means algorithm respectively to get key frame feature of each action for constructing key frame feature library. And similarity measure was utilised to calculate the frequency of different key frames in every interactive video, and statistical histogram representation of interactive videos were obtained. Finally, the decision level fusion was achieved by using SVM classifier based on histogram intersection kernel to obtain impressive results on UT-interaction dataset. The correct recognition rate of the proposed algorithm is 85%, which indicates that the proposed algorithm is simple and effective than others. © 2021 Inderscience Enterprises Ltd.

关键词： K-means clustering

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