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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Palarimath, Suresh Maran, Pyingkodi Thenmozhi, K. Shiny, K.V. Sujatha, T. N.r, Wilfred Blessing College of Computing and Information Sciences University of Technology and Applied Information Technology Department Sciences Salalah Oman Kongu Engineering College Department of Computer Applications Erode India Department of Compute Science Bengaluru India Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Chennai India Sri Krishna College of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India College of Computing and Information Sciences Univesity of Technology and Applied Sciences Information Technology Department Ibri Oman

ISBN: (纸本)9798350385793

Breast cancer poses a significant threat to women's health, being a leading cause of cancer-related mortality among female population. In recent years, machine learning has emerged as a promising approach in medical field, particularly in detection and classification tasks. However, existing algorithms often exhibit suboptimal accuracy, necessitating improved methodologies. This research presents a novel approach using a Modified Support Vector Machine (MSVM) for breast cancer classification into benign and malignant categories. Leveraging the Wisconsin Breast Cancer Dataset (WBCD), preprocessing techniques are applied to enhance data quality. Principle Component Analysis (PCA) reduces dimensionality, while linear Discriminant Analysis (LDA) extracts discriminative features crucial for classification. The proposed MSVM classifier achieves exceptional performance, with a classification accuracy of 99.42%, outperforming other existing methods such as Deep Convolutional Neural Networks and Fuzzy Rule-based Systems. These results highlight the efficacy of the MSVM approach in accurately distinguishing between benign and malignant breast cancer cases, showcasing its potential as a reliable tool for medical image analysis and cancer diagnosis. © 2024 IEEE.

关键词： Convolutional neural networks

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Secondary Teachers’ Self-perceived AI Competences in Relation to Renowned European Digital Competence Frameworks 17th

Secondary Teachers’ Self-perceived AI Competences in Relat...

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17th International Conference on Informatics in Schools: Situation, Evolution, and Perspectives, ISSEP 2024

作者： Jemetz, Michael Dolezal, Dominik Motschnig, Renate Faculty of Computer Science University of Vienna Vienna Austria University of Vienna Vienna Austria HTL Pinkafeld Pinkafeld Austria University of Applied Sciences Technikum Wien Vienna Austria TGM - Vienna Institute of Technology Vienna Austria

ISBN: (纸本)9783031734731

The fast-paced developments in GenAI technology have begun to change school realities. The aim of this study is to identify what competences teachers of technical subjects are relying on when managing AI use in the classroom and to what degree these competences are covered in the DigComp framework. This study used a qualitative research approach utilizing semi-structured interviews. Nine educators from two vocational upper secondary schools in Austria were interviewed and a thematic analysis of the interviews was conducted to identify competences which were then mapped to DigComp2.2. Results show that several of the AI-related competences identified, such as information literacy and privacy can smoothly be mapped to DigComp2.2. However, other competences such as prompting, and the reflected goal-oriented use of GenAI tools appear not to fit in well and hence point to the necessity of adapting DigComp2.2. Furthermore, the importance of interpersonal skills and a well-founded subject knowledge were highlighted as crucial. This research is targeted at curriculum designers, educators, educational researchers, and administrators. It will also speak to teachers who wish to better deal with and prosper from the emergence of GenAI in schools. Future work will address key factors in developing educators’ and students’ AI literacy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Apprentices

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Optimal Planning of Operation Modes of Gas Transmission Systems 14

Optimal Planning of Operation Modes of Gas Transmission Syst...

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14th International Conference on Advanced computer Information Technologies, ACIT 2024

作者： Pyanylo, Yaroslav Prytula, Nazar Khymko, Olga Prytula, Myroslav Prytula, Zoia Pidstryhach Institute for Applied Problems of Mechanics and Mathematics National Academy of Sciences of Ukraine Lviv Ukraine Research and Design Institute of Gas Transport Lviv Ukraine National University«Lviv Polytechnic» Department of Automation and Computer-Integrated Technologies Lviv Ukraine

ISBN: (纸本)9798350350036

The object of the study is the gas transmission system (GTS) and underground gas storage facilities (UGSF). The problem to be solved is the planning of optimal modes of their operation. In the process of studying the operation of GTS and UGS, a significant potential for their joint optimization was revealed. The factors affecting the optimal operation of the GTS and UGS and the problems of implementing optimal modes are analyzed. The developed and implemented combinatorial optimization methods of minimum complexity ensured a quick calculation of optimal modes according to the energy and mode stability criteria. The developed software has been tested and is used by dispatch services as a decision support system. © 2024 IEEE.

关键词： Stability criteria

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Ge_(0.92)Sn_(0.08)/Ge multi-quantum-well LEDs operated at 2-μm-wavelength on a 12-inch Si substrate

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Photonics Research 2023年第10期11卷 1606-1612页

作者： SHAOTENG WU LIN ZHANG RONGQIAO WAN HAO ZHOU KWANG HONG LEE QIMIAO CHEN YI-CHIAU HUANG XIAO GONG CHUAN SENG TAN School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore State Key Laboratory of Superlattices and Microstructures Institute of SemiconductorsChinese Academy of SciencesBeijing 100083China Applied Materials Inc.SunnyvaleCalifornia 95054USA Department of Electrical and Computer Engineering National University of SingaporeSingapore 117576Singapore

The development of an efficient group-IV light source that is compatible with the CMOS process remains a significant goal in Si-based ***,the Ge Sn alloy has been identified as a promising candidate for realizing Si-based light ***,previous research suffered from a small wafer size,limiting the throughput and *** overcome this challenge,we report the successful growth of Ge Sn/Ge multiple-quantum-well(MQW)p-i-n LEDs on a 12-inch(300-mm)Si *** the best of our knowledge,this represents the first report of semiconductor LEDs grown on such a large *** MQW LED epitaxial layer is deposited on a 12-inch(300-mm)(001)-oriented intrinsic Si substrate using commercial reduced pressure chemical vapor *** mitigate the detrimental effects of threading dislocation densities on luminescence,the Ge Sn/Ge is grown *** to the high crystal quality and more directness in the bandgap,enhanced electroluminescence(EL)integrated intensity of 27.58 times is demonstrated compared to the Ge *** MQW LEDs exhibit EL emission near 2μm over a wide operating temperature range of 300 to 450 K,indicating hightemperature *** work shows that Ge Sn/Ge MQW emitters are potential group-IV light sources for large-scale manufacturing.

关键词： quantum emitter overcome

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Estimation of Maximum Friction Coefficient Using Recurrent Artificial Neural Networks 22

Estimation of Maximum Friction Coefficient Using Recurrent A...

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3rd International Conference on Robotics Systems and Vehicle Technology, RSVT 2022

作者： Lampe, Nicolas Ziaukas, Zygimantas Westerkamp, Clemens Jacob, Hans-Georg Institute of Computer Engineering Osnabrück University of Applied Sciences Germany Institute of Mechatronic Systems Leibniz Universität Hannover Germany

ISBN: (纸本)9781450396783

Knowledge of vehicle dynamics and in particular the maximum friction coefficient is required for the optimization of advanced driver assistance systems and the implementation of autonomous driving. Since the maximum friction coefficient cannot be measured directly, estimating this coefficient based on available sensors is a field of interest. In particular, model-based approaches based on Kalman filter derivates are used. However, their accuracy is limited by the accuracy of the physical model. Due to the real-time capability required, more detailed modeling is not possible. In addition, system identification and a robust filter design are needed. As a result, data-based approaches are gaining popularity in vehicle dynamics, which are also suitable for estimating the maximum friction coefficient. In this paper, maximum friction coefficient estimation is presented using recurrent artificial neural networks (RANN) based on vehicle sensors. In order to avoid incorrect estimations during low vehicle excitation, an excitation monitoring approach is introduced. Typical longitudinal and lateral driving maneuvers on different road surfaces simulated in IPG CarMaker with a Dacia Duster are used for training, validation and testing of the RANN. Finally, the data-based approach shows improved results compared to the model-based approach of a sensitivity-based unscented Kalman filter (sUKF) from previous works. © 2022 ACM.

关键词： Advanced driver assistance systems

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Enhancing Fake News Detection Using BERT: A Comparative Analysis of Logistic Regression, RFC, LSTM and BERT

Enhancing Fake News Detection Using BERT: A Comparative Anal...

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2024 International Conference on IT and Industrial Technologies, ICIT 2024

作者： Ramzan, Alishba Ali, Raja Hashim Ali, Nisar Khan, Ayesha Department of Artificial Intelligence Faculty of Computer Science & Engineering GIK Institute of Engineering Sciences & Technology Topi23460 Pakistan Department of Business University of Europe for Applied Sciences Potsdam14469 Germany Faculty of Engineering and Applied Science University of Regina ReginaS4S0A2 Canada

ISBN: (纸本)9798331504465

The research analyzed how to identify fake news effectively, understanding the developing capabilities to differentiate between misinformation and the correct nature of news in today’s complex media environment. Numerous approaches were tested: mostly the ways of determining if a certain message is valid or false. With application of Logistic Regression, Random Forest and lastly LSTM models, the research has been able to achieve 98.7%, 98.8%, and 95% accuracies respectively, showing that these traditional and advanced techniques are very effective. However, it became apparent that these models performed poorly as soon as tested on a new dataset and the discrepancy was not being able to understand context while being trained. Understanding the context is the key in news identification, so the research turned to BERT, the Transformer model pre-trained with huge contextual data to perform this task. Drawing on its deep and diverse knowledge base, BERT has shown a remarkable aptitude for sorting news articles into fake/real categories based on their context. Logistic Regression, Random Forest, and LSTM models demonstrated that although they were able to build models that were highly accurate up to 99% on familiar data, their accuracy dropped disproportionately as soon as new data was given. BERT, despite having a lower overall accuracy of 84% demonstrated a better sensitivity to contextual nuances in the news data. Here, the importance of contextualized conception in the sphere of fake news detection should be emphasized as a way to take advantage of BERT’s complicated comprehension which is a promising alternative of more accurate and effective identification as the media landscapes of the modern times may be complex and diverse. Despite the fact that the classical models surpass in planned environments, BERT’s capability of contextualization made it irreplaceable for evidence as the news sources in uncommon circumstances can digress from base. © 2024 IEEE.

关键词： Logistic regression

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Network Traffic Co-Movement Assessment via Oriented Basis Signal Processing and Ensemble Decision Trees 27

Network Traffic Co-Movement Assessment via Oriented Basis Si...

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Mohebbi, Mohammad Reza Hassan Zada, Mohammad Javad Rostami-Shahrbabaki, Majid Doller, Mario Yamnenko, Iuliia University of Applied Sciences Josef Ressel Center Vision2Move Project Tirol Kufstein Austria University of Passau Department of Computer Science Germany Technical University of Munich Traffic Engineering and Control Germany National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Department of Electronic Devices and Systems Kyiv Ukraine

ISBN: (纸本)9798331505929

Accurate short-term network-wide traffic prediction is essential to guarantee high service quality in urban traffic control systems. Nevertheless, traffic state time series represent network-scale spatiotemporal co-movement patterns and location-specific features. Therefore, hybrid statistical machine learning (ml) algorithms could be utilized to accommodate the aforementioned characteristics. In this paper, a hybrid random forest (rf) and extreme gradient boosting tree (XGBoost) model is introduced for network-wide traffic prediction. Moreover, a multi-noise oriented basis wavelet transform (OBT) filter is employed to pre-process the original time series, and improve the predictive accuracy. The RF model captures the traffic co-movements and the XGBoost extracts the local information. Comparative analysis of the hybrid algorithm and deep learning-based benchmarks indicate better performance of the proposed methodology in hourly traffic state prediction of 30 loop detectors, located in the paris city center. Hence, the hybrid decision-tree-based mechanism is a useful framework for real-time network traffic forecasting offering fewer trainable (hyper-)parameters, and thus, lower computational cost. © 2024 IEEE.

关键词： Time series

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VigilantAI: Real-time detection of anomalous activity from a video stream using deep learning

VigilantAI: Real-time detection of anomalous activity from a...

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2024 International Conference on IT and Industrial Technologies, ICIT 2024

作者： Javed, Danish Arshad, Usama Peerzada, Shuhrabeel Saud, Muhammad Ramiz Ali, Nisar Ali, Raja Hashim Department of Artificial Intelligence Faculty of Computer Science & Engineering GIK Institute of Engineering Sciences & Technology Topi23460 Pakistan Department of Business University of Europe for Applied Sciences Potsdam14469 Germany Faculty of Engineering and Applied Science University of Regina ReginaS4S0A2 Canada

ISBN: (纸本)9798331504465

In an era where artificial intelligence (AI) solutions are increasingly integrated into various sectors, we have utilized Artificial Intelligence for enhancing public safety through realtime detection of illegal activities such as robberies and threats at gunpoint using CCTV footage. With the proliferation of deep learning in object detection, the study focuses on deploying the YoloV5 model, trained on a custom dataset compiled from diverse CCTV sources and movies, to identify specific criminal actions. One of the major problems faced in this field is the availability of a large robust labeled dataset on which a deep learning model can be trained. For this purpose, we have created our own dataset by converting various CCTV footage and movies into images, and then labeling them with the correct class. In addition, we also augmented data by using various data augmentation techniques for the chosen images. This dataset, enriched through augmentation techniques and annotated with bounding boxes, allows for the precise detection of threats, achieving an accuracy rate of 85%. Our system stands out by not only spotting these activities but also by instantly alerting security personnel, facilitating a rapid response to potentially dangerous situations. This capability is important for law enforcement agencies worldwide, offering them an advanced tool to act swiftly and prevent crimes, thereby enhancing public security. The essence of our work demonstrates the practical application and significant impact of AI in bolstering security measures, providing a solid foundation for future enhancements in the field. Through this initiative, we aim to foster a safer environment in public spaces, reducing crime rates and increasing the general public’s sense of safety. © 2024 IEEE.

关键词： Crime

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Traffic Sign Recognition System

Traffic Sign Recognition System

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2024 International Conference on IT and Industrial Technologies, ICIT 2024

作者： Ahmed, Maqsood Ali, Raja Hashim Ali, Nisar Department of Artificial Intelligence Faculty of Computer Science & Engineering GIK Institute of Engineering Sciences & Technology Topi23460 Pakistan Department of Business University of Europe for Applied Sciences Potsdam14469 Germany Faculty of Engineering and Applied Science University of Regina ReginaS4S0A2 Canada

ISBN: (纸本)9798331504465

Traffic Sign Recognition Systems (TSRS) are instrumental in improving road safety by assisting drivers and supporting autonomous vehicles in real-time identification of road regulations. These systems have advanced significantly in recent years, with deep learning approaches achieving promising results. However, despite these developments, existing models face challenges in adapting to the diverse conditions of real-world environments, including variations in lighting, weather, and sign appearance. Addressing this limitation, our study focuses on enhancing the robustness and accuracy of TSRS for practical deployment. In response to this gap, we introduce a novel deep learning model optimized for traffic sign recognition across various conditions. Our approach utilizes a convolutional neural network (CNN) architecture, which was trained on the German Traffic Sign Recognition (GTSR) dataset. We improved the model’s adaptability to new and unseen data while achieving a high accuracy rate of 97.99% by applying techniques like data augmentation and transfer learning. Methodologically, the model workflow includes extensive preprocessing, hyperparameter tuning, and real-time inference evaluations, ensuring suitability for deployment in autonomous driving systems. Our study contributes by providing a scalable, high-accuracy TSRS model that can reliably identify traffic signs in complex environments, supporting both autonomous and assisted driving technologies. The model’s performance paves the way for safer and more efficient road transport, while our results highlight the potential for further interdisciplinary research to expand TSRS capabilities. © 2024 IEEE.

关键词： Convolutional neural networks

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Robust Deep Learning Empowered Real Time Object Detection for Unmanned Aerial Vehicles based Surveillance Applications

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Journal of Mobile Multimedia 2023年第2期19卷 451-476页

作者： Ranjith, C. Prasanna Hardas, Bhalchandra M. Khaja Mohideen, M. Syed Nijil Raj, N. Robert, Nismon Rio Mohan, Prakash Faculty in Information Technology Department University of Technology and Applied Sciences Shinas Oman Electronics Engineirng Dept Shri Ramdeobaba College of Enginering and Management Nagpur India Department of Information Technology University of Technology and Applied Science Salalah Oman Department of Computer Science and Engineering Younus College of Engineering and Technology Kollam India Department of Computer Science Christ University Bangalore India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

Surveillance is a major stream of research in the field of Unmanned Aerial Vehicles (UAV), which focuses on the observation of a person, group of people, buildings, infrastructure, etc. With the integration of real time images and video processing approaches such as machine learning, deep learning, and computer vision, the UAV possesses several advantages such as enhanced safety, cheap, rapid response, and effective coverage facility. In this aspect, this study designs robust deep learning based real time object detection (RDL-RTOD) technique for UAV surveillance applications. The proposed RDL-RTOD technique encompasses a two-stage process namely object detection and objects classification. For detecting objects, YOLO-v2 with ResNet-152 technique is used and generates a bounding box for every object. In addition, the classification of detected objects takes place using optimal kernel extreme learning machine (OKELM). In addition, fruit fly optimization (FFO) algorithm is applied for tuning the weight parameter of the KELM model and thereby boosts the classification performance. A series of simulations were carried out on the benchmark dataset and the results are examined under various aspects. The experimental results highlighted the supremacy of the RDL-RTOD technique over the recent approaches in terms of several performance measures. © 2022 River Publishers.

关键词： computer vision deep learning image processing object detection Surveillance unmanned aerial vehicles

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