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2nd IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2023

作者： Verma, Anand Agarwal, Saurabh Arya, K.V. Petrlik, Ivan Esparza, Roberto Rodriguez, Ciro ABV-IIITM Gwalior Department of Information Technology Madhya Pradesh 474015 India ABV-Indian Institute of Information Technology and Management Multimedia and Information Security Research Group Department of Computer Science and Engineering Gwalior474015 India National University Federico Villarreal Faculty of Industrial and Systems Engineering Lima Peru National University Mayor de San Marcos Faculty of Systems Engineering and Informatics Peru

ISBN: (纸本)9798350305142

Image captioning involves generating a natural language description that accurately represents the content and context of an image. To achieve this, image captioning utilises various machine learning techniques and fields, such as computer vision and natural language processing. In the field of image captioning, a lot of advances have been made with encoder-decoder models and reinforcement learning algorithms. However, there are still problems of imbalance between testing and training, as reinforcement learning only handles single comparator metrics such as CIDEr, SPICE, and BLEU and could not perform better in multiple metrics at once. Which is why a lack of diversity can be seen in generated captions. This idea proposes a general technique for collaborative updating that can bridge the gap between evaluation measures and test metrics to produce captions that are more human-like. To increase the precision of image captions, the approach involves using a compiled reward system that considers multiple evaluation metrics to compare the generated sentence with the provided sentences. We will evaluate the model's performance and the reward updating process on standard datasets like MS COCO. © 2023 IEEE.

关键词： Reinforcement learning

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Automatic detection of Covid-19 based on lung CT images using Deep Convolutional Neural Networks (CNN)

Automatic detection of Covid-19 based on lung CT images usin...

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Information and Communication Technologies (ZCICT), Zimbabwe Conference of

作者： Shawn Mahachi Kudakwashe Zvarevashe Leslie Kudzai Nyandoro Analytics & Informatics Department Faculty of Computer Engineering Informatics and Communications University of Zimbabwe Harare Zimbabwe

ISBN: (纸本)9781665475778

In recent years, the COVID-19 pandemic has spread all over the world. Due to its rapid transmission, techniques that automatically detect COVID-19 infections and distinguish it from other forms of pneumonia are crucial. The scientific community has embarked on finding solutions to quick detection of COVID-19 through implementation of deep learning(DL) techniques that can diagnose COVID-19 using computed tomography (CT) lung scans. The use of CT images has been widely accepted in medical imaging and it is a pertinent screening tool due to its higher sensitivity in recognizing early pneumonic changes. Also, most developed DL models developed have been end-to-end from feature extraction to categorization of the COVID19 infected images. The proposed model results showed high accuracy rates on both training and testing of the model in COVID-19 classification. A customised ResNet-50 architecture has the best results in classifying the images and achieved state of art accuracy of 97% on training and testing using the COVID dataset with 200 epochs. This work presented a computationally efficient and highly accurate model for multi-class classification of normal and infected individuals. The model can help in effective early screening of COVID-19 cases hence reducing the burden on healthcare systems.

关键词： COVID-19 Training Computed tomography Pulmonary diseases Computational modeling Lung Feature extraction

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Fake News Detection Regarding COVID-19 Tweets Using Machine Learning Approaches 8

Fake News Detection Regarding COVID-19 Tweets Using Machine ...

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8th IEEE International Conference on Recent Advances and Innovations in engineering, ICRAIE 2023

作者： Hussin, Muhammad Hazimmuddin Mahmud, Yuzi Mohd Hanafiah, Nurul Izzah Azma Nasruddin, Zan Mohd Ariffin, Nor Hapiza Ince, Murat Senel, Fatih Ahmet Universiti Teknologi MARA School of Computing Sciences College of Computing Informatics and Mathematics Shah Alam Malaysia Sohar University Faculty of Business MIS Department Sohar Oman Isparta University of Applied Sciences Department of Computer Technologies Isparta Turkey Suleyman Demiral University Department of Computer Engineering Isparta Turkey

ISBN: (纸本)9798350315516

The pervasiveness of misinformation surrounding the COVID-19 pandemic has garnered heightened attention due to its implications, as a noteworthy proportion of the populace is being exposed to spurious and unsubstantiated narratives concerning the crisis. This research utilizes a dataset sourced from Codalab, comprising 8,560 tweets, with 4,480 labelled as real and 4,080 as fake. The research explores the effectiveness of different machine learning models, including logistic regression (LR), random forest (RF), and deep learning models such as Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM). In addition to model comparison, experiments were conducted to analyze the impact of different data splits (70:30, 80:20, and 90:10), batch sizes (16, 32, and 64), and the number of epochs (5, 10, and 15) on model performance. The experiments provided insights into the optimal configurations for the models. The results showcase the model's capabilities, with high accuracy achieved across the different models. Specifically, logistic regression achieved an accuracy of 92%, random forest 91%, Bi-LSTM 93%, and CNN 95%. These findings highlight the potential of deep learning models, particularly CNN, in accurately detecting fake news from COVID-19-related tweets. © 2023 IEEE.

关键词： COVID-19

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Driver drowsiness detection using Convolutional Neural Networks-inspired features and Principal component analysis with K-Nearest Neighbors

Driver drowsiness detection using Convolutional Neural Netwo...

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Information and Communication Technologies (ZCICT), Zimbabwe Conference of

作者： Marvelous Alexander Panganai Leslie Kudzai Nyandoro Kudakwashe Zvarevashe Faculty of Computer Engineering Informatics and Communication Analytics & Informatics Department University of Zimbabwe Harare Zimbabwe

ISBN: (纸本)9781665475778

Drowsiness and fatigue are the major reasons for triggering serious and severe road crashes in Zimbabwe and the whole world at large. The developments in technology in recent years brought support and backing to drivers using intelligent automobile systems. Several research studies used datasets of a single driver for training and testing. Some as well used mainly day time images for training and testing the models. Therefore, fatigue and drowsiness is a key possible field of study to prevent numerous number of sleep induced road crashes. In this paper, two methods of feature extraction were proposed which are the MLP (Multilayer - Perceptron), the CNN (Convolutional Neural Network). The PCA (Principal Component Analysis) method was used for dimensionality reduction. Based on these methods, five classifiers where used to detect drowsiness on the driver. The five classifiers used where the LDA, XGBoost, LR, Decision Tree and the K-Nearest neighbors. Experiments were done in order to examine the capacity and usefulness of the approaches contrasted with other techniques. Experimental outcomes demonstrate that the feature extraction technique of CNN provided high accuracy on the five classifiers. The KNN was the average best classifier with a 100% accuracy. Experimental results indicated that the PCA improved the classifiers. This study delivers important and significant answers in practice to curb motor vehicle crashes due to drowsiness.

关键词： Dimensionality reduction Training Roads Feature extraction Fatigue Convolutional neural networks Principal component analysis

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A Condition Coverage-Based Black Hole Inspired Meta-Heuristic for Test Data Generation 4

A Condition Coverage-Based Black Hole Inspired Meta-Heuristi...

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Joint 4th Software engineering Education Workshop, SEED 2021 and 9th International Workshop on Quantitative Approaches to Software Quality, QuASoQ 2021

作者： Ulutas, Derya Yeliz Tosun, Ayse ASELSAN Inc. Ankara Turkey Istanbul University Faculty of Computer and Informatics Engineering Istanbul Turkey

Combinatorial Testing (CT) strategy is one of the well-known methods to achieve high code coverage rates during testing for safety critical systems. While generating test data in CT, we often encounter the problem of test case explosion, especially for the systems with multiple parameters and values. To overcome this challenge, search-based CT (CSST) strategies are introduced. In this study, we propose a new algorithm that inspires from a binary variant of Black Hole Algorithm (BBH) in CSST and adopt BBH according to the CT challenges in an industrial context. The proposed BBH version, BH-AllStar, aims the following: (1) obtaining higher condition coverage, (2) avoiding being stuck in local minima and (3) handling discrete input values. We finalize the solution space of BH-AllStar by reassessing the previously removed stars and incorporating the useful ones into it. We evaluate our approach on a real-life software project in the safety-critical domain with respect to condition coverage, number of test cases and execution time. Compared to BBH, BH-AllStar generates more test cases which achieve up to 43% increase in condition coverage. © 2021 CEUR-WS. All rights reserved.

关键词： Stars

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The Influence of Information and Communication Technology on Economic Growth in Europe

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Journal of the Knowledge Economy 2025年 1-29页

作者： Pažun, Brankica Langović, Zlatko Stojanović, Vladica S. Milićević, Ana Langović Božović, Ivan Department of Informatics Mathematics and Statistics Faculty of Engineering Management University Union Nikola Tesla Belgrade Serbia Department of Business Economy Faculty of Hotel Management and Tourism University of Kragujevac Kragujevac Serbia Department of Informatics & Computer Sciences Investigation & Police Studies University of Criminal Belgrade Serbia Faculty of Geography Department of Tourism University of Belgrade Belgrade Serbia Faculty of Economics University of Kosovska Mitrovica Kosovska Mitrovica Serbia

The aim of this study is to estimate thtrae influence of information and communication technology, based on the use of the ICT development index (measured by the number of Internet users, fixed broadband Internet subscribers, and the number of mobile subscriptions per 100 inhabitants), on economic development measured by gross domestic product (GDP) per capita. For this purpose, data were collected from a panel of 43 European countries from 2000 to 2020, and three econometric models were used to investigate the impact of ICT use on GDP per capita growth. First, multiple linear regression (MLR) and least absolute shrinkage and selection operator (LASSO) regression were used to research the impact of ICT use on individual countries. Then, a generalized linear dynamic data (GLD) panel model was taken to provide a general model of the dependence of the observed variables. Accordingly, a positive and significant relationship between real GDP per capita and ICT use in Europe is confirmed. The obtained results also show that the impact of ICT on economic growth is greater in low-income countries, i.e., those belonging to Eastern and Central Europe. It is crucial that governments follow the dynamics of the ICT sector and implement specific policies.

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LOGICAL ANALYSIS AND CONTRADICTION DETECTION IN HIGH-LEVEL REQUIREMENTS DURING THE REVİEW PROCESS USING SAT-SOLVER

arXiv

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arXiv 2024年

作者： Yatkın, Simge Ovatman, Tolga Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey MGEO Test and Verification Directorate ASELSAN Inc. Ankara Turkey

DO-178C stands out as a guiding standard for aviation system development processes. This standard not only mandates ensuring the consistency of requirements in the software verification process but also recognizes it as a mandatory element. The main objective of this study is to introduce a method for analyzing and identifying inconsistencies between high-level requirements using information obtained from a data dictionary. This method aims to transform high-level requirements into logical expressions and then thoroughly examine them using a SAT Solver to detect inconsistencies. While methods focused on identifying inconsistencies among requirements often appear in the literature, this study presents a novel approach to detect contradictions between non-natural language, systematically structured, and language-independent requirements. The goal of this approach is to significantly reduce the review time of high-level requirements in the software verification process. Evaluations indicate that the use of this method results in substantial time savings in the inconsistency detection process. © 2024, CC BY-SA.

关键词： Model checking

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IoT Educational Management and Consultancy System for Egyptian Public Schools

IoT Educational Management and Consultancy System for Egypti...

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AFRICON Conference

作者： Omar Ashraf Saleh Salma Elsherif Dina Mohamed Sherif Nada Sharaf Yasmine Zaghloul Faculty of Informatics and Computer Science The German International University Cairo Egypt Faculty of Engineering The German International University Cairo Egypt Faculty of Engineering German International University Cairo Egypt

Educational systems in Egypt face a shortage in the number of teachers as there is an annual increment in the number of students who are enrolled in public schools. Therefore, voluntary teaching is proposed to assist the hierarchy of educational objectives and to compensate for this shortage. With the help of technologies such as QR code scanners, Blue-tooth, Wifi, RFID, and computer Vision, a technological system guarantees a high quality and high performance of monitoring teaching activities. Nevertheless, an implementation of a mobile application guides the volunteer, the student, and the school administrators to monitor and access the course content easily along with a rewarding system that credits the volunteers for their effort.

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A Triple-Channel Encrypted Hybrid Fusion Technique to Improve Security of Medical Images

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computers, Materials & Continua 2021年第7期68卷 431-446页

作者： Ahmed S.Salama Mohamed Amr Mokhtar Mazhar B.Tayel Esraa Eldesouky Ahmed Ali Future University in Egypt CairoEgypt Cairo Higher Institute for Engineering Computer Science and ManagementCairoEgypt Department of Electrical Engineering Faculty of EngineeringAlexandria UniversityAlexandria21544Egypt Department of Computer Science Faculty of Computers and InformaticsSuez Canal UniversityIsmailia CityEgypt Higher Future Institute for Specialized Technological Studies CairoEgypt Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia

Assuring medical images protection and robustness is a compulsory necessity *** this paper,a novel technique is proposed that fuses the wavelet-induced multi-resolution decomposition of the Discrete Wavelet Transform(DWT)with the energy compaction of the Discrete Wavelet Transform(DCT).The multi-level Encryption-based Hybrid Fusion Technique(EbhFT)aims to achieve great advances in terms of imperceptibility and security of medical images.A DWT disintegrated sub-band of a cover image is reformed simultaneously using the DCT ***,a 64-bit hex key is employed to encrypt the host image as well as participate in the second key creation process to encode the ***,a PN-sequence key is formed along with a supplementary key in the third layer of the ***,the watermarked image is generated by enclosing both keys into DWT and DCT *** fusions ability of the proposed EbHFT technique makes the best use of the distinct privileges of using both DWT and DCT *** order to validate the proposed technique,a standard dataset of medical images is *** results show higher performance of the visual quality(i.e.,57.65)for the watermarked forms of all types of medical *** addition,EbHFT robustness outperforms an existing scheme tested for the same dataset in terms of Normalized Correlation(NC).Finally,extra protection for digital images from against illegal replicating and unapproved tampering using the proposed technique.

关键词： Medical image processing digital image watermarking discrete wavelet transforms discrete cosine transform encryption image fusion hybrid fusion technique

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A Wideband Rotary Antenna Based on Gap Wavegudie Technology for mmWave Applications

A Wideband Rotary Antenna Based on Gap Wavegudie Technology ...

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European Conference on Antennas and Propagation, EuCAP

作者： Ali Farahbakhsh Davood Zarifi Michal Mrozowski Department of Microwave and Antenna Engineering Faculty of Electronics Telecommunications and Informatics Gdansk University of Technology Gdansk Poland Department of Electrical and Computer Engineering Graduate University of Advanced Technology Kerman Iran School of Electrical and Computer Engineering University of Kashan Kashan Iran

ISBN: (数字)9788831299107

ISBN: (纸本)9798350366327

This study introduces the design of a gap waveguide-based rotary antenna capable of 360° mechanical beam-steering for wideband mmWave applications. This innovative design overcomes the typical limitations of electronically steerable antennas, such as narrow bandwidth, restricted steering range, gain reduction, and high cost and complexity. The designed rotary antenna achieves a gain of 13.8 dBi without any scan loss across the entire 360° steering range from 45 to 60 GHz. The proposed rotary antenna is a promising candidate for a variety of mmWave mechanical beam scanning systems.

关键词： Steerable antennas Costs Electromagnetic scattering Gap waveguide Europe Broadband antennas Impedance Millimeter wave communication Electromagnetics Wideband

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