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Historical Arabic Images Classification and Retrieval Using Siamese Deep Learning Model

computers, Materials & Continua 2022年第7期72卷 2109-2125页

作者： Manal M.Khayyat Lamiaa A.Elrefaei Mashael M.Khayyat Computer Science Department Umm Al-Qura UniversityMakkahSaudi Arabia Computer Science Department King Abdulaziz UniversityJeddahSaudi Arabia Electrical Engineering Department Faculty of Engineering at ShoubraBenha UniversityCairoEgypt Department of Information Systems and Technology College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia

Classifying the visual features in images to retrieve a specific image is a significant problem within the computer vision field especially when dealing with historical faded colored ***,there were lots of efforts trying to automate the classification operation and retrieve similar images *** reach this goal,we developed a VGG19 deep convolutional neural network to extract the visual features from the images ***,the distances among the extracted features vectors are measured and a similarity score is generated using a Siamese deep neural *** Siamese model built and trained at first from scratch but,it didn’t generated high evaluation ***,we re-built it from VGG19 pre-trained deep learning model to generate higher evaluation ***,three different distance metrics combined with the Sigmoid activation function are experimented looking for the most accurate method formeasuring the similarities among the retrieved *** that the highest evaluation parameters generated using the Cosine distance ***,the Graphics Processing Unit(GPU)utilized to run the code instead of running it on the Central Processing Unit(CPU).This step optimized the execution further since it expedited both the training and the retrieval time *** extensive experimentation,we reached satisfactory solution recording 0.98 and 0.99 F-score for the classification and for the retrieval,respectively.

关键词： Visual features vectors deep learning models distance methods similar image retrieval

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Electric Vehicles as Flexible Assets for Households and System Operators: Survey Results

Electric Vehicles as Flexible Assets for Households and Syst...

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International Conference on Smart Systems and Technologies (SST)

作者： Nemanja Mišljenović Matej Žnidarec Goran Knežević Danijel Topić Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (数字)9798350386394

ISBN: (纸本)9798350386400

The transition towards electric vehicles (EVs) is a pivotal response to mitigate environmental concerns and reduce reliance on fossil fuels. This survey-based research aims to provide comprehensive insights into the stance of EV users in Croatia on using EVs as assets for flexibility in operation. Users are surveyed about their EV characteristics and habits, emphasizing charging preferences and possible subsidizing. Because of their larger degree of freedom in operation, family home users are further surveyed about their attitude toward load management, investments for additional sources of electricity and flexibility in operation, and advanced electricity billing models to ensure greater savings. Results show a very conservative stance of users toward actions that deteriorate their comfort level. Further on, users that dwell in a family house are prone to investments if it can generate additional savings and maintain or even increase their comfort level. This survey provided valuable findings for researchers in modeling EVs as a flexible asset in energy management studies, especially in the emerging field of energy communities.

关键词： Surveys Electricity Water heating Transportation Electric vehicles Washing machines Standards Load modeling Resistance heating Investment

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BRANCH - a Labeled Dataset of RGB-D Images and 3D Models for Autonomous Tree Pruning

BRANCH - a Labeled Dataset of RGB-D Images and 3D Models for...

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International Conference on Smart Systems and Technologies (SST)

作者： Jana Dukić Petra Pejić Andrej Bošnjak Emmanuel Karlo Nyarko Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (数字)9798350386394

ISBN: (纸本)9798350386400

In autonomous pruning, the robot must identify the branches that need to be removed to improve the health and shape of the tree. Furthermore, the robot needs depth information about these branches in order to successfully perform tool navigation and finally pruning. In this work, we provide the BRANCH dataset with 70 RGB-D images of pear trees before and after pruning, taken in the real environment from different viewpoints to cover the whole tree. Based on these images, we created point clouds and performed model reconstruction to obtain 3D models of the trees. After overlaying the models before and after pruning, we obtained the points of the pruned branches. Therefore, we also provide labeled point clouds of 52 trees in BRANCH that can be used for further machine learning.

关键词： Point cloud compression Training Solid modeling Three-dimensional displays Neural networks Robot sensing systems Solids Smart systems Image reconstruction Testing

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Experimental Protocol for Biological Experiments on Petri Dishes 14

Experimental Protocol for Biological Experiments on Petri Di...

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14th International Conference on Measurement, MEASUREMENT 2023

作者： Bajtos, Marek Janousek, Ladislav Radil, Roman Carnecka, Lucia Paulecova, Kristina University of Zilina Faculty of Electrical Engineering and Information Technology Department of Electromagnetic and Biomedical Engineering Zilina Slovakia University of Colorado Boulder Department of Electrical Computer and Energy Engineering BoulderCO United States

ISBN: (纸本)9788097262976

In this paper, the experimental protocol for the further exposition of low-frequency magnetic fields on biological cells is established. Experiments for one-week mass controlling of Petri dishes with YPD agar and Saccharomyces cerevisiae inoculum are performed. Firstly, four groups of samples are prepared to find out the proper volume of inoculum with the lowest standard deviation between the samples. Secondly, the dedicated volume of inoculum is used for another set of experiments for comparison with YPD agar plates without the yeast cells. The statistical evaluation of two groups of data is determined. © 2023 Institute of Measurement Science, Slovak Academy o.

关键词： Yeast

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Smart Contracts for Political Transparency: A Blockchain Approach to Campaign Promise Fulfillment Monitoring

Smart Contracts for Political Transparency: A Blockchain App...

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International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Miljenko Švarcmajer Mirko Köhler Ivica Lukić Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

Before elections, political parties make their campaign promises. These promises are often poorly defined and ambiguous. Such campaign promises are difficult to monitor and confirm their fulfillment. This paper discusses current challenges in monitoring political campaign promises. While monitoring platforms exist, their centralized nature raises concerns about data manipulation. The paper suggests leveraging blockchain technology, specifically smart contracts and independent monitoring bodies, to ensure accurate and tamper-proof traceability of (un)fulfilled promises.

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A Deep Learning Approach for Faults Recognition of Dissolved Oxygen Sensor in Wastewater Treatment Plants 28

A Deep Learning Approach for Faults Recognition of Dissolved...

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28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Ghinea, Liliana Maria Miron, Mihaela Ratnaweera, Harsha Electrical Engineering and Electronics Faculty of Automation Computers Department of Automatic Control and Electrical Engineering Department Galaţi Romania Computers Electrical Engineering and Electronics Faculty of Automation Department of Computer Science and Information Technology Galaţi Romania Norwegian University of Life Sciences Faculty of Science and Technology Oslo Norway

ISBN: (纸本)9798350339918

Fault diagnosis in wastewater treatment plants (WWTPs) is important to protect communities and ecosystems from toxic elements discharged into water. In this sense, fault identification of sensors plays an important role as they are the key components of the water plants control, especially because environmental legislation is very strict when referring to failures or anomalies in WWTPs. This paper analyzes the performances of two Deep Learning models, a Feedforward Neural Network (FFNN) and a 1D Convolution Neural Network (1DCNN) for identifying five operating states of the dissolved oxygen (DO) sensor: normal and faulty (bias, stuck, spike and precision degradation faults). The experiments were conducted on the Benchmark Simulator Model No 2 (BSM2) developed by the IWA Task Group. The performance of the Deep Learning (DL) classifiers was evaluated via accuracy, precision, recall, and F1-score metrics. The best overall classification accuracy was obtained by FFNN, 98.32% for training and 98.30% for testing. © 2023 IEEE.

关键词： Wastewater treatment

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A Self-Supervised Learning-Based computer Recognition Platform for Orchidaceae Classification 11

A Self-Supervised Learning-Based Computer Recognition Platfo...

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11th IEEE Global Conference on Consumer Electronics, GCCE 2022

作者： Hu, Wu-Chih Huang, Guan-Zhi Huang, Xiang-Rui Wang, Wei-Chien Chen, Liang-Bi National Penghu University of Science and Technology Department of Computer Science and Information Engineering Penghu Taiwan The University of Sydney Faculty of Engineering and Information Technologies Department of Computer Science Sydney Australia

ISBN: (纸本)9781665492324

This paper proposes an orchid classification platform based on a self-supervised learning model. Users can view orchid information through the computer application platform and upload unknown orchid images to the platform. The orchid information is displayed on the visualization window. In terms of the recognition of orchids, fine-grained classification is used to classify orchids. The fine-grained classification model is usually applied to plants. This model is more suitable for the classification of hundreds of orchid species. The fine-grained classification of orchids is suitable for this task, and the classification system is built on the server side. The user only needs to install the client-side application and operate it directly. © 2022 IEEE.

关键词： Visualization

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Bitcoin Price Prediction Using Cuckoo Search Algorithm for Feature Selection with LSTM Model 2

Bitcoin Price Prediction Using Cuckoo Search Algorithm for F...

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2nd International engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Radmand, Elnaz Pirgazi, Jamshid Odeh, Mahmoud Sorki, Ali Ghanbari University of Science and Technology of Mazandaran Faculty of Electrical and Computer Engineering Behshahr Iran School of Information Technology Zarqa University Cyber Security Department Zarqa Jordan College of Engineering University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798350373363

Deep learning is now a top method for predicting the price of Bitcoin, providing an excellent and well thought out strategy. This technique makes use of a wealth of historical data from reliable sources covering the first ten years of Bitcoin. Technical data and complex patterns are deeply integrated into the dataset to enhance the model's comprehension of complex Bitcoin price patterns. These additions greatly deepen our understanding of the various factors affecting the price of Bitcoin. This method is based on a strategic procedure that involves the careful selection of certain high-performing characteristics. These traits go through a stringent screening process that uses advanced algorithms such as the Cuckoo algorithm. The chosen features are then smoothly incorporated into an encoder-decoder model of LSTM (Long Short-Term Memory), improving the model's ability to predict Bitcoin prices. In this case, it is critical to apply deep learning methods, especially the LSTM encoder-decoder model. Through the integration of technical indicators and sophisticated selection techniques with historical Bitcoin data, this approach leverages deep learning capabilities to identify intricate patterns in Bitcoin pricing movements. In the end, this gives the model the ability to forecast future changes in the price of Bitcoin with accuracy. © 2023 IEEE.

关键词： Forecasting

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Efficient Deep Learning Modalities for Object Detection from Infrared Images

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computers, Materials & Continua 2022年第8期72卷 2545-2563页

作者： Naglaa F.Soliman E.A.Alabdulkreem Abeer D.Algarni Ghada M.El Banby Fathi E.Abd El-Samie Ahmed Sedik Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Electronics and Communications Faculty of EngineeringZagazig UniversityZagazig44519Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of the Robotics and Intelligent Machines Faculty of Artificial IntelligenceKafrelSheikh UniversityKafr el-sheikhEgypt

For military warfare purposes,it is necessary to identify the type of a certain weapon through video stream tracking based on infrared(IR)video *** vision is a visual search trend that is used to identify objects in images or video *** military applications,drones take a main role in surveillance tasks,but they cannot be confident for longtime ***,there is a need for such a system,which provides a continuous surveillance task to support the drone *** a system can be called a Hybrid Surveillance System(HSS).This system is based on a distributed network of wireless sensors for continuous *** addition,it includes one or more drones to make short-time missions,if the sensors detect a suspicious *** paper presents a digital solution to identify certain types of concealed weapons in surveillance applications based on Convolutional Neural Networks(CNNs)and Convolutional Long Short-Term Memory(ConvLSTM).Based on initial results,the importance of video frame enhancement is obvious to improve the visibility of objects in video *** accuracy of the proposed methods reach 99%,which reflects the effectiveness of the presented *** addition,the experimental results prove that the proposed methods provide superior performance compared to traditional ones.

关键词： Deep learning object detection military applications OFDM SPIHT IoT

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Ensemble Transfer Learning for Lymphoma Classification

Ensemble Transfer Learning for Lymphoma Classification

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International Conference on Systems, Signals and Image Processing, IWSSIP

作者： Marija Habijan Irena Galić Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (数字)9798350391886

ISBN: (纸本)9798350391893

The classification of lymphoma types using deep learning models presents a promising avenue for enhancing diagnostic accuracy in medical imaging. This study evaluates the effectiveness of multiple pre-trained convolutional neural networks (CNNs), namely VGG-19, DenseNet201, MobileNetV3, and ResNet50V2, in classifying three common types of lymphoma: chronic lymphocytic leukemia (CLL), follicular lymphoma (FL), and mantle cell lymphoma (MCL). We tailored each model via transfer learning to adapt to the specific task of lymphoma classification. Our results indicate that DenseNet201 achieved the highest accuracy with 98.04%, followed by ResNet50V2, MobileNetV3, and VGG-19 with accuracies of 90.13%, 89.07%, and 87.11% respectively. Additionally, an ensemble approach combining all four models demonstrated a significant performance improvement, achieving an accuracy of 98.89%. These findings underscore the potential of advanced CNN architectures and ensemble methods in improving the diagnostic processes for lymphoma through medical imaging, offering a robust tool for clinical support and a pathway toward automated diagnostic systems.

关键词： Deep learning Adaptation models Accuracy Computational modeling Microprocessors Transfer learning computer architecture

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