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8th International Symposium on Electrical and Electronics engineering, ISEEE 2023

作者： Moldovanu, Simona Pavel, Sorin Aiordachioaie, Dorel Dunarea de Jos University of Galati Faculty of Automation Computers Electrical Engineering and Electronics Department of Computer Science and Information Technology Galati Romania Dunarea de Jos University of Galati Faculty of Automation Computers Electrical Engineering and Electronics Department of Electronics and Telecommunications Galati Romania

ISBN: (纸本)9798350301670

Due to the common limitation of the human visual system, internal features of thermal images cannot be fully discovered. To overcome these drawbacks, a lot of studies analyzed the facial expressions corroborating the features extracted from thermal images with machine learning tools. This paper proposed the first-order features extracted from the image histogram and a binary classification performed by the k-nearest neighbor algorithm (K-NN). The proposed method tests separately the performance of different input data, firstly, the features were extracted from raw images and the robustness of the K-NN algorithm led to an accuracy of 92.6%. Secondly, the features were extracted sequentially from corrupted images with noise. The novelty of the proposed method consists of verifying the quality classification when the images have bad quality, in this case, the accuracy decreased to 70.7%, as a result, the classification of facial expressions is sensitive to the quality of images. © 2023 IEEE.

关键词： Emotion Recognition

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Hierarchical Image Segmentation Based on Bi-Directional Cascade Network

Hierarchical Image Segmentation Based on Bi-Directional Casc...

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Software engineering and computer Science (CSECS), International Conference on

作者： Yunping Zheng ShiQiang Shu Jinzhao Huang Mudar Sarem School of Computer Science and Engineering South China University of Technology Guangzhou China Faculty of Engineering Manara University Latakia Syria

ISBN: (数字)9798331522216

ISBN: (纸本)9798331522223

Image segmentation is the way of splitting digital data into subgroups known as image segments, which reduces the entire image's complexity and allows for further processing or image analysis of each segment. The general segmentation techniques of images are divided into traditional methods and deep learning methods. In this paper, we introduce a deep-learning approach to a traditional image segmentation method (i.e., edge detection) and propose a new framework for hierarchical image segmentation. In the new framework, we first use the Bi-Directional Cascade Network (BDCN) model as our edge detector to preprocess the input image in order to form a feature map with coarse edges. Then, we refine the edges of the image by using the watershed algorithm. Finally, we form a segmentation dendrogram by using the Ultrametric Contour Map (UCM) algorithm. Extensive experiments have demonstrated that our proposed framework has significant advantages in segmentation performance compared to the state-of-the-art algorithms.

关键词： Image segmentation Image analysis Image edge detection Software algorithms Bidirectional control Watersheds Detectors Feature extraction Software engineering Digital information

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Efficient Wideband Diode Mixer for k-Band Applications 2

Efficient Wideband Diode Mixer for k-Band Applications

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2nd International Conference on Advanced Electrical engineering, ICAEE 2022

作者： Sellidj, A. Gaoua, Said Yagoub, M.C.E. University of Science and Technology Houari Boumedienne Faculty of Electronics and Computer Science Algiers Algeria University of Ottawa School of Electrical Engineering and Computer Science Ottawa Canada

ISBN: (数字)9781665417419

ISBN: (纸本)9781665417419

In this work, an efficient k-band diode mixer was designed in TSMC CMOS 180 nm technology. It works in the frequency range of 17-28 GHz with a conversion loss of 7.5 dB for an IF frequency of 3 GHz and a local oscillator power of 15 dBm. It exhibits an isolation better than 30 dB between the input LO and RF ports and the IF output port. Furthermore, the proposed mixer shows low conversion loss particularly for high IF frequencies. © 2022 IEEE.

关键词： CMOS integrated circuits

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Hyperspectral Images-Based Crop Classification Scheme for Agricultural Remote Sensing

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computer Systems Science & engineering 2023年第7期46卷 303-319页

作者： Imran Ali Zohaib Mushtaq Saad Arif Abeer D.Algarni Naglaa F.Soliman Walid El-Shafai Department of Mechanical Engineering National Taiwan University of Science and TechnologyTaipei City106335TaiwanChina Department of Electrical Engineering Riphah International UniversityIslamabad44000Pakistan Department of Mechanical Engineering HITEC University TaxilaTaxila Cantt.47080Pakistan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia

Hyperspectral imaging is gaining a significant role in agricultural remote sensing *** data unit is the hyperspectral cube which holds spatial information in two dimensions while spectral band information of each pixel in the third *** classification accuracy of hyperspectral images(HSI)increases significantly by employing both spatial and spectral *** this work,the data was acquired using an airborne hyperspectral imager system which collected HSI in the visible and near-infrared(VNIR)range of 400 to 1000 nm wavelength within 180 spectral *** dataset is collected for nine different crops on agricultural land with a spectral resolution of 3.3 nm wavelength for each *** data was cleaned from geometric distortions and stored with the class labels and annotations of global localization using the inertial navigation *** this study,a unique pixel-based approach was designed to improve the crops'classification accuracy by using the edge-preserving features(EPF)and principal component analysis(PCA)in *** preliminary processing generated the high-dimensional EPF stack by applying the edge-preserving filters on acquired *** the second step,this high dimensional stack was treated with the PCA for dimensionality reduction without losing significant spectral *** resultant feature space(PCA-EPF)demonstrated enhanced class separability for improved crop classification with reduced dimensionality and computational *** support vector machines classifier was employed for multiclass classification of target crops using *** classification performance evaluation was measured in terms of individual class accuracy,overall accuracy,average accuracy,and Cohen kappa *** proposed scheme achieved greater than 90%results for all the performance evaluation *** PCA-EPF proved to be an effective attribute for crop classification using hyperspectral imaging in the VNIR *** proposed scheme

关键词： Hyperspectral imaging visible and near-infrared edge preserving feature dimensionality reduction crop classification

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Predicting Thyroid Disease using Machine Learning: A Comparative Study of Algorithms

Predicting Thyroid Disease using Machine Learning: A Compara...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： Khushi Katore Parul Balvir Subodh Daronde Computer Science And Medical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Mahartashtra India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Predicting thyroid illness has become a significant problem in recent times. Thyroid illness is getting more frequent in women over thirty, but it is also becoming more prevalent in men, women, and children. Long-term effects include difficulties with the heart, eyes, fertility, and pregnancy. Therefore, it remains essential to assess the thyroid data in instruction to detect the disease at an initial stage and take preventative measures against the lethal thyroid cancer condition. Two prevalent thyroid illnesses that affect the thyroid's ability to produce thyroid hormones and control the body's metabolism are hyperthyroidism and hypothyroidism. Data cleaning procedures were utilised to make the data simple sufficient for analytics to demonstration the likelihood of affected role having thyroid illness. Thirteen clinicopathologic characteristics from the Kaggle data set are employed in this paper's analysis and classification models for thyroid illness with the goal of predicting the recurrence of well-differentiated thyroid cancer. An important factor in the process of illness prediction is machine learning. It is important to guarantee a strong foundation of information that container be integrated and employed as a hybrid model for multifaceted knowledge tasks, such medical diagnostic and prognosis tasks. Additionally, we suggested many machine learning methods and diagnostics for thyroid prevention in this research. Machine Learning Algorithms, support vector machine (SVM), K-NN, Decision Trees, Logistic Regression , Random Forest , XGBoost were used to forecast the projected risk on a patient’s chance of obtaining thyroid disease.

关键词： Support vector machines Logistic regression Accuracy Machine learning algorithms Thyroid cancer Data models Decision trees Random forests Thyroid Diseases

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Transfer Learning for Crowed Counting 2

Transfer Learning for Crowed Counting

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2nd IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer engineering, MI-STA 2022

作者： Khalifa, Othman Omran Albagul, Abdulgani Abdallah Hashim, Aisha Hassan Abdul Malik Hashim, Noreaha Sakinahbt Wan Zainuddin, Kwan Nur Libyan Center for Engineering Rresearch and Information Technology Baniwalid Libya International Islamic University Malaysia Faculty of Engineering Electrical and Computer Engineering Departmet Kuala Lumpur Malaysia

ISBN: (数字)9781665479189

ISBN: (纸本)9781665479189

Transfer learning for crowd counting via CNN is explored in this research to minimize training time and computational cost. The Mall dataset is used to evaluate the effectiveness of the transfer learning approach and is compared with other recent techniques that use their own deep architecture. By using transfer learning, less computation is involved because the pretrained model has already learned the necessary weightage and architecture thus reducing time consumption for training. The ResNet50 model is fine-Tuned to be applying to the Mall dataset. The result of this project shows that using ResNet50 for transfer learning achieved mediocre MAE and MSE compared to other recent techniques such as the DeepCount and DecideNet models. Therefore, application of transfer learning in crowd counting using CNN for ResNet50 on Mall dataset was proven inefficient and further improvements needs to be carried out for this application to be beneficial © 2022 IEEE.

关键词： Convolutional neural networks

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Economical Calculation Basis of Synthetic Gas Production using Alternate Renewable Resources in Combining of Hydrogen and Carbon Dioxide

Economical Calculation Basis of Synthetic Gas Production usi...

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International Scientific Conference on Power and Electrical engineering of Riga Technical University (RTUCON)

作者： Aleksandrs Borisovs Aleksandrs Dolgicers Riga Technical University Faculty of Computer Science Information Technology and Power Engineering Riga Latvia

The main task of this work is to establish the consumption of energy from renewable sources such as solar and wind energy for the production of such a combustible substance as hydrogen, and further optimal calculation... 详细信息

ISBN: (数字)9798350365771

ISBN: (纸本)9798350365788

关键词： Renewable energy sources Wind energy Electricity Hydrogen Water heating Production Carbon dioxide Wind power generation Syngas Resistance heating

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Two-Stage Robust Operation of Power-Gas Integrated System with Penetration of Wind Energy 12

Two-Stage Robust Operation of Power-Gas Integrated System wi...

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12th Iranian Conference on Renewable Energies and Distributed Generation, ICREDG 2025

作者： Hasanzad, Fardin Rezaeieh, Mohammad-Reza Hosseinkhani Talavat, Vahid Rastegar, Hasan Amirkabir University of Technology Department of Electrical Engineering Tehran Iran Urmia University Faculty of Electrical and Computer Engineering Urmia Iran

ISBN: (纸本)9798331525132

The fast development of wind energy installation in power systems accompanied by the interdependency of power and gas systems due to the application of gas-fueled power generating units (GPGUs) and electric-driven gas system compressors (EGSCs) intensified the importance of coordinated operation of power and gas integrated systems (PGISs) with penetration of wind energy. In this paper, a two-stage robust framework is presented for optimal operation of the PGIS under intermittency of wind energy and demand uncertainties, in which the first stage provides day-ahead scheduling of the PGIS, while the second stage addresses the real-time operation. To enable tractable computation, the Column and Constraint Generation (C&CG) algorithm is used to offer an efficient way of solving the proposed model. Numerical simulations on a 24bus-20node PGIS demonstrate the usefulness of the proposed model. © 2025 IEEE.

关键词： Scheduling algorithms

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Big Data Analysis on Intelligent Traffic Surveillance by Incorporating Sensors and IoT Devices on Roads 7

Big Data Analysis on Intelligent Traffic Surveillance by Inc...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Devi, A. Kapruwan, Akanksha Shrimali, Manish Appalakonda, Vengalapudi Maranan, Ramya Barve, Amit Tamil Nadu Coimbatore641 049 India Graphic Era Deemed To Be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Department of Computer Science And It Rajasthan Udaipur313 003 India Department of Artificial Intelligence And Machine Learning Kakinada Andhra Pradesh Surampalem533 437 India SIMATS Saveetha School of Engineering Department of Research And Innovation Tamil Nadu Chennai602105 India Parul University Parul Institute of Technology Faculty of Engineering And Technology Department of Computer Science And Engineering P.O.Limda Gujarat 391760 India

ISBN: (纸本)9798350359299

Rather than relying on public transportation, people are increasingly comfortable driving their automobiles to satisfy their mobility needs. Reasons for this include the automobiles' accessibility and the fact that they can be used whenever needed. Because of this, many cities around the world experience terrible traffic congestion and long wait periods at traffic signals. Smarter and more effective traffic management is possible through the integration of big data, the Internet of Things (IoT), and a cloud platform with a network of linked vehicles and sensors. The objective of this study is to estimate traffic conditions along a route and provide updates to the user. Initially, Deep Learning (DL) models such as YOLO (You Only Look Once) and SSD (Single-Shot Object Detection) are developed and tested to detect vehicles. YOLO produces outstanding performance in vehicle detection, with an 89.5% mean Average Precision (mAP) and an 83.98% Intersection over Union (IoU). This model is then pushed to the cloud. A Global Positioning System (GPS) module is used to determine the user's current location. Based on the user's location, the nearest camera is enabled, and the recorded video is sent to the cloud. The cloud-deployed YOLO model is used to detect and count vehicles. To improve the user experience, this study has created a mobile application. The software contains a navigation map function that allows users to specify the maximum number of vehicles expected along their route. The created system counts the number of vehicles on their path, and if the count exceeds the specified threshold, the mobile app notifies the user about traffic conditions. © 2024 IEEE.

关键词： Network security

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Blockchain-based loyalty system: Feasibility testing

Blockchain-based loyalty system: Feasibility testing

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2023 International Conference on computer Science, Information technology and engineering, ICCoSITE 2023

作者： Nguyen, Danh Khoi Pham, Hoang-Anh Nguyen, Quoc Vinh Huynh, Tuong Nguyen Son, Trang Hong Ho Chi Minh City University of Technology Faculty of Computer Science & Engineering VNU-HCM Ho Chi Minh City Viet Nam Industrial University of Ho Chi Minh City Faculty of Information Technology Ho Chi Minh City Viet Nam Hoa Sen University Faculty of Information Technology Ho Chi Minh City Viet Nam

ISBN: (纸本)9798350320954

A loyalty program brings benefits to both companies and customers. In this paper, we consider the use of loyalty program integration in blockchain technology, one of the most promising advanced technologies, where trust is of prime significance and customer identification will no longer require a physical certificate. Based on the essential characteristics of the loyalty program, 4 possible approaches that integrate into a blockchain platform are identified. An analysis through several observations helps to determine the most suitable one to create a loyalty program that benefits all stakeholders. The performance of the proposed system related to the most suitable approach is evaluated according to the criteria that consider the feasibility of practical usability. © 2023 IEEE.

关键词： Smart contract

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