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International Conference on Electrical, computer and Communication Engineering (ECCE)

作者： Khaled Mahmud Sujon Nusrat Jahan Someyo Kamal Utsho Mahadi Hasan Shuvo Md Najmus Sakib Razin Department of Software Engineering Universiti Teknologi Malaysia (UTM) Johor Bahru Johor Malaysia Department of Computer Science and Engineering Chittagong University of Engineering & Technology Chittagong Bangladesh Department of Software Engineering Universiti Teknologi Malaysia Johor Bahru Johor Malaysia Department of Architecture American International University-Bangladesh Dhaka Bangladesh Department of Electrical & Electronics Engineering Universiti Teknologi Malaysia Johor Bahru Johor Malaysia

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

A persistent issue of class imbalance in intrusion detection systems (IDS) is addressed in this research, particularly in the context of cyber-physical systems and IoT devices. Current IDS approaches often struggle to detect rare attack patterns due to the dominance of normal traffic in imbalanced datasets. To address this, we present a unique strategy that combines Principal Component Analysis (PCA) for selecting features with the Synthetic Minority Oversampling Technique (SMOTE) to deal with class imbalance, applied to four powerful classifiers: Random Forest (RF), Decision Trees (DT), XGBoost (XGB), and AdaBoost. By reducing the feature space with PCA and balancing the dataset with SMOTE, our methodology significantly enhances model performance. Evaluating on the KDDCup ’99 dataset, we observe notable improvements: for instance, Random Forest achieves 96.42% accuracy, 98.52% precision, 94.68% recall 96.55% F1-score, and 93.87% MCC. Similarly, XGBoost achieves 94.78% accuracy, 96.72% precision, 92.94% recall, 94.81% F1-score, and 92.36% MCC. These results demonstrate that the integration of SMOTE and PCA effectively addresses class imbalance and enhances the detection of minority class attacks. Our findings contribute to the advancement of machine learning techniques in IDS and provide a foundation for further research into combining oversampling methods and dimensionality reduction for robust cybersecurity solutions. Future work will focus on exploring alternative oversampling methods, incorporating deep learning models, and assessing the practical deployment of the proposed methodology in real-time network environments.

关键词： Dimensionality reduction Deep learning Accuracy Computational modeling Intrusion detection Real-time systems Decision trees Random forests Principal component analysis Testing

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Darcy-Forchheimer mangetized flow based on differential type nanoliquid capturing Ohmic dissipation effects

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Propulsion and Power Research 2023年第3期12卷 443-455页

作者： M.Waqas Yunjie Xu M.Nasir Md Mottahir Alam Amjad Ali Pasha Kashif Irshad Bandar M.Fadhl M.S.Kausar NUTECH School of Applied Science and Humanities National University of TechnologyIslamabad 44000Pakistan Department of Mechanical Engineeing Lebanese American UniversityBeirutLebanon School of Engineering Huzhou UniversityHuzhou 313000China Faculty of Informatics and Computing University Sultan Zainal AbidinBesut Campus22200 BesutTereng-ganuMalaysia Department of Electrical and Computer Engineering King Abdulaziz UniversityJeddah 21589Saudi Arabia Aerospace Engineering Department King Abdulaziz UniversityJeddah 21589Saudi Arabia Interdisciplinary Research Centre for Renewable Energy and Power System(IRC-REPS) Research InstituteKing Fahd University of Petroleum and Minerals(KFUPM)Dhahran 31261Saudi Arabia Mechanical Engineering Department College of Engineering and Islamic ArchitectureUmm Al-Qura UniversityP.O.Box 5555Makkah 21955Saudi Arabia

Hydromagnetic nanoliquid establish an extraordinary category of nanoliquids that unveil both liquid and magnetic *** interest in the utilization of hydromagnetic nanoliquids as a heat transporting medium stem from a likelihood of regulating its flow along with heat transportation process subjected to an externally imposed magnetic *** analysis reports the hydromagnetic nanoliquid impact on differential type(second-grade)liquid from a convectively heated extending *** well-known Darcy-Forchheimer aspect capturing porosity characteristics is introduced for nonlinear *** conditions elaborating heat-mass transportation effect are *** addition,Ohmic dissipation and suction/injection aspects are also a part of this *** analysis is done by implementing the basic relations of fluid *** modeled physical problem is simplified through order *** resulting systems(partial differential expressions)are rendered to the ordinary ones by utilizing the apposite *** solutions are constructed employing homotopy *** and numeric result are addressed comprehensively to elaborate the nature of sundry parameters against physical *** velocity profile is suppressed with increasing Hartmann number(magnetic parameter)whereas it is enhanced with increment in material parameter(second-grade).With the elevation in thermophoresis parameter,temperature and concentration of nanoparticles are accelerated.

关键词： Darcy-Forchheimer magnetized flow Differential type nanoliquid Ohimc dissipation Robin conditions Homotopy algorithm

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Unveiling Hidden Energy Anomalies: Harnessing Deep Learning to Optimize Energy Management in Sports Facilities

arXiv

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arXiv 2024年

作者： Fadli, Fodil Himeur, Yassine Elnour, Mariam Amira, Abbes Department of Architecture & Urban Planning Qatar University Doha Qatar College of Engineering and Information Technology University of Dubai Dubai United Arab Emirates Department of Electrical Engineering Qatar University Doha Qatar Department of Computer Science University of Sharjah United Arab Emirates Institute of Artificial Intelligence De Montfort University Leicester United Kingdom

Anomaly detection in sport facilities has gained significant attention due to its potential to promote energy saving and optimizing operational efficiency. In this research article, we investigate the role of machine learning, particularly deep learning, in anomaly detection for sport facilities. We explore the challenges and perspectives of utilizing deep learning methods for this task, aiming to address the drawbacks and limitations of conventional approaches. Our proposed approach involves feature extraction from the data collected in sport facilities. We present a problem formulation using Deep Feedforward Neural Networks (DFNN) and introduce threshold estimation techniques to identify anomalies effectively. Furthermore, we propose methods to reduce false alarms, ensuring the reliability and accuracy of anomaly detection. To evaluate the effectiveness of our approach, we conduct experiments on aquatic center dataset at Qatar University. The results demonstrate the superiority of our deep learning-based method over conventional techniques, highlighting its potential in real-world applications. Typically, 94.33% accuracy and 92.92% F1-score have been achieved using the proposed scheme. © 2024, CC BY.

关键词： Anomaly detection

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Design of Subsea storage tanks for Arctic conditions - heat treatment of materials

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Materials Today: Proceedings 2023年

作者： Kishor Tirumanur Shanmugavelu, Arvind Muraliraja, R. Shanmugam, Ragavanantham Pal Singh Pawar, Mayur Vishwakarma, Rajoo Sarkar, Proshanta Department of Naval Architecture and Offshore Engineering Vels Institute of Science Technology & Advanced Studies Chennai 600117 Tamil Nadu India Department of Mechanical Engineering Vels Institute of Science Technology & Advanced Studies Chennai 600117 Tamil Nadu India Advanced Manufacturing Engineering Technology Navajo Technical University NM 87313 United States Department of Civil Engineering IES College of Technology Bhopal 462044 Madhya Pradesh India Department of Mechanical Engineering Suyash Institute of Information Technology Gorakhpur 273016 Uttar Pradesh India Department of Computer Science Kalinga University Naya Raipur 492101 Chhattisgarh India

The Arctic region contains a plethora of recoverable hydrocarbon wealth in the form of oil and gas in existing conventional wells and other undiscovered fields. The main challenges faced in the Arctic region are both environmental and operational. Some environmental challenges are low ambient temperature, winterization, severity and unpredictability of the weather. However, the issue of multi-phase flow in the current operation coupled with the decline of the reservoir pressure are the major concerns in this gas field. In the early stages of field life, the reservoir pressure is sufficient to allow the natural flow from reservoir formation to surface without additional supporting compression. Unfortunately, the pressure naturally declines throughout the entire life cycle of the field. To achieve a better production profile in the arctic region the idea is to implement a Subsea processing concept employing a Subsea Storage Tank (SST) in order to maximize production from additional infield wells, introducing a novel step in this field development phase that will change the entire oil and gas industry especially for subsea processing field architecture. The incorporation of SST in the subsea system will enhance the production rate as well as it avoids the transportation problems through pipe lines. By this work, the production rate is increased by 50% and decrease the oil spill accidents considerably. © 2023 Elsevier Ltd. All rights reserved.

关键词： Heat transfer

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A Novel Voronoi-based Convolutional Neural Network Framework for Pushing Person Detection in Crowd Videos

arXiv

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arXiv 2023年

作者： Alia, Ahmed Maree, Mohammed Chraibi, Mohcine Seyfried, Armin Institute for Advanced Simulation Forschungszentrum Jülich Jülich52425 Germany Department of Computer Simulation for Fire Protection and Pedestrian Traffic Faculty of Architecture and Civil Engineering University of Wuppertal Wuppertal42285 Germany Department of Information Technology An-Najah National University Nablus Palestine Department of Information Technology Arab American University Jenin Palestine

Analyzing the microscopic dynamics of pushing behavior within crowds can offer valuable insights into crowd patterns and interactions. By identifying instances of pushing in crowd videos, a deeper understanding of when, where, and why such behavior occurs can be achieved. This knowledge is crucial to creating more effective crowd management strategies, optimizing crowd flow, and enhancing overall crowd experiences. However, manually identifying pushing behavior at the microscopic level is challenging, and the existing automatic approaches cannot detect such microscopic behavior. Thus, this article introduces a novel automatic framework for identifying pushing in videos of crowds on a microscopic level. The framework comprises two main components: i) Feature extraction and ii) Video labeling. In the feature extraction component, a new Voronoi-based method is developed for determining the local regions associated with each person in the input video. Subsequently, these regions are fed into EfficientNetV1B0 Convolutional Neural Network to extract the deep features of each person over time. In the second component, a combination of a fully connected layer with a Sigmoid activation function is employed to analyze these deep features and annotate the individuals involved in pushing within the video. The framework is trained and evaluated on a new dataset created using six real-world experiments, including their corresponding ground truths. The experimental findings indicate that the suggested framework outperforms seven baseline methods that are employed for comparative analysis purposes. © 2023, CC BY.

关键词： Feature extraction

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CLASS ATTENDANCE SYSTEM IN EDUCATION WITH DEEP LEARNING METHOD

arXiv

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arXiv 2023年

作者： Demir, Hüdaverdi Savaş, Serkan Department of Electronics and Computer Engineering Graduate School of Natural and Applied Sciences Çankırı Karatekin University Çankırı Turkey Department of Informatics Technology Yenikent Ahmet Çiçek Vocational and Technical Anatolian High School Ankara Turkey Department of Computer Engineering Faculty of Engineering and Architecture Kırıkkale University Kırıkkale Turkey

With the advancing technology, the hardware gain of computers and the increase in the processing capacity of processors have facilitated the processing of instantaneous and real-time images. Face recognition processes are also studies in the field of image processing. Facial recognition processes are frequently used in security applications and commercial applications. Especially in the last 20 years, the high performances of artificial intelligence (AI) studies have contributed to the spread of these studies in many different fields. Education is one of them. The potential and advantages of using AI in education;can be grouped under three headings: student, teacher, and institution. One of the institutional studies may be the security of educational environments and the contribution of automation to education and training processes. From this point of view, deep learning methods, one of the sub-branches of AI, were used in this study. For object detection from images, a pioneering study has been designed and successfully implemented to keep records of students' entrance to the educational institution and to perform class attendance with images taken from the camera using image processing algorithms. The application of the study to real-life problems will be carried out in a school determined in the 2022-2023 academic year. © 2023, CC BY.

关键词： Face recognition

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A Multiband Tri-branch Monopole Antenna Base on Step Impedance Technique for WLAN, WiMAX, 5G technology, and IoT Application

A Multiband Tri-branch Monopole Antenna Base on Step Impedan...

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International Conference on Electrical Engineering/Electronics, computer, Telecommunications and Information technology, ECTI-CON

作者： Maleeya Tangjitjetsada Thanakarn Suangun Wanchalerm Chanwattanapong Chatree Mahatthanajatuphat Kanyanat Phimthai Prayoot Akkaraekthalin Department of Electronics and Telecommunication Engineering Faculty of Engineering and Architecture Rajamangala University of Technology Suvarnabhumi (RMUTSB) Nonthaburi Thailand Department of Electrical Engineering Faculty of Engineering University of Phayao Phayao Thailand Department of Electronics and telecommunications Faculty of Engineering Rajamangala University of Technology Thanyaburi Pathum Thani Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok (KMUTNB) Bangkok Thailand

A multiband tri-branch monopole antenna is presented. The antenna structure is constructed of radiating patches with three branches. It is fed by a microstrip transmission line. Essentially, the antenna is a simple structure created by a step impedance technique. In addition, the antenna can be separately regulated for each operational frequency band of 2.44 GHz, 3.36 GHz, and 5.52 GHz by the height of a central patch, a right patch with a thin strip line, and a small patch on the left, respectively. The antenna is manufactured on a size of 50x35 mm 2 FR4 substrate with a thickness of 0.8 mm and a permittivity of 4.3. The suggested antenna is appropriate for network board internet of things (NB-IoT) technology working at 2.4 GHz/5.2 GHz/5.8 GHz (WLAN), 3.5 GHz (WiMAX), and 2.3 GHz/2.6 GHz/3.5 GHz (5G technology). The antenna’s radiation pattern is omnidirectional, and its average gain is approximately 2dBi.

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Affecting Factors of Efficiency in Photovoltaic Energy Systems and Productivity-Enhancing Suggestions

Affecting Factors of Efficiency in Photovoltaic Energy Syste...

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2022 Innovations in Intelligent Systems and Applications Conference, ASYU 2022

作者： Ay, Ilker Kademli, Murat Karabulut, Sener Savas, Serkan Resources Technology Program Hacettepe Ankara Chamber of Industry 1st Organized Industrial Zone Vocational School Hacettepe University Department of Alternative Energy Ankara Turkey Hacettepe Ankara Chamber of Industry 1st Organized Industrial Zone Vocational School Hacettepe University Department of Mechanical Program Ankara Turkey Kirikkale University Faculty of Engineering and Architecture Department of Computer Engineering Kirikkale Turkey

ISBN: (数字)9781665488945

ISBN: (纸本)9781665488945

In recent years, hazardous gases emission from fossil fuels has attracted public concerns due to its worse effects on the ecosystem and living conditions not only mankind but also all creatures living on earth. That's why solar energy has a vital role in alternative energy resources. Solar energy sources will gain more importance in the future. As it is known, the most needed type of energy today is electrical energy. Thus, in this study, the necessary conditions for the photovoltaic (PV) systems used in solar energy production to operate at maximum performance and which parameters are required to control these conditions are examined. The results show that four parameters that we need to measure. These are: maximum operating current of a panel/cell (Impp), maximum operating voltage of a panel/cell (Vmpp), panel surface temperature and light intensity falling on the panel. Except for the panel surface temperature, the rest of the parameters can be measured directly. However, affecting the panel surface temperature;we must not ignore parameters such as ambient temperature, wind speed, humidity and light intensity. Therefore, while determining the panel surface temperature, these parameters should also be measured and a surface temperature should be determined accordingly. © 2022 IEEE.

关键词： Surface temperature

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Handwritten Digit Recognition using Deep Learning Networks

Handwritten Digit Recognition using Deep Learning Networks

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2022 IEEE Conference on Telecommunications, Optics and computer Science, TOCS 2022

作者： Yu, Shiying Liu, Jinmei Shu, Hang Cheng, Zhiwei Wuhan University School of Geodesy and Geomatics Wuhan430000 China Rutgers University School of Arts and Sciences New Jersey NJ08854 United States South West University School of Computer and Information Science Chongqing400010 China Xi'an University of Science and Technology Department of Architecture and Civil Engineering Xi'an710600 China

ISBN: (纸本)9781665470537

Handwritten digit recognition has been widely researched. Numerous classification methods have been proposed. This article improves the recognition of handwritten numbers method based on CNN neural network. Recognition of handwritten digits has made great progress, but due to insufficient recognition accuracy, it will have a certain impact on work efficiency. In this paper, a two-layer CNN network with two fully connected layers can achieve a good prediction performance. In our framework, the activation function used is the ReLU function, to some extent, this avoids the difficulties of gradient disappearance and gradient saturation. At the same time, dropout is added to the full connection layer to discard the neurons in the network with a certain probability, which can avoid certain accidents, and the final trained network will be more robust and the generalization performance will be stronger. This method effectively improves the accuracy of recognition in the final experiment, which achieves 98.75% accuracy on theMNIST dataset and 99.25% on the CIFAR-100 dataset. © 2022 IEEE.

关键词： Deep neural networks

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A Critical Analysis on Neural Networks and Deep Learning based Techniques for the Cloud Computing System and its Impact on Industrial Management 5

A Critical Analysis on Neural Networks and Deep Learning bas...

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5th International Conference on Contemporary Computing and Informatics, IC3I 2022

作者： Latoria, Akhilesh Joshi, S.K. Priya, A. Chakravarthi, M. Kalyan Chandan, Radha Raman Pant, Bhasker Auro University School of Information Technology Gujarat Surat India Uttaranchal University Uttarakhand Dehradun India School of Architecture KoneruLakshmaiah Educational Foundation Andhra Pradesh Guntur India VIT-AP University School of Electronics Engineering Amaravathi India Department of Computer Science Bachhaon Khushipur UP Varanasi India Graphic Era Deemed to Be University Department of CSE Uttarakhand Dehradun India

ISBN: (纸本)9798350398267

Due to the rising use of the internet for communication, commercial financial transactions, and other purposes, cloud computing is described as a crucial feature that has risen lately. Through network systems, this entails providing a flexible growth of different services. The fog is often used as a concept to describe different *** often trust cloud service providers to store enormous amounts of data and information with the intention of accessing it later. This paper is mainly concerned with doing a critical examination of the usage of supervised learning and deep learning in cloud applications, as well as their effects on production *** businesses are concentrating on using technology to increase returns on investments, assemble vast quantities of data, and archive it securely so that it may be utilised for organisational decision-making. Notwithstanding, they are more concerned with the cost and security of storing the information. so in order to accommodate the customers, the cloud services have used a wide range of approaches, including human brains, supervised learning, advanced analytics, and others. This helps to increase the users' credibility and help them use cloud storage to achieve their *** order to understand the overall efficacy of employing neural networks and advanced learning processes connected to cloud technology, this research is capable of using both secondary and primary information. The efficacy will be assessed by the researchers using correlation matrix and other non-parametric approaches. The conclusion is reached after thorough data examination and interpretation. © 2022 IEEE.

关键词： Decision making

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