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Analysis and Performance Evaluation of E-Commerce Implementation 2

Analysis and Performance Evaluation of E-Commerce Implementa...

2nd International Conference on Computing, Networking, Telecommunications and engineering Sciences Applications, CoNTESA 2021

作者： Fetaji, Majlinda Fetaji, Bekim Ali, Maaruf Zeqiri, Izet Snopce, Halil Ebibi, Mirlinda South East European University Faculty of Computer Sciences North Macedonia Tetovo Macedonia Mother Teresa University Faculty of Informatics North Macedonia Skopje Macedonia Epoka University Department of Computer Engineering Tirana Albania South East European University Faculty of Economy North Macedonia Tetovo Macedonia

ISBN: (纸本)9781665436106

The problems facing the implementation and adoption of e-commerce (electronic-commerce) is investigated in the Balkan region, specifically in North Macedonia. The strengths and weaknesses are analysed by using a questionnaire targeting the stakeholders and consumers located in Northern Macedonia. 40 consumers were chosen dealing with 20 selected companies. The total population under examination were gender balanced with their age starting from 18 years old. Each group had their own set of questions. The questions were of mixed modality consisting of binary responses, multiple choices and the use of the Likert Scale. The responses were analysed and recommendations made. © 2021 IEEE.

关键词： Big data

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Pulse rate pattern for gamers during playing video game in angry emotion

Pulse rate pattern for gamers during playing video game in a...

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2020 International Electronics Symposium, IES 2020

作者： Utama, Dhanu Kurnia Dharma Wibawa, Adhi Hakim, Lutfi Purnomo, Mauridhi Hery Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Department of Computer Engineering Surabaya Indonesia Politeknik Negeri Banyuwangi Department of Informatics Engineering Banyuwangi Indonesia

ISBN: (纸本)9781728195308

Playing computer games tends to be more determined or driven by emotion than reason, this is due to the game algorithm that is made to influence the player behavior. In general, the initial concept of developing games is how the game can make people addicted. One consequence that often occurs from playing such games is angry emotions among gamers. However, angry is one of the negative emotions of the human being that can cause poor health. Therefore, this study tries to provide information on pulse rate signal patterns in gamers when angry occurs during playing our chosen game. The chosen game has been validated using a survey to the game developer community as a game that can trigger angry emotion. In this study, 12 subjects volunteered in the experiment by playing the chosen game. Pulse rate sensors were applied during measurement. The pulse rate data from each subject has two phases: The baseline phase (prestimulation) and the playing phase (in-stimulation). After each measurement, the subject was asked to fill a questionnaire to access the level of his anger. This is the way we confirm the pulse rate result. The feature extraction parameters used to obtain the pulse rate pattern are the difference value of the mean and absolute deviation of the mean. Those two parameters at each phase then being compared using paired t-Tests to find out the significance between the baseline stage and the playing games stage. The result showed that when the subject confirmed feeling angry during playing games from the questionnaire, there was an increase in the pulse rate signal in the graph visualization and a significant difference in the paired t-Test. Some subjects showed a significantly high increase in pulse rate. This study concludes that some games can increase the risk of heart attack or other heart diseases. © 2020 IEEE.

关键词： computer games

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UPDExplainer: an Interpretable Transformer-based Framework for Urban Physical Disorder Detection Using Street View Imagery

arXiv

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arXiv 2023年

作者： Hu, Chuanbo Jia, Shan Zhang, Fan Xiao, Changjiang Ruan, Mindi Thrasher, Jacob Li, Xin Lane Department of Computer Science and Electrical Engineering West Virginia University MorgantownWV United States University at Buffalo State University of New York BuffaloNY United States Department of Civil and Environmental Engineering The Hong Kong University of Science and Technology Hong Kong College of Surveying and Geo-Informatics Tongji University Shanghai China

Urban Physical Disorder (UPD), such as old or abandoned buildings, broken sidewalks, litter, and graffiti, has a negative impact on residents’ quality of life. They can also increase crime rates, cause social disorder, and pose a public health risk. Currently, there is a lack of efficient and reliable methods for detecting and understanding UPD. To bridge this gap, we propose UPDExplainer, an interpretable transformer-based framework for UPD detection. We first develop a UPD detection model based on the Swin Transformer architecture, which leverages readily accessible street view images to learn discriminative representations. In order to provide clear and comprehensible evidence and analysis, we subsequently introduce a UPD factor identification and ranking module that combines visual explanation maps with semantic segmentation maps. This novel integrated approach enables us to identify the exact objects within street view images that are responsible for physical disorders and gain insights into the underlying causes. Experimental results on the re-annotated Place Pulse 2.0 dataset demonstrate promising detection performance of the proposed method, with an accuracy of 79.9%. For a comprehensive evaluation of the method’s ranking performance, we report the mean Average Precision (mAP), R-Precision (RPrec), and Normalized Discounted Cumulative Gain (NDCG), with success rates of 75.51%, 80.61%, and 82.58%, respectively. We also present a case study of detecting and ranking physical disorders in the southern region of downtown Los Angeles, California, to demonstrate the practicality and effectiveness of our framework. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Electrically Programmable Non-Volatile Silicon Photonic Content Addressable Memory (CAM) Cell

Electrically Programmable Non-Volatile Silicon Photonic Cont...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Chuanyu Lian Hongyi Sun Yi-Siou Huang Steven A. Vitale Juejun Hu Ichiro Takeuchi Nathan Youngblood Christos Vagionas Carlos A. Ríos Ocampo Department of Materials Science & Engineering University of Maryland College Park MD USA Institute for Research in Electronics and Applied Physics University of Maryland College Park MD USA Advanced Materials and Microsystems Group MIT Lincoln Laboratory Lexington MA USA Department of Materials Science & Engineering MIT Cambridge MA USA Department of Physics Quantum Materials Center University of Maryland College Park MD USA Electrical & Computer Engineering Department The University of Pittsburgh Pittsburgh PA USA Department of Informatics Center for Interdisciplinary Research and Innovation Aristotle University of Thessaloniki Thessaloniki Greece

We experimentally demonstrate the first electrically programmable, non-volatile silicon photonic content addressable memory cell using Sb 2 Se3 phase change material on microring resonators, opening the path for light... 详细信息

ISBN: (纸本)9798350369311

关键词： Phase change materials Associative memory Nonvolatile memory Lasers and electrooptics Silicon photonics Table lookup Resonators Electro-optical waveguides

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Correction: LPM-Net: A Data-Driven Resource-Efficient Predictive Motion Planner for Mobile Robots

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Neural Processing Letters 2025年第1期57卷 1-1页

作者： Amirhosseini, Fakhreddin Nilforoushan, Zahra Mirtaheri, Seyedeh Leili Electrical and Computer Engineering Department Kharazmi University Tehran Iran Department of Informatics Modeling Electronics and System Engineering (DIMES) University of Calabria Arcavacata Italy

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Identification of Diabetic Retinopathy through Machine Learning

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Mobile Information Systems 2021年 2021卷

作者： Bader Alazzam, Malik Alassery, Fawaz Almulihi, Ahmed Faculty of Computer Science and Informatics Amman Arab University Amman Jordan Department of Computer Engineering College of Computers and Information Technology Taif University Taif Saudi Arabia

A cross-sectional study of patients with suspected diabetic retinopathy (DR) who had an ophthalmological examination and a retinal scan is the focus of this research. Specialized retinal images were analyzed and classified using OPF and RBM models (restricted Boltzmann machines). Classification of retinographs was based on the presence or absence of disease-related retinopathy (DR). The RBM and OPF models extracted 500 and 1000 characteristics from the images for disease classification after the system training phase for the recognition of retinopathy and normality patterns. There were a total of fifteen different experiment series, each with a repetition rate of 30 cycles. The study included 73 diabetics (a total of 122 eyes), with 50.7% of them being men and 49.3% being women. The population was on the older side, at 59.7 years old on average. The RBM-1000 had the highest overall diagnostic accuracy (89.47) of any of the devices evaluated. The RBM-500 had a better autodetection system for DR signals in fundus images than the competition (100% sensitivity). In terms of specificity, RBM-1000 and OPF-1000 correctly identified all of the images that lacked DR signs. In particular, the RBM model of machine learning automatic disease detection performed well in terms of diagnostic accuracy, sensitivity, and application in diabetic retinopathy screening. © 2021 Malik Bader Alazzam et al.

关键词： Ophthalmology

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Robust Attacks Detection Model for Internet of Flying Things based on Generative Adversarial Network (GAN) and Adversarial Training

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IEEE Internet of Things Journal 2025年

作者： Gaber, Tarek Ali, Tarek Nicho, Mathew Torky, Mohamed Engineering and Environment University of Salford School of Science Manchester United Kingdom Suez Canal University Faculty of Computers and Informatics Ismailia Egypt Manchester Metropolitan University Department of Computing and Mathematics Manchester United Kingdom Rabdan Academy Research and Innovation Centre Abu Dhabi United Arab Emirates University of Queensland School of Electrical Engineering and Computer Science Australia Egyptian Russian University-Badr City Faculty of Artificial Intelligence Cairo Egypt

The Internet of Flying Things (IoFT) holds significant promise in fields like disaster management and surveillance. However, it is increasingly vulnerable to cyberattacks that can compromise the confidentiality, integrity, and availability (CIA) of sensitive data. Despite the growing interest in proposing Intrusion Detection Systems (IDSs) for IoFT networks, current literature faces key limitations, particularly the shortage of publicly available IoFT datasets with diverse attacks, and the fact that existing IDSs lack robustness against sophisticated adversarial machine learning attacks. This paper is the first study to address these limitations by proposing a more resilient and accurate IDS tailored for IoFT networks (RIDS-IoFT). We introduce a novel IDS that leverages Generative Adversarial Networks (GANs) to generate a hybrid dataset that combines real IoFT traffic data with GAN-generated adversarial attacks, addressing the dataset diversity issue. Additionally, we introduce an innovative adversarial training method to enhance the system’s defense against evolving threats, such as Fast Gradient Sign Method (FGSM), Basic Iterative Method (BIM), and Carlini & Wagner (C&W) attacks. The proposed RIDS-IoFT was evaluated using four machine learning models, Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), and Logistic Regression (LR), on two datasets: ECU-IoFT and CICIDS2018. The IDS’s performance was assessed based on its ability to detect both traditional and adversarial attacks. The results show that the Random Forest model achieved the highest detection accuracy, up to 96.5%, demonstrating superior performance across both real and hybrid datasets. The proposed RIDS-IoFT not only enhances detection accuracy but also strengthens resilience against adversarial threats, making it suitable for resource-constrained IoFT environments. In conclusion, this study presents a comprehensive approach to securing IoFT networks by combining real and synthetic d

关键词： Generative adversarial networks

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Diagnosis of COVID-19 Pneumonia via a Novel Deep Learning Architecture

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Journal of computer Science & Technology 2022年第2期37卷 330-343页

作者： Xin Zhang Siyuan Lu Shui-Hua Wang Xiang Yu Su-Jing Wang Lun Yao Yi Pan Yu-Dong Zhang Department of Medical Imaging The Fourth People's Hospital of Huai'anHuai'an 223002China School of Informatics University of LeicesterLeicesterLE17RHU.K. School of Architecture Building and Civil Engineering Loughborough UniversityLoughboroughLE113TUU.K. School of Mathematics and Actuarial Science University of LeicesterLeicesterLE17RHU.K. Key Laboratory of Behavior Sciences Institute of PsychologyChinese Academy of SciencesBeijing 100101China Department of Psychology University of the Chinese Academy of SciencesBeijing 100101China Department of Infection Diseases The Fourth People's Hospital of Huai'anHuaian 223002China Department of Computer Science Georgia State UniversityAtlanta 30302-5060U.S.A. Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz University Jeddah 21589Saudi Arabia

COVID-19 is a contagious infection that has severe effects on the global economy and our daily *** diagnosis of COVID-19 is of importance for consultants,patients,and *** this study,we use the deep learning network AlexNet as the backbone,and enhance it with the following two aspects:1)adding batch normalization to help accelerate the training,reducing the internal covariance shift;2)replacing the fully connected layer in AlexNet with three classifiers:SNN,ELM,and ***,we have three novel models from the deep COVID network(DC-Net)framework,which are named DC-Net-S,DC-Net-E,and DC-Net-R,*** comparison,we find the proposed DC-Net-R achieves an average accuracy of 90.91%on a private dataset(available upon email request)comprising of 296 images while the specificity reaches 96.13%,and has the best performance among all three proposed *** addition,we show that our DC-Net-R also performs much better than other existing algorithms in the literature.

关键词： pneumonia COVID-19 convolutional neural network AlexNet deep learning

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On the Accuracy and Efficiency of Received Signal Strength Modelling for a Forest Environment

On the Accuracy and Efficiency of Received Signal Strength M...

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Machine Learning for Communication and Networking (ICMLCN), IEEE International Conference on

作者： Vasileios P. Rekkas Sotirios P. Sotiroudis Georgios P. Koudouridis Panagiotis Sarigiannidis Zaharias D. Zaharis George K. Karagiannidis Sotirios K. Goudos ELEDIA@AUTH School of Physics Aristotle University of Thessaloniki Thessaloniki Greece Systems Management Global AI Accelerator Ericsson Stockholm Sweden Department of Informatics and Telecommunications Engineering University of Western Macedonia Kozani Greece School of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece Cyber Security Systems and Applied AI Research Center Lebanese American University (LAU) Lebanon Department of Electronics and Communication Engineering Bharath University Chennai Tamilnadu India

ISBN: (数字)9798350343199

ISBN: (纸本)9798350343205

Within telecommunications, the accuracy and efficiency of machine learning models (ML) define their utility, and consequently, the choice of ML mechanisms assumes paramount importance. This study focuses on the exploration and comparison of diverse ML and ensemble learning techniques, with a specific emphasis on their significance in crafting precise and extensive models. To this end, the quality of the received signal and the optimization and functioning of wireless communication networks rely heavily on accurately predicting the received signal strength indicator (RSSI) and path loss (PL). The studied environment, which is highly complex, spans 2000 km2 of the intricate landscapes of the American River Hydrologic Observatory (ARHO) networks and is characterized by a diverse blend of terrain features and vegetation distributions. Notable independent variables under consideration include path distance, canopy coverage, terrain variability, and path angle. The proposed ensemble ML approaches demonstrate remarkable accuracy and efficiency when it comes to modeling and predicting the RSSI values in forested environments.

关键词： Accuracy Wireless networks Vegetation mapping Forestry Predictive models Telecommunications Rivers

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Reverse osmosis desalination process modeling and simulation with membrane fouling

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AIP Conference Proceedings 2024年第1期3034卷

作者： Arash Golabi Abdelkarim Erradi Hazim Qiblawey Ashraf Tantawy Ahmed Bensaid Khaled Shaban Department of Computer Science and Engineering Qatar University Doha Qatar Department of Chemical Engineering Qatar University School of Computer Science and Informatics De Montfort University Leicester UK

Analyzing the correlation between the key RO operating conditions and performance indicators requires accurate reverse osmosis (RO) model that facilitates understanding and evaluating membrane performance. This study aims to develop and simulate a dynamic RO model to assess the performance of the RO process under realistic conditions by considering the effects of membrane fouling. Analyzing performance, managing energy use, assessing design (sizing, configuration, network layout), and estimating energy consumption are all possible with the model. Dynamic models have several advantages over steady-state models, including the ability to test and design advanced controllers. Based on the mathematical model presented, a model of RO pressure vessel modules can be built to represent the dynamic behavior of RO pressure vessels. Through MATLAB simulations, we demonstrate how the fidelity and accuracy of the models can be demonstrated.

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