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8th IET Smart Cities Symposium (SCS 2024)

作者： Mahmoud Elshabrawy Faris H. Rizk Ahmed Mohamed Zaki Marwa M. Eid Ali E. Takieldeen Abdelhameed Ibrahim Abdelaziz A. Abdelhamid Laith Abualigah El-Sayed M. El-kenawy Computer Science and Intelligent Systems Research Center Blacksburg 24060 Virginia USA Faculty of Artificial Intelligence Delta University for Science and Technology Mansoura 35111 Egypt School of ICT Faculty of Engineering Design and Information & Communications Technology (EDICT) Bahrain Polytechnic PO Box 33349 Isa Town Bahrain Department of Computer Science College of Computing and Information Technology Shaqra University 11961 Shaqra Saudi Arabia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan

ISBN: (数字)9781837243105

Treatment plant optimization plays an important role in the implementation of eco-problem solutions. This article is going to cover how machine learning algorithms can determine energy consumption, climate, and wastewater features of electricity at a wastewater treatment plant in eastern Melbourne from 2014 to 2019. This study uses data obtained from the Melbourne Water and Airport weather station, which are freely available to carry out MLPRegressor, SVM, Linear Regression, Decision Tree Regression, random forest regression, and Nearest Neighbor. Through customary measures like MSE, RMSE, EVS, and others, the best model is adequately identified, which is the Random Forest Regressor model, which has 0.889285. (Lowest MSE) With these results, the plant treatment optimization processes, such as sampling for environmental analysis and the control of energy consumption, have improved.

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ETHACA - A Container-Based Cyber Range for Research and Education

ETHACA - A Container-Based Cyber Range for Research and Educ...

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IEEE International Workshop on Computer-Aided Modeling, Analysis, and Design of Communication Links and Networks, CAMAD

作者： Nestoras Chouliaras Ioanna Kantzavelou Grammati Pantziou Leandros Maglaras Panagiota Chouliara-Sidera Antonis Adamakos Panagiotis Chalatsakos Department of Informatics and Computer Engineering University of West Attica Athens Greece School of Computing Edinburgh Napier University Edinburgh UK Department of Computer Science and Biomedical Infromatics University of Thessaly Lamia Greece Senior Software Engineer at *** Athens Greece Faculty of Pure and Applied Sciences Open University of Cyprus Nicosia Cyprus

ISBN: (数字)9798350377644

ISBN: (纸本)9798350377651

The advent of Industry 4.0 and the Industrial Internet of Things (IIoT) has revolutionized manufacturing and production systems, enhancing efficiency and communication through the integration of advanced technologies such as IoT devices and AI. However, this increased digitization has also led to heightened cybersecurity risks, with a notable rise in the frequency and complexity of cyber-attacks. Cyber Ranges emerges as a critical solution in addressing these challenges, offering a controlled environment for simulating cyber-attack scenarios, training cybersecurity teams, and developing robust defense strategies. The escalating demand for cybersecurity professionals has highlighted the crucial need for effective training solutions. This article presents an open-source Cyber Range constructed on container-based architecture, meticulously engineered for efficient operation with minimal resource consumption. In response to the scarcity of cybersecurity experts, this platform prioritizes extensive scalability and robust isolation between exercises, ensuring secure learning environments, and also integrates a complex developed exercise series aimed at strengthening cybersecurity learning experiences. These exercises encompass a variety of scenarios and challenges, catering to professionals at different skill levels. Each exercise replicates real-world cyber threats, offering practical, hands-on learning opportunities. Additionally, a comprehensive questionnaire accompanies these exercises, designed to gather feedback on participants' experiences.

关键词： Training Production systems Scalability Conferences Computational modeling Computer architecture Fourth Industrial Revolution Complexity theory Cyberattack Industrial Internet of Things

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Network-based methods with heterogeneous data to identify severe COVID immune-related genes

Network-based methods with heterogeneous data to identify se...

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International Computer science and Engineering Conference (ICSEC)

作者： Pakorn Sagulkoo Apichat Suratanee Kitiporn Plaimas Department of Family Medicine Faculty of Medicine Center of Biomedical Informatics Chiang Mai University Chiang Mai Thailand Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Mathematics and Computer Science Advance Virtual and Intelligent Computing Center Faculty of Science Chulalongkorn University Bangkok Thailand

ISBN: (纸本)9781665462730

Bioinformatics and systems biology play a vital role in the computational prediction of disease-associated genes using multi-omics data. The network-based approach is one of the most potent tools in disease-associated gene prediction. The two commonly used methods are neighborhood-based and network diffusion techniques. However, there is still a lack of studies comparing the performance of these methods, especially in terms of functional pathway discovery. Thus, this study demonstrated the performance comparison of these two techniques in both numerical accuracies based on the area under the receiver operating characteristic curve (AUROC) and biological meaning efficiency based on functional pathway enrichment. In this study, we analyzed data of severe COVID-19 immune-related genes using heterogeneous data. The prediction results of the COVID-19 immune-related genes in the human protein-protein interaction (PPI) network showed that the network diffusion had better performance in both AUROC and pathway enrichment even though it provided a longer computational time than the neighborhood method.

关键词： COVID-19 Proteins Measurement Computer science High performance computing Systems biology Receivers

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ANN Based Novel Approach to Detect Node Failure in Wireless Sensor Network

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Computers, Materials & Continua 2021年第11期69卷 1447-1462页

作者： Sundresan Perumal Mujahid Tabassum Ganthan Narayana Suresh Ponnan Chinmay Chakraborty Saju Mohanan Zeeshan Basit Mohammad Tabrez Quasim Faculty of Science and Technology Universiti Sains Islam MalaysiaNilai71800Malaysia Advanced Informatics Department Razak Faculty of Technology and InformaticsUniversiti Teknologi MalaysiaKuala Lumpur54100Malaysia Department of ECE Veltech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology600062TamilnaduIndia Department Electronics&Communication Engineering Birla Institute of TechnologyMesraJharkhandIndia Faulty of Information Technology University of Technology and Applied SciencesMuscat133Oman College of Computing and Information Technology University of BishaBisha67714Saudi Arabia

A wireless sensor network(WSN)consists of several tiny sensor nodes to monitor,collect,and transmit the physical information from an environment through the wireless *** node failure is considered as one of the main issues in the WSN which creates higher packet drop,delay,and energy consumption during the *** the node failure occurred mostly due to persistent energy exhaustion during transmission of data *** this paper,Artificial Neural Network(ANN)based Node Failure Detection(NFD)is developed with cognitive radio for detecting the location of the node *** ad hoc on-demand distance vector(AODV)routing protocol is used for transmitting the data from the source node to the base ***,the Mahalanobis distance is used for detecting an adjacent node to the node failure which is used to create the routing path without any node *** performance of the proposed ANN-NFD method is analysed in terms of throughput,delivery rate,number of nodes alive,drop rate,end to end delay,energy consumption,and overhead ***,the performance of the ANN-NFD method is evaluated with the header to base station and base station to header(H2B2H)*** packet delivery rate of the ANN-NFD method is 0.92 for 150 nodes that are high when compared to the H2B2H ***,the ANN-NFD method provides data consistency during data transmission under node and battery failure.

关键词： AODV artificial neural network artificial intelligence Mahalanobis distance node failure throughput wireless sensor network

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Mitosis detection and classification for breast cancer diagnosis: What we know and what is next

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Computers in Biology and Medicine 2025年 191卷 110057-110057页

作者： Khalil, Rafi Ullah Sajjad, Muhammad Dhahbi, Sami Bourouis, Sami Hijji, Mohammad Muhammad, Khan Digital Image Processing Lab Department of Computer Science Islamia College Peshawar Peshawar25000 Pakistan Applied College of Mahail Aseer King Khalid University Muhayil Aseer62529 Saudi Arabia Department of Information Technology College of Computers and Information Technology Taif University Taif21944 Saudi Arabia Faculty of Computers and Information Technology University of Tabuk Tabuk71491 Saudi Arabia Department of Applied AI School of Convergence College of Computing and Informatics Sungkyunkwan University Seoul03063 Korea Republic of

Breast cancer is the second most deadly malignancy in women, behind lung cancer. Despite significant improvements in medical research, breast cancer is still accurately diagnosed with histological analysis. During this procedure, pathologists examine a physical sample for the presence of mitotic cells, or dividing cells. However, the high resolution of histopathology images and the difficulty of manually detecting tiny mitotic nuclei make it particularly challenging to differentiate mitotic cells from other types of cells. Numerous studies have addressed the detection and classification of mitosis, owing to increasing capacity and developments in automated approaches. The combination of machine learning and deep learning techniques has greatly revolutionized the process of identifying mitotic cells by offering automated, precise, and efficient solutions. In the last ten years, several pioneering methods have been presented, advancing towards practical applications in clinical settings. Unlike other forms of cancer, breast cancer and gliomas are categorized according to the number of mitotic divisions. Numerous papers have been published on techniques for identifying mitosis due to easy access to datasets and open competitions. Convolutional neural networks and other deep learning architectures can precisely identify mitotic cells, significantly decreasing the amount of labor that pathologists must perform. This article examines the techniques used over the past decade to identify and classify mitotic cells in histologically stained breast cancer hematoxylin and eosin images. Furthermore, we examine the benefits of current research techniques and predict forthcoming developments in the investigation of breast cancer mitosis, specifically highlighting machine learning and deep learning. © 2025 Elsevier Ltd

关键词： Lung cancer

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An analytical solution to the time fractional Navier–Stokes equation based on the Katugampola derivative in Caputo sense by the generalized Shehu residual power series approach

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Partial Differential Equations in applied Mathematics 2024年 11卷

作者： Sawangtong, W. Dunnimit, P. Wiwatanapataphee, B. Sawangtong, P. Department of Mathematics Faculty of Science Mahidol University Bangkok Thailand Research group for fractional calculus theory and applications Science and Technology Research Institute King Mongkut's University of Technology North Bangkok Bangkok Thailand School of Electrical Engineering Computing and Mathematical Sciences Curtin University Perth 6845 WA Australia Department of Mathematics Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand

The Navier–Stokes equations describe the behavior of viscous fluids and establish a fundamental connection between the application of external forces on fluid motion and the resulting pressure within the fluid. The objective of this study is to solve the two-dimensional time fractional Navier–Stokes equation through the utilization of the residual power series method together with the generalized Shehu transform. The method is called the generalized Shehu residual power series (GSHRPS) approach. The fractional derivative utilized in this research is the Katugampola derivative in the sense of Caputo. The effectiveness of this method is verified by demonstrating its convergence to the solution of the previously described problem. Furthermore, a practical example is presented to show the precision, accuracy, and efficiency of this approach in order to illustrate its effectiveness and benefits. © 2024 The Author(s)

关键词： Fractional Navier–Stokes equations Generalized Shehu transform Katugampola fractional derivative in the sense of Caputo Residual power series method

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Amazon in business to customers and overcoming obstacles 2

Amazon in business to customers and overcoming obstacles

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2nd International Conference on Smart computing and Electronic Enterprise, ICSCEE 2021

作者： Altrad, Abdullah Pathmanathan, P. Ravindran Al Moaiad, Yazeed Endara, Yousef Mohamed Aseh, Khairi El-Ebiary, Yousef A. Baker Farea, Mazen Mohammed Latiff, Nurul Adilah Abdul Saany, Syarilla Iryani Ahmad Faculty of Computer and Info. Tech. Al Madinah International University Malaysia International Institute of Applied Science Swiss School of Management Netherlands Faculty of Computer and Information Technology Malaysia Misurata University Libya UniSZA Faculty of Informatics and Computing Malaysia Al-Madinah International University Faculty of Finance and Admin. Sciences Malaysia Universiti Malaysia Terengganu Faculty of Ocean Engineering Technology and Informatics Malaysia

ISBN: (纸本)9781665432221

Electronic commerce, or people call it e-commerce, it refers to buying and selling products or services on the Internet and transferring money and data to carry out these transactions. More consumers are shifting from traditional transactions to e-commerce, as e-commerce becomes faster and cheaper. In contrast, electronic commerce provides more ease and variety in the composition of goods and services worldwide. E-commerce is the type of electronic business that has been in business for more than 25 years and is used by many users around the world. This research aims to shed light on the nature of electronic commerce and the difficulties it faces on the technical side. This research used descriptive methodology after gathering data from online and indexed research. The research also mentioned five of these obstacles, and how the Amazon overcomes them, and we will take a look at how the opportunities of the Amazon grow so that they have an effect called the Amazon effect, the research reached an important conclusion that eDesk is the leading help desk for online sellers, specifically designed to meet the exact requirements of e-commerce. The research also found solutions to some of the weaknesses faced by electronic commerce platforms. © 2021 IEEE.

关键词： Electronic commerce

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Classification of Breast Thermal Images into Healthy/Cancer Group Using Pre-Trained Deep Learning Schemes

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Procedia Computer science 2023年 218卷 24-34页

作者： Seifedine Kadry Rubén González Crespo Enrique Herrera-Viedma Sujatha Krishnamoorthy Venkatesan Rajinikanth Faculty of Applied Computing and Technology Noroff University College Kristiansand 94612 Norway Computer Science Department School of Engineering and Technology Universidad Internacional de La Rioja 26006 Andalusian Spain Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai 602105 Tamil Nadu India

In the women's community, Breast Cancer (BC) is a severe disease. The World Health Organization reported in 2020 that 2.26 million deaths occur due to BC. BC is curable if detected early. Since thermal imaging is non-invasive and supports disease detection, it is commonly used in clinics. Compared to other methods, it keeps BC early and accurate. The proposed work aims to evaluate the performance of the Pretrained Deep-Learning Methods (PDLM) in detecting BC using the thermal images collected from the benchmark dataset. It includes the following stages: primary image processing, deep feature mining, handcrafted feature mining, feature optimization using Firefly-Algorithm (FA), classification and validation. Visual Lab thermal images were used in the study. The investigational outcome of this study authenticates that the VGG16, along with the DT, provides better detection accuracy (95.5%) compared to other classifiers used in this study. To justify the significance of the implemented technique, the proposed work not only improved accuracy, but also improved precision, sensitivity, specificity, and F1-Scores.

关键词： Breast cancer Thermal imaging Firefly algorithm Feature optimization Classification

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Determinants of customer purchase intention using Zalora mobile commerce application 2

Determinants of customer purchase intention using Zalora mob...

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2nd International Conference on Smart computing and Electronic Enterprise, ICSCEE 2021

作者： El-Ebiary, Yousef A. Baker Pathmanathan, P. Ravindran Tarshany, Yasser Mohamed Abdelrahman Jusoh, Julaily Aida Aseh, Khairi Al Moaiad, Yazeed Al-Kofahi, Mahmoud Pande, Bishwajeet Bamansoor, Samer UniSZA Faculty of Informatics and Computing Malaysia International Institute of Applied Science Swiss School of Management Netherlands MEDIU Faculty of Islamic Sciences Department of Fiqh and Usul Fiqh Malaysia MEDIU Faculty of Computer and Information Technology Malaysia Irbid National University Faculty of Administrative and Financial Sciences Irbid Jordan Gyancity Research Consultancy Pvt Ltd Motihari India Faculty of Computer and Information Technology MEDIU Malaysia

ISBN: (纸本)9781665432221

In the era of modern technologies and with the development of marketing, nowadays the customer can save time buying things without having to visit the store. They just need to shop online, find what they are buying and pay it with just a few simple clicks. With this convenience in mind, the indulgence of people buying online has risen quite well. The customer's decision to purchase a product through Internet commerce under the concept of electronic commerce - which has been developed into e-commerce or mobile commerce - usually takes a serious action with perceived confidence in the services and their suitability. Therefore, customer behaviour is an important factor to facilitate online electronic service. Hence, it is important to understand customer perceptions on these services and their intention of using it. Zalora is one of the fastest online search platforms providing kind of world-famous brands in Malaysia. The objective of this study is to examine factors that influence Malaysian consumers' purchase intention through Zalora mobile application. Based on the theory of planned behaviour along with other factors, the study proposes a theoretical model for the empirical examination of the customer intention for purchasing via Zalora mobile application. Data were collected from sixteen respondents to test the relationship between variables. Data analysis was carried out using PLS-SEM. The empirical results indicated that perceived friendliness, perceived convenience, and perceived influence have a significant impact on purchase intention. The result may be preserved by providing an easy shopping experience to the user when using commerce sites and building trust to maintain the intent to buy. In addition, this analysis is helpful for Zalora marketers to suggest the most effective promotion methods in reference to further marketing of their products. © 2021 IEEE.

关键词： Mobile commerce

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THE ROLE OF SOCIAL MEDIA ON SELECTED BUSINESSES IN NIGERIA IN THE ERA OF COVID-19 PANDEMIC

arXiv

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arXiv 2023年

作者： Ihemebiri, Cajetan Ukwandu, Elochukwu Ofusori, Lizzy Olebara, Comfort Department of Applied Computing and Engineering Cardiff School of Technologies Cardiff Metropolitan University Cardiff United Kingdom School of Management Information Technology & Governance University of KwaZulu-Natal Westville Campus Durban South Africa Department of Computer Science Faculty of Physical Science Imo State University Imo State Owerri Nigeria

As several countries were experiencing unprecedented economic slowdowns due to the outbreak of COVID-19 pandemic in early 2020, small business enterprises started adapting to digital technologies for business transactions. However, in Africa, particularly Nigeria, COVID-19 pandemic resulted to some financial crisis that impacted negatively on the sustainability of small and medium-sized (SMEs) businesses. Thus, this study examined the role of social media on selected SMEs in Nigeria in the heat of the COVID-19 pandemic that led to several lock downs in a bid to curtail the spread of the virus. Cross-sectional survey research design was used alongside convenience population sampling techniques. The population was categorised based on selected SMEs businesses, while a quantitative research approach was adopted, and primary data were collected using a questionnaire. The questionnaires were administered to owners and operators of SMEs in Ikotun and Ikeja areas of Lagos State, Nigeria. A total of 190 questionnaires were distributed, where 183 usable responses were analysed. The findings of the study show that SMEs were aware of the usefulness of social media to their businesses as they largely leveraged it in conducting their businesses during the national lockdowns. The study recommended that labour/trade unions should sensitise and encourage business owners on the benefits of continuous use of social media in carrying out their business transactions. © 2023, CC BY-NC-SA.

关键词： COVID-19

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