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1st International Conference on Intelligent Transportation and Smart Cities, ICITSC 2024

作者： Ma, Yinchao Por, Lip Yee School of Economics and Management Civil Aviation Flight University of China Guanghan618307 China Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur50603 Malaysia

ISBN: (纸本)9781643685205

The new generation of information technology has had a significant impact on people's behavior, and making airports more intelligent and intelligent. Airport intelligence is an important component of smart cities and intelligent transportation. Accurate prediction of passenger throughput is not only one of the keys to achieving a smart airport, but also of great significance for airport management and operation. The artificial intelligence prediction method SVR has advantages such as fast convergence speed and suitability for small sample data. The BA algorithm has advantages in parameter optimization, and the combination of the two can effectively improve prediction performance. This article takes Shuangliu Airport in Sichuan Province, China as the research object. In response to the nonlinear characteristics of the airport's monthly passenger throughput from 2001 to 2019, based on the analysis of the correlation between passenger throughput with historical data and takeoff and landing sorties, a prediction model SVR-BA was established using SVR and BA, while BP-BA and ARMA models were also established. The prediction performance of several types of models was compared from MAPE and MAE, and it was found that the SVR-BA model had better prediction performance and higher robustness. This indicates that after optimizing the parameters of the BA algorithm, the predictive performance of the SVR prediction model can be effectively improved. © 2024 The Authors.

关键词： Airports

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Real-time Privacy Preserving Framework for Covid-19 Contact Tracing

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computers, Materials & Continua 2022年第1期70卷 1017-1032页

作者： Akashdeep Bhardwaj Ahmed A.Mohamed Manoj Kumar Mohammed Alshehri Ahed Abugabah School of Computer Science University of Petroleum&Energy Studies(UPES)Dehradun248007India Department of Computer Science College of Computer and Information SciencesMajmaah UniversityMajmaah11952Saudi Arabia Department of Information Technology Faculty of Computer and InformationAssiut UniversityAssiut71515Egypt Department of Information Technology College of Computer and Information SciencesMajmaah UniversityMajmaah11952Saudi Arabia Department of Information System College of Technological InnovationZayed UniversityAbu DhabiUnited Arab Emirates

The recent unprecedented threat from COVID-19 and past epidemics,such as SARS,AIDS,and Ebola,has affected millions of people in multiple *** have shut their borders,and their nationals have been advised to *** variety of responses to the pandemic has given rise to data privacy *** prevention and control strategies as well as disease control measures,especially real-time contact tracing for COVID-19,require the identification of people exposed to *** tracing frameworks use mobile apps and geolocations to trace ***,while the motive may be well intended,the limitations and security issues associated with using such a technology are a serious cause of *** are growing concerns regarding the privacy of an individual’s location and personal identifiable information(PII)being shared with governments and/or health *** study presents a real-time,trust-based contact-tracing framework that operateswithout the use of an individual’sPII,location sensing,or gathering GPS *** focus of the proposed contact tracing framework is to ensure real-time privacy using the Bluetooth range of individuals to determine others within the *** research validates the trust-based framework using Bluetooth as practical and *** our proposed methodology,personal information,health logs,and location data will be secure and not *** research analyzes 100,000 tracing dataset records from 150 mobile devices to identify infected users and active users.

关键词： Privacy contact tracing mobile apps Bluetooth Covid epidemic

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Improved Chest X-Ray Image Quality Using Median and Gaussian Filter Methods 4

Improved Chest X-Ray Image Quality Using Median and Gaussian...

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4th International Conference on Informatics, Multimedia, Cyber and information system, ICIMCIS 2022

作者： Kesuma, Lucky Indra Ermatita, Ermatita Erwin, Erwin Sari, Purwita Purabaya, Rudhy Ho Universitas Sriwijaya Doctoral Program in Engineering Faculty of Engineering Palembang Indonesia Universitas Sjakhyakirti India Universitas Sriwijaya Information System Faculty of Computer Science Palembang Indonesia Universitas Sriwijaya Faculty of Computer Science Departement of Computer System Palembang Indonesia Universitas Sriwijaya Informatic Management Faculty of Computer Science Palembang Indonesia Universitas Pembangunan Nasional Veteran Jakarta Information System Faculty of Computer Science Jakarta Indonesia

ISBN: (纸本)9781665473279

The lungs are one of the organs of the body that are responsible for the human respiratory process and are very susceptible to dangerous diseases. For this reason, early detection and diagnosis of lung organs is needed, one of which is through examination of Chest X-Ray (CXR) images. Examination of the results of CXR images is still done manually by doctors and radiologists, this requires time and high accuracy. To facilitate the examination, it is necessary to image quality enhancement in order to get better image quality results so as to produce an accurate diagnosis. The initial stage in this research is to apply the contour improvement technique to the image using Morphology Opening, and followed by noise reduction using Median Filter and Gaussian filter. The results of the two methods of noise reduction are compared with the results of image quality in order to find out the best method that can be applied. The implementation of image quality enhancement results was measured quantitatively using Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), and Structural Similarity Index Metrics (SSIM). In the Morphology Opening and Median Filter methods, the values obtained are 39.187, 22.252, and 0.952, respectively. Meanwhile, the Morphology Opening and Gaussian Filter methods obtained values of 38.717, 23.917 and 0.956. Based on these results, it can be concluded that both methods are able to improve image quality well. © 2022 IEEE.

关键词： Image quality

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IoT-Based Transmission Tower Monitoring Communications and Visualization Platform 6

IoT-Based Transmission Tower Monitoring Communications and V...

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6th IEEE Symposium on Future Telecommunication Technologies, SOFTT 2022

作者： Mohd Alias, Mohamad Razin Naim Mohd Dzaki, Dzahir Rashidi Din, Norashidah Md Deros, Siti Noratiqah Mohd Passarella, Rossi Amier Chaai, Azzam Eusuf Chaai Malaysia Universitas Sriwijaya Computer Networking and Information System Faculty of Computer Science Department Palembang Indonesia Malaysia

ISBN: (纸本)9781665455978

Transmission tower fault monitoring has always been a challenge for power system engineers. Vertical and horizontal displacement monitoring is important for evaluating health status of transmission tower's infrastructures. Quick fault detection can help protect the equipment by allowing the disconnection of transmission tower before any significant damage of the equipment. The purpose of this study is to propose an IoT monitoring system for transmission towers. The monitoring system adheres to Three-Layer IoT architecture. In the perception layer, sensors MPU6050 and Anemometer are used to measure a specific parameter. The data, which includes the Roll, Pitch, Yaw, and wind velocity, is gathered and delivered to the ESP32 through network layer using WiFi, which then stored the information in the cloud using InfluxDB. For visualization purposes in the application layer, Grafana is utilized which is hosted in the web dashboard and the mobile application. Flutter framework is used to develop the mobile application. An alert will be displayed in the mobile application when the threshold of the wind speed exceeds 10m/s. © 2022 IEEE.

关键词： Wind

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Blockchain Technology Supports Employee Career Management to Increase Transparency of Employee Career Achievements 6

Blockchain Technology Supports Employee Career Management to...

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6th International Conference on Cybernetics and Intelligent system, ICORIS 2024

作者： Inayatulloh Yasri Fathurrozi, Ahmad Yasir, R. Muhammad Sinlae, Fried Lestari, Tyastuti Sri Priatna, Wowon Hartono, Indra Kusumadi Noe'man, Achmad Information Systems Department School of Information System Bina Nusantara University Jakarta11480 Indonesia Computers Science Department School of Compters Science Bina Nusantara University Jakarta11480 Indonesia Faculty of Computer Science Bhayangkara Jakarta Raya University Jakarta Indonesia

ISBN: (纸本)9798331539603

Career planning for employees is an important part for employees to increase motivation and work performance. On the other hand, career management for companies plays an important role in meeting employee needs in the future. The problem is that employees cannot get real-time and valid information about their career progress based on objective company assessments. Many old employees are not promoted to higher positions without an accurate explanation from the company. Thus, a system is needed that can help employees monitor their career progress. So the aim of this research is to build an employee career management model using blockchain technology to increase the transparency of employee career paths in the company. The research uses a qualitative approach through a literature review to obtain information about employee management phenomena and problems. The literature review is also used to study blockchain technology as an alternative solution to career management problems. The research output is a blockchain technology adoption model for corporate career management and simulation with the Hyperledger framework. © 2024 IEEE.

关键词： Employment

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Priority Based Cyber Incident Handling: Work in Progress 11

Priority Based Cyber Incident Handling: Work in Progress

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11th International Conference on Electrical Engineering, computer science and Informatics, EECSI 2024

作者： Mulyati Stiawan, Deris Widhiarso, Wijang Budiarto, Rahmat Multi Data Palembang University & Faculty of Engineering Universitas Sriwijaya Department of Information System Palembang Indonesia Sriwijaya University Faculty of Computer Science Palembang Indonesia Multi Data Palembang University Department of Informatics Palembang Indonesia College of Computer Science & IT Albaha University Saudi Arabia

ISBN: (纸本)9798350355314

Effective and efficient cyber incident handling is crucial for maintaining the security of information systems and organizational data. This research aims to develop a priority-based cyber incident handling method by adopting the NIST 2.0 framework and utilizing machine learning classification algorithms. This method enables organizations to quickly and accurately identify and respond to incidents based on threat levels and impact, focusing resources on the most critical incidents. The research employs the Support Vector Machine (SVM) classification algorithm to determine incident priorities. The study is still developing for handling cyber incidents, followed by testing and evaluating the system prototype within organizations. Initial results indicate that the priority-based approach using the SVM classification algorithm achieves a True Positive (TP) Rate of 0.999 and a False Positive (FP) Rate of 0.008, with a correlation of 98.88%, resulting in excellent performance in identifying positive cases and a low error rate. © 2024 IEEE.

关键词： Cyber attacks

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N-gram and Word2Vec Feature Engineering Approaches for Spam Recognition on Some Influential Twitter Topics in Saudi Arabia 22

N-gram and Word2Vec Feature Engineering Approaches for Spam ...

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6th International Conference on information system and Data Mining, ICISDM 2022

作者： Balfagih, Ahmed Keselj, Vlado Taylor, Stacey Faculty of Computer Science Dalhousie University Canada Information System Faculty of Computing and Information Technology King Abdulaziz University Saudi Arabia

ISBN: (纸本)9781450396257

Social media platforms, such as Twitter, have become powerful sources of information on people's perception of major events. Many people use Twitter to express their views on various issues and events and use it to develop their opinion on the diverse economic, political, technical, and social occurrences related to their daily lives. Spam and non-relevant tweets are a major challenge for Twitter trend detection. Saudi Arabia is a top ranked country in Twitter usage worldwide, and in recent years has experienced difficulties due to the use and rise of hashtags based on misleading tweets and spam. The goal of this paper is to apply machine learning techniques to identify spam on the Saudi tweets collected to the end of 2020. To date, spam detection on Twitter data has been mostly done in English, leaving other major languages, such as Arabic, insufficiently covered. Additionally, publicly accessible Arabic Twitter datasets are hard to find. For our research, we use eight Twitter datasets on some significant topics in politics, health, national affairs, economy, and sport, to train and evaluate different machine learning algorithms, with a focus on two feature generation techniques based on N-grams and Word2Vec embeddings. One contribution of this paper is providing these new labelled datasets with embeddings. The experimental results show improvement from using embeddings over N-grams in more balanced datasets vs. more unbalanced ones. We also find a superior performance of the Random Forest algorithm over other algorithms in most experiments. © 2022 ACM.

关键词： Embeddings

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Toward Digital Transformation Adoption: A Conceptual Framework from Transformational Leadership Perspective 7

Toward Digital Transformation Adoption: A Conceptual Framewo...

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7th information systems International Conference, ISICO 2023

作者： Marcel Gaol, Ford Lumban Supangkat, Suhono Harso Ranti, Benny Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Smart Cities and Communities Innovation Center Faculty of Digital Communication and Hotel & Tourism Institute of Technology Bandung Bandung Indonesia Computer Science Department Faculty of Computer Science University of Indonesia Depok Indonesia Information System Department Universitas Kristen Krida Wacana Jakarta Indonesia

Digital transformation is about transforming processes, business models, domains, and culture. Studies show that the failure rate of digital transformation is quite high up to 90%. Studies show that the transformational leadership model has a significant impact on digital transformation adoption. This paper identifies the positive and negative attributes of transformational leadership including the components that support and are affected for successful adoption of digital transformation. Furthermore, the paper combines several findings related to the attributes and components in the form of a conceptual framework. The conceptual framework can serve as a guide for organizations for their digital transformation journey. © 2023 The Authors. Published by Elsevier B.V.

关键词： Failure rate

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Learning-Based Artificial Algae Algorithm with Optimal Machine Learning Enabled Malware Detection

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computer systems science & Engineering 2023年第9期46卷 3103-3119页

作者： Khaled M.Alalayah Fatma S.Alrayes Mohamed K.Nour Khadija M.Alaidarous Ibrahim M.Alwayle Heba Mohsen Ibrahim Abdulrab Ahmed Mesfer Al Duhayyim Department of Computer Science College of Science and ArtsNajran UniversitySharurahSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Computer Department Applied CollegeNajran UniversityNajran66462Saudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

Malware is a‘malicious software program that performs multiple cyberattacks on the Internet,involving fraud,scams,nation-state cyberwar,and *** malicious software programs come under different classifications,namely Trojans,viruses,spyware,worms,ransomware,Rootkit,botnet malware,*** is a kind of malware that holds the victim’s data hostage by encrypting the information on the user’s computer to make it inaccessible to users and only decrypting it;then,the user pays a ransom procedure of a sum of *** prevent detection,various forms of ransomware utilize more than one mechanism in their attack flow in conjunction with Machine Learning(ML)*** study focuses on designing a Learning-Based Artificial Algae Algorithm with Optimal Machine Learning Enabled Malware Detection(LBAAA-OMLMD)approach in computer *** presented LBAAA-OMLMDmodelmainly aims to detect and classify the existence of ransomware and goodware in the *** accomplish this,the LBAAA-OMLMD model initially derives a Learning-Based Artificial Algae Algorithm based Feature Selection(LBAAA-FS)model to reduce the curse of dimensionality ***,the Flower Pollination Algorithm(FPA)with Echo State Network(ESN)Classification model is *** FPA model helps to appropriately adjust the parameters related to the ESN model to accomplish enhanced classifier *** experimental validation of the LBAAA-OMLMD model is tested using a benchmark dataset,and the outcomes are inspected in distinct *** comprehensive comparative examination demonstrated the betterment of the LBAAAOMLMD model over recent algorithms.

关键词： computer networks machine learning security malware detection feature selection ransomware

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Natural Language Processing Studies in Humancomputer Interaction: A Bibliometric Analysis and Future Research Framework 22

Natural Language Processing Studies in HumanComputer Interac...

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22nd IEEE Student Conference on Research and Development, SCOReD 2024

作者： Rashid, Farah Syazwani Binti Mohd Kamsin, Amirrudin Bin Noor, Nurul Fazmidar Binti Mohd Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System Technology Kuala Lumpur50603 Malaysia Universiti Teknologi MARA College of Computing Informatics and Mathematics Selangor Shah Alam40450 Malaysia

ISBN: (纸本)9798331510077

This study conducts a bibliometric analysis of Natural Language Processing (NLP) within Human-computer Interaction (HCI) to identify trends, challenges, and future directions. Analyzing 1,710 SCOPUS-indexed documents (1983-2024) using a PRISMA flowchart, the results show that the United States and China are leading contributors. Key developments include emotion recognition, chatbot interfaces, and speech processing, highlighting NLP's role in user-centered technologies. Despite growing applications, challenges such as reliability and ethical concerns persist. This analysis emphasizes the need for ethical frameworks and technological advancements to address deployment issues and align NLP innovations with the United Nations Sustainable Development Goals (SDGs). By mapping global research trends, this study provides insights into the transformative potential of NLP in HCI for developing inclusive and responsive systems. © 2024 IEEE.

关键词： Emotion Recognition

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