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2024 Cognitive Models and Artificial Intelligence Conference, AICCONF 2024

作者： Anarova, Shahzoda Jabbarov, Jamoliddin Uralova, Iroda Tashkent University of Information Technologies Department of Convergence of digital technologies Computer Engineering Faculty Tashkent Uzbekistan Department of Computer science and technology Intelligent systems and computer technologies Faculty Samarkand State University Samarkand Uzbekistan

ISBN: (纸本)9798400716928

This article is devoted to the determination of the fractal size of the damaged part of the human brain on the basis of images obtained from MRI (magnetic resonance imaging). There are various mathematical methods for determining the size of fractal structures, this article uses two variants of the cell counting method and describes the analysis of errors in determining the fractal size of the damaged part of the human brain. The article shows that the damaged part of the human brain has a fractal structure and that it has a fractal dimension. Fractal measurement has also been used to determine the size of the damaged part of the human brain, unlike common methods. In determining the fractal scale, cases of changes in human body organs were not taken into account. In the article, the determination of the size of the damaged part of the human brain is studied on the basis of mathematical formulas. Two different variants of the special cell counting method were used to calculate the fractal measurements, as well as the corresponding number of experimental results. In addition, these measurements were used to assess the extent to which patients suffered brain damage and to provide information on the practical application of these fractal measurements. © 2024 ACM.

关键词： Magnetic resonance imaging

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Handling Imbalanced Data for Improved Classification Performance: Methods and Challenges 3

Handling Imbalanced Data for Improved Classification Perform...

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3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Abokadr, Somiya Azman, Azreen Hamdan, Hazlina Amelina, Nurul University Putra Malaysia Faculty of Computer Science & Information Technology Department of Intelligent Systems Seri Kembangan43400 Malaysia Al Zintan University Faculty of Science Libya University Putra Malaysia Faculty of Computer Science & Information Technology Seri Kembangan43400 Malaysia University Putra Malaysia Faculty of Computer Science and Information Technology Department of Computer Science Malaysia University Putra Malaysia Faculty of Computer Science and Information Technology Department of Multimedia Selangor Darul Ehsan Serdang Malaysia

ISBN: (纸本)9798350305333

Imbalanced data significantly impacts the efficacy of machine learning models. In cases where one class greatly outweighs the other in terms of sample count, models might develop a bias towards the majority class, thereby undermining the performance of the minority class. Imbalanced data act to increase the risk of overfitting, as the model may memorize the majority of class samples instead of learning underlying patterns. This paper addresses these challenges in the classification field by exploring various solutions, including under-sampling, oversampling, SMOTE, cost-sensitive learning, and ensemble deep learning methods. The evaluate the performance of these methods on different datasets and provide insights into their strengths and limitations. The paper presents a taxonomy of strategies for imbalanced binary and multi-class classification problems, including resampling, algorithmic, and hybrid methods. Ultimately, the paper furnishes guidelines to facilitate the selection of the most pertinent method for mitigating imbalanced data challenges within a specific classification context. © 2023 IEEE.

关键词： Classification (of information)

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Challenges in Securing UAV IoT Framework: Future Research Perspective

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IEEE Transactions on Network and Service Management 2025年第3期22卷 2607-2629页

作者： Aljumah, Abdullah Ahanger, Tariq Ahamed Ullah, Imdad Prince Sattam Bin Abdulaziz University College of Computer Engineering and Sciences Saudi Arabia CoBA Prince Sattam Bin Abdulaziz University Department of Management Information Systems Saudi Arabia The University of Sydney School of Computer Science Faculty of Engineering SydneyNSW2006 Australia

Unmanned Aerial Vehicles (UAVs) offer the immense capability for allowing novel applications in a variety of domains including security, military, surveillance, medicine, and traffic monitoring. The prevalence of UAV systems is due to the collaboration and accomplishment of tasks efficiently and effectively. UAVs embedded with camcorders, GPS receivers, and wireless sensors propose enormous promise in realizing the Internet of Things (IoT) service delivery in vast domains. It results in establishing an airborne field of the IoT when empowered with communication protocols of LTE, 4G, and 5G/6G networks. However, numerous difficulties must be addressed before UAVs may be used effectively namely privacy, security, and administration. Conspicuously, in the current article, novel UAV-specific domains enabled by IoT and 5G/6G technology are explored. Moreover, the presented technique assesses sensor requirements and provides an overview of fleet management systems that address aerial networking, privacy, and security concerns. Furthermore, a framework based on the IoT-5G/6G aspect is proposed which can be deployed over UAVs. Finally, in a heterogeneous computational platform, the proposed framework provides a complete IoT architecture that enables secure UAVs. © 2004-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Building Travel Agent Partnerships in Tourism Industry Using Blockchain Technology to Increase Customer Satisfaction 4

Building Travel Agent Partnerships in Tourism Industry Using...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Inayatulloh Fathurrozi, Ahmad Prio Pamungkas, R. Wisnu Sinlae, Fried Priatna, Wowon Lestari, Tyastuti Sri Yasir, Muhammad Noe'man, Achmad Bina Nusantara University Information Systems Department School of Information system Jakarta11480 Indonesia Bhayangkara Jakarta Raya University Faculty of Computer Science Jakarta Indonesia

ISBN: (纸本)9798350353266

Tourist travel is an activity that concern the temporary movement of tourists from their place of origin to a particular destination for recreational purposes. The many parties or agents involved in a tourist trip require coordination between parties to provide the best service for tourists. The negative impact of the lack of coordination and transparency from tourism agents has an impact on tourists. Tourists often receive services that are not as expected because there is no transparency in transactions throughout their tour. Currently, there is no mechanism that unites tourism agents and guarantees transparency and security of information for tourists and stakeholders in the tourism industry. Thus, the aim of this study is to build a blockchain prototype for travel agent partnerships in the tourism industry using blockchain technology. The research method uses a quantitative approach through a literature review by studying the mechanisms of tourist travel and the parties involved in tourist travel, as well as blockchain technology. The research result is a model for adopting blockchain technology for tourism and travel partnerships. The research outcome also show examples of data simulation using the Ganache framework. © 2024 IEEE.

关键词： Customer satisfaction

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A Survey on Acute Leukemia Expression Data Classification Using Ensembles

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computer systems science & Engineering 2023年第11期47卷 1349-1364页

作者： Abdel Nasser H.Zaied Ehab Rushdy Mona Gamal Department of Information Systems Faculty of Computer ScienceMisr International UniversityCairo11828Egypt Department of Information Technology Faculty of Computers and InformaticsZagazig UniversityZagazig44516Egypt Department of Information Systems Faculty of Computers and InformaticsZagazig UniversityZagazig44516Egypt

Acute leukemia is an aggressive disease that has high mortality rates *** error rate can be as high as 40%when classifying acute leukemia into its ***,there is an urgent need to support hematologists during the classification *** than two decades ago,researchers used microarray gene expression data to classify cancer and adopted acute leukemia as a test *** high classification accuracy they achieved confirmed that it is possible to classify cancer subtypes using microarray gene expression *** machine learning is an effective method that combines individual classifiers to classify new *** classifiers are recognized as powerful algorithms with numerous advantages over traditional *** the past few decades,researchers have focused a great deal of attention on ensemble classifiers in a wide variety of fields,including but not limited to disease diagnosis,finance,bioinformatics,healthcare,manufacturing,and *** paper reviews the recent ensemble classifier approaches utilized for acute leukemia gene expression data ***,a framework for classifying acute leukemia gene expression data is *** pairwise correlation gene selection method and the Rotation Forest of Bayesian Networks are both used in this *** outcomes show that the classification accuracy achieved by the acute leukemia ensemble classifiers constructed according to the suggested framework is good compared to the classification accuracy achieved in other studies.

关键词： Leukemia classification ensemble rotation forest pairwise correlation bayesian networks gene expression data microarray gene selection

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Report on the 12th International Workshop on Quantitative Approaches to Software Quality (QuASoQ 2024) 12

Report on the 12th International Workshop on Quantitative Ap...

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12th International Workshop on Quantitative Approaches to Software Quality, QuASoQ 2024

作者： Lichter, Horst Wild, Nils Sunetnanta, Thanwadee Anwar, Toni Research Group Software Construction RWTH Aachen University Aachen Germany Computer Science Academic Group Faculty of Information And Communication Technology Mahidol University Bangkok Thailand Faculty of Science and Information Technology Chair Computer & Information Systems Universiti Teknologi Petronas Bandar Seri Iskandar Perak Malaysia

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Evaluation and Comparison of Emotionally Evocative Image Augmentation Methods 28th

Evaluation and Comparison of Emotionally Evocative Image Aug...

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28th International Conference on Knowledge Based and Intelligent information and Engineering systems, KES 2024

作者： Ignatowicz, Jan Kutt, Krzysztof Nalepa, Grzegorz J. Jagiellonian Human-Centered AI Lab Mark Kac Center for Complex Systems Research Institute of Applied Computer Science Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Lojasiewicza 11 Kraków30-348 Poland

Experiments in affective computing are based on stimulus datasets that, in the process of standardization, receive metadata describing which emotions each stimulus evokes. In this paper, we explore an approach to creating stimulus datasets for affective computing using generative adversarial networks (GANs). Traditional dataset preparation methods are costly and time consuming, prompting our investigation of alternatives. We conducted experiments with various GAN architectures, including Deep Convolutional GAN, Conditional GAN, Auxiliary Classifier GAN, Progressive Augmentation GAN, and Wasserstein GAN, alongside data augmentation and transfer learning techniques. Our findings highlight promising advances in the generation of emotionally evocative synthetic images, suggesting significant potential for future research and improvements in this domain. © 2024 The Authors.

关键词： Generative adversarial networks

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Promoting Cybersecurity Knowledge via Gamification: An Innovative Intervention Design 3

Promoting Cybersecurity Knowledge via Gamification: An Innov...

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3rd International Conference on Distributed Computing and High Performance Computing, DCHPC 2024

作者： Fatokun Faith, B. Long, Zalizah Awang Hamid, Suraya Universiti Kuala Lumpur Malaysian Institute of Information Technology Kuala Lumpur Malaysia Universiti Malaya Faculty of Computer Science & It Department of Information Systems Kuala Lumpur Malaysia

ISBN: (纸本)9798350381580

Cybersecurity is becoming a critical challenge as technology thrives with novel innovations. The increase in cyber threats is quite alarming. Cyber-users' vulnerability is a serious issue as negligent behaviour alongside a lack of basic cybersecurity knowledge constitutes a major cause of attacks today. Several approaches have been introduced via security systems to provide prevailing defence against numerous cybersecurity attacks. However, they are relinquished to limited impact due to a lack of compliance with security guidelines by end-users deficient in basic cybersecurity knowledge. Gamification has played an important role in educational contexts as it promotes motivation and engaging learning. Though still a new research field, literature has postulated that the innovative application of gamification in cybersecurity could be a winning strategy to combat cybersecurity threats. This paper, via an experimental/qualitative approach, proposes a comprehensive cybersecurity gamification design based on established gamification theories to ensure the validity of the cybersecurity gamification tool. The cybersecurity gamification tool is aimed at promoting the cybersecurity knowledge of end-users. Gamification design has the potential to transform a training environment by altering the player's emotional experience alongside their social position and sense of identity. Gamification, if implemented properly, can impact beyond just learning but also boost knowledge, learning experience and engagement of the targeted audience through motivation by the game elements. This research is ongoing and the result of a survey evaluating the impact of the gamification tool in promoting cybersecurity knowledge will be presented in a future publication. © 2024 IEEE.

关键词： Cybersecurity

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Deep Reinforcement Learning Actor-Critic Algorithm based Autonomous Lane Change Decisions in Static Environments 6

Deep Reinforcement Learning Actor-Critic Algorithm based Aut...

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6th International Conference on Intelligent Computing in Data sciences, ICDS 2024

作者： Safae, Belkhyr Jaouad, Boumhidi Nikolov Nikola, S. University Sidi Mohamed Ben LISAC Laboratory Computer Science Departement Faculty of Science Dhar EI Mahraz Abdellah Fez Morocco University of Limerick Department of Computer Science and Information Systems Limerick Ireland

ISBN: (纸本)9798350351200

Deep Reinforcement Learning (DRL) has emerged as a pivotal technology in modern transportation systems, offering considerable potential to enhance driving safety and optimize performance. This study underscores the significance of DRL in autonomous driving (AD) and introduces the Advantage Actor-Critic (A2C) approach for lane change decision-making. A2C, representing policy-based methods, is compared with the Prioritized Replay Deep Q-Network (PRDQN), a value-based approach previously used in similar scenarios. Both approaches were evaluated using the CARLA (Car Learning to Act) simulation environment, focusing on static obstacle scenarios. The comparative analysis aims to assess the performance differences between A2C and PRDQN, highlighting the effectiveness of A2C in improving decision-making accuracy, safety, and adaptability. The results demonstrate the superior performance of A2C in improving safety and decision-making within autonomous driving systems. © 2024 IEEE.

关键词： Deep reinforcement learning

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Real-time System for Translating American Sign Language to Text Using Robust Techniques

Real-time System for Translating American Sign Language to T...

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2023 International Conference on Recent Advances in science and Engineering Technology, ICRASET 2023

作者： Dabwan, Basel A. Jadhav, Mukti E. Abosaq, Hamad A. Olayah, Fekry A. Al Yami, Mohammed Abdelrahman, Yahya A. AlBaydha University Department of Information System AlBaydha Yemen Shri Shivaji Science & Art College Department of Computer Science India Najran University Computer Science Dep. Saudi Arabia Najran University Kingdom of Saudi Arabia Department of Information System Saudi Arabia Najran University Faculty of Computer Science and Information Systems Department of Information Systems Saudi Arabia Najran University College of Computer Science and Information Systems Department of Information System Saudi Arabia

ISBN: (纸本)9798350306927

Sign language serves as a vital medium of communication for the Deaf and dumb people. We present a real-time system to translate ASL into text, addressing the communication gap between deaf and dumb people and normal people. Our system leverages computer vision and machine learning techniques to accurately recognize ASL gestures. In this study, we introduce a convolutional neural network (CNN) and (LSTM) approaches designed to identify hand gestures associated with human actions as captured through images from cameras. By utilizing the hand's spatial positioning and orientation, we construct the training and testing datasets for the CNN-LSTM model, we were used 4680 skeleton images of the ASL alphabet, from A to Z as a dataset. The hand image undergoes an initial filtering process followed by classification, where a classifier determines the classification of the hand gesture. Subsequently, the refined images are harnessed to train the CNN-LSTM model. Our real-time system provides accurate and reliable results in real-time 99.9%, in addition to generating new datasets that are processed and can be trained on any model. © 2023 IEEE.

关键词： Real time systems

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