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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Amila Akagic Emir Buza Medina Kapo Mahdi Bohlouli Department of Computer Science and Informatics Faculty of Electrical Engineering University of Sarajevo Sarajevo Bosnia and Herzegovina Research and Innovation Department Petanux GmbH Bonn Germany

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

This research explores into the utilization of synthetic data within image classification tasks and evaluates its efficiency in comparison to the utilization of real data. To facilitate this investigation, we employ the CIFAKE dataset, comprising the well-established CIFAR10 dataset and an equivalent number of images synthetically generated using the Latent Diffusion Model (LDM). The increasing demand for diverse and abundant labeled datasets has prompted the emergence of synthetic data as a potential solution to address data scarcity. Within this study, we scrutinize the performance of image classification models trained on both real and synthetic datasets. To ensure a comprehensive evaluation, we alternately apply test data across different models. Our analysis encompasses diverse factors, including classification accuracy, generalization capabilities, and robustness in various scenarios. The findings provide valuable insights into the efficacy of synthetic data as a viable alternative or complement to real data in the realm of image classification.

关键词： Training Measurement Neural networks Process control Data models Robustness Information technology Synthetic data Image classification Testing

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Analysis of Enterprise Resource Planning (ERP) system implementation for manufacturing in Indonesia 2

Analysis of Enterprise Resource Planning (ERP) system implem...

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2nd UPY International Conference on Applied Science and Education, UPINCASE 2020

作者： Sari, Marti Wdya Santoso, Banu Department of Informatics Faculty of Science and Technology Universitas PGRI Yogyakarta Yogyakarta Indonesia Department of Computer Engineering Faculty of Computer Science Universitas Amikom Yogyakarta Yogyakarta Indonesia

The purpose of this study is to analyze the success of ERP systems in Indonesia for companies that have implemented ERP systems. There are various kinds of companies with different business fields, such as manufacturing, mining, oil and gas, information technology and construction companies. The method used to achieve this research goal is through the study of literature, by selecting ERP references that support, starting from the theory, framework and information resources. The population involved is the end-user ERP, which consists of various types of industries that have implemented an ERP system. The results of this study indicate the measurement of system quality, service quality, information quality, benefits obtained, user satisfaction and ERP system benefits. © Published under licence by IOP Publishing Ltd.

关键词： Enterprise resource planning

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Combining Drone-based Monitoring and Machine Learning for Online Reliability Evaluation of Wind Turbines 5

Combining Drone-based Monitoring and Machine Learning for On...

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5th International Conference on Computing, Electronics and Communications engineering. iCCECE 2022

作者： Kabir, Sohag Aslansefat, Koorosh Gope, Prosanta Campean, Felician Papadopoulos, Yiannis University of Bradford Faculty of Engineering and Informatics Bradford United Kingdom University of Hull Dept. Computer Science and Technology Kingston Upon Hull United Kingdom University of Sheffield Dept. Computer Science Sheffield United Kingdom

ISBN: (数字)9781665489805

ISBN: (纸本)9781665489805

The offshore wind energy is increasingly becoming an attractive source of energy due to having lower environmental impact. Effective operation and maintenance that ensures the maximum availability of the energy generation process using offshore facilities and minimal production cost are two key factors to improve the competitiveness of this energy source over other traditional sources of energy. Condition monitoring systems are widely used for health management of offshore wind farms to have improved operation and maintenance. Reliability of the wind farms are increasingly being evaluated to aid in the maintenance process and thereby to improve the availability of the farms. However, much of the reliability analysis is performed offline based on statistical data. In this article, we propose a drone-Assisted monitoring based method for online reliability evaluation of wind turbines. A blade system of a wind turbine is used as an illustrative example to demonstrate the proposed approach. © 2022 IEEE.

关键词： Offshore wind farms

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A Novel Optimized Resource Allocation Algorithm using GWO Optimization Technique for WBAN 23

A Novel Optimized Resource Allocation Algorithm using GWO Op...

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Proceedings of the 2023 12th International Conference on Software and Information engineering

作者： Amira Olayan Albashir Adel Youssef Mohamed Samir Abou El-Seoud Faculty of Informatics and Computer Science The British University (BUE) Egypt Department of Electronics and Communication Engineering Arab Academy for Science Technology and Maritime Transport Egypt

ISBN: (纸本)9798400708107

Wireless Body Area Networks have severe challenges in energy management to enhance the longevity of the system. Specifically, for a WBAN system that operates by ambient energy sources. The system combines energy scavenging modules integrated into the sensors carried by patients, enabling data transmission to a personal device. Our approach does not rely on previous information as the characteristics of the scavenged and consumed energy are stochastic. To optimize user utility, a formulation of an optimization problem by employing the Grey Wolf Optimization technique (GWO) compared to previous works that used different optimization techniques, to decompose it into three sub-problems: battery management, collecting rate control, and transmission power allocation. To achieve our goals, we apply the GWO to the introduced online resource allocation algorithm that serves two primary purposes: (1) balancing energy scavenging and consumption of network nodes to ensure system stability, and (2) maximizing user utility. Through simulation results, we validate the effectiveness and optimization capabilities of the algorithm while applying a different optimization technique from the previous works maintained.

关键词： Energy management GWO WBAN

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Towards Utilizing Unlabeled Data for Flood Forecasting with Weakly Supervised Adaptive Learning

Towards Utilizing Unlabeled Data for Flood Forecasting with ...

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Innovations in Intelligent Systems and Applications Conference

作者： Nawaf Abdulla Mehmet Demirci Suat Ozdemir Department of Information Systems Graduate School of Informatics Gazi University Ankara Türkiye Department of Computer Engineering Faculty of Engineering Gazi University Ankara Türkiye Department of Computer Engineering Faculty of Engineering Hacettepe University Ankara Türkiye

The importance of accurate flood forecasting is rising as climate change makes it more difficult to predict when and where floods will occur. Flood predictions use supervised machine learning models, and flood levels are being tracked via the Internet of Things (IoT). Despite advancements in data-driven hydrological forecasting, producing reliable flood forecasts remains challenging. This research paper explores the possibility of utilizing adaptive deep learning with weak supervision to predict flooding in coastal cities that are equipped with Internet of Things technology. This approach uses weakly supervised learning for data labeling and enhancement, a bidirectional long-short-term memory (LSTM) RNN for time-series prediction and classification, and adaptive learning to handle seasonality. The results of the proposed system show that flood forecasting accuracy could be improved by 40% compared to the baseline models.

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Two Layer Symmetric Cryptography Algorithm for Protecting Data from Attacks

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computers, Materials & Continua 2023年第2期74卷 2625-2640页

作者： Muhammad Nadeem Ali Arshad Saman Riaz Syeda Wajiha Zahra Shahab SBand Amir Mosavi Department of Computer Science Abasyn UniversityIslamabad44000Pakistan Department of Computer Science National University of TechnologyIslamabad44000Pakistan Future Technology Research Center National Yunlin University of Science and TechnologyDouliuYunlin64002Taiwan Institute of Information Society University of Public ServiceBudapest1083Hungary John Von Neumann Faculty of Informatics Obuda UniversityBudapestHungary Institute of Information Engineering Automation and MathilticsSlovak University of Technology in BratislavaSlovakia

Many organizations have insisted on protecting the cloud server from the outside,although the risks of attacking the cloud server are mostly from the *** are many algorithms designed to protect the cloud server from attacks that have been able to protect the cloud server ***,the attackers have designed even better mechanisms to break these security *** cryptography is the best data protection algorithm that exchanges data between authentic *** this article,one symmetric cryptography algorithm will be designed to secure cloud server data,used to send and receive cloud server data securely.A double encryption algorithm will be implemented to send data in a secure ***,the XOR function will be applied to plain text,and then salt technique will be ***,a reversing mechanism will be implemented on that data to provide more data *** decrypt data,the cipher text will be reversed,salt will be removed,andXORwill be *** the end of the paper,the proposed algorithm will be compared with other algorithms,and it will conclude how much better the existing algorithm is than other algorithms.

关键词： Cryptography symmetric algorithm encryption decryption cipher text cloud security asymmetric algorithm

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Preventing Cloud Network from Spamming Attacks Using Cloudflare and KNN

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computers, Materials & Continua 2023年第2期74卷 2641-2659页

作者： Muhammad Nadeem Ali Arshad Saman Riaz SyedaWajiha Zahra Muhammad Rashid Shahab S.Band Amir Mosavi Department of Computer Science Abasyn UniversityIslamabad44000Pakistan Department of Computer Science National University of TechnologyIslamabad44000Pakistan Future Technology Research Center National Yunlin University of Science and TechnologyDouliuYunlin64002Taiwan Institute of Information Society University of Public ServiceBudapest1083Hungary John von Neumann Faculty of Informatics Obuda UniversityBudapestHungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaSlovakia

Cloud computing is one of the most attractive and cost-saving models,which provides online services to *** computing allows the user to access data directly from any *** nowadays,cloud security is one of the biggest issues that *** types of malware are wreaking havoc on the *** on the cloud server are happening from both internal and external *** paper has developed a tool to prevent the cloud server from spamming *** an attacker attempts to use different spamming techniques on a cloud server,the attacker will be intercepted through two effective techniques:Cloudflare and K-nearest neighbors(KNN)*** will block those IP addresses that the attacker will use and prevent spamming ***,the KNN classifiers will determine which area the spammer belongs *** the end of the article,various prevention techniques for securing cloud servers will be discussed,a comparison will be made with different papers,a conclusion will be drawn based on different results.

关键词： Intrusion prevention system spamming KNN classification spam cyber security botnet

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Machine Vision Approach Using Multi Features for Detection of Oil Palm Stem Disease 1

Machine Vision Approach Using Multi Features for Detection o...

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1st International Conference on Advanced engineering and Technologies, ICONNIC 2023

作者： Septiarini, Anindita Hamdani, Hamdani Syaifudin, Encik Akhmad Setyaningsih, Emy Nurcahyono, Damar Hadiwijaya, Noor Alam Mulawarman University Dept. of Informatics Samarinda Indonesia Mulawarman University Faculty of Agriculture Samarinda Indonesia Institut Sains Teknologi Akprind Department of Computer System Engineering Yogyakarta Indonesia Politeknik Negeri Samarinda Dept. of Information Technology Samarinda Indonesia

ISBN: (纸本)9798350306484

Indonesia is a significant palm oil producer, widely cultivated due to its status as a prominent vegetable oil-producing plant. Palm oil is a highly sought-after crop in agriculture due to its profitability and the demand for substantial quantities of high-quality oil. Early diagnosis of oil palm diseases is crucial for prompt prevention and eradication measures essential for maintaining high-quality palm oil production. Hence, there is a requirement for a machine vision-based method to classify diseases on plant stems. The proposed method consists of several main processes: pre-processing, feature extraction, and classification. Several machine learning algorithms and feature extraction using color and texture features were performed to develop a machine vision for detecting oil palm stem disease. It applied cross-validation with a K-fold value of 10. Performance evaluation was carried out using three parameters: precision, recall, and accuracy. The Linear Discriminant Analysis (LDA) method achieves the optimum results in testing scenarios combining color features based on HSV color space and textures features, achieving an accuracy value of 90.50%. © 2023 IEEE.

关键词： Palm oil

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DP-FSAEA: Differential Privacy for Federated Surrogate-Assisted Evolutionary Algorithms

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Yan, Yuping Wang, Xilu Ligeti, Peter Jin, Yaochu Trustworthy and General AI Lab School of Engineering Westlake University Hangzhou China Faculty of Technology Bielefeld University Bielefeld Germany Department of Computer Algebra Faculty of Informatics Eötvös Loránd University Hungary

In surrogate-assisted evolutionary optimization, privacy-preservation and trusted data sharing has become an increasingly important concern, especially in scenarios involving distributed sensitive data. Existing privacy-preserving surrogate-assisted evolutionary optimization algorithms heavily rely on the basic federated learning framework. However, recent findings have revealed possible vulnerabilities within this framework, including susceptibility to adversarial threats like gradient leakage and inference attacks. To address the above challenges and enhance privacy protection, this paper proposes to protect the raw data by applying a differentially private stochastic gradient descent method to train surrogate models. A differential evolution operator is designed to generate personalized new samples for multiple clients based on promising and additional auxiliary samples, avoiding the exposure of online newly generated data. Moreover, a similarity-based aggregation algorithm is integrated to effectively construct the global surrogate model. A rigorous security analysis is provided to further validate the effectiveness of the proposed method in privacy protection. Experimental results show that the proposed method exhibits remarkable optimization performance on a set of synthetic problems with federated settings while maintaining the data privacy. IEEE

关键词： Evolutionary algorithms

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A Framework for Tele-rehabilitation Gaming System 6

A Framework for Tele-rehabilitation Gaming System

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6th International Conference on Applied Computational Intelligence in Information Systems, ACIIS 2023

作者： Elaklouk, Ahmed M. S. Zuarni Ramli, Ratna Edris, Ibrahim Padilla-Valdez, Nena Jumaily, Adel Al Universiti Teknologi Brunei School of Computing and Informatics Bandar Seri Begawan Brunei Universiti Teknologi Mara Negeri College of Computing Informatics and Mathematics Seremban Malaysia Universiti Teknologi Brunei Centre for Communication Bandar Seri Begawan Brunei Faculty of Engineering University of Technology Brunei Brunei Department of Computer Science and Software Engineering School of Computing University of Western Australia Australia Ensta Bretagne University France

ISBN: (纸本)9798350317428

Brain damage resulting from stroke and traumatic brain injury is the primary cause of complicated disability and one of the major causes of mortality in most countries, burdening healthcare systems owing to the growth in the number of brain injury victims. Rehabilitation is required to deal with the brain damage. With fewer therapists available, it is very challenging to manage patients and even motivate them to participate actively in their rehabilitation. Patients have frequently complained that many traditional rehabilitation systems are monotonous and uninteresting. The study's objective is to propose a telerehabilitation gaming system framework that can serve as guidance for developing an affordable rehabilitation gaming system that can motivate and engage patients and increase rehabilitation effectiveness. The research methodology is based on synthesizing related research and currently available technologies. The proposed framework includes the therapist and patient attached with vital constructs: tailoring tools, instructional content, game characteristics, tailored games, and performance. It is an internet-based communication structure that connects the Rehabilitation Gaming System to the hospital and other care provider networks. The proposed framework is evaluated by a panel of five experts. The results reveal that all experts believe that the proposed framework can serve as a useful guide for creating gaming interventions that can boost the patient's motivation and engagement and increase the effectiveness of rehabilitation. © 2023 IEEE.

关键词： Serious games

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