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SSRN 2024年

作者： Nugraha, Devin Jaya Yudistira, Novanto Widodo, Agus Wahyu Informatics Engineering Faculty of Computer Science Brawijaya University Jalan Veteran 8 East Java Malang65145 Indonesia

Recent advances in deep learning have been applied in various fields, including the medical field. One of the major medical disorders in ophthalmology is glaucoma. Glaucoma is a medical condition, which is irreversible, that occurs due to damage to the eye's optic nerve that carries sensory information related to vision. Although several deep learning methods have been developed to detect glaucoma, the topic of glaucoma detection is still a challenge with the unbalanced availability of glaucoma fundus image datasets. This research proposes the application of weighted loss in Convolutional Neural Network (CNN) to detect glaucoma in unbalanced fundus image dataset. The dataset used in this research is SMDG-19 which consists of 19 standardized public datasets. This research tested SMDG-19 and its 8 datasets and compared the performance of glaucoma detection without using weighted loss and by using weighted loss. After comparing several CNN model architectures, this research proposes a CNN model that achieves an average recall improvement of 44.75% and an average f1 score improvement of 7.25% in glaucoma detection. With satisfactory recall and f1 score improvement, precision performance is sacrificed with an average decrease of -6.53% in glaucoma detection. The CNN model in this research also managed to get an average increase in AUC of 3.21%, but with an average decrease in accuracy of -4.10%. In addition, this research uses the Gradient Class Activation Map (Grad-CAM) which can interpret the part of the fundus image that gets the attention of the model. © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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High-temperature Corrosion of ∼ 30 Pct Porous FeCr Stainless Steels in Air: Long-Term Evaluation Up to Breakaway (vol 54, pg 2244, 2023)

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METALLURGICAL AND MATERIALS TRANSACTIONS A-PHYSICAL METALLURGY AND MATERIALS SCIENCE 2023年第8期54卷 3370-3370页

作者： Koszelow, D. Advanced Materials Centre Faculty of Electronics Telecommunications and Informatics Gdańsk University of Technology 80-233 Gdańsk Poland Laboratory for Nuclear Materials Laboratory for Synchrotron Radiation and Femtochemistry Paul Scherrer Institut 5232 Villigen Switzerland International Centre of Electron Microscopy for Materials Science Faculty of Metal Engineering and Industrial Computer Science AGH University of Science and Technology Al. Mickiewicza 30 30-059 Krakow Poland

In this work, a long-term (up to 6000 hours) corrosion evaluation of three porous (~ 30 pct of initial porosity) ferritic iron-chromium alloys with different Cr contents (20, 22, and 27 wt pct of Cr) was carried out at 600 °C, 700 °C, 800 °C, and 900 °C in air. Mass gain measurements and SEM analyses revealed that at temperatures above 600 °C, all alloys exhibit breakaway corrosion, whereas at 600 °C, none of the alloys were heavily oxidized even after 6000 hours. Based on the results, the diffusion character of the corrosion of porous chromia-forming alloys was identified. The microstructure changes at high temperatures in porous alloys containing 22 wt pct of Cr were determined in detail by transmission electron microscopy. The proposed prediction model indicated that the lifetimes of the Fe20Cr and Fe22Cr alloys were determined as 1250 hours (± 535 hours) and 1460 hours (± 640 hours), respectively. It is in agreement with the long-term oxidation experiment. For the Fe27Cr alloy, the deviation between predicted and observed lifetimes occurs. The proposed model allows for qualitative estimation of the porous alloys’ lifetime with experimentally validated accuracy.

关键词： Chromium alloys

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MODEL OF THE DECISION-MAKING SYSTEM FOR ASSESSMENT OF THE STATE OF TECHNICAL OBJECTS 28

MODEL OF THE DECISION-MAKING SYSTEM FOR ASSESSMENT OF THE ST...

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28th International Congress on Sound and Vibration, ICSV 2022

作者： Peter, Brancevich Yibin, Li Belarusian State University of Informatics and Radioelectronics Faculty of Computer Systems and Networks Minsk Belarus Shandong University School of Information Science and Engineering Jinan China

ISBN: (纸本)9789811850707

The most wear-out is production equipment with rotary motion (turbines, generators, motors, gearboxes, pumps, compressors, fans, etc.). It is possible to reduce the cost of its operation by introducing modern maintenance systems, which are based on the use of technologies for monitoring, assessing the condition, diagnosing, predicting the development of defects, which, in terms of their organization and functioning, are intelligent systems. The state of the observed technical objects is determined by the parameters and characteristics. The aim of the work is to develop a model of the basic element of the decision-making system and consideration of options for its practical application. The proposed model of the basic element of the decision-making system allows you to create various structures designed to assess the state of complex technical and natural objects. An example of the implementation of a decision-making system for assessing the state of a multi-support power unit is presented, as a combination of basic solver modules. © International Institute of Acoustics and Vibration (IIAV), 2022.

关键词： Intelligent systems

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An Introductory Survey of Entropy Applications to Information Theory, Queuing Theory, engineering, computer Science, and Statistical Mechanics 27

An Introductory Survey of Entropy Applications to Informatio...

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27th International Conference on Automation and Computing, ICAC 2022

作者： Mageed, Ismail A Zhang, Qichun University of Bradford Faculty of Engineering and Informatics Department of Computer Science West Yorkshire BradfordBD7 1DP United Kingdom

ISBN: (数字)9781665498074

ISBN: (纸本)9781665498074

Clausius was the first to coin the term 'entropy' roughly 160 years ago. Since then, many scholars from several scientific fields have continued to enhance, develop, and interpret the data. This study describes the concept of entropy and its applications to information theory, queuing theory, engineering systems, computing, and statistical mechanics. The choice of these applications was subjected to the size limitation of the paper. Having said that, entropy has a great applicability to other scientific disciplines. Even more interesting is the nature of entropy as a measurement of uncertainty, dis-order, or turbulence was the main core of attraction to a considerable number of scholars in all academic areas of research expertise to both employ it and make a significant research advancement. Its goal is to present a coherent picture of how classical entropies might be used in engineering and computer science. It is attempted to create a concise exposition that strikes a balance between conceptual importance and instrumental relevance. A representative list of references closes this review. © 2022 IEEE.

关键词： Surveys

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Formation Drilling RoP Prediction via Deep Neural Networks with TensorFlow 27

Formation Drilling RoP Prediction via Deep Neural Networks w...

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27th International Conference on Automation and Computing, ICAC 2022

作者： Isam, Sherrif Zhang, Qichun University of Bradford Faculty of Engineering and Informatics Department of Computer Science West Yorkshire BradfordBD7 1DP United Kingdom

ISBN: (数字)9781665498074

ISBN: (纸本)9781665498074

Predicting the speed for the drill penetrates the formation (RoP, Rate of Penetration) has been considered as an essential factor for the drilling operation and efficiency. Complexity in predicting the RoP arises from its dependency on many factors, for example, drilling fluid properties, drilling parameters, and the characteristics of the drilled formation. Based on the mentioned factors, the objective of this paper is to use Deep Neural Networks (DNN) with TensorFlow to predict RoP based on the 8 distinct parameters, which are obtained in the Final Well Report-Well 15/9-F-15 of the Volve oil field data provided by Equinor. The obtained results confirmed that the three DNN Models with TensorFlow techniques are applied to estimate the complex lithologies RoP with varying accuracies. As a comparative study, the presented Mode13 in this paper which includes an input layer with 32 neurons, 5 deep layers with 64 neurons, and a single neuron output layer has outperformed the previous 2 smaller models and it has been recorded how all the performance parameters has recorded smaller error values as the models adopted new deep layer and neurons. © 2022 IEEE.

关键词： Deep neural networks

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Human Age Estimation Based on Dental Images 3

Human Age Estimation Based on Dental Images

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3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Raidzan, Nur Ain Mohtar, Itaza Afiani Mustafa, Wan Azani Almusawi, Muntather Hussein, Abbas Hameed Abdul Computer Science Studies College of Computing Teknologi MARA Perak Branch Informatics and Mathematics Universiti Perak Malaysia Faculty of Electrical Engineering & Technology Campus Pauh Putra Perlis Arau Malaysia The Islamic University Faculty of Engineering Najaf Iraq College of MLT Ahl Al Bayt University Karbala Iraq

ISBN: (纸本)9798350317022

Predicting age is one of the most important characteristics in the forensic context for human identification because biological profiles can only be effectively rebuilt by estimating the most precise age. Humans usually count their age using calendars that are pre-programmed to keep track of days. It is not difficult for any living human being to measure their age, but it becomes a challenge when forensics needs to determine the exact age of a dead person involved in a criminal case. By comparing the stage of tooth development in X-rays to established dental growth norms, forensic scientists may estimate age. To deal with such problems, an age estimate system that predicts a person's real age group is required. In this paper, Convolutional Neural Network (CNN) was used to build Human Age Estimation System that can predict a person's actual age group. The system employs Inception-v3 architecture to create the model. The dataset consists of 1404 dental X-ray images of pre-teen in Malaysia. The model was trained using 60% of the dataset, and the remaining 40% was used for testing. Accuracy, precision, recall, and f-measure were used to assess the model. The model achieved 82%, 78%, 76%, and 77%, respectively. As a conclusion, a large amount of data is required for the training and testing process to acquaint the model with the pattern of tooth growth by age group. It is also crucial to rule out any illnesses that impact tooth growth, as well as radiographic images that are not of good quality. © 2023 IEEE.

关键词： age estimation convolutional neural network forensic Inception-V3 machine learning pre-teen dental images)

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A New Handover Management Model for Two-Tier 5G Mobile Networks

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computers, Materials & Continua 2022年第6期71卷 5491-5509页

作者： Mohammad Arifin Rahman Khan Mohammed Golam Kaosar Mohammad Shorfuzzaman Kire Jakimoski Department of Computer Science and Engineering Bangladesh UniversityDhaka1207Bangladesh Discipline of IT Media and CommunicationsMurdoch UniversityPerthWA6150Australia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Faculty of Informatics AUE-FON UniversitySkopjeRepublic of North Macedonia

There has been an exponential rise in mobile data traffic in recent times due to the increasing popularity of portable devices like tablets,smartphones,and *** rapid rise in the use of these portable devices has put extreme stress on the network service providers while forcing telecommunication engineers to look for innovative solutions to meet the increased *** solution to the problem is the emergence of fifth-generation(5G)wireless communication,which can address the challenges by offering very broad wireless area capacity and potential cut-power *** application of small cells is the fundamental mechanism for the *** use of small cells can enhance the facility for higher capacity and ***,it must be noted that small cells deployment will lead to frequent handovers of mobile *** the importance of small cells in 5G,this paper aims to examine a new resource management scheme that can work to minimize the rate of handovers formobile phones through careful resources allocation in a two-tier ***,the resource management problem has been formulated as an optimization issue thatwe aim to overcome through an optimal *** find a solution to the existing problem of frequent handovers,a heuristic approach has been *** solution is then evaluated and validated through simulation and testing,during which the performance was noted to improve by 12%in the context of handover ***,this model has been observed to be more efficient as compared to the existing model.

关键词： Two-tier mobile networks small cell densification management of mobility handover cost average throughput power consumption

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Robotic Process Automation in Smart System Platform: A Review 9

Robotic Process Automation in Smart System Platform: A Revie...

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9th International Conference on ICT for Smart Society, ICISS 2022

作者： Falih, Noor Supangkat, Suhono Harso Lubis, Fetty Fitriyanti School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Faculty of Computer Science Universitas Pembangunan Nasional Veteran Jakarta Jakarta Indonesia

ISBN: (数字)9781665471350

ISBN: (纸本)9781665471350

Robotic Process Automation (RPA) is routine rule-based process automation to reduce costs and increase efficiency. In recent years, RPA becomes an interesting thing for researchers. This paper presents a literature study on RPA the focuses on discussing the definition, tools, benefits, business area domains, and various problems that arise in RPA research. In the end, the research challenges in the RPA field were discussed. © 2022 IEEE.

关键词： Robotics

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Analysis Performance of Support Vector Regression (SVR) for Congestion Prediction 4

Analysis Performance of Support Vector Regression (SVR) for ...

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4th International Conference on informatics, Multimedia, Cyber and Information System, ICIMCIS 2022

作者： Irzavika, Nindy Saptawati, Gusti Ayu Putri Universitas Pembangunan Nasional Veteran Jakarta Faculty of Computer Science Jakarta Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

ISBN: (纸本)9781665473279

The increasing population in urban areas has led to an increase in the number of vehicles. The growth of vehicles is not comparable with the growth of roads that caused traffic congestion. To solve the congestion problem, traffic conditions are predicted. The method used to predict traffic congestion is Support Vector Regression (SVR). The map-Matching algorithm is used to adjust vehicle position data that is not accurate, resulting in an accurate position. In this research, the performance of SVR was tested for congestion prediction. The test was conducted to determine the performance of the SVR model by calculating the Means Absolute Error (MAE) and Root Mean Squared Error (RMSE). K-fold cross-validation is used to show the accuracy of the SVR model on the dataset after being divided into k sections and FCE to determine the level of congestion. We conclude that the SVR has good performance for congestion prediction, this is indicated by a small error value and high accuracy value. The prediction results using SVR are the same as the actual condition. © 2022 IEEE.

关键词： Traffic congestion

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LDP-Fed+: A robust and privacy-preserving federated learning based classification framework enabled by local differential privacy

LDP-Fed+: A robust and privacy-preserving federated learning...

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作者： Wang, Yufeng Zhang, Xu Ma, Jianhua Jin, Qun School of Communications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China Faculty of Computer & Information Sciences Hosei University Tokyo Japan Department of Human Informatics and Cognitive Sciences Waseda University Tokyo Japan

As a distributed learning framework, Federated Learning (FL) allows different local learners/participants to collaboratively train a joint model without exposing their own local data, and offers a feasible solution to legally resolve data islands. However, among them, the data privacy and model security are two challenges. The former means that, if original data are used for trained FL models, various methods can be used to deduce the original data samples, thereby causing the leakage of data. The latter implies that unreliable/malicious participants may affect or destroy the joint FL model, through uploading wrong local model parameters. Therefore, this paper proposes a novel distributed FL training framework, namely LDP-Fed+, which takes into account differential privacy protection and model security defense. Specifically, firstly, a local perturbation module is added at the local learner side, which perturbs the original data of local learners through feature extraction, binary encoding and decoding, and random response. Then, through using the perturbed data, local neural network model is trained to obtain the network parameters that meet local differential protection, to effectively deal with model inversion attacks. Secondly, a security defense module is added on the server side, which uses the auxiliary model and differential index mechanism to select an appropriate number of local disturbance parameters for aggregation to enhance model security defense and deal with membership inference attacks. The experimental results show that, compared with other federated learning models based on differential privacy, LDP-Fed+ has stronger robustness for model security and higher accuracy for model training while ensuring strict privacy protection. © 2022 John Wiley & Sons, Ltd.

关键词： Learning systems

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