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International Conference on ICT For Smart Society (ICISS)

作者： Rizal Broer Bahaweres Angga Pratama Benhard Sitohang Data & Software Engineering Research Division-School of Electrical Engineering & Informatics Institute of Technology Bandung Faculty of Science and Technology State Islamic University Syarif Hidayatullah Jakarta Indonesia

Three dimension computer graphics has become important to present virtual objects to be more realistic and physically correct. One way to enhancing realism is by representing the physics simulation. We tried to implement physics simulation into a serious game. It is because of ability to increase realism in synthesis on virtual object motion. We design a serious game called “Traffic Drive”. Traffic Drive is a vehicle simulation game that has a goal to drive a vehicle, especially cars, with emphasis on safety and presented in a fun way. In physics simulation and game that are vehicle's physics a physics of gear and physics of rollover. It was expected that physics simulation can make the behavior of vehicles in the game resembles the behavior of the vehicle in terms of more realistics. And it is used as the object of research to compare the results of the data simulation with data from manual calculations. Comparison of results from the simulation output data and manual calculations were analyzed using T test hypotheses. From that T test was concluded that the output of data simulation with formula calculation almost the same.

关键词： Physics Games Gears Solid modeling Manuals Mathematical model Vehicles

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Evaluation of OSA Patient Sleep Stage Classification Performance Using a Multi-Channel PSG dataset

Evaluation of OSA Patient Sleep Stage Classification Perform...

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2022 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2022

作者： Na, Younghoon Kim, Dongyoung Kim, Dong-Kyu Lee, Jeong-Gun Division of Software Hallym University Chuncheon Korea Republic of Dept. of Computer Engineering Hallym University Chuncheon Korea Republic of Department of Otorhinolaryngology-Head and Neck Surgery Chuncheon Sacred Heart Hospital Hallym University College of Medicine Chuncheon Korea Republic of Institute of New Frontier Research Division of Big Data and Artificial Intelligence Chuncheon Sacred Heart Hospital Chuncheon Korea Republic of

ISBN: (数字)9781665464345

ISBN: (纸本)9781665464345

In this paper, we conduct a comparative analysis of sleep stage classification for patients having different levels of obstructive sleep apnea (OSA). For the analysis, we use 10 bio-signal channels: 4 EEG (Electroencephalogram) channels (F3-M2, F4-M1, C3-M2, and C4-M1), 2 EOG (Electrooculogram) channels (E1-M2 and E2-M1), and 4 other bio-channels (EMG, ECG, Flow, and Abdomen). In this work, in particular, we consider OSA severity for training and testing. Then, we investigated the detailed impacts of the OSA severity on the accuracy performance of a modern deep learning model with single channel and multiple channels. © 2022 IEEE.

关键词： Deep learning

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Entropy-Based Model Generalization for Sleep Stage Classification 13

Entropy-Based Model Generalization for Sleep Stage Classific...

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13th International Conference on Information and Communication Technology Convergence, ICTC 2022

作者： Kim, Dongyoung Na, Younghoon Woo, Yunhee Kim, Dong-Kyu Lee, Jeong-Gun Hallym University Dept. of Computer Engineering Chuncheon Korea Republic of Hallym University Division of Software Chuncheon Korea Republic of Chuncheon Sacred Heart Hospital Hallym University College of Medicine Department of Otorhinolaryngology-Head and Neck Surgery Chuncheon Korea Republic of Institute of New Frontier Research and Division of Big Data and Artificial Intelligence Chuncheon Sacred Heart Hospital Chuncheon Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

The most recent researches on sleep stage classification have been focused on a model architecture to attain high classification accuracy. Sleep stage classification is performed by human sleep experts and consequently there are some inconsistencies of labels among the scoring experts. To improve robustness of the sleep stage classification model, it is essential to consider those noisy labels which have been commonly mentioned in typical image classification problems as well. To partly solve the problem, we employ 'entropy minimization' in a loss function. By including the entropy, we can make a generalized model with regard to the label noises. To validate the effectiveness of the use of entropy on model generalization, we use two different datasets gathered from two institutions. We train a model only using a dataset obtained from one institution. Then we test the model using the dataset of another institution to investigate the accuracy improvement by the generalization. © 2022 IEEE.

关键词： Entropy

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Classification of Real and Deepfakes Visual Samples with Pre-trained Deep Learning Models 7th

Classification of Real and Deepfakes Visual Samples with Pre...

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Proceedings of the 7th International Conference on Advances in Computing and data Sciences, ICACDS 2023

作者： Nawaz, Marriam Javed, Ali Nazir, Tahira Khan, Muhammad Attique Rajinikanth, Venkatesan Kadry, Seifedine Department of Software Engineering UET Taxila Taxila47050 Pakistan Department of Computing Riphah International University Islamabad Pakistan Department of Computer Science HITEC University Taxila Pakistan Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering SIMATS Chennai602105 India Department of Applied Data Science Noroff University College Kristiansand4612 Norway Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

ISBN: (纸本)9783031379390

Serious security and privacy problems have arisen as a result of significant advancements in the creation of deepfakes. Attackers can easily replace a person’s face with the target person’s face in an image using sophisticated Deep learning (DL) algorithms to spoof their identity. Deepfakes detection algorithms have been proposed in response to the growing concerns about the potential harm caused by deepfakes. However, a reliable deepfakes detector that can keep up with contemporary deepfakes creation techniques is required. In this work, we have proposed an end-to-end methodology for detecting manipulated visual content. We used multiple CNN models i.e., ResNet18, ResNet50, DenseNet65, DenseNet77, and DenseNet100 along with the SVM classifier to compute effective cues from the input facial faces to distinguish between actual and altered content. We have also applied the concept of transfer learning to solve the issue of model over-fitting and improve generalizability against different manipulation algorithms. A comparison study is carried out to evaluate the performance of several feature extractors. Through thorough experiments performed using the Deepfakes Detection Challenge dataset, our results demonstrated that DenseNet100 surpasses the other CNN models by better recognizing deepfakes. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Learning algorithms

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Plant Disease Classification Using VGG-19 Based Faster-RCNN 7th

Plant Disease Classification Using VGG-19 Based Faster-RCNN

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Proceedings of the 7th International Conference on Advances in Computing and data Sciences, ICACDS 2023

作者： Nawaz, Marriam Nazir, Tahira Khan, Muhammad Attique Rajinikanth, Venkatesan Kadry, Seifedine Department of Software Engineering University of Engineering and Technology Taxila Taxila47050 Pakistan Faculty of Computing Department of Computer Science Riphah International University Gulberg Green Campus Islamabad Pakistan Department of Computer Science HITEC University Taxila Pakistan Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering SIMATS Chennai602105 India Department of Applied Data Science Noroff University College Kristiansand4612 Norway Ajman University Ajman346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

ISBN: (纸本)9783031379390

Early plant disease diagnosis can help farmers avoid spending money on costly crop pesticides and aid them to boost food production. Scientists have put a lot of effort to classify plant diseases, but it is difficult to quickly locate and identify different crop abnormalities due to the high degree of resemblance between the normal and damaged parts of plant leaves. Additionally, the procedure of detecting plant diseases has been made more challenging due to the extensive color, size, shape, and intensity variations in the background and foreground of the plant images. To address the existing difficulties, we have introduced an effective deep learning (DL) based system called Faster-RCNN o recognize and classify various types of plant diseases. The suggested method consists of 3 basic steps. In order to identify the area of interest in investigated samples, we first create annotations for them which are later used for Faster-RCNN training. The Faster-RCNN model employs the VGG-19 network to extract the relevant keypoints from the given images which are later passed to the regressor and classification units to identify and categorize the various crop diseases using the estimated features. We evaluated our method on a widely used standard plant sample repository called the PlantVillage database, and the findings show that our approach is reliable for classifying plant diseases under a variety of image-capturing scenarios. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Crops

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A Comparative Study of Two Complex Ontologies in Air Traffic Management

A Comparative Study of Two Complex Ontologies in Air Traffic...

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Conference on Digital Avionics Systems (DASC)

作者： Eduard Gringinger Richard M. Keller Audun Vennesland Christoph G. Schuetz Bernd Neumayr Corporate Research Frequentis AG Vienna Austria Intelligent Systems Division NASA Ames Research Center Moffet Field CA USA Software Engineering Safety and Security SINTEF Trondheim Norway Institute of Business Informatics - Data & Knowledge Engineering Johannes Kepler University Linz Linz Austria

ISBN: (数字)9781728106496

ISBN: (纸本)9781728114972

Over the past 25 years, multiple different data models have been introduced to standardize information management and facilitate data exchange and integration in the aviation domain. As a next step in the evolution of aviation data management, ontologies capturing the semantics (concepts, properties, and relationships) have been produced based on those models. In this paper, we describe a study comparing two recently released and independently-developed complex ontologies focused on Air Traffic Management (ATM) - the NASA ATM Ontology and an ontology derived from the ATM Information Reference Model. We develop a methodology for manually comparing two ontologies and identifying what we describe as exact, light, and mismatches between concepts in the two ontologies. We also describe a classification scheme that characterizes mismatches in terms of the general reason for the mismatch. This approach can be applied to improve existing ATM ontologies and foster interoperability, which will benefit aviation stakeholders.

关键词： Information Exchange Models ATM Information Reference Model NASA ATM Ontology Ontology Matching Ontology Air traffic management Automatic Teller Machines classification scheme MISMATCH aviation data exchange data models Information management

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DESIGN AND EVALUATION OF A RECEPTOR

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PATTERN RECOGNITION 1979年第4期11卷 277-288页

作者： SUTO, Y HAYASHI, Y Medical Equipment Division Toshiba Corporation Kawasaki 213 Japanwas born in Japan in July 193 He received M.S. and Ph.D. degrees in administration engineering from Keio University Yokohama Japan in 197 and 1977 respectively. From 196 to 1978 he was with the Electronic Computer Division Toshiba Corporation Tokyo Japan engaged in the development of computer application software. He is currently with the Medical Equipment Division Toshiba Corporation and is concerned with nuclear medical data processing. Dr. Suto is a member of the Japan Society of Medical Electronics and Biological Engineering the Japan Society of Ergonomics and the Information Processing Society of Japan.YoshioFaculty of Engineering Keio University Yokohama 223 Japan was born in Tokyo Japan in September 192 He received M.D. and Ph.D. degrees from Keio University Tokyo Japan in 1960 and 1967 respectively. From 195 to 1965 he was associated with the school of Medicine Keio University. He is currently a Professor in the Department of Administration Engineering Keio University. He has been concerned with human engineering system engineering and bioengineering and is presently engaged in research in the areas of pattern recognition. Dr. Hayashi is a member of the Japan Society of Ergonomics the Operations Research Society of Japan and the Society of Instrument and Control Engineers of Japan.

A pattern recognition system is divided into two parts, a receptor and a categorizer. In this research, a creative receptor is designed, and then an effective pattern recognition system based on the receptor is constituted. The receptor is composed of a set of unique matrix operators which allow it to perform efficient feature measurement extraction. The feature measurements are ranked in effective order, based on another function of the receptor. For the categorizer, a well-known optimization theory is applied. To evaluate the performance of this system, some evaluation functions are defined. Therefore, the receptor mechanism is optimized by the functions. This system is experimentally evaluated by computer simulations.

关键词： Receptor Categorizer Mapping operator Discriminant function Picture primitives System evaluator

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Statistical data Mining with Slime Mould Optimization for Intelligent Rainfall Classification

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Computer Systems Science & engineering 2023年第10期47卷 919-935页

作者： Ramya Nemani G.Jose Moses Fayadh Alenezi K.Vijaya Kumar Seifedine Kadry Jungeun Kim Keejun Han Department of Mathematics Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Engineering University Institute of Engineering and Technology(UIET)Guru Nanak UniversityHyderabadIndia Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia Department of Computer Science and Engineering GITAM School of TechnologyVishakhapatnam CampusGITAM(Deemed to be a University)VishakhapatnamIndia Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea Division of Computer Engineering Hansung UniversitySeoul02876Korea

Statistics are most crucial than ever due to the accessibility of huge counts of data from several domains such as finance,medicine,science,engineering,and so *** data mining(SDM)is an interdisciplinary domain that examines huge existing databases to discover patterns and connections from the *** varies in classical statistics on the size of datasets and on the detail that the data could not primarily be gathered based on some experimental strategy but conversely for other ***,this paper introduces an effective statistical data Mining for Intelligent Rainfall Prediction using Slime Mould Optimization with Deep Learning(SDMIRPSMODL)*** the presented SDMIRP-SMODL model,the feature subset selection process is performed by the SMO algorithm,which in turn minimizes the computation *** rainfall *** neural network with long short-term memory(CNN-LSTM)technique is *** last,this study involves the pelican optimization algorithm(POA)as a hyperparameter *** experimental evaluation of the SDMIRP-SMODL approach is tested utilizing a rainfall dataset comprising 23682 samples in the negative class and 1865 samples in the positive *** comparative outcomes reported the supremacy of the SDMIRP-SMODL model compared to existing techniques.

关键词： Statistical data mining predictive models deep learning rainfall prediction parameter tuning

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Leveraging Multimodal Ensemble Fusion-Based Deep Learning for COVID-19 on Chest Radiographs

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Computer Systems Science & engineering 2023年第10期47卷 873-889页

作者： Mohamed Yacin Sikkandar K.Hemalatha M.Subashree S.Srinivasan Seifedine Kadry Jungeun Kim Keejun Han Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia Department of Biomedical Engineering Bannari Amman Institute of TechnologySathyamangalam638401India Department of Computer Science and Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Institute of Biomedical Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesSaveetha UniversityChennai602105India Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea Division of Computer Engineering Hansung UniversitySeoul02876Korea

Recently,COVID-19 has posed a challenging threat to researchers,scientists,healthcare professionals,and administrations over the globe,from its diagnosis to its *** researchers are making persistent efforts to derive probable solutions formanaging the pandemic in their *** of the widespread and effective ways to detect COVID-19 is to utilize radiological images comprising X-rays and computed tomography(CT)*** the same time,the recent advances in machine learning(ML)and deep learning(DL)models show promising results in medical ***,the convolutional neural network(CNN)model can be applied to identifying abnormalities on chest *** the epidemic of COVID-19,much research is led on processing the data compared with DL techniques,particularly *** study develops an improved fruit fly optimization with a deep learning-enabled fusion(IFFO-DLEF)model for COVID-19 detection and *** major intention of the IFFO-DLEF model is to investigate the presence or absence of *** do so,the presented IFFODLEF model applies image pre-processing at the initial *** addition,the ensemble of three DL models such as DenseNet169,EfficientNet,and ResNet50,are used for feature ***,the IFFO algorithm with a multilayer perceptron(MLP)classification model is utilized to identify and classify *** parameter optimization of the MLP approach utilizing the IFFO technique helps in accomplishing enhanced classification *** experimental result analysis of the IFFO-DLEF model carried out on the CXR image database portrayed the better performance of the presented IFFO-DLEF model over recent approaches.

关键词： COVID-19 computer vision deep learning image classification fusion model

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Development and testing of a vehicle management system for autonomous spacecraft habitat operations

Development and testing of a vehicle management system for a...

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AIAA Space and Astronautics Forum and Exposition, 2018

作者： Aaseng, Gordon B. Frank, Jeremy D. Iatauro, Michael Knight, C. Levinson, Richard Ossenfort, John Scott, Michael Sweet, Adam Csank, Jeffrey Soeder, James Carrejo, Daniel Loveless, Andrew T. Ngo, Tam Greenwood, Zachary NASA Ames Research Center Moffett Field Intelligent Systems Division M/S/269 CA94035-1000 United States NASA Ames Research Center Moffett Field Intelligent Systems Division M/S 269-1 CA94035-1000 United States NASA Glenn Research Center Power Management and Distribution M/S 301-5 21000 Brookpark Rd ClevelandOH44135 United States NASA Glenn Research Center Power Division M/S 301-5 21000 Brookpark Rd ClevelandOH44135 United States NASA Johnson Space Center Systems Engineering and Test Branch 2101 E NASA Pkwy HoustonTX77058 United States NASA Johnson Space Center Command and Data Handling Branch 2101 E NASA Pkwy HoustonTX77058 United States NASA Johnson Space Center Spacecraft Software Engineering Branch 2101 E NASA Pkwy HoustonTX77058 United States NASA Marshall Space Flight Center Redstone Arsenal Environmental Control and Life Support Systems Branch ES62 HuntsvilleAL35812 United States

ISBN: (数字)9781624105753

ISBN: (纸本)9781624105753

As the increased distance between Earth-based mission control and the spacecraft results in increasing communication delays, small crews cannot take on all functions performed by ground today, and so vehicles must be more automated to reduce the crew workload for such missions. In addition, both near-term and future missions will feature significant periods when crew is not present, meaning the vehicles will need to operate themselves autonomously. NASA’s Advanced Exploration Systems Program pioneers new approaches for rapidly developing prototype systems, demonstrating key capabilities, and validating operational concepts for future human missions beyond low-Earth orbit. Under this program, NASA has developed and demonstrated multiple technologies to enable the autonomous operation of a dormant space habitat. These technologies included a fault-tolerant avionics architecture, novel spacecraft power system and power system controller, and autonomy software to control the habitat. The demonstration involved simulation of the habitat and multiple spacecraft sub-systems (power storage and distribution, avionics, and air-side life-support) during a multi-day test at NASA’s Johnson Space Center. The foundation of the demonstration was ‘quiescent operations’ of a habitat during a 55 minute eclipse period. For this demonstration, the spacecraft power distribution system and air-side life support system were simulated at a high level of fidelity;additional systems were managed, but with lower fidelity operational constraints and system behavior. Operational constraints for real and simulated loads were developed by analyzing on-orbit hardware and evaluating future Exploration capable technology. A total of 13 real and simulated loads were used during the test. Eight scenarios including both nominal and off-nominal conditions were performed. Over the course of the test, every application performed its desired functions successfully during the simulated tests. The results wil

关键词： NASA

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