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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Miyazaki, Takeru Araki, Shunsuke Uehara, Satoshi Kyushu Inst. of Information Sciences Faculity of Management and Information Sciences Fukuoka Japan Kyushu Inst. of Technology Faculty of Computer Science and Systems Engineering Fukuoka Japan The Univ. of Kitakyushu Faculty of Environmental Engineering Fukuoka Japan

ISBN: (纸本)9798350324174

In this paper, we propose a new pseudorandom bit sequence generator, which is based on a method digging out walls. Although each of these sequences lacks enough randomness, synthesized sequences of them can pass all of the NIST statistical test suite. We also describe a conjectural relation between the number of types on the square mazes and their sizes. © 2023 IEEE.

关键词： Digging Out the Walls Maze Generator Pseudorandom Bit Sequence

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ClipArtGAN: An Application of Pix2Pix Generative Adversarial Network for Clip Art Generation

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Multimedia Tools and Applications 2025年 1-25页

作者： Elnabawy, Reham H. Abdennadher, Slim Hellwich, Olaf Eldawlatly, Seif Technische Universität Berlin Berlin Germany Computer Science and Engineering Department Faculty of Media Engineering and Technology German University in Cairo Cairo Egypt Computer Science Department Faculty of Informatics and Computer Science German International University New Administrative Capital Egypt Computer and Systems Engineering Department Faculty of Engineering Ain Shams University 1 El-sarayat st. Abbassia Cairo Egypt Computer Science and Engineering Department The American University in Cairo Cairo Egypt School of Information Technology New Giza University Giza Egypt

An image can convey a thousand words. This statement emphasizes the importance of illustrating ideas visually rather than writing them down. Although detailed image representation is typically instructive, there are several contexts where simplification of image representations may be more informative. One way to provide an abstract representation of an object is via clip art, which is a simple graphic illustration of an object that can be included in documents or presentations. Clip art representation can be utilized in graphic design for concepts expression and daily life objects illustration. Furthermore, clip art had shown remarkable advantages when compared to real images in terms of editing and manipulation since clip art geometry can be easily edited in addition to the feasibility of storing it in a resolution-independent manner. However, it can be difficult to automatically create clip arts that resemble any object. Here, we propose a novel deep learning model, named ClipArtGAN, that utilizes Generative Adversarial Networks (GANs) to convert a photo of an object to its corresponding clip art. To our knowledge, this is the first GAN model based on Pix2Pix architecture that is capable of automatically generating clip art for any arbitrary object. Three training datasets were collected to train and compare two GAN models based on Pix2Pix and CycleGAN architectures. Our results demonstrate that the mean accuracy obtained using ClipArtGAN utilizing Pix2Pix outperforms that of CycleGAN by 58% and that of Google Images-based search by 54%. These results indicate the efficacy of ClipArtGAN in generating representative clip art images. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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Metaheuristic Optimization Through Deep Learning Classification of COVID-19 in Chest X-Ray Images

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computers, Materials & Continua 2022年第11期73卷 4193-4210页

作者： Nagwan Abdel Samee El-Sayed M.El-Kenawy Ghada Atteia Mona M.Jamjoom Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Noha E.El-Attar Tarek Gaber Adam Slowik Mahmoud Y.Shams Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura 35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura 35712Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Faculty of Computers and Artificial Intelligence Benha UniversityEgypt School of Science Engineeringand EnvironmentUniversity of SalfordUK Faculty of Computers and Informatics Suez Canal UniversityIsmailia41522Egypt Koszalin University of Technology Poland Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt

As corona virus disease(COVID-19)is still an ongoing global outbreak,countries around the world continue to take precautions and measures to control the spread of the *** of the excessive number of infected patients and the resulting deficiency of testing kits in hospitals,a rapid,reliable,and automatic detection of COVID-19 is in extreme need to curb the number of *** analyzing the COVID-19 chest X-ray images,a novel metaheuristic approach is proposed based on hybrid dipper throated and particle swarm *** lung region was segmented from the original chest X-ray images and augmented using various transformation ***,the augmented images were fed into the VGG19 deep network for feature *** the other hand,a feature selection method is proposed to select the most significant features that can boost the classification ***,the selected features were input into an optimized neural network for *** neural network is optimized using the proposed hybrid *** experimental results showed that the proposed method achieved 99.88%accuracy,outperforming the existing COVID-19 detection *** addition,a deep statistical analysis is performed to study the performance and stability of the proposed *** results confirm the effectiveness and superiority of the proposed approach.

关键词： Covid-19 feature selection dipper throated optimization particle swarm optimization deep learning

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ChatGPT: Applications, Opportunities, and Threats

ChatGPT: Applications, Opportunities, and Threats

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2023 systems and Information engineering Design Symposium, SIEDS 2023

作者： Bahrini, Aram Khamoshifar, Mohammadsadra Abbasimehr, Hossein Riggs, Robert J. Esmaeili, Maryam Majdabadkohne, Rastin Mastali Pasehvar, Morteza University of Virginia Department of Systems and Information Engineering CharlottesvilleVA United States Iran University of Science and Technology School of Computer Engineering Tehran Iran Azarbaijan Shahid Madani University Faculty of Information Technology and Computer Engineering Tabriz Iran Alzahra University Faculty of Engineering Department of Industrial Engineering Tehran Iran The American University Faculty of Medical Sciences Department of Medicine Managua Nicaragua Islamic Azad University Faculty of Engineering Department of Industrial Engineering Tehran Iran

ISBN: (纸本)9798350300642

Developed by OpenAI, ChatGPT (Conditional Generative Pre-trained Transformer) is an artificial intelligence technology that is fine-tuned using supervised machine learning and reinforcement learning techniques, allowing a computer to generate natural language conversation fully autonomously. ChatGPT is built on the transformer architecture and trained on millions of conversations from various sources. The system combines the power of pre-trained deep learning models with a programmability layer to provide a strong base for generating natural language conversations. In this study, after reviewing the existing literature, we examine the applications, opportunities, and threats of ChatGPT in 10 main domains, providing detailed examples for the business and industry as well as education. We also conducted an experimental study, checking the effectiveness and comparing the performances of GPT-3.5 and GPT-4, and found that the latter performs significantly better. Despite its exceptional ability to generate natural-sounding responses, the authors believe that ChatGPT does not possess the same level of understanding, empathy, and creativity as a human and cannot fully replace them in most situations. © 2023 IEEE.

关键词： Deep learning

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A Comparison of Various Deep Learning Methods for Household Load Forecasting

A Comparison of Various Deep Learning Methods for Household ...

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2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023

作者： Deivamani, Karthikeyan Norouzi, Farshid Shekhar, Aditya Bauer, Pavol Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands Department of DC Systems Energy Conversion and Storage Delft University of Technology Delft Netherlands

ISBN: (纸本)9798350396782

Forecasting energy consumption is vital for smart grid operations to manage demand, plan loads, and optimize grid operations. This work aims at reviewing and experimentally evaluating six univariate deep learning architectures to forecast load for a single household using a real-world dataset. Multi-layer perceptron (MLP), Convolutional neural network (CNN) and recurrent neural networks (Simple RNN, Long Short Term Memory (LSTM)) were the neural network methods that were analysed along with robust LSTM architectures like Bidirectional LSTM and CNN-LSTM Hybrid. All the models were tuned using Bayesian optimization and evaluated using root mean squared error (RMSE) as the metric. In addition to neural network models, Seasonal ARIMA (SARIMA) a statistical model is also presented to observe the performance. As a result, Bi-directional LSTM was observed to have achieved the best performance with the smallest value of RMSE;however, it was also observed that differences in performances between other neural network models were quite low, especially between the RNN architectures. Additionally, although machine learning methods performed better than SARIMA the former model was more complex and computationally intensive. © 2023 IEEE.

关键词： deep learning electric load forecasting smart grid time-series forecasting univariate

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Generating synthetic mixed-type tabular data by decoding samples from a latent-space: a case study in healthcare

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Procedia computer science 2024年 246卷 2254-2263页

作者： Jarosław Drapała Jerzy Świątek Department of Computer Science and Systems Engineering Faculty of Information and Communication Technology Wrocław University of Science and Technology Wrocław 50-370 Poland

Medical data are subject to privacy regulations, which severely limit AI specialists who wish to construct decision support systems for medicine. Large amounts of this data are tabular, indicating that they are organized into a table format, where patient records are represented in rows and measured variables in columns. Furthermore, the variables come in different types—some are numerical, while others are categorical. In this work, we introduce a novel method for constructing generators of synthetic tabular data with mixed types. The key point of our approach is the explicit utilization of a latent space to represent the original data. A case study using real medical data is presented.

关键词： Multidimensional scaling Kernel density estimation Random forest regression Support Vector Machine

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Intrusion Detection in 5G Cellular Network Using Machine Learning

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computer systems science & engineering 2023年第11期47卷 2439-2453页

作者： Ishtiaque Mahmood Tahir Alyas Sagheer Abbas Tariq Shahzad Qaiser Abbas Khmaies Ouahada Knowledge Unit of Systems and Technology UMT Sialkot CampusSialkot51040Pakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Faculty of Computer Science National College of Business Administration and EconomicsLahore54660Pakistan Department of Electrical and Computer Engineering COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Faculty of Computer and Information Systems Islamic University of Madinah Madinah42351Saudi Arabia Department of Computer Science&IT University of SargodhaSargodha40100Pakistan Department of Electrical and Electronic Engineering Science University of JohannesburgP.O Box 524Auckland ParkJohannesburg2006South Africa

Attacks on fully integrated servers,apps,and communication networks via the Internet of Things(IoT)are growing *** devices’effectiveness harms end users,increases cyber threats and identity theft,raises costs,and negatively impacts income as problems brought on by the Internet of Things network go unnoticed for extended *** on Internet of Things interfaces must be closely monitored in real time for effective safety and *** the 1,2,3,and 4G cellular networks,the 5th generation wireless 5G network is indeed the great invasion of mankind and is known as the global advancement of cellular *** to this day,experts are working on the evolution’s sixth generation(6G).It offers amazing capabilities for connecting everything,including gadgets and machines,with wavelengths ranging from 1 to 10 mm and frequencies ranging from 300 MHz to 3 *** gives you the most recent *** countries have already established this technology within their *** is the most crucial aspect of using a 5G *** of the absence of study and network deployment,new technology first introduces new gaps for attackers and *** Protocol(IP)attacks and intrusion will become more prevalent in this *** efficient approach to detect intrusion in the 5G network using a Machine Learning algorithm will be provided in this *** research will highlight the high accuracy rate by validating it for unidentified and suspicious circumstances in the 5G network,such as intruder hackers/*** applying different machine learning algorithms,obtained the best result on Linear Regression Algorithm’s implementation on the dataset results in 92.12%on test data and 92.13%on train data with 92%precision.

关键词： Intrusion detection system machine learning confidentiality integrity availability

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Deep Learning Enabled Intelligent Healthcare Management System in Smart Cities Environment

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computers, Materials & Continua 2023年第2期74卷 4483-4500页

作者： Hanan Abdullah Mengash Lubna A.Alharbi Saud S.Alotaibi Sarab AlMuhaideb Nadhem Nemri Mrim M.Alnfiai Radwa Marzouk Ahmed S.Salama Mesfer Al Duhayyim Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTabuk UniversityTabuk71491Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Information Systems College of Science and Arts at MuhayelKing Khalid UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Electrical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

In recent times,cities are getting smart and can be managed effectively through diverse architectures and *** cities have the ability to support smart medical systems that can infiltrate distinct events(i.e.,smart hospitals,smart homes,and community health centres)and scenarios(e.g.,rehabilitation,abnormal behavior monitoring,clinical decision-making,disease prevention and diagnosis postmarking surveillance and prescription recommendation).The integration of Artificial Intelligence(AI)with recent technologies,for instance medical screening gadgets,are significant enough to deliver maximum performance and improved management services to handle chronic *** latest developments in digital data collection,AI techniques can be employed for clinical decision making *** the other hand,Cardiovascular Disease(CVD)is one of the major illnesses that increase the mortality rate across the ***,wearables can be employed in healthcare systems that instigate the development of CVD detection and *** this motivation,the current study develops an Artificial Intelligence Enabled Decision Support System for CVD Disease Detection and Classification in e-healthcare environment,abbreviated as AIDSS-CDDC *** proposed AIDSS-CDDC model enables the Internet of Things(IoT)devices for healthcare data ***,the collected data is saved in cloud server for *** by,training 4484 CMC,2023,vol.74,no.2 and testing processes are executed to determine the patient’s health *** accomplish this,the presented AIDSS-CDDC model employs data preprocessing and Improved Sine Cosine Optimization based Feature Selection(ISCO-FS)*** addition,Adam optimizer with Autoencoder Gated RecurrentUnit(AE-GRU)model is employed for detection and classification of *** experimental results highlight that the proposed AIDSS-CDDC model is a promising performer compared to other existing models.

关键词： Smart cities e-healthcare decision support system cardiovascular disease deep learning feature selection

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Maize Crop Yield Prediction Using Machine Learning Regression Approach 5th

Maize Crop Yield Prediction Using Machine Learning Regressio...

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5th International Conference on Advances in Distributed Computing and Machine Learning, ICADCML 2024

作者： Bedane, Tarikwa Tesfa Kebede, Kidistie Bizuneh Mohapatra, Sudhir Kumar Das, Tapan Kumar Tripathy, Asis Kumar Department of Software Engineering Addis Ababa Science and Technology University Addis Ababa Ethiopia Agricultural Transformation Institute Addis Ababa Ethiopia Faculty of Emerging Technologies Sri Sri University Cuttack India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9789819735228

Given maize’s significant role as a staple crop, it becomes imperative to carry out precise crop yield prediction to ensure food security. This research employs machine learning algorithms to analyze historical data related to maize crop yields, with the aim of developing a predictive model. The outcome of this study can serve as a valuable tool for pre-harvest planning and measuring crop production per unit of land area. The dataset utilized in this research spans from 2010 to 2021 and was sourced from Ethiopia’s Central Statistical Agency. Three machine learning regression algorithms—Logistic Regression, XGBoost, and Random Forest—were employed to design the predictive model. After data preparation and preprocessing, a total of 436,358 records were used for model development. The models were further trained with reduced features using principal component analysis (PCA). The experimental results demonstrate that the Random Forest algorithm, trained with a reduced number of features through PCA, yielded superior performance, with a lower error rate and a high R2 score of 0.98. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Machine learning

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Comparative Study of Facial Expression Recognition Using Facial Landmark in e-Learning System

Comparative Study of Facial Expression Recognition Using Fac...

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International Conference on Electrical engineering, computer science and Informatics (EECSI)

作者： Fitra Abdurrachman Bachtiar Cesilia Natasya Nainggolan Riza Setiawan Soetedjo Retno Indah Rokhmawati Lailil Muflikhah Department of Informatics Engineering Faculty of Computer Science Brawijaya University Malang Indonesia Department of Information Systems Faculty of Computer Science Brawijaya University Malang Indonesia

ISBN: (数字)9798350355314

ISBN: (纸本)9798350355321

The e-Iearning system or distance learning system has become important, especially during the COVID-19 pan-demic. Several tertiary institutions have made the e-Iearning system an alternative teaching and learning activity. Research on the use of e-Iearning needs to be explored to obtain innovative, creative, and fun learning. One aspect that can be examined in e-learning is students' emotional response to the learning activities. The emotional state of students directly or indirectly affects the learning process. The student's emotional responses can be measured by their facial expressions. In this study, an exploration was carried out on the facial expressions shown by students in HSS Learning, an e-Iearning for learning web programming. The student's facial expressions were extracted using 68 pairs of facial landmarks and additional 6 facial feature vectors. The total number of features used is 142. Machine learning methods for classification are applied to determine the performance of several classification models, including the k-Nearest Neighbor (k- NN) algorithm, Logistic Regression, and Support Vector Machine (SVM). Machine learning performance evaluation is done using the Fl-score and confusion matrix. The results show that the accuracy of the k-NN, Logistic Regression, and SVM are 91.4%, 92.2%, and 92.1 %, respectively. However, the Fl-score macro values are relatively low due to data imbalance with the k- NN, Logistic Regression, and SVM models are 0.250, 0.272, and 0.243, respectively. While the study provides insight into the student learning experience in e-Iearning settings, this study needs to be explored more.

关键词： Support vector machines Logistic regression Emotion recognition Electronic learning Accuracy Computational modeling Machine learning Feature extraction Vectors Emotional responses

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