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6th International Conference on Information Technology, Information Systems and Electrical engineering, ICITISEE 2022

作者： Kharisma, Rizqi Sukma Kusrini Saputro, Uyock Anggoro Sulistiyono, Mulia Rahardi, Majid Bernadhed Muktafin, Elik Hari Universitas Amikom Yogyakarta Faculty of Computer Science Yogvakarta Indonesia Universitas Amikom Yogyakarta Magister of Informatics Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350399615

Enclosed spaces are places with a higher potential for spreading the COVID-19 virus. This is because the COVID-19 virus can be carried in the air. Closed space makes the air last longer in the space. Moreover, closed spaces are widely used, such as homes, schools, malls, offices, places of worship, etc. So for closed spaces, serious attention must be given to avoid the spread of the virus. ADX is an IoT-based tool equipped with UVC rays that can kill viruses, including the COVID-19 virus. This tool can be controlled remotely, manually, or on a timer. So that the device can be activated first before the closed space is used. This study also used the ESP8266 microcontroller in supporting the development of IoT-based ADX tools. The results of this study with an accuracy of the IoT ADX system of 96.10% and the average response time is 1.47 seconds. © 2022 IEEE.

关键词： ADX Covid-19 Enclosed Spaces IoT UVC

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Multi-Label Classification of Film Genres Based on Synopsis Using Support Vector Machine, Logistic Regression and Naïve Bayes Algorithms 6

Multi-Label Classification of Film Genres Based on Synopsis ...

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6th International Conference on Information Technology, Information Systems and Electrical engineering, ICITISEE 2022

作者： Akbar, Jihadul Utami, Ema Yaqin, Ainul Universitas Amikom Yogyakarta Magister of Informatics Engineering Yogyakarta Indonesia Universitas Amikom Yogyakarta Computer Science Faculty Yogyakarta Indonesia

ISBN: (纸本)9798350399615

Movies were still a very popular means of entertainment. The current distribution of internet users causes a large amount of movie data to be created and distributed online. The emergence of movie streaming services makes consumers very interested in using automatic film genre classification. In this study, a multi-label film genre classification will be carried out based on an English synopsis. Data were collected from the Internet Movie Database (IMDb) website. The amount of data used in this study was 10,432 lines of data obtained using scraping techniques on June 7, 2022. Researchers divided the dataset labels into 18 labels representing each genre. Feature extraction using TF-IDF and Stemming. The multi-label classification algorithm used is the Support Vector Machine, Logistic Regression, and Naive Bayes Algorithms. Optimal parameter search using GridSearch of each algorithm. The optimum result in this study was obtained f1-score value of 0.58 using the SVM algorithm with TF-IDF feature extraction with stemming dataset, followed by NB with the f1-score value of 0.48 and LR with an f1-score value of 0.43. © 2022 IEEE.

关键词： Logistics Regression Multilabel Classification Naive Bayes Support Vector Machine Synopsis Movie

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Investigation of the separation zone for split-domain light field visualization 28

Investigation of the separation zone for split-domain light ...

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2024 28th International Conference on Information Technology, IT 2024

作者： Javidi, Kamran Martini, Maria G. Kara, Peter A. School of Computer Science and Mathematics Faculty of Engineering Computing and the Environment Kingston University Wireless Multimedia and Networking Research Group Department of Computer Science Penrhyn Road Campus Kingston upon Thames LondonKT1 2EE United Kingdom Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Networked Systems and Services Budapest1111 Hungary

ISBN: (纸本)9798350369618

Light field visualization commonly provides a single content over the entire field of view. However, the angularly-selective nature of the technology enables the simultaneous visualization of different contents at different viewing angles. Yet segmenting the valid viewing area comes with content interference as well in forms of separation zones, resulting invalid viewing domains. In this paper, we introduce a study on the separation zone of split-domain light field visualization. We created a static-content-based scenario in which we split the valid viewing area into two distinct domains, with a separation zone in between them. This was achieved by merging two light field contents in the middle with an instantaneous switch. The resulting visualization on a light field display was captured by a DSLR camera from two viewing distances, and it was compared to the crosstalk effect caused by insufficient angular resolution. © 2024 IEEE.

关键词： Visualization

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Improving SVM Performance in Determining Cost and Kernel Parameter Values Through Grid Search

Improving SVM Performance in Determining Cost and Kernel Par...

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Technology Innovation and Its Applications (ICTIIA), International Conference on

作者： Visca Sylvia Maya Silvi Lydia Benny Benyamin Nasution Master of Informatics Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Computer Engineering and Informatics Politeknik Negeri Medan Medan Indonesia

ISBN: (数字)9798350351613

ISBN: (纸本)9798350351620

The purpose of this study is to improve the performance of Support Vector Machine (SVM) algorithm in sentiment analysis of trainee reviews through parameter optimization using Grid Search. Trainee reviews were taken from the Vocational and Productivity Training Center (BBPVP) Medan, which is the context of this study. The method used includes collection and preprocessing of review data, followed by the application of SVM for sentiment analysis. Grid Search is used to optimize the cost and kernel parameters of SVM. The results show that after optimization, the resulting SVM model shows significant performance improvement compared to the model before optimization. Accuracy increased from 68.75% to 71.96%, with improvements in other metrics such as precision, recall, and F1 score. These findings suggest that Grid Search is an effective method to optimize SVM parameters in sentiment analysis, providing a more accurate evaluation of the effectiveness of training programs at BBPVP Medan. This research makes an important contribution by applying hyperparameter optimization to SVM and suggests the application of this method for similar analysis in the future.

关键词： Support vector machines Training Productivity Sentiment analysis Analytical models Accuracy Costs Reviews Kernel Optimization

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Generating All Invertible Matrices by Row Operations 35

Generating All Invertible Matrices by Row Operations

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35th International Symposium on Algorithms and Computation, ISAAC 2024

作者： Gregor, Petr Hoang, Hung P. Merino, Arturo Mička, Ondřej Department of Theoretical Computer Science and Mathematical Logic Charles University Prague Czech Republic Algorithm and Complexity Group Faculty of Informatics TU Wien Austria Institute of Engineering Sciences Universidad de O’Higgins Rancagua Chile

ISBN: (纸本)9783959773546

We show that all invertible n × n matrices over any finite field Fq can be generated in a Gray code fashion. More specifically, there exists a listing such that (1) each matrix appears exactly once, and (2) two consecutive matrices differ by adding or subtracting one row from a previous or subsequent row, or by multiplying or dividing a row by the generator of the multiplicative group of Fq. This even holds in the more general setting where the pairs of rows that can be added or subtracted are specified by an arbitrary transition tree that has to satisfy some mild constraints. Moreover, we can prescribe the first and the last matrix if n ≥ 3, or n = 2 and q > 2. In other words, the corresponding flip graph on all invertible n × n matrices over Fq is Hamilton connected if it is not a cycle. This solves yet another special case of Lovász conjecture on Hamiltonicity of vertex-transitive graphs. © Petr Gregor, Hung P. Hoang, Arturo Merino, and Ondřej Mička.

关键词： Matrix algebra

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Enhancing IT Risk Management in XYZ Vocational School: Leveraging COBIT 5 Framework with the APO12 Domain

Enhancing IT Risk Management in XYZ Vocational School: Lever...

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2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2024

作者： Winanti Fernando, Erick Gate, Jullend Universitas Insan Pembangunan Department of Information Systems Indonesia Universitas Multimedia Nusantara Information System Faculty of Engineering and Informatics Tangerang Indonesia Bina Nusantara University Binus Online Learning Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350372229

This article explores the importance of IT risk management, especially at XYZ Vocational School. The agency is equipped to manage business risks, emphasizing the need to improve decision-making and risk mitigation by identifying potential hazards and evaluating internal and external threats, including hacking risks. The research investigates IT risk management at XYZ Vocational School using a qualitative and quantitative method with a focus on the APO12 COBIT 5 domain: Align, plan, and organize. It assesses subdomains such as data collection, risk analysis, profile maintenance, risk articulation, portfolio management, and response strategies. Conclusively, the research reveals institutional IT capabilities at level 1 (Performed Process) in the APO12 subdomain. Recommendations include developing COBIT-compliant SOPs for processes at this level, highlighting the need for further improvements in IT governance. This study provides valuable insights into IT risk management practices, offering recommendations for improving IT governance in educational settings. © 2024 IEEE.

关键词： APO12 Domain COBIT 5 Information technology Risk Management Vocational School

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Efficient Detection of Knee Osteoporosis Using the Swin Transformer on X-Ray Images

Efficient Detection of Knee Osteoporosis Using the Swin Tran...

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2024 International Conference on computer and Applications, ICCA 2024

作者： Sultana, Rahat Zaman, Muhammad Ivković, Nikola Cengiz, Korhan Rafaqat, Iqra Akhunzada, Adnan The University of Lahore Faculty of Computer Science Sargodha Pakistan Comsats University Islamabad Department of Computer Science Islamabad Pakistan University of Zagreb Faculty of Organization and Informatics Varaždin Croatia Biruni University Department of E&e Engineering Istanbul Turkey University of Doha for Science and Technology Department of Data and Cybersecurity Doha24449 Qatar

ISBN: (纸本)9798350367560

Knee Osteoporosis (KOP) is a skeletal disease that is caused by low bone mineral density and the degradation of bone tissue. It increases the risk of bone fractures in the knee region and is commonly seen in older people but usually ignored due to its silent nature. Osteoporosis is mostly diagnosed via X-ray images and Dual-Energy X-ray Absorptiometry (DXA) scans that can be difficult to read due to their sheer volume the subtle variations. This study aims to develop a Deep Learning (DL) utilizing Swin Transform model that can accurately recognize osteoporosis in knee X-ray images at an early stage. The dataset used for this research comprises knee X-rays labeled as either 'normal' or 'osteoporotic,' with image preprocessing including resizing to standard dimensions, converting the images into tensors, and normalizing pixel values to improve model performance. The model is trained and validated on arranged dataset, characterized by metrics such as accuracy, precision, recall or F1-score that gives an idea how well our model performs for prediction as acknowledged for its performance in image classification tasks. The results demonstrate that the Swin Transformer model has an overall accuracy of 89.38% on the data set for identifying osteoporosis features on knee X-ray images. This approach demonstrates the potential of the Swin Transformer model to serve as an effective tool for early osteoporosis diagnosis, offering improved accuracy and reliability compared to traditional convolutional neural networks (CNNs). © 2024 IEEE.

关键词： Diseases

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A Deep Learning Driven Feature Based Steganalysis Approach

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Intelligent Automation & Soft Computing 2023年第8期37卷 2213-2225页

作者： Yuchen Li Baohong Ling Donghui Hu Shuli Zheng Guoan Zhang College of Computer Science and Information Engineering Hefei University of TechnologyHefei230009China College of Information Engineering Anhui Broadcasting Movie and Television CollegeHefei230011China Department of Informatics Faculty of Natural&Mathematical SciencesKing’s College LondonLondonWC2R2LSUK

The goal of steganalysis is to detect whether the cover carries the secret information which is embedded by steganographic *** traditional ste-ganalysis detector is trained on the stego images created by a certain type of ste-ganographic algorithm,whose detection performance drops rapidly when it is applied to detect another type of steganographic *** phenomenon is called as steganographic algorithm mismatch in *** resolve this pro-blem,we propose a deep learning driven feature-based *** advanced steganalysis neural network is used to extract steganographic features,different pairs of training images embedded with steganographic algorithms can obtain diverse features of each *** a multi-classiﬁer implemented as lightgbm is used to predict the matching *** results on four types of JPEG steganographic algorithms prove that the proposed method can improve the detection accuracy in the scenario of steganographic algorithm mismatch.

关键词： Image steganalysis algorithm mismatch convolutional neural network JPEG images

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Comparative Analysis of Brain Waves for EEG-Based Depression Detection in the Prefrontal Cortex Lobe using LSTM

Comparative Analysis of Brain Waves for EEG-Based Depression...

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Conference on New Media Studies (CoNMedia)

作者： Monica Pratiwi Department of Computer Engineering Faculty of Engineering and Informatics Universitas Multimedia Nusantara Tangerang Indonesia

Global anxiety and depression have become 25% more prevalent, with teenagers and women being the most affected. Approximately 280 million people suffer from depression. Doctors and psychologists are able to diagnose depressive disorders through counselling sessions and ask relevant questions to the subject, despite being vulnerable to mistakes due to the examiner's lack of experience. Therefore, automated depression detection development is necessary to validate doctor and psychiatrist assessment. Electroencephalography (EEG) is considered to be a popular option for the detection and investigation of various mental disorders. In this study, a comparison and analysis of each existing brain wave is carried out, namely Alpha (8-12Hz), Beta (13-30Hz), Theta (4–8 Hz), Delta (0.5-4 Hz) and Gamma (30-50Hz). From each wave, an accuracy testing is carried out for three groups of features: linear features, nonlinear features, and a combination of linear and nonlinear features. The given results demonstrate that the combination of linear and nonlinear data consistently yields the highest accuracy outcomes across all waves. Also, the combination of theta waves and linear nonlinear features contributed the highest accuracy (84%) using LSTM as the classifier.

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Improved Accuracy of Decision Tree with XGBoost Technique in Cat Skin Disease Diagnosis

Improved Accuracy of Decision Tree with XGBoost Technique in...

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Science and Information Technology in Smart Administration (ICSINTESA), International Conference of

作者： Nanda Safrina Maya Silvi Lydia Benny Benyamin Nasution Master of Informatics Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Computer Engineering and Informatics Politeknik Negeri Medan Medan Indonesia

ISBN: (数字)9798350376111

ISBN: (纸本)9798350376128

This research aims to develop an expert system for initial diagnoses of skin diseases in cats using the Decision Tree method. It assists cat owners in identifying skin diseases based on observed symptoms. Data from expert veterinarian drh. Ismi Azima, including various types of cat skin diseases and their symptoms, were used. The research employs 80% training data and 20% testing data to develop the classification model. Testing results indicate the Decision Tree model achieves up to 90% average accuracy in diagnosing cat skin diseases based on 50 datasets. This expert system aids in diagnosis and provides treatment and prevention solutions, beneficial for cat owners, especially in areas with limited veterinary access. In conclusion, this Decision Tree-based expert system effectively provides initial diagnoses of skin diseases in cats with high accuracy.

关键词： Accuracy Prevention and mitigation Training data Skin Decision trees Medical diagnosis Expert systems Diseases Testing Python

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