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23rd International Scientific Conference on Electric Power engineering, EPE 2023

作者： Pluzek, Aleksandra Nagi, Lukasz Koziol, Michal Zygarlicka, Malgorzata Opole University of Technology Faculty of Electrical Engineering Automatic Control and Informatics Department of Electrical Power and Renewable Energy Opole Poland Opole University of Technology Faculty of Electrical Engineering Automatic Control and Informatics Department of Computer Science Opole Poland

ISBN: (纸本)9798350335934

During the last few years, there has been a growing interest in the topic of using natural or synthetic esters as an alternative to mineral oils in oil transformers due to the easier way to obtain them and their ability to exhibit better electrical properties. In addition, more and more researchers are describing in their work that doping esters with nanoparticles is capable of further improving these properties. Therefore, in the current work, it was decided to see what effect the addition of a hydrophobic carbon shell iron nanoparticle to ORLEN mineral oil and Midel 7131 synthetic ester would have on selected electrical parameters of the insulating fluid, i.e. breakdown voltage and electrical strength. © 2023 IEEE.

关键词： Nanoparticles

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Facial Palsy Detection Through Changes in Facial Muscle Functionality Using CNN Algorithm 6

Facial Palsy Detection Through Changes in Facial Muscle Func...

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6th International Conference on computer and informatics engineering, IC2IE 2023

作者： Sari, Chindy Aulia Bachtiar, Fitra Abdurrachman Muflikhah, Lailil Widayati, Aris University of Brawijaya Faculty of Computer Science Informatics Engineering Department Malang Indonesia University of Brawijaya Faculty of Medicine Malang Indonesia

ISBN: (纸本)9798350345162

Facial palsy is a common condition in the medical field that results from facial nerve disorders, leading to weakened facial motor functions. Detecting facial palsy typically involves observing the functional ability of facial muscles, a process currently performed manually by healthcare professionals relying on visual cues. However, manual detection is prone to errors, this needs for early and automated facial palsy detection. Overall, our research aims to improve the detection of facial palsy by leveraging deep learning techniques. By automating the detection process, we hope to provide a more accurate and efficient approach for early identification of this condition. In this research, we aim to develop a facial palsy detection system using the Convolutional Neural Network (CNN) algorithm. We utilize three datasets, namely CK+, JAFFE, and YouTube Facial Palsy, which are organized into normal and dysfunctional classes. The dataset is divided into training and testing data in an 80:20 ratio, and preprocessing techniques such as image resizing and grayscale conversion are applied. To find the optimal parameters for our model, we employ k-fold cross validation during the training process. The evaluation results show that the best model achieved optimal performance with the following parameters: 3 hidden layers, a fully connected layer (fc)-1 with a hidden size of 128, fc-2 with a hidden size of 16, and fc-3 with a hidden size of 32. The optimizer used was Adam, with a learning rate of 0.001, a batch size of 64, and trained for 10 epochs. This best model attained an average training accuracy of 0.958, testing accuracy of 1, recall of 1, and precision of 0.99. © 2023 IEEE.

关键词： CNN deep learning face detection facial palsy

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Harvesting Natural Disaster Reports from Social Media with 1D Convolutional Neural Network and Long Short-Term Memory 8

Harvesting Natural Disaster Reports from Social Media with 1...

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8th International Conference on informatics and Computing, ICIC 2023

作者： Budiman, Irwan Faisal, Mohammad Reza Nugrahadi, Dodon Turianto Muliadi Delimayanti, Mera Kartika Prastya, Septyan Eka Lambung Mangkurat University Department of Computer Science Banjarbaru Indonesia Politeknik Negeri Jakarta Computer and Informatics Engineering Department Depok Indonesia Sari Mulia University Department of Information Technology Banjarmasin Indonesia

ISBN: (纸本)9798350342604

The employment of social media platforms is progressively assuming pivotal roles in natural disaster management as early warning and monitoring systems. In emergencies, social media users can post real-time information regarding the disaster they are experiencing, along with their location. In contrast, other users can access this information, and volunteers can search for these posts to render assistance. Nonetheless, the effective utilization of social media for monitoring natural disasters remains challenging due to the lack of automated and prompt tools for identifying natural disaster reports on social media. The analysis of social media data, particularly Twitter data, heavily relies on Natural Language Processing (NLP) algorithms to classify natural disaster reports obtained from eyewitnesses. Previous studies developed a classification model utilizing a 1D Convolutional Neural Network (CNN) with feature extraction based on three word embedding techniques: Word2Vec, fastText, and Glove. Although CNN can optimize features used in the classification process, it lacks the capacity to comprehend the structure of data sequences or sequential data. Thus, this research aims to develop a combined classification model of 1D CNN and Long Short-Term Memory (LSTM). LSTM can comprehend temporal features, such as the word order in a given document. The 1D CNN + LSTM model developed in this research exhibits performance of 82.83%, 88.33%, and 81.77% for Floods, Forest Fires, and Earthquakes. The paired t-test conducted shows a significant increase with a p-value of 0.0035. This research significantly contributes to identifying the 1D CNN + LSTM model that demonstrates the best performance in classifying natural disaster reports. By exploring the effectiveness of 1D CNN + LSTM models with different word embedding techniques and levels of contamination, this research offers various insights into the optimal method for harvesting natural disaster reports from social media. Mor

关键词： Long short-term memory

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SGCL-LncLoc:An Interpretable Deep Learning Model for Improving IncRNA Subcellular Localization Prediction with Supervised Graph Contrastive Learning

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Big Data Mining and Analytics 2024年第3期7卷 765-780页

作者： Min Li Baoying Zhao Yiming Li Pingjian Ding Rui Yin Shichao Kan Min Zeng School of Computer Science and Engineering Central South UniversityChangsha 410083China Center for Artificial Intelligence in Drug Discovery Case Western Reserve UniversityClevelandOH 44106USA Department of Health Outcomes and Biomedical Informatics University of FloridaGainesvilleFL 32603USA

Understanding the subcellular localization of long non-coding RNAs(IncRNAs)is crucial for unraveling their functional *** previous computational methods have made progress in predicting IncRNA subcellular localization,most of them ignore the sequence order information by relying on k-mer frequency features to encode IncRNA *** the study,we develope SGCL-LncLoc,a novel interpretable deep learning model based on supervised graph contrastive ***-LncLoc transforms IncRNA sequences into de Bruijn graphs and uses the Word2Vec technique to learn the node representation of the ***,SGCL-LncLoc applies graph convolutional networks to learn the comprehensive graph ***,we propose a computational method to map the attention weights of the graph nodes to the weights of nucleotides in the IncRNA sequence,allowing SGCL-LncLoc to serve as an interpretable deep learning ***,SGCL-LncLoc employs a supervised contrastive learning strategy,which leverages the relationships between different samples and label information,guiding the model to enhance representation learning for *** experimental results demonstrate that SGCL-LncLoc outperforms both deep learning baseline models and existing predictors,showing its capability for accurate IncRNA subcellular localization ***,we conduct a motif analysis,revealing that SGCL-LncLoc successfully captures known motifs associated with IncRNA subcellular *** SGCL-LncLoc web server is available at http://***:8000/*** source code can be obtained from https://***/CSUBioGroup/SGCL-LncLoc.

关键词： supervised contrastive learning long non-coding RNA(IncRNA) subcellular localization prediction deep learning Graph Convolutional Network(GCN)

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Android and Desktop Based Ground Control Station (GCS) Interface Design for Unmanned Aerial Vehicle (UAV) Communication and Monitoring 24

Android and Desktop Based Ground Control Station (GCS) Inter...

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24th International Electronics Symposium, IES 2022

作者： Putra, Hafizh Irfansyah Bachtiar, Mochamad Mobed Ruswiansari, Maretha Politeknik Elektronika Negeri Surabaya Department of Informatics and Computer Engineering Surabaya Indonesia

ISBN: (数字)9781665489713

ISBN: (纸本)9781665489713

The Indonesian Flying Robot Contest (KRTI) is a competition organized by the National Achievement Center of the Indonesian Ministry of Education, Culture, Research, and Technology. One of the KRTI categories, Technology Development, competes with the work of Unmanned Aerial Vehicle (UAV) technology in the field of Ground Control Station (GCS). GCS serves to communicate and monitor the condition of the UAV in the air. Generally, GCS is only available in applications designed specifically for certain brands of UAVs, resulting in GCS not being able to be used for other types of UAVs. UAVs can experience wind disturbances as well as urgency due to depleted battery capacity. Based on the problems that have been outlined, in this research, a solution was created to design the interface of an Android and Windows-based GCS application for the communication and monitoring of UAVs. The built GCS provides an alternative communication medium with Radio Telemetry for long-distance coverage and Wi-Fi for short-range coverage. The constructed GCS analyzes the roll value movement of the IMU sensor with the linear regression method to inform the UAV stability condition and analyzes the data of GPS sensors, altitude sensors, and UAV battery capacity sensors with the fuzzy inference system method to inform the urgency of the UAV return time. GCS meets real-time needs running on the lowest versions of Windows 10 and Android 5.0 (Lollipop) with an average data runtime of 3.427 milliseconds for Windows and 16.724 milliseconds for Android. The usability of the GCS design is rated Excellent with an effectiveness rate of 84%. Round-Trip-Time USB UART communication media of 13.518 milliseconds at the test limit distance of 100 meters and Wi-Fi UDP Relay of 3.125 milliseconds at an optimal distance of 25 meters. © 2022 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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A hybrid deep learning approach for phenotype prediction from clinical notes

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Journal of Ambient Intelligence and Humanized Computing 2023年第4期14卷 4503-4513页

作者： Khalafi, Sahar Ghadiri, Nasser Moradi, Milad Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan84156-83111 Iran Institute for Artificial Intelligence Center for Medical Statistics Informatics and Intelligent Systems Medical University of Vienna Vienna Austria

Identifying patient cohorts from clinical notes in secondary use of electronic health records is a fundamental task in clinical information management. The patient cohort identification process requires identifying the patient phenotypes. However, the growing number of clinical notes makes it difficult to analyze the data manually. Therefore, automatically extracting clinical concepts is crucial to identify patient phenotypes correctly. This paper’s proposed hybrid model is based on a neural bidirectional sequence model (BiLSTM or BiGRU) and a Convolutional Neural Network (CNN) for identifying patients’ phenotypes in clinical notes. Furthermore, an extra CNN layer is run parallel to the hybrid proposed model to extract more features related to each phenotype. We used pre-trained embeddings such as FastText and Word2vec separately as the input layers to evaluate other embedding’s performance in identifying patient phenotypes. We also measured the effect of adding additional data cleaning steps on discharge reports to identify patient phenotypes using deep learning models. Results demonstrated the proposed hybrid model extracts more features than existing methods of patient phenotype extraction and provides a better F1-score. We show that complementing the proposed hybrid model with an extra CNN in identifying different phenotypes improves the F1 scores. In addition, eliminating punctuation, numbers, and stop words in discharge reports before training hybrid models increased model performance. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Embeddings

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ConvNeXt Model for Breast Cancer Image Classification 6

ConvNeXt Model for Breast Cancer Image Classification

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Setiawan, Devin Karnyoto, Andrea Stevens Intan, Indo Pardamean, Bens Department of Electrical Engineering and Computer Science University of Kansas Lawrence United States Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Department of Informatics Engineering Universitas Dipa Makassar Makassar Indonesia Computer Science Department BINUS Graduate Program Computer Science Program Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798331539603

Breast cancer is an occurrence of cancer that attacks breast tissue and is the most common cancer among women worldwide, affecting one in eight women. In this modern world, breast cancer image classification simplifies the process of analyzing, providing objective and accurate results. By leveraging machine learning algorithms and computer vision techniques, we developed breast cancer detection. The dataset is histopathology dataset from BreakHis and UNHAS Hospital. We chose the ConvNeXt-Tiny model then modified the classifier head as the proposed method. Before the dataset is processed by the model, we augment the images by applying random horizontal and vertical flips, adjustments to brightness, contrast, saturation, and hue using color jitter. The augmentation process simulates real-world variance and enhances the model's ability to generalize to unseen data. Our proposed model gained better performance (accuracy, F1-Score) results compared two other techniques to VGG16 and SVM. According to our experiments, the F1-Score for the ConvNeXt-Tiny model with classifier head modification is higher at 0.9516, than the gain for VGG16 at 0.9292, and the gain for the SVM at 0.83. © 2024 IEEE.

关键词： Lung cancer

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Alapana Generation using Finite State Machines and Generative Adversarial Networks

Alapana Generation using Finite State Machines and Generativ...

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2023 International Research Conference on Smart Computing and Systems engineering, SCSE 2023

作者： Jayatharan, Vithushigan Alwis, Dileeka University of Westminster Computer Science and Engineering London United Kingdom Informatics Institute of Technology Department of Computing Colombo Sri Lanka

ISBN: (纸本)9798350341454

The recent advancements in deep learning techniques and computational power have promoted the development of novel approaches for music generation. In this study, generating alapana, an improvisational form of Carnatic music was proposed, by leveraging Generative Adversarial Networks (GANs) and Finite State Machines (FSM). The goal is to create melodious alapana sequences that follow a given input Raga, ensuring continuity and coherence throughout the generated musical piece. The proposed approach incorporates Carnatic music theory rules into the generation process to enhance the structural coherence of the generated alapana. Additionally, various hyperparameter settings were explored to achieve the best performance. The Fréchet Audio Distance, Percentage of Correct Pitches and the Subjective evaluation through human listeners are the evaluation metrics of this approach. The result of this study demonstrates the potential of using GANs and FSM for generating continuous and pleasing alapana sequences in Carnatic music, contributing to the growing body of research in computational music generation. © 2023 IEEE.

关键词： Music

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A LoRa-Based Emotion Estimation Scheme for Smart Home Automated Actions Using ELMs 8

A LoRa-Based Emotion Estimation Scheme for Smart Home Automa...

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8th IEEE International Smart Cities Conference, ISC2 2022

作者： Karras, Christos Karras, Aristeidis Drakopoulos, Georgios Tsolis, Dimitrios Mylonas, Phivos Sioutas, Spyros Decentralized Systems Computing Group University of Patras Computer Engineering and Informatics Department Greece Ionian University Humanistic and Social Informatics Laboratory Department of Informatics Greece

ISBN: (数字)9781665485616

ISBN: (纸本)9781665485616

Emotion detection is crucial in many IoT deployments from an operational perspective with examples ranging from digital health to smart cities. This is particularly true in smart homes where the interaction between the local IoT ecosystem and the inhabitants are continuous, pervasive, and nuanced. More specifically, emotion estimation from human speech attributes is an integral architectural component of such ecosystems. In this work, we survey the emerging LPWAN technologies and after selecting the most optimal for our use-case, we propose an emotion estimation scheme based on LoRa wireless technology for automated actions in smart home environments. In particular, a voice recognition module coupled with a transmitter are installed in a car. Then, depending on the estimation outcome, the smart home may undertake one or more preemptive actions according to its configuration as the car passenger approaches. The prototype LoRa system has been tested with the widely-used TESS dataset with encouraging results as expressed in the emotion confusion matrix obtained from a extreme learning machine with various kernels. The preceding paves the way for adaptive smart homes tailored to the needs of their inhabitants. © 2022 IEEE.

关键词： Automation

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A Plastic Waste Hotspot Detection System and Its Application to Measuring Environmental Improvement 1

A Plastic Waste Hotspot Detection System and Its Application...

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1st IEEE International Symposium on Consumer Technology, ISCT 2024

作者： Sasaki, Shiori Nakamura, Ryota Wibowo, Prasetyo Musashino University Department of Data Science Tokyo Japan Musashino University Faculty of Data Science Tokyo Japan Politeknik Elektronika Negeri Department of Informatics and Computer Engineering Surabaya Indonesia

ISBN: (纸本)9798350365191

This paper describes a Plastic Waste Hotspot Detection System which has been developed in an international collaborative research project to realize an 'Environmental AI-Human Actions integration' with marine plastic waste reduction actions. In this project, various local conditions are obtained to the system to realize an environmentally resilient society. The nature conservation activities such as marine plastic waste collection and reduction actions are implemented based on these conditions. In the process of integration of Environmental AI and Human Actions, the effects of these activities and activists' contribution to environmental improvement and global-scale knowledge are accumulated, shared, analyzed, and visualized through global-scale analysis. This paper describes the Plastic Waste Hotspot Detection system as one of the systems that integrate AI systems and human actions. This paper examines the feasibility and effectiveness of the system through the fieldwork tests in Phuket, Thailand and Surabaya, Indonesia. © 2024 IEEE.

关键词： Plastic recycling

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