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23rd International Electronics Symposium, IES 2021

作者： Lado, Anita Jaquiline Sooai, Adri Gabriel Mamulak, Natalia Magdalena Rafu Nani, Paskalis Andrianus Bria, Yulianti Paula Batarius, Patrisius Aliandu, Paulina Ngaga, Emerensiana Sinlae, Alfry Aristo Jansen Mau, Sisilia Daeng Bakka Tedy, Frengky Meolbatak, Emiliana Metan Siki, Yovinia Carmeneja Hoar Gumelar, Agustinus Bimo Fanani, Nurul Zainal Yuhana, Umi Laili Universitas Katolik Widya Mandira Dept. of Computer Science Kupang85225 Indonesia Universitas Narotama Faculty of Computer Science Surabaya60117 Indonesia Dept. of Engineering Politeknik Negeri Jember Indonesia Institut Teknologi Sepuluh Nopember Dept. of Informatics Engineering Surabaya60117 Indonesia

ISBN: (纸本)9781665443463

Measuring the performance of several classifiers in the modeling process, based on datasets with certain criteria, becomes important. It used to determine which classifier is more reliable for a particular task. Neural network will be compared its performance against Random Forest using dragon fruit datasets. Consisting of 41 images of healthy and sick fruit and leaf, it is divided into four classes, both classifiers were used in two comparing experiments. The validation used is 10-fold cross-validation. The results obtained are not much different from the prediction accuracy in the range of 70% to 82.9% for both classifiers. © 2021 IEEE.

关键词： Fruits

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Machine Learning-Enabled Power Scheduling in IoT-Based Smart Cities

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computers, Materials & Continua 2021年第5期67卷 2449-2462页

作者： Nabeela Awan Salman Khan Mohammad Khalid Imam Rahmani Muhammad Tahir Nur Alam MD Ryan Alturki Ihsan Ullah Department of Computer Science Government Girls Degree College MardanKPK23200Pakistan Department of Electrical Energy System UETPeshawar25000Pakistan Department of Computer Science College of Computing and InformaticsSaudi Electronic UniversityKSA Department of Smart Computing Kyungdong UniversityGlobal CampusGosung24764South Korea Department of Information Sciences College of Computer and Information SystemsUmm Al-Qura UniversityMakkahSaudi Arabia Robotics Engineering Daegu Gyeongbuk Institute of Science and TechnologySouth Korea

Recent advancements in hardware and communication technologies have enabled worldwide interconnection using the internet of things(IoT).The IoT is the backbone of smart city applications such as smart grids and green energy *** smart cities,the IoT devices are used for linking power,price,energy,and demand information for smart homes and home energy management(HEM)in the smart *** complex smart gridconnected systems,power scheduling and secure dispatch of information are the main research *** challenges can be resolved through various machine learning techniques and data *** this paper,we have proposed a particle swarm optimization based machine learning algorithm known as a collaborative execute-before-after dependency-based requirement,for the smart *** proposed collaborative execute-before-after dependencybased requirement algorithm works in two phases,analysis and assessment of the requirements of end-users and power distribution *** the rst phases,a xed load is adjusted over a period of 24 h,and in the second phase,a randomly produced population load for 90 days is evaluated using particle swarm *** simulation results demonstrate that the proposed algorithm performed better in terms of percentage cost reduction,peak to average ratio,and power variance mean ratio than particle swarm optimization and inclined block rate.

关键词： PSO IBR machine learning IoT smart cities CDBR

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Oversampling Multi-Label Data Based on Natural Neighbor and Label Correlation

SSRN

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SSRN 2023年

作者： Liu, Bin Zhou, Ao Wang, Jin Tsoumakas, Grigorios Key Laboratory of Data Engineering and Visual Computing School of Computer Science of Technology Chongqing University of Posts and Telecommunications Chongqing400065 China School of Informatics Aristotle University of Thessaloniki Thessaloniki54124 Greece

In multi-label learning, addressing the class imbalance issue is of paramount importance. Oversampling methods are preferred as they offer a more general solution independent of the model choice, i.e., they alleviate the imbalance of datasets by augmenting instances in the pre-processing step. Existing neighbor-based oversampling methods employ an empirical number of neighbors (k=5), to identify the local region for new instances creation. However, a single fixed k value cannot fit all labels, because every label usually has its own distinct distribution and complexity. Furthermore, the label assignment for synthetic instances usually depends on the statistics of individual labels within the corresponding neighborhood, ignoring the informative correlation among labels. To overcome these limitations, we propose an oversampling method called Multi-Label Oversampling with Natural neighbor and label Correlation (MLONC). Our approach offers three main advantages: 1) the adaptive number of neighbors for each label related to the data complexity is obtained via natural neighbor detection;2) it encourages generating more instances proximate to the decision boundary of highly imbalanced labels, and diminishes the impact of outliers;3) exploitation of label correlation in label assignment enhances the quality of the synthetic instances. Experimental results demonstrate the effectiveness of MLONC under various base classifiers. © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Surgical Instrument Detection for Laparoscopic Surgery using Deep Learning 19

Surgical Instrument Detection for Laparoscopic Surgery using...

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19th International Conference on Electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON 2022

作者： Boonkong, Apiwat Hormdee, Daranee Sonsilphong, Suphachoke Khampitak, Kovit Khon Kaen University Embedded System RandD Group Computer Engineering Khon Kaen Thailand Khon Kaen University Data Science and Health Informatics Program Academic Affairs Khon Kaen Thailand Khon Kaen University Faculty of Medicine Department of Obstetrics and Gynecology Khon Kaen Thailand

ISBN: (纸本)9781665485845

Laparoscopic surgery has transformed conventional open surgery. Robot-Assisted laparoscopic surgery which is minimally invasive is effective for operations in limited space. Nevertheless, the robotic system which is utilized in such surgery can inflict impair to the surgical instruments, organs, or tissues as a consequence of the narrow field of view and space available to operate, and insufficient tactile response. In laparoscopy, the surgeon observes what he operates via digital images which are displayed on a monitor. In addition to the surgeon's expertise, the image-guided surgery system could offer him significant support if that system is capable of understanding those images. computer Vision has been able to proceed due to the emergence of Machine Learning, and especially Deep Learning. This paper applies Deep Learning for surgical instrument detection for laparoscopic surgery. The fastest model, based on results trained on the COCO 2017 Dataset on TensorFlow Framework, for each of 4 algorithms-Faster-RCNN, SSD, CenterNet and EfficientDet-from Model Zoo, along with YOLOv4 on Darknet Framework, have been investigated in this work. The evaluation metrics used are Precision, Recall and F1-score. The results reveal that each model plays its part well;however, YOLOv4 seemed to breakthrough this statement by offering grate result for all three accuracy metrics. © 2022 IEEE.

关键词： Laparoscopy

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Parking Occupancy Prediction and Traffic Assignment in a University Environment 10

Parking Occupancy Prediction and Traffic Assignment in a Uni...

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10th International Japan-Africa Conference on Electronics, Communications, and Computations, JAC-ECC 2022

作者： Farag, Mohamed Hilal, Amr El-Tawab, Samy Arab Academy for Science Technology & Maritime Transport Alexandria Egypt Virginia Tech Transportation Institute Virginia Tech BlacksburgVA United States Informatics Lab Virginia Tech BlacksburgVA United States Alexandria University Department of Computer and Systems Engineering Egypt James Madison University College of Integrated Science & Engineering HarrisonburgVA United States

ISBN: (纸本)9781665464635

The fourth industrial revolution has given rise to large-scale data-driven models like smart cities and Intelligent transportation. Within these models, applications like smart parking have been growing rapidly in research and industry. However, different scenarios and environments (e.g., shopping areas, residential places, and business complexes) can require special handling due to the various factors impacting people's schedules and behavior. In this paper, we provide an initial investigation of traffic assignment based on parking prediction for a mid-size university environment where parking is concentrated in three parking garages around the campus. Our initial investigation includes results for parking prediction using a statistical method and plans for an augmenting study using variations of Neural Networks. On top of the parking prediction layer, we propose an application layer that directs and fuses the model predictions to produce the parking options provided to the application user. The presented investigation can help the university administration in their consideration of building additional garages. © 2022 IEEE.

关键词： Forecasting

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Machine Learning for Plastic Waste Detection: State-of-the-art, Challenges, and Solutions 3

Machine Learning for Plastic Waste Detection: State-of-the-a...

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3rd International Conference on Communications, Information, Electronic and Energy Systems, CIEES 2022

作者： Tamin, Owen Moung, Ervin Gubin Ahmad Dargham, Jamal Yahya, Farashazillah Omatu, Sigeru Angeline, Lorita Universiti Malaysia Sabah Faculty of Computing And Informatics Kota Kinabalu Malaysia Universiti Malaysia Sabah Faculty of Engineering Kota Kinabalu Malaysia Education Research Centre Higashi-Hiroshima739-0046 Japan Taylor's University School of Computer Science Faculty of Innovation And Technology Selangor Malaysia

ISBN: (数字)9781665491495

ISBN: (纸本)9781665491495

Machine learning has advanced rapidly in recent technologies of artificial intelligence. The abundant and cheap computation in machine learning has created more knowledge and reliability of this method. Therefore, more researchers have relied on machine learning to solve plastic waste pollution, which contributes to global greenhouse gas emissions. Datasets are fundamental requirements to build the foundation of the plastic waste detection system. This paper provides an overview of several state-of-the-art deep learning models, focusing on the dataset used for waste classification and identification. The performance of each deep learning model in classification accuracy is also discussed. The challenges of implementing deep learning in plastic waste detection are identified;(i) Data deficiency for training, (ii) Quality of the existing plastic waste dataset, and (iii) Color spaces. Lastly, the proposed solution to each challenge is presented. © 2022 IEEE.

关键词： Object detection

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Analyzing gaze behavior for text-embellished narrative visualizations under different task scenarios

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Visual informatics 2020年第3期4卷 41-50页

作者： Chris Bryan Aditi Mishra Hidekazu Shidara Kwan-Liu Ma School of Computing Informaticsand Decision Systems EngineeringArizona State UniversityUnited States Department of Computer Science University of CaliforniaDavisUnited States

We conduct an eye tracking study to investigate perception text-embellished narrative visualizations under different task *** stimuli are data visualizations embellished with text-based elements:annotations,captions,labels,and descriptive *** consider three common viewing tasks that occur when these types of graphics are viewed:(1)simple observation,(2)active search to answer a query,and(3)information memorization for later *** overarching goal is to understand,at a perceptual level,if and how task affects how these visualizations are interacted *** analyzing collected gaze data and conducting advanced semantic scanpath analysis,we find,at a high level,diverse patterns of gaze behavior:simple observation and information memorization lead to similar optical viewing strategies,while active search significantly diverges,both in regards to which areas of the visualization are focused upon and how often embellishments are interacted *** discuss study outcomes in the context of embellishing visualizations with text for various usage scenarios.

关键词： Eye tracking Perception User study

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Informal Indonesian Part-of-Speech Tagger Using Hidden Markov Model and Normalization Algorithm

Informal Indonesian Part-of-Speech Tagger Using Hidden Marko...

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International Conference on Electrical engineering and informatics, ICEEI

作者： Fika Hastarita Rachman Firdaus Solihin Nenden Siti Fatonah Tsania Hazhiahadani Sri Herawati Imamah Department of Informatics Engineering University of Trunojoyo Madura Bangkalan Indonesia Department Magister of Computer Science Esa Unggul University Jakarta Indonesia Department of Information Systems University of Trunojoyo Madura Bangkalan Indonesia

Twitter is a social networking platform that can be used as resources to find information in real time. Twitter is one of the social media that does not have writing criteria, users are free to express their thoughts and send messages. There are many messages and tweets that use abbreviations, foreign words, local and mixed languages, which are characteristic of informal language patterns. Informal language patterns do not rely on Indonesian, making the process of annotating word classes less precise and influential on the level of precision. The use of POS Tagging in previous research can annotate word classes well in formal sentences. The problem in informal sentences is solved by the process of word normalization after the preprocessing stage, where informal words are converted into formal words then Part-of-Speech Tagging is carried out on each word in the sentence such as nouns, verbs, adjectives, etc. The dataset used was 846 words, with a informal word count of 99 words. With the Hidden Markov Model commonly used in machine learning as well as the word normalization process, the system is able to increase the resulting precision value, there is a significant increase of up to 10% in the precision produced.

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Measuring Credibility Level of E-commerce Sellers with Structural Equation Modeling and Naive Bayes on Tweets of Service Quality

Measuring Credibility Level of E-commerce Sellers with Struc...

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IEEE Region 10 International Conference TENCON

作者： Marastika wicaksono aji bawono Diana Purwitasari Mauridhi Hery Purnomo Departement of Technology Management Sepuluh Nopember Institute of Technology Telkom University Bogor West Java Departement of Informatics Engineering Sepuluh Nopember Institute of Technology Surabaya East Java Departement of Electrical Engineering Departement of Computer Engineering Sepuluh Nopember Institute of Technology Surabaya East Java

In digital development 4.0, store brands are very important. The problem in this research is the lack of consumer trust to buy quality goods in e-commerce store accounts so that it affects consumer satisfaction. This study aims to address this question feedback from the problems of the customer, then on the other hand a questionnaire with the PLS-SEM (Partial Least Squares Structural Equation Modeling) model to determine the dimensions of the variables selected according to customer experience. To achieve this aim, both negative and positive customer comments were compiled to assess customer satisfaction, employing a comparative analysis method through Naive Bayes algorithm. The overarching goal was to achieve optimal results and extract valuable insights regarding the determinants that influenced customer satisfaction within the domain of online transactions. This research also has an impact on buyers so they can have an understanding of the factors that support trust in customer satisfaction, so that individuals do not hesitate in making purchasing decisions to shop online. The results showed that the algorithm initially recorded a modest accuracy score of 0.37. Meanwhile, after implementing hyperparameter tuning, the accuracy increased significantly to 0.62. In the aspect of Smart PLS questionnaire analysis, a standardized Normed Fit Index (NFI) of 0.707 was recorded, which was slightly below the established threshold of 0.90. The standardized root mean square residual (SRMR) was measured at 0.071, falling below the specified value of 0.08, indicating a commendable model fit. However, the RMS theta value at 0.240 exceeded the threshold of 0.102.

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Identification of Acute Myeloid Leukemia (AML) Subtypes: M1, M2, M3 on White Blood Cells Using Microscopic Images

Identification of Acute Myeloid Leukemia (AML) Subtypes: M1,...

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International Electronics Symposium (IES)

作者： Soniya Jasmine Azzahra Riyanto Sigit Heny Yuniarti Yetti Hernaningsih Ade Imannurohma Departemen of Informatics and Computer Engineering Politeknik Elektronika Negeri Surabaya Surabaya Indonesia Departemen of Clinical Pathology Faculty of Medicine Airlangga University Dr. Soetomo General Academic Hospital Surabaya Indonesia

Leukemia is a form of blood cancer characterized by an overproduction of abnormal white blood cells that invade the body’s immune system. Leukemias are divided into four main groups based on cell type and growth rate. Acute Myeloid Leukemia (AML) is one of them, namely leukemia that occurs in the myeloid series. AML itself is divided into several subtypes, according to the French-American-British (FAB), namely M0-M7. AML subtypes M1, M2, and M3 are influenced by the same cell type, namely myeloblast cells, so a more detailed analysis is needed to classify them. Therefore we need a system that can identify the presence of AML in the blood, this identification is done by classifying white blood cells on microscopic images. To detect the presence of AML in microscopic images, multiple processes will be used, including preprocessing, segmentation, feature extraction, and classification stages by applying the K-NN method. This classification procedure will result in a diagnosis of M1, M2, or M3 blood cells. At the end of phase 1 of this final project, pre-processing and segmentation processes and graphical user interface (GUI) designs have been carried out. This final project resulted in an accuracy of 80% for the identification of subtype M1, 70% for the identification of subtype M2, and 80% for the identification of subtype M3.

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