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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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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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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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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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Guiding the Way: A Systematic Literature Review on Mentoring Practices in Open Source Software Projects

SSRN

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SSRN 2023年

作者： Feng, Zixuan Kimura, Katie Trinkenreich, Bianca Sarma, Anita Steinmacher, Igor Oregon State University School of Electrical Engineering and Computer Science CorvallisOR97331 United States Northern Arizona University School of Informatics Computing and Cyber Systems FlagstaffAZ86011 United States

Context: Mentoring in Open Source Software (OSS) is important to its project’s growth and sustainability. Mentoring allows contributors to improve their technical skills and learn about the protocols and cultural norms of the project. However, mentoring has its challenges: mentors sometimes feel unappreciated, and mentees may have mismatched interests or lack interpersonal skills. Existing research has investigated the different challenges of mentoring in different OSS contexts, but we lack a holistic understanding. Objective: A comprehensive understanding of the current practices and challenges of mentoring in OSS is needed to implement appropriate strategies to facilitate mentoring. Method: This study presents a systematic literature review investigating how literature has characterized mentoring practices in OSS, including their challenges and the strategies to mitigate them. We retrieved 232 studies from four digital libraries. Out of these, 21 were primary studies. Using this, we performed backward and author snowballing, adding another 27 studies. We conducted a completeness check by reviewing the references of the 4 most relevant primary studies, which resulted in us adding 1 additional study. We then conducted a full-text review and evaluated the studies using a set of criteria;as a result, 10 papers were excluded. We then employed an open-coding approach to analyze, aggregate, and synthesize the selected studies. Results: We reviewed 39 studies to investigate the different facets of mentoring in OSS, encompassing motivations, goals, channels, and contributor dynamics. We then identified 13 challenges associated with mentoring in OSS, which fall into three categories: social, process, and technical. We also present a quick-reference strategy catalog to map these strategies to challenges for mitigation. Conclusions: Our study serves as a guideline for researchers and practitioners about mentoring challenges and potential strategies to mitigate these challenge

关键词： Open source software

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Multi-level biomedical NER through multi-granularity embeddings and enhanced labeling

arXiv

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arXiv 2023年

作者： Shahrokh, Fahime Ghadiri, Nasser Samani, Rasoul 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

Biomedical Named Entity Recognition (NER) is a fundamental task of Biomedical Natural Language Processing for extracting relevant information from biomedical texts, such as clinical records, scientific publications, and electronic health records. The conventional approaches for biomedical NER mainly use traditional machine learning techniques, such as Conditional Random Fields and Support Vector Machines or deep learning-based models like Recurrent Neural Networks and Convolutional Neural Networks. Recently, Transformer-based models, including BERT, have been used in the domain of biomedical NER and have demonstrated remarkable results. However, these models are often based on word-level embeddings, limiting their ability to capture character-level information, which is effective in biomedical NER due to the high variability and complexity of biomedical texts. To address these limitations, this paper proposes a hybrid approach that integrates the strengths of multiple models. In this paper, we proposed an approach that leverages fine-tuned BERT to provide contextualized word embeddings, a pretrained multi-channel CNN for character-level information capture, and following by a BiLSTM + CRF for sequence labelling and modelling dependencies between the words in the text. In addition, also we propose an enhanced labelling method as part of pre-processing to enhance the identification of the entity's beginning word and thus improve the identification of multi-word entities, a common challenge in biomedical NER. By integrating these models and the pre-processing method, our proposed model effectively captures both contextual information and detailed character-level information. We evaluated our model on the benchmark i2b2/2010 dataset, achieving an F1-score of 90.11. These results illustrate the proficiency of our proposed model in performing biomedical Named Entity *** Codes 68T50, 68T07 Copyright © 2023, The Authors. All rights reserved.

关键词： Natural language processing systems

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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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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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A Novel Digital Forensic Readiness (DFR) Framework for e-Government

A Novel Digital Forensic Readiness (DFR) Framework for e-Gov...

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Cryptography, informatics, and Cybersecurity (ICoCICs), IEEE International Conference on

作者： Hernowo Adi Nugroho Obrina Candra Briliyant Septia Ulfa Sunaringtyas Badan Siber dan Sandi Negara Jakarta Indonesia School of Computer Science and Informatics Cardiff University Wales United Kingdom Cybersecurity Engineering Politeknik Siber dan Sandi Negara Bogor Indonesia

The increasing reliance on e-Government services has amplified the importance of digital forensic readiness (DFR) in ensuring effective incident response to mitigate the impact of cyber incidents. A specialized DFR framework would streamline incident response procedures within e-Government services, facilitating prompt identification, containment, and remediation of cyber threats. One of the most notable characteristics of DFR for e-Government is the implementation of a formal policy on forensic readiness. This paper proposes novel DFR steps to guide the formulation of such a policy. The proposed framework is tailored by combining two DFR frameworks that Claims and Elyas et al. contributed. The new framework involves identifying crucial DFR parameters and mapping domain parameters between both frameworks. The framework was then implemented in a local government organization in West Java, Indonesia. The result is a security policy recommendation for Digital Forensic Readiness, which consists of 8 domains and 33 subdomains. The security policy provides a practical approach to enhance the DFR capabilities of e-Government services to enhance incident response and cyber resilience.

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Instance-wise Grasp Synthesis for Robotic Grasping

arXiv

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arXiv 2023年

作者： Xu, Yucheng Kasaei, Mohammadreza Kasaei, Hamidreza Li, Zhibin The School of Informatics University of Edinburgh United Kingdom The Department of Artificial Intelligence Bernoulli Institute Faculty of Science and Engineering University of Groningen Netherlands The Department of Computer Science University College London United Kingdom

Generating high-quality instance-wise grasp configurations provides critical information of how to grasp specific objects in a multi-object environment and is of high importance for robot manipulation tasks. This work proposed a novel Single-Stage Grasp (SSG) synthesis network, which performs high-quality instance-wise grasp synthesis in a single stage: instance mask and grasp configurations are generated for each object simultaneously. Our method outperforms state-of-the-art on robotic grasp prediction based on the OCID-Grasp dataset, and performs competitively on the JACQUARD dataset. The benchmarking results showed significant improvements compared to the baseline on the accuracy of generated grasp configurations. The performance of the proposed method has been validated through both extensive simulations and real robot experiments for three tasks including single object pick-and-place, grasp synthesis in cluttered environments and table cleaning task. © 2023, CC BY-NC-ND.

关键词： End effectors

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