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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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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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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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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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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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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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Offline Content-based Recommendation System for Wikimedia Commons Contents 16

Offline Content-based Recommendation System for Wikimedia Co...

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16th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2025 / 8th International Conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Maitaigahasse, Ahidjo Ebongue Fendji, Jean Louis Kedieng Atemkeng, Marcellin Department Mathematics and Computer Science Faculty of Science University of Ngaoundere Ngaoundere454 Cameroon Centre for Research Experimentation and Production School of Chemical Engineering and Mineral Industries University of Ngaoundere Ngaoundere454 Cameroon Wallenberg Research Centre Stellenbosch University Stellenbosch South Africa Department of Robotics and Industrial Informatics School of Chemical Engineering and Mineral Industries University of Ngaoundere Ngaoundere454 Cameroon Department of Mathematics Rhodes University P.O. Box 6140 Grahamstown South Africa

Recommendation systems are essential for enhancing digital experiences, but their reliance on internet connectivity limits accessibility in regions with limited or no access. This paper presents an offline content-based recommendation system designed to operate without internet dependency by leveraging precomputed Term Frequency-Inverse Document Frequency (TF-IDF) vectors and cosine similarity. To optimize offline performance, we store a locally computed TF-IDF matrix, allowing efficient retrieval of relevant media items through matrix multiplication instead of real-time computation. The system is evaluated using twenty simulated personas representing diverse user interests, demonstrating its ability to generate personalized relevant recommendations. By eliminating the need for online data access, our system makes educational content from Wikimedia Commons accessible in remote areas, schools, and offline learning environments. These findings highlight the potential of offline recommendation systems in bridging the digital divide and providing equitable access to personalized learning resources. © 2025 Elsevier B.V.. All rights reserved.

关键词： Interactive computer systems

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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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