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检索条件"机构=Polytechnic School Postgraduate Program in Computer Science"

共 159 条记录，以下是21-30 订阅

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The Age and Updating Stability for a Communication Context in VANET

The Age and Updating Stability for a Communication Context i...

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Brazilian Symposium on Computing System Engineering (SBESC)

作者： Margarete De Sá Sérgio Gorender Distributed Systems Laboratory (LaSiD) Graduate Program in Mechatronics (PPGM) Computer Science Institute (IC) and Polytechnic School (EP) Federal University of Bahia (UFBA)

ISBN: (数字)9798331509002

ISBN: (纸本)9798331532857

VANETs make it possible to execute specific vehicular applications, in particular those with the aim of turning traffic safe and avoiding congestion. However, it is difficult to maintain communication between vehicles when these vehicles move dynamically at high speeds. Context-aware systems have been studied as a way to monitor context information from sensors, which can be used by systems to adapt to changes in their environment or in application requirements. This concept is used by VANET protocols and applications in different ways. Quality of Context (QoC) metrics are used to qualify the context information considering different characteristics of sensors and of the information, being a base for systems to decide if it is safe and interesting to use specific information. In this paper, we propose QoC metrics related to the stability of the updating process for a vehicular communication context. In addition, we also analyze the Age QoC metric and show how to calculate it for distinct context information. We simulate scenarios to verify the QoC metrics behavior. The results showed that the proposed QoC metrics can properly qualify the vehicular communication context.

关键词： Measurement Roads Vehicular ad hoc networks Sensor phenomena and characterization Turning Systems engineering and theory Stability analysis Sensor systems Reliability Monitoring

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Interactive Multimedia Kolb Experiential Learning Model Using Logistic Regression Algorithm to Improve Student Cognitive

Interactive Multimedia Kolb Experiential Learning Model Usin...

引用

7th International Conference on Advances in Visual Informatics, IVIC 2021

作者： Khoirunnisa, Azizah Nurul Munir Rasim Rahman, Eka Fitrajaya Dewi, Laksmi Curriculum Development Study Program School of Postgraduate Studies Indonesia University of Education Bandung Indonesia Computer Science Education Study Program Department of Computer Science Education Indonesia University of Education Bandung Indonesia

ISBN: (纸本)9783030902346

The differences of each student become a challenge for educators in delivering learning materials. More often than not, students often find it difficult to process study materials. Conventional study-based learning may not always have a profound impact on students. Consideration of interactive multimedia as a tool for student-centered learning has not been widely discussed in scientific research. In this paper, the authors investigate interactive multimedia using Kolb’s experiential learning model in improving students’ cognitive abilities. This study uses a pre-experimental method and one-group pre-and post-test design type with the research population in class XI RPL and a sample of SMK students in Bandung. The results show that the application of interactive multimedia using Kolb’s experiential learning model is effective in improving students’ cognitive abilities. On the other hand, it is found that combining abstract learning materials with interactive visuals and activities that involve students’ participation could create exceptionally positive feedback from students. © 2021, Springer Nature Switzerland AG.

关键词： Students

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LLM-based Hierarchical Label Annotation for Foodborne Illness Detection on Social Media

LLM-based Hierarchical Label Annotation for Foodborne Illnes...

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IEEE International Conference on Big Data

作者： Dongyu Zhang Ruofan Hu Dandan Tao Hao Feng Elke Rundensteiner AML ByteDance San Jose USA Worcester Polytechnic Institute Data Science Program Worcester Polytechnic Institute Worcester USA Vanke School of Public Health Tsinghua University Beijing China College of Ag&Environ Sciences North Carolina A&T State University Greensboro USA Computer Science/Data Science Program Worcester Polytechnic Institute Worcester USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Foodborne illnesses pose a threat to public health, leading to morbidity, mortality, and economic burden annually. Social media, while providing a rich timely source for training AI models for surveillance, requires effective tools for annotation. While Large Language Models (LLMs) have shown promise for generating simple labels, here hierarchical labels composed of entity types like food type and symptom (at individual word level) and the foodborne illness event (at complete post level) are required. For this, we introduce ICL2FID, the first LLM-based hierarchical labeling framework designed to annotate social media posts for foodborne illness detection at two levels using only a few demonstration examples. To utilize the interconnection between post and word levels, ICL2FID instructs the LLM to leverage information from one level when predicting the other level. To combat model hallucination and cyclic dependencies, a verification step improves evidence propagation between interconnected word and post-level labeling tasks. Strategies for custom selection of demonstration examples are designed reducing biases and increasing representation. We compare ICL2FID against traditional supervised learning and other LLM methods, demonstrating that it not only achieves superior accuracy but does so at a fraction of the cost and time. These findings highlight ICL2FID’s potential as a viable alternative for hierarchical label generation in scenarios with limited resources and huge data sets. Code is available at https://***/zdy93/ICL2FID.

关键词： Training Social networking (online) Annotations Biological system modeling Surveillance Large language models Supervised learning Mortality Labeling Public healthcare

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Q-Learning Energy Management System (Q-EMS) in Wireless Sensor Network

Q-Learning Energy Management System (Q-EMS) in Wireless Sens...

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Smart Computing, IoT and Machine Learning (SIML), International Conference on

作者： Deden Ardiansyah Mustafid Teddy Mantoro Doctoral Program of Information Systems School of Postgraduate Universitas Diponegoro Semarang Central Java Indonesia Computer Engineering Department Vocational School Universitas Pakuan Bogor West Java Indonesia Media-Tech-Lab Computer Science Department Sampoerna University DKI Jakarta Indonesia

ISBN: (数字)9798350364101

ISBN: (纸本)9798350364118

The energy control of a Wireless Sensor Network (WSN) often leads to an unbalanced state between the battery storage system, energy extraction through photovoltaic systems energy, and energy utilization in the WSN. These disparities result in suboptimal wireless sensor network performance. The reliance on batteries is a major factor contributing to this problem. The Q-Learning Energy Management System (Q-EMS) is designed to address these challenges and improve energy management strategies. The Q-EMS algorithm used a learning process resulting in optimal actions for sensor nodes in different situations. The rewards or punishments nodes receive determine their decisions, which are determined by the Q-EMS algorithm. The Q-learning model approach is aimed at reducing energy consumption and supply in WSNs. Energy supply is categorized into battery-based, transfer-based, and harvesting, while energy consumption can be classified into task-cycle, mobility-based, and data-driven. The development of the Q Learning algorithm model has three scenarios: determining energy needs for WSN, effective energy harvesting strategy, and effective energy transfer. As a result, effective energy demand, harvesting and transfer using the Q-learning algorithm are balanced. However, it needs to be studied in more depth using real data in the field. In future research, I will use real data and optimize the use of the Q-Learning algorithm.

关键词： Wireless communication Wireless sensor networks Energy consumption Q-learning Machine learning algorithms Energy resources Classification algorithms

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Analyzing User Comments on Fintech E-wallet Applications in Indonesia Using the LDA Algorithm

Analyzing User Comments on Fintech E-wallet Applications in ...

引用

International Conference on Information Technology (INCIT)

作者： Albertus Dwiyoga Widiantoro Mustafid Mustafid Ridwan Sanjaya Doctoral Program of Information System School of Postgraduate Studies Diponegoro University Semarang Indonesia Information System Computer Science Fakulcty Soegijapranata Catholic University Semarang Indonesia

The main objective of this article is to compare user comments from 4 Fintech applications in Indonesia using the Latent Dirichlet Allocation (LDA) algorithm. This research is word processing research carried out using text mining. The statistical population is comments from Fintech users Ovo, Dana, LinkAja, and Doku, with data taken from May 2022-May 2023. LDA and the Python programming language are applied to analyze the data and implement text mining algorithms from topic modeling. Each application group has different topic coherence, each application group is analyzed and 4 large categories are found, namely system convenience, system problems, customer service (CS) problems, and service ease. The results of text mining and processing using Coherence and Latent Dirichlet allocation can be used to identify hidden topics and be used as input for system improvements

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Deep Learning in Smart Healthcare: A GAN-based Approach for Imbalanced Alzheimer's Disease Classification 19

Deep Learning in Smart Healthcare: A GAN-based Approach for ...

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19th International Conference on Future Networks and Communications, FNC 2024 / 21st International Conference on Mobile Systems and Pervasive Computing, MobiSPC 2024 / 14th International Conference on Sustainable Energy Information Technology, SEIT 2024

作者： Tufail, Hina Ahad, Abdul Puspitasari, Ira Shayea, Ibraheem Coelho, Paulo Jorge Pires, Ivan Miguel Department of Computer Science University of Management and Technology Sialkot Pakistan Information System Study Program Faculty of Science and Technology Universitas Airlangga Indonesia School of Software Northwestern Polytechnical University Shaanxi Xian China Turkey Polytechnic University of Leiria Leiria Portugal Coimbra Portugal Instituto de Telecomunicações Escola Superior de Tecnologia e Gestão de Águeda Universidade de Aveiro Águeda Portugal

Alzheimer's disease (AD) is a type of dementia that leads to memory loss and impairment, which affects patients' lives badly. It is not curable yet, but its progression can be slowed down if detected at earlier stages. In this research study, we propose a transfer learning-based convolutional neural network (CNN) model to classify magnetic resonance imaging (MRI) into one of four stages of Alzheimer's disease. One of the major limitations of the deep learning-based classification model is the non-availability of healthcare datasets related to AD. The widely used Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset has a major class imbalance issue. We propose a generative adversarial network (GAN) based data augmentation technique to overcome the data imbalance. This promotes the investigation of applying GANs to generate synthetic samples for minority classes in Alzheimer's disease datasets to enhance classification performance. The results show the progression in the overall classification process of AD. © 2024 The Authors. Published by Elsevier B.V.

关键词： Generative adversarial networks

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International Conference of computer and Informatics Engineering (IC2IE)

作者： Wandy Wandy Kusworo Adi Media Anugerah Ayu Doctoral Program of Information Systems School of Postgraduate Studies Diponegoro University Semarang Indonesia Department of Physics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia Department of Computer Science Faculty of Engineering and Technology Sampoerna University South Jakarta Indonesia

ISBN: (数字)9798331505530

ISBN: (纸本)9798331505547

Full-marathon and Half-marathon distances are categorized as road running. Full-marathon running is becoming increasingly popular, and Half-marathon is increasing worldwide in both sexes and all age groups. Some aspects might relate to Full-marathon and Half-marathon running performance during training and races. Technology also plays an essential role in supporting runners and running races. Technology like artificial intelligence (AI) now supports the running athlete, not only predicting performance and results. It can also be used later to help the coach generate training programs for the athlete. This research aimed to find many aspects of marathons and performance and analyze them to see if artificial intelligence could later support them. It used secondary data and a systematic literature review proposed by Kitchenham. Out of the 58 articles, 21 of them (36.21%) received a score of 1 from Q1. Additionally, 19 articles (32.76%) received a score of 1 from both Q2 and Q3. Among the 58 articles, 9 (15.52%) received a total score of 3, with all three Q1, Q2, and Q3 scores being 1. This indicates that artificial intelligence will likely support the content of these nine articles. Several factors were also discovered to be connected to marathons and athletic performance. These findings suggested that additional investigation into marathons and performance, later backed by artificial intelligence, remained pertinent and essential.

关键词： Training Roads Artificial intelligence Informatics

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Intrusion Detection using Deep Neural Network Algorithm on the Internet of Things 11

Intrusion Detection using Deep Neural Network Algorithm on t...

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11th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2022

作者： Ikhwan, Syariful Wibowo, Adi Warsito, Budi School of Postgraduate Studies Diponegoro University Doctoral Program of Information System Semarang Indonesia Institut Teknologi Telkom Purwokerto D3 Telecommunication Engineering Banyumas Indonesia Diponegoro University Computer Science Informatics Department of Science and Mathematics Faculty Semarang Indonesia Diponegoro University Statistics Department of Science and Mathematics Faculty Semarang Indonesia

ISBN: (纸本)9781665460309

The increasing use of IoT devices on future networks is very helpful for humans in their lives. However, the increase in devices connected to IoT networks also increases the potential for attacks against those networks. Vulnerabilities in Internet of Things (IoT) networks can be exposed at any time. Artificial intelligence can be used to protect the IoT network by being able to detect attacks on the network so that they can be prevented. In this study, network detection was carried out using the Deep Neural Network (DNN) algorithm. The test was carried out using the UNSW Bot-IoT dataset with a comparison of training data of 75% of the overall data. The results obtained show the ability of the algorithm to detect attacks on average with 99.999% accuracy. The validation loss and training loss look very small. In this study, there is a validation loss that still occurs in overfitting, but the difference is very small. © 2022 IEEE.

关键词： Internet of things

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Real Time Monitoring System of Drinking Water Quality Using Internet of Things 24

Real Time Monitoring System of Drinking Water Quality Using ...

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24th International Electronics Symposium, IES 2022

作者： Abidin, Zainul Maulana, Eka Nurrohman, Muhammad Yogi Wardana, Fitri Candra Warsito Universitas Brawijaya Faculty of Computer Science Department of Computer Engineering Malang Indonesia Universitas Brawijaya Faculty of Engineering Department of Electrical Engineering Malang Indonesia Universitas Brawijaya Master Program in Environmental Resources and Development Management Postgraduate School Malang Indonesia Universitas Brawijaya Faculty of Mathematics and Natural Sciences Department of Chemistry Malang Indonesia

ISBN: (数字)9781665489713

ISBN: (纸本)9781665489713

In order to reduce Polyethylene Terephthalate (PET) bottle plastic waste, Universitas Brawijaya (UB) provided reverse osmosis drinking water in some buildings. Water monitoring is needed to keep the water quality. Therefore, a water monitoring system based on Total Dissolved Solids (TDS), pH, and water flow was proposed. Since the UB buildings have good internet connection (WiFi), Internet of Things (IoT) was employed to the system to support real time monitoring. So, data of TDS, pH, and water flow can be periodically sent to cloud and displayed to a specific website. The system worked very well and the data can be summarized and used by operator as indicators of performance of the reverse osmosis machine. When water quality and performance decrease, operator must check filters membrane or water pump for further maintenance to support long life use. Although the sensors have been calibrated properly, some measurement errors occur. The measurement error percentages of TDS and pH are 0.62% and 0.94%, respectively. © 2022 IEEE.

关键词： Internet of things

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UCE-FID: Using Large Unlabeled, Medium Crowdsourced-Labeled, and Small Expert-Labeled Tweets for Foodborne Illness Detection

UCE-FID: Using Large Unlabeled, Medium Crowdsourced-Labeled,...

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IEEE International Conference on Big Data

作者： Ruofan Hu Dongyu Zhang Dandan Tao Huayi Zhang Hao Feng Elke Rundensteiner Data Science Program Worcester Polytechnic Institute Worcester USA Vanke School of Public Health Tsinghua University Beijing China ByteDance San Jose USA College of Ag&Environ Sciences North Carolina A&T State University Greensboro USA Computer Science/Data Science Program Worcester Polytechnic Institute Worcester USA

Foodborne illnesses significantly impact public health. Deep learning surveillance applications using social media data aim to detect early warning signals. However, labeling foodborne illness-related tweets for model training requires extensive human resources, making it challenging to collect a sufficient number of high-quality labels for tweets within a limited budget. The severe class imbalance resulting from the scarcity of foodborne illness-related tweets among the vast volume of social media further exacerbates the problem. Classifiers trained on a classimbalanced dataset are biased towards the majority class, making accurate detection difficult. To overcome these challenges, we propose EGAL, a deep learning framework for foodborne illness detection that uses small expert-labeled tweets augmented by crowdsourced-labeled and massive unlabeled data. Specifically, by leveraging tweets labeled by experts as a reward set, EGAL learns to assign a weight of zero to incorrectly labeled tweets to mitigate their negative influence. Other tweets receive proportionate weights to counter-balance the unbalanced class distribution. Extensive experiments on real-world TWEET-FID data show that EGAL outperforms strong baseline models across different settings, including varying expert-labeled set sizes and class imbalance ratios. A case study on a multistate outbreak of Salmonella Typhimurium infection linked to packaged salad greens demonstrates how the trained model captures relevant tweets offering valuable outbreak insights. EGAL, funded by the U.S. Department of Agriculture (USDA), has the potential to be deployed for real-time analysis of tweet streaming, contributing to foodborne illness outbreak surveillance efforts.

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