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IEEJ Transactions on Electronics, Information and Systems 2025年第1期145卷 16-24页

作者： Shouno, Kazuhiro Sato, Lina Fukagawa, Kanta Tanimoto, Hiroshi Muto, Cosy Division of Information Engineering University of Tsukuba 1-1-1 Tennoudai Ibaraki Tsukuba305-8573 Japan Graduate School of Science and Technology Degree Programs in Systems and Information Engineering Master’s Program in Computer Science University of Tsukuba Japan Kitami Institute of Technology 165 Koen-cho Hokkaido Kitami090-8507 Japan Faculty of Education Nagasaki University 1-14 Bunkyo-machi Nagasaki852-8521 Japan

This paper proposes a measurement technique for an integrated complex filter. The proposed method is based on two measurement methods with integrated circuitry for calibration. It is accomplished by applying square waves to an FUT (Filter Under Test) instead of sinusoidal waves or by superposing transfer characteristics obtained by applying a single sinusoidal wave to an FUT. In both of the above methods, the output signal connected to the external measurement equipment via a 50 Ω coaxial cable and so on. In this case, the output signal of the FUT should be amplified by internal or external amplifiers, which degrades the response. However, it is cancelled out by the proposed calibration circuitry with some analog switches. In addition to the above, for the RC polyphase filter, the source resistance and the load capacitance degrade the measurement results and their effect is discussed. The validity of the proposal is confirmed through computer simulation for a third-order RCPF whose passband is 8–40 MHz fabricated by Phenitec 0.6 µm CMOS process. It is shown that the measurement error is −1.10 dB for the square-wave method and −1.00 dB and −0.43 degrees for the superposition method at 40 MHz. © 2025 The Institute of Electrical Engineers of Japan.

关键词： Capacitance

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Hoax Detection in Social Media: A Literature Review

Hoax Detection in Social Media: A Literature Review

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International Conference on Artificial Intelligence and Data Sciences (AiDAS)

作者： Carsten Sebastian Saputra Norbert Wijaya Maria Susan Anggreainy Afdhal Kurniawan Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta Indonesia

For both print and digital media, the accuracy of the information has long been a problem that has impacted society and the business world. Information travels so quickly on social networks and is amplified that it can be skewed, false, or misleading. Inaccurate or incorrect information has great potential to affect millions of real-world users directly. Therefore, finding or getting accurate, trusted, and precise information becomes a problem. People start to spread fake news, which can cause certain events, such as mental disturbance, riots, and unnecessary anxiety. To prevent these problems, we will solve/research them with CountVectorizer and TF-IDF, which is what this study is all about (Discusses the detection of hoax news using CountVectorizer and TF-IDF). It was found that using these methods makes it possible to detect which words are most common in fake news and real news.

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Leveraging ChemBERTa Robustness in Drug-Drug Interaction Classification via Molecular Decomposition

Leveraging ChemBERTa Robustness in Drug-Drug Interaction Cla...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Christian Kenneth Alyssa Imani Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

Understanding drug-drug interactions (DDIs) can benefit us by enhancing drug effectiveness and preventing side effects that arise from consuming multiple drugs administered together. However, as the number of novel drugs increases, predicting potential DDIs becomes more challenging. Previous studies have used the molecular language model ChemBERTa to predict potential DDIs, but the predictions were only tested on unseen drugs and not dissimilar drugs, while also performing poorly. In this study, we demonstrate that a simple molecular preprocessing method using BRICS decomposition can enhance ChemBERTa robustness in predicting potential DDIs for unseen-dissimilar drugs. Specifically, we employ fragmentation with a minimum fragment size of 5 (BRICS-5), which balances the number of fragments and decomposition speed. Using this method, we were able to improve ChemBERTa robustness, reflected by an approximate increase of 1.5% in recall and 2.2% in the f1-score compared to non-decomposed molecules. Our findings suggest that this preprocessing method could be useful in the development of future AI-based approaches for solving challenging molecular tasks across different settings and various model architectures.

关键词： Drugs Seminars Accuracy Data preprocessing Predictive models Robustness Data models Intelligent systems Information technology

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Deep learning for oil palm fruit ripeness classification with densenet 6

Deep learning for oil palm fruit ripeness classification wit...

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6th International Conference on Information Management and Technology, ICIMTech 2021

作者： Herman, Herman Cenggoro, Tjeng Wawan Susanto, Albert Pardamean, Bens Bina Nusantara University BINUS Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Bioinformatics and Data Science Research Center Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program - Master of Computer Science Program Bioinformatics and Data Science Research Center Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665449373

Oil palm fruit farming is one of the most leading agriculture industries in the South East Asia region. Unfortunately, most of the harvesting method is still done through manual labor. Multiple research has been conducted to help farmers automatically detect the ripeness level of the oil palm fruit. We conducted an experiment of detecting the ripeness level of oil palm fruit by using one of the state-of-the-art computer vision approaches, which was deep learning with a Convolutional Neural Network (CNN). The dataset used consists of 7 levels of ripeness with 400 images of oil palm fruits. The models used in this test scenario were both AlexNet and DenseNet. The result of this study showed that DenseNet has been proven to outperform AlexNet by 8.5% in terms of accuracy and 8% in F1 score. © 2021 IEEE.

关键词： computer vision

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Location Analysis Probing Rockets in Ground with Telemetry Data

Location Analysis Probing Rockets in Ground with Telemetry D...

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International Symposium on Instrumentation Systems, Circuits and Transducers (INSCIT)

作者： Nilton Rodrigues Cantanhede Ewaldo Eder Carvalho Santana Paulo Fernandes da Silva Júnior Raimundo Carlos Silvério Freire Tiago Pereira Santana Lúcio Flávio de Albuquerque Campos Gabrielle Muniz Fernandes Maria Tereza Cunha de Albuquerque Sophia Viana Macedo Graduate Program in Electrical Engineering Federal University of Maranhão São Luís Brazil Graduate Program in Computer and System Engineering State University of Maranhão São Luís Brazil Graduate Program in Electrical Engineering Federal University of Campina Grande Campina Grande Brazil Graduate Program in Computational Engineering and Systems State University of Maranhão São Luís Brazil Undergraduate Program in Computational Engineering State University of Maranhão São Luís Brazil Graduate Program in Electrical Engineering Federal University of Paraiba João Pessoa Brazil

ISBN: (数字)9798350350869

ISBN: (纸本)9798350350876

Rocket launch centers are crucial in technological development across various fields, including meteorology, long-distance real-time communications, geoprocessing, and satellite operations. However, one of the challenges in tracking probing rockets is accurately identifying their position from the moment of launch until the initial seconds, due to ground noise that interferes with traditional radar location systems. In this study is proposed an innovative method to precisely determine the distance between the rocket and the ground telemetry station. We utilize onboard telemetry signals processed by the Acquisition and Processing Data Trajectory (APDtraj) system, developed by Delsis Aerospace for the Launch Center. This system enables the identification of the rocket's position both on the ground and during the early stages of flight, using telemetry data processed by APDtraj. This monitoring equipment alone calculated the distance between the Telemetry Station and the rocket just moments before launch lift-off with an error of only 16.844 meters from the actual position of origin. Based on our studies, we were able to measure the radial distance and location between the rocket and the telemetry station just four seconds after launch, even without reliable radar information, which is often disrupted by ground noise. This technological advancement not only enhances the efficiency of rocket launch operations but also expands overall tracking capabilities.

关键词： Rockets Transducers Satellites Radar measurements Spaceborne radar Noise Radar tracking Real-time systems Trajectory Telemetry

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Mitigating Berkson's Paradox with Neural Propensity Score Matching in E-Commerce Deals

Mitigating Berkson's Paradox with Neural Propensity Score Ma...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Nicholas Dominic Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

Within observational Data Science workloads, Berkson's paradox can lead to false causal inferences. One of the prominent quasi-experimental methods to mitigate this selection bias is Propensity Score Matching (PSM). An approach called Neural PSM (NPSM) was developed to overcome the drawbacks of conventional regression-based PSM, including its limited flexibility to model high-dimensional data and non-linear relationships that could cause imperfect covariate balance. In this study, a three-layer depth of Deep Neural Networks was designed to estimate propensity scores and finally balance both control and treatment groups of the Groupon dataset. An unsupervised k-Nearest Neighbor algorithm then helped the model to efficiently detect and cluster similar matching points. From the five salient features presented, NPSM successfully achieved lower differences in Cohen's d effect size, i.e., 0.313 for coupon duration, 0.017 for promotion length, 0.425 for quantity sold, -0.199 for limited supply, and 0.395 for Facebook likes. While these results mostly outperformed Linear Regression (LR) and Random Forest (RF) models, further evaluation is needed to verify the true effectiveness of NPSM in mitigating Berkson's paradox in broader e-commerce contexts.

关键词： Radio frequency Social networking (online) High dimensional data Linear regression Nearest neighbor methods Data science Electronic commerce Intelligent systems Cybernetics Context modeling

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Model Prediction Using Random Forest Algorithm and Time Series Analysis Parts Distribution with in Indonesia Automotive Industry

Model Prediction Using Random Forest Algorithm and Time Seri...

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International Conference on Informatics and Computing (ICIC)

作者： H Andy Achmad Arief Ramadhan H Warnars Harco Leslie Widodo Budiharto Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia School of Computing Telkom University Bandung Indonesia

Car spare parts are parts of a car that consist of components that form a certain unity and function, there are some parts that are damaged more often and therefore must be replaced more often. In this research, to predict the availability of spare parts using the times series moving average prediction method with random forest. The dataset used in this research is 19,068 consisting of location, transaction number, transaction date, part number, quantity and odo-km. Distribution of spare parts classified based on recapitulation of spare part distribution is RAE=54.5541% and RSE=63.8888%, distribution of spare parts based on location is RAE=55.5541% and RSE=65.2118%, maintenance based on ODO-KM is RAE=33.0949% and RSE=41.2241 % and distribution quantity RAE=33.749% and RSE=36.2317%. The results of spare part distribution analysis using the Confusion Matrix with probability accuracy are TP Rate=0.903, FP Rate=0.067, Precision=0.911, Recall=0.903, F-Measure=0.893, MCC=0.857, ROC Area=0.983 and PRC Area=0.948. The aim of this research is to determine the factors needed to build a machine learning-based spare parts distribution model, implementation, measurement and prediction in the automotive industry in Indonesia.

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Mitigating Berkson's Paradox with Neural Propensity Score Matching in E-Commerce Deals 6

Mitigating Berkson's Paradox with Neural Propensity Score Ma...

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

作者： Dominic, Nicholas Pardamean, Bens Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta Indonesia BINUS Graduate Program - Master of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798331539603

关键词： Marketplaces

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Trends, Opportunities, and Challenges in Detecting Depressive Disorders Through Mobile Devices: A Review 2

Trends, Opportunities, and Challenges in Detecting Depressiv...

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2nd International Conference on computer System, Information Technology, and Electrical Engineering, COSITE 2023

作者： Elwirehardja, Gregorius Natanael Isnan, Mahmud Perbangsa, Anzaludin Samsinga Muchtar, Kahlil Pardamean, Bens Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mahmud Isnan Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350343069

Depressive Disorders (DD) is one of the most prevalent mental disorders in the world that may lead to suicide cases. To prevent the latter, ubiquitous early detection systems may be effective. Recent studies have since researched the development of such systems by exploiting several forms of data, including video, audio, Ecological Momentary Assessments (EMA), and passive sensing data using sensors embedded in mobile devices. To summarize the trends, opportunities, and existing challenges in this field, this study reviewed 15 papers to answer four research questions. EMA was the most popular data to be used in this task, but other approaches, such as using video, audio, and typing behaviors, may be considered due to the subjectivity of EMA. These data were typically recorded using smartphones and analyzed using Machine Learning (ML). However, most of the developed systems had yet to be implemented. Overall, it was concluded that further studies may need to explore usages of more objective data in multimodal approaches as well as consider using Mobile Cloud Computing (MCC) to deploy these systems to provide more effective and efficient diagnoses. Future studies must also take into account the existing challenges of the data and infrastructures, such as the weaknesses of several data types, limitations of mobile devices, as well as the challenges of diagnosis approaches. © 2023 IEEE.

关键词： Machine learning

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Knowledge Graph-Enhanced Semantic Cache for Low-Latency and Cost-Effective Inference in Large Language Models

Knowledge Graph-Enhanced Semantic Cache for Low-Latency and ...

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International Conference on Information Management and Technology (ICIMTech)

作者： Nicholas Dominic Bens Pardamean Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350390025

ISBN: (纸本)9798350390032

In organizational knowledge management, Large Language Model (LLM) caches act as a semantic repository gathered from previous LLM responses. Due to intensive calls from multiple users, LLM may suffer from high inference latency. While there are many prior available approaches to solve this problem, most of them are inherently complex. This paper introduced a Knowledge Graph-enhanced Semantic Cache mechanism as an alternative, lightweight technique to boost retrieval for similar prompts. The latest state-of-the-art open-source LLM, named Google's Gemma-2B-it, was used to generate sample prompts and responses as a draft, while a knowledge graph (KG) was built from Wikipedia sentences. To create embeddings of prompts and KG, all-MiniLM-L6-v2 from SentenceTransformer was used. This new cache system resulted in up to 28% improvement over a standard model. In particular, reinforcement with KG cache embeddings yielded more than 85% semantic cache accuracy. To map the next trajectory of this pilot study, an overview of the extended framework for LLM knowledge management was also presented in this paper. The framework includes the new KG- enhanced cache system equipped with scalable security and fallback mechanisms that can promote green technology through substantial improvements in latency, throughput, and overall LLM costs.

关键词： Large language models Semantics Throughput Knowledge management Internet Trajectory Security Online services Low latency communication Standards

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