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7th International Conference on Information Technology Systems and Innovation, ICITSI 2022

作者： Parjuangan, Sabam Suhardi Nughara, I Gusti Bagus Baskara Khoirunnisa, Elvin Doctoral Program of School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung40132 Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung40132 Indonesia University of Twente Faculty of Electrical Engineering Mathematics Computer Science Enschede Netherlands

ISBN: (数字)9781665465557

ISBN: (纸本)9781665465557

Service systems' business processes show the existence of mutual interaction between users. Running a particular business subdomain requires the role of each user. This role is in line with the concept contained in blockchain-based smart contracts. However, determining the subdomains that require smart contract coding is challenging in developing business processes that will run on the blockchain. We aim to determine the service-oriented business process (in the case of marketplace service) and the subdomain of the business process that requires smart contract coding. This article is essential because it shows how developing IT services without a smart contract differs from developing IT services with a smart contract. The main differences between developing services that need smart contracts and those that do not are that certain process can be run automatically and the record of activity in blockchain cannot be changed by anyone (tamper-proof). We outlined methods for designing marketplace business processes and defined subdomains to be encoded into smart contract code. After reviewing the three marketplace platforms, we determined eight subdomains of the marketplace business process. Two subdomains require smart contract coding from eight, namely the payment and complaint subdomains. © 2022 IEEE.

关键词： Smart contract

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Study on Autonomous Delivery of Guidewire Based on Improved YOLOV5s on Vascular Model Platform

Study on Autonomous Delivery of Guidewire Based on Improved ...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Wang, Shuang Liu, Zheng Shu, Xiongpeng Cao, Yongfeng Zhang, Ling Xie, Le School of Materials Science and Engineering Shanghai Jiao Tong University Shanghai200240 China School of Mathematics and Computer Science Quanzhou Normal University Quanzhou362000 China Institute of Forming Technology and Equipment Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9781665481090

Cardiovascular interventional surgery robot is an effective means to solve the problem of X-ray radiation injury and physiological fatigue of surgeons. With the development of robotics, robotic autonomous surgery has become one of the research hotspots. However, the current intelligence level of cardiovascular interventional surgery robots is low. Aiming at the guidewire delivery process of the designed cardiovascular interventional surgery robot, this paper proposes a new control strategy based on improved YOLOV5s deep learning algorithm, which can realize the autonomous delivery of the guidewire under image navigation. The development of the vascular model platform experiment proved the feasibility of the scheme. © 2022 IEEE.

关键词： Image enhancement

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Multi-Scale Dynamic and Hierarchical Relationship Modeling for Facial Action Units Recognition

Multi-Scale Dynamic and Hierarchical Relationship Modeling f...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Zihan Wang Siyang Song Cheng Luo Songhe Deng Weicheng Xie Linlin Shen Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University Shenzhen Institute of Artificial Intelligence and Robotics for Society National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Universiry of Leicester Monash University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Human facial action units (AUs) are mutually related in a hierarchical manner, as not only they are associated with each other in both spatial and temporal domains but also AUs located in the same/close facial regions show stronger relationships than those of different facial regions. While none of existing approach thoroughly model such hi-erarchical inter-dependencies among AUs, this paper proposes to comprehensively model multi-scale AU-related dynamic and hierarchical spatiotemporal relationship among AUs for their occurrences recognition. Specifically, we first propose a novel multi-scale temporal differencing network with an adaptive weighting block to explicitly capture facial dynamics across frames at different spatial scales, which specifically considers the heterogeneity of range and mag-nitude in different AUs' activation. Then, a two-stage strategy is introduced to hierarchically model the relationship among AUs based on their spatial distribution (i.e., local and cross-region AU relationship modelling). Experimental results achieved on BP4D and DISFA show that our approach is the new state-of-the-art in the field of AU occurrence recognition. Our code is publicly available at https://***/CVI-SZU/MDHR.

关键词： Gold Adaptation models computer vision Graphical models Codes Adaptive systems Computational modeling

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Features Reduction with PCA Technique for Malware Detection Using Machine Learning

Features Reduction with PCA Technique for Malware Detection ...

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International Conference on Computing, engineering, and Design (ICCED)

作者： Teddy Mantoro Agung Ginanjar Media Anugerah Ayu Umar Aditiawarman Department of Computer Science Faculty of Engineering and Technology Sampoerna University Jakarta Indonesia Postgraduate Program - School of Computer Science Nusa Putra University Sukabumi Indonesia

Malware had been a problem for quite some times since it spreads easily and can cause various problems. Currently, malware is also one of the big threats for internet users. With a huge number of internet users today, techniques that can automatically detect malware before it infects the system is required. This study aims to develop malware detection using machine learning approach with Principal Component Analysis (PCA) as feature reduction. PCA (Principal Component Analysis) is expected to be able to reduce the number of features which then could also reduce the learning time but do not reduce its accuracy significantly. There were four machine learning classifiers used in this study, i.e. K-Nearest Neighbor, Decision Tree, Naïve bayes, and Random Forest. The n-components used in this study were 20 and 34 and the ratio of test and train in the dataset was 35% for test and 65% for training. The results have shown that the best performance come from the detection using random forest with 34 n-component and 100 n-estimator with the average accuracy was 0.991688.

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Modeling and analysis of a CT-guided lung puncture robot

Modeling and analysis of a CT-guided lung puncture robot

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Zhang, Ling Wang, Shuang Shu, Xiongpeng Xie, Le Shanghai Jiao Tong University School of Materials Science and Engineering Shanghai200240 China Quanzhou Normal University School of Mathematics and Computer Science Quanzhou362000 China Institute of Forming Technology and Equipment Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9781665481090

In this paper, a five-degree-of-freedom lung puncture biopsy surgical robot system was proposed to solve the clinical problem of small CT aperture and limited puncture space, and the overall size of the robot is within 20∗20∗20 cm. By kinematic analysis of the robot configuration, a working space that can completely cover the patient's chest was obtained. The design of the parallel manipulators was characterized with higher accuracy and stiffer than the serial robots, making the robot end-effector meet the remote immobile puncture effect and reduce the damage to the patient's skin surface. The finite element model of the structure was constructed and statically simulated by Abaqus, and the deformation and strength of the mechanism met the requirements during the operation. © 2022 IEEE.

关键词： Degrees of freedom (mechanics)

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RAINER: A Robust Ensemble Learning Grid Search-Tuned Framework for Rainfall Patterns Prediction

arXiv

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arXiv 2025年

作者： Li, Zhenqi Zhong, Junhao Wang, Hewei Xu, Jinfeng Li, Yijie You, Jinjiang Zhang, Jiayi Wu, Runzhi Dev, Soumyabrata The Fu Foundation School of Engineering and Applied Science Columbia University United States Biomedical Engineering Department Pratt School of Engineering Duke University United States School of Computer Science University College Dublin Ireland Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Robotics Institute School of Computer Science Carnegie Mellon University United States Faculty of Science and Engineering University of Nottingham Ningbo China China College of Intelligence and Computing Tianjin University China The ADAPT SFI Research Centre

Rainfall prediction remains a persistent challenge due to the highly nonlinear and complex nature of meteorological data. Existing approaches lack systematic utilization of grid search for optimal hyperparameter tuning, relying instead on heuristic or manual selection, frequently resulting in sub-optimal results. Additionally, these methods rarely incorporate newly constructed meteorological features such as differences between temperature and humidity, to capture critical weather dynamics. Furthermore, there is a lack of systematic evaluation of ensemble learning techniques and limited exploration of diverse advanced models introduced in the past one or two years. To address these limitations, we propose a Robust ensemble LeArning grId search-tuNed framEwoRk (RAINER) for rainfall prediction. RAINER incorporates a comprehensive feature engineering pipeline, including outlier removal, imputation of missing values, feature reconstruction, and dimensionality reduction via Principal Component Analysis (PCA). The framework integrates novel meteorological features to capture dynamic weather patterns and systematically evaluates non-learning mathematical-based methods and a variety of machine learning models, from weak classifiers (e.g., KNN, LASSO, and Random Forest) to advanced neural networks such as Kolmogorov-Arnold Networks (KAN). By leveraging grid search for hyperparameter tuning and ensemble voting techniques, RAINER achieves state-of-the-art results within real-world dataset. Extensive experiments demonstrate the framework’s superior performance across diverse metrics, such as Accuracy, Precision, Recall, F1-score, and AUC, highlighting the pivotal role of feature engineering, grid search, and ensemble learning in rainfall prediction tasks. © 2025, CC BY.

关键词： Dimensionality reduction

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Sentiment Analysis in Indonesian Trading using Lexicon-based and Support Vector Machine

Sentiment Analysis in Indonesian Trading using Lexicon-based...

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computer science, Information Technology and engineering (ICCoSITE), International Conference on

作者： Raymond Sunardi Oetama Yanfi Yanfi Masagus M. Ikhsan Assiddiq U.P Sani Muhamad Isa Information System DepartmentFaculty of Engineering and Informatics Universitas Multimedia Nusantara Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia

Many individuals now trade online utilizing trading software in the digital world. Binomo is one of Indonesia's most popular trading platforms. This is because some influencers made several promises to Binomo customers. Since many customers were deceived, this case became quite popular. This study was executed to see how Indonesians felt about the Binomo application after the case went viral. The solution taken was in the form of sentiment analysis because there had been no previous research on sentiment analysis that discussed the Binomo case. The data was scanned using Netlytic tools, a cloud-based text and social network analyzer capable of identifying any talks on social media sites such as Twitter. The sentiment analysis of Binomo trading tweets by using the Multi-Perspective Question Answering lexicon utilized the KNIME tool. But unfortunately, the accuracy of sentiment analysis results is low. Furthermore, the Support Vector Machine technique is also being conducted. The Term Frequency-Inverse Document Frequency method is applied to perform feature extraction whilst the chi-square approach is utilized to identify features that are thought to be useful for inclusion in the classification process and to exclude features that are irrelevant to the target class. The obtained accuracy is 86%. The study proposes that words from the algorithm's outputs can be utilized to improve the quality of sentiment analysis using the lexicon. As an outcome of the algorithm, positive and negative terms are added to the lexicon, increasing the accuracy of sentiment analysis using the new vocabulary from 58.984% to 71.146%.

关键词： Support vector machines Training Sentiment analysis Vocabulary Dictionaries Social networking (online) Blogs

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MOSFET Based Active Rectifier Design in Wireless Capacitive Power Transfer for Electric Vehicles

MOSFET Based Active Rectifier Design in Wireless Capacitive ...

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Smart Mechatronics (ICSMech), IEEE International Conference on

作者： Klemens Raphael Dragon Zener Sukra Lie Aam Muharam Juan Luciano Ika Muhamad Arjuna Perdana Amin Abdul Hapid Computer Engineering Department Automotive and Robotics Program BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia Research Center for Transportation Technology Badan Riset dan Inovasi Nasional Bandung Jawa Barat Indonesia

ISBN: (数字)9798350368918

ISBN: (纸本)9798350368925

While the rise of electric vehicles reflects a push for clean energy as envisioned by international agreements like the Paris Agreement, wireless charging's convenience can overcome limitations and hasten their adoption, making it a strategic step alongside regulations such Permenhub No. 15/2022 and SDG 9 “Industry, Innovation, and Infrastructure” for cleaner air and sustainable transportation. The aim is to convert the high frequency AC voltage from the coupling capacitor into a stable DC voltage. Further increase the system sensitivity to the load variations, by designing a MOSFET based active rectifier. By changing load parameters, the accuracy of PFC adaptation to the battery State of Charge (SOC) obtained is at 98% with 2% tolerance for errors. It is concluded that the research is said to be successful because it has met the parameters of general research hypothesis where the system is able to convert high frequency AC voltage into a stable DC voltage.

关键词： Wireless communication MOSFET Wireless sensor networks Rectifiers High-voltage techniques Electric vehicles Batteries State of charge High frequency Voltage control

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Applying CNN Models with Stereo Camera for Object Detection and Distance Estimation in Automatic Micro Electric Vehicle (MEVi)

Applying CNN Models with Stereo Camera for Object Detection ...

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Smart Mechatronics (ICSMech), IEEE International Conference on

作者： Farrell Putra Harimanto Nurul Hasanah Muhammad Khristamto Aditya Pradana Muhammad Nurul Puji Computer Engineering Department Automotive & Robotics Program BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia Research Center for Smart Mechatronics National Research and Innovation Agency KST Samaun Samadikun Bandung Indonesia

ISBN: (数字)9798350368918

ISBN: (纸本)9798350368925

Technological advancements in the automotive industry are currently focused on autonomous driving systems or driver assistance systems. Depth estimation is also an important feature of the autonomous driving system as it can accurately estimate the distance to the detected objects. In current developments technologies such as stereo cameras are more popular for a more accurate depth estimation. The process for object detection consists of a combination of classification, localization, and segmentation. Deep convolutional neural networks are the basis of object detection. The methods for object detection are divided into two categories namely the Region Proposal-based method such as RCNN, and the Classification-based method which provides for methods such as YOLO and MobileNet. As a part of the research conducted by the National Research and Innovation Agency (BRIN) on Automatic Micro Electric Vehicles or MEVi, this paper analyzed and compared the performance of several CNN models for object detection as well as the performance of depth sensing using ZED stereo camera which was used for the autonomous system in MEVi. Among several models that had been compared, YOLO v8 model demonstrates high accuracy and moderate speed as indicated by its high mAP (0.50) and FPS (38.46). Based on depth sensing results, the accuracy of distance measurement also depended on brightness and environment with the average of accuracy achieved indoors was 98.2% and outdoors was 96.3%.

关键词： YOLO Accuracy Depth measurement Brightness Cameras Electric vehicles Distance measurement Sensors Convolutional neural networks Autonomous vehicles

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Enhancing Security Information and Event Management (SIEM) by Incorporating Machine Learning for Cyber Attack Detection

Enhancing Security Information and Event Management (SIEM) b...

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International Conference on Computing, engineering, and Design (ICCED)

作者： Media Anugerah Ayu Dimas Erlangga Teddy Mantoro Dini Handayani Department of Computer Science Faculty of Engineering and Technology Sampoerna University Jakarta Indonesia Postgraduate Program - School of Computer Science Nusa Putra University - Sukabumi Indonesia

Network security is a crucial component of Information Technology, yet organizations continue to grapple with meeting established security benchmarks. Given the rise in cyber-attacks and the continuous emergence of new attack types, it’s practically infeasible to persistently update attack patterns or signatures within security parameters. Key tools such as Intrusion Detection Systems (IDS) and Security Information and Event Management (SIEM) are instrumental in monitoring network traffic and identifying potential threats. However, these tools face limitations, such as the high volume of alerts produced by IDS and the use of rule-based method, also the inability of SIEM tools to analyze logs comprehensively to identify inappropriate activities. This research has conducted anomaly detection using machine learning process to classify cyber-attacks network flow collected from IDS that installed incident network infrastructure. The analysis of IDS using machine learning, integrated with SIEM. The algorithm used in this research was Random Forest Classifier using CSE-CID-IDS2018 dataset pre-processed with Principal Component Analysis (PCA). Results of the experiments show that Random Forest Classifier Model, when combined with Principal Component Analysis (PCA), yields the most commendable results when applied to a 70/30 training/testing data ratio with accuracy of 0.99953.

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