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8th International Conference on Information Management and Technology, ICIMTech 2023

作者： Rochadiani, Theresia Herlina Soeparno, Haryono Gaol, Ford Lumban Arifin, Yulyani Bina Nusantara University Binus Graduate Program - Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia Pradita University Informatics Department Tangerang15810 Indonesia

ISBN: (纸本)9798350326093

The Internet of Things (IoT) was evaluated as part of this study. The IoT system in this study is the air quality monitoring system. The system consists of three layers, three end devices with air quality monitoring sensors as the perception layer, the LoRaWAN network as the network layer, and the monitoring website as the presentation layer. Before being made available to the public, IoT hardware and software must undergo testing. For IoT systems, there are no established standards or quality criteria. This study's goal is to evaluate the methodology for an air quality monitoring system. Website speed is measured using Pingdom tools. The website received a performance grade of B. Pingdom offers advice for enhancing website performance. Sucuri is utilized as a security test tool to evaluate the website's security, and consequently, no malware was discovered. The latency and data dependability are monitored to assess the system's performance fully. The outcome demonstrates that all system components consistently and accurately receive data with a latency of less than 1 s. © 2023 IEEE.

关键词： Internet of things

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Pap Smear Medical Image Classification Using Deep Learning: A Systematic Review 6

Pap Smear Medical Image Classification Using Deep Learning: ...

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6th International Conference on computer and Informatics Engineering, IC2IE 2023

作者： Selvano, Elvan Elwiehardja, Gregorius Natanael Pardamean, Bens Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta Indonesia

ISBN: (纸本)9798350345162

Cervical cancer has been known as one of the most prevalent medical disorders globally and a leading cause of death. Early detection, particularly through Pap tests, plays a vital role in its prevention. Previous studies have leveraged machine learning and deep learning techniques to classify the medical images obtained from Pap tests. In this study, a Systematic Literature Review methodology was used to examine 15 relevant papers that have been filtered from queries to Google Scholar which have gone through 4 stages of filtering that include: identification, screening, eligibility, and inclusion. This study addresses two research questions regarding the datasets and deep learning techniques for classifying pap smear images in recent years. The performance of the models was analyzed and potential areas for improvements are suggested. The findings of this study reveal that the Herlev university Hospital and SIPaKMed datasets are the most used. The methodologies used by researchers range from machine learning techniques, transfer learning using Convolutional Neural Networks, and utilize state-of-the-art models with novel optimizing methodology. While there are exciting opportunities in the field, challenges include model generalization and interpretability. © 2023 IEEE.

关键词： cervical cancer deep learning machine learning pap test

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Systematic Literature Review on Metaheuristic Algorithms in SNP Analysis for Diseases 6

Systematic Literature Review on Metaheuristic Algorithms in ...

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

作者： Sulistyawan, Jason Sebastian Nugroho, Kuncahyo Setyo Pardamean, Bens Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798331539603

Single-nucleotide polymorphism (SNP) analysis has become a pivotal strategy for drug discovery within bioinformatics, especially for incurable diseases like cancer. With the increasing number of researchers starting to embrace metaheuristic methods, a review was done by gathering papers from the Google Scholar database from 2018 to 2023, which resulted in 20 papers after title, abstract, and content filtering. The findings show that the Genetic Algorithm and the Harmony Search Algorithm have become popular approaches in SNP analysis, particularly in studies on breast cancer, age-related macular degeneration, and colorectal cancer. However, the review shows that while researchers have proven most methods effective in finding disease-related SNPs, a more measurable study in SNP analysis is needed, due to the lack of elaboration on measurement metrics in the found studies. © 2024 IEEE.

关键词： metaheuristic algorithms single nucleotide polymorphisms

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Identifying The Types and Platforms of the Metaverse - a Systematic Literature Review

Identifying The Types and Platforms of the Metaverse - a Sys...

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2022 IEEE Creative Communication and Innovative Technology, ICCIT 2022

作者： Suryodiningrat, Satrio Pradono Prabowo, Harjanto Ramadhan, Arief Santoso, Harry Budi Bina Nusantara University Computer Science Binus Graduate Program Jakarta Indonesia Bina Nusantara University Computer Science Graduate Program Jakarta Indonesia Universitas Indonesia Faculty of Computer Science Depok Indonesia

ISBN: (纸本)9781665468763

Metaverse is gaining a lot of traction lately, especially after Mark Zuckerberg's Facebook changed its name to Meta as a preparation to enter another universe called metaverse. The eXtended Reality (XR) technology is also rapidly growing since the birth of Virtual Reality (VR), followed by Augmented Reality (AR), and Mixed Reality is the latest development of XR technology. As the XR technology is the perfect companion to support the metaverse and the understanding of the metaverse is already clear but unfortunately, there is a lot of misconception about the types of the metaverse as well as the platform for each type. This paper aims to point out the types and platforms of the metaverse. 49 articles have been collected and reviewed carefully as a quality assessment process, and 45 articles have been selected for further review. This review informs everybody that there are many types and platforms of the metaverse nowadays. © 2022 IEEE.

关键词： Mixed reality

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Systematic Literature Review of Transformer Model Implementations in Detecting Depression 6

Systematic Literature Review of Transformer Model Implementa...

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6th International Conference on computer and Informatics Engineering, IC2IE 2023

作者： Nanggala, Kenjovan Pardamean, Bens Elwirehardja, Gregorius Natanael Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798350345162

This systematic literature review explores the application of transformer models in early detection of human depression, encompassing text, audio, and video data modalities. Transformer architectures, notably BERT for text, have proven adept at capturing crucial contextual and linguistic patterns associated with depression. For audio and video data, hybrid approaches that combine transformer models with other architectures are prevalent. Key features considered include eye gaze, head pose, facial muscle movements, and audio characteristics such as MFCC and Log-mel Spectrogram, along with text embeddings. Performance comparisons underscore the superiority of text-based data in consistently delivering the most promising results, followed by audio and video modalities when utilizing transformer models. The fusion of multiple modalities emerges as an effective strategy for enhancing predictive accuracy, with the amalgamation of audio, video, and text data yielding the most precise outcomes. However, it is noteworthy that unimodal approaches also exhibit potential, with text data exhibiting superior performance over audio and video data. Nevertheless, several challenges persist in this research domain, including imbalanced datasets, the limited availability of comprehensive and diverse samples, and the inherent complexities in interpreting visual cues. Addressing these challenges remains imperative for the continued advancement of depression detection using transformer-based models across various modalities. © 2023 IEEE.

关键词： deep learning major depressive disorder multimodal transformer unimodal

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Semi-automated meningioma segmentation with bounding boxes 9th

Semi-automated meningioma segmentation with bounding boxes

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Heryanto, Nur Adhianti Isnan, Mahmud Henry, Matthew Martianus Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

Segmentation is manually performed by physicians, which takes considerable time and may be subject to observers. Automating this task can increase efficiency and consistency. Existing studies on meningioma segmentation used data from limited study centers, indicating the need for research on multi-center data to assess generalizability. In this work, two semi-automated methods with bounding box priors, LiteMedSAM and BBU-Net, are evaluated on the brain tumor segmentation (BraTS) 2023 meningioma dataset collected from five study-centers. Preprocessing included exclusion of small tumors, z-score normalization, and extraction of slices that contain tumors, generating 25,602 2D axial magnetic resonance imaging (MRI) scans. A fine-tuning strategy is adopted for LiteMedSAM while BBU-Net is trained from scratch. The models are evaluated using a five-fold cross-validation, with data split at the case level. Results show that while U-Net models can achieve performance close to LiteMedSAM, the foundation model has overall better performance, with more than 90% in all evaluation scores. © 2024 The Authors.

关键词： Magnetic resonance imaging

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Evaluating the Effectiveness of Transformer Models for Molecular Property Prediction: A Review 9

Evaluating the Effectiveness of Transformer Models for Molec...

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9th International Conference on Information Technology and Digital Applications, ICITDA 2024

作者： Imani, Alyssa Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Binus Graduate Program Bina Nusantara University Computer Science Departement Jakarta11480 Indonesia

ISBN: (纸本)9798331510732

Transformer models, originally successful in natural language processing, are now being applied to chemical and biological studies, excelling in areas such as molecular property prediction, material science, and drug discovery. BERT, a Transformer-based model, has become foundational in cheminformatics, particularly for QSAR (Quantitative Structure-Activity Relationship) modeling and ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) evaluations in drug discovery. However, achieving higher accuracy often requires designing more complex models, which can compromise their interpretability. This posing a challenge for researchers who need to understand the reasoning behind the predictions. The trade-off between accuracy and interpretability presents a critical challenge in applying black box models to real-world problems in cheminformatics. This work compares Transformer-based models with traditional machine learning and deep learning approaches, focusing on both interpretability and performance. The goal is to highlight the strengths and limitations of each method, offering insights into their optimal use in drug discovery and material science. © 2024 IEEE.

关键词： Prediction models

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Automated Treadmill Control Strategy for Gait Rehabilitation Based on Human-Machine Interaction

Automated Treadmill Control Strategy for Gait Rehabilitation...

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2023 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, ChileCon 2023

作者： MacEdo, Breno C. Moreno, Brayan S. MacHado, Fabiana Morais, Joao V. Mello, Ricardo C. Frizera, Anselmo Federal University of Espírito Santo Graduate Program in Electrical Eng. ES Vitória Brazil Federal University of Espírito Santo Undergraduate Program in Computer Eng. ES Vitória Brazil

ISBN: (纸本)9798350369533

Human mobility involves the nervous and muscular systems, essential for the planning and execution of movements such as walking. According to the WHO, approximately 3.8% of the global population faces challenges in walking. This article presents the development of a human-machine interface with the goal of testing the system and validating user perception to make it more accessible, efficient, and enjoyable for patients. The integration of serious games into the system was chosen to establish an interactive environment that blends gait training, driving physical rehabilitation. This approach focuses on integrating technology into conventional therapy to offer a safer and more motivating environment. The system was validated with twelve volunteers in a series of tasks that offered different degrees of autonomy when interacting with the treadmill. The evaluation of the control strategy in the tasks used the SUS questionnaire, indicating an acceptability score above 70. The results demonstrate the adaptability of the treadmill to individuals' speed due to the developed control model, promoting its use in applications combined with serious games, thus enhancing the user experience without increasing complexity. © 2023 IEEE.

关键词： Serious games

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Smart Services Implementation in Smart Cities: A Comprehensive Review of State-of-the-art Technologies 23

Smart Services Implementation in Smart Cities: A Comprehensi...

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5th International Conference on Management science and Industrial Engineering, MSIE 2023

作者： Arin, Ikrar Adinata Supangkat, Suhono Harso Gaol, Ford Lumban Ranti, Benny Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Indonesia Smart City and Community Innovation Center Institut Teknologi Bandung Indonesia Computer Science Department Faculty of Computer Science University of Indonesia Indonesia

ISBN: (纸本)9798400708381

This paper discusses comprehensively the advanced technologies used in the implementation of smart services, which are key elements of a smart city. Smart cities are becoming eco-friendly, livable, and sustainable to deliver essential services to their citizens by leveraging internet and communication technology advancements. Starting from a short literature review about smart cities and smart services, a recent trend in the technology used to implement smart services is collected. Smart service can be defined as a service conveyed using intelligent products that possess awareness and connectivity. All kinds of smart services can be connected in smart cities to offer citizens comprehensive solutions. Pre-emptive services, self-services, or highly interactive services, for example, have the potential to be implemented using current technology such as the internet of things, artificial intelligence, cloud computing, big data, machine learning, machine-to-machine communications, and the digital twin. Therefore, there is scope for research and implementation of other technology for smart services in the future. © 2023 ACM.

关键词： Smart city

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Speed Bump Detection Through Inertial Sensors and Deep Learning in a Multi-contextual Analysis

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SN computer science 2023年第1期4卷 1-20页

作者： Menegazzo, Jeferson von Wangenheim, Aldo Graduate Program in Computer Science (PPGCC) Image Processing and Computer Graphics Lab (LAPIX) Brazilian National Institute for Digital Convergence (INCoD) Department of Informatics and Statistics (INE) Federal University of Santa Catarina (UFSC)-Florianópolis Santa Catarina Brazil

Speed bumps are vertical raisings of the road pavement used to force drivers to slow down to ensure greater safety in traffic. However, these obstacles have disadvantages in terms of efficiency and safety, where the presence of speed bumps can affect travel time and fuel consumption, cause traffic jams, delay emergency vehicles, and cause vehicle damage or accidents when not properly signaled. Due to these factors, the availability of geolocation information for these obstacles can benefit several applications in Intelligent Transportation System (ITS), such as Advanced Driver Assistance Systems (ADAS) and autonomous vehicles, allowing to trace more efficient routes or alert the driver of the presence of the obstacle ahead. Speed bump detection applications described in the literature employ cameras or inertial sensors, represented by accelerometers and gyroscopes. While camera-based solutions are mature with evaluation in different contextual conditions, those based on inertial sensors do not offer multi-contextual analyses, being mostly simple applications of proof of concept, not applicable in real-world scenarios. For this reason, in this work, we propose the development of a reliable speed bump detection model based on inertial sensors, capable of operating reliably in contextual variations: different vehicles, driving styles, and environments in which vehicles can travel to. For the model development and validation, we collect nine datasets with contextual variations, using three different vehicles, with three different drivers, in three different environments, in which there are three different surface types, in addition to variations in conservation state and the presence of obstacles and anomalies. The speed bumps are present in two different pavement types, asphalt and cobblestone. We use the collected data in experiments to evaluate aspects such as the influence of the placement of the sensors for vehicle data collection and the data window size. Afterwar

关键词： Autonomous vehicles Deep learning Inertial sensors Intelligent transport systems Road conditions Speed bump

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