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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3336-3349页

作者： Chen, Liuyin Lu, Di Zhai, Jianxue Cai, Kaican Wang, Long Zhang, Zijun City University of HongKong School of Data Science Hong Kong Nanfang Hospital Southern Medical University Department of Thoracic Surgery Guangzhou510515 China University of Science and Technology Beijing Department of Computer Science and Technology Beijing100083 China University of Science and Technology Beijing Shunde Graduate School Foshan China

Lung function evaluation is important to many medical applications, but conducting pulmonary function tests is constrained by different conditions. This article presents a pioneer study of an integrated invertible deep learning method for lung function estimation via using computed tomography (CT) images. First, the projection method is proposed to flatten the three-dimensional (3-D) image onto a two-dimensional (2-D) plane, with preserving location information in 3-D. Next, the MBConv transformer-based encoder-decoder structure is developed to extract latent features. Finally, we develop an invertible normalizing flow (NF) model to infer lung function based on the extracted features and design two loss functions for two directions. The method enables both estimating the lung function based on CT images and metadata as well as generating the corresponding simulated CT image according to the lung function. Computational studies show that the proposed regression model outperforms all state-of-the-art image regression models. A comprehensive comparative analysis also demonstrates the effectiveness of using generated images and confirms the superiority of the proposed method. To the best of our knowledge, this work is the first of its kind in combining encoder-decoder network with NFs to ensure the effectiveness of the fully invertible framework, especially in lung CT image analysis. © 2020 IEEE.

关键词： Metadata

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Implementing Vision Transformer to Model Emotions Recognition from Facial Expressions 4

Implementing Vision Transformer to Model Emotions Recognitio...

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4th International Conference on Artificial Intelligence and data sciences, AiDAS 2023

作者： Chowanda, Andry Nadia Diana School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Cyber Security Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350318432

Emotions are essential to social interaction between interlocutors. It provides essential meaning to social interaction. It is important to recognise emotions along with verbal cues to fully understand the true meaning of a conversation in a social interaction setting. Several techniques can be applied to recognise emotions in the social interaction setting automatically. Emotions can be recognised and interpreted from the interlocutor's facial cues (e.g., facial expression recognition task) by using a sensor such as a camera. Recognising emotions from facial cues has several problems to be solved. This research aims to model robust emotion recognition from facial cues to recognise emotions (particularly from Asian people), as there is only limited research done in this area. This research aims to propose an emotion recognition model from facial cues by implementing Vision Transformers architecture. The dataset implemented in this research was explicitly performed by local (i.e., Indonesian) actors (The Indonesian Mixed Emotion dataset-IMED). The results show that the proposed model can achieve the best testing accuracy of 100% with the best training loss of 0.0001, with 0.53 hours of training times for 50 epochs. © 2023 IEEE.

关键词： Emotion Recognition

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Recognition of Facial Expressions and Emotions on the Face using the YOLOv5 Algorithm

Recognition of Facial Expressions and Emotions on the Face u...

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2023 International Workshop on Artificial Intelligence and Image Processing, IWAIIP 2023

作者： Gunawan, Alexander A. S. Kusnadi, Maximilianus Raymond Joseph, James Tristan Josan, William Edbert, Ivan Sebastian Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Cyber Security Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350382914

This study examines the significant influence of emotional states on individuals, recognizing that people are often confronted with numerous challenges and problems in their lives. Such challenges can give rise to feelings of despair and tension, especially in those who struggle to manage them effectively. Depression, which is not just limited to adults, can also have a significant impact on children who may not fully grasp their socio-economic conditions. One of the serious consequences of unresolved emotional distress, particularly depression, is an elevated risk of suicide. Within this framework, facial recognition technology presents itself as a powerful instrument for identifying human emotions. The purpose of this study is to assess the efficacy of facial recognition systems in detecting indicators of depression. Using the YOLOv5 algorithm and comparing it with its precursors, YOLOv4 and YOLOv7, the research conducts a simulation experiment to examine the ability of the system to precisely interpret a range of emotional states, including happiness, sadness, and anger. The research findings suggest that the YOLO algorithm, particularly in its most recent versions, displays exceptional precision in identifying and classifying intricate facial expressions, thus showcasing its potential as a beneficial device in mental health evaluations. © 2023 IEEE.

关键词： Face recognition

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Exploring the Spectrum of Biometric Technologies: A Systematic Literature Review of Conventional and Unconventional Modalities 9th

Exploring the Spectrum of Biometric Technologies: A Systemat...

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

作者： Aldo Arista, W.P. Bryan Fernando, K.S. Sidharta, Sidharta Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Biometric recognition is a process of validating and identifying people based on their distinct physiological or behavioral traits. Through this literature review, we strive to reduce the knowledge gap among the public about existing biometric modalities, how the biometrics technology is applied, what algorithms are often used in processing the biometrics data, and what common benchmarks are often used to test and measure the performance of each biometrics. This paper provides a systematic literature review with the Kitchenham and Cochrane method, aimed at exploring variant types of biometric modalities and how each of them collects data, how biometric modalities are applied in various industries, common algorithms or methods used in biometric technologies, and establishing metrics and benchmarks for evaluating the performance of biometric technology. As a result, each biometric modality has its way of collecting data, for example, fingerprint recognition uses techniques like contrast correction, noise filtering, and sharpening to extract the fingerprint pattern. Biometric modalities are already used in many sectors of industries, and fingerprint readers or facial feature detection cameras are used to reduce the authentication process in the banking sector. Minutiae-based algorithm commonly used for fingerprint biometric modality, Eigenface for facial detection, and Euclidean distance or Support Vector Machine (SVM) for gait detection. Accuracy, False Acceptance Rate (FAR), False Rejection Rate (FRR), Equal Error Rate (EER), and Receiver Operating Characteristic (ROC) metrics and benchmarks are used to evaluate the performance of biometric technology. © 2024 The Authors.

关键词： Face recognition

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Unsupervised Content-Based Book Recommendation System Using DeepWalk 11

Unsupervised Content-Based Book Recommendation System Using ...

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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Zebua, Hans Jonathan Hamdi, Emmanuel Brandon Kurniadi, Felix Indra School of Computer Science Bina Nusantara University Computer Science Department Jakarta11530 Indonesia

ISBN: (纸本)9798350389654

Reading is a beneficial activity that anyone can do to increase their skills such as critical thinking skills and knowledge, unfortunately, low literacy rates are still prevalent in developing country such as Indonesia, to address this problem a book recommendation system is needed to increase people's interest in reading, this paper introduces the utilization of DeepWalk for creating an unsupervised content-based book recommendation system. It's starts by first doing pre-processing to the the dataset by cleaning the dataset and turning the dataset into graph, implement the DeepWalk's algorithm to the graph, and then conduct a study case to use the model to recommend books similar to the book 'Anna Karenina' as the basis for the recommendation. It is found that based on the results, the model achieves its goal in recommending a sensible and robust collection of recommended books and thus makes DeepWalk a promising algorithm in making a book recommendation system. © 2024 IEEE.

关键词： Adversarial machine learning

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Implementation of YOLO v7 algorithm in estimating traffic flow in Malang 9th

Implementation of YOLO v7 algorithm in estimating traffic fl...

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

作者： Saputri, Hanis Amalia Avrillio, Matthew Christofer, Louis Simanjaya, Victorius Alam, Islam Nur Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

The escalating prevalence of traffic congestion in Malang, which has worsened over time, has resulted in increased inconvenience and traffic-related issues. As a solution, we propose the development of an intelligent transportation system based on traffic flow estimation. While this endeavor presents challenges, advancements in traffic data accessibility and deep learning algorithms, including You Only Look Once (YOLO), R-CNN based ResNet101, Inception V2, and others, with YOLOv7 demonstrating superior performance, continue to enhance traffic flow estimation. Consequently, this study aims to describe and assess the YOLOv7 model's suitability for estimating traffic flow. To achieve this, we propose a traffic monitoring system utilizing the YOLOv7 model in conjunction with a SORT filter, capable of tracking vehicle count and average speed using a custom dataset comprising car images and CCTV footage of Malang's traffic. Subsequently, the number and average speed of cars passing on the road were computed by delineating two virtual boxes. According to the findings, our YOLOv7 system exhibited higher average precision, achieving 61.3%, 78.6%, and 62.1% for each test, and boasted a faster average inference time of 90.67 seconds, meeting the requirements of an intelligent transportation system. Nevertheless, with an average percent deviation in vehicle count exceeding 30% compared to the pre-trained model, vehicle detection is suboptimal, consequently impacting average vehicle speed detection adversely. Hence, our custom model necessitates further optimization and training before practical implementation in real-world traffic estimation. © 2024 The Authors.

关键词： Traffic congestion

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The Feasibility of Using Synthetic-Generated dataset for Training Sentiment Analysis Model 9

The Feasibility of Using Synthetic-Generated Dataset for Tra...

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

作者： Nugroho, Bianca Chelsea Putri Widjaja, Denovan Wijaya, Nicole Sutoyo, Rhio School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331510732

New features in machine learning applications are constantly being explored demanding the availability and quality of data. To address this, the exploration of synthetic data emerges as a promising solution. This paper intends to solve the problem of data availability by finding out the feasibility of using synthetic data as a solution. Many researchers tried to use algorithms such as SMOTE and ADASYN, but encountered challenges in producing semantically and grammatically acceptable material. One of the few front-runners in synthetic text data generation is GPT 3.5. A trained sentiment analysis model would benefit users greatly by providing valuable insights into given text data, helping to understand emotions, opinions, and trends. The criteria of a high-quality dataset is providing various examples for each class. High-quality training data is crucial for accurate sentiment analysis, ensuring correct patterns, bias minimization, and reliable predictions. This paper utilizes GPT 3.5 to generate the synthetic data, with BERT and CNN-LSTM as the model architectures. Each architecture employs three models, each with a unique train-test combination that serves its own purpose. The performance of the six trained models is compared based on accuracy, precision, recall, and F1 score to evaluate the quality and efficacy of synthetic data. The conducted evaluation shows that data augmentation using synthetic data is feasible. This finding is presented as the main contribution of the study. However, fully synthetic data-trained models require more refining. © 2024 IEEE.

关键词： data assimilation

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Stunting Prediction in Children Using Random Forest Algorithm 6

Stunting Prediction in Children Using Random Forest Algorith...

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

作者： Kanz, Ahmad Fauzan Johanes, Luvky Pratama Alam, Islam Nur Wulandhari, Lili Ayu Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798331539603

Stunted child growth and development caused by chronic malnutrition has remained a significant global public health problem worldwide, especially in low-and middle-income countries. Thus, it is essential to employ effective predictive models that can identify those at risk so as to implement appropriate interventions. In this research, a predictive model for childhood stunting based on artificial intelligence (AI) methods will be introduced. The machine learning algorithms and statistical techniques are employed in the model which integrates several socio-economic, demographic, dietary and health related factors to predict stunting among children under five years of age. This includes training the model using survey data that is nationally representative as well as longitudinal studies to ensure its reliability and applicability across populations with different characteristics. Some of the metrics used to assess the predictive performance include accuracy, sensitivity, specificity and area under receiver operating characteristic curve (AUC-ROC). Many interpretability techniques have been utilized to highlight the primary factors associated with an increased risk of stunting thereby becoming a valuable tool for policy makers and health care providers in planning focused interventions and resource allocation effectively. Utilizing AI, this predictive model presents a proactive strategy to address childhood stunting, ultimately enhancing child health and well-being globally. © 2024 IEEE.

关键词： Electronic health record

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Re-Enhancing Multi-Target Classification Techniques for CODEBRIM dataset using Deep Learning Algorithm 13

Re-Enhancing Multi-Target Classification Techniques for CODE...

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13th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2024

作者： Alexsah, Rizky P. Gunawan, Alexander Agung Santoso Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350368086

The existence of concrete defects such as corrosion stains, cracks, efflorescence, exposed bars, and spallation has greatly compromised the safety and convenience of bridge users. These calamities can lead to deaths and have an impact on both communities and the economy. Effective maintenance management relies on the receipt of timely and accurate information. Technological progress facilitates the instantaneous handling of up-to-the-minute data. The objective of this work is to enhance the performance of YOLO models for current applications. The CODEBRIM custom dataset is used, which consists of 7,261 photographs illustrating concrete flaws. The results demonstrated that the YOLOv9m version attained the highest levels of accuracy and precision, reaching 74.99% and 89.41% correspondingly. Both YOLOv9 and YOLOv10 are outperformed in terms of accuracy, but they excel in training time, with YOLOv9 producing a more convergent result. © 2024 IEEE.

关键词： Deep learning

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Study of Cross-Site Request Forgery on Web-Based Application: Exploitations and Preventions 8

Study of Cross-Site Request Forgery on Web-Based Application...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Siahaan, Crisdeo Nuel Rufisanto, Mario Nolasco, Raymond Achmad, Said Siahaan, Chrisando Ryan Pardomuan Cyber Security Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Cross-Site Request Forgery (CSRF) is a prominent web exploit that continues to pose significant security risks, even on highly ranked websites. This research focuses on identifying the underlying vulnerability, understanding the techniques employed, and proposing effective preventive measures. The hybrid method which is systematic report review method and lab-based scenarios method is conducted to identify CSRF exploitation and prevention mechanisms. Using a scenario-based testing approach, this study investigated the cause and effect of CSRF attacks, uncovering numerous techniques for exploiting this vulnerability. Based on the report review, 35 CSRF vulnerability reports from the Hacker one Bug Bounty Platform are analyzed. The result shows there is a lack of awareness and implementation of primary defenses, resulting in a high violation of end-user data integrity. The result also extensively describes some of the profound causes and effects of CSRF attacks and uncovers a broad range of techniques for exploiting this vulnerability. Furthermore, this study provides actionable recommendations to enhance system security and raise awareness among developers and users. The findings from this research serve as a valuable resource for improving security practices and mitigating CSRF attacks. © 2023 The Authors. Published by Elsevier B.V.

关键词： cross-site request forgery vulnerability web exploitation

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