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Exploring the Impact of Word Embedding Dimensions on Depression Data Classification Using BiLSTM Model

Procedia Computer Science 2023年 227卷 298-306页

作者： Merinda Lestandy Abdurrahim Department of Electrical Engineering Faculty of Engineering Universitas Muhammadiyah Malang Malang 65144 Indonesia Informatics Master Program Faculty of Industrial Technology Universitas Islam Indonesia Yogyakarta 55584 Indonesia

Depression is a mental health issue that impacts over 300 million individuals worldwide. Daily anxiety impacts the relationships of depressed individuals with their family and friends, affects their health, and, in the worst-case scenario, leads to suicide. Due to the an increase of social networks, a vast majority of people now express their thoughts, feelings, and emotions via social media. The researchers mainly focused on features and building predictions, making it difficult for them to detect depression via social media. Word embedding offers a potential solution for processing and transforming unstructured data into meaningful representations. Embedded words represent textual data as numeric numerals with equivalent meaning. Using the Bidirectional Long Short-Term Memory (Bi-LSTM) paradigm, this investigation attempts to experiment with various weighting dimensions using the Word2Vec and GloVe methods. The data used is Depression: Reddit Dataset labelled: depressed and non-depressed. The experimental results of word weight dimensions using Word2Vec with a dimension of 500 are better than Glove. The results of accuracy, precision, recall and f1-score for the Word2Vec method were 96.22%, 97.02%, 95.30% and 96.15%, while the Glove method was 95.91%, 96.40%, 95.30% and 95.85% with a dimension of 200. Overall, Word Embedding Dimensions influences the results of this study. Based on these findings, it is concluded that the proposed Word2Vec method is a significant approach.

关键词： Social Media Depression Word Embedding BiLSTM

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Prefallkd: Pre-Impact Fall Detection Via CNN-ViT Knowledge Distillation 48

Prefallkd: Pre-Impact Fall Detection Via CNN-ViT Knowledge D...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Chi, Tin-Han Liu, Kai-Chun Hsieh, Chia-Yeh Tsao, Yu Chan, Chia-Tai National Yang Ming Chiao Tung University Department of Biomedical Engineering Taiwan Academia Sinica Research Center for Information Technology Innovation Taiwan Fu Jen Catholic University Bachelor's Program in Medical Informatics and Innovative Applications Taiwan

ISBN: (纸本)9781728163277

Fall accidents are critical issues in an aging and aged society. Recently, many researchers developed "pre-impact fall detection systems"using deep learning to support wearable-based fall protection systems for preventing severe injuries. However, most works only employed simple neural network models instead of complex models considering the usability in resource-constrained mobile devices and strict latency requirements. In this work, we propose a novel pre-impact fall detection via CNN-ViT knowledge distillation, namely PreFallKD, to strike a balance between detection performance and computational complexity. The proposed PreFallKD transfers the detection knowledge from the pre-trained teacher model (vision transformer) to the student model (lightweight convolutional neural networks). Additionally, we apply data augmentation techniques to tackle issues of data imbalance. We conduct the experiment on the KFall public dataset and compare PreFallKD with other state-of-the-art models. The experiment results show that PreFallKD could boost the student model during the testing phase and achieves reliable F1-score (92.66%) and lead time (551.3 ms). © 2023 IEEE.

关键词： Inertial measurement units Knowledge distillation Pre-impact fall detection Vision transformer Wearable sensors

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An Introduction to the Three-Body Problem Optimization Algorithm: A Novel Swarm-Based Optimization Method

An Introduction to the Three-Body Problem Optimization Algor...

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Disruptive Technologies (ICDT), International Conference on

作者： Ivan Diryana Sudirman Patah Herwanto Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Bandung Indonesia Informatics Engineering Department STMIK IM Bandung Indonesia

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

The Three-Body Problem Optimization Algorithm (3BPOA) is a metaheuristic method inspired by how gravity interacts if there are three bodies, a classical physics problem. 3BPOA uses 3 best agents or leaders where these 3 best agents will interact with each other and also influence the movement of the swarm. The interaction of these 3 best bodies or agents is also influenced by the distance between the three. In this study, 3BPOA was tested on the Rastrigrin mathematical function using various scenarios from 5 dimensions to 100 dimensions, also using various population sizes. The test results show that the 3BPOA algorithm has good performance in low dimensions and quite good in medium dimensions. In high dimensions, if using a large population, 3BPOA has good potential.

关键词： TV Metaheuristics Refining Optimization methods Benchmark testing Particle swarm optimization Optimization Physics Thermal stability Standards

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Comprehensive Analysis and Remediation of Insecure Direct Object References (IDOR) Vulnerabilities in Android APIs 1

Comprehensive Analysis and Remediation of Insecure Direct Ob...

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1st IEEE International Conference on Cryptography, informatics, and Cybersecurity, ICoCICs 2023

作者： Yulianto, Semi Abdullah, Roni Reza Soewito, Benfano Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia National Institute of Science and Technology Faculty of Informatics Engineering Jakarta12630 Indonesia Bina Nusantara University Binus Graduate Program. Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350339437

The escalating visibility of secure direct object reference (IDOR) vulnerabilities in API security, as indicated in the compilation of OWASP Top 10 API Security Risks, highlights a noteworthy peril to sensitive data. This study explores IDOR vulnerabilities found within Android APIs, intending to clarify their inception while evaluating their implications for application security. This study combined the qualitative and quantitative approaches. Insights were obtained from an actual penetration test on an Android app into the primary reasons for IDOR vulnerabilities, underscoring insufficient input validation and weak authorization methods. We stress the frequent occurrence of IDOR vulnerabilities in the OWASP Top 10 API vulnerability list, highlighting the necessity to prioritize them in security evaluations. There are mitigation recommendations available for developers, which recognize its limitations involving a possibly small and homogeneous selection of tested Android applications, the testing environment that could cause some inaccuracies, and the impact of time constraints. Additionally, the study noted insufficient threat modeling and root cause analysis, affecting its generalizability and real-world relevance. However, comprehending and controlling IDOR dangers can enhance Android API security, protect user data, and bolster application resilience. © 2023 IEEE.

关键词： Sensitive data

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IoT-Based Smart Parking Management System Using ESP32 Microcontroller 9

IoT-Based Smart Parking Management System Using ESP32 Microc...

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9th International Conference on Electrical engineering, Computer Science and informatics, EECSI 2022

作者： Simatupang, Joni Welman Lubis, Aida Mahdalena Vincent President University Study Program of Electrical Engineering Cikarang 17530 Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung 40132 Indonesia

ISBN: (数字)9786239213565

ISBN: (纸本)9781665470438

In recent years, the number of vehicles has increased significantly. As a result, drivers waste a lot of time looking for an open parking spot or queuing to enter one. It leads to fuel waste and raises air pollution at the same time. One solution is to implement an innovative system that provides a comprehensive parking solution. This work focuses on designing a smart parking management system that allows drivers to see the parking slots, then reserve a certain slot before arriving to the parking area. It is a web-based system where the drivers do not have to download any mobile application. Practically, the system implemented ultrasonic sensors to check slot availability. In addition, an indicator is installed at each slot to differentiate the one that is vacant, reserved, and occupied. They are controlled by ESP32 modules, and the data was sent wirelessly by using MQTT protocol. Quantitatively, the ultrasonic sensors are set to have 100 cm of distance as the threshold to differentiate vacant and occupied slots. After experiments, it was found that the sensor has an accuracy of 94.72%. However, there is an approximately 10 seconds of delay since the reservation is made until it can be successfully confirmed by the system. Finally, the system also informs the drivers of how long they have been parking, as well as the parking fee based on the duration of time. © 2022 Institute of Advanced engineering and Science (IAES).

关键词： ESP32 IoT Microcontroller MQTT Smart Parking

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An Examination of the Possible Application of Artificial Intelligence Audit Process in Organizations 6

An Examination of the Possible Application of Artificial Int...

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

作者： Sardjono, Wahyu Pranata, Dewi Sagita Isamesal, Muhammad Information Systems Management Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia Cyber Security Studi Program Department of Informatics Faculty of Natural Mathematical & Engineering Sciences King’s College London West Minster WC2R 2LS United Kingdom Management Department Faculty of Economics and Busines Universitas of Airlangga Surabaya10430 Indonesia

ISBN: (纸本)9798331539603

This study investigates how artificial intelligence (AI) can transform audit processes, including how AI can be used in businesses to execute non-technical audit tasks and what effect this will have on those tasks. A qualitative case study methodology is used in the research design to give detailed insights into how AI is being adopted by businesses. This study aims to analyze the aspects that impact AI implementation from the viewpoints of managers, auditors, and other stakeholders. The findings show that artificial intelligence (AI) has a great deal of promise for non-technical audit procedures in businesses. AI can automate tedious jobs, evaluate large data sets to identify hazards and enhance teamwork and communication. These developments may lead to increased productivity, maybe better audit quality, and more effective processes. But there are still difficulties. Data quality is essential to the success of AI integration, and auditors may exhibit bias in favor of human professionals. Careful thought must also be given to ethical issues about transparency and possible job displacement. All things considered, this study adds significant knowledge to both the academic literature and real-world auditing applications. It improves our comprehension of the function of AI in the industry and provides useful information for people involved in information technology development, audit, and legislation. © 2024 IEEE.

关键词： Data quality

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Artificial Intelligence-Based System ‘SiMoniK’ for MSMEs

Artificial Intelligence-Based System ‘SiMoniK’ for MSMEs

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Information Technology International Seminar (ITIS)

作者： Sri Lestari Yulmaini Aswin Sulyono Ruki Rizal Nul Fikri Faculty of Computer Science Informatics Engineering Study Program Bandar Lampung Indonesia Faculty of Economics Business Management Study Program Bandar Lampung Indonesia

Artificial intelligence was recently implemented in various fields. One of these applied artificial intelligence systems was the Performance Monitoring System “SiMoniK” for MSMEs. This system was designed through the C4.5 algorithm and the Porat-Rank method. The C4.5 algorithm was applied to classify the MSMEs into three classifications, e.g. the micro enterprise classification, the small enterprise classification, and the medium enterprise classification. Moreover, the Porat-Rank method was also applied to distribute the solving-problem recommendations and to improve the independence of MSMEs. The result of this study was that the C4.5 algorithm could classify the MSME data correctly in the form of an accuracy value of 99.2%. Besides, the Porat-Rank method could also provide recommendations in the form of outstanding quality with an NDCG mean value of 0.87.

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Segmentation of Brain Tumor Objects in Magnetic Resonance Imaging (MRI) Image using Connected Component Label Algorithm

Segmentation of Brain Tumor Objects in Magnetic Resonance Im...

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2021 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2021

作者： Mimboro, Prasetyo Sunyoto, Andi Kharisma, Rizqi Sukma Program Universitas Amikom Yogyakarta Pt Perkebunan Nusantara Iv Informatics Engineering Departement Yogyakarta Indonesia

ISBN: (纸本)9781665499699

Several techniques in computer vision can be used in object grouping, one of which is image segmentation. Segmentation is a field of computer vision that aims to group all pixels in an image into regions that have homogeneity in their characteristics. In this study, the segmentation technique will be carried out to partition objects in magnetic resonance image (MRI) images. Object segmentation uses a connected component labeling algorithm to classify regions or objects of brain tumors in MRI images. The dataset used in this study amounted to 80 images, of which 30 images contained meningioma brain tumor disease, 30 images with glioma brain tumor disease, and 20 images without indications of brain tumor disease. Based on tests conducted, the average accuracy obtained in detecting brain tumor objects is 96.66%. © 2021 IEEE.

关键词： Magnetic resonance imaging

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Lung Nodule Analysis in CT Images: Deep Learning for Segmentation and Measurement 24

Lung Nodule Analysis in CT Images: Deep Learning for Segment...

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8th International Conference on Medical and Health informatics, ICMHI 2024

作者： Li, Ai-Hsien Adams Aruperes, Cristian Daniel Lai, Yen-Jun Chien, Ting-Ying Chiu, Yen-Ling Chan, Chien-Lung Division of Cardiology Far Eastern Memorial Hospital New Taipei TaiwanGraduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan TaiwanDepartment of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan TaiwanInnovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan Taiwan Department of Information Management Yuan Ze University Taoyuan Taiwan Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan TaiwanGraduate Institute of Medicine Yuan Ze University Taoyuan Taiwan TaiwanDepartment of Medical Research Far Eastern Memorial Hospital New Taipei Taiwan Department of Radiology Far Eastern Memorial Hospital New Taipei Taiwan Department of Information Management Yuan Ze University Taoyuan Taiwan

ISBN: (纸本)9798400716874

In the realm of research, the global health challenge posed by lung cancer remains pronounced, contributing substantially to annual cancer-related fatalities. The critical imperative lies in the early identification of pulmonary nodules, frequently indicative of impending lung cancer, to enhance patient outcomes and diminish mortality rates. Computed Tomography (CT) imaging stands out as a pivotal diagnostic instrument for the timely detection of these nodules. The swift proliferation of medical imaging data has underscored the pressing necessity for precise and efficient methodologies dedicated to nodule segmentation and measurement. These approaches are crucial in assisting radiologists in their diagnostic and clinical decision-making endeavors. In this study, we introduced a thorough method for analyzing lung nodules, leveraging dataset from Far Eastern Memorial Hospital (FEMH) comprising original CT images and manually annotated ground truth masks obtained with the assistance of radiologists at FEMH. This dataset is utilized for the segmentation of nodules. We employed advanced deep learning models, specifically the U-Net architecture, identified as the optimal model through our training process. We made substantial progress in nodule segmentation, attaining an Intersection over Union (IoU) score of 0.824 and a Dice Coefficient of 0.903 for the FEMH dataset. Furthermore, our performance improved when utilizing the merged dataset comprising FEMH and Luna16, yielding an IoU score of 0.862 and a Dice Coefficient of 0.926. Luna16 has been extensively utilized in numerous studies related to nodule detection and segmentation. In the next phase of the study, the best-performing model from our segmentation phase was utilized to predict nodule masks on the merged dataset. Subsequently, we measured the size of each predicted nodule by comparing it with the size ground truth mask in millimeters. In detail, this study achieved the Pearson Correlation Coefficient (PCC) at 0.

关键词： Computerized tomography

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Humans vs. ChatGPT: Evaluating Annotation Methods for Financial Corpora

Humans vs. ChatGPT: Evaluating Annotation Methods for Financ...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Kaikaus, Jamshed Li, Haoen Brunner, Robert J. University of Illinois Urbana-Champaign Informatics Program UrbanaIL United States Carnegie Mellon University Department of Electrical and Computer Engineering PittsburghPA United States University of Illinois Urbana-Champaign Department of Accountancy UrbanaIL United States

ISBN: (纸本)9798350324457

Given the vast amount of unstructured financial text data available today, there is a high demand for reliable, quality annotations to facilitate robust model development. However, traditional methods can often be expensive and time-inefficient. In this study, we investigate annotations for emotion, sentiment, and cognitive dissonance generated by the large language models (LLMs), GPT-3.5 and GPT-4, for quarterly earnings conference calls and compare them against human annotations obtained via traditional methods. We also investigate different prompt engineering choices on LLM annotation quality, experimenting with 4 styles of prompts centered around varying the amount of contextual information given and how it is presented to the models. Our results show the GPT models are not only more consistent and reliable than human annotators, but also provide annotations in a more cost- and time-efficient manner. © 2023 IEEE.

关键词： earnings calls emotion recognition large language models sentiment analysis

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