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2024 International Conference on Artificial Intelligence, Blockchain, Cloud Computing, and data Analytics, ICoABCD 2024

作者： Tano, Jose Jonathan Kelvin, Davis Nadia, Nadia Junior, Franz Adeta School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350368451

Sign language is the primary means of communication for deaf individuals, yet not everyone is proficient in its use, creating communication barriers. With the advancement of artificial intelligence, there is potential to utilize AI for detecting and interpreting sign language, thus facilitating better communication. This research aims to compare the performance of various object detection models to identify the most effective one for detecting sign language. Our study focuses on evaluating four different models: Faster R-CNN with ResNet50 backbone, Faster R-CNN with MobileNetV3 backbone, SSDLite, and YOLOv8. Using a dataset of 1,728 images representing American Sign Language with multiple classes, we preprocess the data and assess the models based on their Mean Average Precision (mAP) values. Our goal is to determine which model provides the highest accuracy and efficiency in detecting sign language. The evaluation revealed that the Faster R-CNN with ResNet50 backbone achieved the highest mAP value of 0.87 and 0.66, followed by Faster R-CNN with MobileNetV3 backbone at 0.88 and 0.63, YOLOv8 at 0.79 and 0.66, and SSDLite at 0.73 and 0.53. This research demonstrates the potential for AI to bridge communication gaps and improve sign language detection methods. Future studies could enhance these models by increasing the dataset size and incorporating more diverse sign language variations to improve performance and reliability further. © 2024 IEEE.

关键词： Mobile telecommunication systems

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Significance of Big data Analytics and The Internet of Things Aspects in Sustainability: A Systematic Literature Review

Significance of Big Data Analytics and The Internet of Thing...

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2024 International of Seminar on Application for Technology of Information and Communication, iSemantic 2024

作者： Intama, Fabiano Salmaen Nugroho, Fadil Gunawan, Alexander Agung Santoso Syahputra, Muhammad Edo Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350366648

With an emphasis on supply chain management and agriculture, this study investigates how Big data Analytics (BDA) and the Internet of Things (IoT) affect sustainability in a variety of industries. This study aims to evaluate how BDA and IoT integration might improve corporate effectiveness and sustainability practices. We incorporate qualitative and quantitative data to assess how well these technologies promote sustainable practices, using the Sustainable Supply Chain Management-IRIS paradigm. According to our research, integrating BDA with IoT greatly increases resource management, stimulates better decision-making, decreases environmental impact, and increases organizational sustainability and efficiency. Precision farming and the Internet of Things in agriculture increase yields while consuming less resources. These technologies improve the resilience and efficiency of supply chains' operations. According to our study's findings, IoT and BDA are revolutionary technologies that have the potential to significantly advance sustainability objectives across a range of industries, promoting their wider use for a sustainable future. © 2024 IEEE.

关键词： Sustainable agriculture

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Large Language Models for Financial Aid in Financial Time-series Forecasting

Large Language Models for Financial Aid in Financial Time-se...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Islam, Md Khairul Karmacharya, Ayush Sue, Timothy Fox, Judy University of Virginia Computer Science Department United States University of Virginia School of Data Science Charlottesville United States

ISBN: (纸本)9798350362480

Considering the difficulty of financial time series forecasting in financial aid, much of the current research focuses on leveraging big data analytics in financial services. One modern approach is to utilize "predictive analysis", analogous to forecasting financial trends. However, many of these time series data in Financial Aid (FA) pose unique challenges due to limited historical datasets and high dimensional financial information, which hinder the development of effective predictive models that balance accuracy with efficient runtime and memory usage. Pre-trained foundation models are employed to address these challenging tasks. We use state-of-the-art time series models including pre-trained LLMs (GPT-2 as backbone), transformers, and linear models to demonstrate their ability to outperform traditional approaches, even with minimal ("few-shot") or no fine-tuning ("zero-shot"). Our benchmark study, which includes financial aid with seven other time series tasks, shows the potential of using LLMs for scarce financial datasets. © 2024 IEEE.

关键词： Decentralized finance

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Public Perception and Acceptance of AI-based Mental Health Assessment Tools 9th

Public Perception and Acceptance of AI-based Mental Health A...

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

作者： Steven, Alex Sandro Ibrahim, Muhammad Amien Fredyan, Renaldy Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

A survey consisting of 18 questions that lasted for 3 days from 10th May 2024 to 13th May 2024 was conducted to the public across Indonesian. In order to find out about public perception and acceptance of AI-based mental health assessment tools. One hundred and thirty participants responded to the survey, however only 107 of them passed the data quality check. from the 107 respondents, 61.7 % of them are males, and 38.3% of them are females, and 88% of the respondents are in the age range of 18-29 with the last level of education around high school and undergraduate. Using the score of 1-5, Most were familiar with AI, but over two-thirds hadn't used AI in mental health. They weakly believed in AI effectiveness (mean score: 3.08) and doubted it could match traditional methods (mean score: 2.88). Nearly 80% saw AI as helpful for early detection and intervention;54.2% found current AI credible. Comfort with AI tools was moderate (score: 3.12), but confidence in AI vs. professional assessments was low (score: 2.78). Trust in AI tools is expected to grow in 10 years, with nearly 80% expecting widespread use and higher comfort (score: 3.64). AI is seen as beneficial for increasing mental health service use (score: 3.77) and improving access for underserved populations (70.1%). Privacy and security concerns were high (72.9%). Overall, the public sees AI-based mental health tools positively but still prefers human experts. The level of trust is expected to grow as technology progresses. Privacy concerns need addressing, but overall, acceptance is high. © 2024 The Authors.

关键词： Electronic health record

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Face Recognition as Base Protocol in Online Transactions 9th

Face Recognition as Base Protocol in Online Transactions

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

作者： Aziz, Mohammad Fauzi Taraka, Gavin Sadiya Sidharta, Sidharta Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

From each to each year, from decades and decades, the fraud and scam are growing more and more powerful, although we cannot avoid it 100% since the fraud and scams using any method to make a crack of hole or other chances to steal that make a company and other e-commerce suffer losses in small or large numbers. This leads to new rules that can make e-commerce stand a chance and avoid the losses of either the e-commerce itself or the client that is trying to make online transactions. This paper has a goal to answer 5 research questions about face recognition as base protocol in online transaction. Method that we use is systematic literature review that has 7 steps from deciding research question until data synthesis. For the result from 5 research questions are answered very well, which is RQ1. How effective face recognition can decrease fraud identity on online transactions. RQ2. What kind of struggle and problem makes this technology unable to integrate with online payment. RQ3. What kind of perception and the acceptance of the user about the face recognition method for authentication in online transactions? RQ4. How far the technology is for face recognition can integrate with the system online payment transaction that increases the efficiency transaction with or without compromising security. RQ5. What kind of law of implication and etiquette from the user face recognition in online transactions and how can the system be made to follow the regulation that already exists. From that our conclusion is clear which is face recognition is safe and secure but since the technology is very well and good, then need proper technology to support it further. © 2024 The Authors.

关键词： Authentication Protocol

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iLIAC: An approach of identifying dissimilar groups on unstructured numerical image dataset using improved agglomerative clustering technique

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Multimedia Tools and Applications 2024年第39期83卷 86359-86381页

作者： S, Sreedhar Kumar Ahmed, Syed Thouheed Fathima, Afifa Salsabil Mathivanan, Sandeep Kumar Jayagopal, Prabhu Saif, Abdu Gupta, Sachin Kumar Sinha, Garima Department of Computer Science and Engineering School of Engineering and Technology CMR University Karnataka Bengaluru India Department of Electrical Engineering Indian Institute of Technology Hyderabad Telangana Kandi India School of Computer Science and Engineering REVA University Bangalore India School of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida203201 India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Communication and Computer Engineering Faculty of Engineering and IT Taiz University Taiz Yemen Katra India Department of Computer Science Engineering Jain University Bangalore India

Unstructured Numerical Image dataset Separation (UNIDS) method employing an enhanced unsupervised clustering technique. The objective is to delineate an optimal number of distinct groups within the input grayscale (G-S) image content, utilizing the improved limited iteration agglomerative clustering (iLIAC) clustering technique for the separation and enhancement of picture elements. The UNIDS method is structured into two primary stages: partitioning and validation. In the partitioning stage, the UNIDS method identifies an appropriate number of discrete clusters within the grayscale image using the iLIAC technique, eliminating the need for predetermined procedures. Subsequently, the method evaluates the similarity and deviation among data elements within the same group in the resultant image dataset. Additionally, it assesses the proximity and inters severance among clusters in the outcome of the image dataset through the partitioning process. Empirical results indicate that the UNIDS system excels in the spontaneous identification of an optimal number of discrete clusters within the input G-S image. The system demonstrates superior thickness, reduced deviation among data elements within the same cluster, increased inter-separation, and diminished inter-closeness between cluster elements. Furthermore, empirical analysis establishes the superior performance of the UNIDS approach compared to existing clustering techniques. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Iterative methods

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Evaluating Back Translation and Misspelling Correction Utilization on Indonesian AES 5

Evaluating Back Translation and Misspelling Correction Utili...

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5th International Conference on Artificial Intelligence and data sciences, AiDAS 2024

作者： Amadea Tanaka, Elvina Christian, Steven Anderies Chowanda, Andry School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331528553

This paper aims to tackle the problem of Automatic Essay Scoring (AES), a method used to predict whether an answer is correct given a question based on the provided guidelines and criteria. AES helps teachers to score student answers based on the guidelines fed into the model, saving time and decreases the human-error rate. We had chosen the UKARA challenge dataset, an Indonesian binary classification dataset consisting of two different questions separated into two datasets A and B. Prior studies utilized a stacking approach with XGBoost and a Neural Network model, achieving F1-scores of 88.4% and 75.9% for problem A and B respectively. The latest study utilizing the UKARA dataset used SBERT sentence embeddings and a Neural Network model, resulting in F1-scores of 89.4% for problem A and 75.7% for problem B. Although the F1-score for problem A was satisfactory, the F1-score for problem B remained relatively low. Therefore, this research aim to improve the performance of problem B. This research also discusses the various data augmentation techniques that can be applied to the dataset, such as back translation and misspelling correction Peter Norvig method. Based on the latest research, we decided to use SBERT sentence embeddings and neural networks for the model. The experiment managed to improve the result for problem B with a maximum F1-score of 77.2% along with 89.7% for problem A. © 2024 IEEE.

关键词： Back-Translation Indonesian AES IndoSBERT Sentence Embed-ding Neural Network

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Analyzing Sleep Health from Lifestyle data Using Lasso Regression 9

Analyzing Sleep Health from Lifestyle Data Using Lasso Regre...

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9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Riupassa, Ascendiazorg Rambi, Putri Ireine Ibrahim, Muhammad Amien Fredyan, Renaldy School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

Optimal sleep is the primary determinant of one's overall well-being and contentment. This study investigates the correlation between lifestyle choices and the quality of sleep by use the Lasso regression technique. The Lasso regression algorithm was employed to analyse lifestyle data obtained from Kaggle. The findings indicate that the Lasso regression model exhibits strong performance, with a training score of 0.9113 and a testing score of 0.8995. Additionally, the model demonstrates a low error rate of 0.1757. The regression coefficient analysis reveals that sleep length, physical activity, stress levels, and the occurrence of sleep disorders are the lifestyle factors with the greatest impact on sleep quality. These findings enhance our comprehension of how lifestyle choices impact the quality of sleep and can be utilised to create more efficient interventions for enhancing sleep quality and general well-being. © 2024 IEEE.

关键词： Sleep research

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The Influence of Social Media on Stress Detection Using TF-IDF and Machine Learning Strategies 11

The Influence of Social Media on Stress Detection Using TF-I...

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

作者： Yulistiani, Risma Kurniadi, Felix Indra School of Computer Science Bina Nusantara University Computer Science Department Jakarta11530 Indonesia

ISBN: (纸本)9798350389654

Stress is an intrinsic aspect of human existence, frequently provoked by a range of factors such as work responsi-bilities and social relationships. Although short-term stress might have beneficial effects, long-term stress can result in adverse health outcomes. In modern times, social media has become a venue for individuals to openly convey their emotions, especially feelings of stress. This study investigates the correlation between the utilization of social media and the identification of stress, employing advanced machine learning methods. We utilize the TF-IDF method to extract features and assess several machine learning techniques on refined models. The contribution on this research is applying TF -IDF and several machine learning algorithms on stress detection. The results showed Logistic regression algorithms provide exceptional performance with the accuracy 0,9 and 0,79 respectively in both Reddit and Twitter datasets, exhibiting high accuracy, precision, and recall in accurately recognizing occurrences of stress. The Support Vector Machine (SVM) exhibits potential, especially in identifying stress-positive cases, albeit with significantly poorer precision when compared to logistic regression and random forest. These findings highlight the capacity of machine learning methods to comprehend the influence of social media on the expression of stress. © 2024 IEEE.

关键词： Adversarial machine learning

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Handling Sparsity Problem Using Removal and Imputation in the HUPA-UCM Diabetes Type 1 dataset 5

Handling Sparsity Problem Using Removal and Imputation in th...

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5th International Conference on Artificial Intelligence and data sciences, AiDAS 2024

作者： Kurniadi, Felix Indra Yulistiani, Risma School of Computer Science Bina Nusantara University Computer Science Department Jakarta11530 Indonesia

ISBN: (纸本)9798331528553

Diabetes is a medical condition characterized by the pancreatic gland's failure to secrete insulin, leading to abnormally elevated levels of glucose in the bloodstream. Type 1 diabetes is caused by an autoimmune attack on the β}-cells located in the islets of the pancreas, which are responsible for producing insulin. Monitoring blood glucose levels in individuals with diabetes is of utmost significance, since maintaining optimal blood sugar levels can significantly enhance the lifespan of patients. Hence, the anticipation of blood glucose levels holds significant importance, particularly for patients to ascertain their medical state. This study utilizes data from the HUPA-UCM dataset. Regrettably, the utilized data is subject to a sparsity constraint, characterized by a multitude of 0 values in each feature, which has the potential to disrupt prediction accuracy. In order to address this issue, we provide two methods for dealing with sparsity: elimination and imputation. When the features surpass a specific threshold, the elimination procedure is carried out, whereas imputation is done on data that falls below the threshold value. In addition, we utilize multiple machine learning techniques, including K-Nearest Neighbor, Decision Tree, and Random Forest, to predict blood glucose levels. The findings of this method demonstrate that the suggested sparsity treatment enhances the prediction outcomes, albeit without a substantial impact. The Decision Tree technique yielded an accuracy value of 0.912 without sparsity handling, and an accuracy value of 0.91 with sparsity handling, producing the best results. © 2024 IEEE.

关键词： Machine learning

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