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Enhancing Social Media Accessibility: Automatic Alternative Text Generation in X by Image Captioning 14

Enhancing Social Media Accessibility: Automatic Alternative ...

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14th International Conference on System Engineering and Technology, ICSET 2024

作者： Wibowo, Jessica Lynn Gunawan, Gabriel Seemore Edbert, Ivan Sebastian Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331504526

In the contemporary digital landscape, social media's reliance on visual content poses significant accessibility barriers for individuals with visual impairments, sidelining a substantial user base. This study applies Artificial Intelligence to improve accessibility on social media by automating alternative text generation for images on platform X (formerly Twitter). The Generative Image-to-text Transformer (GIT) model is analyzed and then integrated with the Twitter API. Using the Flickr30k dataset, further fine-tuning and evaluation processes are performed on the model. Concurrently, the model demonstrates higher accuracy of 106% over the baseline VGG model as quantified by ROUGE metrics. The results confirm our model's capability to produce descriptive and contextually relevant captions that significantly enhance accessibility for visually impaired users. Furthermore, the integration of this AI-driven solution into social media platforms showcases the practical application of advanced machine learning techniques in addressing real-world accessibility challenges. This research highlights the potential of AI to transform digital communication, making it more inclusive and ensuring that visually impaired users can fully participate in the social media experience. © 2024 IEEE.

关键词： Tweets

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Ensemble of pre-trained vision transformer models in plant disease classification, an efficient approach 9th

Ensemble of pre-trained vision transformer models in plant d...

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

作者： Hamdi, Emmanuel Brandon Hidayaturrahman Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Plant disease is one prevalent factor that has a significant impact worldwide on food security and production, with plants contributing over 80% of food that is consumed by human, due to the importance of plants for worldwide food security and a projected food demand rise of 59-102% by 2050, a method for early detection of plant disease can be a significant solution in the prevention of food shortage around the world, in the present day advancement in technology has made possible for Artificial Intelligence techniques such as Convolutional Neural Network (CNN) and Vision Transformer (ViT) to automatically detect and classify plant disease with success, this research compared 20 efficient and speed-focused Pretrained ViT model in plant disease classification with the top 7 performing models ensembled together, the results show that ensemble of ViT model beats all other models with a 7 model ensemble using average voting method resulted in an accuracy of Accuracy of 0.9816, Precision of 0.9737, Recall of 0.9723, and F1 score of 0.9723, although it's recommended to use lower model ensemble such as the 3 model ensemble with max voting that has an Accuracy of 0.9804, Precision of 0.9708, Recall of 0.9703, and F1 score of 0.9705 due to negligible difference between model ensemble and lower cost of the ensemble. © 2024 The Authors.

关键词： Artificial Intelligence Deep Learning Ensemble Plant Disease Pretrained Model Vision Transformer

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Sentiment Analysis of Quick Count Results of the 2024 Indonesian Presidential Election on Social Media 11

Sentiment Analysis of Quick Count Results of the 2024 Indone...

引用

11th International Conference on Electrical Engineering, computer science and Informatics, EECSI 2024

作者： Atmojo, Axell Prita Aurelie Arienaldi, Nadine Aulyaa Edbert, Ivan Sebastian Aulia, Alvina School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350355314

Sentiment analysis is a technique of analyzing text to determine the emotion of the message. The main objective of this sentiment analysis is to investigate whether Indonesian citizens on the platform X felt positive, negative, or neutral towards the Quick Count results of the recent 2024 Indonesian Presidential Election, in light of public distrust surrounding the legitimacy of the election organizers. The goal of this study is also to compare the performance of two classifier models;Naive Bayes and LSTM, based on standard metrics like accuracy, precision, recall, and f1-score. The data used were posts scraped from X taken after the election day on February 14th, along with datasets sourced from Kaggle and GitHub. The data preprocessing followed a sequence of data cleansing, case folding, and stop word removal. The final dataset is then split and vectorized using TF-IDF for Naive Bayes, and tokenized for LSTM. The result of this research shows 75.142% of accuracy for the Naive Bayes classifier and 80.03 % of accuracy for the LSTM-based classifier. From the 2,639 posts classified by the Naive Bayes model, 64.7% show positive sentiment towards the Quick Count results, whereas the LSTM model produced 58.2% positive sentiment of the same amount. © 2024 IEEE.

关键词： Deep learning

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Discrete Cosine Transform-Based Feature Extraction for Retinal Abnormality Classification

Discrete Cosine Transform-Based Feature Extraction for Retin...

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2024 Beyond Technology Summit on Informatics International Conference, BTS-I2C 2024

作者： Setiawan, Benny Chandra Harianto, Eric Silvanus Prasetyo, Simeon Yuda Purwanto, Eko Setyo School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331508579

Retinal abnormalities, such as CNV, DME, and DRUSEN, significantly impact vision and require timely diagnosis for effective treatment. This study explores a machine learning-based approach to classify retinal abnormalities using OCT (Optical Coherence Tomography) images. The dataset, sourced from Kaggle, was preprocessed using the DCT (Discrete Cosine Transform) for feature extraction. Five machine learning models-Random Forest, Decision Tree, Logistic Regression, Gaussian Naive Bayes, and a Neural Network (MLPClassifier)-were employed to classify the extracted features. The Random Forest model achieved the highest performance, with accuracy of 86.47%, outperforming other models. Evaluation metrics such as accuracy, precision, recall, F1-score, and confusion matrix were used to comprehensively assess model performance. Comparison with similar studies highlighted the effectiveness of this approach, though challenges such as dataset limitations and generalization to diverse populations remain. Future work will focus on integrating advanced deep learning methods, expanding datasets, and improving model interpretability to enhance clinical applicability. © 2024 IEEE.

关键词： Ophthalmology

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Optimizing Pretrained ResNet Models for Alzheimer's Disease Detection in MRI Images 7

Optimizing Pretrained ResNet Models for Alzheimer's Disease ...

引用

7th International Conference on Informatics and Computational sciences, ICICoS 2024

作者： Prasetyo, Simeon Yuda Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350375886

Alzheimer's disease (AD) is a serious concern in healthcare because it affects cognitive skills and quality of life. Recent advances in medical imaging and machine learning techniques provide great opportunities for early identification and diagnosis of Alzheimer's disease. This study explores the optimization of pretrained ResNet models for AD detection in MRI images, focusing on ResNet50, ResNet101, and ResNet152 architectures. The research comprises two phases: transfer learning and fine-tuning. Results show that deeper model architectures perform better in the transfer learning phase, with ResNet152 demonstrating superior performance, achieving an accuracy of 99.417%. Fine-tuning significantly enhances model accuracy, with all models achieving nearly perfect scores. ResNet152 emerges as the top-performing model, emphasizing the importance of fine-tuning in optimizing classification accuracy for AD detection. Future study might concentrate on fine-tuning model architectures and using sophisticated regularization approaches to improve AD classification. This work helps to the development of accurate diagnostic methods for early Alzheimer's disease identification, allowing for prompt intervention and treatment techniques. © 2024 IEEE.

关键词： Neurodegenerative diseases

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Development of a Fuzzy Logic and IoT-Based Automated Irrigation System for Water Spinach Cultivation

Development of a Fuzzy Logic and IoT-Based Automated Irrigat...

引用

2024 International Conference on Information Technology Systems and Innovation, ICITSI 2024

作者： Wijaya, Marcolino Bachri, Fairuz Athallah Ferial Ahmadi, Ferdi Ngarianto, Heri Achmad, Said Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331511470

This study presents the development of an IoT-based irrigation system with fuzzy logic to enhance water spinach cultivation. Urban farming has helped increase urban resilience to food security concerns. This method encourages water conservation, organic fertilizers, and composting, among other sustainable agriculture methods. This technique combines technology, plant physiology, and soil science, resulting in long-term sustainable agriculture. Previous research primarily focused on developing an automated plant sprinkler system that used a microcontroller to provide the plant with adequate water and nutrients based on sensor data without any application to make it easier for users to monitor the plant and control the automated sprinkler from afar. This project implements a cloud computing method by using Blynk apps to control and monitor sensor data. The system was tested directly by evaluating the functionality of sensors and the application to ensure they operated as intended. The results showed that the sensors reliably monitored environmental parameters, and the application successfully controlled the irrigation system remotely, maintaining ideal conditions for plant growth. These findings highlight the potential of IoT technology to support sustainable urban farming and expand opportunities for its implementation. © 2024 IEEE.

关键词： Sprinkler systems (irrigation)

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Comparative Analysis of Machine Learning Algorithms for Laptop Value Estimation 9th

Comparative Analysis of Machine Learning Algorithms for Lapt...

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

作者： Hansel, Kent Christopher Tanoto, Vincentius Axelle Suri, Puti Andam Fajar, Muhamad Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

The increasing prevalence of laptops in homes, schools, and workplaces makes accurate value estimations essential to guide buyers in making informed decisions. This study compares the effectiveness of four machine learning algorithms-Multiple Linear Regression (MLR), Artificial Neural Network (ANN), Random Forest (RF), and Support Vector Machine (SVM)-in predicting laptop prices based on specifications such as brand, processor speed, RAM size, storage capacity, screen size, and weight. The performance of these models is evaluated using Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and R-squared (R2) metrics. Results indicate that MLR and RF provide superior performance, achieving the lowest RMSE of 0.02 and the highest R2 score of 1.0, demonstrating their suitability for datasets with linear feature relationships. The findings highlight the significance of selecting appropriate models based on the nature of the dataset, suggesting that while ANN and SVM are potent for complex, non-linear data, MLR and RF are preferable for linear datasets. © 2024 The Authors.

关键词： Multiple linear regression

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Unsupervised Content-Based Book Recommendation System Using DeepWalk 11

Unsupervised Content-Based Book Recommendation System Using ...

引用

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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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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Optimizing the Use of Technology in Efforts to Improve Public Health, Especially with Appropriate Food Calorie Intake 9th

Optimizing the Use of Technology in Efforts to Improve Publi...

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

作者： Lo, Yossy Frenlie Fernandi, Fery Saputri, Hanis Amalia Sari, Azani Cempaka Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

NutriFitPal is a web-based online application developed to help someone monitor and control their calorie intake and maintain a healthy lifestyle. This web application was built using React TypeScript for frontend development and Firebase for backend operations. The main features contained in this web application are user authentication, real-time data storage, an extensive food database, and personalized calorie recommendations based on individual user data. In testing its reliability and functionality, this web application carries out black-box testing and System Usability Scale (SUS). Future improvements will focus on incorporating user feedback and expanding the food database. © 2024 The Authors.

关键词： Food ingredients

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