A Lagrangian Dual-based Theory-guided Deep Neural Network

The theory-guided neural network (TgNN) is a kind of method which improves the effectiveness and efficiency of neural network architectures by incorporating scientific knowledge or physical information. Despite its great success, the theory-guided (deep) neural network possesses certain limits when maintaining a tradeoff between training data and domain knowledge during the training process. In this paper, the Lagrangian dual-based TgNN (TgNN-LD) is proposed to improve the effectiveness of TgNN. We convert the original loss function into a constrained form with fewer items, in which partial differential equations (PDEs), engineering controls (ECs), and expert knowledge (EK) are regarded as constraints, with one Lagrangian variable per constraint. These Lagrangian variables are incorporated to achieve an equitable tradeoff between observation data and corresponding constraints, in order to improve prediction accuracy, and conserve time and computational resources adjusted by an ad-hoc procedure. To investigate the performance of the proposed method, the original TgNN model with a set of optimized weight values adjusted by ad-hoc procedures is compared on a subsurface flow problem, with their L2 error, R square (R2), and computational time being analyzed. Experimental results demonstrate the superiority of the Lagrangian dual-based TgNN.Comment: 12 pages, 10 figure

Deteksi Seksisme Online menggunakan Support Vector Machine dan Naïve Bayes

AbstrakSeksisme online menjadi topik penting di media sosial yang mempengaruhi perkembangan internet, menimbulkan efek negatif dan menjadi ancaman serius bagi wanita yang menjadi target. Penelitian ini menggunakan machine learning untuk mendeteksi seksisme pada kalimat bahasa Inggris. Algoritma yang digunakan adalah Support Vector Machine dan Naive Bayes. Grid search diterapkan pada model untuk mencari kombinasi hyperparameter terbaik sehingga menghasilkan skor terbaik. Pelatihan dibagi menjadi dua tugas, yaitu (1) pelatihan model menggunakan data tanpa penanganan imbalanced dan (2) pelatihan model menggunakan data yang telah dilakukan SMOTE. Hasil dari pelatihan model menunjukkan model SVM+SMOTE menghasilkan rata-rata skor F1 terbaik paling tinggi yaitu sebesar 0,96. Pengujian menggunakan data uji menunjukkan model SVM+SMOTE menghasilkan skor F1 tertinggi, yaitu sebesar 0,90 dengan 1467 kalimat diklasifikasikan benar 'not sexist’, 47 kalimat ‘not sexist’ diklasifikasikan sebagai ‘sexist’, 189 kalimat ‘sexist’ diklasifikasikan benar dan 297 kalimat ‘sexist’ diklasifikasikan sebagai ‘not sexist’.Kata kunci: Seksisme, Deteksi, SVM, Naive Bayes, SMOTEAbstractOnline sexism has become a significant issue on social media, impacting internet progress and posing a serious threat to targeted women. This research uses machine learning to detect sexism in English sentences. The algorithms used are Support Vector Machine and Naive Bayes. Grid search is applied in the model to find the best combination of hyperparameters to produce the best score. The training is divided into two tasks: (1) training the model using unhandle the imbalanced data and (2) training the model using data with SMOTE. The training results show that the SVM+SMOTE model produces the highest average best F1 score is 0.96. The testing results show that the SVM+SMOTE model produces the highest F1 score is 0.90 with 1467 sentences correctly classified as 'not sexist', 47 'not sexist' sentences classified as 'sexist', 189 sentences classified as 'sexist' correctly and 297 'sexist' sentences were classified as 'not sexist'.Keywords: Sexism, Detection, SVM, Naive Bayes, SMOT

An Optimized Machine Learning and Deep Learning Framework for Facial and Masked Facial Recognition

In this study, we aimed to find an optimized approach to improving facial and masked facial recognition using machine learning and deep learning techniques. Prior studies only used a single machine learning model for classification and did not report optimal parameter values. In contrast, we utilized a grid search with hyperparameter tuning and nested cross-validation to achieve better results during the verification phase. We performed experiments on a large dataset of facial images with and without masks. Our findings showed that the SVM model with hyperparameter tuning had the highest accuracy compared to other models, achieving a recognition accuracy of 0.99912. The precision values for recognition without masks and with masks were 0.99925 and 0.98417, respectively. We tested our approach in real-life scenarios and found that it accurately identified masked individuals through facial recognition. Furthermore, our study stands out from others as it incorporates hyperparameter tuning and nested cross-validation during the verification phase to enhance the model's performance, generalization, and robustness while optimizing data utilization. Our optimized approach has potential implications for improving security systems in various domains, including public safety and healthcare. Doi: 10.28991/ESJ-2023-07-04-010 Full Text: PD

Aspect-Based Sentiment Analysis from User-Generated Content in Shopee Marketplace Platform

A number of businesses, such as TripAdvisor, Open Table, and Yelp, have successfully utilized aspect-based sentiment analysis in order to gain insights from reviews provided by customers and enhance the quality of their goods or services. Businesses are able to swiftly discover any unfavorable sentiment or possible harm to their brand when they analyze client input across numerous aspects from social media, online reviews, and conversations with customer care representatives. This study aims to explain how aspect-based semantic analysis of market-collected user-generated data through performance comparisons of Doc2vec and TF-IDF vectorization. Both Doc2Vec and TF-IDF have their own distinctive qualities, which might vary according on the nature of the job, the dataset, and the volume of the available training data. For the objectives of this research, the data was obtained from several of fashion merchants that run their companies by means of the Shopee platform, which is a well-known online marketplace platform in Indonesia. In this research, the accuracy and F1 Score achieved by Doc2Vec vectorization was superior to those achieved by TF-IDF vectorization. Our findings shows that Doc2Vec vectorization is better for classifying customer ratings because it can pull out the semantic meaning of words in a document. The findings also shows that the score of c and gamma parameter have significant impact to the score of Accuracy and F1 Score of the classifier.By precisely categorizing client sentiment, this study enables businesses to improve their services, respond to customers' problems, and increase their customer satisfaction