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1
САВВИН, Н. В., Д. Н. ВАСЕНИН та Д. С. СВИРИДОВ. "ОБОСНОВАНИЕ МЕТОДА ОБРАБОТКИ ИНФОРМАЦИИ ДЛЯ ПОВЫШЕНИЯ ТОЧНОСТИ КРАТКОСРОЧНОГО ПРОГНОЗА ЭЛЕКТРОПОТРЕБЛЕНИЯ (НА ПРИМЕРЕ КОМПЛЕКСА ОБЪЕКТОВ ИНЖЕНЕРНОГО КАМПУСА УНИВЕРСИТЕТА)". Инженерные системы и сооружения, № 1(59) (4 квітня 2025): 149–54. https://doi.org/10.36622/2074-188x.2025.36.19.014.
Повний текст джерелаАнотація:
В данной статье представлен новый метод краткосрочного прогнозирования электрической нагрузки, в котором акцент делается на интеграцию календарных данных и уникального метода временного кодирования. Проведённый анализ показал, что погодные переменные оказывают незначительное влияние на точность прогнозов. В связи с этим предложен новый подход, позволяющий моделям лучше понимать временные закономерности, используя синусоидальные и косинусоидальные преобразования минут, часов, дней недели и года. Для прогнозирования нагрузки применялись модели машинного обучения: LSTM (долгая краткосрочная память), Bi-LSTM (двунаправленная LSTM), CNN LSTM (сверточная нейронная сеть с LSTM) и CNN-Bi-LSTM. По результатам экспериментов Bi-LSTM показала наилучшую точность. This article presents a new method for short-term forecasting of electrical load, which focuses on the integration of calendar data and a unique time coding method. The analysis showed that weather variables have little effect on the accuracy of forecasts. In this regard, a new approach is proposed that allows models to better understand temporal patterns using sinusoidal and cosine transformations of minutes, hours, days of the week, and year. Machine learning models were used to predict the load: LSTM (long-term short-term memory), Bi LSTM (bidirectional LSTM), CNN-LSTM (convolutional neural network with LSTM) and CNN-Bi-LSTM. According to the experimental results, Bi-LSTM showed the best accuracy.
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Roy, Dilip Kumar, Tapash Kumar Sarkar, Sheikh Shamshul Alam Kamar, Torsha Goswami, Md Abdul Muktadir, Hussein M. Al-Ghobari, Abed Alataway, Ahmed Z. Dewidar, Ahmed A. El-Shafei, and Mohamed A. Mattar. "Daily Prediction and Multi-Step Forward Forecasting of Reference Evapotranspiration Using LSTM and Bi-LSTM Models." Agronomy 12, no. 3 (February 27, 2022): 594. http://dx.doi.org/10.3390/agronomy12030594.
Повний текст джерелаАнотація:
Precise forecasting of reference evapotranspiration (ET0) is one of the critical initial steps in determining crop water requirements, which contributes to the reliable management and long-term planning of the world’s scarce water sources. This study provides daily prediction and multi-step forward forecasting of ET0 utilizing a long short-term memory network (LSTM) and a bi-directional LSTM (Bi-LSTM) model. For daily predictions, the LSTM model’s accuracy was compared to that of other artificial intelligence-based models commonly used in ET0 forecasting, including support vector regression (SVR), M5 model tree (M5Tree), multivariate adaptive regression spline (MARS), probabilistic linear regression (PLR), adaptive neuro-fuzzy inference system (ANFIS), and Gaussian process regression (GPR). The LSTM model outperformed the other models in a comparison based on Shannon’s entropy-based decision theory, while the M5 tree and PLR models proved to be the lowest performers. Prior to performing a multi-step-ahead forecasting, ANFIS, sequence-to-sequence regression LSTM network (SSR-LSTM), LSTM, and Bi-LSTM approaches were used for one-step-ahead forecasting utilizing the past values of the ET0 time series. The results showed that the Bi-LSTM model outperformed other models and that the sequence of models in ascending order in terms of accuracies was Bi-LSTM > SSR-LSTM > ANFIS > LSTM. The Bi-LSTM model provided multi-step (5 day)-ahead ET0 forecasting in the next step. According to the results, the Bi-LSTM provided reasonably accurate and acceptable forecasting of multi-step-forward ET0 with relatively lower levels of forecasting errors. In the final step, the generalization capability of the proposed best models (LSTM for daily predictions and Bi-LSTM for multi-step-ahead forecasting) was evaluated on new unseen data obtained from a test station, Ishurdi. The model’s performance was assessed on three distinct datasets (the entire dataset and the first and the second halves of the entire dataset) derived from the test dataset between 1 January 2015 and 31 December 2020. The results indicated that the deep learning techniques (LSTM and Bi-LSTM) achieved equally good performances as the training station dataset, for which the models were developed. The research outcomes demonstrated the ability of the developed deep learning models to generalize the prediction capabilities outside the training station.
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Saragih, Letare, Maria Nababan, Yohana Simatupang, and Junita Amalia. "ANALISIS SELF-ATTENTION PADA BI-DIRECTIONAL LSTM DENGAN FASTTEXT DALAM MENDETEKSI EMOSI BERDASARKAN TEXT." ZONAsi: Jurnal Sistem Informasi 4, no. 2 (November 15, 2022): 144–56. http://dx.doi.org/10.31849/zn.v4i2.10846.
Повний текст джерелаАнотація:
Cuitan Twitter yang sudah dilabeli berdasarkan jenis emosinya merupakan salah satu bentuk pengekspresian emosi dalam bentuk teks. Teks dapat dijadikan sebagai objek dalam melakukan emotion detection. Tujuan penelitian ini adalah untuk mengetahui pengaruh self-attention pada pemodelan Bi-LSTM dengan FastText dalam mendeteksi emosi pada cuitan Twitter. Pengaruh dilihat dengan membandingkan hasil evaluasi recall, precison, F1-score dan akurasi dari pemodelan Bi-LSTM, Bi-LSTM + Self-Attention dan Self-Attention. FastText digunakan untuk mengubah setiap kata menjadi vector matrix. Bi-LSTM digunakan untuk proses klasifikasi. Dan self-attention untuk membantu model untuk memilih kata yang paling dapat merepresentasikan makna dari kalimat terutama pada kalimat review yang panjang. Hasil yang diperoleh menunjukkan bahwa dari ketiga model, Bi-LSTM memiliki hasil evaluasi yang lebih baik dibandingkan dengan kedua model lainnya. Berdasarkan hasil tersebut, dapat disimpulkan bahwa penambahan self-attention pada model Bi-LSTM tidak memberikan pengaruh pada hasil evaluasi model untuk klasifikasi emosi.
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Nilasari, Ni Ketut Novia, Made Sudarma, and Nyoman Gunantara. "Prediksi Nilai Cryptocurrency Dengan Metode Bi-LSTM dan LSTM." Majalah Ilmiah Teknologi Elektro 22, no. 2 (December 19, 2023): 221. http://dx.doi.org/10.24843/mite.2023.v22i02.p09.
Повний текст джерелаАнотація:
Semakin pesatnya perkembangan teknologi saat ini, dapat memudahkan seluruh kegiatan manusia, sehingga mengakibatkan seluruh aspek tidak bisa lepas dari teknologi tanpa terkecuali bidang keuangan. Dengan berkembangnya teknologi diiringi juga dengan dikenalnya berbagai instrument investasi. Setiap melaksanakan investasi tentu akan selalu ada berbagai resiko yang menyertainya termasuk investasi cryptocurrency salah satunya bitcoin. Tidak seperti mata uang konvensional, bitcoin bersifat tidak desentralisasi sehingga perkembangan harganya tidak dalam pengawasan atau kontrol pihak manapun, dimana jika uang konvensional ada lembaga tertentu yang mengawasi dan mengontrol pergerakannya. Hal tersebut mengakibatkan harga nilai tukar dari bitcoin menjadi tidak konsisten atau tidak stabil. Dengan terdapatnya metode prediksi, pengguna bitcoin bisa menetapkan waktu yang pas untuk menjalankan transaksi. Penelitian ini memiliki tujuan guna memprediksi harga bitcoin dengan menggunakan metode LSTM serta Bi-LSTM. Berdasarkan hasil penelitian diperoleh hasil prediksi terbaik menggunakan metode Bi-LSTM dengan RMSE 1482.73 sedangkan dengan LSTM menghasilkan RMSE sebesar 1768.69 sehingga dapat disimpulkan dari sisi akurasi Bi-LSTM memberikan hasil yang lebih akurat hanya saja dengan Bi-LSTM membutuhkan resourse yang lebih banyak.
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Fernando, Yeremias. "Analisis Sentimen pada Ulasan Konsumen Ayam Goreng Nelongso di Google Maps Menggunakan Metode Bidirectional Long Short-Term Memory (Bi-LSTM)." Jurnal Ilmiah Mahasiswa Sains Unisma Malang 3, no. 1 (February 25, 2025): 65. https://doi.org/10.33474/jimsum.v3i1.26592.
Повний текст джерелаАнотація:
This study analyzes consumer review sentiments of Ayam Goreng Nelongso on Google Maps using the Bidirectional Long Short-Term Memory (Bi-LSTM) method. The data used consists of 4,450 reviews, with varying training and testing data ratios ranging from 90:10 to 10:90. The evaluation results show that the Bi-LSTM model performs excellently, with an average accuracy of 98.33%, precision of 99.44%, recall of 99.44%, and F1-score of 99.44%. These findings demonstrate that Bi-LSTM can reliably and consistently identify positive, negative, and neutral sentiments in consumer review data, providing valuable insights for improving the services and product quality of the MSME Ayam Goreng Nelongso.Keywords: Sentiment Analysis, Ayam Goreng Nelongso, Google Maps, Bidirectional Long Short-Term Memory, Bi-LSTM. ABSTRAKPenelitian ini menganalisis sentimen ulasan konsumen terhadap Ayam Goreng Nelongso di Google Maps menggunakan metode Bidirectional Long Short-Term Memory (Bi-LSTM). Data yang digunakan mencakup 4.450 ulasan dengan rasio data latih dan uji bervariasi, mulai dari 90:10 hingga 10:90. Hasil evaluasi menunjukkan bahwa model Bi-LSTM memiliki performa sangat baik dengan rata-rata akurasi 98,33%, presisi 99,44%, recall 99,44%, dan F1-score 99,44%. Temuan ini menunjukkan bahwa Bi-LSTM mampu secara andal dan konsisten mengidentifikasi sentimen positif, negatif, dan netral pada data ulasan konsumen, memberikan wawasan yang bermanfaat untuk peningkatan layanan dan kualitas produk UMKM Ayam Goreng Nelongso.Kata Kunci: Analisis Sentimen, Ayam Goreng Nelongso, Google Maps, Bidirectional Long Short-Term Memory, Bi-LSTM.
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Lai, Stevanus Alditian, and Yonathan Purbo Santosa. "BUS ROUTE DEMAND PREDICTION WITH DEEP LEARNING." Proxies : Jurnal Informatika 4, no. 2 (August 29, 2024): 110–35. http://dx.doi.org/10.24167/proxies.v4i2.12439.
Повний текст джерелаАнотація:
bus companies currently have several obstacles in providing their fleets from one city to another because of the highly dynamic demand from passengers, bus companies must be able to analyze which routes will have a lot of demand so that bus companies can provide more fleets on the routes that will have high demand. Deep learning method is relatively new for bus company to predict the bus route demand, this study is try to create and implement LSTM Autoencoder-Bi-LSTM Hybrid Models and Bi-LSTM to forecast bus route demand to support the decision making process in order to optimize bus fleet deployment each route. The results shows that LSTM Autoencoder-Bi-LSTM Hybrid Models and Bi-LSTM models doesn't differ very much.
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Fajri, Haidar Ahmad, and Kirey Oleisan. "Enhancing Energy Consumption Forecasting with a Multi-Model Deep Learning Approach." Indonesian Journal of Mathematics and Applications 3, no. 1 (April 10, 2025): 55–68. https://doi.org/10.21776/ub.ijma.2025.003.01.5.
Повний текст джерелаАнотація:
High energy consumption highlights the need for accurate primary energy forecasts to be critical for policy development, resource optimization and sustainable growth. Indonesia, the fourth largest energy-consuming country in Asia-Pacific, will face challenges in managing energy consumption for economic advancement if it does not conduct proper forecasts with large and limited data. Deep learning models, such as Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (Bi-LSTM), and Transformer, excel at extracting insights and modelling temporal dependencies with minimal error, making them ideal for energy forecasting. The hybrid CNN-Bi-LSTM-Transformer model leverages complementary strengths: CNN captures initial patterns, Bi-LSTM manages temporal dependencies, and Transformer enhance global relationships. This model outperforms others model, including Linear Regression, CNN, Bi-LSTM, LSTM, CNN-LSTM, CNN-Bi-LSTM, CNN-Transformer, LSTM-Transformer, Bi-LSTM-Transformer, and hybrid CNN-LSTM-Transformer. It achieves a Mean Squared Error (MSE) of $\num{6.0006e-4}$ on train data, $\num{3.4485e-4}$ on test data and computation time of 8.20 minutes from 25 iterations, with 128 units of CNN layer, 150 units of LSTM layer, and four heads of attention in Transformer. The model also reports a Mean Absolute Error (MAE) of $\num{1.4000e-4}$ for training and $\num{1.5000e-4}$ test data and a Mean Absolute Percentage Error (MAPE) of $1.56$\% for train data and $1.75$\% for test data. This model also effectively tracks energy consumption trends with minimal fluctuations, accurately mirroring the original data and avoiding irregular variations, ensuring reliable future predictions in the long- and short-term.
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Suebsombut, Paweena, Aicha Sekhari, Pradorn Sureephong, Abdelhak Belhi, and Abdelaziz Bouras. "Field Data Forecasting Using LSTM and Bi-LSTM Approaches." Applied Sciences 11, no. 24 (December 13, 2021): 11820. http://dx.doi.org/10.3390/app112411820.
Повний текст джерелаАнотація:
Water, an essential resource for crop production, is becoming increasingly scarce, while cropland continues to expand due to the world’s population growth. Proper irrigation scheduling has been shown to help farmers improve crop yield and quality, resulting in more sustainable water consumption. Soil Moisture (SM), which indicates the amount of water in the soil, is one of the most important crop irrigation parameters. In terms of water usage optimization and crop yield, estimating future soil moisture (forecasting) is an essentially valuable task for crop irrigation. As a result, farmers can base crop irrigation decisions on this parameter. Sensors can be used to estimate this value in real time, which may assist farmers in deciding whether or not to irrigate. The soil moisture value provided by the sensors, on the other hand, is instantaneous and cannot be used to directly compute irrigation parameters such as the best timing or the required water quantity to irrigate. The soil moisture value can, in fact, vary greatly depending on factors such as humidity, weather, and time. Using machine learning methods, these parameters can be used to predict soil moisture levels in the near future. This paper proposes a new Long-Short Term Memory (LSTM)-based model to forecast soil moisture values in the future based on parameters collected from various sensors as a potential solution. To train and validate this model, a real-world dataset containing a set of parameters related to weather forecasting, soil moisture, and other related parameters was collected using smart sensors installed in a greenhouse in Chiang Mai province, Thailand. Preliminary results show that our LSTM-based model performs well in predicting soil moisture with a 0.72% RMSE error and a 0.52% cross-validation error (LSTM), and our Bi-LSTM model with a 0.76% RMSE error and a 0.57% cross-validation error. In the future, we aim to test and validate this model on other similar datasets.
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Yan, Xiuwei, Sijia Liu, Songlei Wang, Jiarui Cui, Yongrui Wang, Yu Lv, Hui Li, et al. "Predictive Analysis of Linoleic Acid in Red Meat Employing Advanced Ensemble Models of Bayesian and CNN-Bi-LSTM Decision Layer Fusion Based Hyperspectral Imaging." Foods 13, no. 3 (January 28, 2024): 424. http://dx.doi.org/10.3390/foods13030424.
Повний текст джерелаАнотація:
Rapid non-destructive testing technologies are effectively used to analyze and evaluate the linoleic acid content while processing fresh meat products. In current study, hyperspectral imaging (HSI) technology was combined with deep learning optimization algorithm to model and analyze the linoleic acid content in 252 mixed red meat samples. A comparative study was conducted by experimenting mixed sample data preprocessing methods and feature wavelength extraction methods depending on the distribution of linoleic acid content. Initially, convolutional neural network Bi-directional long short-term memory (CNN-Bi-LSTM) model was constructed to reduce the loss of the fully connected layer extracted feature information and optimize the prediction effect. In addition, the prediction process of overfitting phenomenon in the CNN-Bi-LSTM model was also targeted. The Bayesian-CNN-Bi-LSTM (Bayes-CNN-Bi-LSTM) model was proposed to improve the linoleic acid prediction in red meat through iterative optimization of Gaussian process acceleration function. Results showed that best preprocessing effect was achieved by using the detrending algorithm, while 11 feature wavelengths extracted by variable combination population analysis (VCPA) method effectively contained characteristic group information of linoleic acid. The Bi-directional LSTM (Bi-LSTM) model combined with the feature extraction data set of VCPA method predicted 0.860 Rp2 value of linoleic acid content in red meat. The CNN-Bi-LSTM model achieved an Rp2 of 0.889, and the optimized Bayes-CNN-Bi-LSTM model was constructed to achieve the best prediction with an Rp2 of 0.909. This study provided a reference for the rapid synchronous detection of mixed sample indicators, and a theoretical basis for the development of hyperspectral on-line detection equipment.
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B.Ramasubba Reddy. "Enhancing Cloudburst Prediction System Using Bi-LSTM in Realtime: Machine Learning." Communications on Applied Nonlinear Analysis 32, no. 1s (November 2, 2024): 221–31. http://dx.doi.org/10.52783/cana.v32.2161.
Повний текст джерелаАнотація:
Nowadays Technology is enhanced, but still facing floods and toofan due to uncertain rainfall conditions and artificial rains, it is a challenging task for hydrology and meteorology. Now a new cloudburst prediction system is a requirement for hydrology and meteorology. cloudburst prediction holds significant importance in fields like hydrology and meteorology, aiding farmers, water resource managers, and emergency responders in preparing for and managing the impacts of extreme weather events like droughts and floods. One effective technique for rainfall prediction involves employing machine learning models such as Bi-LSTM (Bidirectional Long Short-Term Memory). Bi-LSTM, a neural network architecture known for its prowess in sequence modeling tasks like voice recognition and natural language processing (NLP), operates by processing data bidirectionally, facilitating a thorough understanding of sequential patterns. By analyzing historical rainfall data as a sequence, a Bi-LSTM model can effectively forecast future rainfall values with notable accuracy.In this proposed methodology, a Bi-LSTM model is trained using a CSV dataset comprising historical rainfall data. The performance of the Bi-LSTM model is then compared with that of an LSTM (Long Short-Term Memory) model, another widely used architecture for sequence prediction tasks. The results indicate that the Bi-LSTM model achieves an accuracy of 96.55%, surpassing the accuracy of the LSTM model, which stands at 94.36%. This comparison underscores the effectiveness of the Bi-LSTM approach in rainfall prediction, showcasing its potential to provide valuable insights for decision-making in various domains affected by weather variability.
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Zhang, Chun-Xiang, Shu-Yang Pang, Xue-Yao Gao, Jia-Qi Lu, and Bo Yu. "Attention Neural Network for Biomedical Word Sense Disambiguation." Discrete Dynamics in Nature and Society 2022 (January 10, 2022): 1–14. http://dx.doi.org/10.1155/2022/6182058.
Повний текст джерелаАнотація:
In order to improve the disambiguation accuracy of biomedical words, this paper proposes a disambiguation method based on the attention neural network. The biomedical word is viewed as the center. Morphology, part of speech, and semantic information from 4 adjacent lexical units are extracted as disambiguation features. The attention layer is used to generate a feature matrix. Average asymmetric convolutional neural networks (Av-ACNN) and bidirectional long short-term memory (Bi-LSTM) networks are utilized to extract features. The softmax function is applied to determine the semantic category of the biomedical word. At the same time, CNN, LSTM, and Bi-LSTM are applied to biomedical WSD. MSH corpus is adopted to optimize CNN, LSTM, Bi-LSTM, and the proposed method and testify their disambiguation performance. Experimental results show that the average disambiguation accuracy of the proposed method is improved compared with CNN, LSTM, and Bi-LSTM. The average disambiguation accuracy of the proposed method achieves 91.38%.
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Poetra, Chandra Kirana, Syafrial Fachri Pane, and Nuraini Siti Fatonah. "Meningkatkan Akurasi Long-Short Term Memory (LSTM) pada Analisis Sentimen Vaksin Covid-19 di Twitter dengan Glove." Jurnal Telematika 16, no. 2 (January 19, 2022): 85–90. http://dx.doi.org/10.61769/telematika.v16i2.400.
Повний текст джерелаАнотація:
Covid-19 began to appear in early 2020. The spread of this outbreak is often discussed on Twitter, especially about vaccine procurement. For this reason, it is necessary to have a sentiment analysis on the opinion on vaccine procurement. Sentiment analysis will use the Long Short Term Memory (LSTM) method. However, the level of accuracy of LSTM itself is not accurate enough compared to another method, such as Bi-LSTM. Therefore, it is necessary to optimize so that the LSTM model can predict accurately and compete with the accuracy of Bi-LSTM. Optimization is done by using the Glove method. The Glove method works by counting the occurrences of one word with another and then converting it to a vector. Words that often appear together will have vector values that are close to each other. This vector value is then used as a reference and inserted into the embedding layer of the LSTM model. The application of LSTM coupled with the Glove method resulted in an accuracy of 89% (87% for LSTM and 88% for Bi-LSTM). In this study, the Glove method could increase the accuracy of the used model by 2%. Covid-19 mulai muncul di awal tahun 2020. Penyebaran wabah ini sering dibicarakan di Twitter, terutama tentang pengadaan vaksin. Untuk itu, perlu adanya analisis sentimen terhadap opini pengadaan vaksin. Analisis sentimen akan menggunakan metode Long Short Term Memory (LSTM). Namun, tingkat akurasi LSTM sendiri belum cukup akurat dibandingkan dengan metode lainnya, seperti Bi-LSTM. Oleh karena itu, perlu dilakukan optimalisasi agar model LSTM dapat memprediksi secara akurat dan dapat menyaingi akurasi Bi-LSTM. Optimalisasi dilakukan dengan menggunakan metode Glove. Metode Glove bekerja dengan menghitung kemunculan satu kata dengan kata lainnya lalu mengonversinya menjadi vektor. Kata yang sering muncul secara bersamaan akan memiliki nilai vektor yang saling mendekati. Nilai vektor ini kemudian dijadikan referensi dan dimasukkan ke lapisan embedding pada model LSTM. Penerapan LSTM yang ditambah dengan metode Glove menghasilkan akurasi sebesar 89% (87% untuk LSTM dan 88% untuk Bi-LSTM). Dalam penelitian ini penerapan metode Glove dapat meningkatkan akurasi model sebesar 2%.
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Han, Chenyu, and Xiaoyu Fu. "Challenge and Opportunity: Deep Learning-Based Stock Price Prediction by Using Bi-Directional LSTM Model." Frontiers in Business, Economics and Management 8, no. 2 (April 2, 2023): 51–54. http://dx.doi.org/10.54097/fbem.v8i2.6616.
Повний текст джерелаАнотація:
Stock price prediction is a challenging and important task in finance, with many potential applications in investment, risk management, and portfolio optimization. In this paper, we propose a bi-directional long short-term memory (Bi-LSTM) model for predicting the future price of a stock based on its historical prices. The Bi-LSTM model is a variant of the popular LSTM model that is capable of processing input sequences in both forward and backward directions, allowing it to capture both short- and long-term dependencies in the data. We apply the Bi-LSTM model to historical stock price data for Apple Inc. and evaluate its performance using mean squared error (MSE) and visual inspection of actual vs. predicted prices. Our experiments show that the Bi-LSTM model is able to make accurate predictions on the testing data and capture some of the trends and patterns in the data, although it may struggle with sudden changes in the market. Overall, our results suggest that the Bi-LSTM model is a promising tool for stock price prediction and has many potential applications in finance and investment.
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Af'idah, Dwi Intan, Puput Dewi Anggraeni, Muhammad Rizki, Aji Bagus Setiawan, and Sharfina Febbi Handayani. "Aspect-Based Sentiment Analysis for Indonesian Tourist Attraction Reviews Using Bidirectional Long Short-Term Memory." JUITA : Jurnal Informatika 11, no. 1 (May 6, 2023): 27. http://dx.doi.org/10.30595/juita.v11i1.15341.
Повний текст джерелаАнотація:
The tourism sector in Indonesia experienced growth and made a positive contribution to the national economy, but this growth has yet to reach its target. Therefore, the government of Indonesia has implemented a sustainable tourism development program by establishing ten priority tourism destinations. Aspect-based sentiment analysis (ABSA) towards tourist attraction reviews can assist the government in developing potential goals. The ABSA process compares with two deep learning models (LSTM and Bi-LSTM), which are considered to obtain good performance in text analysis. The shortcomings of previous ABSA research should have examined the performance of the aspect classification and sentiment classification models sequentially. This makes the performance obtained from the ABSA task invalid. Thus, this study is conducted to determine the version of the aspect classification model and the sentiment classification model individually and simultaneously. This study aims to develop an aspect-based tourist attraction sentiment analysis as an intelligent system solution for sustainable tourism development by applying the binary relevance mechanism and the best deep learning model from LSTM or Bi-LSTM. The test results showed that Bi-LSTM was superior in aspect and sentiment classification individually and simultaneously. Likewise, the aspect classification and sentiment classification test results sequentially Bi-LSTM outperformed that of LSTM. The average accuracy and f1 score of Bi-LSTM are 92.22% and 71,06%. Meanwhile, LSTM obtained 90,63% of average precision and 70,4% of f1 score.
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Sri Sai Sankar, Padaga, and Dr J. Avanija. "SENTIMENT ANALYSIS OF CODE-MIXED LANGUAGES." International Journal of Interpreting Enigma Engineers 01, no. 01 (2024): 31–38. http://dx.doi.org/10.62674/ijiee.2024.v1i01.005.
Повний текст джерелаАнотація:
In the multilingual context of today, code-mixed language is frequently utilized. It happens when a sentence contains both foreign language vocabulary and grammar. Finding the sentence's polarity value is the aim of sentiment analysis of code-mixed language. It is mostly concerned with sentiment analysis of tweets that contain extra Hindi and English words and symbols. The collection is composed of 20,000 tweets, which produces word-level representations of the tweets for use as input in several models, including CNN, LSTM, and Bi-LSTM. When compared to other models, the Bi-LSTM model performs better. The precision of CNN, LSTM and Bi-LSTM is 65.00%, 58.59% and 54.24% respectively.
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Hansun, Seng. "Deep Learning Approach in Predicting Property and Real Estate Indices." International Journal of Advances in Soft Computing and its Applications 14, no. 1 (March 28, 2022): 61–71. http://dx.doi.org/10.15849/ijasca.220328.05.
Повний текст джерелаАнотація:
Abstract The real estate market is one of the most impacted sectors from the Corona Virus Disease 2019 (COVID-19) pandemic that happened in early 2020 globally. Here, we tried to apply an extension of the Long Short-Term Memory (LSTM) deep learning method, known as the Bidirectional LSTM (Bi-LSTM) networks for stock price prediction. Our focus is on six stocks that were included in the LiQuid45 (LQ45) property and real estate sectors. A simple three-layers Bi-LSTM network is proposed for predicting the stocks’ closing prices. We found that the prediction results fall in the reasonable prediction category, except for Pembangunan Perumahan Tbk (PTPP). Bumi Serpong Damai Tbk (BSDE) got the highest accuracy result with more than 90% score, while PTPP got the lowest score with less than 8% score. The proposed Bi-LSTM network could provide a baseline result for developing a good trading strategy. Keywords: Bi-LSTM networks, deep learning, LQ45, property and real estate, stock price prediction.
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Nguyen, Tuyet Nam Thi, Tan Dat Trinh, Pham Cung Le Thien Vu, and Pham The Bao. "Statistical and machine learning approaches for estimating pollution of fine particulate matter (PM2.5) in Vietnam." Journal of Environmental Engineering and Landscape Management 32, no. 4 (November 13, 2024): 292–304. http://dx.doi.org/10.3846/jeelm.2024.22361.
Повний текст джерелаАнотація:
This study aims to predict fine particulate matter (PM2.5) pollution in Ho Chi Minh City, Vietnam, using autoregressive integrated moving average (ARIMA), linear regression (LR), random forest (RF), long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), and convolutional neural network (CNN) combining Bi-LSTM (CNN+Bi-LSTM). Two experiments were set up: the first one used data from 2018–2020 and 2021 as training and test data, respectively. Data from 2018–2021 and 2022 were used as training and test data for the second experiment, respectively. Consequently, ARIMA showed the worst performance, while CNN+Bi-LSTM achieved the best accuracy, with an R² of 0.70 and MAE, MSE, RMSE, and MAPE of 5.37, 65.4, 8.08 µg/m³, and 29%, respectively. Additionally, predicted air quality indexes (AQIs) of PM2.5 were matched the observed ones up to 96%, reflecting the application of predicted concentrations for AQI computation. Our study highlights the effectiveness of machine learning model in monitoring of air pollution.
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Simanihuruk, Laurensia, and Hari Suparwito. "Long Short-Term Memory and Bidirectional Long Short-Term Memory Algorithms for Sentiment Analysis of Skintific Product Reviews." ITM Web of Conferences 71 (2025): 01016. https://doi.org/10.1051/itmconf/20257101016.
Повний текст джерелаАнотація:
In the era of ever-evolving digital technology, conducting customer sentiment analysis through product reviews has become crucial for businesses to improve their offerings and increase customer satisfaction. This research aims to analyze the sentiment of SKINTIFIC skincare products on the Shopee online store platform using advanced deep learning models: Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (Bi-LSTM). These models were evaluated using learning rate, number of units, and dropout rate. The dataset consists of 9,184 product reviews extracted through the Shopee API. The reviews were pre-processed using stemming, normalization, and stopword removal techniques. The Bi-LSTM model showed superior performance, achieving an average accuracy of 95.91% and an average F1 score of 95.82%, compared to the standard LSTM model. The optimal configuration for Bi-LSTM included a learning rate 0.01, 64 units, and a dropout rate 0.2. These findings underscore the effectiveness of Bi-LSTM in understanding and classifying consumer sentiment toward specific products.
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Xu, Hesheng, and Bin Hu. "Legal Text Recognition Using LSTM-CRF Deep Learning Model." Computational Intelligence and Neuroscience 2022 (March 17, 2022): 1–10. http://dx.doi.org/10.1155/2022/9933929.
Повний текст джерелаАнотація:
In legal texts, named entity recognition (NER) is researched using deep learning models. First, the bidirectional (Bi)-long short-term memory (LSTM)-conditional random field (CRF) model for studying NER in legal texts is established. Second, different annotation methods are used to compare and analyze the entity recognition effect of the Bi-LSTM-CRF model. Finally, other objective loss functions are set to compare and analyze the entity recognition effect of the Bi-LSTM-CRF model. The research results show that the F1 value of the model trained on the word sequence labeling corpus on the named entity is 88.13%, higher than that of the word sequence labeling corpus. For the two types of entities, place names and organization names, the F1 values obtained by the Bi-LSTM-CRF model using word segmentation are 67.60% and 89.45%, respectively, higher than the F1 values obtained by the model using character segmentation. Therefore, the Bi-LSTM-CRF model using word segmentation is more suitable for recognizing extended entities. The parameter learning result using log-likelihood is better than that using the maximum interval criterion, and it is ideal for the Bi-LSTM-CRF model. This method provides ideas for the research of legal text recognition and has a particular value.
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Halim, Kevin Yudhaprawira, Dodon Turianto Nugrahadi, Mohammad Reza Faisal, Rudy Herteno, and Irwan Budiman. "Gender Classification Based on Electrocardiogram Signals Using Long Short Term Memory and Bidirectional Long Short Term Memory." Jurnal Ilmiah Teknik Elektro Komputer dan Informatika 9, no. 3 (July 16, 2023): 606–18. https://doi.org/10.26555/jiteki.v9i3.26354.
Повний текст джерелаАнотація:
Gender classification by computer is essential for applications in many domains, such as human-computer interaction or biometric system applications. Generally, gender classification by computer can be done by using a face photo, fingerprint, or voice. However, researchers have demonstrated the potential of the electrocardiogram (ECG) as a biometric recognition and gender classification. In facilitating the process of gender classification based on ECG signals, a method is needed, namely Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (Bi-LSTM). Researchers use these two methods because of the ability of these two methods to deal with sequential problems such as ECG signals. The inputs used in both methods generally use one-dimensional data with a generally large number of signal features. The dataset used in this study has a total of 10,000 features. This research was conducted on changing the input shape to determine its effect on classification performance in the LSTM and Bi-LSTM methods. Each method will be tested with input with 11 different shapes. The best accuracy results obtained are 79.03% with an input shape size of 100×100 in the LSTM method. Moreover, the best accuracy in the Bi-LSTM method with input shapes of 250×40 is 74.19%. The main contribution of this study is to share the impact of various input shape sizes to enhance the performance of gender classification based on ECG signals using LSTM and Bi-LSTM methods. Additionally, this study contributes for selecting an appropriate method between LSTM and Bi-LSTM on ECG signals for gender classification.
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Wang, Weisheng, Yongkang Hao, Xiaozhen Zheng, Tong Mu, Jie Zhang, Xiaoyuan Zhang, and Zhenhao Cui. "Runoff Prediction for Hydrological Applications Using an INFO-Optimized Deep Learning Model." Processes 12, no. 8 (August 22, 2024): 1776. http://dx.doi.org/10.3390/pr12081776.
Повний текст джерелаАнотація:
Runoff prediction is essential in water resource management, environmental protection, and agricultural development. Due to the large randomness, high non-stationarity, and low prediction accuracy of nonlinear effects of the traditional model, this study proposes a runoff prediction model based on the improved vector weighted average algorithm (INFO) to optimize the convolutional neural network (CNN)-bidirectional long short-term memory (Bi-LSTM)-Attention mechanism. First, the historical data are analyzed and normalized. Secondly, CNN combined with Attention is used to extract the depth local features of the input data and optimize the input weights of Bi-LSTM. Then, Bi-LSTM is used to study the time series feature depth analysis data from both positive and negative directions simultaneously. The INFO parameters are optimized to provide the optimal parameter guarantee for the CNN-Bi-LSTM-Attention model. Based on a hydrology station’s water level and flow data, the influence of three main models and two optimization algorithms on the prediction accuracy of the CNN-Bi-LSTM-Attention model is compared and analyzed. The results show that the fitting coefficient, R2, of the proposed model is 0.948, which is 7.91% and 3.38% higher than that of Bi-LSTM and CNN-Bi-LSTM, respectively. The R2 of the vector-weighted average optimization algorithm (INFO) optimization model is 0.993, which is 0.61% higher than that of the Bayesian optimization algorithm (BOA), indicating that the method adopted in this paper has more significant forecasting ability and can be used as a reliable tool for long-term runoff prediction.
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Yadav, Omprakash, Rachael Dsouza, Rhea Dsouza, and Janice Jose. "Soccer Action video Classification using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 1060–63. http://dx.doi.org/10.22214/ijraset.2022.43929.
Повний текст джерелаАнотація:
Abstract: This paper proposes a deep learning approach for the classification of different soccer actions like Goal, Yellow Card and Soccer Juggling from an input soccer video. The approach used for the same included a Hybrid model which consisted of VGG16 CNN model and Bidirectional Long short-term memory (Bi-LSTM) a Recurrent Neural Network (RNN) model. Our approach involved manually annotating approximately 400 soccer clips from 3 action classes for training. Using the VGG16 model to extract the features from the frames of these clips and then training the bi-LSTM on the features obtained. Bi-LSTM being useful in predicting input sequence problems like videos. Keywords: Soccer Videos, Convolution Neural Networks (CNNs), Recurrent Neural Network (RNN), Bidirectional Long shortterm memory (Bi-LSTM)
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Phua, Yeong Tsann, Sujata Navaratnam, Chon-Moy Kang, and Wai-Seong Che. "Sequence-to-sequence neural machine translation for English-Malay." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 2 (June 1, 2022): 658. http://dx.doi.org/10.11591/ijai.v11.i2.pp658-665.
Повний текст джерелаАнотація:
Machine translation aims to translate text from a specific language into another language using computer software. In this work, we performed neural machine translation with attention implementation on English-Malay parallel corpus. We attempt to improve the model performance by rectified linear unit (ReLU) attention alignment. Different sequence-to-sequence models were trained. These models include long-short term memory (LSTM), gated recurrent unit (GRU), bidirectional LSTM (Bi-LSTM) and bidirectional GRU (Bi-GRU). In the experiment, both bidirectional models, Bi-LSTM and Bi-GRU yield a converge of below 30 epochs. Our study shows that the ReLU attention alignment improves the bilingual evaluation understudy (BLEU) translation score between score 0.26 and 1.12 across all the models as compare to the original Tanh models.
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Wu, Yuhan, Chun Xiang, Heng Qian, and Peijian Zhou. "Optimization of Bi-LSTM Photovoltaic Power Prediction Based on Improved Snow Ablation Optimization Algorithm." Energies 17, no. 17 (September 4, 2024): 4434. http://dx.doi.org/10.3390/en17174434.
Повний текст джерелаАнотація:
To enhance the stability of photovoltaic power grid integration and improve power prediction accuracy, a photovoltaic power prediction method based on an improved snow ablation optimization algorithm (Good Point and Vibration Snow Ablation Optimizer, GVSAO) and Bi-directional Long Short-Term Memory (Bi-LSTM) network is proposed. Weather data is divided into three typical categories using K-means clustering, and data normalization is performed using the minmax method. The key structural parameters of Bi-LSTM, such as the feature dimension at each time step and the number of hidden units in each LSTM layer, are optimized based on the Good Point and Vibration strategy. A prediction model is constructed based on GVSAO-Bi-LSTM, and typical test functions are selected to analyze and evaluate the improved model. The research results show that the average absolute percentage error of the GVSAO-Bi-LSTM prediction model under sunny, cloudy, and rainy weather conditions are 4.75%, 5.41%, and 14.37%, respectively. Compared with other methods, the prediction results of this model are more accurate, verifying its effectiveness.
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Li, Jiawei. "Research on Stock Portfolio Construction Based on Bi-LSTM Neural Networks." Journal of Computing and Electronic Information Management 13, no. 2 (June 28, 2024): 37–41. http://dx.doi.org/10.54097/u1wm896m.
Повний текст джерелаАнотація:
In recent years, the rapid globalisation of China's financial market has provided more opportunities for investors, but also brought about a more complex investment environment. This paper constructs stock portfolios based on Bi-LSTM neural networks, aiming to improve the accuracy of stock price prediction and the optimisation of investment portfolios using deep learning techniques. The theoretical part introduces the portfolio theory, including the mean-variance model and the capital asset pricing model, and explores the advantages of LSTM, Bi-LSTM and ATT-LSTM in processing time series data. The constituent stocks of CSI 300 index are selected in the research design part, and the stocks are screened using entropy weighted TOPSIS method and analysed based on the data from January 2018 to April 2024. The closing price and logarithmic return are predicted by constructing and using LSTM, Bi-LSTM and ATT-LSTM models, and then the trading strategies of EMA, MACD double conditions are determined, and the investment weights are determined by Monte Carlo method for the investment portfolio. The results of the empirical study show that the Bi-LSTM model has the optimal prediction performance, and based on the prediction data of the model, the trading strategy using the dual conditions of EMA and MACD achieves a higher investment return than the strategy using only MACD. In summary, this paper demonstrates the superiority of Bi-LSTM model in stock price prediction through empirical research, and proposes an effective portfolio construction method and trading strategy, which helps investors make more effective decisions in the complex market environment.
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Aldhyani, Theyazn H. H., and Hasan Alkahtani. "A Bidirectional Long Short-Term Memory Model Algorithm for Predicting COVID-19 in Gulf Countries." Life 11, no. 11 (October 21, 2021): 1118. http://dx.doi.org/10.3390/life11111118.
Повний текст джерелаАнотація:
Accurate prediction models have become the first goal for aiding pandemic-related decisions. Modeling and predicting the number of new active cases and deaths are important steps for anticipating and controlling COVID-19 outbreaks. The aim of this research was to develop an accurate prediction system for the COVID-19 pandemic that can predict the numbers of active cases and deaths in the Gulf countries of Saudi Arabia, Oman, the United Arab Emirates (UAE), Kuwait, Bahrain, and Qatar. The novelty of the proposed approach is that it uses an advanced prediction model—the bidirectional long short-term memory (Bi-LSTM) network deep learning model. The datasets were collected from an available repository containing updated registered cases of COVID-19 and showing the global numbers of active COVID-19 cases and deaths. Statistical analyses (e.g., mean square error, root mean square error, mean absolute error, and Spearman’s correlation coefficient) were employed to evaluate the results of the adopted Bi-LSTM model. The Bi-LSTM results based on the correlation metric gave predicted confirmed COVID-19 cases of 99.67%, 99.34%, 99.94%, 99.64%, 98.95%, and 99.91% for Saudi Arabia, Oman, the UAE, Kuwait, Bahrain, and Qatar, respectively, while testing the Bi-LSTM model for predicting COVID-19 mortality gave accuracies of 99.87%, 97.09%, 99.53%, 98.71%, 95.62%, and 99%, respectively. The Bi-LSTM model showed significant results using the correlation metric. Overall, the Bi-LSTM model demonstrated significant success in predicting COVID-19. The Bi-LSTM-based deep learning network achieves optimal prediction results and is effective and robust for predicting the numbers of active cases and deaths from COVID-19 in the studied Gulf countries.
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Ding, Xianghua, Jingnan Wang, Yiqi Liu, and Uk Jung. "Multivariate Time Series Anomaly Detection Using Working Memory Connections in Bi-Directional Long Short-Term Memory Autoencoder Network." Applied Sciences 15, no. 5 (March 6, 2025): 2861. https://doi.org/10.3390/app15052861.
Повний текст джерелаАнотація:
“Normal” events are characterized as data patterns or behaviors that align with expected operational conditions, while “anomalies” are defined as deviations from these patterns, potentially signaling faults, errors, or unexpected system behaviors. The timely and accurate detection of anomalies plays a critical role in domains such as industrial manufacturing, financial transactions, and other related domains. In the context of Industry 4.0, the proliferation of sensors has resulted in a massive influx of time series data, making the anomaly detection of such multivariate time series data a popular research area. Long Short-Term Memory (LSTM) has been extensively recognized as an effective framework for modeling and processing time series data. Previous studies have combined Bi-directional Long Short-Term Memory (Bi-LSTM) architecture with Autoencoder (AE) for multivariate time series anomaly detection. However, due to the inherent limitations of LSTM, Bi-LSTM-AE still cannot overcome these drawbacks. Our study replaces the LSTM units within the Bi-LSTM-AE architecture of existing research with Working Memory Connections for LSTM units and demonstrates that this architecture performs better in the field of multivariate time series anomaly detection compared to using standard LSTM units. The model we proposed not only outperforms the baseline models but also demonstrates greater robustness across various scenarios.
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Lasri, Imane, Anouar Riadsolh, and Mourad ElBelkacemi. "Self-Attention-Based Bi-LSTM Model for Sentiment Analysis on Tweets about Distance Learning in Higher Education." International Journal of Emerging Technologies in Learning (iJET) 18, no. 12 (June 21, 2023): 119–41. http://dx.doi.org/10.3991/ijet.v18i12.38071.
Повний текст джерелаАнотація:
For limiting the COVID-19 spread, countries around the world have implemented prevention measures such as lockdowns, social distancing, and the closers of educational institutions. Therefore, most academic activities are shifted to distance learning. This study proposes a deep learning approach for analyzing people’s sentiments (positive, negative, and neutral) from Twitter regarding distance learning in higher education. We collected and pre-processed 24642 English tweets about distance learning posted between July 20, 2022, and November 06, 2022. Then, a self-attention-based Bi-LSTM model with GloVe word embedding was used for sentiment classification. The proposed model performance was compared to LSTM (Long Short Term Memory), Bi-LSTM (Bidirectional-LSTM), and CNN-Bi-LSTM (Convolutional Neural Network-Bi-LSTM). Our proposed model obtains the best test accuracy of 95% on a stratified 90:10 split ratio. The results reveal generally neutral sentiments about distance learning for higher education, followed by positive sentiments, particularly in psychology and computer science, and negative sentiments in biology and chemistry. According to the obtained results, the proposed approach outperformed the state-of-art methods.
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Liao, JunHua, LunXin Liu, HaiHan Duan, YunZhi Huang, LiangXue Zhou, LiangYin Chen, and ChaoHua Wang. "Using a Convolutional Neural Network and Convolutional Long Short-term Memory to Automatically Detect Aneurysms on 2D Digital Subtraction Angiography Images: Framework Development and Validation." JMIR Medical Informatics 10, no. 3 (March 16, 2022): e28880. http://dx.doi.org/10.2196/28880.
Повний текст джерелаАнотація:
Background It is hard to distinguish cerebral aneurysms from overlapping vessels in 2D digital subtraction angiography (DSA) images due to these images’ lack of spatial information. Objective The aims of this study were to (1) construct a deep learning diagnostic system to improve the ability to detect posterior communicating artery aneurysms on 2D DSA images and (2) validate the efficiency of the deep learning diagnostic system in 2D DSA aneurysm detection. Methods We proposed a 2-stage detection system. First, we established the region localization stage to automatically locate specific detection regions of raw 2D DSA sequences. Second, in the intracranial aneurysm detection stage, we constructed a bi-input+RetinaNet+convolutional long short-term memory (C-LSTM) framework to compare its performance for aneurysm detection with that of 3 existing frameworks. Each of the frameworks had a 5-fold cross-validation scheme. The receiver operating characteristic curve, the area under the curve (AUC) value, mean average precision, sensitivity, specificity, and accuracy were used to assess the abilities of different frameworks. Results A total of 255 patients with posterior communicating artery aneurysms and 20 patients without aneurysms were included in this study. The best AUC values of the RetinaNet, RetinaNet+C-LSTM, bi-input+RetinaNet, and bi-input+RetinaNet+C-LSTM frameworks were 0.95, 0.96, 0.92, and 0.97, respectively. The mean sensitivities of the RetinaNet, RetinaNet+C-LSTM, bi-input+RetinaNet, and bi-input+RetinaNet+C-LSTM frameworks and human experts were 89% (range 67.02%-98.43%), 88% (range 65.76%-98.06%), 87% (range 64.53%-97.66%), 89% (range 67.02%-98.43%), and 90% (range 68.30%-98.77%), respectively. The mean specificities of the RetinaNet, RetinaNet+C-LSTM, bi-input+RetinaNet, and bi-input+RetinaNet+C-LSTM frameworks and human experts were 80% (range 56.34%-94.27%), 89% (range 67.02%-98.43%), 86% (range 63.31%-97.24%), 93% (range 72.30%-99.56%), and 90% (range 68.30%-98.77%), respectively. The mean accuracies of the RetinaNet, RetinaNet+C-LSTM, bi-input+RetinaNet, and bi-input+RetinaNet+C-LSTM frameworks and human experts were 84.50% (range 69.57%-93.97%), 88.50% (range 74.44%-96.39%), 86.50% (range 71.97%-95.22%), 91% (range 77.63%-97.72%), and 90% (range 76.34%-97.21%), respectively. Conclusions According to our results, more spatial and temporal information can help improve the performance of the frameworks. Therefore, the bi-input+RetinaNet+C-LSTM framework had the best performance when compared to that of the other frameworks. Our study demonstrates that our system can assist physicians in detecting intracranial aneurysms on 2D DSA images.
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Appati, Justice Kwame, Ismail Wafaa Denwar, Ebenezer Owusu, and Michael Agbo Tettey Soli. "Construction of an Ensemble Scheme for Stock Price Prediction Using Deep Learning Techniques." International Journal of Intelligent Information Technologies 17, no. 2 (April 2021): 72–95. http://dx.doi.org/10.4018/ijiit.2021040104.
Повний текст джерелаАнотація:
This study proposes a deep learning approach for stock price prediction by bridging the long short-term memory with gated recurrent unit. In its evaluation, the mean absolute error and mean square error were used. The model proposed is an extension of the study of Hossain et al. established in 2018 with an MSE of 0.00098 as its lowest error. The current proposed model is a mix of the bidirectional LSTM and bidirectional GRU resulting in 0.00000008 MSE as the lowest error recorded. The LSTM model recorded 0.00000025 MSE, the GRU model recorded 0.00000077 MSE, and the LSTM + GRU model recorded 0.00000023 MSE. Other combinations of the existing models such as the bi-directional LSTM model recorded 0.00000019 MSE, bi-directional GRU recorded 0.00000011 MSE, bidirectional LSTM + GRU recorded 0.00000027 MSE, LSTM and bi-directional GRU recorded 0.00000020 MSE.
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Wang, Pengyu, Yaqiong Zhang, and Wanqing Guo. "Demand Forecast of Railway Transportation Logistics Supply Chain Based on Machine Learning Model." International Journal of Information Technologies and Systems Approach 16, no. 3 (May 18, 2023): 1–17. http://dx.doi.org/10.4018/ijitsa.323441.
Повний текст джерелаАнотація:
The deep learning method based on long short-term memory (LSTM), gated recurrent unit (GRU), and bidirectional LSTM (Bi-LSTM) was constructed by researching the factors affecting railway transportation logistics. Moreover, a simulation study on Tianjin Station was conducted. The deep learning model suitable for the logistics demand forecasting of Tianjin Station was established, and the changing trend of logistics supply chain demand in Tianjin Station in the future was analyzed. Moreover, a strategy for railway construction and regional cooperation was proposed. In this study, three deep learning neural networks, namely LSTM, GRU, and Bi-LSTM, were used to construct a demand forecasting model for the logistics supply chain in Tianjin Station. Bi-LSTM, which has bidirectional storage performance and the highest prediction accuracy, is superior to the traditional neural network structure in terms of period and fluctuation.
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Singh, Amankumar, Riya Thapliyal, Ritika Vanave, Rajashree Shedge, and Snehal Mumbaikar. "Analysis of hyperparameters in Sentiment Analysis of Movie Reviews using Bi-LSTM." ITM Web of Conferences 44 (2022): 03012. http://dx.doi.org/10.1051/itmconf/20224403012.
Повний текст джерелаАнотація:
Movie reviews are an important factor in determining a film’s success because instead of depending solely on the number of views as a parameter for the success of the movie, movie reviews are used to acquire additional insights into the movie. Existing systems use LSTM for sentiment analysis but there is no study available how various hyperparameters affect the performance of the model. Bi-LSTM along with dropout layers provide good accuracy in sentiment analysis. The suggested method outperforms CNN and Natural Language Toolkit in terms of accuracy.The proposed model is tested using different hyper parameters including dropout rate,number of Bi-LSTM layers and Bi-LSTM nodes. 64 LSTM nodes, 2 Bi-directional Layers, and a 0.2 Dropout rate should be used for optimal accuracy. Effect of different text vectorization algorithms and activation functions was also studied. The combination of Tf-idf text vectorization and the ReLU activation function yields the best results.
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Li, Dong, Jiping Liu, and Yangyang Zhao. "Prediction of Multi-Site PM2.5 Concentrations in Beijing Using CNN-Bi LSTM with CBAM." Atmosphere 13, no. 10 (October 19, 2022): 1719. http://dx.doi.org/10.3390/atmos13101719.
Повний текст джерелаАнотація:
Air pollution is a growing problem and poses a challenge to people’s healthy lives. Accurate prediction of air pollutant concentrations is considered the key to air pollution warning and management. In this paper, a novel PM2.5 concentration prediction model, CBAM-CNN-Bi LSTM, is constructed by deep learning techniques based on the principles related to spatial big data. This model consists of the convolutional block attention module (CBAM), the convolutional neural network (CNN), and the bi-directional long short-term memory neural network (Bi LSTM). CBAM is applied to the extraction of feature relationships between pollutant data and meteorological data and assists in deeply obtaining the spatial distribution characteristics of PM2.5 concentrations. As the output layer, Bi LSTM obtains the variation pattern of PM2.5 concentrations from spatial data, overcomes the problem of long-term dependence on PM2.5 concentrations, and achieves the task of accurately forecasting PM2.5 concentrations at multiple sites. Based on real datasets, we perform an experimental evaluation and the results show that, in comparison to other models, CBAM-CNN-Bi LSTM improves the accuracy of PM2.5 concentration prediction. For the prediction tasks from 1 to 12 h, our proposed prediction model performs well. For the 13 to 48 h prediction task, the CBAM-CNN-Bi LSTM also achieves satisfactory results.
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Behera, Bibhuti Bhusana, Binod Kumar Pattanayak, and Rajani Kanta Mohanty. "Deep Ensemble Model for Detecting Attacks in Industrial IoT." International Journal of Information Security and Privacy 16, no. 1 (January 1, 2022): 1–29. http://dx.doi.org/10.4018/ijisp.311467.
Повний текст джерелаАнотація:
In this research work, a novel IIoT attack detection framework is designed by following four major phases: pre-processing, imbalance processing, feature extraction, and attack detection. The attack detection is carried out using the projected ensemble classification framework. The projected ensemble classification framework encapsulates the recurrent neural network, CNN, and optimized bi-directional long short-term memory (BI-LSTM). The RNN and CNN in the ensemble classification framework is trained with the extracted features. The outcome acquired from RNN and CNN is utilized for training the optimized BI-LSTM model. The final outcome regarding the presence/absence of attacks in the industrial IoT is portrayed by the optimized BI-LSTM model. Therefore, the weight of BI-LSTM model is fine-tuned using the newly projected hybrid optimization model referred as cat mouse updated slime mould algorithm (CMUSMA). The projected hybrids the concepts of both the standard slime mould algorithm (SMA) and cat and mouse-based optimizer(CMBO), respectively.
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Wang, Yina, Wenjie Hao, Yanjun Yu, Junyou Yang, and Guang Yang. "A Novel Prediction Method of Transfer-Assisted Action Oriented to Individual Differences for the Excretion Care Robot." Sensors 23, no. 24 (December 7, 2023): 9674. http://dx.doi.org/10.3390/s23249674.
Повний текст джерелаАнотація:
The excretion care robot’s (ECR) accurate recognition of transfer-assisted actions is crucial during its usage. However, transfer action recognition is a challenging task, especially since the differentiation of actions seriously affects its recognition speed, robustness, and generalization ability. We propose a novel approach for transfer action recognition assisted by a bidirectional long- and short-term memory (Bi-LSTM) network combined with a multi-head attention mechanism. Firstly, we utilize posture sensors to detect human movements and establish a lightweight three-dimensional (3D) model of the lower limbs. In particular, we adopt a discrete extended Kalman filter (DEKF) to improve the accuracy and foresight of pose solving. Then, we construct an action prediction model that incorporates a fused Bi-LSTM with Multi-head attention (MHA Bi-LSTM). The MHA extracts key information related to differentiated movements from different dimensions and assigns varying weights. Utilizing the Bi-LSTM network effectively combines past and future information to enhance the prediction results of differentiated actions. Finally, comparisons were made by three subjects in the proposed method and with two other time series based neural network models. The reliability of the MHA Bi-LSTM method was verified. These experimental results show that the introduced MHA Bi-LSTM model has a higher accuracy in predicting posture sensor-based excretory care actions. Our method provides a promising approach for handling transfer-assisted action individual differentiation in excretion care tasks.
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Nasimov, Rashid, Deepak Kumar, M. Rizwan, Amrish K. Panwar, Akmalbek Abdusalomov, and Young-Im Cho. "A Novel Approach for State of Health Estimation of Lithium-Ion Batteries Based on Improved PSO Neural Network Model." Processes 12, no. 9 (August 26, 2024): 1806. http://dx.doi.org/10.3390/pr12091806.
Повний текст джерелаАнотація:
The operation and maintenance of futuristic electric vehicles need accurate estimation of the state of health (SOH) of lithium-ion batteries (LIBs). To address this issue, a robust neural network framework is proposed to estimate the SOH. This article developed a novel approach that combines improved particle swarm optimization (IPSO) with bidirectional long short-term memory (Bi-LSTM) to effectively address the issue of precisely estimating SOH. The proposed IPSO-Bi-LSTM model is more effective than the other models for SOH estimation. This is because Bi-LSTM can capture both past and future appropriate information, making it more suitable for modeling complicated temporal sequences. The IPSO main objective is to optimize the model hyperparameters. To increase the model’s accuracy, the IPSO improves the parameters. The PSO-Bi-LSTM model performed better than the other approaches, according to experimental findings based on the NASA-PCOE battery dataset, and all of the SOH estimated outcomes, such as root mean square errors, were less than 0.50%. This result suggests that the proposed PSO-Bi-LSTM model has the ability to robustly estimate the SOH with a high accuracy.
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Onsu, Romario, Daniel Febrian Sengkey, and Feisy Diane Kambey. "Implementasi Bi-LSTM dengan Ekstraksi Fitur Word2Vec untuk Pengembangan Analisis Sentimen Aplikasi Identitas Kependudukan Digital." Jurnal Teknologi Terpadu 10, no. 1 (July 29, 2024): 46–55. http://dx.doi.org/10.54914/jtt.v10i1.1225.
Повний текст джерелаАнотація:
Pemerintah Indonesia berupaya meningkatkan layanan publik berbasis digital, termasuk aplikasi Identitas Kependudukan Digital (IKD) yang diluncurkan pada 2022 oleh Dirjen Kependudukan dan Pencatatan Sipil. Sejak diluncurkan, IKD mendapat berbagai tanggapan dari masyarakat. Data ulasan di Google Play Store menunjukkan penurunan rating dari Juni hingga Desember 2023. Analisis ulasan penting untuk memahami kepuasan pengguna dan mengidentifikasi masalah serta memandu perbaikan aplikasi. Penelitian ini bertujuan melakukan analisis sentimen ulasan pengguna IKD menggunakan metode Bidirectional Long Short-Term Memory (Bi-LSTM) dan Word2Vec. Bi-LSTM dan Word2Vec digunakan untuk mengembangkan analisis sentimen dari penelitian sebelumnya yang masih menggunakan metode Machine Learning. Penelitian ini diharapkan berkontribusi dalam pengembangan model analisis sentimen menggunakan Deep Learning untuk aplikasi IKD. Data ulasan dikumpulkan dari Google Play Store dengan teknik scraping pada periode Januari-Desember 2023 dan dibagi menjadi kategori positif dan negatif. Model Bi-LSTM dilatih dengan variasi Word2Vec CBOW dan Skip-Gram dengan dimensi 100, 200, dan 300. Hasil penelitian menunjukkan kombinasi Bi-LSTM dan Word2Vec CBOW dengan dimensi 200 dan proporsi data 80/20 menghasilkan akurasi tertinggi sebesar 96,06%, dengan precision 96,44%, recall 95,64%, dan f1 score 96,04%. Semua kombinasi Bi-LSTM dan Word2Vec menunjukkan hasil lebih tinggi dibandingkan algoritma Machine Learning lainnya.
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Dyanggi, Anak Agung Mayra Candra, I. Wayan Agus Surya Darma, and Ni Nyoman Ayu J. Sastaparamitha. "FastText and Bi-LSTM for Sentiment Analysis of Tinder Application Reviews." Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) 12, no. 1 (May 23, 2024): 72. https://doi.org/10.24843/jim.2024.v12.i01.p07.
Повний текст джерелаАнотація:
Nowadays technology affects all aspects of society, one of the innovations and creativity in the field of technology is the emergence of online dating application media. The application makes it easy for users to find a partner according to their respective criteria. The most popular online dating app is Tinder. The rise of the use of online dating applications invites controversial sentiments in the community. With this problem, a sentiment analysis is needed to find out the opinions and views of users about Tinder. This study proposed the fastText and Bi-LSTM models used to determine the optimization performance of the fastText and Bi-LSTM methods in sentiment analysis and compares the performance of the fastText and Bi-LSTM models with the fastText and Bidirectional Encoder Representations from Transformers (BERT) models. Based on the experiment, fastText and Bi-LSTM produced the highest performance in the 4th fold scenario with 88% accuracy. Based on the comparison of the three model performances, the fastText and BI-LSTM models can outperform the fastText and BERT models on sentiment analysis of user review datasets in the Tinder application.
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Lee, Chien-Hsing, Phuong Nguyen Thanh, Chao-Tsung Yeh, and Ming-Yuan Cho. "Three-Phase Load Prediction-Based Hybrid Convolution Neural Network Combined Bidirectional Long Short-Term Memory in Solar Power Plant." International Transactions on Electrical Energy Systems 2022 (September 16, 2022): 1–15. http://dx.doi.org/10.1155/2022/2870668.
Повний текст джерелаАнотація:
The economic renewable energy generations have been rapidly developed because of the sharp reduction in the costs of solar panels. It is imperative to forecast the three-phase load power for more effective energy planning and optimization in a smart solar microgrid installed on a building in the Linyuan District, Taiwan. To alleviate this problem, this article proposes a convolution neural network bidirectional long short-term memory (CNN-Bi-LSTM) to accurately predict the short-term three-phase load power in building the energy management system in the smart solar microgrid with the collected data from advanced metering infrastructure (AMI), which have not been investigated before. The three-phase load-predicting methodology is developed using weather parameters and different collected data from AMI. The project evaluates the performance of the CNN-Bi-LSTM model by utilizing hyper-parameter optimization to attain the optimum parameters. The prediction models are trained based on hourly historical input features, selected based on the Pearson correlation coefficient. The performances’ optimal structure CNN-Bi-LSTM are validated and compared with the bidirectional LSTM (Bi-LSTM), LSTM, the Gated Recurrent Unit (GRU), and the recurrent neural network (RNN) models. The obtained optimized structure of CNN-Bi-LSTM demonstrates the effectiveness of the proposed models in the short-term prediction of three-phase load power in a smart solar microgrid for building with a maximum enhancement of 68.36% and 8.81% average MSE, and 30.26% and 36.36% average MAE during the testing and validating operations.
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Gunawan, Akbar Rikzy, and Rifda Faticha Alfa Aziza. "Sentiment Analysis Using LSTM Algorithm Regarding Grab Application Services in Indonesia." Journal of Applied Informatics and Computing 9, no. 2 (March 17, 2025): 322–32. https://doi.org/10.30871/jaic.v9i2.8696.
Повний текст джерелаАнотація:
This study aims to analyze the sentiment of user reviews for the Grab Indonesia application using Long Short-Term Memory (LSTM) algorithms. Two variants of LSTM, namely Stacked LSTM and Bi-Directional LSTM, were compared to determine the most effective model in classifying user review sentiments. Both models were enhanced with Multi-Head Attention mechanisms to capture more complex contextual relationships in sequential data. The data used consists of 2,000 user reviews collected through scraping from the Google Play Store, with sentiment labels of positive and negative. Data preprocessing included labeling, case folding, stopword removal, tokenization, stemming, and the application of the SMOTE technique to address class imbalance. The results show that the Bi-Directional LSTM model achieved the highest validation accuracy of 87%, with an F1-score of 0.90 for the negative class and 0.82 for the positive class, while the Stacked LSTM recorded an accuracy of 84%, with an F1-score of 0.87 for the negative class and 0.78 for the positive class. Overall, the Bi-Directional LSTM demonstrated better performance in identifying both negative and positive sentiments, providing a good balance between precision and recall. This study proves that Bi-Directional LSTM with Multi-Head Attention can improve sentiment analysis performance on user reviews of digital applications, with potential applications in various other platforms.
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Wang, Hao. "Enhancing Stock Price Forecasting Accuracy Using LSTM and Bi-LSTM Models." ITM Web of Conferences 70 (2025): 04008. https://doi.org/10.1051/itmconf/20257004008.
Повний текст джерелаАнотація:
Accurately predicting stock price trends is of critical importance in the financial sector, enabling both individuals and enterprises to make informed and profitable decisions. In recent years, researchers have employed a variety’ of techniques to forecast stock market trends, yet the challenge of improving accuracy remains. This research introduces an innovative approach to predicting stock prices, employing two sophisticated models: Long Short-Tenn Memory (LSTM) and Bidirectional Long Short-Tenn Memory (Bi-LSTM) networks. Through rigorous analysis, the research demonstrates that, with proper hypeiparameter tuning. LSTM models are capable of making highly accurate predictions of future stock trends, a capability’ that is also exhibited by Bi-LSTM models. The study’ evaluates the models by’ measuring the Root Mean Square Error (RMSE) while varying key factors. Publicly available stock market information. such as the highest and lowest prices, and opening and closing prices, is utilized for evaluating model effectiveness. The results indicate that the Bi-LSTM model is superior to the LSTM model in terms of RMSE. making it a more effective methodology for stock price forecasting and aiding in strategic decision-making.
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H. Nayef, Bahera, Siti Norul Huda Sheikh Abdullah, Rossilawati Sulaiman, and Ashwaq Mukred Saeed. "Text Extraction with Optimal Bi-LSTM." Computers, Materials & Continua 76, no. 3 (2023): 3549–67. http://dx.doi.org/10.32604/cmc.2023.039528.
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Kone, Vinayak Sudhakar, Atrey Mahadev Anagal, Swaroop Anegundi, Priya Jadekar, and Priyadarshini Patil. "Emoji Prediction Using Bi-Directional LSTM." ITM Web of Conferences 53 (2023): 02004. http://dx.doi.org/10.1051/itmconf/20235302004.
Повний текст джерелаАнотація:
Messengers and social media dominate today’s internet usage across the globe. For the large population, a typical day starts with messages flooding on mobiles, from simple good morning wishes, business meeting invites, reminders, and schedules for the day and the list is endless. A striking feature of today’s digital communication is the variety of emojis used, without which text communication almost look incomplete. Emojis are graphic symbols/logograms used with text communication to enhance the effectiveness of emotions and set an undertone that makes texting a more fun experience for the users. Emojis are the visual language of the new generation. They give consumers a means to communicate their feelings while reducing the quantity of text that needs to be typed by the sender. Every social media and messenger platform like Facebook, Instagram, Twitter, WhatsApp, and many more have its own emoji set. To lure more and more users, many new emojis are added day by day. Predicting and suggesting emojis based on the text, emotion and user patterns to the user is an important feature of today’s messengers and social media applications. If you start typing a message, relevant emojis will be displayed from which users can choose an emoji, further enhancing the user texting experience. This process is done using natural language processing and machine learning techniques. In this paper, we study emoji prediction techniques and propose an emoji prediction model using bi-directional LSTMs. We compare emoji prediction NLP techniques, including RNN, LSTM, LSTM networks, and Bi-LSTM. Based on our implementation, we suggest that the bi-directional LSTM model is the most effective technique. Our model outperforms many baseline approaches with an accuracy of 94% when tested on a CodaLab Twitter data set with 60000 rows and two columns. Our study shows the effectiveness and efficiency of bi-directional LSTMs for text-based systems for communication.
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S, Gowthami, Srivanth S, and Matisvar M V. "Stroke Prediction Using Bi-directional LSTM." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 10 (October 1, 2023): 1–11. http://dx.doi.org/10.55041/ijsrem26141.
Повний текст джерелаАнотація:
Abstract—The greatest cause of disability in adults and the elderly—including many social and financial challenges—is stroke. A stroke can result in death if it is not addressed. Patients who have had a stroke typically have aberrant biosignals, such as an altered ECG. Individuals can therefore immediately obtain the right therapy if they are observed and have their bio-signals precisely analyzed and analysed in real-time. However, the majority of stroke diagnostic and prediction systems rely on pricey, challenging to use image processing technologies like CT or MRI. In this study, we created an artificial intelligence (AI)-based stroke prediction system that uses real-time biosignals to identify stroke. Our system made use of both deep learning (Long Short-Term Memory) and machine learning (Random Forest) methods. Real-time EMG (Electromyography) bio-signals from the thighs and calves were gathered, the key characteristics were identified, and prediction models based on regular activities were created. For our suggested system, prediction accuracy values of 90.38% for Random Forest and 98.958% for LSTM were found. This approach may be viewed as an alternative, affordable, real-time diagnosis tool that can accurately anticipate strokes and may one day be used to other illnesses like heart disease. Keywords— Bone Mass Density (BMD), feature extraction, evolutionary algorithm - genetic algorithm, Deep learning modules, Jaccard index
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Manikalyan, D. "TOXIC COMMENT CLASSIFICATION USING BI-LSTM." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 03 (March 7, 2024): 1–9. http://dx.doi.org/10.55041/ijsrem29141.
Повний текст джерелаАнотація:
In the age of internet and social media platforms, the problem of toxic remarks has become increasingly prominent. This research addresses the application of advanced deep learning technique, specifically C Bidirectional Long Short-Term Memory networks (Bi-LSTM), for the classification of toxic comments. Additionally, we employ pre-trained GloVe word embeddings to enhance the performance of the models. The project intends to increase the accuracy and efficiency of toxic comment classification, enabling platforms to automatically recognize and filter out harmful information. By utilizing Bi-LSTM architectures, which excel in capturing spatial and temporal correlations in textual data, we can effectively identify toxic language. The integration of GloVe embeddings significantly strengthens the semantic comprehension of words, contributing to more exact categorization results. Through a comprehensive analysis and evaluation of the proposed models on benchmark datasets such as the Jigsaw Multilingual Toxic Comment Classification dataset, we demonstrate the effectiveness of Bi- LSTM with GloVe embeddings in accurately identifying toxic comments. By reaching high classification accuracy, precision, recall, and F1 scores, the models highlight their potential in minimizing the harmful impact of toxic comments in online contexts. The results of this study have significant implications for online platforms, social media companies, and community moderators seeking automated solutions for content moderation. Key Words: Toxic comment classification, Bidirectional Long Short-Term Memory(BI-LSTM), GloVe embeddings, Deep learning.
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Hasan, Mohammad Kamrul, Shoeb Akibul Islam, Md Sabbir Ejaz, Md Mahbubul Alam, Nahid Mahmud, and Tanvir Ahmed Rafin. "Classifying Bengali Newspaper Headlines with Advanced Deep Learning Models: LSTM, Bi-LSTM, and Bi-GRU Approaches." Asian Journal of Research in Computer Science 16, no. 4 (December 16, 2023): 372–88. http://dx.doi.org/10.9734/ajrcos/2023/v16i4398.
Повний текст джерелаАнотація:
Reading newspapers is beneficial for people of all ages and the global community. The enjoyment of gathering diverse data from various sources adds to the overall experience. To enhance specificity in Bengali news headlines, recognizing the news genre becomes crucial. Recognizing the genre of the news, it is a very challenging task in Bengali Text Classification with the help of AI. A very few research works is done on Bengali News headline classification and we have done a model to provide a solution to the addressed issue. Due to the continuous change of the structure of the news headlines, we have employed a neural network adoption connection to our methodology experiment on a mixture of primary and secondary dataset. Achieving significant results, we implemented a Bengali dataset in Multi Classification using Long-Short Term Memory (LSTM), Bi- Long-Short Term Memory (Bi-LSTM), and Bi-Gated Recurrent Unit (Bi-GRU). The dataset is established by aggregating news headlines from various Bengali news portals and websites, showcasing robust categorization performance in the end product. Six categories were employed for the classification of Bengali newspaper headlines. The Bi-LSTM Model emerged with the highest training accuracy at 97.96% and the lowest validation accuracy at 77.91%. Furthermore, it demonstrated enhanced sensitivity and specificity.
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Xi, Yanhui, Feng Zhou, and Weijie Zhang. "Partial Discharge Detection and Recognition in Insulated Overhead Conductor Based on Bi-LSTM with Attention Mechanism." Electronics 12, no. 11 (May 24, 2023): 2373. http://dx.doi.org/10.3390/electronics12112373.
Повний текст джерелаАнотація:
Insulated overhead conductor (IOC) faults cannot be detected by the ordinary protection devices due to the existence of the insulation layer. The failure of insulated overhead conductors is regularly accompanied by partial discharge (PD); thus, IOC faults are often judged by the PDs of insulated overhead conductors. In this paper, an intelligent PD detection model based on bidirectional long short-term memory with attention mechanism (AM-Bi-LSTM) is proposed for judging IOC faults. First, the original signals are processed using discrete wavelet transform (DWT) for de-noising, and then the signal statistical-feature and entropy-feature vectors are fused to characterize the PD signals. Finally, an AM-Bi-LSTM network is proposed for PD detection, in which the AM is able to assign the inputs different weights and highlight their effective characteristics; thus, the identification accuracy and computational complexity have been greatly improved. The validity and accuracy of the proposed model were evaluated with an ENET common dataset. The experiment results demonstrate that the AM-Bi-LSTM model exhibits a higher performance than the existing models, such as LSTM, Bi-LSTM, and AM-LSTM.
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Bansal, Ashish. "Punctuation and Capitalization Restoration using Bi-LSTM Network." International Journal of Science and Research (IJSR) 8, no. 4 (April 5, 2019): 2020–25. http://dx.doi.org/10.21275/sr24809041216.
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Yan, Haoyang. "Research on Gold Price Prediction Based on LSTM Modeling." Advances in Economics, Management and Political Sciences 94, no. 1 (October 12, 2024): 202–10. http://dx.doi.org/10.54254/2754-1169/94/2024ox0166.
Повний текст джерелаАнотація:
Gold plays a pivotal role in asset allocation, and the construction of gold price prediction models represents a complex yet rewarding task within the field of finance. The problem of international gold price is addressed in this paper forecasting by proposing a standard Long-Short Term Memory (LSTM) model and introducing bi-directional LSTM (Bi-LSTM) networks and multivariate analysis to compare the forecasting accuracy of the relevant models. This comparison is based on the daily gold price of the London Bullion Market Association (LBMA) for 2013-2022. This methodology takes into account various factors that influence the gold market and incorporates them as input sources into the Multi-factor LSTM model, thereby enhancing the interpretability of the LSTM model. Correlation analysis and the Granger test are employed to analyze these influencing factors. However, the results of this study suggest that while the Multi-factor LSTM model may be more accurate in predicting outliers, it tends to increase the number of small errors. On the other hand, the inclusion of Bi-LSTM networks can improve the overall accuracy of gold price prediction.
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Siringoringo, Rimbun, Jamaluddin Jamaluddin, Resianta Perangin-angin, Eva Julia Gunawati Harianja, Gortap Lumbantoruan, and Eviyanti Novita Purba. "MODEL BIDIRECTIONAL LSTM UNTUK PEMROSESAN SEKUENSIAL DATA TEKS SPAM." METHOMIKA Jurnal Manajemen Informatika dan Komputerisasi Akuntansi 7, no. 2 (October 31, 2023): 265–71. http://dx.doi.org/10.46880/jmika.vol7no2.pp265-271.
Повний текст джерелаАнотація:
This study examines the LSTM-based model for processing spam in text data. Spam poses several dangers and risks, both for individuals and organizations. Spam can be a nuisance that hampers both individual and organizational productivity. Much spam contains fraudulent or phishing attempts to obtain sensitive information. Spam detection using deep learning involves the utilization of algorithms and deep neural network models to accurately classify messages as either spam or not spam. Typically, spam detection systems use a combination of these methods to improve the accuracy of identifying spam messages. This study applies the Bi-LSTM deep learning model to sequentially process text (sequencing). The performance of the model is determined based on the loss and accuracy. The data used are the Spam SMS and Spam Email datasets. The test results show that the Bi-LSTM model demonstrates better performance on all tested datasets. Bi-LSTM is able to capture textual patterns from both the context and the text itself, as it can combine information from both directions. The test results prove that the Bi-LSTM model is more effective in text comprehension.