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Добірка наукової літератури з теми "Bi-LSTM"

Автор: Grafiati
Опубліковано: 26 травня 2025

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Статті в журналах з теми "Bi-LSTM"

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.

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Анотація:
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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2

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.

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Анотація:
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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3

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.

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Анотація:
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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4

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.

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Анотація:
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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5

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.

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Анотація:
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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6

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.

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Анотація:
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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7

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.

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Анотація:
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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8

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.

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Анотація:
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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9

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.

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Анотація:
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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10

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.

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Анотація:
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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Більше джерел

Дисертації з теми "Bi-LSTM"

1

Khaghani, Farnaz. "A Deep Learning Approach to Predict Accident Occurrence Based on Traffic Dynamics." Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/98801.

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Анотація:
Traffic accidents are of concern for traffic safety; 1.25 million deaths are reported each year. Hence, it is crucial to have access to real-time data and rapidly detect or predict accidents. Predicting the occurrence of a highway car accident accurately any significant length of time into the future is not feasible since the vast majority of crashes occur due to unpredictable human negligence and/or error. However, rapid traffic incident detection could reduce incident-related congestion and secondary crashes, alleviate the waste of vehicles’ fuel and passengers’ time, and provide appropriate information for emergency response and field operation. While the focus of most previously proposed techniques is predicting the number of accidents in a certain region, the problem of predicting the accident occurrence or fast detection of the accident has been little studied. To address this gap, we propose a deep learning approach and build a deep neural network model based on long short term memory (LSTM). We apply it to forecast the expected speed values on freeways’ links and identify the anomalies as potential accident occurrences. Several detailed features such as weather, traffic speed, and traffic flow of upstream and downstream points are extracted from big datasets. We assess the proposed approach on a traffic dataset from Sacramento, California. The experimental results demonstrate the potential of the proposed approach in identifying the anomalies in speed value and matching them with accidents in the same area. We show that this approach can handle a high rate of rapid accident detection and be implemented in real-time travelers’ information or emergency management systems.
M.S.
Rapid traffic accident detection/prediction is essential for scaling down non-recurrent conges- tion caused by traffic accidents, avoiding secondary accidents, and accelerating emergency system responses. In this study, we propose a framework that uses large-scale historical traffic speed and traffic flow data along with the relevant weather information to obtain robust traffic patterns. The predicted traffic patterns can be coupled with the real traffic data to detect anomalous behavior that often results in traffic incidents in the roadways. Our framework consists of two major steps. First, we estimate the speed values of traffic at each point based on the historical speed and flow values of locations before and after each point on the roadway. Second, we compare the estimated values with the actual ones and introduce the ones that are significantly different as an anomaly. The anomaly points are the potential points and times that an accident occurs and causes a change in the normal behavior of the roadways. Our study shows the potential of the approach in detecting the accidents while exhibiting promising performance in detecting the accident occurrence at a time close to the actual time of occurrence.
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2

Zhang, Jiahui. "Bi-Objective Dispatch of Multi-Energy Virtual Power Plant: Deep-Learning based Prediction and Particle Swarm Optimization." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019.

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Анотація:
This paper addresses the coordinative operation problem of multi-energy virtual power plant (ME-VPP) in the context of energy internet. A bi-objective dispatch model is established to optimize the performance of ME-VPP on both economic cost(EC) and power quality (PQ).Various realistic factors are considered, which include environmental governance, transmission ratings, output limits, etc. Long short-term memory (LSTM), a deep learning method, is applied to the promotion of the accuracy of wind prediction. An improved multi-objective particle swarm optimization (MOPSO) is utilized as the solving algorithm. A practical case study is performed on Hongfeng Eco-town in Southwestern China. Simulation results of three scenarios verify the advantages of bi-objective optimization over solely saving EC and enhancing PQ. The Pareto frontier also provides a visible and flexible way for decision-making of ME-VPP operator. Two strategies, “improvisational” and “foresighted”, are compared by testing on IEEE 118-bus benchmark system. It is revealed that “foresighted” strategy, which incorporates LSTM prediction and bi-objective optimization over 5-hr receding horizon, takes 10 Pareto dominances in 24 hours.
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3

Gustafsson, Anton, and Julian Sjödal. "Energy Predictions of Multiple Buildings using Bi-directional Long short-term Memory." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-43552.

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Анотація:
The process of energy consumption and monitoring of a buildingis time-consuming. Therefore, an feasible approach for using trans-fer learning is presented to decrease the necessary time to extract re-quired large dataset. The technique applies a bidirectional long shortterm memory recurrent neural network using sequence to sequenceprediction. The idea involves a training phase that extracts informa-tion and patterns of a building that is presented with a reasonablysized dataset. The validation phase uses a dataset that is not sufficientin size. This dataset was acquired through a related paper, the resultscan therefore be validated accordingly. The conducted experimentsinclude four cases that involve different strategies in training and val-idation phases and percentages of fine-tuning. Our proposed modelgenerated better scores in terms of prediction performance comparedto the related paper.
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4

Su, Ruei-Ye, and 蘇瑞燁. "A Bi-directional LSTM-CNN Model with Attention for Chinese Sentiment Analysis." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/2y9j7r.

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Анотація:
碩士
樹德科技大學
資訊工程系碩士班
107
With the massive development of social media, people are used to sharing personal ideas and opinions on social media service platforms and most people have personal viewpoints on certain specific topics. As time goes on, large amounts of data are generated, which contain potentially valuable information from the perspective of business. In the field of NLP (Natural Language Processing), sentiment analysis in Chinese messages is one of the major approaches to grasping Internet public opinion. This paper originally proposed a LSAEB-CNN （Bi-LSTM Self-Attention of Emoticon-Based Convolutional Neural Network）, which is a deep learning method that combines Bi-directional Long Short-Term Memory （Bi-LSTM） with Convolutional Neural Networks （CNN）, and embeds emoticons into Self-Attention. The method could effectively identify different emotional polarities without external knowledge, but the focus in Self-Attention excessive attention to problems. This paper thus proposes a further improved method: Bi-LSTM Multi-Head Attention of Emoticon-Based Convolutional Neural Network （LMAEB-CNN） on Self-Attention. Most importantly, the method lets each vector perform multi-layer operations. The data was collected from Plurk, the micro-blogging service, with deep learning conducted in Keras. Chinese micro-blogs were checked for sentiment polarity classification and the study achieved an accuracy rate of about 98.9%, which is significantly higher than other methods.
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5

Lu, Kuo-Hao, and 呂國豪. "Intelligent Raising Knowledge Computing System Using NLP and Bi-LSTM: Design and Implementation." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/f787z7.

Повний текст джерела
Анотація:
碩士
國立屏東科技大學
資訊管理系所
106
The pig raising industry has been developing over one hundred years in Taiwan. Accumulated a large number of livestock husbandry knowledge and experience. For the advent of the era of smart agriculture. These valuable animal husbandry expertise are urgently needed to be converted into a computerized intelligent management system. However, there is currently no suitable Knowledge Graph in the field of pig breeding in Taiwan. Therefore, the following technical problems need to be solved, which include: (1) How to correctly determine the segmentation of the animal husbandry vocabulary so that the computer can understand the animal husbandry statement. (2) How to effectively extract the knowledge in the livestock field and classify it into the correct animal husbandry theme. (3) How to build a knowledge Graph of livestock husbandry and show the relationship between entities and entities. This study established “Intelligent Raising Knowledge Computing System”. The proposed system is mainly based on the deep learning schemes, which are Natural Language Processing (NLP) and Bidirectional Long Short Term Memory (Bi-LSTM). To correctly determine the segmentation of the animal husbandry vocabulary, this study proposed the Accuracy Livestock Word Segmentation Scheme (ALWS). ALWS uses the NLP to translate Internet information into a computer understandable language. ALWS also adopts Directed Acyclic Graph, Dynamic Programming, and Hidden Markov Model to effectively understand the animal husbandry vocabulary and build word vectors for deep learning. To build an effective knowledge Graph of livestock husbandry, this study proposed Intelligent Knowledge Unit Construction Scheme (IKUC). IKUC uses Bidirectional Long Short Term Memory to deeply learn livestock domain word vectors. The purpose is to understand contextual semantics and build knowledge units in conjunction with Conditional Random Field (CRF). Finally the knowledge units are classified into the correct categories. This study created four categories, which are Production Management, Feeding Management, Childbirth Management, and Breeding Management. We built knowledge Graph and collected expert knowledge of livestock husbandry to provide reference for pig farmers. The performance evaluation of the proposed methods are as follows. (1) For ALWS, we built a livestock dictionary and verify dictionary performance with accuracy, recall rate, and F-Measure. ALWS was compared with the Academia Sinica dictionary, the performance of the F-Measure is increased by 6.04. (2) For IKUC scheme, which was compared to the CNN and Bi-LSTM. Experiments show that the effectiveness of the proposed IKUC method is improved by 9.33% and has the smoother learning rate. Through the implementation of the paper's system and performance verification, this study built the first domestic animal husbandry pig knowledge map demonstration system. It is expected that this proposed system can collect expert knowledge of livestock husbandry through the developed knowledge extraction technology and provide the references for Taiwanese pig producers to improve the efficiency of pig feeding quality.
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HUANG, SHIH-MENG, and 黃仕孟. "A Holistic and Local Feature Learning Method for Machine Health Monitoring with Convolutional Bi-Directional LSTM Networks." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/xrkeg3.

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Анотація:
碩士
靜宜大學
資訊工程學系
107
In the modern industry, machine health monitoring systems are critical to the modern manufacturing industry. In the machine health monitoring, due to the advanced sensor technologies, the data-driven approach is becoming the most popular way. However, how to deal with the noise data and realize the spatial and temporal correlations presented in the data is a challenge. Traditional research focuses on the use of feature extraction methods to judge normal tool and wear tool for tool state. However, while detecting the characteristics of the worn tool, the tool is likely to cause damage to the workpiece and machine will be downtime. It will have a huge burden on time and material costs. With the rapid development and application of deep learning in recent years, data feature methods can be used to predict tool wear. It provides instant visibility into tool wear. The study proposes a deep learning model called Holistic-Local LSTM (HLLSTM) to predict tool wear. HLLSTM learns short-term data characteristics by segmenting data into fragment data. HLLSTM learns short-term data features by segmenting data into fragment data and short-term data features classify into holistic training and local training to capture more implicit feature information. HLLSTM learns short-term data features by segment data. Short-term data features classify into overall training and local training to capture more implicit feature information to predict tool wear. The data of the study is from 2010 phm society conference data challenge. It contains multiple sensor parameters and tool wear values. The model through the supervised learning method can train to learn the sensor parameters to predict the condition of tool wear. Finally, we compare the prediction error values of the HLLSTM model with the currently used neural network model. The model verifies the prediction accuracy by error values. The HLLSTM model presented in this paper predicts more accurate tool wear values. The HLLSTM model can reduce the mean absolute error between the actual and the predicted tool wear value by a factor of two compared to other neural network models.
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7

Matos, Pedro Ferreira de. "Recognition of genetic mutations in text using deep learning." Master's thesis, 2018. http://hdl.handle.net/10773/25972.

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Deep learning is a sub-area of automatic learning that attempts to model complex structures in the data through the application of different neural network architectures with multiple layers of processing. These methods have been successfully applied in areas ranging from image recognition and classification, natural language processing, and bioinformatics. In this work we intend to create methods for named-entity recognition (NER) in text using techniques of deep learning in order to identify genetic mutations.
Deep Learning é uma subárea de aprendizagem automática que tenta modelar estruturas complexas no dados através da aplicação de diferentes arquitecturas de redes neuronais com várias camadas de processamento. Estes métodos foram aplicados com sucesso em áreas que vão desde o reconhecimento de imagem e classificação, processamento de linguagem natural e bioinformática. Neste trabalho pretendemos criar métodos para reconhecimento de entidades nomeadas (NER) no texto usando técnicas de Deep Learning, a fim de identificar mutações genéticas.
Mestrado em Engenharia de Computadores e Telemática
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(9835070), Chetanpal Singh. "Sentiment Analysis from Social Media Data using Deep Learning." Thesis, 2024. https://figshare.com/articles/thesis/Sentiment_Analysis_from_Social_Media_Data_using_Deep_Learning/29147042.

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The widespread use of social media platforms has led to the vast amounts of textual data generated by individuals and organisations on a daily basis. Understanding these textual data is important for valuable information that can be extracted and utilised for better understanding public opinion, assessing brand perception, and making data-driven decisions. One of the techniques to analyse these textual data is through sentiment analysis. Sentiment analysis refers to the process of understanding and estimating public opinion, emotions, and attitudes expressed online. Sentiment analysis serves as a lens to view the valuable insights from the social media data, transforming it into actionable information that informs decision-making and shapes effective communication approaches.

The process of conducting sentiment analysis in social media is challenging. The first challenge is due to the inherent complexity of human language and the dynamic nature of online interactions. The informal and context-dependent nature of social media communication introduces ambiguity and nuance that traditional language approaches struggle to decipher accurately. Slang, abbreviations, and the constant evolution of language on these platforms further complicate the task. The second challenge is due to the prevalence of facial image expressions, and cultural references which demands a nuanced understanding of context, making it difficult to discern the true sentiment behind a statement. The third challenge is due to the high degree of noise, with irrelevant or off-topic information potentially influencing sentiment analysis. It can be highly subjective, varying across individuals and cultural contexts. Continuous changes in trending topics and the rapid dissemination of information further intensify the challenge of maintaining the relevance and accuracy of sentiment analysis approaches over time. Navigating these complexities requires sophisticated and constant approach adaptation and a deep understanding of the intricacies of human expression on social media.

To address these issues, this research has developed three approaches for effectively dealing with sentiment analysis in social media data. The first approach is based on the development of an integrated non-linear deep learning algorithm using Bidirectional Long-Short Term Memory (Bi-LSTM), Long-Short Term Memory (LSTMs) and Convolutional Neural Networks (CNNs) for sentiment classification and Random Forest is used for sentiment classification to perform the learning process of online reviews. LSTMs and CNNs are adopted in the study as they have demonstrated great information displaying abilities when managing testing and huge text-based datasets from a wide scope of application ranges. The feature mapping employs a Random Forest classifier, which optimises the learning process through decision tree learning. This method effectively reduces feature overlap using non-linear mapping and classifies the data with a Random Forest, which optimises class imbalance. Hence, the proposed approach has a higher performance compared to the other approaches with a positive class accuracy of 99%, a neutral class accuracy of 99.23% and a negative class accuracy of 99.45%.

The second approach is based on the integration of Bi-LSTM, recurrent neural networks (RNNs) and convolutional neural network-recurrent neural network (CNN-RNN) for human face sentiment classification. Bi-LSTM and RNNs are used for object-based segmentation in images, while CNN-RNN is adopted for non-linear mapping with facial images. The main emphasis of the proposed approach is on feature selection, dimension reduction, non-linear mapping of features and consequently the reduction of noises and overlapping concerning the study’s domain. The combination of CNN and RNN-Bi LSTM, along with object-based segmentation, allows for effective feature extraction and sequential data, leading to improved accuracy and performance in sentiment classification tasks. The proposed approach achieves a high accuracy (99.67%) and precision (99.89%), indicating that it correctly identifies angry faces with very few false positives. The recall (99.78%) and F-score (99.88%) are also high, indicating a balanced performance.

The third approach is based on the development of various machine learning and deep learning algorithms such as Naïve Bayes, Random Forest, Support Vector Machine (SVM), Logistic Regression and LSTM-RNN for text classification of sentiments. This approach is developed to accelerate the learning of sentiment and mapped features through deep learning. The proposed approach has improved transformation giving the most weight to non-sparse features, SoftMax connected layers learn and validate features and labels. The proposed approach improves the performance metrics by 20% in accuracy, 10% to 12% in precision, and 12-13% in recall.

The results show that the first and third approaches are capable of non-linear text and sentiment-based text analysis, the first proposed approach is efficient, and practical and can be easily implemented for sentiment classification of online reviews, and the approach performs well in comparison to other existing approach in terms of accuracy, precision, recall and F-score. The second approach is based on the image sentiment analysis of social media data, and it is found to be efficient, practical and can be easily implemented for human face sentiment classification. The third deep learning approach significantly improves the performance metrics, and the results show it is more effective and efficient in sentiment analysis of social media data.

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Частини книг з теми "Bi-LSTM"

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Malik, Shaily, Poonam Bansal, Pratham Sharma, Rocky Jain, and Ankit Vashisht. "Image Retrieval Using Multilayer Bi-LSTM." In Advances in Intelligent Systems and Computing, 745–55. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2597-8_64.

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Anil Kumar, Pagidirayi, and B. Anuradha. "Bi-LSTM Based Speech Emotion Recognition." In Communications in Computer and Information Science, 173–91. Cham: Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-80839-5_14.

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Phukan, Rituraj, Nomi Baruah, Shikhar Kr Sarma, and Darpanjit Konwar. "Parts-of-Speech Tagger in Assamese Using LSTM and Bi-LSTM." In Advances in Data-Driven Computing and Intelligent Systems, 19–31. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9524-0_3.

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Kumar, N. A. Abhinav, R. V. Sanjay, V. Vani, and N. Karthik. "WENSA: WhatsApp Emotion and Sentiment Analyzer Using LSTM and BI-LSTM." In Smart Innovation, Systems and Technologies, 259–74. Singapore: Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-8355-7_22.

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Varghese, Jeena, and Aswathy Wilson. "Conflicting Statements Detection Using Bi-Directional LSTM." In Applications of Artificial Intelligence, Big Data and Internet of Things in Sustainable Development, 133–44. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003245469-9.

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Sai Kesav, R., H. B. Barathi Ganesh, B. Premjith, and K. P. Soman. "Ink Recognition Using TDNN and Bi-LSTM." In Lecture Notes in Electrical Engineering, 35–45. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9885-9_3.

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Kashid, Shamal, Krishan Kumar, Parul Saini, Abhishek Dhiman, and Alok Negi. "Bi-RNN and Bi-LSTM Based Text Classification for Amazon Reviews." In Lecture Notes in Networks and Systems, 62–72. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-30396-8_6.

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Al-Qerem, Ahmad, Mohammed Raja, Sameh Taqatqa, and Mutaz Rsmi Abu Sara. "Utilizing Deep Learning Models (RNN, LSTM, CNN-LSTM, and Bi-LSTM) for Arabic Text Classification." In Studies in Systems, Decision and Control, 287–301. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-43490-7_22.

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Rahul Gandh, D., V. P. Harigovindan, and Amrtha Bhide. "LSTM and Bi-LSTM Based Prediction of Water Quality for Smart Aquaculture." In Lecture Notes in Electrical Engineering, 533–44. Singapore: Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-97-7018-2_36.

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Gul, Malik Junaid Jami, M. Hafid Firmansyah, Seungmin Rho, and Anand Paul. "BI-LSTM-LSTM Based Time Series Electricity Consumption Forecast for South Korea." In Transactions on Computational Science and Computational Intelligence, 897–902. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70296-0_71.

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Тези доповідей конференцій з теми "Bi-LSTM"

1

Togatorop, Andrew Reinhard Marulak, and Mohammad Isa Irawan. "Nickel Price Prediction Using Bi-Directional LSTM (Bi-LSTM) and Attention Bi-Directional LSTM Network (At-Bi-LSTM)." In 2024 IEEE International Symposium on Consumer Technology (ISCT), 450–56. IEEE, 2024. https://doi.org/10.1109/isct62336.2024.10791230.

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Fadili, Yousra, Yassine El Yamani, Jihad Kilani, Najib El Kamoun, Youssef Baddi, and Faycal Bensalah. "An Enhancing Timeseries Anomaly Detection Using LSTM and Bi-LSTM Architectures." In 2024 11th International Conference on Wireless Networks and Mobile Communications (WINCOM), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/wincom62286.2024.10655101.

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Özer, Serpil, and Ümmühan Nida Erol. "Electric Vehicle Stock Price Prediction using LSTM, Bi-LSTM and GRU." In 2024 8th International Artificial Intelligence and Data Processing Symposium (IDAP), 1–7. IEEE, 2024. http://dx.doi.org/10.1109/idap64064.2024.10710919.

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Anbazhagan, K., Supriya Kurlekar, T. V. Brindha, and D. Sudhish Reddy. "Twitter Based Emotion Recognition Using Bi-LSTM." In 2024 International Conference on Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), 1–5. IEEE, 2024. http://dx.doi.org/10.1109/tqcebt59414.2024.10545185.

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Vatsal, Deven, Kailash Chand Sharma, Ankita Sharma, Palak Batra, and Anannya Sindhja. "Dynamic Line Rating Forecasting Using Bi-LSTM." In 2024 23rd National Power Systems Conference (NPSC), 1–6. IEEE, 2024. https://doi.org/10.1109/npsc61626.2024.10987204.

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Raman, Sreekumar Nedumpally, Narendran Sobanapuram Muruganandam, Charles Jeyaseelan, Gautham Arayalpuram, Sreeshanth Parapurathe, and Anushob Kavikkal Anand. "Exodus Bi-Directional LSTM Based Multivariate Texmoji Classification." In 2023 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM), 57–65. IEEE, 2023. http://dx.doi.org/10.1109/ccem60455.2023.00017.

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Luo, Deming, Zhaoguo Zhang, Yi Ning, Yi Lu, Wei Song, Xingguo Qin, and Jinlong Chen. "Att-Bi-LSTM for Scenic Passenger Flow Prediction." In 2024 9th International Conference on Intelligent Computing and Signal Processing (ICSP), 1767–76. IEEE, 2024. http://dx.doi.org/10.1109/icsp62122.2024.10743437.

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Lamba, Puneet Singh, Achin Jain, Harsh Taneja, Prakhar Priyadarshi, Arvind Panwar, and Arun Kumar Dubey. "Ensemble Sentiment Analysis Using Bi-LSTM and CNN." In 2024 4th International Conference on Advancement in Electronics & Communication Engineering (AECE), 575–79. IEEE, 2024. https://doi.org/10.1109/aece62803.2024.10911211.

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Chrisnanto, Julian Evan, Muhamad Maulana Rahmadi, Diva Dien Al Haq, Nurfauzi Fadillah, Nada Syifa Qolbiyah, Ariq Khalingga, Ujang Subhan, Yulison Herry Chrisnanto, and Ferry Faizal. "Bi-LSTM Approach for Seagrass Transplantation Site Detection." In 2024 14th International Conference on System Engineering and Technology (ICSET), 38–43. IEEE, 2024. https://doi.org/10.1109/icset63729.2024.10774941.

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Nirmala Devi, K., Vani Rajasekar, P. Jayanthi, Kavin Balasubramani, Kaviya Kandasamy, and Keerthana Gowrisankar. "Cyberbullying Detection and Severity Classification Using Bi-LSTM." In 2024 9th International Conference on Communication and Electronics Systems (ICCES), 338–44. IEEE, 2024. https://doi.org/10.1109/icces63552.2024.10859875.

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