Relevant bibliographies by topics / LSTM ALGORITHM

Academic literature on the topic 'LSTM ALGORITHM'

Author: Grafiati
Published: 11 September 2023

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Journal articles on the topic "LSTM ALGORITHM"

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Liang, Bushun, Siye Wang, Yeqin Huang, Yiling Liu, and Linpeng Ma. "F-LSTM: FPGA-Based Heterogeneous Computing Framework for Deploying LSTM-Based Algorithms." Electronics 12, no. 5 (February 26, 2023): 1139. http://dx.doi.org/10.3390/electronics12051139.

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Long Short-Term Memory (LSTM) networks have been widely used to solve sequence modeling problems. For researchers, using LSTM networks as the core and combining it with pre-processing and post-processing to build complete algorithms is a general solution for solving sequence problems. As an ideal hardware platform for LSTM network inference, Field Programmable Gate Array (FPGA) with low power consumption and low latency characteristics can accelerate the execution of algorithms. However, implementing LSTM networks on FPGA requires specialized hardware and software knowledge and optimization skills, which is a challenge for researchers. To reduce the difficulty of deploying LSTM networks on FPGAs, we propose F-LSTM, an FPGA-based framework for heterogeneous computing. With the help of F-LSTM, researchers can quickly deploy LSTM-based algorithms to heterogeneous computing platforms. FPGA in the platform will automatically take up the computation of the LSTM network in the algorithm. At the same time, the CPU will perform the pre-processing and post-processing in the algorithm. To better design the algorithm, compress the model, and deploy the algorithm, we also propose a framework based on F-LSTM. The framework also integrates Pytorch to increase usability. Experimental results on sentiment analysis tasks show that deploying algorithms to the F-LSTM hardware platform can achieve a 1.8× performance improvement and a 5.4× energy efficiency improvement compared to GPU. Experimental results also validate the need to build heterogeneous computing systems. In conclusion, our work reduces the difficulty of deploying LSTM on FPGAs while guaranteeing algorithm performance compared to traditional work.
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Hong, Juan, and Wende Tian. "Prediction in Catalytic Cracking Process Based on Swarm Intelligence Algorithm Optimization of LSTM." Processes 11, no. 5 (May 11, 2023): 1454. http://dx.doi.org/10.3390/pr11051454.

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Deep learning can realize the approximation of complex functions by learning deep nonlinear network structures, characterizing the distributed representation of input data, and demonstrating the powerful ability to learn the essential features of data sets from a small number of sample sets. A long short-term memory network (LSTM) is a deep learning neural network often used in research, which can effectively extract the dependency relationship between time series data. The LSTM model has many problems such as excessive reliance on empirical settings for network parameters, as well as low model accuracy and weak generalization ability caused by human parameter settings. Optimizing LSTM through swarm intelligence algorithms (SIA-LSTM) can effectively solve these problems. Group behavior has complex behavioral patterns, which makes swarm intelligence algorithms exhibit strong information exchange capabilities. The particle swarm optimization algorithm (PSO) and cuckoo search (CS) algorithm are two excellent algorithms in swarm intelligent optimization. The PSO algorithm has the advantage of being a simple algorithm with fast convergence speed, fewer requirements on optimization function, and easy implementation. The CS algorithm also has these advantages, using the simulation of the parasitic reproduction behavior of cuckoo birds during their breeding period. The SIM-LSTM model is constructed in this paper, and some hyperparameters of LSTM are optimized by using the PSO algorithm and CS algorithm with a wide search range and fast convergence speed. The optimal parameter set of an LSTM is found. The SIM-LSTM model achieves high prediction accuracy. In the prediction of the main control variables in the catalytic cracking process, the predictive performance of the SIM-LSTM model is greatly improved.
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Khataei Maragheh, Hamed, Farhad Soleimanian Gharehchopogh, Kambiz Majidzadeh, and Amin Babazadeh Sangar. "A New Hybrid Based on Long Short-Term Memory Network with Spotted Hyena Optimization Algorithm for Multi-Label Text Classification." Mathematics 10, no. 3 (February 2, 2022): 488. http://dx.doi.org/10.3390/math10030488.

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An essential work in natural language processing is the Multi-Label Text Classification (MLTC). The purpose of the MLTC is to assign multiple labels to each document. Traditional text classification methods, such as machine learning usually involve data scattering and failure to discover relationships between data. With the development of deep learning algorithms, many authors have used deep learning in MLTC. In this paper, a novel model called Spotted Hyena Optimizer (SHO)-Long Short-Term Memory (SHO-LSTM) for MLTC based on LSTM network and SHO algorithm is proposed. In the LSTM network, the Skip-gram method is used to embed words into the vector space. The new model uses the SHO algorithm to optimize the initial weight of the LSTM network. Adjusting the weight matrix in LSTM is a major challenge. If the weight of the neurons to be accurate, then the accuracy of the output will be higher. The SHO algorithm is a population-based meta-heuristic algorithm that works based on the mass hunting behavior of spotted hyenas. In this algorithm, each solution of the problem is coded as a hyena. Then the hyenas are approached to the optimal answer by following the hyena of the leader. Four datasets are used (RCV1-v2, EUR-Lex, Reuters-21578, and Bookmarks) to evaluate the proposed model. The assessments demonstrate that the proposed model has a higher accuracy rate than LSTM, Genetic Algorithm-LSTM (GA-LSTM), Particle Swarm Optimization-LSTM (PSO-LSTM), Artificial Bee Colony-LSTM (ABC-LSTM), Harmony Algorithm Search-LSTM (HAS-LSTM), and Differential Evolution-LSTM (DE-LSTM). The improvement of SHO-LSTM model accuracy for four datasets compared to LSTM is 7.52%, 7.12%, 1.92%, and 4.90%, respectively.
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Alamri, Nawaf Mohammad H., Michael Packianather, and Samuel Bigot. "Optimizing the Parameters of Long Short-Term Memory Networks Using the Bees Algorithm." Applied Sciences 13, no. 4 (February 16, 2023): 2536. http://dx.doi.org/10.3390/app13042536.

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Improving the performance of Deep Learning (DL) algorithms is a challenging problem. However, DL is applied to different types of Deep Neural Networks, and Long Short-Term Memory (LSTM) is one of them that deals with time series or sequential data. This paper attempts to overcome this problem by optimizing LSTM parameters using the Bees Algorithm (BA), which is a nature-inspired algorithm that mimics the foraging behavior of honey bees. In particular, it was used to optimize the adjustment factors of the learning rate in the forget, input, and output gates, in addition to cell candidate, in both forward and backward sides. Furthermore, the BA was used to optimize the learning rate factor in the fully connected layer. In this study, artificial porosity images were used for testing the algorithms; since the input data were images, a Convolutional Neural Network (CNN) was added in order to extract the features in the images to feed into the LSTM for predicting the percentage of porosity in the sequential layers of artificial porosity images that mimic real CT scan images of products manufactured by the Selective Laser Melting (SLM) process. Applying a Convolutional Neural Network Long Short-Term Memory (CNN-LSTM) yielded a porosity prediction accuracy of 93.17%. Although using Bayesian Optimization (BO) to optimize the LSTM parameters mentioned previously did not improve the performance of the LSTM, as the prediction accuracy was 93%, adding the BA to optimize the same LSTM parameters did improve its performance in predicting the porosity, with an accuracy of 95.17% where a hybrid Bees Algorithm Convolutional Neural Network Long Short-Term Memory (BA-CNN-LSTM) was used. Furthermore, the hybrid BA-CNN-LSTM algorithm was capable of dealing with classification problems as well. This was shown by applying it to Electrocardiogram (ECG) benchmark images, which improved the test set classification accuracy, which was 92.50% for the CNN-LSTM algorithm and 95% for both the BO-CNN-LSTM and BA-CNN-LSTM algorithms. In addition, the turbofan engine degradation simulation numerical dataset was used to predict the Remaining Useful Life (RUL) of the engines using the LSTM network. A CNN was not needed in this case, as there was no feature extraction for the images. However, adding the BA to optimize the LSTM parameters improved the prediction accuracy in the testing set for the LSTM and BO-LSTM, which increased from 74% to 77% for the hybrid BA-LSTM algorithm.
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Abubaker, Shaikh Shoieb, and Syed Rouf Farid. "Stock Market Prediction Using LSTM." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 3178–84. http://dx.doi.org/10.22214/ijraset.2022.42039.

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Abstract: Different machine learning algorithms are discussed in this literature review. These algorithms can be used for predicting the stock market. The prediction of the stock market is one of the challenging tasks that must have to be handled.In this paper, it is discussed how the machine learning algorithms can be used for predicting the stock value. Different attributes are identified that can be used for training the algorithm for this purpose. Some of the other factors are also discussed that can have an effect on the stock value. Keywords: Machine learning, stock market prediction, literature review, artificial neural network, support vector machine, genetic algorithm, investment decision, RNN, LSTM.
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Fang, Wei, Jinguang Jiang, Shuangqiu Lu, Yilin Gong, Yifeng Tao, Yanan Tang, Peihui Yan, Haiyong Luo, and Jingnan Liu. "A LSTM Algorithm Estimating Pseudo Measurements for Aiding INS during GNSS Signal Outages." Remote Sensing 12, no. 2 (January 10, 2020): 256. http://dx.doi.org/10.3390/rs12020256.

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Aiming to improve the navigation accuracy during global navigation satellite system (GNSS) outages, an algorithm based on long short-term memory (LSTM) is proposed for aiding inertial navigation system (INS). The LSTM algorithm is investigated to generate the pseudo GNSS position increment substituting the GNSS signal. Almost all existing INS aiding algorithms, like the multilayer perceptron neural network (MLP), are based on modeling INS errors and INS outputs ignoring the dependence of the past vehicle dynamic information resulting in poor navigation accuracy. Whereas LSTM is a kind of dynamic neural network constructing a relationship among the present and past information. Therefore, the LSTM algorithm is adopted to attain a more stable and reliable navigation solution during a period of GNSS outages. A set of actual vehicle data was used to verify the navigation accuracy of the proposed algorithm. During 180 s GNSS outages, the test results represent that the LSTM algorithm can enhance the navigation accuracy 95% compared with pure INS algorithm, and 50% of the MLP algorithm.
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Li, Hailin, Zhizhou Zhao, and Xue Du. "Research and Application of Deformation Prediction Model for Deep Foundation Pit Based on LSTM." Wireless Communications and Mobile Computing 2022 (July 6, 2022): 1–12. http://dx.doi.org/10.1155/2022/9407999.

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Deep foundation pit is a door with a long history, but it has new disciplines; in this paper, firstly, the modeling method and process of LSTM (long short-term memory) network are discussed in detail, then the optimization algorithm used in the model is described in detail, and the parameter selection methods such as initial learning rate, activation function, and iteration number related to LSTM network training are introduced in detail. LSTM network is used to process the deformation data of deep foundation pit, and random gradient descent, momentum, Nesterov, RMSProp, AdaGmd, and Adam algorithms are selected in the same example for modeling prediction and comparison. Two examples of horizontal displacement prediction of pile and vertical displacement prediction of column in deep foundation pit show that the LSTM network model established by different optimization algorithms has different prediction accuracy, and the LSTM network model established by Adam optimization algorithm has the highest accuracy, which proves that the selection of optimization algorithm plays an important role in LSTM and also verifies the feasibility of LSTM network in the data processing and prediction of deep foundation pit deformation.
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Qin, Wanting, Jun Tang, Cong Lu, and Songyang Lao. "Trajectory prediction based on long short-term memory network and Kalman filter using hurricanes as an example." Computational Geosciences 25, no. 3 (March 5, 2021): 1005–23. http://dx.doi.org/10.1007/s10596-021-10037-2.

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AbstractTrajectory data can objectively reflect the moving law of moving objects. Therefore, trajectory prediction has high application value. Hurricanes often cause incalculable losses of life and property, trajectory prediction can be an effective means to mitigate damage caused by hurricanes. With the popularization and wide application of artificial intelligence technology, from the perspective of machine learning, this paper trains a trajectory prediction model through historical trajectory data based on a long short-term memory (LSTM) network. An improved LSTM (ILSTM) trajectory prediction algorithm that improves the prediction of the simple LSTM is proposed, and the Kalman filter is used to filter the prediction results of the improved LSTM algorithm, which is called LSTM-KF. Through simulation experiments of Atlantic hurricane data from 1851 to 2016, compared to other LSTM and ILSTM algorithms, it is found that the LSTM-KF trajectory prediction algorithm has the lowest prediction error and the best prediction effect.
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Ulum, Dinar Syahid Nur, and Abba Suganda Girsang. "Hyperparameter Optimization of Long-Short Term Memory using Symbiotic Organism Search for Stock Prediction." International Journal of Innovative Research and Scientific Studies 5, no. 2 (April 29, 2022): 121–33. http://dx.doi.org/10.53894/ijirss.v5i2.415.

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Producing the best possible predictive result from long-short term memory (LSTM) requires hyperparameters to be tuned by a data scientist or researcher. A metaheuristic algorithm was used to optimize hyperparameter tuning and reduce the computational complexity to improve the manual process. Symbiotic organism search (SOS), which was introduced in 2014, is an algorithm that simulates the symbiotic interactions that organisms use to survive in an ecosystem. SOS offers an advantage over other metaheuristic algorithms in that it has fewer parameters, allowing it to avoid parameter determination errors and produce suboptimal solutions. SOS was used to optimize hyperparameter tuning in LSTM for stock prediction. The stock prices were time-series data, and LSTM has proven to be a popular method for time-series forecasting. This research employed the Indonesia composite index dataset and assessed it using root mean square error (RMSE) as a key indicator and the fitness function for the metaheuristic approach. Genetic algorithm (GA) and particle swarm optimization (PSO) were used as benchmarking algorithms in this research. The hybrid SOS-LSTM model outperformed GA-LSTM and PSO-LSTM with an RMSE of 78.799, compared to the GA-LSTM model with an RMSE of 142.663 and the PSO-LSTM model with an RMSE of 529.170.
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Chen, Wantong, Hailong Wu, and Shiyu Ren. "CM-LSTM Based Spectrum Sensing." Sensors 22, no. 6 (March 16, 2022): 2286. http://dx.doi.org/10.3390/s22062286.

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This paper presents spectrum sensing as a classification problem, and uses a spectrum-sensing algorithm based on a signal covariance matrix and long short-term memory network (CM-LSTM). We jointly exploited the spatial cross-correlation of multiple signals received by the antenna array and the temporal autocorrelation of single signals; we used the long short-term memory network (LSTM), which is good at extracting temporal correlation features, as the classification model; we then input the covariance matrix of the signals received by the array into the LSTM classification model to achieve the fusion learning of spatial correlation features and temporal correlation features of the signals, thus significantly improving the performance of spectrum sensing. Simulation analysis shows that the CM-LSTM-based spectrum-sensing algorithm shows better performance compared with support vector machine (SVM), gradient boosting machine (GBM), random forest (RF), and energy detection (ED) algorithm-based spectrum-sensing algorithms for different signal-to-noise ratios (SNRs) and different numbers of secondary users (SUs). Among them, SVM is a classical machine-learning algorithm, GBM and RF are two integrated learning methods with better generalization capability, and ED is a classical, traditional, and spectrum-sensing algorithm.
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Dissertations / Theses on the topic "LSTM ALGORITHM"

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Paschou, Michail. "ASIC implementation of LSTM neural network algorithm." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254290.

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LSTM neural networks have been used for speech recognition, image recognition and other artificial intelligence applications for many years. Most applications perform the LSTM algorithm and the required calculations on cloud computers. Off-line solutions include the use of FPGAs and GPUs but the most promising solutions include ASIC accelerators designed for this purpose only. This report presents an ASIC design capable of performing the multiple iterations of the LSTM algorithm on a unidirectional and without peepholes neural network architecture. The proposed design provides arithmetic level parallelism options as blocks are instantiated based on parameters. The internal structure of the design implements pipelined, parallel or serial solutions depending on which is optimal in every case. The implications concerning these decisions are discussed in detail in the report. The design process is described in detail and the evaluation of the design is also presented to measure accuracy and error of the design output.This thesis work resulted in a complete synthesizable ASIC design implementing an LSTM layer, a Fully Connected layer and a Softmax layer which can perform classification of data based on trained weight matrices and bias vectors. The design primarily uses 16-bit fixed point format with 5 integer and 11 fractional bits but increased precision representations are used in some blocks to reduce error output. Additionally, a verification environment has also been designed and is capable of performing simulations, evaluating the design output by comparing it with results produced from performing the same operations with 64-bit floating point precision on a SystemVerilog testbench and measuring the encountered error. The results concerning the accuracy and the design output error margin are presented in this thesis report. The design went through Logic and Physical synthesis and successfully resulted in a functional netlist for every tested configuration. Timing, area and power measurements on the generated netlists of various configurations of the design show consistency and are reported in this report.
LSTM neurala nätverk har använts för taligenkänning, bildigenkänning och andra artificiella intelligensapplikationer i många år. De flesta applikationer utför LSTM-algoritmen och de nödvändiga beräkningarna i digitala moln. Offline lösningar inkluderar användningen av FPGA och GPU men de mest lovande lösningarna inkluderar ASIC-acceleratorer utformade för endast dettaändamål. Denna rapport presenterar en ASIC-design som kan utföra multipla iterationer av LSTM-algoritmen på en enkelriktad neural nätverksarkitetur utan peepholes. Den föreslagna designed ger aritmetrisk nivå-parallellismalternativ som block som är instansierat baserat på parametrar. Designens inre konstruktion implementerar pipelinerade, parallella, eller seriella lösningar beroende på vilket anternativ som är optimalt till alla fall. Konsekvenserna för dessa beslut diskuteras i detalj i rapporten. Designprocessen beskrivs i detalj och utvärderingen av designen presenteras också för att mäta noggrannheten och felmarginal i designutgången. Resultatet av arbetet från denna rapport är en fullständig syntetiserbar ASIC design som har implementerat ett LSTM-lager, ett fullständigt anslutet lager och ett Softmax-lager som kan utföra klassificering av data baserat på tränade viktmatriser och biasvektorer. Designen använder huvudsakligen 16bitars fast flytpunktsformat med 5 heltal och 11 fraktions bitar men ökade precisionsrepresentationer används i vissa block för att minska felmarginal. Till detta har även en verifieringsmiljö utformats som kan utföra simuleringar, utvärdera designresultatet genom att jämföra det med resultatet som produceras från att utföra samma operationer med 64-bitars flytpunktsprecision på en SystemVerilog testbänk och mäta uppstådda felmarginal. Resultaten avseende noggrannheten och designutgångens felmarginal presenteras i denna rapport.Designen gick genom Logisk och Fysisk syntes och framgångsrikt resulterade i en funktionell nätlista för varje testad konfiguration. Timing, area och effektmätningar på den genererade nätlistorna av olika konfigurationer av designen visar konsistens och rapporteras i denna rapport.
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Shaif, Ayad. "Predictive Maintenance in Smart Agriculture Using Machine Learning : A Novel Algorithm for Drift Fault Detection in Hydroponic Sensors." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-42270.

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The success of Internet of Things solutions allowed the establishment of new applications such as smart hydroponic agriculture. One typical problem in such an application is the rapid degradation of the deployed sensors. Traditionally, this problem is resolved by frequent manual maintenance, which is considered to be ineffective and may harm the crops in the long run. The main purpose of this thesis was to propose a machine learning approach for automating the detection of sensor fault drifts. In addition, the solution’s operability was investigated in a cloud computing environment in terms of the response time. This thesis proposes a detection algorithm that utilizes RNN in predicting sensor drifts from time-series data streams. The detection algorithm was later named; Predictive Sliding Detection Window (PSDW) and consisted of both forecasting and classification models. Three different RNN algorithms, i.e., LSTM, CNN-LSTM, and GRU, were designed to predict sensor drifts using forecasting and classification techniques. The algorithms were compared against each other in terms of relevant accuracy metrics for forecasting and classification. The operability of the solution was investigated by developing a web server that hosted the PSDW algorithm on an AWS computing instance. The resulting forecasting and classification algorithms were able to make reasonably accurate predictions for this particular scenario. More specifically, the forecasting algorithms acquired relatively low RMSE values as ~0.6, while the classification algorithms obtained an average F1-score and accuracy of ~80% but with a high standard deviation. However, the response time was ~5700% slower during the simulation of the HTTP requests. The obtained results suggest the need for future investigations to improve the accuracy of the models and experiment with other computing paradigms for more reliable deployments.
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Malina, Ondřej. "Detekce začátku a konce komplexu QRS s využitím hlubokého učení." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442595.

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This thesis deals with the issue of automatic measurement of the duration of QRS complexes in ECG signals. Special emphasis is then placed on the possibility of automatic detection of QRS complexes while exciting cardiac tissue with a pacemaker. The content of this work is divided into four logical units, where the first part deals with the heart as an organ. It describes the origin and spread of excitement in the heart, its possible pathologies and their manifestations in ECG recording, it also deals with pacing and measuring ECG recording during simultaneous pacing. The second part of the thesis contains a brief introduction to the topic of machine and deep learning. The third part of the thesis contains a search of current approaches using methods based on deep learning to solve the detection of QRSd. The fourth part deals with the design and implementation of its own model of deep learning, able to detect the beginnings and ends of QRS complexes from ECG recordings. It describes the data preprocessing implemented in the MATLAB programming environment. The actual implementation of the model was performed in the Python using the PyTorch and NumPy moduls.
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Olsson, Charlie, and David Hurtig. "An approach to evaluate machine learning algorithms for appliance classification." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20217.

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A cheap and powerful solution to lower the electricity usage and making the residents more energy aware in a home is to simply make the residents aware of what appliances that are consuming electricity. Meaning the residents can then take decisions to turn them off in order to save energy. Non-intrusive load monitoring (NILM) is a cost-effective solution to identify different appliances based on their unique load signatures by only measuring the energy consumption at a single sensing point. In this thesis, a low-cost hardware platform is developed with the help of an Arduino to collect consumption signatures in real time, with the help of a single CT-sensor. Three different algorithms and one recurrent neural network are implemented with Python to find out which of them is the most suited for this kind of work. The tested algorithms are k-Nearest Neighbors, Random Forest and Decision Tree Classifier and the recurrent neural network is Long short-term memory.
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Freberg, Daniel. "Evaluating Statistical MachineLearning and Deep Learning Algorithms for Anomaly Detection in Chat Messages." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-235957.

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Automatically detecting anomalies in text is of great interest for surveillance entities as vast amounts of data can be analysed to find suspicious activity. In this thesis, three distinct machine learning algorithms are evaluated as a chat message classifier is being implemented for the purpose of market surveillance. Naive Bayes and Support Vector Machine belong to the statistical class of machine learning algorithms being evaluated in this thesis and both require feature selection, a side objective of the thesis is thus to find a suitable feature selection technique to ensure mentioned algorithms achieve high performance. Long Short-Term Memory network is the deep learning algorithm being evaluated in the thesis, rather than depend on feature selection, the deep neural network will be evaluated as it is trained using word embeddings. Each of the algorithms achieved high performance but the findings ofthe thesis suggest Naive Bayes algorithm in conjunction with a feature counting feature selection technique is the most suitable choice for this particular learning problem.
Att automatiskt kunna upptäcka anomalier i text har stora implikationer för företag och myndigheter som övervakar olika sorters kommunikation. I detta examensarbete utvärderas tre olika maskininlärningsalgoritmer för chattmeddelandeklassifikation i ett marknadsövervakningsystem. Naive Bayes och Support Vector Machine tillhör båda den statistiska klassen av maskininlärningsalgoritmer som utvärderas i studien och bådar kräver selektion av vilka särdrag i texten som ska användas i algoritmen. Ett sekundärt mål med studien är således att hitta en passande selektionsteknik för att de statistiska algoritmerna ska prestera så bra som möjligt. Long Short-Term Memory Network är djupinlärningsalgoritmen som utvärderas i studien. Istället för att använda en selektionsteknik kommer djupinlärningsalgoritmen nyttja ordvektorer för att representera text. Resultaten visar att alla utvärderade algoritmer kan nå hög prestanda för ändamålet, i synnerhet Naive Bayes tillsammans med termfrekvensselektion.
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Almqvist, Olof. "A comparative study between algorithms for time series forecasting on customer prediction : An investigation into the performance of ARIMA, RNN, LSTM, TCN and HMM." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-16974.

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Time series prediction is one of the main areas of statistics and machine learning. In 2018 the two new algorithms higher order hidden Markov model and temporal convolutional network were proposed and emerged as challengers to the more traditional recurrent neural network and long-short term memory network as well as the autoregressive integrated moving average (ARIMA). In this study most major algorithms together with recent innovations for time series forecasting is trained and evaluated on two datasets from the theme park industry with the aim of predicting future number of visitors. To develop models, Python libraries Keras and Statsmodels were used. Results from this thesis show that the neural network models are slightly better than ARIMA and the hidden Markov model, and that the temporal convolutional network do not perform significantly better than the recurrent or long-short term memory networks although having the lowest prediction error on one of the datasets. Interestingly, the Markov model performed worse than all neural network models even when using no independent variables.
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Blanco, Martínez Alejandro. "Study and design of classification algorithms for diagnosis and prognosis of failures in wind turbines from SCADA data." Doctoral thesis, Universitat de Vic - Universitat Central de Catalunya, 2018. http://hdl.handle.net/10803/586097.

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Actualmente las operaciones de mantenimiento preventivo de los parques eólicos se soportan sobre técnicas de Machine Learning para reducir los costes de las paradas no planificadas. Por eso se necesita una predicción de fallos con cierta anticipación que funcione sobre los datos de SCADA. Estos datos necesitan ser procesados en distintas etapas descritas en esta tesis, con resultados publicados en cada una de ellas. En una primera fase se limpian los valores extremos (Outliers), indicando cómo deben ser tratados para no eliminar la información sobre los fallos. En una segunda, las distintas variables son seleccionadas por diversos métodos de selección de características (Feature Selection). En la misma fase, se compara el uso de variables transformadas mediante Autoencoders. En una tercera se construye el modelo, mediante métodos supervisados y no supervisados, obteniendo resultados destacables con Self Organizing Maps (SOM) y con técnicas de Deep Learning incluyendo redes ANN y LSTM multicapa.
Nowadays, the preventive maintenance operations of wind farms are supported by Machine Learning techniques to reduce the costs of unplanned downtime. That is why an early fault prediction that works with SCADA data is required. These data need to be processed at different stages described in this thesis, with results published in each of them. In a first phase, the extreme values (Outliers) are cleaned, indicating how they should address in order not to eliminate the information about the faults. In a second step, the different variables are selected by different Feature Selection methods. At the same step, the use of variables transformed by Autoencoders is also compared. In a third, the model is constructed using Supervised and Unsupervised methods, obtaining outstanding results with Self Organizing Maps (SOM) and Deep Learning techniques including ANN and LSTM multi-layer networks.
Actualment les operacions de manteniment preventiu dels parcs eòlics se suporten sobre tècniques de Machine Learning per a reduir els costos de les parades no planificades. Per això es necessita una predicció de fallades amb certa anticipació que funcioni sobre les dades de SCADA. Aquestes dades necessiten ser processades en diferents etapes descrites a aquesta tesi, amb resultats publicats en cadascuna d'elles. En una primera fase es netegen els valors extrems (Outliers), indicant com han de ser tractats per no eliminar la informació sobre les fallades. En una segona, les diferents variables són seleccionades per diversos mètodes de selecció de característiques (Feature Selection). En la mateixa fase, es compara l'ús de variables transformades mitjançant Autoencoders. En una tercera es construeix el model, mitjançant mètodes supervisats i no supervisats, obtenint resultats destacables amb Self Organizing Maps (SOM) i amb tècniques de Deep Learning incloent xarxes ANN i LSTM multicapa.
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Arvidsson, Philip, and Tobias Ånhed. "Sequence-to-sequence learning of financial time series in algorithmic trading." Thesis, Högskolan i Borås, Akademin för bibliotek, information, pedagogik och IT, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-12602.

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Predicting the behavior of financial markets is largely an unsolved problem. The problem hasbeen approached with many different methods ranging from binary logic, statisticalcalculations and genetic algorithms. In this thesis, the problem is approached with a machinelearning method, namely the Long Short-Term Memory (LSTM) variant of Recurrent NeuralNetworks (RNNs). Recurrent neural networks are artificial neural networks (ANNs)—amachine learning algorithm mimicking the neural processing of the mammalian nervoussystem—specifically designed for time series sequences. The thesis investigates the capabilityof the LSTM in modeling financial market behavior as well as compare it to the traditionalRNN, evaluating their performances using various measures.
Prediktion av den finansiella marknadens beteende är i stort ett olöst problem. Problemet hartagits an på flera sätt med olika metoder så som binär logik, statistiska uträkningar ochgenetiska algoritmer. I den här uppsatsen kommer problemet undersökas medmaskininlärning, mer specifikt Long Short-Term Memory (LSTM), en variant av rekurrentaneurala nätverk (RNN). Rekurrenta neurala nätverk är en typ av artificiellt neuralt nätverk(ANN), en maskininlärningsalgoritm som ska efterlikna de neurala processerna hos däggdjursnervsystem, specifikt utformat för tidsserier. I uppsatsen undersöks kapaciteten hos ett LSTMatt modellera finansmarknadens beteenden och jämförs den mot ett traditionellt RNN, merspecifikt mäts deras effektivitet på olika vis.
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Nitz, Pettersson Hannes, and Samuel Vikström. "VISION-BASED ROBOT CONTROLLER FOR HUMAN-ROBOT INTERACTION USING PREDICTIVE ALGORITHMS." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-54609.

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The demand for robots to work in environments together with humans is growing. This calls for new requirements on robots systems, such as the need to be perceived as responsive and accurate in human interactions. This thesis explores the possibility of using AI methods to predict the movement of a human and evaluating if that information can assist a robot with human interactions. The AI methods that were used is a Long Short Term Memory(LSTM) network and an artificial neural network(ANN). Both networks were trained on data from a motion capture dataset and on four different prediction times: 1/2, 1/4, 1/8 and a 1/16 second. The evaluation was performed directly on the dataset to determine the prediction error. The neural networks were also evaluated on a robotic arm in a simulated environment, to show if the prediction methods would be suitable for a real-life system. Both methods show promising results when comparing the prediction error. From the simulated system, it could be concluded that with the LSTM prediction the robotic arm would generally precede the actual position. The results indicate that the methods described in this thesis report could be used as a stepping stone for a human-robot interactive system.
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Alsulami, Khalil Ibrahim D. "Application-Based Network Traffic Generator for Networking AI Model Development." University of Dayton / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1619387614152354.

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Book chapters on the topic "LSTM ALGORITHM"

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Minu, R. I., G. Nagarajan, Samarjeet Borah, and Debahuti Mishra. "LSTM-RNN-Based Automatic Music Generation Algorithm." In Smart Innovation, Systems and Technologies, 327–39. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9873-6_30.

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Sinhmar, Abhinav, Vinamra Malhotra, R. K. Yadav, and Manoj Kumar. "Spam Detection Using Genetic Algorithm Optimized LSTM Model." In Computer Networks and Inventive Communication Technologies, 59–72. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3728-5_5.

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Wu, Jin, Lei Wang, and Yu Wang. "An Improved CNN-LSTM Model Compression Pruning Algorithm." In Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery, 727–36. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89698-0_75.

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Lin, Zhaochen, Xinran Zhang, and Fenghua He. "A GNN-LSTM-Based Fleet Formation Recognition Algorithm." In Lecture Notes in Electrical Engineering, 7272–81. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-6613-2_702.

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Xiao, Tian, Qingliang Long, Lexi Xu, Guanghai Liu, Zixiang Di, Bei Li, Zhaoning Wang, Shiyu Zhou, and Fei Xue. "5G Construction Efficiency Enhancement Based on LSTM Algorithm." In Lecture Notes in Electrical Engineering, 1089–96. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9968-0_132.

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Wang, Kang, Ning Zhang, Kedi Hu, and Tongbo Cao. "Multispectral Image Compression Algorithm Based on Sliced Convolutional LSTM." In Lecture Notes in Electrical Engineering, 424–28. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0386-1_54.

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Ninagawa, Chuzo. "Example Source Code of LSTM Neural Network Learning Algorithm." In AI Time Series Control System Modelling, 201–37. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4594-6_9.

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Kumar, Neeraj, Ritu Chauhan, and Gaurav Dubey. "Forecasting of Stock Price Using LSTM and Prophet Algorithm." In Lecture Notes in Electrical Engineering, 141–55. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3067-5_12.

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Wang, Kang, Ning Zhang, Kedi Hu, and Tongbo Cao. "Multispectral Image Compression Algorithm Based on Silced Convolutional LSTM." In Lecture Notes in Electrical Engineering, 887–91. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0390-8_112.

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Cao, Yu, Hongyang Bai, Huaju Liang, and Guanyu Zou. "An Integrated Navigation Algorithm Based on LSTM Neural Network." In Lecture Notes in Electrical Engineering, 3203–12. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-6613-2_311.

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Conference papers on the topic "LSTM ALGORITHM"

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Pratiwi, Monica, Adhi Dharma Wibawa, and Mauridhi Hery Purnomo. "EEG-based Happy and Sad Emotions Classification using LSTM and Bidirectional LSTM." In 2021 3rd International Conference on Electronics Representation and Algorithm (ICERA). IEEE, 2021. http://dx.doi.org/10.1109/icera53111.2021.9538698.

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Hu, Weifei, Feng Tang, Zhenyu Liu, and Jianrong Tan. "A New Robot Path Planning Method Based on LSTM Neural Network and Rapidly-Exploring Random Tree Algorithm." In ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/detc2021-71234.

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Abstract As an important field of robot research, robot path planning has been studied extensively in the past decades. A series of path planning methods have been proposed, such as A* algorithm, Rapidly-exploring Random Tree (RRT), Probabilistic Roadmaps (PRM). Although various robot path planning algorithms have been proposed, the existing ones are suffering the high computational cost and low path quality, due to numerous collision detection and exhausting exploration of the free space. In addition, few robot path planning methods can automatically and efficiently generate path for a new environment. In order to address these challenges, this paper presents a new path planning algorithm based on the long-short term memory (LSTM) neural network and traditional RRT. The LSTM-RRT algorithm first creates 2D and 3D environments and uses the traditional RRT algorithm to generate the robot path information, then uses the path information and environmental information to train the LSTM neural network. The trained network is able to promptly generate new path for randomly generated new environment. In addition, the length of the generated path is further reduced by geometric relationships. Hence, the proposed LSTM-RRT algorithm overcomes the shortcomings of the slow path generation and the low path quality using the traditional RRT method.
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Ambrose, Albeena, Vasanthi Sundramoorthy, Dhivya Arjunan, Keertheka Subramanian, and Lavanya Nedunchezhiyan. "Sign language recognition using LSTM algorithm." In 24TH TOPICAL CONFERENCE ON RADIO-FREQUENCY POWER IN PLASMAS. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0165366.

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Lin, Zhaochen, Xinran Zhang, Ning Hao, and Fenghua He. "An LSTM-based Fleet Formation Recognition Algorithm." In 2021 40th Chinese Control Conference (CCC). IEEE, 2021. http://dx.doi.org/10.23919/ccc52363.2021.9550097.

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Wijaya, Nurhadi, Yudianingsih, Evrita Lusiana, Sugeng Winardi, Zaidir, and Agus Qomaruddin Munir. "LongSpam: Spam Email Detection using LSTM Algorithm." In 2022 Seventh International Conference on Informatics and Computing (ICIC). IEEE, 2022. http://dx.doi.org/10.1109/icic56845.2022.10007009.

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Damaraji, Galih Malela, Adhistya Erna Permanasari, Indriana Hidayah, Michael Stephen Moses Paknahan, and Aiie Kusuma Wardhana. "Detecting Pregnancy Risk Type Using LSTM Algorithm." In 2022 4th International Conference on Biomedical Engineering (IBIOMED). IEEE, 2022. http://dx.doi.org/10.1109/ibiomed56408.2022.9987932.

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Ren, Qiankun. "Air quality prediction based on LSTM algorithm." In Sixth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2021), edited by Qingsehng Zeng. SPIE, 2022. http://dx.doi.org/10.1117/12.2624653.

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Chandramouleesvar, V., M. E. Swetha, and P. Visalakshi. "Development of LSTM Model for Fall Prediction Using IMU." In International Research Conference on IOT, Cloud and Data Science. Switzerland: Trans Tech Publications Ltd, 2023. http://dx.doi.org/10.4028/p-stigt6.

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One of the major contributor leading to cause of unintentional injuries after motor vehicle crashes and poisoning, is Falls. The existing Fall Prediction Algorithms are used to predict falls in older or disabled people by analyzing their fall history, capturing their movements through visual sensors (cameras, thermal imaging etc.) in a fixed environment, using inertial sensors to identify the patterns of movements. These algorithms are monologues for each person as they learn from their history and predict falls specific only to that person. The algorithm proposed in this paper aims to predict falls using kinematic data such as accelerometer, magnetometer, and gyroscopic values, for any user. This work involves developing an algorithm capable of predicting falls and to achieve this, we use Long Short-Term Memory (LSTM). The benefit of this algorithm is to prevent trauma to the body or at least reduce the impact of fall and the fatality caused by it. In the future, this algorithm can be used to design a device to predict falls in real-time to scenario be used by everyone irrespective of gender, age, and health.
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Li, Guanlin, Xiao Li, Bei Zhuang, Yueying Li, Shangjing Lin, Ji Ma, and Jin Tian. "Shared-bicycle demand forecast using convolutional LSTM network." In 2023 3rd International Conference on Automation Control, Algorithm and Intelligent Bionics (ACAIB 2023), edited by Samir Ladaci and Suresh Kaswan. SPIE, 2023. http://dx.doi.org/10.1117/12.2686548.

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Mi, Yachao. "Daily temperature prediction exploiting linear regression and LSTM-based model." In International Conference on Computer Vision, Application, and Algorithm (CVAA 2022), edited by Hilal Imane. SPIE, 2023. http://dx.doi.org/10.1117/12.2673698.

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