Suchergebnisse

Abstract Estimates of government expenditure for the next period are very important in the government, for instance for the Ministry of Finance of the Republic of Indonesia, because this can be taken into consideration in making policies regarding how much money the government should bear and whether there is sufficient availability of funds to finance it. As is the case in the health, education and social fields, modeling technology in machine learning is expected to be applied in the financial sector in government, namely in making modeling for spending predictions. In this study, it is proposed the application of Long Short-Term Memory (LSTM) Model for expenditure predictions. Experiments show that LSTM model using three hidden layers and the appropriate hyperparameters can produce Mean Square Error (MSE) performance of 0.2325, Root Mean Square Error (RMSE) of 0.4820, Mean Average Error (MAE) of 0.3292 and Mean Everage Presentage Error (MAPE) of 0.4214. This is better than conventional modeling using the Auto Regressive Integrated Moving Average (ARIMA) as a comparison model. ; Perkiraan pengeluaran belanja pemerintah untuk periode kedepan merupakan hal yang sangat penting di pemerintah dalam hal ini pada Kementerian Keuangan Republik Indonesia, karena hal ini dapat dijadikan bahan pertimbangan dalam mengambil kebijakan terkait berapa nilai uang yang harus ditanggung pemerintah serta apakah ada ketersediaan dana yang cukup dalam membiayai belanja tersebut untuk periode yang akan datang. Seperti halnya pada bidang kesehatan, pendidikan dan sosial, teknologi pemodelan pada Machine Learning diharapkan dapat diterapkan di bidang keuangan pada pemerintahan, yaitu dalam membuat pemodelan untuk prediksi belanja. Pada penelitian ini, diusulkan penerapan model Long Short-Term Memory (LSTM) untuk prediksi belanja. Eksperimen menunjukkan model LSTM dengan menggunakan tiga hidden layers dan hyperparameter yang tepat dapat menghasilkan performa Mean Square Error (MSE) sebesar 0.2325, Root Mean Square Error (RMSE) sebesar 0.4820, Mean Average Error (MAE) sebesar 0.3292 dan Mean Everage Presentage Error (MAPE) sebesar 0.4214. Ini lebih baik dibandingkan pemodelan konvensional menggunakan Auto Regressive Integrated Moving Average (ARIMA) sebagai model pembanding.

Three experiments were performed that compared recall for synthetic and natural lists of monosyllabic words. In the first experiment, presentation intervals of 1, 2, and 5 s per word were used. Although free recall was consistently poorer overall for the synthetic lists at all presentation rates, the decrement for synthetic stimuli did not increase differentially with faster rates. In a second experiment, zero, three, and six digits were presented visually for retention prior to free recall of each spoken word list in a preload paradigm. Fewer subjects were able to correctly recall all of the digits for the six-digit list than the three-digit list when the following word lists were synthetic. The third experiment required ordered recall of lists of natural and synthetic words. Differences in ordered recall between the synthetic and natural word lists were substantially larger for the primacy portion of the serial position curve than the recency portion. These results indicate that difficulties observed in the perception and comprehension of synthetic speech are due, in part, to increased processing demands in short-term memory.

The text classification process has been well studied, but there are still many improvements in the classification and feature preparation, which can optimize the performance of classification for specific applications. In the paper we implemented dictionary based approach and long-short term memory approach. In the first approach, dictionaries will be padded based on field's specific input and use automation technology to expand. The second approach, long short term memory used word2vec technique. This will help us in getting a comprehensive pipeline of end-to-end implementations. This is useful for many applications, such as sorting emails which are spam or ham, classifying news as political or sports-related news, etc

Irrelevant sound tends to break through selective attention and impair cognitive performance. This observation has been brought under systematic scrutiny by laboratory studies measuring interference with memory performance during exposure to irrelevant sound. These studies established that the degree of interference depends on the properties of the irrelevant sound as well as those of the cognitive task. The way in which this interference increases or diminishes as characteristics of the sound and of the cognitive task are changed reveals key functional characteristics of auditory distraction. A number of important practical implications that arise from these studies are discussed, including the finding that relatively quiet background sound will have a marked effect on efficiency in performing cognitive tasks.

In the marine environment, shore-based radars play an important role in military surveillance and sensing. Sea clutter is one of the main factors affecting the performance of shore-based radar. Affected by marine environmental factors and radar parameters, the fluctuation law of sea clutter amplitude is very complicated. In the process of training a sea clutter amplitude prediction model, the traditional method updates the model parameters according to the current input data and the parameters in the current model, and cannot utilize the historical information of sea clutter amplitude. It is only possible to learn the short-term variation characteristics of the sea clutter. In order to learn the long-term variation law of sea clutter, a sea clutter prediction system based on the long short-term memory neural network is proposed. Based on sea clutter data collected by IPIX radar, UHF-band radar and S-band radar, the experimental results show that the mean square error of this prediction system is smaller than the traditional prediction methods. The sea clutter suppression signal is extracted by comparing the predicted sea clutter data with the original sea clutter data. The results show that the proposed sea clutter prediction system has a good effect on sea clutter suppression.

When individuals undertake to memorize long sequences of items, they show a strong tendency to break the sequences into smaller subgroups. This type of spontaneous grouping can be called "natural" grouping. This report reveals that certain specific grouping patterns are spontaneously utilized significantly more often than others for various particular sequence lengths. Furthermore, those persons who employ these "natural" grouping patterns obtain significantly better recall results. The most "natural" subgroup size was found to be three digits with two digits being the next most "natural." These findings should be useful for any application in which number codes are used (telephone numbers, license plates, stock numbers, etc.).

To protect the environment and achieve the Sustainable Development Goals (SDGs), reducing greenhouse gas emissions has been actively promoted by global governments. Thus, clean energy, such as wind power, has become a very important topic among global governments. However, accurately forecasting wind power output is not a straightforward task. The present study attempts to develop a fuzzy seasonal long short-term memory network (FSLSTM) that includes the fuzzy decomposition method and long short-term memory network (LSTM) to forecast a monthly wind power output dataset. LSTM technology has been successfully applied to forecasting problems, especially time series problems. This study first adopts the fuzzy seasonal index into the fuzzy LSTM model, which effectively extends the traditional LSTM technology. The FSLSTM, LSTM, autoregressive integrated moving average (ARIMA), generalized regression neural network (GRNN), back propagation neural network (BPNN), least square support vector regression (LSSVR), and seasonal autoregressive integrated moving average (SARIMA) models are then used to forecast monthly wind power output datasets in Taiwan. The empirical results indicate that FSLSTM can obtain better performance in terms of forecasting accuracy than the other methods. Therefore, FSLSTM can efficiently provide credible prediction values for Taiwan's wind power output datasets.

PURPOSE: Morse code as a form of communication became widely used for telegraphy, radio and maritime communication, and military operations, and remains popular with ham radio operators. Some skilled users of Morse code are able to comprehend a full sentence as they listen to it, while others must first transcribe the sentence into its written letter sequence. Morse thus provides an interesting opportunity to examine comprehension differences in the context of skilled acoustic perception. Measures of comprehension and short-term memory show a strong correlation across multiple forms of communication. This study tests whether this relationship holds for Morse and investigates its underlying basis. Our analyses examine Morse and speech immediate serial recall, focusing on established markers of echoic storage, phonological-articulatory coding, and lexical-semantic support. We show a relationship between Morse short-term memory and Morse comprehension that is not explained by Morse perceptual fluency. In addition, we find that poorer serial recall for Morse compared to speech is primarily due to poorer item memory for Morse, indicating differences in lexical-semantic support. Interestingly, individual differences in speech item memory are also predictive of individual differences in Morse comprehension. CONCLUSIONS: We point to a psycholinguistic framework to account for these results, concluding that Morse functions like "reading for the ears" (Maier et al., 2004) and that underlying differences in the integration of phonological and lexical-semantic knowledge impact both short-term memory and comprehension. The results provide insight into individual differences in the comprehension of degraded speech and strategies that build comprehension through listening experience. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.16451868