scholarly journals Predicting the Kijang Emas Bullion Price using LSTM Networks

2020 ◽  
Vol 8 (2) ◽  
pp. 11-18
Author(s):  
Mohammad Hafiz Ismail ◽  
Tajul Rosli Razak
Keyword(s):  
Deep Learning ◽  
The Other ◽  
Test Dataset ◽  
Long Period ◽  
Price Data ◽  
Learning Techniques ◽  
Future Value ◽  
Bidirectional Lstm

This study investigates the potential of Deep Learning techniques, specifically LSTM networks, in forecasting Kijang Emas future value over a long period. Six LSTM models comprising of Simple LSTM, Bidirectional LSTM, and Stacked LSTM architecture were built and trained against a 15-year historical price data for Kijang Emas. The models’ performance was then measured against ARIMA (5,1,0) as a baseline reference and evaluated against the RAE, MSE and RMSE metric. The results revealed that LSTM networks models performed well in forecasting Kijang Emas price based on the test dataset where the average RMSE was between 49.9 to 50.3 while the Bidirectional LSTM was found to exhibit better performance as compared to the other LSTM models.

Deep Learning Applications in Agriculture

2021 ◽  
pp. 325-345
Author(s):  
Hari Kishan Kondaveeti ◽  
Gonugunta Priyatham Brahma ◽  
Dandhibhotla Vijaya Sahithi
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Deep Learning ◽  
Rapid Growth ◽  
Agricultural Sector ◽  
The Other ◽  
Learning Methods ◽  
Agriculture Sector ◽  
Learning Techniques ◽  
Recent Trends

Deep learning (DL), a part of machine learning (ML), comprises a contemporary technique for processing the images and analyzing the big data with promising outcomes. Deep learning methods are successfully being used in various sectors to gain better results. Agriculture sector is one of the sectors that could be benefitted from the deep learning techniques since the current agriculture techniques cannot keep up with the rapid growth in population. In this chapter, the recent trends in the applications of deep learning techniques in the agricultural sector and the survey of the research efforts that employ deep learning techniques are going to be discussed. Also, the models that are implemented are going to be analyzed and compared with the other existing models.

A Review on Cyber Security Mechanisms Using Machine and Deep Learning Algorithms

2020 ◽  
pp. 23-41 ◽  
Author(s):  
Thiyagarajan P.
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Information Security ◽  
Cyber Security ◽  
Learning Algorithms ◽  
The Other ◽  
The Internet ◽  
Learning Techniques

Digitalization is the buzz word today by which every walk of our life has been computerized, and it has made our life more sophisticated. On one side, we are enjoying the privilege of digitalization. On the other side, security of our information in the internet is the most concerning element. A variety of security mechanisms, namely cryptography, algorithms which provide access to protected information, and authentication including biometric and steganography, provide security to our information in the Internet. In spite of the above mechanisms, recently artificial intelligence (AI) also contributes towards strengthening information security by providing machine learning and deep learning-based security mechanisms. The artificial intelligence (AI) contribution to cyber security is important as it serves as a provoked reaction and a response to hackers' malicious actions. The purpose of this chapter is to survey recent papers which are contributing to information security by using machine learning and deep learning techniques.

scholarly journals Extraction of Ship Images using Deep Learning

2020 ◽  
Vol 9 (5) ◽  
pp. 635-638
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Environmental Changes ◽  
The Other ◽  
Traditional Methods ◽  
Network Algorithm ◽  
Marine Industry ◽  
The Neural Network ◽  
Learning Techniques ◽  
Neural Network Algorithm

Ship Extraction is very important in the marine industry. Extraction of ships is helpful to the fishers to find the other ships nearly around the particular area. Still today the fishers are to find the ships using some traditional methods. But now it became difficult due to environmental changes. So, by using the deep learning techniques like the CNN algorithm the ship extraction can be identified effectively. Generally, the ships are identified as narrow bow and parallel hull edge, etc. Here, the Existing system they have used the Tensor flow, to see the performance of the datasets, using Recall and precision. In the proposed system, we are using CNN deep learning techniques to identify the ships. By finding the ships with the techniques, the time will be saved and the productivity can be increased. The features of the ship image are taken and trained using the neural network algorithm and then the prediction is done by testing the images.

A Novel Hybrid Deep Multi-Criteria Model for Recommender System

2021 ◽  
Author(s):  
Nour Salim Nassar
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Recommender Systems ◽  
Recommender System ◽  
Real World ◽  
Deep Neural Network ◽  
Recommendation Systems ◽  
The Other ◽  
Learning Techniques ◽  
Novel Model

Abstract Recommender systems are everywhere books, products, movies, and more. Traditional recommender systems typically use a single criterion in the recommendation, while studies have shown that multi-criteria recommending is more accurate. Novel deep learning techniques have produced remarkable achievements in many fields. The use of such techniques in recommendation systems has started to get attention recently, and several models of recommendation have been proposed based on deep learning. However, there is still no work for using deep learning in hybrid multi-criteria recommender systems. In this work, a model for a hybrid deep multi-criteria recommender system was presented. The model mainly includes two major parts: In the first one, the model obtains the user ID, item ID, and the item metadata to be used as input to a deep neural network in order to predict the criteria ratings. In the second part, the obtained ratings act as an input to another deep neural network, where the overall rating is predicted. Our experiments were conducted on a real-world dataset. They demonstrated the superiority of the proposed novel model over the other models in all measures used to evaluate performance. This indicates the successful use of hybrid deep multi-criteria in the recommendation systems.

scholarly journals A Study on Sensor System Latency in VR Motion Sickness

2021 ◽  
Vol 10 (3) ◽  
pp. 53
Author(s):  
Ripan Kumar Kundu ◽  
Akhlaqur Rahman ◽  
Shuva Paul
Keyword(s):  
Deep Learning ◽  
Motion Sickness ◽  
Statistical Methods ◽  
Kalman Filtering ◽  
Curve Fitting ◽  
Dead Reckoning ◽  
Learning Techniques ◽  
Bidirectional Lstm ◽  
The Dead ◽  
Convolutional Lstm

One of the most frequent technical factors affecting Virtual Reality (VR) performance and causing motion sickness is system latency. In this paper, we adopted predictive algorithms (i.e., Dead Reckoning, Kalman Filtering, and Deep Learning algorithms) to reduce the system latency. Cubic, quadratic, and linear functions are used to predict and curve fitting for the Dead Reckoning and Kalman Filtering algorithms. We propose a time series-based LSTM (long short-term memory), Bidirectional LSTM, and Convolutional LSTM to predict the head and body motion and reduce the motion to photon latency in VR devices. The error between the predicted data and the actual data is compared for statistical methods and deep learning techniques. The Kalman Filtering method is suitable for predicting since it is quicker to predict; however, the error is relatively high. However, the error property is good for the Dead Reckoning algorithm, even though the curve fitting is not satisfactory compared to Kalman Filtering. To overcome this poor performance, we adopted deep-learning-based LSTM for prediction. The LSTM showed improved performance when compared to the Dead Reckoning and Kalman Filtering algorithm. The simulation results suggest that the deep learning techniques outperformed the statistical methods in terms of error comparison. Overall, Convolutional LSTM outperformed the other deep learning techniques (much better than LSTM and Bidirectional LSTM) in terms of error.

scholarly journals Designing Image Based Captcha using Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 3678-3682
Author(s):  
Piyush Sable
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Computer Networks ◽  
The Other ◽  
Learning Approaches ◽  
Semantic Data ◽  
Other Hand ◽  
Learning Techniques ◽  
The Creation

Captchas, or Completely Automated Public Turing Tests to Tell Computers and Humans Apart, were created in response to programmers' ability to breach computer networks via computer attack programmes and bots. Because of its ease of development and use, the Text Captcha is the most well-known Captcha scheme. Hackers and programmers, on the other hand, have weakened the assumed security of Captchas, leaving websites vulnerable to assault. Text Captchas are still widely used since it is assumed that the attack speeds are moderate, typically two to five seconds for each image, and that this is not considered a significant concern. Style Area Captcha (SACaptcha) is a revolutionary image-based Captcha suggested in this paper, which relies on semantic data comprehension, pixel-level segmentation, and deep learning approaches. The suggested SACaptcha highlights the creation of image-based Captchas utilising deep learning techniques for boosting the security purpose, demonstrating that text Captchas are no longer secure.

scholarly journals Deep Learning Applied to Intracranial Hemorrhage Detection

2021 ◽  
Author(s):  
Luis Cortes-Ferre ◽  
Miguel Angel Gutiérrez-Naranjo ◽  
Juan José Egea-Guerrero ◽  
Marcin Balcerzyk
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Intracranial Hemorrhage ◽  
The Other ◽  
Patient Diagnosis ◽  
Learning Techniques ◽  
Computed Tomography Scans ◽  
Novel Method ◽  
The One ◽  
Urgent Procedure

Intracranial hemorrhage is a serious health problem requiring rapid and often intensive medical care. Identifying the location and type of any hemorrhage present is a critical step in treating the patient. Diagnosis requires an urgent procedure and the detection of the hemorrhage is a hard and time-consuming process for human experts. In this paper, we propose a novel method based on Deep Learning techniques which can be useful as decision support system. Our proposal is two-folded. On the one hand, the proposed technique classifies slices of computed tomography scans for hemorrhage existence or not, achieving 92.7% accuracy and 0.978 ROC-AUC. On the other hand, our method provides visual explanation to the chosen classification by using the so-called Grad-CAM method. TRANSLATE with x English ArabicHebrewPolish BulgarianHindiPortuguese CatalanHmong DawRomanian Chinese SimplifiedHungarianRussian Chinese TraditionalIndonesianSlovak CzechItalianSlovenian DanishJapaneseSpanish DutchKlingonSwedish EnglishKoreanThai EstonianLatvianTurkish FinnishLithuanianUkrainian FrenchMalayUrdu GermanMalteseVietnamese GreekNorwegianWelsh Haitian CreolePersian TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster Portal Back TRANSLATE with x English ArabicHebrewPolish BulgarianHindiPortuguese CatalanHmong DawRomanian Chinese SimplifiedHungarianRussian Chinese TraditionalIndonesianSlovak CzechItalianSlovenian DanishJapaneseSpanish DutchKlingonSwedish EnglishKoreanThai EstonianLatvianTurkish FinnishLithuanianUkrainian FrenchMalayUrdu GermanMalteseVietnamese GreekNorwegianWelsh Haitian CreolePersian TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster Portal Back

scholarly journals Assistive Vision Technology using Deep Learning Techniques

2021 ◽  
Vol 9 (VII) ◽  
pp. 2695-2704
Author(s):  
Dr. Neeta Verma
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
The Other ◽  
Web Page ◽  
Short Term ◽  
Term Memory ◽  
Learning Techniques ◽  
Long Short Term Memory ◽  
Deep Learning Model ◽  
The Web

One of the most important functions of the human visual system is automatic captioning. Caption generation is one of the more interesting and focused areas of AI, with numerous challenges to overcome. If there is an application that automatically captions the scenes in which a person is present and converts the caption into a clear message, people will benefit from it in a variety of ways. In this, we offer a deep learning model that detects things or features in images automatically, produces descriptions for the images, and transforms the descriptions to audio for louder readout. The model uses pre-trained CNN and LSTM models to perform the task of extracting objects or features to get the captions. In our model, first task is to detect objects within the image using pre trained Mobilenet model of CNN (Convolutional Neural Networks) and therefore the other is to caption the pictures based on the detected objects by using LSTM (Long Short Term Memory) and convert caption into speech to read out louder to the person by using SpeechSynthesisUtterance interface of the Web Speech API. The interface of the model is developed using NodeJS as a backend for the web page. Caption generation entails a number of complex steps, including selecting the dataset, training the model, validating the model, creating pre-trained models to check the images, detecting the images, and finally generating captions.

Construction of an Ensemble Scheme for Stock Price Prediction Using Deep Learning Techniques

2021 ◽  
Vol 17 (2) ◽  
pp. 72-95
Author(s):  
Justice Kwame Appati ◽  
Ismail Wafaa Denwar ◽  
Ebenezer Owusu ◽  
Michael Agbo Tettey Soli
Keyword(s):  
Deep Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Absolute Error ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Learning Techniques ◽  
Proposed Model ◽  
Bidirectional Lstm ◽  
Gated Recurrent Unit

This study proposes a deep learning approach for stock price prediction by bridging the long short-term memory with gated recurrent unit. In its evaluation, the mean absolute error and mean square error were used. The model proposed is an extension of the study of Hossain et al. established in 2018 with an MSE of 0.00098 as its lowest error. The current proposed model is a mix of the bidirectional LSTM and bidirectional GRU resulting in 0.00000008 MSE as the lowest error recorded. The LSTM model recorded 0.00000025 MSE, the GRU model recorded 0.00000077 MSE, and the LSTM + GRU model recorded 0.00000023 MSE. Other combinations of the existing models such as the bi-directional LSTM model recorded 0.00000019 MSE, bi-directional GRU recorded 0.00000011 MSE, bidirectional LSTM + GRU recorded 0.00000027 MSE, LSTM and bi-directional GRU recorded 0.00000020 MSE.

scholarly journals Hybrid Recommendation Scheme Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Fangpeng Ming ◽  
Liang Tan ◽  
Xiaofan Cheng
Keyword(s):  
Deep Learning ◽  
Massive Data ◽  
Nonlinear Interactions ◽  
Recommendation Algorithm ◽  
Long Tail ◽  
Test Dataset ◽  
Learning Techniques ◽  
Recommendation Algorithms ◽  
Cn Algorithm ◽  
Hybrid Recommendation

Big data has been developed for nearly a decade, and the information data on the network is exploding. Facing the complex and massive data, it is difficult for people to get the demanded information quickly, and the recommendation algorithm with its characteristics becomes one of the important methods to solve the massive data overload problem at this stage. In particular, the rise of the e-commerce industry has promoted the development of recommendation algorithms. Traditional, single recommendation algorithms often have problems such as cold start, data sparsity, and long-tail items. The hybrid recommendation algorithms at this stage can effectively avoid some of the drawbacks caused by a single algorithm. To address the current problems, this paper makes up for the shortcomings of a single collaborative model by proposing a hybrid recommendation algorithm based on deep learning IA-CN. The algorithm first uses an integrated strategy to fuse user-based and item-based collaborative filtering algorithms to generalize and classify the output results. Then deeper and more abstract nonlinear interactions between users and items are captured by improved deep learning techniques. Finally, we designed experiments to validate the algorithm. The experiments are compared with the benchmark algorithm on (Amazon item rating dataset), and the results show that the IA-CN algorithm proposed in this paper has better performance in rating prediction on the test dataset.

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