scholarly journals Comparison Study on Improved Movie Recommender Systems

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 04) ◽  
pp. 1470-1478
Author(s):  
R. Lavanya ◽  
Ebani Gogia ◽  
Nihal Rai
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Recommendation System ◽  
Singular Value ◽  
Experimental Result ◽  
Comparison Study ◽  
Deep Learning Neural Network ◽  
Value Decomposition ◽  
Crucial Part

Recommendation system is a crucial part of offering items especially in services that offer streaming. For streaming movie services on OTT, RS are a helping hand for users in finding new movies for leisure. In this paper, we propose a machine learning an approach based on auto encoders to produce a CF system which outputs movie rating for a user based on a huge DB of ratings from other users. Utilising Movie Lens dataset, we explore the use of deep learning neural network based Stacked Auto encoders to predict user s ratings on new movies, thereby enabling movie recommendations. We consequently implement Singular Value Decomposition (SVD) to recommend movies to users. The experimental result showcase that our R S out performs a user-based neighbourhood baseline in terms of MSE on predicted ratings and in a survey in which user judge between recommendation s from both systems.

scholarly journals A Deep Learning Neural Network for Detecting the Diabetic Retinopathy

2019 ◽  
Vol 8 (10) ◽  
pp. 3522-3525
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Diabetic Patients ◽  
Retinal Screening ◽  
Deep Learning Neural Network

Diabetic Retinopathy (DR) is the leading cause of disease to blindness of people globally. The retinal screening examinations of diabetic patients is needed to prevent the disease. There are many untreated and undiagnosed cases present in especially in India. DR requires smart technique to detect it. In this paper, we proposed a deep learning based architecture for detecting the DR. The experiments are done on the DR Dataset available in UCI machine Learning Repository. The results obtained from the experiments are satisfactory.

scholarly journals Prediksi Status Pinjaman Bank dengan Deep Learning Neural Network

2021 ◽  
Vol 7 (2) ◽  
pp. 130-135
Author(s):  
Sukri Syafrudin ◽  
Ranu Agastya Nugraha ◽  
Kartika Handayani ◽  
Safitri Linawati ◽  
Windu Gata
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Big Data ◽  
Deep Learning ◽  
Bank Loan ◽  
Deep Learning Neural Network

Penilaian risiko pada penentuan status pinjaman merupakan proses yang penting dalam usaha simpan pinjam. Prediksi dalam mengklasifikasikan apakah nasabah akan melunasi atau tidak akan menentukan pengambilan keputusan dan tindaklanjutnya yang berdampak pada kinerja entitas dalam menjalankan usahanya. Berbagai teknik dalam prediksi status pinjaman dengan machine learning diterapkan dengan hasil yang meningkat dalam akurasi dan performance. Metode Deep Learning Neural Network (DNN) merupakan salah satu metode machine learning yang sekarang ini memiliki peran penting di era big data dimana data yang besar, dengan kemampuan mesin yang mumpuni dan kompleksitas pada suatu fitur mampu ditangani oleh DNN. Penelitian ini memvariasikan beberapa arsitektur dan parameter dalam pembangunan model DNN dengan score yang terbaik. Pengujian terhadap dataset bank loan status dengan metode DNN menghasilkan akurasi 82.27% tidak lebih baik dari metode SVM dengan hasil akurasi 84%. Namun metode DNN masih lebih baik dibandingkan dengan metode lainnya. Karena penggunaan variasi arsitektur dan parameter DNN tidak berpengaruh signifikan dalam menghasilkan score terbaik. Data yang diproses benar-benar diperlukan pemrosesan lebih lanjut sebelum dilakukan pemodelan.

scholarly journals Genetic Algorithm Based Hybrid Model Of convolutional Neural Network And Random Forest Classifier For Sentiment Classification

2021 ◽  
Vol 12 (2) ◽  
pp. 3216-3223
Author(s):  
Siji George C G, Et. al.
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Random Forest ◽  
Convolutional Neural Network ◽  
Sentiment Analysis ◽  
Random Forest Classifier ◽  
Sentiment Classification ◽  
Experimental Result ◽  
Text Input

Sentiment analysis is one of the active research areas in the field of datamining. Machine learning algorithms are capable to implement sentiment analysis. Due to the capacity of self-learning and massive data handling, most of the researchers are using deep learning neural networks for solving sentiment classification tasks. So, in this paper, a new model is designed under a hybrid framework of machine learning and deep learning which couples Convolutional Neural Network and Random Forest classifier for fine-grained sentiment analysis. The Continuous Bag-of-Word (CBOW) model is used to vectorize the text input. The most important features are extracted by the Convolutional Neural Network (CNN). The extracted features are used by the Random Forest(RF) classifier for sentiment classification. The performance of the proposed hybrid CNNRF model is comparedwith the base model such as Convolutional Neural Network (CNN) and Random Forest (RF) classifier. The experimental result shows that the proposed model far beat the existing base models in terms of classification accuracy and effectively integrated genetically-modified CNN with Random Forest classifier.

scholarly journals SURVEY ON EVOLVING DEEP LEARNING NEURAL NETWORK ARCHITECTURES

2019 ◽  
Vol 2019 (2) ◽  
pp. 73-82 ◽  
Author(s):  
Dr. Abul Bashar
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Feature Extraction ◽  
Deep Learning ◽  
Human Performance ◽  
Deep Neural Networks ◽  
Network Architectures ◽  
Neural Network Architectures ◽  
Deep Learning Neural Network

The deep learning being a subcategory of the machine learning follows the human instincts of learning by example to produce accurate results. The deep learning performs training to the computer frame work to directly classify the tasks from the documents available either in the form of the text, image, or the sound. Most often the deep learning utilizes the neural network to perform the accurate classification and is referred as the deep neural networks; one of the most common deep neural networks used in a broader range of applications is the convolution neural network that provides an automated way of feature extraction by learning the features directly from the images or the text unlike the machine learning that extracts the features manually. This enables the deep learning neural networks to have a state of art accuracy that mostly expels even the human performance. So the paper is to present the survey on the deep learning neural network architectures utilized in various applications for having an accurate classification with an automated feature extraction.

scholarly journals Augmented Reality Maintenance Assistant Using YOLOv5

2021 ◽  
Vol 11 (11) ◽  
pp. 4758
Author(s):  
Ana Malta ◽  
Mateus Mendes ◽  
Torres Farinha
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Object Recognition ◽  
Augmented Reality ◽  
Real Time ◽  
Recognition System ◽  
High Accuracy ◽  
Video Streams ◽  
The Neural Network ◽  
Deep Learning Neural Network

Maintenance professionals and other technical staff regularly need to learn to identify new parts in car engines and other equipment. The present work proposes a model of a task assistant based on a deep learning neural network. A YOLOv5 network is used for recognizing some of the constituent parts of an automobile. A dataset of car engine images was created and eight car parts were marked in the images. Then, the neural network was trained to detect each part. The results show that YOLOv5s is able to successfully detect the parts in real time video streams, with high accuracy, thus being useful as an aid to train professionals learning to deal with new equipment using augmented reality. The architecture of an object recognition system using augmented reality glasses is also designed.

scholarly journals Detection and Severity Evaluation of Combined Rail Defects Using Deep Learning

Vibration ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 341-356
Author(s):  
Jessada Sresakoolchai ◽  
Sakdirat Kaewunruen
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Machine Learning Techniques ◽  
Rolling Stock ◽  
Raw Data ◽  
Learning Techniques ◽  
Combined Defects

Various techniques have been developed to detect railway defects. One of the popular techniques is machine learning. This unprecedented study applies deep learning, which is a branch of machine learning techniques, to detect and evaluate the severity of rail combined defects. The combined defects in the study are settlement and dipped joint. Features used to detect and evaluate the severity of combined defects are axle box accelerations simulated using a verified rolling stock dynamic behavior simulation called D-Track. A total of 1650 simulations are run to generate numerical data. Deep learning techniques used in the study are deep neural network (DNN), convolutional neural network (CNN), and recurrent neural network (RNN). Simulated data are used in two ways: simplified data and raw data. Simplified data are used to develop the DNN model, while raw data are used to develop the CNN and RNN model. For simplified data, features are extracted from raw data, which are the weight of rolling stock, the speed of rolling stock, and three peak and bottom accelerations from two wheels of rolling stock. In total, there are 14 features used as simplified data for developing the DNN model. For raw data, time-domain accelerations are used directly to develop the CNN and RNN models without processing and data extraction. Hyperparameter tuning is performed to ensure that the performance of each model is optimized. Grid search is used for performing hyperparameter tuning. To detect the combined defects, the study proposes two approaches. The first approach uses one model to detect settlement and dipped joint, and the second approach uses two models to detect settlement and dipped joint separately. The results show that the CNN models of both approaches provide the same accuracy of 99%, so one model is good enough to detect settlement and dipped joint. To evaluate the severity of the combined defects, the study applies classification and regression concepts. Classification is used to evaluate the severity by categorizing defects into light, medium, and severe classes, and regression is used to estimate the size of defects. From the study, the CNN model is suitable for evaluating dipped joint severity with an accuracy of 84% and mean absolute error (MAE) of 1.25 mm, and the RNN model is suitable for evaluating settlement severity with an accuracy of 99% and mean absolute error (MAE) of 1.58 mm.

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