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 Deep learning neural network seismic big-data analysis of earthquake correlations in distinct seismic regions

2021 ◽  
Vol 8 (84) ◽  
Author(s):  
Antonios Konstantaras ◽  
Nikolaos S. Petrakis ◽  
Theofanis Frantzeskakis ◽  
Emmanouil Markoulakis ◽  
Katerina Kabassi ◽  
...  
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Deep Learning ◽  
Data Analysis ◽  
Big Data Analysis ◽  
Seismic Regions ◽  
Deep Learning Neural Network

scholarly journals Artificial Intelligence Sebagai Penggerak Industri 4.0 dan Tantangannya Bagi Sektor Pemerintah dan Swasta

2019 ◽  
Vol 10 (1) ◽  
pp. 68
Author(s):  
Reza Yogaswara
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Big Data ◽  
Deep Learning ◽  
Internet Of Things ◽  
Competitive Advantage ◽  
Smart City ◽  
Chief Information Officer ◽  
Deep Learning Neural Network

Artificial Intelligence (AI) atau kecerdasan buatan menjadi penggerak revolusi industri 4.0 yang menjanjikan banyak kemudahan bagi sektor pemerintah maupun industri. Internet of Things (IoT) dan big data contohnya dimana AI dapat diimplementasikan, teknologi yang telah banyak diadopsi di era industri 4.0 ini mampu menghubungkan setiap perangkat, seseorang dapat mengotomatisasi semua perangkat tanpa harus berada di lokasi, lebih dari itu, saat ini telah banyak mesin yang dapat menginterprestasi suatu kondisi atau kejadian tertentu dengan bantuan AI, sebagaimana telah kamera cerdas pendeteksi kepadatan volume kendaraan di jalan raya menggunakan teknologi Deep Learning Neural Network, yang telah diimplementasikan pada beberapa Pemerintah Daerah Kabupaten dan Kota dalam mendukung program Smart City yang telah dicanangkan. Pada sektor industri, banyak juga dari mereka yang telah mengotomatisasi mesin produksi dan manufaktur menggunakan robot dan Artificial Intelligence, sehingga Industri 4.0 akan meningkatkan daya saing melalui perangkat cerdas, setiap entitas yang mampu menguasai teknologi ini disitulah keunggulan kompetitifnya (competitive advantage). Namun ditengah perkembangan industri 4.0 yang cukup masif pemerintah harus bergerak cepat dalam mengadopsi platform ini, jika tidak, mereka akan menurunkan efisiensi proses bisnis untuk menjaga stabilitas layanan publik. Oleh sebab itu diperlukan keilmuan dan pemahaman yang benar bagi pemerintah dalam menghadapai era Industri 4.0, dimana Chief Information Officer (CIO) dapat mengambil peranan penting dalam memberikan dukungan yang didasari atas keilmuan mereka terkait tren teknologi industri 4.0, khususnya AI yang telah banyak diadopsi di berbagai sektor.

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 APPLICATION OF DEEP LEARNING NEURAL NETWORK FOR CLASSIFICATION OF BIG DATA OF ASTRONOMIC OBSERVATIONS

2017 ◽  
Vol 22 (4) ◽  
pp. 270-275
Author(s):  
A. A. Gorbunov ◽  
◽  
E. A. Isaev ◽  
V. A. Samodurov ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Deep Learning ◽  
Deep Learning Neural Network

scholarly journals Deep learning neural network seismic big-data analysis of earthquake correlations in distinct seismic regions

2021 ◽  
Vol 8 (84) ◽  
Author(s):  
Antonios Konstantaras ◽  
Nikolaos S. Petrakis ◽  
Theofanis Frantzeskakis ◽  
Emmanouil Markoulakis ◽  
Katerina Kabassi ◽  
...  
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Deep Learning ◽  
Data Analysis ◽  
Big Data Analysis ◽  
Seismic Regions ◽  
Deep Learning Neural Network

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 ENNGene: an Easy Neural Network model building tool for Genomics

2021 ◽  
Author(s):  
Eliska Chalupova ◽  
Ondrej Vaculik ◽  
Filip Jozefov ◽  
Jakub Polacek ◽  
Tomas Majtner ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Big Data ◽  
Deep Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Model Building ◽  
Evolutionary Conservation ◽  
Learning Methods

Background: The recent big data revolution in Genomics, coupled with the emergence of Deep Learning as a set of powerful machine learning methods, has shifted the standard practices of machine learning for Genomics. Even though Deep Learning methods such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are becoming widespread in Genomics, developing and training such models is outside the ability of most researchers in the field. Results: Here we present ENNGene - Easy Neural Network model building tool for Genomics. This tool simplifies training of custom CNN or hybrid CNN-RNN models on genomic data via an easy-to-use Graphical User Interface. ENNGene allows multiple input branches, including sequence, evolutionary conservation, and secondary structure, and performs all the necessary preprocessing steps, allowing simple input such as genomic coordinates. The network architecture is selected and fully customized by the user, from the number and types of the layers to each layer's precise set-up. ENNGene then deals with all steps of training and evaluation of the model, exporting valuable metrics such as multi-class ROC and precision-recall curve plots or TensorBoard log files. To facilitate interpretation of the predicted results, we deploy Integrated Gradients, providing the user with a graphical representation of an attribution level of each input position. To showcase the usage of ENNGene, we train multiple models on the RBP24 dataset, quickly reaching the state of the art while improving the performance on more than half of the proteins by including the evolutionary conservation score and tuning the network per protein. Conclusions: As the role of DL in big data analysis in the near future is indisputable, it is important to make it available for a broader range of researchers. We believe that an easy-to-use tool such as ENNGene can allow Genomics researchers without a background in Computational Sciences to harness the power of DL to gain better insights into and extract important information from the large amounts of data available in the field.

Deep Learning

2019 ◽  
pp. 114-130 ◽  
Author(s):  
Kamaljit I. Lakhtaria ◽  
Darshankumar Modi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Big Data ◽  
Deep Learning ◽  
Language Processing ◽  
Boltzmann Machine ◽  
Feed Forward Neural Network ◽  
Deep Boltzmann Machine ◽  
Methods And Techniques

Deep learning is a subset of machine learning. As the name suggests, deep learning means more and more layers. Deep leaning basically works on the principle of neurons. With the increase in big data or large quantities of data, deep learning methods and techniques have been widely used to extract the useful information. Deep learning can be applied to computer vision, bioinformatics, and speech recognition or on natural language processing. This chapter covers the basics of deep learning, different architectures of deep learning like artificial neural network, feed forward neural network, CNN, recurrent neural network, deep Boltzmann machine, and their comparison. This chapter also summarizes the applications of deep learning in different areas.

Heterogeneous Parallel Processing Enabled Deep Learning Pattern Recognition of Seismic Big Data in Syrna and Kandelioussa

2021 ◽  
Author(s):  
Antonios Konstantaras ◽  
Theofanis Frantzeskakis ◽  
Emmanouel Maravelakis ◽  
Alexandra Moshou ◽  
Panagiotis Argyrakis
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Pattern Recognition ◽  
Big Data ◽  
Deep Learning ◽  
Parallel Processing ◽  
Seismic Data ◽  
Seismic Activity ◽  
Point Cloud ◽  
Deep Learning Neural Network

<p>This research aims to depict ontological findings related to topical seismic phenomena within the Hellenic-Seismic-Arc via deep-data-mining of the existing big-seismological-dataset, encompassing a deep-learning neural network model for pattern recognition along with heterogeneous parallel processing-enabled interactive big data visualization. Using software that utilizes the R language, seismic data were 3D plotted on a 3D Cartesian plane point cloud viewer for further investigation of the formed three-dimensional morphology. As a means of mining information from seismic big data, a deep neural network was trained and refined for pattern recognition and occurrence manifestation attributes of seismic data of magnitudes greater than Ms 4.0. The deep learning neural network comprises of an input layer with six input neurons for the insertion of year, month, day, latitude, longitude and depth, followed by six hidden layers with a hundred neurons each, and one output layer of the estimated magnitude level. This approach was conceptualised to investigate for topical patterns in time yielding minor, interim and strong seismic activity, such as the one depicted by the deep learning neural network, observed in the past ten years on the region between Syrna and Kandelioussa. This area’s coordinates are around 36,4 degrees in latitude and 26,7 degrees in longitude, with the deep learning neural network achieving low error rates, possibly depicting a pattern in seismic activity.</p><p>References</p><p>Axaridou A., I. Chrysakis, C. Georgis, M. Theodoridou, M. Doerr, A. Konstantaras, and E. Maravelakis. 3D-SYSTEK: Recording and exploiting the production workflow of 3D-models in cultural heritage. IISA 2014 - 5th International Conference on Information, Intelligence, Systems and Applications, 51-56, 2014.</p><p>Konstantaras A. Deep Learning and Parallel Processing Spatio-Temporal Clustering Unveil New Ionian Distinct Seismic Zone. Informatics, 7 (4), 39, 2020.</p><p>Konstantaras A.J. Expert knowledge-based algorithm for the dynamic discrimination of interactive natural clusters. Earth Science Informatics. 9 (1), 95-100, 2016.</p><p>Konstantaras A.J. Classification of distinct seismic regions and regional temporal modelling of seismicity in the vicinity of the Hellenic seismic arc. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 6 (4), 1857-1863, 2012.</p><p>Konstantaras A., F. Vallianatos, M.R. Varley, J.P. Makris. Soft-Computing modelling of seismicity in the southern Hellenic Arc. IEEE Geoscience and Remote Sensing Letters, 5 (3), 323-327, 2008.</p><p>Konstantaras A., M.R. Varley, F. Vallianatos, G. Collins and P. Holifield. Recognition of electric earthquake precursors using neuro-fuzzy methods: methodology and simulation results. Proc. IASTED Int. Conf. Signal Processing, Pattern Recognition and Applications (SPPRA 2002), Crete, Greece, 303-308, 2002.</p><p>Maravelakis E., Konstantaras A., Kilty J., Karapidakis E. and Katsifarakis E. Automatic building identification and features extraction from aerial images: Application on the historic 1866 square of Chania Greece. 2014 International Symposium on Fundamentals of Electrical Engineering (ISFEE), Bucharest, 1-6, 2014. doi: 10.1109/ISFEE.2014.7050594.</p><p>Maravelakis E., A. Konstantaras, K. Kabassi, I. Chrysakis, C. Georgis and A. Axaridou. 3DSYSTEK web-based point cloud viewer. IISA 2014 - 5th International Conference on Information, Intelligence, Systems and Applications, 262-266, 2014.</p><p>Maravelakis E., Bilalis N., Mantzorou I., Konstantaras A. and Antoniadis A. 3D modelling of the oldest olive tree of the world. International Journal Of Computational Engineering Research. 2 (2), 340-347, 2012.</p>

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