MIT-World Peace University (MIT-WPU) is a top educational institute located in Pune, India. It is recognized within&nbsp;UGC under Government of Maharashtra Act No. XXXV 2017.&nbsp;

It is part of the MIT group of institutions (established in 1983) which covers 10+ campuses across India and has more than 50,000+ students enrolled across various disciplines at a given point of time. The institute houses 15 schools ranging from Engineering &amp; Technology, Design, Pharmacy, Commerce, Governance among others. It offers undergraduate, postgraduate and PhD programs among different disciplines.&nbsp;

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MIT World Peace University

In today’s era, stock prediction has become one of the dominant real world application. Most of the times, scientists attempted to establish a direct connection between information macroeconomic factors and stock returns; however, with the revelation of nonlinear slants in financial exchange record returns, there has been a significant shift in the scientists’ focus toward the nonlinear expectation of stock returns. Even though various articles on nonlinear measurable presenting of stock returns have appeared since then, the huge demand is the nonlinear model which is specified before the estimation is performed. Predicting stock value is a difficult task that necessitates a solid algorithmic structure to calculate returns. Because stock prices are volatile and depend on the market up and down, forecasting stock prices becomes difficult. It has never been easy to invest in a portfolio of assets; the abnormalities of the financial market prevent simple models from accurately predicting future asset values. Machine learning, which is teaching computers to execute activities that would ordinarily need human intelligence, is the current scientific study hot topic. This paper explores gated recurrent units (GRU), simple recurrent neural network (Simple RNN) and long short term memory (LSTM) models for stock price prediction. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

World University root node

School of Computer Engineering & Technology

Engineering and Technology

Lecture Notes in Electrical Engineering

Stock Price Prediction Using GRU, SimpleRNN and LSTM

Age estimation has been paid great attention in the field of intelligent surveillance, face recognition, biometrics, etc. In contrast to other facial variations, aging variation presents several unique characteristics, which make age estimation very challenging. The overall process of age estimation is performed using three important steps. In the first step, the pre-processing is performed from the input image based on Viola-Jones algorithm to detect the face region. In the second step, feature extraction is done based on three important features such as local transform directional pattern (LTDP), active appearance model (AAM), and the new feature, deep appearance model (Deep AM). After feature extraction, the classification is carried out based on the extracted features using deep belief network (DBN), where the DBN classifier is trained optimally using the proposed learning algorithm named as crow-sine cosine algorithm (CS). Copyright © 2021, IGI Global.

International Journal of Ambient Computing and Intelligence

Deep appearance model and crow-sine cosine algorithm-based deep belief network for age estimation

Automatic age estimation from the face images is a growing research interest nowadays. Various literature works have contributed towards the age detection scheme, besides only a few have resulted in providing good performance. This is due to the influence of the external factors, such as environment, lifestyle, and various expressions present in the face image. This paper proposes a deep belief network with the crow optimization algorithm for the age detection purpose. The proposed Crow Deep Belief Network (CDBN) finds the age of the person in the image through the initial training with the face features. The features for the training of the proposed CDBN are provided by the scattering transform and the Active Appearance Model (AAM). The training of the CDBN with the features provides the optimal weights used for the age detection. The experimentation of the proposed CDBN is done by four standard databases, namely the IMDB database, the Adience database, the AFAD database, and the FG-NET database based on the metrics, such as Mean Absolute Error (MAE), Accuracy of error of one age category (AEO) and Accuracy of an Exact Match (AEM). Among them, the proposed model has the minimum MAE with a value of 2.186 for FG-NET database, and maximum AEO and AEM with the values of 0.972, and 0.971, respectively for IMDB database. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.

Fulltext

Journal of Signal Processing Systems

CDBN: Crow Deep Belief Network Based on Scattering and AAM Features for Age Estimation

Facial based human age estimation has become popular now a days due to tremendous increase in real time applications. Age estimation process comes with various challenges such as variation in lighting conditions, poses and facial expression. Performance of age estimation is evaluated with the help of measure 'Mean Absolute Error' (MAE). Main purpose of this work is improving facial based age estimation accuracy by building a Neural Network (NN). Beauty of NN is it learns nonlinear features of input data efficiently. For building NN model input images undergoes preprocessing, feature extraction, training and testing phases. NN model is built by providing training using various training algorithms. For prediction of age estimation neural network have shown superior performance for trainlm algorithm as compared to trainscg and traingdm algorithm. Experimentation are performed on partitioned training images and evaluated on testing images. We achieved low mean absolute error of 4.53 for 70 percent training images dataset.

Indian journal of computer science & Engineering

Gabor Feature Extraction driven facial age estimation using multilayer perceptron neural network

Facial based human age estimation has attracted lot of attention nowadays. Age estimation has become quite challenging task due to variation in lighting conditions, poses, and facial expression. Despite so much research in facial based human age estimation still there is room to improve performance. To improve accuracy we present age estimation using deep belief network. Deep belief network have shown superior performance as compared to other classification models. Success of deep belief network lies in contrastive divergence algorithm. Facial images passes though viola johns facial detection algorithm, once face is detected facial featured are extracted using active appearance and scattering transform feature method. These feature extraction model not only extracts geometric features but also extracts texture features. Subsequently deep belief network classification model is built on partitioned training images and evaluated on testing images. We performed experimentation on training images. Dataset and results are obtained by varying training percentages. Compared to other age estimation models we achieved low mean absolute error of 4.95 for 70% training images dataset. This study shows that due to inclusion of deep belief network performance is excelled. © 2020, Springer Nature Switzerland AG.

EAI/Springer Innovations in Communication and Computing

Facial Based Human Age Estimation Using Deep Belief Network

Age estimation is an interesting and challenging research area, gaining significant importance in the recent era and is employed in various applications, such as intelligent surveillance, face recognition, biometrics, and so on. Various techniques are employed in the literature for age estimation from the face images. This paper introduces the age estimation scheme from the face image, and estimation is done by defining a novel feature extraction strategy, named Local Transform Directional Pattern (LTDP). The database containing the input images has many unwanted regions, and thus, the Viola Jones algorithm detects the required face region. After detecting the face regions, active appearance model extracts the active appearance features, and the proposed LTDP extracts the texture features. The proposed LTDP feature extraction model modifies the existing Local Directional Pattern (LDP) with several other texture feature extraction models. After the feature extraction, the Cuckoo search based Deep Belief Network (CDBN) classifier estimates the age of the person from the face image based on the extracted features. The simulation results reveal that the proposed LTDP with the CDBN classifier achieved high performance with the values of 2.3416, 0.9803, and 0.9724 for MAE, AEO, and AEM, respectively.

Evolutionary Intelligence- Springer

Local transform directional pattern and optimization driven DBN for age estimation

Recently facial based age estimation has become increasingly important because of many potential real time applications. Age estimation is predicting someone's age by analyzing his/her biometric trait such as bone density, dental structure or face. Amongst these face is important trait so facial based age estimation has become more popular due to its vast real time applications. Age estimation is defined as to label the face image automatically with the exact age or age group. Estimating age from images has been one of the most challenging problems within the field of facial analysis due to uncontrollable nature of the aging process, high variance of observations within the same age range, lighting, facial expressions, pose, occlusion, blur, camouflage due to beards, moustache, glasses, makeup and the difficulty to gather complete and sufficient training data. This paper presents analysis of earlier techniques proposed by researchers for facial based age estimation. Different feature extraction and estimator learning methods used in this domain are also discussed. © 2017 IEEE.

2017 International Conference on Energy, Communication, Data Analytics and Soft Computing, ICECDS 2017

Comprehensive review on facial based human age estimation

Steganography is the art of hiding the existence of data in another transmission medium to achieve secret communication. It does not replace cryptography but rather boosts the security using its obscurity features. Steganography method used in this paper is based on biometrics. And the biometric feature used to implement steganography is skin tone region of images [1]. Here secret data is embedded within skin region of image that will provide an excellent secure location for data hiding. For this skin tone detection is performed using HSV (Hue, Saturation and Value) color space. Additionally secret data embedding is performed using frequency domain approach - DWT (Discrete Wavelet Transform), DWT outperforms than DCT (Discrete Cosine Transform). Secret data is hidden in one of the high frequency sub-band of DWT by tracing skin pixels in that sub-band. Different steps of data hiding are applied by cropping an image interactively. Cropping results into an enhanced security than hiding data without cropping i.e. in whole image, so cropped region works as a key at decoding side. This study shows that by adopting an object oriented steganography mechanism, in the sense that, we track skin tone objects in image, we get a higher security. And also satisfactory PSNR (Peak-Signal-to-Noise Ratio) is obtained. © 2010 IEEE.