scholarly journals Writer Identification Based on Hand Writing using Artificial Neural Network

2021 ◽  
Vol 3 (2) ◽  
pp. 257-264
Author(s):  
Rosalia Arum Kumalasanti ◽  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Social Interaction ◽  
Social Relations ◽  
Image Data ◽  
Training Phase ◽  
Testing Phase ◽  
Training Stage ◽  
Artificial Neural ◽  
Hand Writing

Humans are social beings who depend on social interaction. Social interaction that is often used is communication. Communication is one of the bridges to connect social relations between humans. Communication can be delivered in two ways, namely verbal or nonverbal. Handwriting is an example of nonverbal communication using paper and writing utensils. Each individual's writing has its own uniqueness so that handwriting often becomes the character or characteristic of the author. The handwriting pattern usually becomes a character for the writer so that people who recognize the writing will easily guess the ownership of the related handwriting. However, handwriting is often used by irresponsible people in the form of handwriting falsification. The acts of writing falcification often occur in the workplace or even in the field of education. This is one of the driving factors for creating a reliable system in tracking someone's handwriting based on their ownership. In this study, we will discuss the identification of a person's handwriting based on their ownership. The output of this research is in the form of ID from the author and accuracy in the form of percentage of system reliability in identifying. The results of this study are expected to have a good impact on all parties, in order to minimize plagiarism. Identification of handwriting to be built consists of two main processes, namely the training phase and the testing phase. At the training stage, the handwritten image is subjected to several processes, namely threshold, wavelet conversion, and then will be trained using the Backpropagation Artificial Neural Network. In the testing phase, the process is the same as in the training phase, but at the end of the process, a comparison will be made between the image data that has been stored during training with a comparison image. Backpropagation ANN can work optimally if it is trained using input data that has determined the size, learning rate, parameters, and the number of nodes on the network. It is expected that the offered method can work optimally so that it produces an accurate percentage in order to minimize handwriting falcification.

SIMPLEX: An Activation Function with Improved Loss Function Results in Validation

2020 ◽  
Vol 17 (1) ◽  
pp. 147-153
Author(s):  
Sarfaraz Masoood ◽  
Nida Safdar Jan
Keyword(s):  
Neural Network ◽  
Activation Function ◽  
Mathematical Function ◽  
Training Phase ◽  
Complex Relationship ◽  
Activation Functions ◽  
Testing Phase ◽  
Experimental Problem ◽  
Overall Performance ◽  
Artificial Neural

An activation function is a mathematical function used for squashing purposes in artificial neural networks, whose domain and the range are two important most features to judge its potency. Overfitting of a neural network, is an issue that has gained considerable importance. This is a consequence of a function developing some complex relationship during the training phase and then these do not show up during the testing phase due to which these relationships aren’t actually relations, but are merely a consequence of sampling noise that arises during the training phase and is absent during testing phase. This creates a significant gap in accuracy which if minimized could result in better results in terms of overall performance of an ANN (Artificial Neural Network). The activation function proposed in this work is called SIMPLEX. Over a set of experiments, it was observed, to have the least overfitting issue among the rest of the analyzed activation functions over the MNIST dataset, selected as the experimental problem.

scholarly journals Virtual Voice Assistant Based on Voice Flow

2021 ◽  
Vol 9 (11) ◽  
pp. 775-781
Author(s):  
Dhruv Piyush Parikh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Social Interaction ◽  
Social Problems ◽  
Psychological Impact ◽  
Human Interaction ◽  
Symptoms Of Depression ◽  
Network Layout ◽  
Artificial Neural ◽  
Voice Flow

Abstract: We face a perennial pandemic that forces everyone to stay within their premises, which engenders a decline in social interaction between individuals. Moreover, some people fear missing out, which correlates to the fact that individual proclivity towards human interaction is drained, which increases symptoms of depression and raises the bar for anxiety—inspiring from such social circumstances, we want to develop a system that aids in mitigating these social problems. Our system has an artificial neural network layout that enhances personalisation through voice application. An Autonomous Virtual Assistant System is an effective method that shall help a person deprived of social interaction get engaged in a gregarious task, deal with various problems faced by introverts by reducing the psychological impact of COVID 19. Keywords: Google Assistant, Intergromat, APIs, Voiceflow, Speech to text

scholarly journals Jaringan Syaraf Tiruan Perambatan Balik Untuk Pengenalan Wajah

2016 ◽  
Vol 6 (1) ◽  
pp. 30
Author(s):  
Nahdi Sabuari ◽  
Rizal Isnanto ◽  
Kusworo Adi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Face Recognition ◽  
Face Detection ◽  
Image Data ◽  
Training Algorithm ◽  
Accuracy Rate ◽  
Average Accuracy ◽  
The Face ◽  
Artificial Neural

This research discusses about face detection and face recognition in an image. Face detection has only two classifications, i.e face and not face. Face recognition is compatible with some classifications of a number individuals who want to be recognized. Face detection and face recognition in thi study using Haar-Like Feature method and Artificial Neural Network Backpropagation. A method Haar-Like Feature used for detection and extraction in an image, because the clasification on this method showed success at used to detect image of the face. Artificial Neural Network Backpropagation is a training algorithm that is used to do training simulated on facial image data training stored in a database. This study uses Ms. Excel 2007 as database with 10 individual sample image, every image in each individuals having three distance with every range has four defferent light intensities, so that the data training stored in the database reached 120 data training. The results shows that the face detection and face recognition which is developed can recognize a face image with an average accuracy rate reaches 80,8% for each distance.

A LOCAL-PATCH BASED MULTI-STAGE ARTIFICIAL-NEURAL-NETWORK TRAINING PROCEDURE AND ITS APPLICATION TO MATERIAL CHARACTERIZATION

2007 ◽  
Vol 04 (03) ◽  
pp. 439-458 ◽  
Author(s):  
YUNHUA LUO ◽  
ARVIND SHAH
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Training Data ◽  
Training Procedure ◽  
Training Time ◽  
Multi Stage ◽  
Training Stage ◽  
Local Patch ◽  
Artificial Neural

A local-patch based multi-stage artificial-neural-network (ANN) training procedure is proposed in this paper, to improve the accuracy of an ANN trained by a backpropagation (BP) algorithm and, at the same time, to reduce the overall training time. In the proposed procedure a conventional one-stage training procedure is split into multiple stages: an initial training stage and subsequent re-training stage(s). In the initial stage the training data are so selected that the trained ANN has adequate ability of generalization, that is, if provided with a set of new input, the ANN can predict the right region where the output is located, but the accuracy of the solution is not necessarily high. In the following re-training stage(s), local patches of training data, either selected from an existing data pool or generated by numerical methods such as finite element method, are used to re-train the ANN to improve the accuracy. Several factors that may have significant effects on the proposed procedure were investigated based on function approximation. As an example of application, the procedure was then used to train an ANN with finite element data to characterize material parameters.

Prediction of recycled coarse aggregate concrete mechanical properties using multiple linear regression and artificial neural network

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Suhas Vijay Patil ◽  
K. Balakrishna Rao ◽  
Gopinatha Nayak
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Artificial Neural Network ◽  
Prediction Model ◽  
Coarse Aggregate ◽  
Recycled Aggregate ◽  
Training Phase ◽  
Content Type ◽  
Recycled Coarse Aggregate ◽  
Artificial Neural

Purpose Recycling construction waste is a promising way towards sustainable development in construction. Recycled aggregate (RA) is obtained from demolished concrete structures, laboratory crushed concrete, concrete waste at a ready mix concrete plant and the concrete made from RA is known as RA concrete. The purpose of this study is to apply multiple linear regressions (MLRs) and artificial neural network (ANN) to predict the mechanical properties, such as compressive strength (CS), flexural strength (FS) and split tensile strength (STS) of concrete at the age of 28 days curing made completely from the recycled coarse aggregate (RCA). Design/methodology/approach MLR and ANN are used to develop a prediction model. The model was developed in the training phase by using data from a previously published research study and a developed model was further tested by obtaining data from laboratory experiments. Findings ANN shows more accuracy than MLR with an R2-value of more than 0.8 in the training phase and 0.9 in a testing phase. The high R2-value indicates strong relation between the actual and predicted values of mechanical properties of RCA concrete. These models will help construction professionals to save their time and cost in predicting the mechanical properties of RCA concrete at 28 days of curing. Originality/value ANN with rectified linear unit transfer function and backpropagation algorithm for training is used to develop a prediction model. The outcome of this study is the prediction model for CS, FS and STS of concrete at 28 days of curing.

scholarly journals Tomographic Image Reconstruction based on Artificial Neural Network (ANN) Techniques

2019 ◽  
Vol 18 ◽  
pp. 89
Author(s):  
M. Argyrou ◽  
P. Paschalis ◽  
D. Maintas ◽  
E. Stiliaris
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Image Reconstruction ◽  
Network Architecture ◽  
Reconstruction Technique ◽  
Training Phase ◽  
Tomographic Image ◽  
Tomographic Image Reconstruction ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

A new approach for tomographic image reconstruction from projections using Artificial Neural Network (ANN) techniques is presented in this work. The design of the proposed reconstruction system is based on a simple but efficient network architecture, which best utilizes all available input information. Due to the computational complexity, which grows quadratically with the image size, the training phase of the system is characterized by relatively large CPU times. The trained network, on the contrary, is able to provide all necessary information in a quick and efficient way giving results comparable to other time consuming iterative reconstruction algorithms. The performance of the network studied with a large number of software phantoms is directly compared to the well known Algebraic Reconstruction Technique (ART). For a given image and projections size, the role of the hidden layers in the network architecture is examined and the quality dependence of the reconstructed image on the size of the geometrical patterns used in the training phase is also investigated.

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