PREDICTING SOCIAL NETWORK ADDICTION USING VARIANT SIGMOID TRANSFER FEED-FORWARD NEURAL NETWORKS (FNN-SNA)

Researchers have reflected on personal traits that may predict Social Networking Sites (SNS) addiction. However, most of the researchers involved in the findings of personality traits predictor for social networking addiction either postulate or based their conclusions on analytical tools. Moreso, a review of the literature reveals that the prediction of social networking addiction using classifiers have not been well researched. We examined the prediction of SNS addiction from a well-structured questionnaire consisting of sixteen (16) personality traits. The questionnaire was administered on the google form with a response rate of 95% out of the 102-sample size. Additionally, a three (3) variant sigmoid transfer feed- forward neural networks was developed for the prediction of SNS addiction. Result indicated that pertinence (β = 0.251, p 0.01) was the most powerful predictor of social networking addiction in general and less obscurity addiction (β = 0.244, p 0.01). Experimental results also showed that the developed classifier correctly predict SNS addiction with 98% accuracy compared to similar classifiers.

Artificial neural network based meta-heuristic for performance improvement in physical internet supply chain network

<p>Nowadays, reducing total costs while enhancing customer satisfaction is a major task for many supply chain systems. To deal with this issue, the physical internet (PI) paradigm can be represented as a potential replacement for the current logistics system. This paper devoted the cost reduction and lead time improvement in a PI-SCN using a hybrid framework based on an artificial neural network (ANN) and an improved slime mould algorithm (ISMA). To address the performance of the proposed framework, a real-case study in Morocco is considered. The new trainer ISMA’s performance has been investigated in three approximation datasets from the University of California at Irvine (UCI) machine-learning repository regarding nine recent metaheuristics. The experimental results highlight the effectiveness of ISMA according to other meta heuristics for training feed-forward neural networks (FNNs) to converge speed and to avoid local minima.</p>

Fast Recognition using Bar-based Data Mapping for Gamelan Music Genre Classification

This research aims to develop a gamelan music genre classifier based on the musical mode system determined based on the dominant notes in a certain order. Only experts can discriminate the musical mode system of compositions. The Feed Forward Neural Networks method was used to classify gamelan compositions into three musical mode systems. The challenge is to recognize the musical mode system of compositions between the initial melody without having to analyze the entire melody using a small amount of data for the dataset. Instead of conducting a melodic extraction from audio signal data, the text-based skeletal melody data, which is a form of extracted melodic features, are used for the dataset. Unique corpuses are controlled based on the cardinality of the one-to-many relationship, and a data mapping technique based on the bars is used to increase the number of corpuses. The results show that the proposed method is suitable to solve the specified problems, where the accuracy in recognizing the class of unseen compositions between the initial melody achieves at 86.7%.

Utilizing Artificial Neural Network for Load Prediction Caused by Fluid Sloshing in Tanks

In this research, neural network models were used to predict the action of sloshing phenomena in a tank containing fluid under harmonic excitation. A new methodology is proposed in this analysis to test and simulate fluid sloshing behavior in the tank. The sloshing behavior was first modeled using the smooth particle hydrodynamics (SPH) method. The backpropagation of the error algorithm was then used to apply the two multilayer feed-forward neural networks and the recurrent neural network. The findings of the SPH process are employed in the training and testing of neural networks. Input neural network data include the tank position, velocity, and acceleration, neural output data, and fluid sloshing curve wave position. The findings of the neural networks were correlated with the experimental evidence provided in the literature. The findings revealed that neural networks can be used to predict fluid sloshing.

Using A Feed Forward Neural Network Algorithm to Predict Prices of Multiple Cryptocurrencies

This paper initially presents a nontechnical overview of cryptocurrency, its history, and the technicalities of its usage as a means of exchange. Bitcoin’s working methodology and mathematical baseline is further presented in more depth. For the remaining majority of the paper, recent cryptocurrency price data of Bitcoin, Ethereum, Tether, Dogecoin, and Binance coin was used to train a machine learning model of Feed Forward Neural Networks to predict future prices for each of the datasets. Further and in conclusion, the results are discussed, and the efficiency and accuracy of these models are evaluated.