Comparative study for 8 computational intelligence algorithms for human identification

Abdulrahman, Shaymaa Adnan; Khalifa, Wael; Roushdy, Mohamed; Salem, Abdel-Badeeh M.

doi:10.1016/j.cosrev.2020.100237

Cited by 24 publications

(11 citation statements)

References 41 publications

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“…Thus, including further days only hinders performance, as the time needed grows, but with no substantial improvement in accuracy metric is yielded. Hardware dependence is a negative characteristic of Artificial Neural Networks highlighted in a comparative study for human identification [ 67 ].…”

Section: Behavior Algorithms Resultsmentioning

confidence: 99%

Non-Invasive Challenge Response Authentication for Voice Transactions with Smart Home Behavior

Hayashi

Ruggiero

2020

Sensors

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Smart speakers, such as Alexa and Google Home, support daily activities in smart home environments. Even though voice commands enable friction-less interactions, existing financial transaction authorization mechanisms hinder usability. A non-invasive authorization by leveraging presence and light sensors’ data is proposed in order to replace invasive procedure through smartphone notification. The Coloured Petri Net model was created for synthetic data generation, and one month data were collected in test bed with real users. Random Forest machine learning models were used for smart home behavior information retrieval. The LSTM prediction model was evaluated while using test bed data, and an open dataset from CASAS. The proposed authorization mechanism is based on Physical Unclonable Function usage as a random number generator seed in a Challenge Response protocol. The simulations indicate that the proposed scheme with specialized autonomous device could halve the total response time for low value financial transactions triggered by voice, from 7.3 to 3.5 s in a non-invasive manner, maintaining authorization security.

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Section: Behavior Algorithms Resultsmentioning

confidence: 99%

Non-Invasive Challenge Response Authentication for Voice Transactions with Smart Home Behavior

Hayashi

Ruggiero

2020

Sensors

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“…An object is classified to the major class among its nearest neighbors. Common distance metrics are Hamming distance, Manhattan distance, Makowski distance [24]. • Random forest is a supervised learning algorithm that randomly creates and uses ensemble approach to merge multiple tress into a forest.…”

Section: Discussionmentioning

confidence: 99%

A Hybrid Mutual Information-LASSO-Genetic Algorithm Selection Approach for Classifying Breast Cancer

Abdel-Naby

Alfonse

Roushdy

2021

Digital Transformation Technology

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Breast cancer is the most common cancer among women due to many factors such as heredity and unhealthy lifestyles. Early and accurate diagnosis of this cancer improves the patient quality of life and increases the survival rate. Microarray technology provides effective way to early diagnosis cancer. However, the nature of its data complicates the classification process. A hybrid approach of mutual information (MI), least absolute shrinkage and selection operator (LASSO) and genetic algorithm (GA) is proposed to face this challenge. The proposed approach is assessed using logistic regression (LR), support vector machine (SVM), K-nearest neighbor (KNN), and random forest (RF). Compared to the state-of-the-art models, the proposed approach can effectively diagnose the breast cancer with few numbers of genes. For the benchmarked Van't veer dataset, it obtained a classification accuracy of 96% with only 23 features. KeywordsCancer classification • Microarray data • Genetic algorithm • LASSO • Mutual information 1 Introduction Breast cancer is the most commonly occurring cancer in women and the second most common cancer overall. Early detection and diagnosis have been proven to significantly improve patient survival rates [1], quality of life, as well as to significantly reduce the cost and complexity of cancer treatment. Microarray technology has become benchmark technique for early cancer diagnosis [2]. It monitors each gene many times under different conditions or alternately evaluates each gene in M. Abd-elnaby (B) • M.

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“…First, a tomato photosynthetic rate prediction model was built based on the support vector regression (SVR) algorithm, which was used to predict the photosynthetic rate of tomatoes grown in different environments. Support vector machine (SVM) is a typical kernel machine learning method, which minimizes the boundary between empirical risk and Vapnik-Chervonenkis (VC) dimension, without compromising the accuracy of the data approximation and the complexity of the approximation function to get good classification and promotion ability [23] . The SVM for regression (SVR) has been widely used in various modeling studies because of its unique performance in solving small sample sets, non-linear and high-dimensional regression problems.…”

Section: Photosynthetic Rate Prediction Modelmentioning

confidence: 99%

Irrigation decision model for tomato seedlings based on optimal photosynthetic rate

Wan

Chen

et al. 2021

International Journal of Agricultural and Biological Engineering

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Soil moisture is a major environmental factor that influences tomato growth and development. Suitable soil moisture not only increases tomato production but also saves irrigation water. In this study, an irrigation decision model was developed, which called soil moisture regulation model, for optimizing growth of tomato seedlings while saving water. The data used for modeling were collected from a multi-gradient nested experiment, in which temperature, photosynthetic photon flux density (PPFD), carbon dioxide (CO 2 ) concentration and soil moisture were variables and the corresponding photosynthetic rate was measured. Subsequently, a prediction model of tomato photosynthetic rate was constructed using support vector regression (SVR) algorithm. With photosynthetic rate prediction model as fitness function, genetic algorithm (GA) was used to find the optimal soil moisture under each combination of the above environmental factors. Finally, back propagation neural network (BPNN) algorithm was used to establish a decision model of tomato irrigation, which could provide the optimal soil moisture under current environment. For the soil moisture regulation model constructed here, the coefficient of determination was 0.9738, the mean square error of the test set was 1.51×10 -5 , the slope of the verified straight line was 0.9752, and the intercept was 0.00916. This model demonstrated high precision, which thereby provides a theoretical basis for accurate irrigation control in the greenhouse facility environment.

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Comparative study for 8 computational intelligence algorithms for human identification

Cited by 24 publications

References 41 publications

Non-Invasive Challenge Response Authentication for Voice Transactions with Smart Home Behavior

Non-Invasive Challenge Response Authentication for Voice Transactions with Smart Home Behavior

A Hybrid Mutual Information-LASSO-Genetic Algorithm Selection Approach for Classifying Breast Cancer

Irrigation decision model for tomato seedlings based on optimal photosynthetic rate

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