Comparison of swarm intelligence algorithms for high dimensional optimization problems

High dimensional optimization considers being one of the most challenges that face the algorithms for finding an optimal solution for real-world problems. These problems have been appeared in diverse practical fields including business and industries. Within a huge number of algorithms, selecting one algorithm among others for solving the high dimensional optimization problem is not an easily accomplished task. This paper presents a comprehensive study of two swarm intelligence based algorithms: 1- particle swarm optimization (PSO), 2-cuckoo search (CS).The two algorithms are analyzed and compared for problems consisting of high dimensions in respect of solution accuracy, and runtime performance by various classes of benchmark functions

Federated deep learning for automated detection of diabetic retinopathy

Diabetic retinopathy (DR) is a primary cause of impaired vision that can lead to permanent blindness if not detected and treated early. Unfortunately, DR frequently has no early warning signs and may not generate any symptoms. According to recent figures, over 382 million people worldwide suffer from DR, with the number expected to climb to 592 million by 2030. Patients with DR may not be treated in time given the apparent large number of DR patients and inadequate medical resources in specific places, resulting in missed treatment possibilities and eventually irreversible vision loss. Color fundus diagnosis requires highly experienced experts to recognize the existence of tiny features and the relevance of DR. Unfortunately, manually diagnosing DR is time-consuming, tedious and error-prone. At the same time, the effect of manual interpretation is highly dependent on the medical expert experiences. Deep learning is a machine learning algorithm with potential for detecting the significance of DR. However, deep learning still suffers from high computational cost, requires tons of training data, over fitting, and non-trivial hyper parameter tuning. Thus, in order to build a model that can compete with medical experts, deep learning algorithms must feed a huge number of instances or pool data from other institutions. Federated learning allows deep learning algorithms to learn from a diverse set of data stored in multiple databases. Federated learning is a novel method for training deep learning models on local DR patient data, with just model parameters exchanged between medical facilities. The objectives of this research is to avoid the requirement sharing DR patient data, since such approaches expedite the development of deep learning models through the use of federated learning. Primarily, we propose a federated learning which decentralizes deep learning by eliminating the need to pool data in a single location. In this research, we present a practical method for the federated learning of deep network based on retinal image of diabetic retinopathy

A comparative study of clonal selection algorithm for effluent removal forecasting in septic sludge treatment plant

The development of effluent removal prediction is crucial in providing a planning tool necessary for the future development and the construction of a septic sludge treatment plant (SSTP), especially in the developing countries. In order to investigate the expected functionality of the required standard, the prediction of the effluent quality, namely biological oxygen demand, chemical oxygen demand and total suspended solid of an SSTP was modelled using an artificial intelligence approach. In this paper, we adopt the clonal selection algorithm (CSA) to set up a prediction model, with a wellestablished method – namely the least-square support vector machine (LS-SVM) as a baseline model. The test results of the case study showed that the prediction of the CSA-based SSTP model worked well and provided model performance as satisfactory as the LS-SVM model. The CSA approach shows that fewer control and training parameters are required for model simulation as compared with the LS-SVM approach. The ability of a CSA approach in resolving limited data samples, nonlinear sample function and multidimensional pattern recognition makes it a powerful tool in modelling the prediction of effluent removals in an SSTP

Preventive and curative personality profiling based on EEG, ERP, and big five personality traits: a literature review

Healthy lifestyle is a significant factor that impacts on the budget for medicine. According to psychological studies, personality traits based on the Big Five personality traits especially the neuroticism and conscientiousness, have the ability to predict healthy lifestyle profiling. Electrophysiological signals have been used to explore the nature of individual differences and personality that are related to perception. In this paper, we reviewed studies examining healthy lifestyle profile i.e., preventive and curative using electroencephalography (EEG) and event-related potential (ERP) signals. This study proposed a general experimental model by reviewing the literature to build suitable experimental design for implementing artificial intelligence techniques based on the machine learning

Convolutional Neural Networks and Deep Belief Networks for Analysing Imbalanced Class Issue in Handwritten Dataset

Imbalanced class is one of the trials in classifying materials of big data. Data disparity produces a biased output of a model regardless how recent the technology is. However, deep learning algorithms such as convolutional neural networks and deep belief networks have proven to provide promising results in many research domains, especially in image processing as well as time series forecasting, intrusion detection, and classification. Therefore, this paper will investigate the effect of imbalanced data discrepancy of classes in MNIST handwritten dataset using convolutional neural networks and deep belief networks. Based on the experiment conducted, the results show that although the algorithm is suitable for multiple domains and have shown stability, the imbalanced distribution of data still able to affect the overall performance of the models

Big data framework to evaluate and analyze Notational Covid-19 Immunization Programmed (NCIP) in Malaysia: a comparative study

Many governments around the world have launched their open government data (OGD) portal to improve the government’s transparency by sharing their data with the public such as National Covid-19 Immunization Pro-grammed (NCIP), which has been published at https://github.com/CITF-Malaysia/citf-public. However, increasing the number of datasets, data types, volume and complexity will be raised the integration issues. There-fore, it is essential to evaluate and analyses those huge amounts of these da-tasets. NCIP provides multiple data sources and datasets. These may raise the Big Data (BD) issues and pose various evaluation and analysis prob-lems to produce valuable information. To generate meaningful linked data to support the purposes of this research study, the relationship between these disparate datasets needs to be identified and construct a comprehen-sive framework. In order to understand the causes of OGD development of big data, this study involves a detailed examination and comparison of ex-isting theories and actual approaches to handle public sector open data con-cerns. According to the review, the framework was dominantly adopted over architecture, infrastructures, theoretical and conceptual framework in previous research to examine the revolution of government public accessi-ble data. According to the findings, most existing frameworks do not con-sider the demand for public open data in health such as NCPI. Previous re-search on OGD for health has a lesser number of advanced BD frameworks. In the public sector, there is still a lack of investment and use of Big Data. The findings will aid academics in doing empirical research on the revealed need, as well as offer decision-makers with a better understanding of how to leverage OGD adoption in health by taking relevant actions

Study of Adam and AdaMax optimizers on AlexNet architecture for voice biometric authentication system

Biometric authentication has password or token authentication in significance. Even though several methods for biometric authentication systems have been developed, the Deep Learning method is considered to be significantly more efficient than the other methods, especially Convolutional Neural Network (CNN). For this paper, the CNN architecture that was evaluated is AlexNet since it is compatible with a small dataset. Considering optimization techniques are important in Deep Learning method, this research will use the proposed voice dataset to determine if Adam or AdaMax is the optimal optimizer for the AlexNet architecture. The proposed dataset consists of seven celebrity classes, with 20 audio files in each class that is collected from Google and Youtube. In improving the model's accuracy, k- fold with cross-validation approach was selected. The experiment proved that the AdaMax optimizer outperforms Adam on the proposed dataset

Comparative performance of machine learning algorithms for cryptocurrency forecasting

Machine Learning is part of Artificial Intelligence that has the ability to make future forecastings based on the previous experience. Methods has been proposed to construct models including machine learning algorithms such as Neural Networks (NN), Support Vector Machines (SVM) and Deep Learning. This paper presents a comparative performance of Machine Learning algorithms for cryptocurrency forecasting. Specifically, this paper concentrates on forecasting of time series data. SVM has several advantages over the other models in forecasting, and previous research revealed that SVM provides a result that is almost or close to actual result yet also improve the accuracy of the result itself. However, recent research has showed that due to small range of samples and data manipulation by inadequate evidence and professional analyzers, overall status and accuracy rate of the forecasting needs to be improved in further studies. Thus, advanced research on the accuracy rate of the forecasted price has to be done

Performance analysis of machine learning algorithms for missing value imputation

Data mining requires a pre-processing task in which the data are prepared, cleaned, integrated, transformed, reduced and discretized for ensuring the quality. Missing values is a universal problem in many research domains that is commonly encountered in the data cleaning process. Missing values usually occur when a value of stored data absent for a variable of an observation. Missing values problem imposes undesirable effect on analysis results, especially when it leads to biased parameter estimates. Data imputation is a common way to deal with missing values where the missing value's substitutes are discovered through statistical or machine learning techniques. Nevertheless, examining the strengths (and limitations) of these techniques is important to aid understanding its characteristics. In this paper, the performance of three machine learning classifiers (K-Nearest Neighbors (KNN), Decision Tree, and Bayesian Networks) are compared in terms of data imputation accuracy. The results shows that among the three classifiers, Bayesian has the most promising performance. © 2015 The Science and Information (SAI) Organization Limited

Systematic Review on Missing Data Imputation Techniques with Machine Learning Algorithms for Healthcare

Missing data is one of the most common issues encountered in data cleaning process especially when dealing with medical dataset. A real collected dataset is prone to be incomplete, inconsistent, noisy and redundant due to potential reasons such as human errors, instrumental failures, and adverse death. Therefore, to accurately deal with incomplete data, a sophisticated algorithm is proposed to impute those missing values. Many machine learning algorithms have been applied to impute missing data with plausible values. However, among all machine learning imputation algorithms, KNN algorithm has been widely adopted as an imputation for missing data due to its robustness and simplicity and it is also a promising method to outperform other machine learning methods. This paper provides a comprehensive review of different imputation techniques used to replace the missing data. The goal of the review paper is to bring specific attention to potential improvements to existing methods and provide readers with a better grasps of imputation technique trends