Weighted Heuristic Ensemble of Filters

Feature selection has become increasingly important in data mining in recent years due to the rapid increase in the dimensionality of big data. However, the reliability and consistency of feature selection methods (filters) vary considerably on different data and no single filter performs consistently well under various conditions. Therefore, feature selection ensemble has been investigated recently to provide more reliable and effective results than any individual one but all the existing feature selection ensemble treat the feature selection methods equally regardless of their performance. In this paper, we present a novel framework which applies weighted feature selection ensemble through proposing a systemic way of adding different weights to the feature selection methods-filters. Also, we investigate how to determine the appropriate weight for each filter in an ensemble. Experiments based on ten benchmark datasets show that theoretically and intuitively adding more weight to ‘good filters’ should lead to better results but in reality it is very uncertain. This assumption was found to be correct for some examples in our experiment. However, for other situations, filters which had been assumed to perform well showed bad performance leading to even worse results. Therefore adding weight to filters might not achieve much in accuracy terms, in addition to increasing complexity, time consumption and clearly decreasing the stability

Heuristic ensembles of filters for accurate and reliable feature selection

Feature selection has become increasingly important in data mining in recent years. However, the accuracy and stability of feature selection methods vary considerably when used individually, and yet no rule exists to indicate which one should be used for a particular dataset. Thus, an ensemble method that combines the outputs of several individual feature selection methods appears to be a promising approach to address the issue and hence is investigated in this research. This research aims to develop an effective ensemble that can improve the accuracy and stability of the feature selection. We proposed a novel heuristic ensemble of filters (HEF). It combines two types of filters: subset filters and ranking filters with a heuristic consensus algorithm in order to utilise the strength of each type. The ensemble is tested on ten benchmark datasets and its performance is evaluated by two stability measures and three classifiers. The experimental results demonstrate that HEF improves the stability and accuracy of the selected features and in most cases outperforms the other ensemble algorithms, individual filters and the full feature set. The research on the HEF algorithm is extended in several dimensions; including more filter members, three novel schemes of mean rank aggregation with partial lists, and three novel schemes for a weighted heuristic ensemble of filters. However, the experimental results demonstrate that adding weight to filters in HEF does not achieve the expected improvement in accuracy, but increases time and space complexity, and clearly decreases stability. Therefore, the core ensemble algorithm (HEF) is demonstrated to be not just simpler but also more reliable and consistent than the later more complicated and weighted ensembles. In addition, we investigated how to use data in feature selection, using ALL or PART of it. Systematic experiments with thirty five synthetic and benchmark real-world datasets were carried out

Cyber Security Body of Knowledge and Curricula Development

The cyber world is an ever-changing world and cyber security is most important and touches the lives of everyone on the cyber world including researchers, students, businesses, academia, and novice user. The chapter suggests a body of knowledge that incorporates the view of academia as well as practitioners. This research attempts to put basic step and a framework for cyber security body of knowledge and to allow practitioners and academicians to face the problem of lack of standardization. Furthermore, the chapter attempts to bridge the gap between the different audiences. The gap is so broad that the term of cyber security is not agreed upon even in spelling. The suggested body of knowledge may not be perfect, yet it is a step forward

Reinforced concrete bridge damage detection using arithmetic optimization algorithm with deep feature fusion

Inspection of Reinforced Concrete (RC) bridges is critical in order to ensure its safety and conduct essential maintenance works. Earlier defect detection is vital to maintain the stability of the concrete bridges. The current bridge maintenance protocols rely mainly upon manual visual inspection, which is subjective, unreliable and labour-intensive one. On the contrary, computer vision technique, based on deep learning methods, is regarded as the latest technique for structural damage detection due to its end-to-end training without the need for feature engineering. The classification process assists the authorities and engineers in understanding the safety level of the bridge, thus making informed decisions regarding rehabilitation or replacement, and prioritising the repair and maintenance efforts. In this background, the current study develops an RC Bridge Damage Detection using an Arithmetic Optimization Algorithm with a Deep Feature Fusion (RCBDD-AOADFF) method. The purpose of the proposed RCBDD-AOADFF technique is to identify and classify different kinds of defects in RC bridges. In the presented RCBDD-AOADFF technique, the feature fusion process is performed using the Darknet-19 and Nasnet-Mobile models. For damage classification process, the attention-based Long Short-Term Memory (ALSTM) model is used. To enhance the classification results of the ALSTM model, the AOA is applied for the hyperparameter selection process. The performance of the RCBDD-AOADFF method was validated using the RC bridge damage dataset. The extensive analysis outcomes revealed the potentials of the RCBDD-AOADFF technique on RC bridge damage detection process

Construction of Pilgrim Framework-Information Seeking Based on New Norm Selection Criteria of Hajj

The pilgrimage quota for each country is 0.1% of the total population. The demand to perform Hajj increases yearly and demands more quotas, but it is limited due to providing exemplary services and maintaining comfort for pilgrims. The selection process is challenging and informative. Upon registration, the Malaysian waiting period was between 89 and 116 years. With COVID-19 and the new norm, the waiting period will be much longer, making things worse and more ridiculous. This paper explains and proposes constructing a proper framework for fulfilling the need and selecting future candidates for Hajj in Malaysia

Heuristic Ensemble of Filters for Reliable Feature Selection

Feature selection has become ever more important in data mining in recent years due to the rapid increase in the dimensionality of data. Filters are preferable in practical applications as they are much faster than wrapper based approaches, but their reliability and consistency vary considerably on different data and yet no rule exists to indicate which one should be used for a particular given dataset. In this paper, we propose a heuristic ensemble approach that combines multiple filters with heuristic rules to improve the overall performance. It consists of two types of filters: subset filters and ranking filters, and a heuristic consensus algorithm. The experimental results demonstrate that our ensemble algorithm is more reliable and effective than individual filters as the features selected by the ensemble consistently achieve better accuracy for typical classifiers on various datasets

An Early and Smart Detection of Corn Plant Leaf Diseases Using IoT and Deep Learning Multi-Models

Plant leaf diseases have various causes, leading to severe disorders. The early and accurate detection and classification of these diseases are fundamental for fostering healthy crop production. In recent years, smart agricultural systems have garnered significant attention due to their capability to enhance efficiency by deploying sensor networks and Internet of Things (IoT) devices that collect and analyze environmental data. However, traditional plant disease detection methods are manual, time-consuming, and often need help handling the data’s complexity and dynamism. These manual methods do not use heterogeneous data to make better decisions. Corn holds significant importance yet it faces numerous diseases that include main three diseases such as blight, common rust, and grey leaf spot. The advancement of computer technology has led to a pivotal focus on corn leaf diseases classification application based on deep learning. Convolutional Neural Networks (CNNs) have revealed remarkable achievements within Precision Agriculture (PA) due to their ability to enhance information. To this end, this work introduces a CNN-based architecture, the Multi-Model Fusion Network (MMF-Net). Its primary objective is to classify diseases within the realm of PA. MMF-Net integrates multi-contextual features using RL-block and PL-blocks 1 & 2, thus effectively combining different model streams trained on heterogeneous data. The RL-block uses spatial range to process coarse grained images to convolve the local context, while PL-block 1 extracts fine-grained global context by expanding the perceptual area of images. PL-block 2 deals with real-life environmental parameters as features. The extracted features are syndicated using multiple classifiers that ensemble three individual blocks at the decision level to improve the accuracy. After fusion, it uses adaptively the majority voting scheme to generate the final decision probability score of the base model. Multiple experiments are conducted involving the corn leaf diseases dataset and a real-life numerical dataset, generating an impressive 99.23% accuracy in the classification of corn leaf diseases. Overall, MMF-Net provides a promising and smart solution to identify plant leaf diseases in PA effectively

Tuna Swarm Algorithm With Deep Learning Enabled Violence Detection in Smart Video Surveillance Systems

In smart video surveillance systems, violence detection becomes challenging to ensure public safety and security. With the proliferation of surveillance cameras in public areas, there is an increasing need for automated algorithms that can accurately and efficiently detect violent behavior in real time. This article presents a Tuna Swarm Optimization with Deep Learning Enabled Violence Detection (TSODL-VD) technique to classify violent actions in surveillance videos. The TSODL-VD technique enables the recognition of violence and can be a measure to avoid chaotic situations. In the presented TSODL-VD technique, the residual-DenseNet model is applied for feature vector generation from the input video frames and then passed into the stacked autoencoder (SAE) classifier. The SAE model is enforced to recognize the events into violence and non-violence events. To improve the violence detection effectiveness of the TSODL-VD procedure, the TSO protocol is utilized as a hyperparameter optimizer for the residual-DenseNet model. The performance validation of the TSODL-VD procedure has experimented on a benchmark violence dataset. The experimental results demonstrate that the TSODL-VD technique accomplishes precise and rapid detection outcomes over the recent state-of-the-art approaches

Short-Term Load Forecasting in Smart Grids Using Hybrid Deep Learning

Load forecasting in Smart Grids (SG) is a major module of current energy management systems, that play a vital role in optimizing resource allocation, improving grid stability, and assisting the combination of renewable energy sources (RES). It contains the predictive of electricity consumption forms over certain time intervals. Load Forecasting remains a stimulating task as load data has exhibited changing patterns because of factors such as weather change and shifts in energy usage behaviour. The beginning of advanced data analytics and machine learning (ML) approaches; particularly deep learning (DL) has mostly enhanced load forecasting accuracy. Deep neural networks (DNNs) namely Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) have achieved popularity for their capability to capture difficult temporal dependencies in load data. This study designs a Short-Load Forecasting scheme using a Hybrid Deep Learning and Beluga Whale Optimization (LFS-HDLBWO) approach. The major intention of the LFS-HDLBWO technique is to predict the load in the SG environment. To accomplish this, the LFS-HDLBWO technique initially uses a Z-score normalization approach for scaling the input dataset. Besides, the LFS-HDLBWO technique makes use of convolutional bidirectional long short-term memory with an autoencoder (CBLSTM-AE) model for load prediction purposes. Finally, the BWO algorithm could be used for optimal hyperparameter selection of the CBLSTM-AE algorithm, which helps to enhance the overall prediction results. A wide-ranging experimental analysis was made to illustrate the better predictive results of the LFS-HDLBWO method. The obtained value demonstrates the outstanding performance of the LFS-HDLBWO system over other existing DL algorithms with a minimum average error rate of 3.43 and 2.26 under FE and Dayton grid datasets, respectively

Intracranial Haemorrhage Diagnosis Using Willow Catkin Optimization With Voting Ensemble Deep Learning on CT Brain Imaging

Intracranial haemorrhage (ICH) has become a critical healthcare emergency that needs accurate assessment and earlier diagnosis. Due to the high rates of mortality (about 40%), the early classification and detection of diseases through computed tomography (CT) images were needed to guarantee a better prognosis and control the occurrence of neurologic deficiencies. Generally, in the earlier diagnoses test for severe ICH, CT imaging of the brain was implemented in the emergency department. Meanwhile, manual diagnoses are labour-intensive, and automatic ICH recognition and classification techniques utilizing artificial intelligence (AI) models are needed. Therefore, the study presents an Intracranial Haemorrhage Diagnosis using Willow Catkin Optimization with Voting Ensemble (ICHD-WCOVE) Model on CT images. The presented ICHD-WCOVE technique exploits computer vision and ensemble learning techniques for automated ICH classification. The presented ICHD-WCOVE technique involves the design of a multi-head attention-based CNN (MAFNet) model for feature vector generation with optimal hyperparameter tuning using the WCO algorithm. For automated ICH detection and classification, the majority voting ensemble deep learning (MVEDL) technique is used, which comprises recurrent neural network (RNN), Bi-directional long short-term memory (BiLSTM), and extreme learning machine-stacked autoencoder (ELM-SAE). The experimental analysis of the ICHD-WCOVE approach can be tested by a medical dataset and the outcomes signified the betterment of the ICHD-WCOVE technique over other existing approaches