Swallow Swarm Optimization Research Articles

Population Diversity Management of Swallow Swarm Optimization Algorithm for Fuzzy Classification Problem

Population Diversity Management of Swallow Swarm Optimization Algorithm for Fuzzy Classification Problem

RCNN with Swallow Swarm Optimization for Liver Disease Detection and Classification

One of the most serious medical conditions that can endanger a person's life and health is liver disease. The second-leading cause of death for men and sixth-leading cause of death for women, respectively, is liver cancer. In 2008, liver cancer claimed the lives of almost 750,000 people, killing 960,000 of them. The segmentation and identification of CT images produced by computer tomography has emerged as a major topic in medical image processing. There are few choices for liver segmentation due to the enormous amount of time and resources necessary to train a deep learning model. As part of this research, we created the Region utilizing Convolutional Neural Network, a novel way of extracting the liver from CT scan images (RCNN). The suggested CNN approach, which employs softmax to isolate the liver from the background, contains of three convolutional layers and two entirely associated layers. In the CapsNet and CAL layers, there are class dependencies and an efficient mechanism to connect CAL and subsequent CapsNet processing. Finally, the classification is carried out using the SSO-CSAE model, an approach known as the swallow swarm optimization that is based on the Convolutional Sparse Autoencoder (CSAE) The MICCAI SLiver '07, 3Dircadb01, and LiTS17 benchmark datasets were used to validate the proposed RCNN-SSO approach. When compared to other frameworks, the proposed framework performed well in numerous categories.

A Novel Adaptive Swallow Swarm Optimisation Method for Medical Image Segmentation in Brain MRI Using Machine Learning

Automatic segmentation has to be effective to provide sufficient results for clinical use due to inherent challenges involved in medical images, including poor image quality, different imaging, segmentation protocols or variations in patients. Further, they are reduced to previously invisible classes by a lack of image-specific adaptation and the absence of generality. When handling some complex cases, it may lead to ineffectiveness, taking into account the differences in distribution between training and test data. To solve these issues in the proposed work, noise removal and segmentation algorithm is introduced by using Adaptive Swallow Swarm Optimization (ASSO) with Convolutional Neural Network (CNN) and is called as ASSO-CNNseg to find traces of cancer in Brain Tumour images. In the noise removal step nonlinear filtering algorithm is introduced to remove noises from the image samples efficiently. After the removal of noises from the image samples, then segmentation is performed by using the deep learning algorithm consists of some steps: image-based fine tuning, interaction-oriented uncertainty, and segmentation process. In the fine-tuning algorithm, ASSO algorithm is introduced for weighted loss feature taking the network and interaction-based uncertainty taken into account during image-dependent fine-tuning. Next, here the initial effort make use of CNNseg to tackle their earlier unknown objects are the image segmentation context is presented. The experimental outcomes demonstrate the highly accurate results of the proposed ASSO-CNNseg method with less number interactions between users and less consumption of time for user compared to classical interactive segmentation techniques such as Slic-seg and BIFSeg.

IoT enabled healthcare environment using intelligent deep learning enabled skin lesion diagnosis model

Melanoma skin cancer is a widely occurring disease over the globe and it is needed to identify the disease at the initial stages. Recent technologies like Internet of Things (IoT) have aimed to design efficient healthcare system. Besides, deep learning and transfer learning are progressively important in the healthcare diagnoses of various diseases. This study focuses on the design of Intelligent Internet of Things with Deep learning Enabled Skin Lesion Diagnosis (IIoT-DLSLD) model for healthcare environment. The proposed IIoT-DLSLD technique enables the IoT devices to capture the dermoscopic images and classify the skin lesions into distinct class labels. In addition, the IIoT-DLSLD technique employs Gaussian filtering (GF) based preprocessing technique to eradicate the presence of noise. Besides, Otsu thresholding with swallow swarm optimization (OT-SSO) algorithm is employed for skin lesion segmentation. For skin lesion classification, MobileNet v2 based feature extraction and emperor penguin optimizer (EPO) based deep wavelet neural network (DWNN) is utilized. The experimental validation of the IIoT-DLSLD technique take place against ISIC dataset and the results portrayed the betterment of the IIoT-DLSLD algorithm over the other state of art skin lesion classifiers.

Binary swallow swarm optimization with convolutional neural network brain tumor classifier for magnetic resonance imaging images

SummaryThe brain tumor classification is implemented through biopsy, which is not normally executed before classic mind surgery. Machine learning (ML) algorithms assist radiologists in tumor analysis, not including obtrusive evaluations. The conventional ML strategies need separate feature extraction to tumor detect thus it needs more computation time to perform classification. Deep learning (DL) based convolution neural networks (CNNs) have been focused on brain tumor detection. In this paper, the CNN algorithm is improved based on meta‐heuristics, which are used for pre‐trained systems for databases to categorize MRI brain tumor images. Pre‐trained DL, binary swallow swarm optimization (BSSO) is used for improving the weight and predispositions of the CNN algorithm. It is a block‐wise calibrating system which is dependent on transfer learning. The current technique is assessed over a publically accessible magnetic resonance imaging (MRI) brain tumor database containing three categories as glioma, meningioma, and pituitary by the most noteworthy rate among everyone brain tumor in medical training. The proposed strategy is assessed over T1‐weighted contrast‐enhanced MRI (CE‐MRI) benchmark data. To assess the execution, utilize the proposed strategies to the CE‐MRI dataset for tumor detection and in the general execution of the BSSO‐CNN model is estimated using the execution assessment measurements such as precision, sensitivity (recall), specificity, F1‐score, and accuracy. Exploratory outcomes demonstrated with the purpose of the proposed strategy higher when compared to other methods to all metrics.

Modelling of Metaheuristics with Machine Learning-Enabled Cybersecurity in Unmanned Aerial Vehicles

The adoption and recent development of Unmanned Aerial Vehicles (UAVs) are because of their widespread applications in the private and public sectors, from logistics to environment monitoring. The incorporation of 5G technologies, satellites, and UAVs has provoked telecommunication networks to advance to provide more stable and high-quality services to remote areas. However, UAVs are vulnerable to cyberattacks because of the rapidly expanding volume and poor inbuilt security. Cyber security and the detection of cyber threats might considerably benefit from the development of artificial intelligence. A machine learning algorithm can be trained to search for attacks that may be similar to other types of attacks. This study proposes a new approach: metaheuristics with machine learning-enabled cybersecurity in unmanned aerial vehicles (MMLCS-UAVs). The presented MMLCS-UAV technique mainly focuses on the recognition and classification of intrusions in the UAV network. To obtain this, the presented MMLCS-UAV technique designed a quantum invasive weed optimization-based feature selection (QIWO-FS) method to select the optimal feature subsets. For intrusion detection, the MMLCS-UAV technique applied a weighted regularized extreme learning machine (WRELM) algorithm with swallow swarm optimization (SSO) as a parameter tuning model. The experimental validation of the MMLCS-UAV method was tested using benchmark datasets. This widespread comparison study reports the superiority of the MMLCS-UAV technique over other existing approaches.

An automated deep learning enabled brain signal classification for epileptic seizure detection on complex measurement systems

Recent advancements in machine learning and deep learning models find them helpful in designing effective complex measurement systems. At the same time, examining the brain’s activities using Electroencephalography (EEG) is essential to determine the mental state or thought of a person. It is essential in several application areas, such as Brain-Computer Interface (BCI), emotion recognition, and mental disease diagnosis. The proper brain signal classification using EEG finds helpful diagnose epileptic seizures. Since the traditional seizure detection process is a lengthy and challenging task, the automated identification of epilepsy is a significant problem. In order to resolve the issues that exist in the traditional brain signal classification models, this study designs Automated Deep Learning-Enabled Brain Signal Classification for Epileptic Seizure Detection (ADLBSC-ESD). The proposed ADLBSC-ESD technique aims to classify the brain signals to determine the existence of seizures or not. In addition, the presented model involves the design of the Improved Teaching and Learning-Enabled Optimization (ITLBO) technique for selecting features from EEG signals. Moreover, the Deep Belief Network (DBN) model is used for an effectual classification of EEG signals, and the hyperparameters of the DBN model are optimally tuned using the Swallow Swarm Optimization Algorithm (SSA). In order to ensure the improved brain signal classification performance of the ADLBSC-ESD technique, a series of simulations take place, and the outcomes are investigated concerning different measures. The experimental values highlighted the better performance of the ADLBSC-ESD technique over the current state of art techniques with maximum accuracy of 0.8316 and 0.8609 under binary and multiple classes, respectively.

Computer-aided diagnosis for breast cancer detection and classification using optimal region growing segmentation with MobileNet model

Globally, breast cancer is considered a major reason for women’s morality. Earlier and accurate identification of breast cancer is essential to increase survival rates. Therefore, computer-aided diagnosis (CAD) models are developed to help radiologists in the detection of mammographic lesions. Presently, machine-learning (ML) and deep-learning (DL) models are widely employed in the disease diagnostic process. In this view, this paper designs a novel CAD using optimal region growing segmentation with a MobileNet (CAD-ORGSMN) model for breast cancer identification and classification. The proposed CAD-ORGSMN model involves different stages of operations, namely, pre-processing, segmentation, feature extraction, and classification. Primarily, the proposed model uses a Weiner filtering (WF)–based pre-processing technique to remove the existence of noise in the mammogram images. The CAD-ORGSMN model involves a glowworm swarm optimization (GSO)–based region growing technique for image segmentation where the initial seed points and threshold values are optimally created by the GSO algorithm. Besides, a MobileNet-based feature extractor is used in which the hyperparameters of the MobileNet model are optimally selected using a swallow swarm optimization (SSO) algorithm. Lastly, variational autoencoder is applied as a classifier to determine the class labels for the input mammogram images. The utilization of the GSO algorithm for the region growing technique and the SSO algorithm for hyperparameter optimization helps to considerably improve the breast cancer detection performance of the CAD-ORGSMN model. The performance validation of the CAD-ORGSMN model takes place against the Mini-MIAS database, and the obtained results highlighted the promising performance of the CAD-ORGSMN model over the recent state-of-the-art methods in terms of different measures.

Hyperparameter optimization based deep convolution neural network model for automated bone age assessment and classification

Bone age assessment (BAA) is widely employed in therapeutic investigation of endocrinology issues in children. BAA is usually carried out by radiological investigation of the left hand. Since manual BAA is prone to observer variations, it is needed to design automated BAA approaches. Recently developed deep learning (DL) models have demonstrated fascinating outcomes in automatic BAA. This paper presents a novel hyperparameter optimization based deep learning model for automated bone age assessment and classification (HPTDL-BAAC). The proposed HPTDL-BAAC technique aims to predict the bone age and classify it into several stages using X-ray images. The proposed model involves data normalization for pre-processing. For feature extraction, a Regional Convolutional Neural Network (RCNN) based mask with SqueezeNet is employed as a baseline model. For boosting the performance of SqueezeNet method, swallow swarm optimization (SSO) algorithm is used for hyperparameter optimization. Finally, SoftMax classifier-based age prediction and weighted extreme learning machine (WELM) based stage classification models are applied to determine the proper bone age and class label. A wide range of simulations were performed on Digital Hand Atlas (DHA) Database and the outcomes are examined with respect to several measures. The experimental outcomes highlighted the supremacy of HPTDL-BAAC technique over the other existing techniques.

Feature Selection with Optimal Stacked Sparse Autoencoder for Data Mining

Data mining in the educational field can be used to optimize the teaching and learning performance among the students. The recently developed machine learning (ML) and deep learning (DL) approaches can be utilized to mine the data effectively. This study proposes an Improved Sailfish Optimizer-based Feature Selection with Optimal Stacked Sparse Autoencoder (ISOFS-OSSAE) for data mining and pattern recognition in the educational sector. The proposed ISOFS-OSSAE model aims to mine the educational data and derive decisions based on the feature selection and classification process. Moreover, the ISOFS-OSSAE model involves the design of the ISOFS technique to choose an optimal subset of features. Moreover, the swallow swarm optimization (SSO) with the SSAE model is derived to perform the classification process. To showcase the enhanced outcomes of the ISOFS-OSSAE model, a wide range of experiments were taken place on a benchmark dataset from the University of California Irvine (UCI) Machine Learning Repository. The simulation results pointed out the improved classification performance of the ISOFS-OSSAE model over the recent state of art approaches interms of different performance measures.

Optimal Deep Learning Based Inception Model for Cervical Cancer Diagnosis

Prevention of cervical cancer becomes essential and is carried out by the use of Pap smear images. Pap smear test analysis is laborious and tiresome work performed visually using a cytopathologist. Therefore, automated cervical cancer diagnosis using automated methods are necessary. This paper designs an optimal deep learning based Inception model for cervical cancer diagnosis (ODLIM-CCD) using pap smear images. The proposed ODLIM-CCD technique incorporates median filtering (MF) based pre-processing to discard the noise and Otsu model based segmentation process. Besides, deep convolutional neural network (DCNN) based Inception with Residual Network (ResNet) v2 model is utilized for deriving the feature vectors. Moreover, swallow swarm optimization (SSO) based hyperparameter tuning process is carried out for the optimal selection of hyperparameters. Finally, recurrent neural network (RNN) based classification process is done to determine the presence of cervical cancer or not. In order to showcase the improved diagnostic performance of the ODLIM-CCD technique, a series of simulations occur on benchmark test images and the outcomes highlighted the improved performance over the recent approaches with a superior accuracy of 0.9661.

A New Data Fusion Model for Medical Image Encryption in IoT Environment

An improvement of the Internet of Things (IoT) was forecast for changing the healthcare industry and is generating the increase of the Internet of Medical Things (IoMT). The IoT revolution was surpassed the present-day human service with promise social prospects, mechanical, and financial. During this condition, it can be essential for framing an effectual approach for guaranteeing the safety and reliability of t patient’s symptomatic information which are transmitted and received in IoT criteria. This study introduces a new data fusion model in IoT environment. The proposed model is called SSOECC-MIC model focuses on the design of effective encryption scheme with optimal key generation process for IoT environment. To achieve this, the SSOECC-MIC model designs an ECC model for the encryption and decryption of medical images effectively. To further improve the security performance of the ECC model, the optimal key generation process is carried out by the use of swallow swarm optimization (SSO) algorithm. For examining the enhanced performance of the SSOECC-MIC model, a wide ranging experimental analysis is carried out. The experimental outcomes reported the betterment of the SSOECC-MIC model over recent models.

Big Data Analytics with Artificial Intelligence Enabled Environmental Air Pollution Monitoring Framework

Environmental sustainability is the rate of renewable resource harvesting, pollution control, and non-renewable resource exhaustion. Air pollution is a significant issue confronted by the environment particularly by highly populated countries like India. Due to increased population, the number of vehicles also continues to increase. Each vehicle has its individual emission rate; however, the issue arises when the emission rate crosses the standard value and the quality of the air gets degraded. Owing to the technological advances in machine learning (ML), it is possible to develop prediction approaches to monitor and control pollution using real time data. With the development of the Internet of Things (IoT) and Big Data Analytics (BDA), there is a huge paradigm shift in how environmental data are employed for sustainable cities and societies, especially by applying intelligent algorithms. In this view, this study develops an optimal AI based air quality prediction and classification (OAI-AQPC) model in big data environment. For handling big data from environmental monitoring, Hadoop MapReduce tool is employed. In addition, a predictive model is built using the hybridization of ARIMA and neural network (NN) called ARIMA-NN to predict the pollution level. For improving the performance of the ARIMA-NN algorithm, the parameter tuning process takes place using oppositional swallow swarm optimization (OSSO) algorithm. Finally, Adaptive neuro-fuzzy inference system (ANFIS) classifier is used to classify the air quality into pollutant and non-pollutant. A detailed experimental analysis is performed for highlighting the better prediction performance of the proposed ARIMA-NN method. The obtained outcomes pointed out the enhanced outcomes of the proposed OAI-AQPC technique over the recent state of art techniques.

Automated Deep Learning Based Cardiovascular Disease Diagnosis Using ECG Signals

Automated biomedical signal processing becomes an essential process to determine the indicators of diseased states. At the same time, latest developments of artificial intelligence (AI) techniques have the ability to manage and analyzing massive amounts of biomedical datasets results in clinical decisions and real time applications. They can be employed for medical imaging; however, the 1D biomedical signal recognition process is still needing to be improved. Electrocardiogram (ECG) is one of the widely used 1-dimensional biomedical signals, which is used to diagnose cardiovascular diseases. Computer assisted diagnostic models find it difficult to automatically classify the 1D ECG signals owing to time-varying dynamics and diverse profiles of ECG signals. To resolve these issues, this study designs automated deep learning based 1D biomedical ECG signal recognition for cardiovascular disease diagnosis (DLECG-CVD) model. The DLECG-CVD model involves different stages of operations such as pre-processing, feature extraction, hyperparameter tuning, and classification. At the initial stage, data pre-processing takes place to convert the ECG report to valuable data and transform it into a compatible format for further processing. In addition, deep belief network (DBN) model is applied to derive a set of feature vectors. Besides, improved swallow swarm optimization (ISSO) algorithm is used for the hyperparameter tuning of the DBN model. Lastly, extreme gradient boosting (XGBoost) classifier is employed to allocate proper class labels to the test ECG signals. In order to verify the improved diagnostic performance of the DLECG-CVD model, a set of simulations is carried out on the benchmark PTB-XL dataset. A detailed comparative study highlighted the betterment of the DLECG-CVD model interms of accuracy, sensitivity, specificity, kappa, Mathew correlation coefficient, and Hamming loss.

Swarm intelligence‐based secure high‐order optimal density selection for industrial internet‐of‐things (IIoT) data on cloud environment

SummaryIndustrial internet‐of‐things (IIoT) cloud computing gives users with the ease of outsourcing calculations and carrying the risk of privacy disclosure. To solve this issue, secure high‐order clustering algorithm by fast search (SHOCFS) technique is introduced recently. The major factors of SHOCFS algorithm are speed improvement and detecting optimal density peaks. The optimal selection of density points in the SHOCFS algorithm still becomes an important issue to be considered. The swallow swarm optimization (SSO) is introduced in the proposed work for choosing optimal density peaks in clustering algorithm. This paper motivates to design a new secure cloud service system for cloud in IIoT environment. The secure high‐order optimal density selection (SHODS3O‐CFS) approach is proposed in hybrid cloud environment. The major contribution of the work is to develop an optimization‐based secure clustering algorithm for finding the best density points in clustering algorithm. It may reduce the overlapping peak points in the cluster and increase the security of system in hybrid cloud. The experimental results of the proposed algorithm and existing methods are measured via clustering metrics such as accuracy, clustering center quality, and speed ratio. The metrics like clustering center quality, Rand index (RI), encryption time (ms), and speedup ratio have been used to measure the results of the proposed system and existing methods. From the results, it is concluded that the proposed work has achieved higher average RI of 93.4%, which is 29.68% and 17.4% higher when compared with the privacy preservation high‐order clustering algorithm by fast search (PPHOCFS) and SHOCFS methods.

Binarization of the Swallow Swarm Optimization for Feature Selection

Binarization of the Swallow Swarm Optimization for Feature Selection

A wrapper metaheuristic framework for handwritten signature verification

Handwritten signature verification is one of the most fast, intuitive and cost-effective tools for user authentication. However, the analysis of the handwritten signature may become complicated due to the large dimensionality. Feature selection is treated as one of the most commonly applied preprocessing techniques in data mining to deal with the dimensionality. Since the feature selection can be considered as an optimization problem, we propose a wrapper metaheuristic framework for the handwritten signature verification, which uses the k-nearest neighbor classification algorithm to guide a metaheuristic optimization algorithm in order to find the best subset of features. The following metaheuristics are used in the work: swallow swarm optimization (SSO), binary dragonfly swarm optimization (BBA), population algorithm with memory (PAO), genetic algorithm (GA), binary bat algorithm (BBA), binary whale optimization algorithm (WOA) and particle swarm optimization (PSO). The experiments are carried on the SVC2004 dataset by comparing the employed metaheuristics with each other from different perspectives, including the classification performance, the relatives frequencies of selected features and the feature subset size. According to results, the proposed wrapper framework obtains extremely promising results for handwritten signature verification. In particular, the proposed framework can increase the classification accuracy at least or more than 8%, even though selecting between 2 and 6 features among 100 features in most cases. It is also observed that the proposed framework with PSO can achieve slightly better results than with the other metaheuristics in most cases. It can therefore be suggested that the traditional metaheuristics like PSO and GA maintain their position and entrench their popularity in a wide range of fields despite a variety of the recently introduced metaheuristics.

An Optimization Model for Stock Market Direction Prediction

Stock market direction prediction becomes an essential task in the business sector. The inherent volatile behavior of stock markets worldwide makes the prediction process difficult. The improvement in the prediction accuracy of the stock market direction prediction helps to avoid the risks involved in the investment process. In this aspect, this study designs a swallow swarm optimization (SSO) with a fuzzy support vector machine (FSVM) model for stock market direction prediction. The proposed SSO-FSVM model encompasses preprocessing, feature extraction, FSVM, and SSO based parameter tuning. The usage of the SSO algorithm to fine-tune the parameters involved in the FSVM model helps to significantly improve the overall predictive performance. To validate the improved performance of the SSO-FSVM model, a wide range of experiments were carried out using two benchmark datasets. The experimental outcomes reported the betterment of the SSO-FSVM model over the recent approaches in terms of several evaluation metrics.

Mining serial positioning episode rules by natural exponent inertia weight based swallow swarm optimization algorithm with constraint based event sequences

The Frequent Episode Mining (FEM) is a challenging framework to identify frequent episodes from a sequence database. In a sequence, an ordered collection of events defines an episode, and frequent episodes are only considered by the earlier studies. Also, it doesn’t support for the serial based episode rule mining. In this work, the episode rules are mined with precise and serial based rule mining considering the temporal factor, so that, the occurrence time of the consequent is specified in contrast to the traditional episode rule mining. The proposed work has a larger number of candidates and specific time constraints to generate the fixed-gap episodes, and mining such episodes from whole sequence where the time span between any two events is a constant which is utilized to improve the proposed framework’s performance. In order to improve the efficiency, an Optimal Fixed-gap Episode Occurrence (OFEO) is performed using the Natural Exponent Inertia Weight based Swallow Swarm Optimization (NEIWSSO) algorithm. The temporal constraints significantly evaluate the effectiveness of episode mining, and a noticeable advantage of the present work is to generate optimal fixed-gap episodes for better prediction. The effective use of memory consumption and performance enhancement is achieved by developing new trie-based data structure for Mining Serial Positioning Episode Rules (MSPER) using a pruning method. The position of frequent events is updated in the precise-positioning episode rule trie instead of frequent events to reduce the memory space. The benchmark datasets Retail, Kosarak, and MSNBC is used to evaluate the proposed algorithm’s efficiency. Eventually, it is found that it outperforms the existing techniques with respect to memory consumption and execution time. On an average, the proposed algorithm achieves 28 times lesser execution time and consumes 45.5% less memory space for the highest minimum support value on the Retail dataset compared to existing methods.

WITHDRAWN: A novel wrapper FS based on binary swallow swarm optimization with score-based criteria fusion for gene expression microarray data

In recent days, Gene Expression Microarray (GEM) data increases its significance to diagnose the diseases appropriately. The key issue of analyzing gene expression information for huge amount of genes with less samples it to extract the disease-related data from the large number of unwanted information and noise. Feature choice, removing unwanted and inappropriate genes was a major process for investigating this challenge. So, the Feature Selection (FS) and categorization are the primary tasks in analyzing the GEM. In this article, a Swallow Swarm Optimization (SSO) with Score-based Criteria Fusion (Optimized SCF (OSCF)) wrapper FStechnique is developed to predict the cancer with a high classification efficiency. Novel wrapper FStechnique follows the procedure of Binary Weight SSO (BWSSO) and the fitness is evaluated by combining the SCF and classification accuracy. OSCF algorithm fuses SCF and classification accuracy to measure the genes significance and redundancy for verifying the genes dependency. It could improve the efficiency of the classifiers such as K-Nearest Neighbours (KNN), Support Vector Machine (SVM) and Recursive Neural Networks (RNN), and evaluate its improvement using four different GEM datasets (Prostate cancer, Small Round Blue Cell Tumours (SRBCT), Leukemia, and Lymphoma). Experimental results verify that the OSCF technique shows better results when compared to conventional techniques based on precision, recall, accuracy and Area Under Curve (AUC).