Performance Analysis of Smart System with Algorithmic Optimization for Cavities Detection

The Dragonfly Algorithm (DA), a swarm intelligence algorithm inspired by the behavior of dragonflies, has been recently proposed and it was found to have a higher performance as compared to other swarm intelligence algorithms. However, it still has certain limitations, and its performance can be further improved. DA has a low exploitation phase, and this leads to problems such as low accuracy of solutions, falling in local optima and low convergence rate. We propose to enhance the exploitation of DA by using the hill climbing algorithm as a local search so as to increase its effectiveness and efficiency in producing high accuracy solutions. The hill climbing algorithm is chosen to be used as a local search for DA since it always optimizes the current solution until the local optima is obtained However, it has not been employed in any existing hybrid of DA. The proposed algorithm performance is to be evaluated by employing it as a training algorithm for an Artificial Neural Network (ANN) to optimize its connection weights. A classification dataset will be used for the training and testing of the ANN trained by the proposed algorithm. The root mean squared error of the ANN will be taken as the objective function of the optimized dragonfly algorithm. The accuracy of the resultant neural network will be compared to that of an ANN trained by the original DA and the time taken for the training process by both algorithms will be compared. Based on the analysis, the optimized algorithm is expected to outperform the original DA by allowing the resultant neural network to have a higher classification accuracy.

Computer software is frequently used for medical decision support systems in different areas. Magnetic Resonance Images (MRI) are widely used images for brain classification issue. This paper presents an improved method for brain classification of MRI images. The proposed method contains three phases, which are, feature extraction, dimensionality reduction, and an improved classification technique. In the first phase, the features of MRI images are obtained by discrete wavelet transform (DWT). In the second phase, the features of MRI images have been reduced, using principal component analysis (PCA). In the last (third) stage, an improved classifier is developed. In the proposed classifier, Dragonfly algorithm is used instead of backpropagation as training algorithm for artificial neural network (ANN). Some other recent training-based Neural Networks, SVM, and KNN classifiers are used for comparison with the proposed classifier. The classifiers are utilized to classify image as normal or abnormal MRI human brain image. The results show that the proposed classifier is outperformed the other competing classifiers.

Because face images are naturally two-dimensional data, there have been several 2D feature extraction methods to deal with facial images while there are few 2D effective classifiers. Meanwhile, there is an increasing interest in the multilinear subspace analysis and many methods have been proposed to operate directly on these tensorial data during the past several years. One of these popular unsupervised multilinear algorithms is Multilinear Principal Component Analysis (MPCA) while another of the supervised multilinear algorithm is Multilinear Discriminant Analysis (MDA). Then a MPCA+MDA method has been introduced to deal with the tensorial signal. However, due to the no convergence of MDA, it is difficult for MPCA+MDA to obtain a precise result. Hence, to overcome this limitation, a new MPCA plus General Tensor Discriminant Analysis (GTDA) solution with well convergence is presented for tensorial face images feature extraction in this paper. Several experiments are carried out to evaluate the performance of MPCA+GTDA on different databases and the results show that this method has the potential to achieve comparative effect as MPCA+MDA.

Skin cancer, a widespread and possibly life-threatening condition, requires an earlier and accurate diagnosis for efficient involvement. Dermoscopic images, providing a window into skin lesions, gives a useful resource for medical specialists in this context. This article goals to develop a complex architecture to detect skin cancer employing dermatoscopy images, integrating cutting-edge techniques in image analysis, machine learning, and artificial intelligence. This manuscript introduces the Dragonfly Algorithm with Deep Learning for Skin Cancer Diagnoses on Dermoscopy Images (DFADL-SCDDI) method. Our technique integrates advanced methods namely feature extraction, preprocessing, classification, and parameter optimization to increase the reliability and of accuracy identification. For image preprocessing, we exploit the Gabor filter (GF), a robust tool for enriching texture and structure data in images. Feature extraction has been executed employing a Capsule Network (CapsNet). CapsNet is a deep learning (DL) model that exceeds in capturing hierarchical and in-depth features in images. Classification is conducted by a Gated Recurrent Unit (GRU), a kind of recurrent neural network (RNN) ability to model sequential patterns and dependencies within the feature representations. To additional improve the model's effectiveness; we implement the Dragonfly Algorithm (DA) for parameter tuning. The DA has a powerful optimization system stimulated by nature, developed for enhancing hyperparameters efficiently, consequently higher the model's diagnostic accuracy. The proposed architecture is assessed on a large database of dermatoscopy images, signifying its effectiveness in skin cancer detection. The outcomes exhibit substantial enhancement in reliability and accuracy compared to traditional systems.

The Spiking Neural Network (SNN) model is a third-generation neural network model that uses spike pulse trains to detect and classify electrical problems in the electrical equipment under consideration. To diagnose the thermal issue in the early stages, it is required to evaluate and monitor the electrical components. The detection method in Infrared Thermography (IRT) is hybrid optimization, which is a Dragonfly Algorithm (DA)–Ant Colony Optimization (ACO) technique that produces a higher exploration and exploitation rate while moving toward an optimal solution with a higher convergence rate. The innovative hybrid DA–ACO method used in this study aims to find the best weights for the SNN model while also extracting the most useful characteristics for defect detection in electrical equipment. To improve classification accuracy, the optimum features picked using the created hybrid DA–ACO are provided to the SNN model. Based on conventional and other optimization methods, the proposed method provided superior results from the execution results.

This paper presents a novel approach to solve the supervised dimensionality reduction problem and feature extraction by encoding an image object as a general tensor of 2-D/3-D order. In this paper a multilinear principal component analysis (MPCA) for tensor object feature extraction and then a multilinear discriminant analysis (MDA), to find the best subspaces have been proposed. It should be noted that both of the algorithms work with tensor objects so the structure of the objects has been never broken. Therefore we achieve a better result than the result of the traditional methods. The focus of these algorithms is avoiding the curse of dimensionality. Finally, a comprehensive experiments on ORL and FERET databases has been provided by encoding face images as 2-D or 3-D tensors to demonstrate that MPCA+MDA algorithm based on higher order tensors has the potential to outperform the traditional vector-based subspace learning algorithms such as Eigenface and Fisherface, especially in the cases with small sample sizes and curse of dimensionality dilemma.

Challenges in detecting machinery faults, particularly in multivariate sensor environments, necessitate advanced feature extraction and classification techniques. This study introduces a novel approach that combines Multilinear Principal Component Analysis (MPCA) with a 1D-Convolutional Neural Network (1D-CNN) for efficient fault detection. By constructing Frequency Domain (FD) tensors from multivariate sensor data and applying MPCA for dimensionality reduction, our methodology enhances the capabilities of a 1D-CNN in feature learning and fault classification. The efficacy of this approach is validated through experiments on a Machinery Fault Simulator (MFS) with acoustic and vibration sensors, demonstrating notable improvements in fault detection accuracy compared to benchmark methods. The study results demonstrate that the proposed approach exhibits high accuracy in identifying machine fault conditions and outperforms the benchmark methods. The findings of this study have significant inferences for machine fault detection and fill the gap of more effective and reliable techniques in this domain.

High-strength concrete (HSC) is defined as concrete that meets a special combination of uniformity and performance requirements, which cannot be attained routinely via traditional constituents and normal mixing, placing, and curing procedures. It is a complex material since modeling its behavior is a difficult task. This paper intends to show the feasible applicability of optimized convolutional neural networks (CNN) for predicting the slump in HSC. The following are the parameters that given as the input for the prediction of slump: cement (kg/m3), slag (kg/m3), fly ash (kg/m3), water (kg/m3), super-plasticizer (kg/m3), coarse aggregate (kg/m3), and fine aggregate (kg/m3). In order to make the prediction more accurate, the design of CNN is assisted with optimization logic by making some fine-tuned filter size of the convolutional layer. For this optimization purpose, this work presents a new "hybrid" algorithm that incorporates the concept of sea lion optimization algorithm (SLnO) and dragonfly algorithm (DA) and is named as Levy updated-sea lion optimization algorithm (LU-SLnO). Finally, the performance of the proposed work is compared and proved over the state-of-the-art models with respect to error measure and convergence analysis.

Due to the significant ongoing expansion of computer networks in our lives nowadays, the demand for network security and protection from cyber-attacks has never been more imperative to either clients or businesses alike, which signifies the key role of cyber intrusion detection systems in network security. This article proposes a cyber-intrusion detecting system classification with MLP trained by a hybrid metaheuristic algorithm and feature selection based on multi-objective wrapper method. The classifier, named as HADMLP is trained using a hybridization of the artificial bee colony along with the dragonfly algorithm. A multi-objective artificial bee colony model which is wrapper-based is used for selection of feature. Hence, collective name of the proposed technique referred as MO-HADMLP. For performance evaluation, the proposed method was assessed using ISCX 2012 and KDD CUP 99 datasets. The results of our experiments indicate a significant enhancement to the efficacy of network intrusion detection when compared to other approaches.KeywordsIntrusion Detecting System (IDS)Selection of Feature (SoF)Multi-objective Optimization (MO)Multilayer Perceptron (MLP)Artificial Bee Colony (ABC)Dragonfly Algorithm (DA)

Diabetes Mellitus (DM) is a global health challenge, and accurate early detection is critical for effective management. The study explores the potential of machine learning for improved diabetes prediction using microarray gene expression data and PIMA data set. Researchers utilizing a hybrid feature extraction method such as Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) followed by metaheuristic feature selection algorithms as Harmonic Search (HS), Dragonfly Algorithm (DFA), Elephant Herding Algorithm (EHA). Evaluated the performance of a system by using the following classifiers as Non-Linear Regression—NLR, Linear Regression—LR, Gaussian Mixture Model—GMM, Expectation Maximization—EM, Bayesian Linear Discriminant Analysis—BLDA, Softmax Discriminant Classifier—SDC, and Support Vector Machine with Radial Basis Function kernel—SVM-RBF classifier on two publicly available datasets namely the Nordic Islet Transplant Program (NITP) and the PIMA Indian Diabetes Dataset (PIDD). The findings demonstrate significant improvement in classification accuracy compared to using all genes. On the Nordic islet transplant dataset, the combined ABC-PSO feature extraction with EHO feature selection achieved the highest accuracy of 97.14%, surpassing the 94.28% accuracy obtained with ABC alone and EHO selection. Similarly, on the PIMA Indian diabetes dataset, the ABC-PSO and EHO combination achieved the best accuracy of 98.13%, exceeding the 95.45% accuracy with ABC and DFA selection. These results highlight the effectiveness of our proposed approach in identifying the most informative features for accurate diabetes prediction. It is observed that the parametric values attained for the datasets are almost similar. Therefore, this research indicates the robustness of the FE and FS along with classifier techniques with two different datasets.

This paper introduces a pioneering Hybrid Parallel Multi-linear Face Recognition algorithm that capitalizes on multi-linear methodologies, such as Multi-linear Principal Component Analysis (MPCA), Linear Discriminant Analysis (LDA), and Histogram of Oriented Gradients (HOG), to attain exceptional recognition performance. The Hybrid Feature Selection (HFS) algorithm is meticulously crafted to augment the classification performance on the CK+ and FERET datasets by amalgamating the strengths of feature extraction techniques and feature selection methods. HFS seamlessly incorporates Principal Component Analysis (PCA), Local Discriminant Analysis (LDA), and HOG. The primary aim of this algorithm is to autonomously identify a subset of the most distinctive features from the extracted feature pool, thus elevating classification accuracy, precision, recall, and F1-Score. By amalgamating these methodologies, the algorithm adeptly diminishes dimensionality while conserving pivotal features. Experimental trials on facial image datasets, CK+ and FERET, underscore the algorithm's supremacy in terms of accuracy and computational efficiency when contrasted with conventional linear techniques and even certain deep learning approaches. The proposed algorithm proffers an encouraging solution for real-world face recognition applications where precision and operational efficiency are of paramount significance.

Predicting heat transfer rate of a ribbed triple-tube heat exchanger working with nanofluid using neural network enhanced by advanced optimization algorithms

In this paper, a new intrusion detection system (IDS) is presented to deal with distributed denial of service (DDoS) attacks. A combined algorithm based on Harris Hawks Optimization (HHO) and Dragonfly Algorithm (DA) is proposed to select relevant features and eliminate irrelevant and redundant features from the NSL-KDD dataset. The extracted features are presented to a multilayer perceptron (MLP) neural network. This network (as a classifier) divides the network traffic into two classes, normal and attack categories. Performance of the proposed model is evaluated with two standard and widely-used datasets in the field of intrusion detection: NSL-KDD and UNSW-NB15. The results of the simulations clearly show the superiority of the proposed method compared to the previous methods in terms of critical evaluation criteria such as accuracy, precision, recall, and F-Measure. Specifically, the proposed method exhibited improvements of 96.9%, 97.6%, 96%, and 96.8% in these metrics, respectively (compared to the baseline method). The main reason for these improvements is the ability of the combined algorithm to intelligently select the optimal features and reduce the dimensions of the data. This careful selection of features allows the MLP neural network to focus on critical information, increasing the classification accuracy and ultimately improving the performance of the intrusion detection system. This research showed that combining optimization algorithms and machine learning works well. So, it is effective for tackling DDoS attacks. It can lead to better intrusion detection systems. These systems will be more efficient and accurate.

With the rapid development of processing and storage technologies and the success of the Internet, computing resources have become cheaper, more powerful and more ubiquitously available than ever before. This technological trend has enabled the realization of a new computing model, called cloud computing. In cloud, scheduling is an important application. In cloud environments, load balancing task scheduling is an important problem that directly affects resource utilization. Undoubtedly, load balancing scheduling is a serious aspect that should be considered because of its significant impact on both the back end and the front end of the cloud research industry. Good resource utilization is achieved whenever an effective load balance is achieved in the cloud. But, load balancing in cloud computing is an NP-hard optimization problem. In order to accomplish this problem, a novel load balancing task scheduling algorithm in cloud using Adaptive Dragonfly algorithm (ADA) is proposed. The ADA is a combination of dragonfly algorithm and firefly algorithm. Moreover, to attain the better performance, multi-objective function is developed based on three parameters namely, completion time, processing costs and load. Finally, the performance of proposed methodology is evaluated in terms of different metrics namely, execution cost and execution time. The experimental results demonstrate that a proposed approach accomplishes better load balancing result compared to other approaches.

This paper presents a fast, precise method for segmenting white blood cell(WBC) based on visual salient features, which is a two-stage algorithm consisting of adaptive WBC location based on salient map(AWLSM) by simulating the process of human perception with bottom-up strategies and extract pr ecise cell structure in cell salient attention window(CASW) using parameter controlled adaptive salient mechanism (PCASM), the first step locates seve ral CASWs in the blood cell image and the second step is to extract nucleus and cytoplasm accurately in each CASW. The experimental results demonstrate that the proposed method has sufficient accuracy and speediness to be used in the automatic blood analyzer. Keywords: Human perception, WBC location, Visual salient features, Cell salient attention window 1. INTRODUCTION The abnormal fluctuation of peripheral while blood cells(WBC) can be used as a prognostic marker for recurrence or metastases for a variety of diseases to help hemanalysis diagnose possible diseases of patients, whereas differential blood count(DBC) is a tedious, time-consuming task. An automatic blood analyzer(ABA) based on digital image processing techniques could not only lighten the labor burden of DBC work but also enha nce the accuracy of examination. The process of an ABA always require four steps: image acquisition, segmentation, feature extraction, and classiÞ cation. Segmentation plays a crucial role in the subsequent steps, and thus improvement of segmentation is the most widespread effort in many researches. Several approaches have been studied in the literature such as thresholding, edge detection, fuzzy C-means clustering, scale-space Þ ltering, neural network, watershed transformation, EM algorithm, active contour and morphological operations. Due to the complexity of cell image and its background, only single approach cannot extract nucleus and cytoplasm accurately, the current segmentation methods are a combination of several techniques in fact. The contrast between the nucleus and cy toplasm boundaries is high er than the one between cytoplasm and background, therefore, it is much easier to locate and extract nucleus region, and in the traditional method, nucleus region is early or merely extracted, and nucleus and cy toplasm is segmented separately. To extract nucleus region, There are many effective thresholding. Cseke