Metaheuristic Optimization Algorithm for Signals Classification of Electroencephalography Channels

Digital signal processing of electroencephalography (EEG) data is now widely utilized in various applications, including motor imagery classification, seizure detection and prediction, emotion classification, mental task classification, drug impact identification and sleep state classification. With the increasing number of recorded EEG channels, it has become clear that effective channel selection algorithms are required for various applications. Guided Whale Optimization Method (Guided WOA), a suggested feature selection algorithm based on Stochastic Fractal Search (SFS) technique, evaluates the chosen subset of channels. This may be used to select the optimum EEG channels for use in Brain-Computer Interfaces (BCIs), the method for identifying essential and irrelevant characteristics in a dataset, and the complexity to be eliminated. This enables (SFS-Guided WOA) algorithm to choose the most appropriate EEG channels while assisting machine learning classification in its tasks and training the classifier with the dataset. The (SFS-Guided WOA) algorithm is superior in performance metrics, and statistical tests such as ANOVA and Wilcoxon rank-sum are used to demonstrate this.     

Advance Artificial Intelligence Technique for Designing Double T-Shaped Monopole Antenna

Machine learning (ML) has taken the world by a tornado with its prevalent applications in automating ordinary tasks and using turbulent insights throughout scientific research and design strolls. ML is a massive area within artificial intelligence (AI) that focuses on obtaining valuable information out of data, explaining why ML has often been related to stats and data science. An advanced meta-heuristic optimization algorithm is proposed in this work for the optimization problem of antenna architecture design. The algorithm is designed, depending on the hybrid between the Sine Cosine Algorithm (SCA) and the Grey Wolf Optimizer (GWO), to train neural network-based Multilayer Perceptron (MLP). The proposed optimization algorithm is a practical, versatile, and trustworthy platform to recognize the design parameters in an optimal way for an endorsement double T-shaped monopole antenna. The proposed algorithm likewise shows a comparative and statistical analysis by different curves in addition to the ANOVA and T-Test. It offers the superiority and validation stability evaluation of the predicted results to verify the procedures’ accuracy.     

Optimized Ensemble Algorithm for Predicting Metamaterial Antenna Parameters

Metamaterial Antenna is a subclass of antennas that makes use of metamaterial to improve performance. Metamaterial antennas can overcome the bandwidth constraint associated with tiny antennas. Machine learning is receiving a lot of interest in optimizing solutions in a variety of areas. Machine learning methods are already a significant component of ongoing research and are anticipated to play a critical role in today's technology. The accuracy of the forecast is mostly determined by the model used. The purpose of this article is to provide an optimal ensemble model for predicting the bandwidth and gain of the Metamaterial Antenna. Support Vector Machines (SVM), Random Forest, K-Neighbors Regressor, and Decision Tree Regressor were utilized as the basic models. The Adaptive Dynamic Polar Rose Guided Whale Optimization method, named AD-PRS-Guided WOA, was used to pick the optimal features from the datasets. The suggested model is compared to models based on five variables and to the average ensemble model. The findings indicate that the presented model using Random Forest results in a Root Mean Squared Error (RMSE) of (0.0102) for bandwidth and RMSE of (0.0891) for gain. This is superior to other models and can accurately predict antenna bandwidth and gain.     

Hybrid Sine Cosine and Stochastic Fractal Search for Hemoglobin Estimation

The sample's hemoglobin and glucose levels can be determined by obtaining a blood sample from the human body using a needle and analyzing it. Hemoglobin (HGB) is a critical component of the human body because it transports oxygen from the lungs to the body's tissues and returns carbon dioxide from the tissues to the lungs. Calculating the HGB level is a critical step in any blood analysis job. The HGB levels often indicate whether a person is anemic or polycythemia vera. Constructing ensemble models by combining two or more base machine learning (ML) models can help create a more improved model. The purpose of this work is to present a weighted average ensemble model for predicting hemoglobin levels. An optimization method is utilized to get the ensemble's optimum weights. The optimum weight for this work is determined using a sine cosine algorithm based on stochastic fractal search (SCSFS). The proposed SCSFS ensemble is compared to Decision Tree, Multilayer perceptron (MLP), Support Vector Regression (SVR) and Random Forest Regressors as model-based approaches and the average ensemble model. The SCSFS results indicate that the proposed model outperforms existing models and provides an almost accurate hemoglobin estimate.     

A novel global hydrodynamic analysis of the molecular flexibility of the dietary fibre polysaccharide konjac glucomannan

Konjac glucomannans have been widely considered in health food products although their hydrodynamic properties have been poorly understood. The weight-average molecular weight (M_w); sedimentation coefficient (s⁰_20,w) and intrinsic viscosities ([η]) have been estimated for five different preparations. The decrease in both intrinsic viscosity and sedimentation coefficient with molecular weight enables the estimation of molecular flexibility in terms of persistence length (L_p) using the traditional Bohdanecky–Bushin and Yamakawa–Fujii analyses for intrinsic viscosity and sedimentation data respectively. However, this requires an assumption of the mass per unit length M_L. Advantage can now be taken of a recent development in data interpretation which allows the estimation of L_p from combined intrinsic viscosity and sedimentation coefficient data and also an estimate for M_L. Using this “global” procedure an estimate of (13 ± 1) nm is found for L_p and a value of (330 ± 10) g mol⁻¹ nm⁻¹ for M_L.The value for L_p suggests a molecule of considerable flexibility, comparable to galactomannans (L_p  8–10 nm) but not as flexible as pullulan (L_p  1–2 nm).     

In-situ modification of natural fabrics by Cu-BTC MOF for effective release of insect repellent (<Emphasis Type="Italic">N,N</Emphasis>-diethyl-3-methylbenzamide)

Insect repellent fabrics are now of interest to secure human beings from the harmful effects of insects, but control the release of insect repellent from such fabrics is much important. The present study showed a good strategy to release effectively of N,N-diethyl-3-methylbenzamide (DEET) as insect repellent, from natural fabrics. Copper-benzene-1,3,5-tricarboxylic acid (Cu-BTC) as metal organic framework (MOF) material was in-situ incorporated into the matrix of natural fabrics including Cotton, Linen and Silk. DEET was then loaded onto the modified fabrics and the release of DEET from fabrics was studied. The successfulness of Cu-BTC incorporation was confirmed by scanning electron microscope, energy dispersive X-ray, X-ray diffraction and attenuated total reflectance—fourier transform infrared spectroscopy. The measured contents of Cu and Cu-BTC in fabrics were ranged in 35.9–38.9 and 115.4–130.3 mg/g fabrics, respectively. After loading the DEET into fabrics, the measured content of DEET was followed the order of Silk?<?Linen?<?Cotton and the modified fabrics exhibited much higher DEET by percent of 65–110%. Due to MOF modification, the released amount of DEET from fabrics was considerably increased by value of 205–220 mg/g and the release time became as long as 24–36 h. The release rate was fitted well to zero order model as the rate is independent of the reactant concentration. The so-obtained product can be applicable as disposable insect repellent materials for controllable and effective release of DEET for such a long residence time exceeded 9 days.     

Meta-heuristics for Feature Selection and Classification in Diagnostic BreastCancer

One of the most common kinds of cancer is breast cancer. The early detection of it may help lower its overall rates of mortality. In this paper, we robustly propose a novel approach for detecting and classifying breast cancer regions in thermal images. The proposed approach starts with data preprocessing the input images and segmenting the significant regions of interest. In addition, to properly train the machine learning models, data augmentation is applied to increase the number of segmented regions using various scaling ratios. On the other hand, to extract the relevant features from the breast cancer cases, a set of deep neural networks (VGGNet, ResNet-50, AlexNet, and GoogLeNet) are employed. The resulting set of features is processed using the binary dipper throated algorithm to select the most effective features that can realize high classification accuracy. The selected features are used to train a neural network to finally classify the thermal images of breast cancer. To achieve accurate classification, the parameters of the employed neural network are optimized using the continuous dipper throated optimization algorithm. Experimental results show the effectiveness of the proposed approach in classifying the breast cancer cases when compared to other recent approaches in the literature. Moreover, several experiments were conducted to compare the performance of the proposed approach with the other approaches. The results of these experiments emphasized the superiority of the proposed approach.     

Optimized superpixel and AdaBoost classifier for human thermal face recognition

Infrared spectrum-based human recognition systems offer straightforward and robust solutions for achieving an excellent performance in uncontrolled illumination. In this paper, a human thermal face recognition model is proposed. The model consists of four main steps. Firstly, the grey wolf optimization algorithm is used to find optimal superpixel parameters of the quick-shift segmentation method. Then, segmentation-based fractal texture analysis algorithm is used for extracting features and the rough set-based methods are used to select the most discriminative features. Finally, the AdaBoost classifier is employed for the classification process. For evaluating our proposed approach, thermal images from the Terravic Facial infrared dataset were used. The experimental results showed that the proposed approach achieved (1) reasonable segmentation results for the indoor and outdoor thermal images, (2) accuracy of the segmented images better than the non-segmented ones, and (3) the entropy-based feature selection method obtained the best classification accuracy. Generally, the classification accuracy of the proposed model reached to 99% which is better than some of the related work with around 5%.     

On the hydrodynamic analysis of conformation in mixed biopolymer systems

A brief review is given of some of the advances in hydrodynamic methodologies for studying the conformation and flexibility of biomacromolecules in mixed systems. We consider first of all the evaluation of conformation type and flexibility in polymer systems with a quasi‐continuous distribution of molecular weight, using polysaccharides and mucin glycoproteins as our main examples, and then conformation determination in discrete or paucidisperse systems such as aggregated antibody preparations. Copyright © 2010 Society of Chemical Industry     

Hybrid Grey Wolf and Dipper Throated Optimization in Network Intrusion Detection Systems

The Internet of Things (IoT) is a modern approach that enables connection with a wide variety of devices remotely. Due to the resource constraints and open nature of IoT nodes, the routing protocol for low power and lossy (RPL) networks may be vulnerable to several routing attacks. That’s why a network intrusion detection system (NIDS) is needed to guard against routing assaults on RPL-based IoT networks. The imbalance between the false and valid attacks in the training set degrades the performance of machine learning employed to detect network attacks. Therefore, we propose in this paper a novel approach to balance the dataset classes based on metaheuristic optimization applied to locality-sensitive hashing and synthetic minority oversampling technique (LSH-SMOTE). The proposed optimization approach is based on a new hybrid between the grey wolf and dipper throated optimization algorithms. To prove the effectiveness of the proposed approach, a set of experiments were conducted to evaluate the performance of NIDS for three cases, namely, detection without dataset balancing, detection with SMOTE balancing, and detection with the proposed optimized LSH-SOMTE balancing. Experimental results showed that the proposed approach outperforms the other approaches and could boost the detection accuracy. In addition, a statistical analysis is performed to study the significance and stability of the proposed approach. The conducted experiments include seven different types of attack cases in the RPL-NIDS17 dataset. Based on the proposed approach, the achieved accuracy is (98.1%), sensitivity is (97.8%), and specificity is (98.8%).