A White Shark Equilibrium Optimizer with a Hybrid Deep-Learning-Based Cybersecurity Solution for a Smart City Environment.

Smart grids (SGs) play a vital role in the smart city environment, which exploits digital technology, communication systems, and automation for effectively managing electricity generation, distribution, and consumption. SGs are a fundamental module of smart cities that purpose to leverage technology and data for enhancing the life quality for citizens and optimize resource consumption. The biggest challenge in dealing with SGs and smart cities is the potential for cyberattacks comprising Distributed Denial of Service (DDoS) attacks. DDoS attacks involve overwhelming a system with a huge volume of traffic, causing disruptions and potentially leading to service outages. Mitigating and detecting DDoS attacks in SGs is of great significance to ensuring their stability and reliability. Therefore, this study develops a new White Shark Equilibrium Optimizer with a Hybrid Deep-Learning-based Cybersecurity Solution (WSEO-HDLCS) technique for a Smart City Environment. The goal of the WSEO-HDLCS technique is to recognize the presence of DDoS attacks, in order to ensure cybersecurity. In the presented WSEO-HDLCS technique, the high-dimensionality data problem can be resolved by the use of WSEO-based feature selection (WSEO-FS) approach. In addition, the WSEO-HDLCS technique employs a stacked deep autoencoder (SDAE) model for DDoS attack detection. Moreover, the gravitational search algorithm (GSA) is utilized for the optimal selection of the hyperparameters related to the SDAE model. The simulation outcome of the WSEO-HDLCS system is validated on the CICIDS-2017 dataset. The widespread simulation values highlighted the promising outcome of the WSEO-HDLCS methodology over existing methods.

Leveraging Marine Predators Algorithm with Deep Learning for Lung and Colon Cancer Diagnosis.

Cancer is a deadly disease caused by various biochemical abnormalities and genetic diseases. Colon and lung cancer have developed as two major causes of disability and death in human beings. The histopathological detection of these malignancies is a vital element in determining the optimal solution. Timely and initial diagnosis of the sickness on either front diminishes the possibility of death. Deep learning (DL) and machine learning (ML) methods are used to hasten such cancer recognition, allowing the research community to examine more patients in a much shorter period and at a less cost. This study introduces a marine predator's algorithm with deep learning as a lung and colon cancer classification (MPADL-LC3) technique. The presented MPADL-LC3 technique aims to properly discriminate different types of lung and colon cancer on histopathological images. To accomplish this, the MPADL-LC3 technique employs CLAHE-based contrast enhancement as a pre-processing step. In addition, the MPADL-LC3 technique applies MobileNet to derive feature vector generation. Meanwhile, the MPADL-LC3 technique employs MPA as a hyperparameter optimizer. Furthermore, deep belief networks (DBN) can be applied for lung and color classification. The simulation values of the MPADL-LC3 technique were examined on benchmark datasets. The comparison study highlighted the enhanced outcomes of the MPADL-LC3 system in terms of different measures.

Artificial Hummingbird Algorithm with Transfer-Learning-Based Mitotic Nuclei Classification on Histopathologic Breast Cancer Images.

Recently, artificial intelligence (AI) is an extremely revolutionized domain of medical image processing. Specifically, image segmentation is a task that generally aids in such an improvement. This boost performs great developments in the conversion of AI approaches in the research lab to real medical applications, particularly for computer-aided diagnosis (CAD) and image-guided operation. Mitotic nuclei estimates in breast cancer instances have a prognostic impact on diagnosis of cancer aggressiveness and grading methods. The automated analysis of mitotic nuclei is difficult due to its high similarity with nonmitotic nuclei and heteromorphic form. This study designs an artificial hummingbird algorithm with transfer-learning-based mitotic nuclei classification (AHBATL-MNC) on histopathologic breast cancer images. The goal of the AHBATL-MNC technique lies in the identification of mitotic and nonmitotic nuclei on histopathology images (HIs). For HI segmentation process, the PSPNet model is utilized to identify the candidate mitotic patches. Next, the residual network (ResNet) model is employed as feature extractor, and extreme gradient boosting (XGBoost) model is applied as a classifier. To enhance the classification performance, the parameter tuning of the XGBoost model takes place by making use of the AHBA approach. The simulation values of the AHBATL-MNC system are tested on medical imaging datasets and the outcomes are investigated in distinct measures. The simulation values demonstrate the enhanced outcomes of the AHBATL-MNC method compared to other current approaches.

Machine learning-based Decision Tree J48 with grey wolf optimizer for environmental pollution control.

ABSTRACTDue to industrialization, activities of human and urbanization, environment is getting polluted. Air pollution has become a main issue in the metropolitan areas of the world. To protect people from diseases, monitoring air quality plays an important thing. This air pollutant may lead to many health issues like respiratory and cardiac problems. The major air pollutants are NO, C6H6, CO, etc. Many research works have been done in predicting air pollution-based health issues, predicting air pollution levels, monitoring and controlling the polluted levels. But they are not efficient, cost of maintenance is high and insufficient tool for monitoring it. To overcome these issues, this paper implements hybrid algorithm of Decision Tree J48 and Grey Wolf Optimizer (DT-GWO). This DT-GWO is a better model to addresses the predicting of Air Quality Index (AQI), which minimizes the error rate, accurately and effectively predicting the air quality. The AQI values are categorised as good, moderate, unhealthy, very unhealthy and hazardous. The dataset used in this work is collected from Kaggle website which contains air pollutants details with air quality index values. Accuracy obtained for decision Tree J48 is 93.72%, grey wolf optimizer is 96.83% and our proposed work DT-GWO is 99.78%.

Improved Bald Eagle Search Optimization with Synergic Deep Learning-Based Classification on Breast Cancer Imaging.

Medical imaging has attracted growing interest in the field of healthcare regarding breast cancer (BC). Globally, BC is a major cause of mortality amongst women. Now, the examination of histopathology images is the medical gold standard for cancer diagnoses. However, the manual process of microscopic inspections is a laborious task, and the results might be misleading as a result of human error occurring. Thus, the computer-aided diagnoses (CAD) system can be utilized for accurately detecting cancer within essential time constraints, as earlier diagnosis is the key to curing cancer. The classification and diagnosis of BC utilizing the deep learning algorithm has gained considerable attention. This article presents a model of an improved bald eagle search optimization with a synergic deep learning mechanism for breast cancer diagnoses using histopathological images (IBESSDL-BCHI). The proposed IBESSDL-BCHI model concentrates on the identification and classification of BC using HIs. To do so, the presented IBESSDL-BCHI model follows an image preprocessing method using a median filtering (MF) technique as a preprocessing step. In addition, feature extraction using a synergic deep learning (SDL) model is carried out, and the hyperparameters related to the SDL mechanism are tuned by the use of the IBES model. Lastly, long short-term memory (LSTM) was utilized to precisely categorize the HIs into two major classes, such as benign and malignant. The performance validation of the IBESSDL-BCHI system was tested utilizing the benchmark dataset, and the results demonstrate that the IBESSDL-BCHI model has shown better general efficiency for BC classification.

Joint Power Control and Phase Shift Design for Future PD-NOMA IRS-Assisted Drone Communications under Imperfect SIC Decoding.

Intelligent reflecting surfaces (IRS) and power-domain non-orthogonal multiple access (PD-NOMA) have recently gained significant attention for enhancing the performance of next-generation wireless communications networks. More specifically, IRS can smartly reconfigure the incident signal of the source towards the destination node, extending the wireless coverage and improving the channel capacity without consuming additional energy. On the other side, PD-NOMA can enhance the number of devices in the network without using extra spectrum resources. This paper proposes a new optimization framework for IRS-enhanced NOMA communications where multiple drones transmit data to the ground Internet of Things (IoT) devices under successive interference cancellation errors. In particular, the power budget of each drone, PD-NOMA power allocation of IoT devices, and the phase shift matrix of IRS are simultaneously optimized to enhance the total spectral efficiency of the system. Given the system model and optimization setup, the formulated problem is coupled with three variables, making it very complex and non-convex. Thus, this work first transforms and decouples the problem into subproblems and then obtains the efficient solution in two steps. In the first step, the closed-form solutions for the power budget and PD-NOMA power allocation subproblem at each drone are obtained through Karush-Kuhn-Tucker (KKT) conditions. In the second step, the subproblem of efficient phase shift design for each IRS is solved using successive convex approximation and DC programming. Numerical results demonstrate the performance of the proposed optimization scheme in comparison to the benchmark schemes.

Intelligent Reflecting Surfaces Enhanced Mobile Edge Computing: Minimizing the Maximum Computational Time.

Intelligent reflecting surfaces (IRS) and mobile edge computing (MEC) have recently attracted significant attention in academia and industry. Without consuming any external energy, IRS can extend wireless coverage by smartly reconfiguring the phase shift of a signal towards the receiver with the help of passive elements. On the other hand, MEC has the ability to reduce latency by providing extensive computational facilities to users. This paper proposes a new optimization scheme for IRS-enhanced mobile edge computing to minimize the maximum computational time of the end users' tasks. The optimization problem is formulated to simultaneously optimize the task segmentation and transmission power of users, phase shift design of IRS, and computational resource of mobile edge. The optimization problem is non-convex and coupled on multiple variables which make it very complex. Therefore, we transform it to convex by decoupling it into sub-problems and then obtain an efficient solution. In particular, the closed-form solutions for task segmentation and edge computational resources are achieved through the monotonical relation of time and Karush-Kuhn-Tucker conditions, while the transmission power of users and phase shift design of IRS are computed using the convex optimization technique. The proposed IRS-enhanced optimization scheme is compared with edge computing nave offloading, binary offloading, and edge computing, respectively. Numerical results demonstrate the benefits of the proposed scheme compared to other benchmark schemes.

Bayesian dynamic profiling and optimization of important ranked energy from gray level co-occurrence (GLCM) features for empirical analysis of brain MRI.

Accurate classification of brain tumor subtypes is important for prognosis and treatment. Researchers are developing tools based on static and dynamic feature extraction and applying machine learning and deep learning. However, static feature requires further analysis to compute the relevance, strength, and types of association. Recently Bayesian inference approach gains attraction for deeper analysis of static (hand-crafted) features to unfold hidden dynamics and relationships among features. We computed the gray level co-occurrence (GLCM) features from brain tumor meningioma and pituitary MRIs and then ranked based on entropy methods. The highly ranked Energy feature was chosen as our target variable for further empirical analysis of dynamic profiling and optimization to unfold the nonlinear intrinsic dynamics of GLCM features extracted from brain MRIs. The proposed method further unfolds the dynamics and to detailed analysis of computed features based on GLCM features for better understanding of the hidden dynamics for proper diagnosis and prognosis of tumor types leading to brain stroke.

Optimal Deep Transfer Learning-Based Human-Centric Biomedical Diagnosis for Acute Lymphoblastic Leukemia Detection.

Human-centric biomedical diagnosis (HCBD) becomes a hot research topic in the healthcare sector, which assists physicians in the disease diagnosis and decision-making process. Leukemia is a pathology that affects younger people and adults, instigating early death and a number of other symptoms. Computer-aided detection models are found to be useful for reducing the probability of recommending unsuitable treatments and helping physicians in the disease detection process. Besides, the rapid development of deep learning (DL) models assists in the detection and classification of medical-imaging-related problems. Since the training of DL models necessitates massive datasets, transfer learning models can be employed for image feature extraction. In this view, this study develops an optimal deep transfer learning-based human-centric biomedical diagnosis model for acute lymphoblastic detection (ODLHBD-ALLD). The presented ODLHBD-ALLD model mainly intends to detect and classify acute lymphoblastic leukemia using blood smear images. To accomplish this, the ODLHBD-ALLD model involves the Gabor filtering (GF) technique as a noise removal step. In addition, it makes use of a modified fuzzy c-means (MFCM) based segmentation approach for segmenting the images. Besides, the competitive swarm optimization (CSO) algorithm with the EfficientNetB0 model is utilized as a feature extractor. Lastly, the attention-based long-short term memory (ABiLSTM) model is employed for the proper identification of class labels. For investigating the enhanced performance of the ODLHBD-ALLD approach, a wide range of simulations were executed on open access dataset. The comparative analysis reported the betterment of the ODLHBD-ALLD model over the other existing approaches.

Feature Subset Selection with Optimal Adaptive Neuro-Fuzzy Systems for Bioinformatics Gene Expression Classification.

Recently, bioinformatics and computational biology-enabled applications such as gene expression analysis, cellular restoration, medical image processing, protein structure examination, and medical data classification utilize fuzzy systems in offering effective solutions and decisions. The latest developments of fuzzy systems with artificial intelligence techniques enable to design the effective microarray gene expression classification models. In this aspect, this study introduces a novel feature subset selection with optimal adaptive neuro-fuzzy inference system (FSS-OANFIS) for gene expression classification. The major aim of the FSS-OANFIS model is to detect and classify the gene expression data. To accomplish this, the FSS-OANFIS model designs an improved grey wolf optimizer-based feature selection (IGWO-FS) model to derive an optimal subset of features. Besides, the OANFIS model is employed for gene classification and the parameter tuning of the ANFIS model is adjusted by the use of coyote optimization algorithm (COA). The application of IGWO-FS and COA techniques helps in accomplishing enhanced microarray gene expression classification outcomes. The experimental validation of the FSS-OANFIS model has been performed using Leukemia, Prostate, DLBCL Stanford, and Colon Cancer datasets. The proposed FSS-OANFIS model has resulted in a maximum classification accuracy of 89.47%.

Arithmetic Optimization with RetinaNet Model for Motor Imagery Classification on Brain Computer Interface.

Brain Computer Interface (BCI) technology commonly used to enable communication for the person with movement disability. It allows the person to communicate and control assistive robots by the use of electroencephalogram (EEG) or other brain signals. Though several approaches have been available in the literature for learning EEG signal feature, the deep learning (DL) models need to further explore for generating novel representation of EEG features and accomplish enhanced outcomes for MI classification. With this motivation, this study designs an arithmetic optimization with RetinaNet based deep learning model for MI classification (AORNDL-MIC) technique on BCIs. The proposed AORNDL-MIC technique initially exploits Multiscale Principal Component Analysis (MSPCA) approach for the EEG signal denoising and Continuous Wavelet Transform (CWT) is exploited for the transformation of 1D-EEG signal into 2D time-frequency amplitude representation, which enables to utilize the DL model via transfer learning approach. In addition, the DL based RetinaNet is applied for extracting of feature vectors from the EEG signal which are then classified with the help of ID3 classifier. In order to optimize the classification efficiency of the AORNDL-MIC technique, arithmetical optimization algorithm (AOA) is employed for hyperparameter tuning of the RetinaNet. The experimental analysis of the AORNDL-MIC algorithm on the benchmark data sets reported its promising performance over the recent state of art methodologies.

Intelligent Diagnostic Prediction and Classification Models for Detection of Kidney Disease.

Kidney disease is a major public health concern that has only recently emerged. Toxins are removed from the body by the kidneys through urine. In the early stages of the condition, the patient has no problems, but recovery is difficult in the later stages. Doctors must be able to recognize this condition early in order to save the lives of their patients. To detect this illness early on, researchers have used a variety of methods. Prediction analysis based on machine learning has been shown to be more accurate than other methodologies. This research can help us to better understand global disparities in kidney disease, as well as what we can do to address them and coordinate our efforts to achieve global kidney health equity. This study provides an excellent feature-based prediction model for detecting kidney disease. Various machine learning algorithms, including k-nearest neighbors algorithm (KNN), artificial neural networks (ANN), support vector machines (SVM), naive bayes (NB), and others, as well as Re-cursive Feature Elimination (RFE) and Chi-Square test feature-selection techniques, were used to build and analyze various prediction models on a publicly available dataset of healthy and kidney disease patients. The studies found that a logistic regression-based prediction model with optimal features chosen using the Chi-Square technique had the highest accuracy of 98.75 percent. White Blood Cell Count (Wbcc), Blood Glucose Random (bgr), Blood Urea (Bu), Serum Creatinine (Sc), Packed Cell Volume (Pcv), Albumin (Al), Hemoglobin (Hemo), Age, Sugar (Su), Hypertension (Htn), Diabetes Mellitus (Dm), and Blood Pressure (Bp) are examples of these traits.