The study described in this paper consists of a method that applies gabor wavelet transform technique to extract relevant features related to image of tomato leaf in conjunction with using Support Vector Machines (SVMs) with alternate kernel functions in order to detect and identify type of disease that infects tomato plant. Initially, we collected real samples of diseased tomato leaves, next we isolated each leaf in single image, wavelet based feature technique has been employed to identify an optimal feature subset. Finally, a support vector machine classier with different kernel functions including Cauchy kernel, Invmult Kernel and Laplacian Kernel was employed to evaluate the ability of this approach to detect and identify where tomato leaf infected with Powdery mildew or early blight. To evaluate the performance of presented approach, we present tests on dataset consisted of 100 images for each type of tomato diseases. Extensive experimental results demonstrate that the proposed approach provides excellent annotation with accuracy 99.5 %. Efficient result obtained from the proposed approach can lead to tighter connection between agriculture specialists and computer system, yielding more effective and reliable results.

COVID-19 has affected all peoples’ lives. Though COVID-19 is on the rising, the existence of misinformation about the virus also grows in parallel. Additionally, the spread of misinformation has created confusion among people, caused disturbances in society, and even led to deaths. Social media is central to our daily lives. The Internet has become a significant source of knowledge. Owing to the widespread damage caused by fake news, it is important to build computerized systems to detect fake news. The paper proposes an updated deep neural network for identification of false news. The deep learning techniques are The Modified-LSTM (one to three layers) and The Modified GRU (one to three layers). In particular, we carry out investigations of a large dataset of tweets passing on data with respect to COVID-19. In our study, we separate the dubious claims into two categories: true and false. We compare the performance of the various algorithms in terms of prediction accuracy. The six machine learning techniques are decision trees, logistic regression, k nearest neighbors, random forests, support vector machines, and naïve Bayes (NB). The parameters of deep learning techniques are optimized using Keras-tuner. Four Benchmark datasets were used. Two feature extraction methods were used (TF-ID with N-gram) to extract essential features from the four benchmark datasets for the baseline machine learning model and word embedding feature extraction method for the proposed deep neural network methods. The results obtained with the proposed framework reveal high accuracy in detecting Fake and non-Fake tweets containing COVID-19 information. These results demonstrate significant improvement as compared to the existing state of art results of baseline machine learning models. In our approach, we classify the data into two categories: fake or nonfake. We compare the execution of the proposed approaches with Six machine learning procedures. The six machine learning procedures are Decision Tree (DT), Logistic Regression (LR), K Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machine (SVM), and Naive Bayes (NB). The parameters of deep learning techniques are optimized using Keras-tuner. Four Benchmark datasets were used. Two feature extraction methods were used (TF-ID with N-gram) to extract essential features from the four benchmark datasets for the baseline machine learning model and word embedding feature extraction method for the proposed deep neural network methods. The results obtained with the proposed framework reveal high accuracy in detecting Fake and non-Fake tweets containing COVID-19 information. These results demonstrate significant improvement as compared to the existing state of art results of baseline machine learning models.

Feature Selection (F.S.) reduces the number of features by removing unnecessary, redundant, and noisy information while keeping a relatively decent classification accuracy. F.S. can be considered an optimization problem. As the problem is challenging and there are many local solutions, stochastic optimization algorithms may be beneficial. This paper proposes a novel approach to dimension reduction in feature selection. As a seminal attempt, this work uses binary variants of the recent Marine Predators Algorithm (MPA) to select the optimal feature subset to improve classification accuracy. MPA is a new and novel nature-inspired metaheuristic. This research proposes an algorithm that is a hybridization between MPA and k-Nearest Neighbors (k-NN) called MPA-KNN. K-Nearest Neighbors (k-NN) is used to evaluate the selected features on medical datasets with feature sizes ranging from tiny to massive. The proposed methods are evaluated on 18 well-known UCI medical dataset benchmarks and compared with eight well-regarded metaheuristic wrapper-based approaches. The core exploratory and exploitative processes are adapted in MPA to select the optimal and meaningful features for achieving the most accurate classification. The results show that the proposed MPA-KNN approach had a remarkable capability to select the optimal and significant features. It performed better than the well-established metaheuristic algorithms we tested. The algorithms we used for comparison are Grey Wolf Optimizer (GWO), MothFlame Optimization Algorithm (MFO), Sine Cosine Algorithm (SCA), Whale Optimization Algorithm (WOA), Slap Swarm Algorithm (SSA), Butterfly Optimization Algorithm (BFO), and Harris Hawks Optimization (HHO). This paper is the first work that implements MPA for Feature Selection problems. The results ensure that the proposed MPA-KNN approach has a remarkable capability to select the optimal and significant features and performed better than several metaheuristic algorithms. MPA-KNN achieves the best averages accuracy, Sensitivity, and Specificity rates of all datasets.

Economic load dispatch (ELD) in power system problems involves scheduling the power generating units to minimize cost and satisfy system constraints. Although previous works propose solutions to reduce CO2 emission and production cost, an optimal allocation needs to be considered on both cost and emission-leading to combined economic and emission dispatch (CEED). Metaheuristic optimization algorithms perform relatively well on ELD problems. The gradient-based optimizer (GBO) is a new metaheuristic algorithm inspired by Newton's method that integrates both the gradient search rule and local escaping operator. The GBO maintains a good balance between exploration and exploitation. Also, the possibility of the GBO getting stuck in local optima and premature convergence is rare. This paper tests the performance of GBO in solving ELD and CEED problems. We test the performance of GBO on ELD for various scenarios such as ELD with transmission losses, CEED and CEED with valve point effect. The experimental results revealed that GBO has been obtained better results compared to eight other metaheuristic algorithms such as Slime mould algorithm (SMA), Elephant herding optimization (EHO), Monarch butterfly optimization (MBO), Moth search algorithm (MSA), Earthworm optimization algorithm (EWA), Artificial Bee Colony (ABC) Algorithm, Tunicate Swarm Algorithm (TSA) and Chimp Optimization Algorithm (ChOA). Therefore, the simulation results showed the competitive performance of GBO as compared to other benchmark algorithms.

This study integrates a tunicate swarm algorithm (TSA) with a local escaping operator (LEO) for overcoming the weaknesses of the original TSA. The LEO strategy in TSA–LEO prevents searching deflation in TSA and improves the convergence rate and local search efficiency of swarm agents. The efficiency of the proposed TSA–LEO was verified on the CEC’2017 test suite, and its performance was compared with seven metaheuristic algorithms (MAs). The comparisons revealed that LEO significantly helps TSA by improving the quality of its solutions and accelerating the convergence rate. TSA–LEO was further tested on a real-world problem, namely, segmentation based on the objective functions of Otsu and Kapur. A set of well-known evaluation metrics was used to validate the performance and segmentation quality of the proposed TSA–LEO. The proposed TSA-LEO outperforms other MA algorithms in terms of fitness, peak signal-to-noise ratio, structural similarity, feature similarity, and segmentation findings.