Plant disease, especially crop plants, is a major threat to global food security since many diseases directly affect the quality of the fruits, grains, and so on, leading to a decrease in agricultural productivity. Farmers have to observe and determine whether a leaf was infected by naked eyes. This process is unreliable, inconsistent, and error prone. Several works on deep learning techniques for detecting leaf diseases had been proposed. Most of them built their models based on limited resolution images using convolutional neural networks (CNNs). In this research, we aim at detecting early disease on plant leaves with small disease blobs, which can only be detected with higher resolution images, by an artificial neural network (ANN) approach. After a pre-processing step using a contrast enhancement method, all the infested blobs are segmented for the whole dataset. A list of several measurement-based features that represents the blobs are chosen and then selected based on their influences on the model’s performance using a wrapper-based feature selection algorithm, which is built based on a hybrid metaheuristic. The chosen features are used as inputs for an ANN. We compare the results obtained using our methods with another approach using popular CNN models (AlexNet, VGG16, ResNet-50) enhanced with transfer learning. The ANN’s results are better than those of CNNs using a simpler network structure (89.41% vs 78.64%, 79.92%, and 84.88%, respectively). This shows that our approach can be implemented on low-end devices such as smartphones, which will be of great assistance to farmers on the field.

Diabetes leads to health problems for hundreds of millions of people globally every year. Available medical records of patients quantify symptoms, body features, and clinical laboratory test values, which can be used to perform biostatistics analysis aimed at finding patterns or features undetectable by current practice. In this work, we proposed a machine learning model to predict the early onset of diabetes patients. It is a novel wrapper-based feature selection utilizing Grey Wolf Optimization (GWO) and an Adaptive Particle Swam Optimization (APSO) to optimize the Multilayer Perceptron (MLP) to reduce the number of required input attributes. Moreover, we also compared the results achieved using this method and several conventional machine learning algorithms approaches such as Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbor (KNN), Naïve Bayesian Classifier (NBC), Random Forest Classifier (RFC), Logistic Regression (LR). Computational results of our proposed method show not only that much fewer features are needed, but also higher prediction accuracy can be achieved (96% for GWO - MLP and 97% for APGWO - MLP). This work has the potential to be applicable to clinical practice and become a supporting tool for doctors/physicians.

Epileptic seizure is a neurological condition caused by short and unexpectedly occurring electrical disruptions in the brain. It is estimated that roughly 60 million individuals worldwide have had an epileptic seizure. Experiencing an epileptic seizure can have serious consequences for the patient. Automatic seizure detection on electroencephalogram (EEG) recordings is essential due to the irregular and unpredictable nature of seizures. By thoroughly analyzing EEG records, neurophysiologists can discover important information and patterns, and proper and timely treatments can be provided for the patients. This research presents a novel machine learning-based approach for detecting epileptic seizures in EEG signals. A public EEG dataset from the University of Bonn was used to validate the approach. Meaningful statistical features were extracted from the original data using discrete wavelet transform analysis, then the relevant features were selected using feature selection based on the binary particle swarm optimizer. This facilitated the reduction of 75% data dimensionality and 47% computational time, which eventually sped up the classification process. After having been selected, relevant features were used to train different machine learning models, then hyperparameter optimization was utilized to further enhance the models’ performance. The results achieved up to 98.4% accuracy and showed that the proposed method was very effective and practical in detecting seizure presence in EEG signals. In clinical applications, this method could help relieve the suffering of epilepsy patients and alleviate the workload of neurologists.

Falls are one of the most serious dangers for elderly people who live alone at home. It has become a widespread issue all across the world. Reliable fall detection systems can help to mitigate the negative consequences of accidentally falling. Several techniques for automatically falling detection machines have been suggested in the past. The existing technologies are classified into three types of fall detectors: wearable sensor-based, ambient device-based, and computer vision-based approaches. This paper focuses on a dataset comprising signals from wearable sensors, ambient sensors, and vision devices. We propose a novel feature subset selection to reduce the number of effective input attributes based on a hybridized metaheuristic - an Adaptive Particle Swarm and Grey Wolf Optimization (APGWO). Classification results use various machine learning classifiers such as Logistic Regression (LR), K-Nearest Neighbor, Support Vector Machine (SVM), Decision Tree (DT), Naïve Bayes (NB), Random Forest (RF), and Multilayer Perceptron (MLP), show that the proposed approach is highly effective. Classification accuracy and F ₁ score can reach as high as 99% and 96%, respectively.

Among physical attributes, dimensions and weight of agricultural products are important parameters that are useful for designing grading/packaging systems. Size and weight measurements usually require extensive time and labor. Moreover, it is often difficult to accurately do the measurements for the products having irregular shapes, such as sweet potatoes. Even though there are many previous works on the topic, they either lack accuracy or require multiple captures and computing power to reconstruct the three-dimensional representation of the products. This paper proposes the measurement of sweet potato features, including the body length, width, and thickness, by using a simple vision system that requires only a single camera to capture a top-view image of the products. After the background segmentation step, the product is virtually sliced into many equal slices along the longitudinal axis. The volume of the product is calculated as the summation of the volumes of these individual slices. Since there is a high correlation between the products’ weight and volume, the weight of the product can be calculated. The experimental results obtained show that the proposed approach is accomplished with highly competitive results, which have an accuracy of up to 96% (with an R² of 0.98) for the volume estimation task and an accuracy of up to 95% (with an R² of 0.96) for the weight estimation task. Due to its simplicity, this model can be used to design and develop the sizing/weighing/packaging systems.

Parkinson’s Disease (PD) is a brain disorder that causes uncontrollable movements. According to estimation, roughly ten million individuals worldwide have had or are developing PD. This disorder can have severe consequences that affect the patient’s daily life. Therefore, several previous works have worked on PD detection. Automatic Parkinson’s Disease detection in voice recordings can be an innovation compared to other costly methods of ruling out examinations since the nature of this disease is unpredictable and non-curable. Analyzing the collected vocal records will detect essential patterns, and timely recommendations on appropriate treatments will be extremely helpful. This research proposed a machine learning-based approach for classifying healthy people from people with the disease utilizing Grey Wolf Optimization (GWO) for feature selection, along with Light Gradient Boosted Machine (LGBM) to optimize the model performance. The proposed method shows highly competitive results and has the ability to be developed further and implemented in a real-world setting.