Malaysian Journal of Computer Science

ENHANCING BRIX VALUE PREDICTION IN STRAWBERRIES USING MACHINE LEARNING: A FUSION OF PHYSIOCHEMICAL AND COLOR-BASED FEATURES FOR IMPROVED SWEETNESS ASSESSMENT

2024-05-03T06:02:32+08:00

This study contributes to the ongoing wave of artificial intelligence integration by applying machine learning techniques to automate the assessment of strawberry quality. This research focuses on determining if the sweetness of strawberries can be predicted using a combination of physiochemical variables, their interaction parameters, and color-based features extracted from image data. This research used a 150-sample collection of strawberry images and physiochemical characteristics such as salinity, specific gravity, pH, and Brix. Normalized raw and derived feature variables and selected dataset transformations were done. We then split the dataset into mutual exclusivity training and test sets. Exponential Gaussian Process Regression (GPR) suited well due to low validation errors. This best model predicted Brix values for the remaining test samples. The Mean Absolute Percentage Error(MAPE) showed 98.783% forecast accuracy (Acc). We also examined the model's coefficient of determination (R²) values, which were 0.78 and 0.9739 for training and testing, respectively. The Mean Square Error (MSE) and Mean Absolute Error (MAE) obtained after training were 0.32994 and 0.0453, and testing was 0.35286 and 0.0663. Using input characteristics with high Acc and low error rates, deep learning models like Recurrent Neural Network (RNN) and its derivatives were constructed. Using physiochemical and visual data, machine learning and deep learning models successfully predict strawberry sweetness. This prediction accuracy shows the complex link between internal components and Brix readings, enabling high-quality strawberry production.

USAGE OF PARTICLE SWARM OPTIMIZATION IN DIGITAL IMAGES SELECTION FOR MONKEYPOX VIRUS PREDICTION AND DIAGNOSIS

2024-05-03T06:10:09+08:00

Identifying skin diseases by using digital images of skin that are also automated, efficient, and accurate is critical for biomedical image analysis. Many researchers have developed numerous machine-learning techniques for the prediction and diagnosis of various diseases that help clinicians identify infections early and provide crucial data for virus management. In this work, we use the inherent attributes of Particle Swarm Optimization (PSO), such as exploration and exploitation, to identify images for monkeypox virus prediction and diagnosis. Alongside, monkeypox, chickenpox, smallpox, cowpox, measles, tomato flu, and normal skin images were all considered in this study for monkeypox virus prediction and diagnosis. We collect photos from the International Skin Imaging Collaboration (ISIC) for analysis and experimentation purposes. Finally, we compare the proposed model Particle Swarm Optimization- Monkeypox Virus (PSOMPX) for monkeypox virus identification with four distinct pre-trained deep learning models (e.g., VGG16, ResNet50, InceptionV3, and Ensemble). Then we use four performance evaluation metrics—accuracy, precision, recall, and F1 score—to evaluate the model and analyze the outcomes of experiments. The experimental results obtained through the PSOMPX model significantly outperform other models due to its numerous traits.

ENHANCING IIOT SECURITY WITH MACHINE LEARNING AND DEEP LEARNING FOR INTRUSION DETECTION

2024-05-10T21:31:20+08:00

The rapid growth of the Internet of Things (IoT) and digital industrial devices has significantly impacted various aspects of life, underscoring the importance of the Industrial Internet of Things (IIoT). Given its importance in industrial contexts that affect human life, the IIoT represents a key subset of the broader IoT landscape. Due to the proliferation of sensors in smart devices, which are viewed as points of contact, as the gathering of data and information regarding the IIoT systems and devices operating on the IoT, there is an urgent requirement for developing effective security methods to counter such threats as well as protecting IIoT systems. In this study, we develop and evaluate a well-optimized intrusion detection system (IDS) based on deep learning (DL) and machine learning (ML) techniques to enhance IIoT security. Leveraging the Edge-IIoTset dataset, specifically designed for IIoT cybersecurity evaluations, we focus on detecting and mitigating 14 distinct attack types targeting IIoT and IoT protocols. These attacks are categorized into five threat groups: information collection, malware, DDoS, man-in-the-middle attacks, and injection attacks. We conducted experiments using machine learning algorithms (k-nearest neighbors, decision tree) and a neural network on the KNIME platform, achieving a remarkable 100% accuracy with the decision tree model. This high accuracy demonstrates the effectiveness of our approach in protecting industrial IoT networks.