The Internet of Things (IoT) technology has the means to shape the future of many industries. Data is the language of the communication between different nodes through the network; the networks are the communication channel. The cloud is the home and destination of the data which adds intelligence through data analytics software, Precision agriculture uses the IoT features to help in managing crops production, by optimizing the quality of the crops through applying required nutrients and reduce the harmful impacts on the environment due to the application of excess pesticides. In this paper, we deployed a sensing network to gather the field data of some crops (Potatoes, Tomatoes, etc.), then fed these data to a machine learning algorithm to get a warning message finally displaying both the data and the warning message through a Graphical User Interface (GUI).

Network Intrusion Detection Systems (NIDS) are critical for protecting computer networks from unauthorized activities. Traditional NIDS rely on rule-based signatures, which can be limiting in detecting emerging threats. This study investigates the effectiveness of the random forest classifier in advancing NIDS capabilities through machine learning. Using the CICIDS-2017 dataset, the data are preprocessed to enhance their quality by removing redundancies. feature selection and permutation importance were employed to identify the most relevant features. The methodology involves rigorous testing and analysis of the random forest classifier’s performance, focusing on f1-score rates compared to other machine learning models. Results demonstrate that by optimizing class weights, applying a custom prediction function and leveraging 26 key features, the random forest classifier achieves an outstanding 99.8% in the weighted f1-score and 93.31% in the macro f1-score in various attack types. This research highlights the potential of machine learning to significantly enhance NIDS effectiveness, offering a robust defense mechanism against evolving cybersecurity threats in modern networks.

As a trending medical imaging technique, Elastography and B-mode (ultrasound) are combined as a diagnostic tool to differentiate between benign and malignant breast lesions based on their stiffness and geometric properties. Image processing techniques are applied to the resulting images for feature extraction. Data preprocessing methods and principal component analysis (PCA) as a dimensionality reduction technique are applied to the dataset. In this paper, supervised learning algorithm “support vector machine (SVM)” is used for the classification of combined elastogram and B-mode images. Model validation is performed with K-fold cross-validation to ensure the generalization of the algorithm. Accuracy, confusion matrix, and logistic loss are then evaluated for the used algorithm. The maximum classification accuracy is 94.12% when using SVM with radial basis function (RBF) kernel.

Breast cancer constitutes a significant threat to women’s health and is considered the second leading cause of their death. Breast cancer is a result of abnormal behavior in the functionality of the normal breast cells. Therefore, breast cells tend to grow uncontrollably, forming a tumor that can be felt like a breast lump. Early diagnosis of breast cancer is proved to reduce the risks of death by providing a better chance of identifying a suitable treatment. Machine learning and artificial intelligence play a key role in healthcare systems by assisting physicians in diagnosing early, better, and treating various diseases. For achieving the early detection of breast cancer, this paper proposes a Machine Learning-based two-level top-down hierarchical approach for breast cancer detection and classification into three classes: normal, benign, and malignant, using the Mammographic Image Analysis Society (MIAS) mammography dataset. Different data preprocessing techniques are applied before using feature extraction techniques and machine learning algorithms for classification. The first classification stage which distinguishes between normal and abnormal cases is comprised of Gray Level Co-occurrence Matrix (GLCM) as a feature extraction technique and random forest as a classifier, followed by the second classification stage which classifies the abnormal cases into benign or malignant cases and is comprised of Local Binary Patterns (LBP) as a feature extraction technique and random forest as a classifier. The classification accuracy for the first stage is 97% and an F1-score of 0.98 and 0.97 for normal and abnormal classes. While for the second stage, the classification accuracy is 75% and an F1-score of 0.76 and 0.74 for benign and malignant classes. The overall hierarchical classification system achieves a classification accuracy of 85%, Matthews correlation coefficient (MCC) of 0.76, and F1-score of 0.98, 0.7, and 0.74 for normal, benign, and malignant test cases.

Automatic lane detection is a classical task in autonomous vehicles that traditional computer vision techniques can perform. However, such techniques lack reliability for achieving high accuracy while maintaining adequate time complexity in the context of real-time detection in complex and dynamic road scenes. Deep neural networks have proved their ability to achieve competing accuracy and time complexity while training them on manually labeled data. Yet, the unavailability of segmentation masks for host lanes in harsh road environments hinders fully supervised methods’ operability on such a problem. This work proposes integrating traditional computer vision techniques and deep learning methods to develop a reliable benchmarking framework for lane detection tasks in complex and dynamic road scenes. Firstly, an automatic segmentation algorithm based on a sequence of traditional computer vision techniques has been experimented. This algorithm precisely segments the semantic region of the host lane in the complex urban images of nuScenes dataset used in this framework; hence corresponding weak labels are generated. After that, the developed data is qualitatively evaluated to be used in training and benchmarking five state-of-the-art FCN-based architectures: SegNet, Modified SegNet, U-Net, ResUNet, and ResUNet++. The performance evaluation of the trained models is done visually and quantitatively by considering lane detection a binary semantic segmentation task. The output results show robust performance, especially ResUNet++, which outperforms all the other models while testing them in different complex road scenes with dynamic scenarios and various lighting conditions.