The major concern in Pakistani agriculture is the reduction of growing weed. This research aims to provide a weed detection tool for future agri-robots. The weed detection tool incorporates the use of machine-learning procedure explicitly implementing Support Vector Machines (SVMs) and blob analysis for the effective classification of crop and weed. Weed revealing is based on characteristic features i.e. red green blue (RGB) components which differentiate soil and plant. Morphological features—centroidand length aid to distinguish shape of crop and weed leaves. Following feature extraction, the positive and negative margins are separated by a hyper-plane. The separating hyper-plane acts as the decision surface. Sample input consists of multiple digital field images of carrot crops. Training samples of seventy two images are taken. Accuracy of the outcomes discloses that SVM and blob analysis attain above 50-95% accuracy.
Internet of Things (IoT) is the major technology of the 4 th industrial revolution in which various types of devices are connected together to work smartly without the intervention of humans. IoT seems to impart a great impact on our social, economic, and commercial lives. IoT applications are converting from smart home and smart me to the smart cities or smart planet. However, the large number of devices interconnected with each other by multi protocols puts the security of IoT networks on the verge of threats. Making the IoT devices more secure is also not feasible because of their limited computational power. Hence, there is a need for advancement in methods to secure IoT networks. Machine Learning (ML) models have been hot topics in security research in past years. As the IoT devices generate tons of data on a daily basis which can be used to train ML algorithms, it could be a reasonable solution to provide security to IoT systems. In this work, the main goal is to provide a broader survey of research works in the IoT security field regarding ML implementation. We briefly described the security issues in IoT networks and their impact on the privacy of important data. We then shed light on different ML algorithms and models and discussed their advantages, disadvantages, and applications in IoT individually. Moreover, the ML models currently working in IoT networks for security purposes are discussed. We also talked about the limitations of using ML models to secure the IoT networks which could provide new future research directions.
Automated brain tumor detection is an important application in the medical field. There is a lot of methods developed for this task. In this paper, we have implemented an algorithm which detects the type of brain tumor from MRI image using supervised classification techniques. The major part of the work includes feature extraction using DWT and then reduction of features by using PCA. These reduced features are submitted to different classifiers like SVM, k-NN, Naïve Bayes and LDA. The results from each classifier are then submitted to a voting algorithm that chooses the most frequent result. The dataset for training contains 160 MRI images. The algorithm is processed on 200 * 200 images to reduce processing time. This method is tested and found to be much beneficial and rapid. It could be utilized in the field of MRI classification and can assist doctors to detect the tumor type and diagnose about patient abnormality level.
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