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Internet of Things (IoT) security refers to different aspects of security, including methods, tactics, and technologies used to protect these devices from unauthorized access. However, it is connected with multiple physical devices to perform huge tasks simultaneously and secure the data transmitted through the IoT network. Furthermore, the IoT is used to transmit sensitive data and validate security performance. Mostly Machine Learning (ML) algorithms are widely utilized for the process of anomaly detection. However the application of the ML model fails to detect attacks in IoT, to overcome this, a novel Stacked Long Short Term Memory based Willow Catkin Optimization (SLSTM-WCO) algorithm is proposed to detect intrusion anomalies in IoT networks. The complex patterns and abnormalities are predicted by determining the regularization method. Also, the deep learning (DL) model such as stacked LSTM detects the anomaly accurately and improves the effectiveness. The detection performance is validated by using benchmark datasets such as BoT-IoT, IoT network Intrusion, IoT-23, MQTT, and MQTTset which enhanced the efficiency. The outcome of the SLSTM-WCO method improved accuracy by 99.49% and improved anomaly detection in IoT networks compared to existing methods. [ABSTRACT FROM AUTHOR]

In recent times, the images and videos have emerged as one of the most important information source depicting the real time scenarios. Digital images nowadays serve as input for many applications and replacing the manual methods due to their capabilities of 3D scene representation in 2D plane. The capabilities of digital images along with utilization of machine learning methodologies are showing promising accuracies in many applications of prediction and pattern recognition. One of the application fields pertains to detection of diseases occurring in the plants, which are destroying the widespread fields. Traditionally the disease detection process was done by a domain expert using manual examination and laboratory tests. This is a tedious and time consuming process and does not suffice the accuracy levels. This creates a room for the research in developing automation based methods where the images captured through sensors and cameras will be used for detection of disease and control its spreading. The digital images captured from the field's forms the dataset which trains the machine learning models to predict the nature of the disease. The accuracy of these models is greatly affected by the amount of noise and ailments present in the input images, appropriate segmentation methodology, feature vector development and the choice of machine learning algorithm. To ensure the high rated performance of the designed system the research is moving in a direction to fine tune each and every stage separately considering their dependencies on subsequent stages. Therefore the most optimum solution can be obtained by considering the image processing methodologies for improving the quality of image and then applying statistical methods for feature extraction and selection. The training vector thus developed is capable of presenting the relationship between the feature values and the target class. In this article, a highly accurate system model for detecting the diseases occurring in citrus fruits using a hybrid feature development approach is proposed. The overall improvement in terms of accuracy is measured and depicted. [ABSTRACT FROM AUTHOR]