IoT architectures facilitate us to generate data for large and remote agriculture areas and the same can be utilized for Crop predictions using this machine learning algorithm. Recommendations are based on the following N, P, K, pH, Temperature, Humidity, and Rainfall these attributes decide the crop to be recommended. The data set has 2200 instances and 8 attributes. Nearly 22 different crops are recommended for a different combination of 8 attributes. Using the supervised learning method, the optimum model is attained using selected machine learning algorithms in WEKA. The Machine learning algorithm selected for classifying is multilayer perceptron rules-based classifier JRip, and decision table classifier. The main objective of this case study is to end up with a model which predicts the high yield crop and precision agriculture. The proposed system modeling incorporates the trending technology, IoT, and Agriculture needy measurements. The performance assessed by the selected classifiers is 98.2273%, the Weighted average Receiver Operator Characteristics is 1 with the maximum time taken to build the model being 8.05 s.

Applications of learning models for text-based datasets as well as image pixels-based datasets grow rapidly for prediction purposes. Pre-processing becomes challenging in carrying out image filtering and classifying. Retinal Fundus images plays important role in Diabetic Retinopathy (DR) diagnosis and treatment planning in various stages. Diabetic Retinopathy is diagnosed by observing the variation in retinal blood vessel, exudates, micro aneurysm, hemorrhages, and the new blood vessel growth inside the retina. The objective of this study is to enrich the diagnosis for the Diabetic Retinopathy from the retinal fundus images by applying machine learning algorithms. The proposed work implements normalization, parameter tuning, and optimal feature selection method to improve the classification accuracy offered by selected algorithms like decision tree algorithm and K-nearest neighborhood classifiers. The highest accuracy of 81.99%, Weighted Average of Receiver Operating Characteristics (ROC) 0.907 are obtained by k-Nearest Neighbor (KNN) classifier due to its best performance.

Intrusion detection models using machine-learning algorithms are used for Intrusion prediction and prevention purposes. Wireless sensor network has a possibility of being attacked by various kinds of threats that will de-promote the performance of any network. These WSN are also affected by the sensor networks that send wrong information because of some environmental causes in- built disturbances misaligned management of the sensors in creating intrusion to the wireless sensor networks. Even though signified routing protocols cannot assure the required security in wireless sensor networks. The idea system provides a key solution for this kind of problem that arises in the network and predicts the abnormal behavior of the sensor nodes as well. But built model by the proposed system various approaches in detecting these kinds of intrusions in any wireless sensor networks in the past few years. The proposed system methodology gives a phenomenon control over the wireless sensor network in detecting the inclusions in its early stages itself. The Data set pre-processing is done by a method of applying the minimum number of features for intrusion detection systems using a machine learning algorithm. The main scope of this article is to improve the prediction of intrusion in a wireless sensor network using AI- based algorithms. This also includes the finest feature selection methodologies to increase the performance of the built model using the selected classifier, which is the Bayes category algorithm. Performance accuracy in the prediction of different attacks in wireless sensor networks is attained at nearly 95.8% for six selected attributes, a Precision level of 0.958, and the receiver operating characteristics or the area under the curve is equal to 0.989.

Mental stress is a psychological or emotional strain that typically occurs because of threatening, challenging, and overwhelming conditions and affects human behavior. Various factors, such as professional, environmental, and personal pressures, often trigger it. In recent years, various deep learning (DL)-based schemes using electroencephalograms (EEGs) have been proposed. However, the effectiveness of DL-based schemes is challenging because of the intricate DL structure, class imbalance problems, poor feature representation, low-frequency resolution problems, and complexity of multi-channel signal processing. This paper presents a novel hybrid DL framework, BDDNet, which combines a deep convolutional neural network (DCNN), bidirectional long short-term memory (BiLSTM), and deep belief network (DBN). BDDNet provides superior spectral–temporal feature depiction and better long-term dependency on the local and global features of EEGs. BDDNet accepts multiple EEG features (MEFs) that provide the spectral and time-domain features of EEGs. A novel improved crow search algorithm (ICSA) was presented for channel selection to minimize the computational complexity of multichannel stress detection. Further, the novel employee optimization algorithm (EOA) is utilized for the hyper-parameter optimization of hybrid BDDNet to enhance the training performance. The outcomes of the novel BDDNet were assessed using a public DEAP dataset. The BDDNet-ICSA offers improved recall of 97.6%, precision of 97.6%, F1-score of 97.6%, selectivity of 96.9%, negative predictive value NPV of 96.9%, and accuracy of 97.3% to traditional techniques.

Wireless Sensor Networks (WSNs) play a vital role in applications like environmental monitoring and military operations but face significant security challenges such as Denial of Service (DoS), Sybil, and sinkhole attacks. Traditional security mechanisms are ineffective due to their reliance on static rules, high false positive rates, and limited scalability. This chapter introduces a machine learning (ML)-based real-time threat detection system using Decision Trees (DT), Support Vector Machines (SVM), and Neural Networks (NN) to identify network anomalies dynamically. The system preprocesses real-time sensor data, extracts relevant features, and continuously retrains models to detect both known and novel attacks with high accuracy. Compared to existing systems, the proposed approach demonstrates superior performance, achieving 95.1% accuracy versus 85.7% and 82.3% for previous models. It enhances detection accuracy (97.5%) and true positive rate (95.8%), making it more reliable for intrusion detection.

In the sphere of agriculture, the challenge of recognizing and detecting plant illnesses is proposed in this study, and it is anticipated that this problem will be challenging. A system has been proposed to automatically detect and track cases of pest diseases in agricultural settings. When performing image processing, it is necessary to first obtain the image and then use a number of preprocessing techniques, such as feature extraction, segmentation, and modification. In order to identify any unwanted organisms, it is necessary to compare the original image with the one that was obtained. Upon receiving the Matlab result that was achieved, the controller is notified. The controller is responsible for carrying out the essential management measures in accordance with the level of the pest's impact on the paddy crops. A pesticide will be sprayed on the crop in proportion to the percentage of the crop that is impacted. Therefore, the rice field may be identified in a very short amount of time and with a high degree of efficiency.