Unmanned aerial vehicles (UAVs), commonly known as drones, have found extensive applications across diverse sectors, such as agriculture, delivery, surveillance, and military. In recent times, drone swarming has emerged as a novel field of research, which involves multiple drones working in collaboration towards a shared objective. This innovation holds immense potential in transforming the way we undertake tasks, including military operations, environmental monitoring, and search and rescue missions. However, the emergence of drone swarms also brings new security challenges, as they can be susceptible to hacking and intrusion. To address these concerns, we propose utilizing a timed probabilistic automata (TPA)-based intrusion detection system (IDS) to model the normal behavior of drone swarms and identify any deviations that may indicate an intrusion. This IDS system is particularly efficient and adaptable in detecting different types of attacks in drone swarming. Its ability to adapt to evolving attack patterns and identify zero-day attacks makes it an invaluable tool in protecting drone swarms from malicious attacks.

The Selection of the mining method for underground minerals extraction is the crucial task for the mining engineers. Underground minerals extraction is a multi-criteria decision making problem due to many criteria to be considered in the selection process. There are many studies on selection of underground mining method using Multi Criteria Decision Making (MCDM) techniques or approaches. Extracting minerals from the underground involves many geological characteristics also called as input parameters. The geological characteristics of any mineral deposit vary from one location to another location. Thus only one mineral extraction method is not suitable for different deposit characteristics. There are many mineral extraction methods available for different characteristics of the ore deposit. As of now only MCDM approach or Hybrid MCDM approaches or MCDM approaches with fuzzy logic were used for selecting a mining method for underground metal mine. In this study, only fuzzy logic approach is used for selecting a mining method for different deposit characteristics. The proposed model considers five deposit characteristics as input parameters and seven underground mining methods output parameters The developed fuzzy logic based approach is also validated by the deposit characteristics of two Indian mines. The model produced the suitable mining method for extraction of the minerals at the specified Indian mines and the same mining methods are used by the mine authorities.

Unmanned Aerial Vehicles (UAVs) equipped with remote sensing (RS) abilities have revolutionized agriculture practices, especially in the area of plant disease detection, with the incorporation of deep learning (DL) techniques. Using the UAVs to capture high-resolution images of agricultural regions, RS information becomes widely accessible for monitoring and identifying crop health and signs of diseases. DL models, specifically convolutional neural networks (CNNs), are employed to analyze these images, enabling automated recognition and detection of plant diseases with enhanced accuracy. This integration of UAV-based RS and DL models facilitates early disease detection, allowing farmers to implement timely interventions, such as targeted pesticide application or crop management strategies, ensuring food security and reducing yield losses. Moreover, this model’s scalability and efficiency improve its value for precision agriculture, optimizing resource usage and promoting sustainable farming practices. This manuscript proposes an Optimal Fuzzy Deep Neural Networks-based Plant Disease Detection and Classification (OFDNN-PDDC) technique on UAV-based Remote Sensed Data. The purpose of the OFDNN-PDDC technique is to proficiently detect and classify the distinct kinds of plant diseases. The OFDNN-PDDC technique follows a three-stage process to enhance plant disease detection performance. At the primary level, the OFDNN-PDDC technique employs an improved ShuffleNetv2 model for learning complex and intrinsic feature patterns on the RS data. Besides, the OFDNN-PDDC technique utilizes a fuzzy restricted Boltzmann machine (FRBM) model to detect plant diseases. Finally, the hyperparameter selection of the OFDNN-PDDC technique is performed by the tent chaotic salp swarm algorithm (TCSSA) model. To illustrate the superior outcomes of the OFDNN-PDDC technique, detailed experiments were accomplished by utilizing APD and CPD datasets. The experimental validation of the OFDNN-PDDC technique portrayed a superior accuracy value of 96.18% and 98.85% over existing techniques under the used datasets.