In recent years, human–drone interaction has received increasing interest from the scientific community. When interacting with a drone, humans assume a variety of roles, the nature of which are determined by the drone’s application and degree of autonomy. Common methods of controlling drone movements include by RF remote control and ground control station. These devices are often difficult to manipulate and may even require some training. An alternative is to use innovative methods called natural user interfaces that allow users to interact with drones in an intuitive manner using speech. However, using only one language of interacting may limit the number of users, especially if different languages are spoken in the same region. Moreover, environmental and propellers noise make speech recognition a complicated task. The goal of this work is to use a multilingual speech recognition system that includes English, Arabic, and Amazigh to control the movement of drones. The reason for selecting these languages is that they are widely spoken in many regions, particularly in the Middle East and North Africa (MENA) zone. To achieve this goal, a two-stage approach is proposed. During the first stage, a deep learning based model for multilingual speech recognition is designed. Then, the developed model is deployed in real settings using a quadrotor UAV. The network was trained using 38,850 records including commands and unknown words mixed with noise to improve robustness. An average class accuracy of more than 93% has been achieved. After that, experiments were conducted involving 16 participants giving voice commands in order to test the efficiency of the designed system. The achieved accuracy is about 93.76% for English recognition and 88.55%, 82.31% for Arabic and Amazigh, respectively. Finally, hardware implementation of the designed system on a quadrotor UAV was made. Real time tests have shown that the approach is very promising as an alternative form of human–drone interaction while offering the benefit of control simplicity.

The past decade has witnessed a growing demand for drone-based fire detection systems, driven by escalating concerns about wildfires exacerbated by climate change, as corroborated by environmental studies. However, deploying existing drone-based fire detection systems in real-world operational conditions poses practical challenges, notably the intricate and unstructured environments and the dynamic nature of UAV-mounted cameras, often leading to false alarms and inaccurate detections. In this paper, we describe a two-stage framework for fire detection and geo-localization. The key features of the proposed work included the compilation of a large dataset from several sources to capture various visual contexts related to fire scenes. The bounding boxes of the regions of interest were labeled using three target levels, namely fire, non-fire, and smoke. The second feature was the investigation of YOLO models to undertake the detection and localization tasks. YOLO-NAS was retained as the best performing model using the compiled dataset with an average mAP50 of 0.71 and an F1_score of 0.68. Additionally, a fire localization scheme based on stereo vision was introduced, and the hardware implementation was executed on a drone equipped with a Pixhawk microcontroller. The test results were very promising and showed the ability of the proposed approach to contribute to a comprehensive and effective fire detection system.

The field of biometric identification has seen significant advancements over the years, with research focusing on enhancing the accuracy and security of these systems. One of the key developments is the integration of deep learning techniques in biometric systems. However, despite these advancements, certain challenges persist. One of the most significant challenges is scalability over growing complexity. Traditional methods either require maintaining and securing a growing database, introducing serious security challenges, or relying on retraining the entire model when new data is introduced—a process that can be computationally expensive and complex. This challenge underscores the need for more efficient methods to scale securely. To this end, we introduce a novel approach that addresses these challenges by integrating multimodal biometrics, cancelable biometrics, and incremental learning techniques. This work is among the first attempts to seamlessly incorporate deep cancelable biometrics with dynamic architectural updates, applied incrementally to the deep learning model as new users are enrolled, achieving high performance with minimal catastrophic forgetting. By leveraging a One-Dimensional Convolutional Neural Network (1D-CNN) architecture combined with a hybrid incremental learning approach, our system achieves high recognition accuracy, averaging 98.98% over incrementing datasets, while ensuring user privacy through cancelable templates generated via a pre-trained CNN model and random projection. The approach demonstrates remarkable adaptability, utilizing the least intrusive biometric traits like facial features and fingerprints, ensuring not only robust performance but also long-term serviceability.

Cloud computing is an emerging paradigm where computing services are provided across the web. Virtualization powers the cloud by mutualizing physical resources thus ensuring flexibility and high availability of the cloud. Certainly fault tolerance like load balancing or advancement programming security aim to foster availability but classic reactive fault tolerance techniques prove to be greedy in terms of memory and recovery time. Elsewhere, proactive fault tolerance is possible by preemptive virtual machine migration requiring a strong and accurate failure predictor. In quest of an effective approach for proactive fault tolerance we introduce in this paper a probabilistic model of the cloud with a failure generator for evaluating a proposed approach based on three scenarios of virtual machine migration.

In order to implement clustering under the condition that the number of clusters is not known a priori, the authors propose a novel automatic clustering algorithm in this chapter, based on particle swarm optimization algorithm. ACPSO can partition images into compact and well separated clusters without any knowledge on the real number of clusters. ACPSO used a novel representation scheme for the search variables in order to determine the optimal number of clusters. The partition of each particle of the swarm evolves using evolving operators which aim to reduce dynamically the number of naturally occurring clusters in the image as well as to refine the cluster centers. Experimental results on real images demonstrate the effectiveness of the proposed approach.

DNA Microarray datasets, also known as “omics” data, are important for the diagnosis of numerous diseases, including cancer and tumors. In the analysis of these data, feature selection techniques and classification algorithms are the workhorse for choosing candidate genes that serve as cancer biomarkers. However, microarray datasets present a challenge; they contain a greater number of features than the samples, which affects the performance of algorithms used in the analysis process. In order to extract precise information, it is necessary to employ a method that is both robust and performant. This paper emphasizes the importance of accurate and stable gene selection for the discovery of knowledge derived from high-dimensional data. A novel hybrid framework was put forth for consideration, comprising three distinct stages: Clustering, Parallel Filtering, and Hybrid-Parallel Optimization. In each step, a combination of techniques and algorithms is used to improve the results in terms of stability and/or accuracy. The proposal is evaluated and tested according to different scenarios; using thirteen gene expression datasets and two classifiers: Artificial Neural Network (ANN) and Naïve Bayes (NB).Comparison with related work demonstrates the efficacy of this approach, which enhances classification accuracy and stability while reducing the number of selected genes.