A brain tumour is one of the major reasons for death in humans, and it is the tenth most common type of tumour that affects people of all ages. However, if detected early, it is one of the most treatable types of tumours. Brain tumours are classified using biopsy, which is not usually performed before definitive brain surgery. An image classification technique for tumour diseases is important for accelerating the treatment process and avoiding surgery and errors from manual diagnosis by radiologists. The advancement of technology and machine learning (ML) can assist radiologists in tumour diagnostics using magnetic resonance imaging (MRI) images without invasive procedures. This work introduced a new hybrid CNN-based architecture to classify three brain tumour types through MRI images. The method suggested in this paper uses hybrid deep learning classification based on CNN with two methods. The first method combines a pre-trained Google-Net model of the CNN algorithm for feature extraction with SVM for pattern classification. The second method integrates a finely tuned Google-Net with a soft-max classifier. The proposed approach was evaluated using MRI brain images that contain a total of 1426 glioma images, 708 meningioma images, 930 pituitary tumour images, and 396 normal brain images. The reported results showed that an accuracy of 93.1% was achieved from the finely tuned Google-Net model. However, the synergy of Google-Net as a feature extractor with an SVM classifier improved recognition accuracy to 98.1%.

Flying Ad Hoc Network (FANET) or drones’ technologies have gained much attraction in the last few years due to their critical applications. Therefore, various studies have been conducted on facilitating FANET applications in different fields. In fact, civil airspaces have gradually adopted FANET technology in their systems. However, FANET’s special roles made it complex to support emerging security threats, especially intrusion detection. This paper is a step forward towards the advances in FANET intrusion detection techniques. It investigates FANET intrusion detection threats by introducing a real-time data analytics framework based on deep learning. The framework consists of Recurrent Neural Networks (RNN) as a base. It also involves collecting data from the network and analyzing it using big data analytics for anomaly detection. The data collection is performed through an agent working inside each FANET. The agent is assumed to log the FANET real-time information. In addition, it involves a stream processing module that collects the drones’ communication information, including intrusion detection-related information. This information is fed into two RNN modules for data analysis, trained for this purpose. One of the RNN modules resides inside the FANET itself, and the second module resides at the base station. An extensive set of experiments were conducted based on various datasets to examine the efficiency of the proposed framework. The results showed that the proposed framework is superior to other recent approaches.

The search algorithm based on symbiotic organisms’ interactions is a relatively recent bio-inspired algorithm of the swarm intelligence field for solving numerical optimization problems. It is meant to optimize applications based on the simulation of the symbiotic relationship among the distinct species in the ecosystem. The task scheduling problem is NP complete, which makes it hard to obtain a correct solution, especially for large-scale tasks. This paper proposes a modified symbiotic organisms search-based scheduling algorithm for the efficient mapping of heterogeneous tasks to access cloud resources of different capacities. The significant contribution of this technique is the simplified representation of the algorithm’s mutualism process, which uses equity as a measure of relationship characteristics or efficiency of species in the current ecosystem to move to the next generation. These relational characteristics are achieved by replacing the original mutual vector, which uses an arithmetic mean to measure the mutual characteristics with a geometric mean that enhances the survival advantage of two distinct species. The modified symbiotic organisms search algorithm (G_SOS) aims to minimize the task execution time (makespan), cost, response time, and degree of imbalance, and improve the convergence speed for an optimal solution in an IaaS cloud. The performance of the proposed technique was evaluated using a CloudSim toolkit simulator, and the percentage of improvement of the proposed G_SOS over classical SOS and PSO-SA in terms of makespan minimization ranges between 0.61–20.08% and 1.92–25.68% over a large-scale task that spans between 100 to 1000 Million Instructions (MI). The solutions are found to be better than the existing standard (SOS) technique and PSO.

Drone Forensics (DRF) is one of the subdomains of digital forensics, which aims to capture and analyse the drone’s incidents. It is a diverse, unclear, and complex domain due to various drone field standards, operating systems, and infrastructure-based networks. Several DRF models and frameworks have been designed based on different investigation processes and activities and for the specific drones’ scenarios. These models make the domain more complex and unorganized among domain forensic practitioners. Therefore, there is a lack of a generic model for managing, sharing, and reusing the processes and activities of the DRF domain. This paper aims to develop A Drone Forensic Metamodel (DRFM) for the DRF domain using the metamodeling development process. The metamodeling development process is used for constructing and validating a metamodel and ensuring that the metamodel is complete and consistent. The developed DRFM consists of three main stages: (1) identification stage, (2) acquisition and preservation stage, and (3) examination and data analysis stage. It is used to structure and organize DRF domain knowledge, which facilitates managing, organizing, sharing, and reusing DRF domain knowledge among domain forensic practitioners. That aims to identify, recognize, extract and match different DRF processes, concepts, activities, and tasks from other DRF models in a developed DRFM. Thus, allowing domain practitioners to derive/instantiate solution models easily. The consistency and applicability of the developed DRFM were validated using metamodel transformation (vertical transformation). The results indicated that the developed DRFM is consistent and coherent and enables domain forensic practitioners to instantiate new solution models easily by selecting and combining concept elements (attribute and operations) based on their model requirement.

The Database Forensics Investigation (DBFI) field is focused on capturing and investigating database incidents. DBFI is a subdomain of the digital forensics domain, which deals with database files and dictionaries to identify, acquire, preserve, examine, analyze, reconstruct, present, and document database incidents. Several frameworks and models have been offered for the DBFI field in the literature. However, these specific models and frameworks have redundant investigation processes and activities. Therefore, this study has two aims: (i) conducting a compressive survey to discover the challenges and issues of the DBFI field and (ii) developing a Unified forensic model for the database forensics field. To this end, the design science research (DSR) method was used in this study. The results showed that the DBFI field suffers from many issues such as the lack of standardization, multidimensional nature, heterogeneity, and ambiguity, making it complex for those working in this domain. In addition, a model was proposed in this paper, called the Unified Forensic Model (UFM), which consists of five main stages: initialization stage, acquiring stage, investigation stage, restoring and recovering stage, and evaluation stage. Each stage has several processes and activities. The applicability of UFM was evaluated from two perspectives: completeness and implementation perspectives. UFM is a novel model covering all existing DBFI models and comprises two new stages: the recovering and restoring stage and the evaluation stage. The proposed UFM is so flexible that any forensic investigator could employ it easily when investigating database incidents.