One of the most severe types of cancer caused by the uncontrollable proliferation of brain cells inside the skull is brain tumors. Hence, a fast and accurate tumor detection method is critical for the patient’s health. Many automated artificial intelligence (AI) methods have recently been developed to diagnose tumors. These approaches, however, result in poor performance; hence, there is a need for an efficient technique to perform precise diagnoses. This paper suggests a novel approach for brain tumor detection via an ensemble of deep and hand-crafted feature vectors (FV). The novel FV is an ensemble of hand-crafted features based on the GLCM (gray level co-occurrence matrix) and in-depth features based on VGG16. The novel FV contains robust features compared to independent vectors, which improve the suggested method’s discriminating capabilities. The proposed FV is then classified using SVM or support vector machines and the k-nearest neighbor classifier (KNN). The framework achieved the highest accuracy of 99% on the ensemble FV. The results indicate the reliability and efficacy of the proposed methodology; hence, radiologists can use it to detect brain tumors through MRI (magnetic resonance imaging). The results show the robustness of the proposed method and can be deployed in the real environment to detect brain tumors from MRI images accurately. In addition, the performance of our model was validated via cross-tabulated data.

The internet-of-Vehicle (IoV) can facilitate seamless connectivity between connected vehicles (CV), autonomous vehicles (AV), and other IoV entities. Intrusion Detection Systems (IDSs) for IoV networks can rely on machine learning (ML) to protect the in-vehicle network from cyber-attacks. Blockchain-based Federated Forests (BFFs) could be used to train ML models based on data from IoV entities while protecting the confidentiality of the data and reducing the risks of tampering with the data. However, ML models are still vulnerable to evasion, poisoning and exploratory attacks by adversarial examples. The BFF-IDS offers partial defence against poisoning but has no measure for evasion attacks, the most common attack/threat faced by ML models. Besides, the impact of adversarial examples transferability in CAN IDS has largely remained untested. This paper investigates the impact of various possible adversarial examples on the BFF-IDS. We also investigated the statistical adversarial detector’s effectiveness and resilience in detecting the attacks and subsequent countermeasures by augmenting the model with detected samples. Our investigation results established that BFF-IDS is very vulnerable to adversarial examples attacks. The statistical adversarial detector and the subsequent BFF-IDS augmentation (BFF-IDS(AUG)) provide an effective mechanism against the adversarial examples. Consequently, integrating the statistical adversarial detector and the subsequent BFF-IDS augmentation with the detected adversarial samples provides a sustainable security framework against adversarial examples and other unknown attacks.

In recent years, various studies have begun to use deep learning models to conduct research in the field of human activity recognition (HAR). However, there has been a severe lag in the absolute development of such models since training deep learning models require a lot of labeled data. In fields such as HAR, it is difficult to collect data and there are high costs and efforts involved in manual labeling. The existing methods rely heavily on manual data collection and proper labeling of the data, which is done by human administrators. This often results in the data gathering process often being slow and prone to human-biased labeling. To address these problems, we proposed a new solution for the existing data gathering methods by reducing the labeling tasks conducted on new data based by using the data learned through the semi-supervised active transfer learning method. This method achieved 95.9% performance while also reducing labeling compared to the random sampling or active transfer learning methods.

Traffic sign detection (TSD) is crucial for real-world applications like driverless vehicles, intelligent driver-assistance systems, and traffic management. Recent advancements in TSD have demonstrated promising outcomes. Nonetheless, challenges persist in terms of speed, accuracy, memory consumption, the capability of the backbone to generate features, and computational cost, especially in handling diverse traffic sign characteristics. To overcome these challenges, we propose Sign-YOLO (You Only Look Once), a novel attention-based one-stage method that integrates YOLOv7 with the squeeze-and-excitation (SE) model and special attention mechanism. Sign-YOLO enhances the feature representation capacity of the model in the presence of variations in traffic sign sizes. The SE block adjusts channel-specific feature responses by actively considering the relationships between channels. By selectively focusing on relevant features, the attention mechanism helps the model better capture and understand the distinctive characteristics of traffic signs, thereby improving detection accuracy. Sign-YOLO effectively reduces the computational cost and memory consumption; further, it effectively enriches the robustness of extracted features. The proposed approach enables the model to allocate more attention to relevant regions of the input, thereby reducing the impact of size discrepancies and contributing to the overall robustness of the system. The experimental findings highlight the success of Sign-YOLO in TSD tasks. Our proposed method exhibits cutting-edge performance on the German Traffic Signs Detection Benchmark (GTSDB) dataset, simultaneously achieving a 98% reduction in model size and memory consumption. Sign-YOLO attains a 99.10% mean average precision (mAP) on the GTSDB dataset. In comparison to both two- and one-stage detectors, our approach exhibits an improvement of 3.33%. The proposed approach is the swiftest and most lightweight framework in terms of memory usage, making it the ideal option for implementation in real-time applications.

Manta ray foraging optimization algorithm (MRFO) is a novel metaheuristic optimization algorithm based on the smart conduct of manta rays. MRFO algorithm is among the most newly developed swarm based metaheuristic approach that emulates the supportive conduct performed by manta rays in search of their food. MRFO algorithm has been found to be efficient in resolving a number of optimization difficulties in various domains, due to its ability in providing an equilibrium between global and local search during the search procedure, resulting in near optimal results. As a result, researchers have developed several variants of MRFO since its introduction. This paper provides an in-depth examination of recent MRFO growths. First, the paper is introduced on natural inspiration context of MRFO and its conceptual optimization framework. Further, MRFO is discussed in the aspect of its modifications, hybridizations, and applications across different domain. Then, the meta-analysis of the developments of MRFO since its introduction is presented. Thereafter, the paper presented a discussion on possible future research directions. This study will be useful for researchers and practitioners in fields such as optimization, engineering design, machine learning, scheduling, and image processing.

The proliferation of immersive services, including virtual reality/augmented reality, holographic content, and the metaverse, has led to an increase in the complexity of communication networks, and consequently, the complexity of network management. Recently, digital twin network technology, which applies digital twin technology to the field of communication networks, has been predicted to be an effective means of managing complex modern networks. In this paper, a digital twin network data pipeline architecture is proposed that demonstrates an integrated structure for flow within the digital twin network and network modeling from a data perspective. In addition, a network traffic modeling technique using data feature extraction techniques is proposed to realize the digital twin network, which requires the use of massive streaming data. The proposed method utilizes the data generated in the OMNeT++ environment and verifies that the learning time is reduced by approximately 25% depending on the feature extraction interval, while the accuracy remains similar.