FANET (flying ad-hoc networks) is currently a trending research topic. Unmanned aerial vehicles (UAVs) have two significant challenges: short flight times and inefficient routing due to low battery power and high mobility. Due to these topological restrictions, FANETS routing is considered more complicated than MANETs or VANETs. Clustering approaches based on artificial intelligence (AI) approaches can be used to solve complex routing issues when static and dynamic routings fail. Evolutionary algorithm-based clustering techniques, such as moth flame optimization, and ant colony optimization, can be used to solve these kinds of problems with routes. Moth flame optimization gives excellent coverage while consuming little energy and requiring a minimum number of cluster heads (CHs) for routing. This paper employs a moth flame optimization algorithm for network building and node deployment. Then, we employ a variation of the K-Means Density clustering approach to choosing the cluster head. Choosing the right cluster heads increases the cluster’s lifespan and reduces routing traffic. Moreover, it lowers the number of routing overheads. This step is followed by MRCQ image-based compression techniques to reduce the amount of data that must be transmitted. Finally, the reference point group mobility model is used to send data by the most optimal path. Particle swarm optimization (PSO), ant colony optimization (ACO), and grey wolf optimization (GWO) were put to the test against our proposed EECP-MFO. Several metrics are used to gauge the efficiency of our proposed method, including the number of clusters, cluster construction time, cluster lifespan, consistency of cluster heads, and energy consumption. This paper demonstrates that our proposed algorithm performance is superior to the current state-of-the-art approaches using experimental results.

Today, people frequently communicate through interactions and exchange knowledge over the social web in various formats. Social connections have been substantially improved by the emergence of social media platforms. Massive volumes of data have been generated by the expansion of social networks, and many people use them daily. Therefore, one of the current problems is to make it easier to find the appropriate friends for a particular user. Despite collaborative filtering’s huge success, accuracy and sparsity remain significant obstacles, particularly in the social networking sector, which has experienced astounding growth and has a large number of users. Social connections have been substantially improved by the emergence of social media platforms. In this work, a social and semantic-based collaborative filtering methodology is proposed for personalized recommendations in the context of social networking. A new hybrid collaborative filtering (HCoF) approach amalgamates the social and semantic suggestions. Two classification strategies are employed to enhance the performance of the recommendation to a high rate. Initially, the incremental K-means algorithm is applied to all users, and then the KNN algorithm for new users. The mean precision of 0.503 obtained by HCoF recommendation with semantic and social information results in an effective collaborative filtering enhancement strategy for friend recommendations in social networks. The evaluation’s findings showed that the proposed approach enhances recommendation accuracy while also resolving the sparsity and cold start issues.

Vehicular Ad-hoc Networks (VANETs) are evolving rapidly with the advent of fog computing, which enhances their capabilities by bringing computational resources closer to the network’s edge. This paper presents a comprehensive review and novel taxonomy of fog computing integration within VANETs, highlighting its potential to significantly improve latency, reliability, and security. We explore various architectures, communication protocols, and services that utilize fog computing to meet the unique challenges of VANETs. Notably, the introduction of a ’Distributed Results’ section provides empirical evidence supporting the practical viability and scalability of fog-enhanced VANET architectures, thereby bridging the gap between theoretical research and real-world applicability. Additionally, a comparative analysis with existing surveys delineates this paper’s unique contributions, particularly its focused examination of security and privacy issues tailored for vehicular environments. By identifying current research gaps and suggesting future directions, this work serves as a crucial resource for both researchers and practitioners aiming to implement and optimize fog computing solutions in vehicular networks.

Content-based Image Retrieval (CBIR) aids radiologist to identify similar medical images in recalling previous cases during diagnosis. Although several algorithms have been introduced to extract the content of the medical images, the process is still a challenge due to the nature of the feature itself where most of them are extracted in low level form. In addition to the dimensionality reduction problem caused by the low-level features, current features are also insufficient to convey the semantic meaning of the images. This paper reviews the recent works in CBIR that attempts to reduce the semantic gap in extracting the features from medical images, precisely for mammogram images. Approaches such as the use of relevance feedback, ontology as well as machine learning algorithms are summarized and discussed.

The functional characteristics of existing inter-vehicle communication networks are used to offer a certificateless authentication scheme that integrates fifth-generation (5G) communication and fog computing. Thus, CLA-FC5G, a novel certificateless authentication scheme for 5G-assisted vehicular networks equipped with fog computing and device-to-device communication in this paper. As opposed to prior schemes, which relayed on 802.11p-based inter-vehicle communication, our CLA-FC5G employs D2D technology, enabling vehicles to communicate directly while ensuring their respective safety and lightening the communication overhead. Our scheme contains six polynomial-time algorithms to handle the system setup, key generation, and message signing and verification processes. The results demonstrate that our method is both secure and efficient due to its lack of communication and computational overhead. We have discovered that our proposed CLA-FC5G system can lower overhead and is extremely efficient and scalable, which is well-suited for mass vehicular networks. The output reveals that CLA-FC5G, which affects both security and effectiveness, meets practical safety requirements for a believable 5G setting.