Vehicular Ad hoc Networks (VANETs) an important category in networking focuses on many applications, such as safety and intelligent traffic management systems. The high node mobility and sparse vehicle distribution (on the road) compromise VANETs network scalability and rapid topology, hence creating major challenges, such as network physical layout formation, unstable links to enable robust, reliable, and scalable vehicle communication, especially in a dense traffic network. This study discusses a novel optimization approach considering transmission range, node density, speed, direction, and grid size during clustering. Whale Optimization Algorithm for Clustering in Vehicular Ad hoc Networks (WOACNET) was introduced to select an optimum cluster head (CH) and was calculated and evaluated based on intelligence and capability. Initially, simulations were performed, Subsequently, rigorous experimentations were conducted on WOACNET. The model was compared and evaluated with state-of-the-art well-established other methods, such as Gray Wolf Optimization (GWO) and Ant Lion Optimization (ALO) employing various performance metrics. The results demonstrate that the developed method performance is well ahead compared to other methods in VANET in terms of cluster head, varying transmission ranges, grid size, and nodes. The developed method results in achieving an overall 46% enhancement in cluster optimization and an F-value of 31.64 compared to other established methods (11.95 and 22.50) consequently, increase in cluster lifetime.

Vehicular ad hoc networks (VANETs) are vital to many Intelligent Transportation System (ITS)-enabled technologies, including efficient traffic control, media applications, and encrypted financial transactions. Due to an increase in traffic, vehicular network topology is constantly changing, and sparse vehicle distribution (on highways) hinders network scalability. Thus, there is a challenge for all vehicles (in the network) to maintain a stable route, which would increase network instability. Concerning IoT-based network transportation, this study proposes a bio-inspired, cluster-based algorithm for routing, i.e., the intelligent, probability-based, and nature-inspired whale optimization algorithm (p-WOA), which produces cluster formation in vehicular communication. Various parameters, such as communication range, number of nodes, velocity, and route along the highway were considered, and their probaabilities were incorporated into the fitness function, hence resulting in randomness reduction. Results were compared to existing methods such as Ant Lion Optimizer (ALO) and Grey Wolf Optimization (GWO), demonstrating that the developed p-WOA technique produces an optimal number of cluster heads (CH). The results achieved by calculating the Packet Delivery Ratio (PDR), average throughput, and latency demonstrate the superiority of the proposed method over other well-established methodologies (ALO and GWO). This study confirms statistically that VANETs employing ITS applications optimize their clusters by a factor of 75, which has the twin benefits of decreasing communication costs and routing overhead and extending the life of the cluster as a whole.

Recent advances in artificial intelligence (AI), notably deep learning, have sparked widespread curiosity with bioinformatics, particularly the challenges presented by medical imaging. It’s been really helpful in enabling the Computer Aided Diagnosis CAD system to provide precise outcomes. Nonetheless, it is still a difficult task to identify breast cancer in mammography images. The purpose of this effort is to lower False Positive Rate FPR and False Negative Rate FNR and increase Matthews’s correlation coefficient MCC value. Two highly tailored object detection models, YOLOv5 and Mask R-CNN, are utilized to get the job done. YOLOv5 is able to detect the mass and determine whether it is benign or malignant. However, YOLOV5’s limited real estate necessitates certain tweaks to the original model in order to get the desired effects. Tumor borders and size are both identified by Mask RCNN as it traverses breast parenchyma in search of malignancies. Stages of cancer are based on the magnitude of the patients’ tumours. This model employs YOLOv5+Mask RCNN and is trained on the INbreast, CBIS-DDSM, and BNS dataset. The proposed model is compared against the baseline version of YOLOv5 to determine how well it performs. The proposed method improves performance, with an FPR of 0.049%, a FNR of 0.029%, and a high MCC value of 92.02%. Based on the results of the studies, combining YOLOv5 with Mask RCNN improves accuracy by 0.06 percentage points compared to using either method alone. Furthermore, this effort may aid in determining the patient’s prognosis and allowing clinicians to be more accurate and predictable in the diagnosing process at an early stage.