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Co-Saliency object detection is the process of identifying common and repetitive objects from the group of images. Earlier studies have looked over several state-of-art deep neural network methodologies for co-saliency detection approach. The Deep CNN approaches rely heavily on co-saliency detection due to their potent feature extraction capabilities both deep and wide. This article assess the performance of several state-of-art deep learning model (VGG19, Inceptionv3, modifiedResNet, MobileNetV2 and PoolNet) for the purpose of co-saliency detection among images from benchmark datasets. All the models were trained on 70% part of the dataset and remaining were used for testing purpose. Experimental results show that modified ResNetmodel outperforms getting 96.53% accuracy as compared to other state-of-the-art deep neural network models. [ABSTRACT FROM AUTHOR]

Vehicular Ad hoc Networks (VANETs) have gained significant recognition as a prospective technology for augmenting road safety and optimizing traffic efficiency through facilitating instantaneous communication between vehicles and roadside infrastructure. However, routing in VANETs faces significant challenges due to the dynamic network topology and security threats. In this context, trust-based routing offers an effective solution by improving reliability, security, and quality of service (QoS) in vehicle-to-infrastructure communication. However, trust-based routing in IOVs requires reliable trust evaluation mechanisms, privacy preservation, authentication, and access control. Challenges arise from the dynamic nature of IOVs, necessitating scalable and efficient trust computation algorithms. Moreover, ensuring the resilience of trust-based routing against malicious attacks, such as Sybil attacks or collusion among malicious vehicles, is an issue of great importance that necessitates attention and resolution. This research paper proposes a novel Graph-Based Trust-Enabled Routing (GBTR) scheme specifically designed for VANETs. The scheme incorporates direct trust, indirect trust, and contextual trust to evaluate the trustworthiness of participating nodes. Direct trust is determined based on factors such as frequency and consistency of successful communication, communication delay, and a mobility factor that incorporates punishment/reward parameters. Indirect trust is calculated using feedback trust value and link reliability, also considering the mobility factor. The contextual trust incorporates factors like location, time of day, weather conditions, and traffic density for each node pair. Routing decisions are made based on the final trust scores obtained from these trust evaluations. The route request/reply mechanism and route maintenance mechanism ensure the selection of the most reliable and trustworthy routes, thereby improving network performance. Additionally, a trust update algorithm with a concept of less reward and more penalty is employed to periodically update the trust values of participating vehicles. This approach enhances security, reliability, robustness, and efficiency of network resource usage, reducing congestion and enabling real-time trust evaluation while minimizing false positives. The simulation results substantiate that the GBTR scheme, as proposed, surpasses existing routing schemes across various performance metrics, including packet delivery ratio (PDR%), dropped packet ratio (DPR%), end-to-end delay (ms), throughput (Kbps), and normalized routing load (packets/sec). These outcomes underscore the efficacy of the proposed scheme in enhancing network performance and bolstering reliability. Overall, the graph-based trust-enabled routing scheme presented in this research contributes to enhancing the reliability and security of VANETs, thereby supporting the development of intelligent transportation systems. [ABSTRACT FROM AUTHOR]