Appa Institute of Engineering and Technology

The position-based routing of Vehicular Ad hoc Network (VANET) vulnerable to various security attacks because of dependency on computing, control, and communication technologies. The Internet of Things (IoT)-enabled VANET application leads to the challenges such as integrity, access control, availability, privacy protection, non-repudiation, and confidentiality. Several security solutions have been introduced for two decades in two categories as cryptography-based and trust-based. Due to the high computation complexity, cryptography-based solutions are outperformed by recent intelligent trust-based mechanisms. The trust-based techniques are lightweight and effective against the well-known security threats in VANET. The objective of this paper has to design a novel position-based routing in which the conduct of vehicles assessed to accomplish reliable VANET communications. Attack Resilient Position-based VANET Protocol (ARPVP) proposed to detect and prevent malicious vehicles in the network using the trust evaluation technique and artificial intelligence (AI). In the first phase of ARPVP, the periodic self-trust assessment algorithm has designed using various trust parameters to detect unreliable vehicles in the network. In the second phase of ARPVP, the position-based route formation algorithm has designed using the AI technique Ant Colony Optimization (ACO). ACO solves the problem of reliable route formation by neglecting the attacker's using a trust-based fitness function. The trust parameters of each vehicle as mobility, buffer occupancy, and link quality parameters had measured in both phases of ARPVP. Simulation outcomes of the proposed model outperformed state-of-art protocols in terms of average throughput, communication delay, overhead, and Packet Delivery Ratio (PDR).

Objective: The objective is to provide a precise segmentation technique based on ACRF which can handle the variations between major and minor vessels and reduces the interference present in the model due to over fitting and can provide a high-quality reconstructed image. Therefore, a robust method with statistical properties needs to be presented to enhance the performance of the model. Moreover, a statistical framework is required to classify images precisely. Methods: Adaptive Conditional Random Field (ACRF) model to detect DR disease in early stages. Here, major vessel potentials and minor vessel potential features are extracted which in precise segmentation of vessel and non-vessel regions. This feature enhances the efficiency of the model. These major vessel and minor vessel potential features rebuild the retinal vasculature parts precisely and help to capture the contextual information present in the ground truth and label images. This method utilizes an ACRF model to reduce interference and computation complexity. Here, two efficient features are extracted to segment fundus images efficiently such as major vessel potentials and minor vessel potentials. The proposed ACRF model can provide the design patterns for both input images and labels with the help of major vessel potentials, unlike state-of-art-techniques which provide patterns for only labels and model the contextual information only in labels which is very essential while performing vessel segmentation Results: The performance results are tested on the DRIVE dataset. Experimental results verify the superiority of the proposed vessel segmentation technique based on the ACRF model in terms of accuracy, sensitivity, specificity, and F1measure and segmentation quality. Conclusion: A highly efficient vessel segmentation technique is evaluated to describe major and minor vessel regions efficiently based on the ACRF to recognize DR in early stages and to ensure an effective diagnosis using eye fundus images. The segmentation process decomposes input images into RGB components through histogram labels based on the proposed ACRF model. Here, the Gabor filtering approach is used for pre-processing and predicting parameters. The proposed segmentation method can provide the smooth boundaries of minor and major vessel regions. The proposed ACRF model can provide the design patterns for both input images and labels with the help of major vessel potentials, unlike state-of-art-techniques which provide patterns for only labels and model the contextual information only in labels.

Vehicular ad hoc networks (VANETs) gained the probable to change transportations perspectives using wireless communication network entities such as buses, cars, cell phones, traffic signals, etc. But factors like the heavy reliance on control technologies, computing, communication, severe mobility, etc. lead to several vulnerabilities in the network. The communication among two vehicles in VANETs should be secure as it mainly includes one or more intermediate vehicles to forward the data from the source to the objective. Hence the intermediate vehicles must be reliable for guaranteed data transmission. The malicious nodes may become part of such communications that can accept the data and drop (or misuse) data to create congestion. This paper proposed the Robust and Reliable Secure Clustering and Data Transmission (R2SCDT/RRSCDT) protocol to mitigate the challenges of malicious vehicles in the network. The methodology is dependent on the trust evaluation of vehicles to detect malicious nodes for secure Cluster Head (CH) selection and data transmission. The clustering performs to isolate the vehicles into clusters and choose the CH for each cluster. The CH choice performed using the trust evaluation of each vehicle and detects the attackers with less trust value. During the data transmission phase, the nodes are selected via the trust evaluation model and raise the alarm if any node has been detected as malicious. The purpose of trust-based clustering and data transmission functionality R2SCDT is to achieve guaranteed QoS performance with minimum time and control overheads. The simulation result shows the proposed model significantly improves performance compared to state-of-art security solutions of VANET.

In recent years, the number of maize disease species has increased, which obviously increases the level of damages in leaves. The reason for maize leaf disease is due to variations in agriculture systems, the variants of pathogen, and it also occurs due to the scarcity of plant conservation measures. The disease in maize leaves can be exhibited by varied symptoms; however, it might be complex for farmers to identify the disease in naked eye. Therefore, this paper intends to present a new automatic system for identifying and diagnosing maize leaf diseases. The proposed model includes two major phases: Proposed Feature Extraction and Classification. The first phase is feature extraction, where the proposed 4D-Local Binary Pattern (4D-LBP) based texture features will be extracted. More particularly, Dimension 1 insists pixel intensity, dimension 2 insists angle, dimension 3 insists local frequency from intensity patch and dimension 4 insists global frequency as well. Once the features get extracted, they are subjected for classification process, where the optimized Convolutional Neural Network (CNN) is used, where the count of convolutional layers is optimally tuned. For this optimal selection, a new Adaptive Opposition based Spider Monkey optimization (AOSMO), which is the enhanced version of SMO algorithm. At last, the performance of proposed work is evaluated over other traditional models with respect to accuracy.