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Owing to a rise in the number of vehicles on route, traffic has been increased dramatically in recent years. It has been noted that driving certain vehicles manually has become a challenging task. The challenges faced during an accident are the congestion of vehicles which blocks the ambulances during emergencies. Currently, traffic congestion is posing a major challenge for the public transportation system. To overcome these issues, a Priority federated learning with Multi-head CNN (PFL-MHCN2) has been proposed. Smart sensors and cameras equipped with IoT capabilities were used to collect data for the proposed model. Using this technique, signals from one junction are transmitted to another junction and updated in the cloud. A congestion spot is identified based on the input characteristics that are contained in the cloud and sensor data that are received. Initially, pre-processing reduces noisy values and predicts missing values in the acquired data. After pre-processing, the data are transferred to the detection layer. The Detection layer detects congestion free route using Federated Multi-head CNN. The proposed model is evaluated based on parameters like accuracy, precision, specificity, F1 score, and Miss rate. The proposed PFL-MHCN2 model produces a 0.65% miss rate, which is less than existing methods. The proposed technique improves the accuracy of 1.45%, 1.66%, and 4.32% better than TCC-SVM, TC2S-DNN and MSR2C-ABPNN respectively.

The vehicular ad hoc network (VANET) topology will change the mobility of the nodes and the data delivery will be efficient in the vehicle environment. This technique uses the density, mobility, dissemination in the requirements of emergency message broadcasting. The emergency message is broadcast on the road causes many issues like reliability, latency and scalability. Beacons are used in the VANET to broadcast messages and get the information from neighbours. When more vehicles transmit the messages in equal time lead a frequent broadcast storm the vehicles are faced the message delivery failure. Adaptive Scheduled Partitioning and Broadcasting technique (ASPBT) is used in our paper for message reliability, and the transmission efficiency will adjust the partitions and beacon automatically for reducing retransmissions. The partition size is determined using the density of network transmission of each partition schedule is estimated using the Black Widow Optimization (BWOA). The emergency message gets low delay and redundancy of the message is reducing, ASPBT include the forwarding of novel with the selection of optimal partition. The performance analysis is done with the existing methods for the determination of efficiency, redundancy, collision, and delay. The efficiency of proposed technique as 98% comparing with existing broadcast schemes of VANET.

A proposed real coded genetic algorithm based radial basis function neural network classifier is employed to perform effective classification of healthy and cancer affected lung images. Real Coded Genetic Algorithm (RCGA) is proposed to overcome the Hamming Cliff problem encountered with the Binary Coded Genetic Algorithm (BCGA). Radial Basis Function Neural Network (RBFNN) classifier is chosen as a classifier model because of its Gaussian Kernel function and its effective learning process to avoid local and global minima problem and enable faster convergence. This paper specifically focused on tuning the weights and bias of RBFNN classifier employing the proposed RCGA. The operators used in RCGA enable the algorithm flow to compute weights and bias value so that minimum Mean Square Error (MSE) is obtained. With both the lung healthy and cancer images from Lung Image Database Consortium (LIDC) database and Real time database, it is noted that the proposed RCGA based RBFNN classifier has performed effective classification of the healthy lung tissues and that of the cancer affected lung nodules. The classification accuracy computed using the proposed approach is noted to be higher in comparison with that of the classifiers proposed earlier in the literatures.

Medical images need to be transmitted with the patient’s information without altering the image data. The present paper discusses secured indexing of lung CT image (SILI) which is a secured way of indexing the lung CT images with the patient information. Authentication is provided using the sender’s logo information and the secret key is used for embedding the watermark into the host image. Watermark is embedded into the region of Noninterest (RONI) of the lung CT image. RONI is identified by segmenting the lung tissue from the CT scan image. The experimental results show that the proposed approach is robust against unauthorized access, noise, blurring, and intensity based attacks.