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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 growth of swarm intelligence approaches and machine learning models in the field of medical image processing is extravagant, and the applicability of these approaches for various types of cancer classification has as well grown in the recent years. Considering the growth of these machine learning models, in this work attempt is taken to develop an optimized deep learning neural network classifier for classifying the nodule tissues in the lung cancer images which is an important application in biomedical area. The optimized model developed is the hybrid version of adaptive multi-swarm particle swarm optimizer with the new improved firefly algorithm resulting in better exploration and exploitation mechanism to determine near-optimal solutions. Multi-swarm particle swarm optimizer (MSPSO) possesses strong exploration capability due to its regrouping schedule nature, and the improved firefly algorithm (ImFFA) possesses better exploitation mechanism due to its inherit attractiveness and intensity feature. At this juncture, the new adaptive MSPSO–ImFFA is applied to the deep learning neural classifier to overcome the local and global minima occurrences and premature convergence by tuning its weight values. As a result, in this work the new adaptive MSPSO–ImFFA-based deep learning neural network classifier is employed to classify the lung cancer tissues of the considered lung computed tomography images. Results obtained prove the effectiveness of the deep learning classifier for the considered lung image sample datasets in comparison with the other methods compared from the previous literature works.

In the last decade, exponential growth of Internet of Things (IoT) and cloud computing takes the healthcare services to the next level. At the same time, lung cancer is identified as a dangerous disease which increases the global mortality rate annually. Presently, support vector machine (SVM) is the effective image classification tool especially in medical imaging. Feature selection and parameter optimization are the effective ways to improve the results of SVM and are conventionally resolved individually. This paper presents an optimal SVM for lung image classification where the parameters of SVM are optimized and feature selection takes place by modified grey wolf optimization algorithm combined with genetic algorithm (GWO-GA). The experimentation part takes place on three dimensions: test for parameter optimization, feature selection, and optimal SVM. For assessing the performance of the presented approach, a benchmark image database is employed which comprises of 50 low-dosage and stored lung CT images. The presented method exhibits its superior results on all the applied test images under several aspects. In addition, it achieves average classification accuracy of 93.54 which is significantly higher than the compared methods.

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.

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.