In this paper, Deep Neural Networks (DNN) with Bat Algorithms (BA) offer a dynamic form of traffic control in Vehicular Adhoc Networks (VANETs). The former is used to route vehicles across highly congested paths to enhance efficiency, with a lower average latency. The latter is combined with the Internet of Things (IoT) and it moves across the VANETs to analyze the traffic congestion status between the network nodes. The experimental analysis tests the effectiveness of DNN-IoT-BA in various machine or deep learning algorithms in VANETs. DNN-IoT-BA is validated through various network metrics, like packet delivery ratio, latency and packet error rate. The simulation results show that the proposed method provides lower energy consumption and latency than conventional methods to support real-time traffic conditions.

Increasing the growth of big data, particularly in healthcare-Internet of Things (IoT) and biomedical classes, tends to help patients by identifying the disease early through methods for the analysis of medical data. Hence, nanotechnology-based IOT biosensors play a significant role in the medical field. Problem. However, the consistency continues to decrease where missing data occurs in such medical data from nanotechnology-based IOT biosensors. Furthermore, each region has its own special features, which further lowers the accuracy of prediction. The proposed model initially reconstructs lost or partial data in order to address the challenge of handling the medical data structures with incomplete data. Methods. An adaptive architecture is proposed to enhance the computing capabilities to predict the disease automatically. The medical databases are managed by unpredictable environments. This optimized paradigm for diagnosis produces the fuzzy, genetically categorized decision tree algorithm. This work uses a normalized classifier namely fuzzy-based decision tree (FDT) algorithm for classifying the data collected via nanotechnology-based IOT biosensors, and this helps in the identification of nondeterministic instances from unstructured datasets relating to the medical diagnosis. The FDT algorithm is further enhanced by using genetic algorithms for effective classification of instances. Finally, the proposed system uses two larger datasets to verify the predictive precision. In order to describe a fuzzy decision tree algorithm based upon the fitness function value, a modified decision classification rule is used. The structure and unstructured databases are configured for processing. Results and Conclusions. This evaluation of test patterns helps to track the efficiency of FDT with optimized rules during the training and testing stages. The proposed method is validated against nanotechnology-based IOT biosensors data in terms of accuracy, sensitivity, specificity, and F -measure. The results of the simulation show that the proposed method achieves a higher rate of accuracy than the other methods. Other metrics relating to the model with and without feature selection show an improved sensitivity, specificity, and F -measure rate than the existing methods.

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The biometrics is now a days trending security method used in the industries. The face recognition is one way of applying biometrics, and liveness detection is add on security to the system which will help the security system to identify between the fake and the real identities. In this case the fake identities are photographs as printed media. And mobile or tablet as display devices. The entire system is developed on the raspberry pi board because of it efficiency with powerful architecture and theportability.

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Context:Breast cancer is a biggest threat to women. X-ray mammography is the most effective method for early detection and screening of breast cancer. It is a tough challenge for the radiologist in reading mammography since it does not provide consistent result every time.Aim:To improve the primary sign of this disease, computer-aided diagnosis schemes have been developed. Using monitor, digital images of mammography are displayed and they can be lightened or darkened before they are printed on the film. Time factor is important to identify the abnormality in body such as breast cancer and lung cancer. Hence, to detect the tissues and treatment stages, image-processing techniques are improved in several medical areas. In this project, using low-level preprocessing techniques and image segmentation, the breast cancer detection is done.Methods:With the help of Bayes algorithm and neural networks (NNs), the type of the mammogram and stages is identified. For segmentation process, region-growing algorithm is used, which helps to find the affected portion, i.e., region of interest. Gray-level co-occurrence matrix (GLCM) and texture feature are used for feature extraction.Results:Bayes algorithm is used for probability of identification, whereas NNs is used to reduce the probability level from 0–1000 to 0–1 in case of classification.

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