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Detecting and categorizing Pap smear images automatically has been a difficult task over the past five decades. There is a significant need for the design and implementation of low‐cost, high‐efficiency screening systems. To address these issues, we have proposed a novel approach for cervical cancer classification. At first, each image is pre‐processed and cropped into 2043*1362 pixels. These images are separated into 681*681 pixels with six square patches. Image augmentation techniques such as image enhancement, image flipping, and image rotation are used to reduce the number of parameters necessary for subsequent processes. The cancer‐affected regions are correctly segmented with the help of kernel weighted fuzzy local information c‐means clustering (KWFLICM) model. The KWFLICM model mainly extracts the cytoplasm and nucleus present in the cell from the background region. The features of the cervical image are extracted using a convolution neural network (CNN), and these retrieved features, such as shape, color, and texture, are crucial for classification. Finally, the Sooty Tern Optimization (STO) algorithm with CNN‐based long short‐term memory classifier (CNN‐LSTM) identifies the four types of CC: normal, light dysplastic, severe dysplastic, and carcinoma. The images for the experiment were obtained from Herlev University Hospital in Denmark, and the software used for the implementation was MATLAB. According to the comparative analysis, the proposed CNN‐LSTM with STO algorithm demonstrates better results using various measures such as accuracy, specificity, sensitivity, and F‐score. Depending upon the experimental results, the proposed CNN‐LSTM with STO algorithm provides 99.80% accuracy, 99% specificity, 98.83% sensitivity, and 97.8 F‐score. The proposed methodology offers an improvement of 28.5% and 19.46% when compared with the random forest and ensemble classifier.

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Healthcare application is one of the most promising developments to provide on-time demand services to the end users, vehicles, and other Road Side Units (RSUs) in the urban environment. In recent years, several application interfaces have been developed to connect, communicate, and share the required services from one source to another. However, the urban environment holds a complex entity of both homogenous and heterogeneous devices to which the communication/sensing range between the devices leads to connectivity breakage, lack of needed service in time, and other environmental constraints. Also, security plays a vital role in allowing everyone in the urban area to access/request services according to their needs. Again, this leads to a massive breakthrough in providing reliable service to authentic users or a catastrophic failure of service denial involving unauthorized user access. This paper proposes a novel topological architecture, Secure Authentication Relay-based Urban Network (S-ARUN), designed for healthcare and other smart city applications for registered transportation stakeholders. The registered stakeholders hold a built-in data security framework with three subsystems connected to the S-ARUN topology: (1) authentication subsystem: the stakeholder must identify themselves to the source responder as part of the authentication subsystem before transmitting the actual data service request; (2) connectivity subsystem: to periodically check the connection state of stakeholders as they travel along with the road pattern; and (3) service subsystem: each source responder will keep a separate queue for collecting data service requests, processing them quickly, and sending the results to the appropriate stakeholder. The Kerberos authentication method is used in working with S-ARUN's model to connect the stakeholders securely and legitimately. The performance of the proposed S-ARUN is assessed, and the performance metric toward key generation and other data security-related metrics is tested with existing schemes.

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The cervical cancer patient’s death rate can be minimized by accurate and early detection of cervical cancer (CC). One of the popular techniques called the Pap test or Pap smear is widely used for the early detection of CC. The manual analysis consumed more time in the case of CC detection. The existing techniques met few shortcomings in terms of poor accuracy, more computational complexity, higher feature dimensionality, poor reliability, and higher time-consumption with poor hyperparameters optimization. Hence, the computer-aided diagnostic model provides reliable and accurate CC detection at the initial stage. In this paper, we proposed MASO optimized DenseNet 121 architecture for the early detection of cervical cancer. At first, different kinds of augmentation techniques such as horizontal flip, vertical flip, zooming, shearing, height shift, width shift, rotation, and brightness to increase the number of training samples. The Mutation based Atom Search Optimization (MASO) algorithm is established to optimize the hyperparameters in DenseNet 121 architecture suchnumber of neurons in the dense layer, learning rate value, and the batch sizes. Different kinds of performance metrics such as accuracy, specificity, sensitivity, precisions, recall, F-score, and confusion matrix evaluate the performance of MASO optimized DenseNet 121 architecture for CC detection. A single normal class with three abnormal classes namely Carcinoma, Light dysplastic, and Sever dysplastic were selected from the Hervel dataset for experimental investigation. The proposed MASO optimized DenseNet 121 architecture achieves 98.38% accuracy, 98.5% specificity, 98.83% sensitivity, 98.58% precision, 99.3% recall and 98.25% F-score values than other existing methods.