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Due to the increasing number of applications for various purposes, including commercial, scientific, environmental, and military ones, Underwater Wireless Sensor Networks (UWSNs) have recently attracted substantial attention from academia and enterprises in research and development. Monitoring pollutants, tactical surveillance, tsunami alerts, and offshore drilling are some important applications. Due to acoustic transmission disruptions brought on by extreme noise, extraordinarily lengthy propagation delays, a high bit error rate, a constrained bandwidth, and interference, efficient sensor communication in UWSNs is a difficult challenge. Therefore, designing efficient communication among sensors and sinks is one of the fundamental research themes in UWSNs. This paper proposes an energy-efficient optimal cluster head (CH)-based routing in UWSNs. The proposed methodology consists of three stages namely, cluster formation, CH selection, and priority-based routing. In this study, initially, the clusters are formed using a k-means clustering algorithm. Then, the CHs are selected using the Adaptive Honey Badger optimization (AHBO) algorithm, which is used to reduce energy consumption and delay. AHBO is a combination of a honey badger, Levy flight, and genetic algorithm operators. After the CH selection process, data packets are transferred toward the base station through autonomous underwater vehicles (AUVs). The efficiency of the proposed approach is analyzed based on different metrics and performance compared with the different methods. [ABSTRACT FROM AUTHOR]

Nowadays, cervical cancer has emerged as one of the major causes of death and incredibly difficult to identify. In this research, three novel methods have been developed for the automatic detection and classification of cervical cancer in Pap images. There are five steps in this framework which includes data collection, preprocessing, segmentation, feature extraction, and classification of images. The proposed method is implemented using Herlev Pap smear data set. The input image from the data set is pre-processed by using an Anisotropic Diffusion Filter with Unsharp Masking technique that removes the noise in the Pap data. In the next step, an enhanced image sequence is segmented automatically by the proposed Advance Map-Based Superpixel Segmentation (AMBSS) algorithm. Finally, cervix cancer images are procured by utilizing an AMBSS and classified by Support Vector Machine classifier. The accuracy obtained is 85.4%. In order to improve the accuracy AMBSS with quasi newton-based Feed Forward Neural Network classification is used and the accuracy of 96.0% is obtained. Furthermore, AMBSS with Deep auto encoder-based Extreme Learning Machine classification is performed and achieved the accuracy of 99.1%. The findings show that the precision of classification typically exceeds other intelligent methods previously applied. [ABSTRACT FROM AUTHOR]

Cervical cancer is a disease that develops in the cervix's tissue. Cervical cancer mortality is being reduced due to the growth of screening programmers. Cervical cancer screening is a big issue because the majority of cervical cancer screening treatments are invasive. Hence, there is apprehension about standard screening procedures, as well as the time it takes to learn the results. There are different methods for detecting problems in the cervix using Pap (Papanicolaou-stained) test, colposcopy, Computed Tomography (CT), Magnetic Resonance Image (MRI) and ultrasound. To obtain a clear sketch of the infected regions, using a decision tree approach, the captured image has to be segmented and analyzed. The goal of creating a decision tree is to establish prediction model that anticipate the feature vector based on the input variable. This paper deals with investigating various techniques of segmentation for detecting the cervical cancer. It proposes a novel method to develop an assistance system for the detection diagnosis of cervical cancer, based on work of Martin, Byriel and Norup. The analysis is focused on Pap smear pictures of single cells. Smear testing is a method of detecting abnormalities in the blood. Image processing is an effective method for extracting data. It is used to determine the size of cervical carcinoma and the length of the uterus. Martin's database, which is open source and utilised for analysis and validation, is obtainable for research purposes. Cervical malignancy information utilizing three grouping strategies to anticipate the disease and afterward analyzed the outcomes showed that choice tree is the best classifier indicator with the test dataset. Further investigations ought to be led to improve execution. [ABSTRACT FROM AUTHOR]

The term Internet of Things is used to describe a wide range of webenabled services and products, including cutting-edge gadgetry. To put it simply, cloud computing is the method of making information and software normally housed on separate local servers available to consumers through the Internet on demand. Cloud services mediate between IoT devices and the applications that control them. Because of this, essential data may be easily accessed without the need for costly infrastructure. The cloud based IoT paradigm connects the virtual and real realms. Two devices need to check their identities against each other before they can exchange data. Creating a one-of-a-kind identifier for every piece of electrical equipment; this might be a number or a name A person's right to privacy has been violated because this data can be used to harm them. Therefore, the suggested system will only store essential information about the user. If you want to keep the exam's identifiers secret, you should choose a method that leaves no paper trail. Therefore, protecting the privacy of users is so crucial. Viruses and other malicious software pose a significant risk to your network and the data it contains. Many IoT applications necessitate the construction of a distributed, scalable, and geographically dispersed datacenter network to meet the demands of their users. The providers of the desired information services are the ones responsible for automating the extensive data classification, analysis, and processing. Protective measures should ideally be handled by the security service desk. Using current approaches exposes too much data and services to risk. The goal of this completely new layout is to create a framework that can't be harmed by environmental elements in any way. [ABSTRACT FROM AUTHOR]

Every year, cervical cancer (CC) is the leading cause of death in women around the world. If detected early enough, this cancer can be treated, and patients will receive adequate care. This study introduces a novel ultrasound-based method for detecting CC. The Oriented Local Histogram Technique (OLHT) is used to improve the image corners in the cervical image (CI), and the Dual-Tree Complex Wavelet Transform (DT-CWT) is used to build a multi-resolution image (CI). Wavelet, and Local Binary Pattern are among the elements retrieved from this improved multi-resolution CI (LBP). The retrieved appearance is trained and tested using a feed-forward propagation neural network, and the ANFIS classifier is utilized to classify them. The purpose of this classifier is to distinguish between normal and pathological cervical pictures. Sensitivity is 97.52 percent, specificity is 99.46 percent, accuracy is 98.39 percent, precision is 97.48 percent, PPV is 97.38 percent, NPV is 92.27 percent, LRP is 141.81 percent, 0.0946 percent LRN, FPR is 96.82 percent, and NPR is 91.46 percent for the CC detection categorization. The proposed methodology outperforms standard CC identification and classification methodologies. [ABSTRACT FROM AUTHOR]

Objective: The purpose of this study was to evaluate an improved oriented speckle reducing anisotropic diffusion (IADF) filter that suppress the speckle noise from ultrasound B-mode images and shows better result than previous filters such as anisotropic diffusion, wavelet denoising and local statistics. Methods: The clinical ultrasound images of the cervix were obtained by ATL HDI 5000 ultrasound machine from the Regional Cancer Centre, Medical College campus, Thiruvananthapuram. The standardized ways of organizing and storing the image were in the format of bmp and the dimensions of 256 × 256 with the help of an improved oriented speckle reducing anisotropic diffusion filter. For analysis, 24 ultrasound cervix images were tested and the performance measured. Results: This provides quality metrics in the case of maximum peak signal-to-noise ratio (PSNR) of 31 dB, structural similarity index map (SSIM) of 0.88 and edge preservation accuracy of 88%. Conclusion: The IADF filter is the optimal method and it is capable of strong speckle suppression with less computational complexity. [ABSTRACT FROM AUTHOR]