En-PaFlower: An Ensemble Approach using PSO and Flower Pollination Algorithm for Cancer Diagnosis

Machine learning now is used across many sectors and provides consistently precise predictions. The machine learning system is able to learn effectively because the training dataset contains examples of previously completed tasks. After learning how to process the necessary data, researchers have proven that machine learning algorithms can carry out the whole work autonomously. In recent years, cancer has become a major cause of the worldwide increase in mortality. Therefore, early detection of cancer improves the chance of a complete recovery, and Machine Learning (ML) plays a significant role in this perspective. Cancer diagnostic and prognosis microarray dataset is available with the biopsy dataset. Because of its importance in making diagnoses and classifying cancer diseases, the microarray data represents a massive amount. It may be challenging to do an analysis on a large number of datasets, though. As a result, feature selection is crucial, and machine learning provides classification techniques. These algorithms choose the relevant features that help build a more precise categorization model. Accurately classifying diseases is facilitated as a result, which aids in disease prevention. This work aims to synthesize existing knowledge on cancer diagnosis using machine learning techniques into a compact report.  Current research work aims to propose an ensemble-based machine learning model En-PaFlower using Particle Swarm Optimization (PSO) as the feature selection algorithm, Flower Pollination algorithm (FPA) as the optimization algorithm with the majority voting algorithm. Finally, the performance of the proposed algorithm is evaluated over three different types of cancer disease datasets with accuracy, precision, recall, specificity, and F-1 Score etc as the evaluation parameters. The empirical analysis shows that the proposed methodology shows highest accuracy as 95.65%

Heuristics Techniques for Scheduling Problems with Reducing Waiting Time Variance

In real computational world, scheduling is a decision making process. This is nothing but a systematic schedule through which a large numbers of tasks are assigned to the processors. Due to the resource limitation, creation of such schedule is a real challenge. This creates the interest of developing a qualitative scheduler for the processors. These processors are either single or parallel. One of the criteria for improving the efficiency of scheduler is waiting time variance (WTV). Minimizing the WTV of a task is a NP-hard problem. Achieving the quality of service (QoS) in a single or parallel processor by minimizing the WTV is a problem of task scheduling. To enhance the performance of a single or parallel processor, it is required to develop a stable and none overlap scheduler by minimizing WTV. An automated scheduler\u27s performance is always measured by the attributes of QoS. One of the attributes of QoS is ‘Timeliness’. First, this chapter presents the importance of heuristics with five heuristic-based solutions. Then applies these heuristics on 1‖WTV minimization problem and three heuristics with a unique task distribution mechanism on Qm|prec|WTV minimization problem. The experimental result shows the performance of heuristic in the form of graph for consonant problems

Application of virtual reality, augmented reality, and mixed reality in endourology and urolithiasis: An update by YAU endourology and Urolithiasis Working Group

The integration of virtual reality (VR), augmented reality (AR), and mixed reality (MR) in urological practices and medical education has led to modern training systems that are cost-effective and with an increased expectation toward surgical performance and outcomes. VR aids the user in interacting with the virtual environment realistically by providing a three-dimensional (3D) view of the structures inside the body with high-level precision. AR enhances the real environment around users by integrating experience with virtual information over physical models and objects, which in turn has improved understanding of physiological mechanisms and anatomical structures. MR is an immersive technology that provides virtual content to interact with real elements. The field of urolithiasis has adapted the technological advancements, newer instruments, and methods to perform endourologic treatment procedures. This mini-review discusses the applications of Virtual Reality, Augmented Reality, and Mixed Reality in endourology and urolithiasis.publishedVersio

Application of virtual reality, augmented reality, and mixed reality in endourology and urolithiasis: An update by YAU endourology and Urolithiasis Working Group

The integration of virtual reality (VR), augmented reality (AR), and mixed reality (MR) in urological practices and medical education has led to modern training systems that are cost-effective and with an increased expectation toward surgical performance and outcomes. VR aids the user in interacting with the virtual environment realistically by providing a three-dimensional (3D) view of the structures inside the body with high-level precision. AR enhances the real environment around users by integrating experience with virtual information over physical models and objects, which in turn has improved understanding of physiological mechanisms and anatomical structures. MR is an immersive technology that provides virtual content to interact with real elements. The field of urolithiasis has adapted the technological advancements, newer instruments, and methods to perform endourologic treatment procedures. This mini-review discusses the applications of Virtual Reality, Augmented Reality, and Mixed Reality in endourology and urolithiasis