An Intelligent Obstacle and Edge Recognition System using Bug Algorithm

Obstacle avoidance is an important task in robotics as the autonomous robot's aim is to reach the destination without collision.  One type of autonomous robot that can detect obstacles and edges and take alternative paths free of obstacles and edges is a real-time obstacle avoiding edge detection robot. This paper proposes a robotic Robot with an intelligence built into it that guides itself whenever an obstacle comes along its way by bug algorithm. This robotic Robot is constructed using AT mega 8 families’ micro-controller (Arduino Uno R3). The ultrasonic sensor is used to detect any obstacle with edges and sends a command to the microcontroller. The micro-controller, based on the received input signal, redirects the robot to push in an alternative direction by actuating the motors that are interfaced with it via a motor driver. Depending on the situation the robot is able to choose the correct path [1]. A decision making process of obstacle avoiding edge detection occurs spontaneously here. This robot was designed to think about its day-to-day potentialities

Breast cancer management pathways during the COVID-19 pandemic: outcomes from the UK ‘Alert Level 4’ phase of the B-MaP-C study

Abstract: Background: The B-MaP-C study aimed to determine alterations to breast cancer (BC) management during the peak transmission period of the UK COVID-19 pandemic and the potential impact of these treatment decisions. Methods: This was a national cohort study of patients with early BC undergoing multidisciplinary team (MDT)-guided treatment recommendations during the pandemic, designated ‘standard’ or ‘COVID-altered’, in the preoperative, operative and post-operative setting. Findings: Of 3776 patients (from 64 UK units) in the study, 2246 (59%) had ‘COVID-altered’ management. ‘Bridging’ endocrine therapy was used (n = 951) where theatre capacity was reduced. There was increasing access to COVID-19 low-risk theatres during the study period (59%). In line with national guidance, immediate breast reconstruction was avoided (n = 299). Where adjuvant chemotherapy was omitted (n = 81), the median benefit was only 3% (IQR 2–9%) using ‘NHS Predict’. There was the rapid adoption of new evidence-based hypofractionated radiotherapy (n = 781, from 46 units). Only 14 patients (1%) tested positive for SARS-CoV-2 during their treatment journey. Conclusions: The majority of ‘COVID-altered’ management decisions were largely in line with pre-COVID evidence-based guidelines, implying that breast cancer survival outcomes are unlikely to be negatively impacted by the pandemic. However, in this study, the potential impact of delays to BC presentation or diagnosis remains unknown

A Comparative Analysis of the Ensemble Method for Liver Disease Prediction

Early diagnosis of liver disease is very important in order to save human lives and take appropriate measure to control the disease. In several fields, especially in the field of medical science, the ensemble method was successfully applied. This research work uses different ensemble methods to investigate the early detection of liver disease. The selected dataset for this analysis is made up of attributes such as total bilirubin, direct bilirubin, age, sex, total protein, albumin, and globulin ratio. This research mainly aims at measuring and comparing the efficiency of different ensemble methods. AdaBoost, LogitBoost, BeggRep, BeggJ48 and Random Forest are the ensemble method used in this research. The study shows that LogitBoost is the most accurate model than other ensemble approaches.ISBN för värdpublikation: 978-1-7281-6309-3</p