Stephan Punitha

COVID-19 has affected many people across the globe. Though vaccines are available now, early detection of the disease plays a vital role in the better management of COVID-19 patients. An Artificial Neural Network (ANN) powered Computer Aided Diagnosis (CAD) system can automate the detection pipeline accounting for accurate diagnosis, overcoming the limitations of manual methods. This work proposes a CAD system for COVID-19 that detects and classifies abnormalities in lung CT images using Artificial Bee Colony (ABC) optimised ANN (ABCNN). The proposed ABCNN approach works by segmenting the suspicious regions from the CT images of non-COVID and COVID patients using an ABC optimised region growing process and extracting the texture and intensity features from those suspicious regions. Further, an optimised ANN model whose input features, initial weights and hidden nodes are optimised using ABC optimisation classifies those abnormal regions into COVID and non-COVID classes. The proposed ABCNN approach is evaluated using the lung CT images collected from the public datasets. In comparison to other available techniques, the proposed ABCNN approach achieved a high classification accuracy of 92.37% when evaluated using a set of 470 lung CT images.

India has an estimated 12 million visually impaired people and is home to the world’s largest number in any country. Smart walking stick devices use various technologies including machine vision and different sensors for improving the safe movement of visually impaired persons. In machine vision, accurately recognizing an object that is near to them is still a challenging task. This paper provides a system to enable safe navigation and guidance for visually impaired people by implementing an object recognition module in the smart walking stick that uses a local feature extraction method to recognize an object under different image transformations. To provide stability and robustness, the Weighted Guided Harris Corner Feature Detector (WGHCFD) method is proposed to extract feature points from the image. WGHCFD discriminates image features competently and is suitable for different real-world conditions. The WGHCFD method evaluates the most popular Oxford benchmark datasets, and it achieves greater repeatability and matching score than existing feature detectors. In addition, the proposed WGHCFD method is tested with a smart stick and achieves 99.8% recognition rate under different transformation conditions for the safe navigation of visually impaired people.

India is severely affected by the COVID-19 pandemic and is facing an unprecedented public health emergency. While the country’s immediate measures focus on combating the coronavirus spread, it is important to investigate the impacts of the current crisis on India’s renewable energy transition and air quality. India’s economic slowdown is mainly compounded by the collapse of global oil prices and the erosion of global energy demand. A clean energy transition is a key step in enabling the integration of energy and climate. Millions in India are affected owing to fossil fuel pollution and the increasing climate heating that has led to inconceivable health impacts. This paper attempts to study the impact of COVID-19 on India’s climate and renewable energy transitions through machine learning algorithms. India is observing a massive collapse in energy demand during the lockdown as its coal generation is suffering the worst part of the ongoing pandemic. During this current COVID-19 crisis, the renewable energy sector benefits from its competitive cost and the Indian government’s must-run status to run generators based on renewable energy sources. In contrast to fossil fuel-based power plants, renewable energy sources are not exposed to the same supply chain disruptions in this current pandemic situation. India has the definite potential to surprise the global community and contribute to cost-effective decarbonization. Moreover, the country has a good chance of building more flexibility into the renewable energy sector to avoid an unstable future.

Abstract: Breast cancer is essential to be detected in primitive localized stage for enhancing the possibility of survival since it is considered as the major malediction to the women society around the globe. Most of the intelligent approaches devised for breast cancer necessitates expertise that results in reliable identification of patterns that conclude the presence of oncology cells and determine the possible treatment to the breast cancer patients in order to enhance their survival feasibility. Moreover, the majority of the existing scheme of the literature incurs intensive labor and time, which induces predominant impact over the diagnosis time utilized for detecting breast cancer cells. An Intelligent Artificial Bee Colony and Adaptive Bacterial Foraging Optimization (IABC-ABFO) scheme is proposed for facilitating better rate of local and global searching ability in selecting the optimal features subsets and optimal parameters of ANN considered for breast cancer diagnosis. In the proposed IABC-ABFO approach, the traditional ABC algorithm used for cancer detection is improved by integrating an adaptive bacterial foraging process in the onlooker bee and the employee bee phase that results in an optimal exploitation and exploration. The results investigation of the proposed IABC-ABFO approach facilitated using Wisconsin breast cancer data set confirmed an enhanced mean classification accuracy of 99.52% on par with the existing baseline cancer detection schemes.

The recent outbreak of the novel Coronavirus Disease (COVID-19) has given rise to diverse health issues due to its high transmission rate and limited treatment options. Almost the whole world, at some point of time, was placed in lock-down in an attempt to stop the spread of the virus, with resulting psychological and economic sequela. As countries start to ease lock-down measures and reopen industries, ensuring a healthy workplace for employees has become imperative. Thus, this paper presents a mobile app-based intelligent portable healthcare (pHealth) tool, called i WorkSafe, to assist industries in detecting possible suspects for COVID-19 infection among their employees who may need primary care. Developed mainly for low-end Android devices, the i WorkSafe app hosts a fuzzy neural network model that integrates data of employees' health status from the industry's database, proximity and contact tracing data from the mobile devices, and user-reported COVID-19 self-test data. Using the built-in Bluetooth low energy sensing technology and K Nearest Neighbor and K-means techniques, the app is capable of tracking users' proximity and trace contact with other employees. Additionally, it uses a logistic regression model to calculate the COVID-19 self-test score and a Bayesian Decision Tree model for checking real-time health condition from an intelligent e-health platform for further clinical attention of the employees. Rolled out in an apparel factory on 12 employees as a test case, the pHealth tool generates an alert to maintain social distancing among employees inside the industry. In addition, the app helps employees to estimate risk with possible COVID-19 infection based on the collected data and found that the score is effective in estimating personal health condition of the app user.

Breast cancer is the biggest curse for the women society in the world since the survival factor of the infected patients is ensured only when it is detected at the early localized stage. The majority of the intelligent schemes proposed for detecting the breast cancer relies on the human skill that helps in trustworthy determination of essential pattern that confirms the existence of the infected cancer cells for deciding upon the course of treatment. Further, most of the research works contributed in the literature for detecting breast cancer necessitates huge time and laborinvolved that increases the time of diagnosis. This Intelligent Artificial Bee Colony and Enhanced Monarchy Butterfly Optimization Technique (IABC-EMBOT) is proposed for effective breast cancer diagnosis. The core idea behind the formulation of IABC-EMBOT relies on two significant ameliorations that, i) focuses on the modification of Monarchy Butterfly Optimization that enhances the exploration degree based on the rate of exploitation of the searching space and ii) concentrates on the elimination in the limitations of the ABC scheme by enhancing the possibility of search diversification process through phenomenal update facilitated through the dynamic and adaptive butterfly operator that improves the search globally. The proposed IABC-EMBOT scheme investigated using the Wisconsin data set is proven to facilitate an improved average classification accuracy of 97.53%.