ijtrseditor@gmail.com   ISSN No.:-2454-2024(Online)

Volume 6 Issue V

Author: Rachakonda Hrithik Sagar, Abhishek Bingi, Aashray Pola, Krishna Sai Raj Goud, Tuiba Ashraf, Subrata Sahana
Organisation: Department of Computer Science Engineering, School of Engineering and Technology, Sharda University, Greater Noida, India, Department of Electronics and Computer Engineering, Sreenidhi Institute of Science and Technology, Ghatkesar, Hyderabad, India
Email: hrithiksagar36@gmail.com
DOI Number: https://doi.org/10.30780/IJTRS.V06.I05.001

The incidence of skin cancer is increasing by epidemic proportions. According to WHO, Skin Cancer is the world’s 6th most common cancer. It can be classified into Basal cell carcinoma, Squamous cell carcinoma and Melanoma among which Melanoma is more difficult to predict. By using this method, we can assist dermatologists to detect at an early stage as Computer Vision plays a vital role in diagnosis.

In this paper, to detect skin cancer we are using machine learning-based algorithms. Traditionally classification algorithms are Convolutional neural networking which Consists of initialization, adding a convolutional layer, summing pooling layer, summing flattening layer, summing a dense layer, then compiling Convolutional neural networks and fitting the CNN model to a dataset. We used machine learning model architecture to determine if the skin images of the patients are harmful or harmless via using machine learning libraries provided in python. We have chosen this approach to be more precise and specific in recognizing about cancer and ultimately declining the mortality rate caused by it.

Keywords: Convolutional Neural Networking, Skin cancer, Malignant, Melanoma, Basal cell carcinoma, Squamous cell carcinoma.
Author: Sarwat Sohail, Saima Shahzad Mirza, Muhammad Sohail, Shahid Aslam Mirza
Organisation: Department of Management Science, Virtual University of Pakistan, Lahore, Microbiology laboratory, Department of Energy System Engineering, Punjab Bioenergy Institute, University of Agriculture, Faisalabad, Pakistan, Department of Management Science, Shaheed Zulfikar Ali Bhutto Institute of Science & Technology, Dubai
Email: ss.sarwatsohail@gmail.com
DOI Number: https://doi.org/10.30780/IJTRS.V06.I05.002

A novel pandemic of COVID-19 is the biggest crisis for the whole world after the Second World War. This Covid-19 created potential risk not only for the health of the people, but also for the global economy. Now a days Covid-19 became the global issue and became the reason of destruction of the global economy. This pandemic has affected not only the human life, but also the economy of all the countries so badly and caused the global economic recession. This pandemic affected the all aspects of our life. It has been disturbed the overall economy of countries, with this it also affected the social life of people.

The purposes of this review article to discuss the impact of Covid-19 on the global economy as well as on different industries like production industries, services industries, financial markets and how it affected the oil prices at the global level. This study also aims to discuss how Covid-19 has affected the economy of Pakistan. Covid-19 has turned the world upside down and also changed our way of life. It has been affected all sectors of any economy. Covid-19 affected the businesses and creates unemployment, which is the most alarming thing for the economy. It affected the production sector, the services sector, health, education, travel and financial market. This is the review article that concisely discusses the recent reports and discussion about this pandemic and tries to make some implications according to these reports and discussions.

Keywords: Covid-19, Economic Growth, Economy, Pakistan, Pandemic.
Author: Ankita Yadav, Riya Fagna, Aparna Vyas
Organisation: Department of Mathematics, Manav Rachna University, Faridabad, Haryana, India
Email: ankita.yadavcom1998@gmail.com
DOI Number: https://doi.org/10.30780/IJTRS.V06.I05.003

In this data age century with increment in the modern technology there is a development in the theory of multidimensional data to provide the higher directional sensitivity in imaging. A numeric image is a portrayal of a real image which is taken as a set of numbers that can be gathered and picked up by a digital computer. In order to decode the image into numbers it is divided into small segments called pixels (picture elements). Whenever there is a transmission of images or due to some environment factor there is an addition of noise to the images takes place that ultimately results in the reduction of originality of the image. It is very important to remove the noise from the images so that it is safeguard. Shearlets are a multiscale foundation which authorize efficient encoding of anisotropic feature in multivariate problem classes. In this paper, we have set forth the noise removal transform by hard thresholding for denoising. We can denoise the noisy image by wiping out the fine details, to enhance the quality of the images.

Keywords: Wavelets, Wavelet Transform, Shearlet, Shearlet Transform, Image De- noising.