CarryIT: Design and Implementation of an Effective Parcel Carrying System

A plethora of courier services are available in Bangladesh which are more expensive, less effective and takes ages to deliver the product. Besides, in the ongoing COVID-19 pandemic situation, it's onerous to move from one place to another, and if a person wants to send a product, he/she needs to go to the courier office. Moreover, many people need to move from their house for attending office, meetings, and traveling from one district to another district for special purposes. In this paperwork, a system is introduced to reduce the cost and time to send a product from one place to another place. Basically, an application is developed to meet the people who want to send a product and another one who want to carry the product. For carrying the product one can easily earn money. After scrutinizing the cost of courier services in Bangladesh, an algorithm is introduced to calculate the carrying cost of the product and an algorithm is also developed for product security.

Feature fusion based VGGFusionNet model to detect COVID-19 patients utilizing computed tomography scan images.

COVID-19 is one of the most life-threatening and dangerous diseases caused by the novel Coronavirus, which has already afflicted a larger human community worldwide. This pandemic disease recovery is possible if detected in the early stage. We proposed an automated deep learning approach from Computed Tomography (CT) scan images to detect COVID-19 positive patients by following a four-phase paradigm for COVID-19 detection: preprocess the CT scan images; remove noise from test image by using anisotropic diffusion techniques; make a different segment for the preprocessed images; and train and test COVID-19 detection using Convolutional Neural Network (CNN) models. This study employed well-known pre-trained models, including AlexNet, ResNet50, VGG16 and VGG19 to evaluate experiments. 80% of images are used to train the network in the detection process, while the remaining 20% are used to test it. The result of the experiment evaluation confirmed that the VGG19 pre-trained CNN model achieved better accuracy (98.06%). We used 4861 real-life COVID-19 CT images for experiment purposes, including 3068 positive and 1793 negative images. These images were acquired from a hospital in Sao Paulo, Brazil and two other different data sources. Our proposed method revealed very high accuracy and, therefore, can be used as an assistant to help professionals detect COVID-19 patients accurately.

Covid-19 Detection in Chest X-ray Through Random Forest Classifier using a Hybridization of Deep CNN and DWT Optimized Features

Chest X-ray image contains sufficient information that finds wide-spread applications in diverse disease diagnosis and decision making to assist the medical experts. This paper has proposed an intelligent approach to detect Covid-19 from the chest X-ray image using the hybridization of deep convolutional neural network (CNN) and discrete wavelet transform (DWT) features. At first, the X-ray image is enhanced and segmented through preprocessing tasks, and then deep CNN and DWT features are extracted. The optimum features are extracted from these hybridized features through minimum redundancy and maximum relevance (mRMR) along with recursive feature elimination (RFE). Finally, the random forest-based bagging approach is used for doing the detection task. An extensive experiment is performed, and the results confirm that our approach gives satisfactory performance compare to the existing methods with an overall accuracy of more than 98.5%.