Efficient deep learning-based approach for malaria detection using red blood cell smears.

Malaria is an extremely malignant disease and is caused by the bites of infected female mosquitoes. This disease is not only infectious among humans, but among animals as well. Malaria causes mild symptoms like fever, headache, sweating and vomiting, and muscle discomfort; severe symptoms include coma, seizures, and kidney failure. The timely identification of malaria parasites is a challenging and chaotic endeavor for health staff. An expert technician examines the schematic blood smears of infected red blood cells through a microscope. The conventional methods for identifying malaria are not efficient. Machine learning approaches are effective for simple classification challenges but not for complex tasks. Furthermore, machine learning involves rigorous feature engineering to train the model and detect patterns in the features. On the other hand, deep learning works well with complex tasks and automatically extracts low and high-level features from the images to detect disease. In this paper, EfficientNet, a deep learning-based approach for detecting Malaria, is proposed that uses red blood cell images. Experiments are carried out and performance comparison is made with pre-trained deep learning models. In addition, k-fold cross-validation is also used to substantiate the results of the proposed approach. Experiments show that the proposed approach is 97.57% accurate in detecting Malaria from red blood cell images and can be beneficial practically for medical healthcare staff.

A milk transportation problem with milk collection centers and vehicle routing.

In this work, we study, model, and propose two approaches to solve a raw milk transportation problem inspired by a real case of a milk company in Chile. The milk is produced by a set of farms scattered in a large rural area. The company must collect all the production daily using a truck fleet. We address the location of milk collection centers to reduce transportation costs. Each center has a limited capacity and a reduced truck fleet, composed of small trucks, to collect a substantial proportion of the produced milk. Once the milk is accumulated in the collection centers, a fleet of big trucks, traveling from a processing plant, collects the milk of each collection center and some large farms. We propose a mixed-integer linear programming model, a three-stage approach based on mathematical models, and an iterated local search approach to face this problem. We evaluate these approaches' performance using a small case and several real-world examples, including a clustering approach to divide the instance into small sub-instances. The results obtained for the real-world instance show improvements of up to 10% percent when milk collection centers are allowed.

Development of Computational Approaches with a Fragment-Based Drug Design Strategy: In Silico Hsp90 Inhibitors Discovery.

A semi-exhaustive approach and a heuristic search algorithm use a fragment-based drug design (FBDD) strategy for designing new inhibitors in an in silico process. A deconstruction reconstruction process uses a set of known Hsp90 ligands for generating new ones. The deconstruction process consists of cutting off a known ligand in fragments. The reconstruction process consists of coupling fragments to develop a new set of ligands. For evaluating the approaches, we compare the binding energy of the new ligands with the known ligands.