A molecule called hemoglobin is found in red blood cells that holds oxygen all over the body. Hemoglobin is elastic, round, and stable in a healthy human. This makes it possible to float across red blood cells. But the composition of hemoglobin is unhealthy if you have sickle cell disease. It refers to compact and bent red blood cells. The odd cells obstruct the flow of blood. It is dangerous and can result in severe discomfort, organ damage, heart strokes, and other symptoms. The human life expectancy can be shortened as well. The early identification of sickle calls will help people recognize signs that can assist antibiotics, supplements, blood transfusion, pain-relieving medications, and treatments etc. The manual assessment, diagnosis, and cell count are time consuming process and may result in misclassification and count since millions of red blood cells are in one spell. When utilizing data mining techniques such as the multilayer perceptron classifier algorithm, sickle cells can be effectively detected with high precision in the human body. The proposed approach tackles the limitations of manual research by implementing a powerful and efficient MLP (Multi-Layer Perceptron) classification algorithm that distinguishes Sickle Cell Anemia (SCA) into three classes: Normal (N), Sickle Cells(S) and Thalassemia (T) in red blood cells. This paper also presents the precision degree of the MLP classifier algorithm with other popular mining and machine learning algorithms on the dataset obtained from the Thalassemia and Sickle Cell Society (TSCS) located in Rajendra Nagar, Hyderabad, Telangana, India. Doi: 10.28991/esj-2021-01270 Full Text: PDF

Pattern recognition is one of the prime concepts in current technologies in both private and public sectors. The analysis and recognition of two or more patterns is a complex task due to several factors. The consideration of two or more patterns requires huge space for keeping the storage media as well as computational aspect. Vector logic gives very good strategy for recognition of patterns. This paper proposes pattern recognition in multimodal authentication system with the use of vector logic and makes the computation model hard and less error rate. Using PCA two to three biometric patterns will be fusion and then various key sizes will be extracted using LU factorization approach. The selected keys will be combined using vector logic, which introduces a memory model often called Context Dependent Memory Model (CDMM) as computational model in multimodal authentication system that gives very accurate and very effective outcome for authentication as well as verification. In the verification step, Mean Square Error (MSE) and Normalized Correlation (NC) as metrics to minimize the error rate for the proposed model and the performance analysis will be presented.

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