International Journal of Computational Biology and Drug Design (3 papers in press)

Regular Issues

• Importance of safety maintenance of the survived with recent former infection experience during a pandemic syndrome episode: A Study by Difference Equation Approach  
  by Subhasis Bhattacharya, Suman Paul, Sudip Mukherjee 
  Abstract: During the outbreak of a highly infectious disease conceded by a virus, handling of healthcare catastrophe is the most momentous part. Any type of known or unknown relaxation may generate enormous loss in terms of population. Present study consider the concern that survived one who has some fresh former infection history can be fingered with appropriate care throughout the syndrome period otherwise a huge harm can be advent by the state. The study follow difference equation modelling considering two aspects where the survived with former infection history handled with care and not reckoned as a part of sustained population and the other is they encompassed with the general population category. The study considers an example of a hypothetical state with some give infection rate, death rate and quarantine rate. By using R- programme language the study observes that proper care for such group of population is very significant to reduce the situation like human loss.
  Keywords: Infectious disease; SARS-CoV-2; 2019-nCov; Difference Equation; Survived from the infected; Quarantine rate; Death Rate.
  
  
• MPFP: Malnourished People Food Predictor Model Using Machine Learning Technique  
  by S. Vairachilai, Thota Chandan, Periyanayagi S, S.P. Raja 
  Abstract: Malnutrition presents a significant global health challenge, particularly prevalent in developing nations, necessitating accurate predictions of food intake for affected individuals. To address this, our research introduces the Malnourished People Food Prediction (MPFP) model, utilising machine learning algorithms for precise forecasts. The extensive dataset, incorporating food consumption data from 170 countries, reveals detailed dietary patterns among malnourished populations. Key findings indicate increased consumption of cereals, vegetal products, pulses, starchy roots, and vegetable oils, accompanied by reduced intake of animal products, eggs, milk, meat, and stimulants. The MPFP model strategically incorporates four regression algorithms multiple linear regression (MLR), random forest regression (RFR), support vector regression (SVR), and K-nearest neighbour (K-NN). Across diverse datasets and correlation levels, RFR consistently outperforms MLR, SVR, and K-NN, establishing its effectiveness in providing accurate predictions. Our analyses cover protein and fat supply quantities, as well as food quantity data measured in kilograms and kilocalories.
  Keywords: Malnutrition; Food Intake Prediction; Correlation Analysis; Dietary Patterns; Nutritional Assessment; Machine Learning Algorithms.
  DOI: 10.1504/IJCBDD.2026.10073943
   
  
• Leveraging Deep Learning Architecture Optimisation for Enhanced Early Detection of Bone Marrow Cancer  
  by G. Deepa, Y. Kalpana 
  Abstract: Early bone marrow cancer detection improves patient outcomes and therapy success. This study introduces a deep learning method that enhances bone marrow cancer diagnosis by utilising various optimal designs. The framework preprocesses input data with a Gaussian filter to reduce noise and preserve critical features for exact analysis. GrabCut and Gaussian mixture models segment relevant data. This method accurately isolates sick regions, which is crucial for diagnosis. The feature extraction step uses a hybrid Gabor-GLCM filter method. Both texture-based and frequency-based aspects are captured to fully understand cell characteristics that distinguish healthy from cancerous tissue. The gathered data is then processed using a sequential convolutional neural network (CNN) tailored to the classification task. The framework performs well on a test dataset, with 0.999 accuracy, 0.999 precision, and 1.0 recall. These impressive measurements demonstrate the optimised deep learning approach's ability to diagnose bone marrow cancer early and accurately. Prompt action can improve therapy success and patient prognosis.
  Keywords: Bone Marrow Cancer; Grabcut and Gaussian; Convolutional Neural Network; Patient Prognosis; Acute Myeloid Leukemia; Blood Cells; Bone Marrow.
  DOI: 10.1504/IJCBDD.2026.10075443