International Journal of Computational Biology and Drug Design (9 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.
  
  
• Costunolide and Lupeol Reinforce IRF3 Gene Activity in Human Immune Response against COVID-19  
  by Fasila Y, Jayaprakash Chinnappan 
  Abstract: Coronavirus disease 2019 (COVID-19), caused by the SARS-CoV-2 virus strain, is a significant threat worldwide due to its fast spreading among people. Currently, many vaccines have been used for prevention and to improve the immune system of people. Still, these vaccines are not prevented enough, and many severe side effects exist, including death in many patients. In this study, firstly, we attempted to identify the target gene responsible for driving the immune response against viruses through network and functional analyses; secondly, potential active components present in the ingredients of Kabasura Kudineer were identified, and its effect against COVID-19; thirdly, the transcription factor (IRF3) and active compounds of Kabasura Kudineer were involved in virtual screening. Pharmacokinetic properties were compared, and the top value was notified. The overall work revealed that the selected active compounds increase the transcription factor enhancement regarding the immune response. Keywords: COVID-19, Kabasura Kudineer, IRF3, immune response.
  Keywords: COVID-19; Kabasura Kudineer; IRF3; Immune response.
  DOI: 10.1504/IJCBDD.2024.10067250
   
  
• Liver Tumour Segmentation and Classification Using MV3CNN-KHO: a Combination of Multiparameterised Inception V3 CNN And Krill Herd Optimisation  
  by A.Bathsheba Parimala, R.S. Shanmugasundaram 
  Abstract: The segmentation and classification of liver tumours are crucial in medical imaging, aiding early detection and treatment planning for liver diseases. Deep learning-based liver lesion segmentation has the potential to enhance the precision and effectiveness of liver disease detection. Recent studies have shown promising results in liver cancer prediction using CNN-based techniques. This work proposes a Multiparameterised Inception v3 CNN to improve feature extraction for liver cancer prediction. Additionally, KHO optimization can be applied to identify ideal hyperparameters, further enhancing the system's performance. By integrating KHO, the proposed model can achieve higher accuracy in predicting liver cancer, benefiting both patients and medical professionals. The study, conducted on the LiTS dataset, evaluates accuracy, sensitivity, and specificity, with the MIV3CNN-KHO model achieving 96% accuracy, 0.96 sensitivity, and 0.94 specificity. The implementation was done using Jupyter Notebook, with Python as the programming language. The optimized system offers an improved solution for liver cancer detection and prognosis, making it a valuable tool in medical imaging.
  Keywords: Segmentation Classification; Multiparameterized Inception V3; Kril Herd Optimization; Convolutional Neural Network; Medical Imaging; Feature Extraction; Model Evaluation; Accuracy Analysis.
  DOI: 10.1504/IJCBDD.2024.10067313
   
  
• ADMET Analysis and Molecular Docking of Phytocompounds of Magnolia Champaca Leaf Essential Oil as Potential Inhibitors of -Glucosidase, Estrogen Receptor-, TNF-, and Xanthine Oxidase  
  by Chiranjibi Sahoo, Ananya Nayak, Ayushman Gadnayak, Sudipta Jena, Asit Ray, Pratap Chandra Panda, Sanghamitra Nayak, Ambika Sahoo 
  Abstract: Magnolia champaca extracts have various pharmacological properties like antifertility, antibacterial, anti-inflammatory, and antioxidant activities. However, the pharmacological activities of Magnolia champaca leaf essential oils are still unknown. This study aimed to examine the antidiabetic, antifertility, anti-inflammatory, and antioxidant properties of the phytoconstituents of MCLEO using computational biological approaches. After analysing 65 compounds, 28 satisfied ADMET properties and were selected for molecular docking to provide mechanistic insights into -Glucosidase, Estrogen Receptor-, Tumour Necrosis Factor-, and Xanthine Oxidase inhibition. Crystallographic structures with PDB ID: 8D43, 1R5K, 5MU8, and 2CKJ of -G, ER-, TNF-, and XO, respectively were used as models for molecular docking. The result showed that cis-Muurola-3,5-diene and Selin-11en-4--ol showed docking scores of 8.489 and 9.146 against -G and ER-, respectively. -Cadinene showed docking scores of 7.951 and 8.268 against TNF- and XO. This is the first in silico study to discover prospective biactive compounds from MCLEO which could be useful in developing novel and effective medications.
  Keywords: Magnolia champaca; Michelia champaca; antidiabetic; antifertility; antiinflammatory; antioxidant; ADMET analysis; molecular docking.
  DOI: 10.1504/IJCBDD.2024.10067412
   
  
• AdaCluCSL: An Approach for Autism Spectrum Disorder Prediction Using Adaptive Clustering Smote and Cost-Sensitive Learning  
  by Kavitha M, M. Kasthuri 
  Abstract: Machine learning struggles to predict autism spectrum disorder (ASD) due to real-world datasets’ underlying class imbalance. Adaptive Cluster SMOTE and Cost-Sensitive Learning (AdaCluCSL) is a new approach that improves ASD prediction accuracy. Combining adaptive k-means clustering SMOTE with cost-sensitive learning achieves this. Because positive ASD samples are few, conventional classification methods sometimes have poor predictive accuracy. AdaCluCSL uses adaptive clustering to find complex clusters in the underrepresented class and address this disparity. It then creates synthetic samples using SMOTE with cluster-specific weights. Then, the enriched samples are used with the original cases to train a cost-sensitive classifier that allocates additional costs to minority class misclassifications. Experimental tests on benchmark ASD datasets show that AdaCluCSL improves ASD prediction accuracy. Precision and recall rates have improved significantly, outperforming traditional methods. The AdaCluCSL algorithm can advance ASD prediction. It solves uneven data distribution issues, making early diagnosis and intervention more reliable.
  Keywords: Prediction of Autism Spectrum Disorder (ASD); K-Means Cluster; Synthetic Minority Oversampling Technique (SMOTE); Cost Sensitive Learning; Imbalance Classification; Adaptive Cluster SMOTE; and Cost-Se.
  DOI: 10.1504/IJCBDD.2024.10067421
   
  
• Strengthening IoT Security: Assessing Ensemble Machine Learning for Cloud DDoS Attack Protection  
  by Bijay Kumar Paikaray, Lalmohan Pattnaik 
  Abstract: The vulnerability to Distributed Denial of Service attacks has significantly increased due to the simultaneous advancement of cloud services and the Internet of Things. This has facilitated the ability of unscrupulous individuals to interrupt cloud services and harm the reputation of organisations. Due to the unique characteristics and constraints of IoT devices, traditional approaches to identifying distributed denial of service attacks can prove inadequate within an IoT setting. The performance of the five supervised learning models Logistic Regression (LR), Ridge Classifier (RC), AdaBoostClassifier (ADB), and ExtraTreesClassifier (ETC) are evaluated in the accurate identification of IoT-based network activities. The evaluation of the learning models is done on a subclass of CIC DoS, CICIDS-2017, and CSECICIDS-2018 datasets, with a special focus on 2017 data. The feature engineering approach is employed to improve the learning model's accuracy. The experimental results revealed the highest level of accuracy rate of 99.97% for ExtraTreeClassifier.
  Keywords: DDoS; IoT; Machine Learning; IDS.
  DOI: 10.1504/IJCBDD.2025.10069480
   
  
• Innovating Prosthetic Foot Design: Integrating Big Data and Computational Biology for Enhanced Lower Limb Rehabilitation  
  by Sushree Sangita Nayak, Aswini Kumar Mohapatra, Srikanta Maharana, Bijay Kumar Paikaray 
  Abstract: This paper aims to discover in-depth knowledge of the design technology, material used, and clinical use of dynamic response prosthetic feet in rehabilitating lower limb amputees. Studies were done using electronic databases such as PubMed, Google Scholar, SCOPUS, and Research Network 20102024. Out of 162 papers, 43 papers relevant studies were included. Significant advances in the research and development of prosthetic legs over the last two decades have improved the functioning and quality of life of many lower-limb amputees living in industrialised countries. The disadvantage of this new RandD is that most end users live in developing countries and cannot benefit from this new technology due to the cost, durability, maintenance, and availability of these components. Research is needed to design and develop cost-effective prosthetic legs that meet economic, environmental, and physical standards and withstand adverse climatic and working conditions.
  Keywords: Foot Design; Finite element analysis; Rehabilitation; Big Data.
  DOI: 10.1504/IJCBDD.2025.10069522
   
  
• Advancing Surgical Instrument Recognition Through Shape Recognition Techniques in the Medical Industry  
  by Bijaya Paikray, Sonia Rathee, Shalu Mehta, Amita Yadav, Tiruveeduula Gopikirishna, Bijay Kishor Shishir Sekhar Pattanaik 
  Abstract: Computer assisted intervention (CAI) system is anticipating surgical workflow. Its goal is to use of instruments, supporting the intraoperative clinical decision support. The shape recognition of surgical instruments enables the identification of the different surgical instruments which are almost similar in shape and size and also helps to understand the category of each instrument. The proposed method is where one can extract the feature set of an object and compare it with the feature set of the universal collection of objects. The patchbased segmentation algorithm that is being suggested can get an F-score of 0.90. With a variety of instrument layouts, on average, the recommended force based grasping protocol achieves a 92% picking success rate, and the recommended attention based instrument recognition module achieves a 95.6% recognition accuracy. The end result consists of the name of the object, along with the percent classification and percent recognition with the universal object collection.
  Keywords: Universal object collection; Euclidean distance; percent classification; percent recognition; object pixels.
  DOI: 10.1504/IJCBDD.2025.10069727
   
  
• Improving Multiple Sclerosis Identification with an Advanced U-Net Architecture Featuring Dilated Convolutions  
  by M. Divya, Dhilipan J, A. Saravanan 
  Abstract: Multiple Sclerosis (MS) is a disease that impacts the CNS, which can lead to brain, spinal cord, and optic nerve problems. A total of 2.8 million are estimated to suffer from MS. Every five minutes, a new case of MS is reported globally. Numerous deep neural network models have been developed using different types of MS data, including MRI and clinical data. However, there is no standard approach available for the identification of abnormalities (lesions) in the DGM and MTL of the brain using MRI. This research proposed a U-Net-based modified architecture with dilated convolution operation for detecting MS. In this, the images from the BioGPS dataset are trained using the proposed U-Net model for extracting image features automatically. These outcome features are fed into softmax classification for performing pixel-wise probabilities of class labels. This network learns special features effectively, requiring much less computation than the traditional U-Net. This work gained 97.26% accuracy in predicting the abnormalities in MS, which is comparatively higher than the existing methods.
  Keywords: Multiple Sclerosis lesions; Modified U-Net; Dilated Convolution; Pixel-wise Classification; BioGPS dataset; Deep Grey Matter (DGM) and Mesial Temporal Lobe (MTL); Magnetic Resonance Imaging (MRI); Cen.
  DOI: 10.1504/IJCBDD.2025.10070187