http://nopr.niscpr.res.in/handle/123456789/62405 2026-10-10T20:23:11Z 2026-10-10T20:23:11Z Rana, Anita Bajwa, Harjit Kaur http://nopr.niscpr.res.in/handle/123456789/62415 2023-08-09T04:44:26Z 2023-08-01T00:00:00Z

Title: Therapeutics of Bioactive Compounds from Medicinal Plants and Honeybee Products against Cancer Authors: Rana, Anita; Bajwa, Harjit Kaur Abstract: Every year, more than 12 million people are diagnosed with cancer worldwide. Cancer diagnosis is difficult for anybody to bear, and dealing with treatment is sometimes more complicated than the disease itself. When it comes to cancer treatment choices, chemotherapy is the most well-known since it is frequently used and recommended by specialists all over the world. On the other hand, chemotherapy is recognized for destroying healthy cells, and this destruction led to several negative impacts on the body. Recent advancements in biology have allowed scientists to better study the possible use of other methods, including phytotherapy and apitherapy for treating or managing many malignant conditions. Phytotherapy and apitherapy are among the best alternatives to chemotherapy as plants and honeybee products are chief sources of phytochemicals with anticancer properties. For example, hesperidin, melittin, apamin, artepillin, 10-hydroxy-2-decenoic acid (10-HDA), Major Royal Jelly Proteins (MRJP), jelleins, royalisin and caffeic acid phenethyl ester are important plant and bee engineered product constituents which by inducing apoptosis and arresting cell cycle control the proliferation of cancer cells. In general, this review highlights problems related to cancer treatment using chemicals. It discusses phytotherapy and apitherapy as an alternative to chemotherapy, while plants and bee products rich in natural anticancer compounds have greater potency to treat cancer. Page(s): 805-817

2023-08-01T00:00:00Z Mall, Pawan Kumar Narayan, Vipul Srivastava, Swapnita Sabarwal, Munish Kumar, Vimal Awasthi, Shashank Tyagi, Lalit http://nopr.niscpr.res.in/handle/123456789/62414 2023-08-09T04:37:41Z 2023-08-01T00:00:00Z

Title: Rank Based Two Stage Semi-Supervised Deep Learning Model for X-Ray Images Classification Authors: Mall, Pawan Kumar; Narayan, Vipul; Srivastava, Swapnita; Sabarwal, Munish; Kumar, Vimal; Awasthi, Shashank; Tyagi, Lalit Abstract: Deep learning approaches rely on a wide-scale labeled dataset to attain a high level of performance. Although labeled data is more difficult and costly to access in some applications, such as bioinformatics and medical imaging, wide variety of ongoing research on the topic of Semi-Supervised Deep Learning (SSDL) can improve and fix underlying problems in this domain. The motivation for the suggested model Rank Based Two-Stage Semi-Supervised Deep Learning (RTS-SS-DL) is the same as how doctors deal with unobserved or suspect cases in day to day practice. The physicians deal with these suspect instances with the help of professional assistance from their colleagues. Before beginning therapy, some patients seek the opinion of a variety of skilled professionals. The patients are treated by the most appropriate (vote count) professional diagnosis. Our model (RTS-SS-DL) has achieved impressive metrics including 92.776% accuracy, 97.376% specificity, 86.932% sensitivity, 96.192% precision, 85.644% MCC (Matthews Correlation Coefficient), 3.808% FDR (False Discovery Rate), 2.624% FPR (False Positive Rate), 91.072% f1-score, 90.85% NPV (Negative Predictive Value), and 13.068% FNR (False Negative Rate) for the unseen dataset. The outcome of this research results in an SSDL model that is both more precise and effective. Page(s): 818-830

2023-08-01T00:00:00Z Dauji, Saha http://nopr.niscpr.res.in/handle/123456789/62413 2023-08-09T04:33:31Z 2023-08-01T00:00:00Z

Title: Comparison of NDT Data Fusion for Concrete Strength using Decision Tree and Artificial Neural Network Authors: Dauji, Saha Abstract: Fusion of Non-Destructive Test (NDT) data results in more accurate estimation of concrete strength when compared to any single NDT data. Estimation of concrete strength from NDT results assumes importance for health assessment and evaluation of existing concrete buildings, particularly those near the end of their design life. Application of machine learning tools and response surface method has found popularity in recent years for this purpose. In this study, universally popular Artificial Neural Network (ANN) and relatively un-explored Decision Tree (DT) are applied to estimate concrete strength from rebound number and ultrasonic pulse velocity data collected from literature, in single and combined forms. A ranking system based on ratios of multiple performance measures was demonstrated for cases where different models are adjudged better considering different performance measures. From the results, it was concluded that fusion of NDT data resulted in better accuracy, for both ANN and DT. Comparing the selected performance measures as well as the ranks of the two machine learning tools, ANN models were found to perform better as compared to the DT models. The narrow range of multiple performance metrics obtained for three different data divisions (into modelling and evaluation sets) in all cases imparted confidence in the robustness of the approach of model development adopted in this study. Page(s): 831-840

2023-08-01T00:00:00Z G, Anurekha P, Geetha http://nopr.niscpr.res.in/handle/123456789/62412 2023-08-09T04:27:37Z 2023-08-01T00:00:00Z

Title: Autism Gene Subset Selection from Microarray data – A Wrapper Approach Authors: G, Anurekha; P, Geetha Abstract: Autism spectrum disorder is a complex neurodevelopment disorder that affects an individual's social behavior. Microarray analysis is an extensively used technique to detect autism. Microarray data can provide additional insight into the etiology of the disorder. Identifying the specific set of genes associated with autism from complex microarray data poses a significant research challenge due to its high dimensionality. However, Gene subset selection is classified as an np-hard problem that can be handled by the meta-heuristic algorithm. In this paper, a novel meta-heuristic Game Theory Based Whale Optimization Algorithm is proposed. The proposed algorithm uses a two-person zero-sum game theory and convergence parameter to increase convergence rate and avoid local optima. The performance of the proposed algorithm is tested with 23 mathematical benchmark functions and compared with other state-of-the-art algorithms. Further, the proposed algorithm is employed as a wrapper-based gene subset selection model with a support vector machine. Furthermore, the outcomes demonstrate that the gene selection model utilizing a wrapper-based approach is capable of effectively identifying a subset of autism-related genes with desirable accuracy. Page(s): 841-850

2023-08-01T00:00:00Z