Material Science, intermetallics, magnetic nanomaterials, metal and metal oxide nanoparticles-synthesis and characterization, Heusler alloys, nanotechnology for biological and agricultural applications
A Systematic Review on Physics-Based Optimization Techniques Mohammed Akheel, Aditya Anjanikar, Soham Navale, Sangeeta Pant, Anuj Kumar, Neeru Bhagat Advanced Metaheuristics for Scheduling in Distributed Manufacturing Systems, 2025 The need for optimization techniques has been long sought after heuristic, deterministic, and probabilistic algorithms have been applied to improve the efficiency and accuracy of these existing algorithms or create new algorithms that are superior to them. Metaheuristic algorithms that use optimization techniques, such as physics-based optimization, can be used when the solution space is approximate and when other algorithms aren't the best suited in these cases. When there is a complex solution space or when existing algorithms are not ideal, the physics-based optimization techniques provide better options. By taking inspiration from physics-based approaches, these techniques close the gap between the existing abstract ideas and real-world implementations. This paper discussed various physics-based optimization techniques, including their variants, to identify their origin, methodology, and applications.
Decoding characteristics of key physical properties in silver nanoparticles by attaining centroids for cytotoxicity prediction through data cleansing Anjana S Desai, Anindita Bandopadhyaya, Aparna Ashok, Maneesha, Neeru Bhagat Machine Learning Science and Technology, 2024 This research underscores the profound impact of data cleansing, ensuring dataset integrity and providing a structured foundation for unraveling convoluted connections between diverse physical properties and cytotoxicity. As the scientific community delves deeper into this interplay, it becomes clear that precise data purification is a fundamental aspect of investigating parameters within datasets. The study presents the need for data filtration in the background of machine learning (ML) that has widened its horizon into the field of biological application through the amalgamation of predictive systems and algorithms that delve into the intricate characteristics of cytotoxicity of nanoparticles. The reliability and accuracy of models in the ML landscape hinge on the quality of input data, making data cleansing a critical component of the pre-processing pipeline. The main encounter faced here is the lengthy, broad and complex datasets that have to be toned down for further studies. Through a thorough data cleansing process, this study addresses the complexities arising from diverse sources, resulting in a refined dataset. The filtration process employs K-means clustering to derive centroids, revealing the correlation between the physical properties of nanoparticles, viz, concentration, zeta potential, hydrodynamic diameter, morphology, and absorbance wavelength, and cytotoxicity outcomes measured in terms of cell viability. The cell lines considered for determining the centroid values that predicts the cytotoxicity of silver nanoparticles are human and animal cell lines which were categorized as normal and carcinoma type. The objective of the study is to simplify the high-dimensional data for accurate analysis of the parameters that affect the cytotoxicity of silver NPs through centroids.
Research Network Analysis and Machine Learning Modeling on Heusler Alloys Aparna Ashok, , Anjana Desai, Rajesh Mahadeva, Shashikant P. Patole, Brajesh Pandey, Neeru Bhagat, , , , , and Engineered Science, 2023 Heusler alloys are an incredible class of inter-metallic materials with different compositions and over 1500 members. Though discovered a century back, they are an active area of physics and material science research. Novel properties and potential fields of applications materialize constantly. Even the alloy system is extensively investigated owing to its shape memory behavior and prospective relevance in the development of actuator devices, where strains are controlled by applying an external magnetic field. Heusler alloys are currently the material of interest due to their properties leading to their use as shape memory alloys and topological insulators. Hence, predicting and determining their composition and structure is imperative before synthesis. Utilizing the conventional method in determining the possible changes in the properties and the structure of the proposed compositions is tedious and time-consuming. In the current consumerism-driven environment, we require a faster method to predict the structure of the proposed alloy or compound or other parameters for the desired application. Once the prediction is made, it must be tested experimentally by synthesizing the material and characterizing its behavior. This analysis is focusing on network analysis with a supervised machine learning approach to study the properties of Heusler alloys with their application as shape memory alloys.
Meta-Analysis of Cytotoxicity Studies Using Machine Learning Models on Physical Properties of Plant Extract-Derived Silver Nanoparticles Anjana Desai, Aparna Ashok, Zehra Edis, Samir Bloukh, Mayur Gaikwad, Rajendra Patil, Brajesh Pandey, Neeru Bhagat International Journal of Molecular Sciences, 2023 Silver nanoparticles (Ag-NPs) demonstrate unique properties and their use is exponentially increasing in various applications. The potential impact of Ag-NPs on human health is debatable in terms of toxicity. The present study deals with MTT(3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyl-tetrazolium-bromide) assay on Ag-NPs. We measured the cell activity resulting from molecules’ mitochondrial cleavage through a spectrophotometer. The machine learning models Decision Tree (DT) and Random Forest (RF) were utilized to comprehend the relationship between the physical parameters of NPs and their cytotoxicity. The input features used for the machine learning were reducing agent, types of cell lines, exposure time, particle size, hydrodynamic diameter, zeta potential, wavelength, concentration, and cell viability. These parameters were extracted from the literature, segregated, and developed into a dataset in terms of cell viability and concentration of NPs. DT helped in classifying the parameters by applying threshold conditions. The same conditions were applied to RF to extort the predictions. K-means clustering was used on the dataset for comparison. The performance of the models was evaluated through regression metrics, viz. root mean square error (RMSE) and R2. The obtained high value of R2 and low value of RMSE denote an accurate prediction that could best fit the dataset. DT performed better than RF in predicting the toxicity parameter. We suggest using algorithms for optimizing and designing the synthesis of Ag-NPs in extended applications such as drug delivery and cancer treatments.
