2.Book Chapter (12)

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    PERFORMANCE ANALYSIS OF ABSTRACT-BASED CLASSIFICATION OF MEDICAL JOURNALS USING MACHINE LEARNING TECHNIQUES
    (Springer Link, 2021-09-14) Deepika, A; Radha, N
    Researchers face many challenges in finding the opt web-based resources by giving the queries based on keyword search. Due to advent of Internet, there are huge biological literatures that are deposited in the medical database repository in recent years. Nowadays, as many web-based medical researchers evolved in the field of medicine, there is need for an intelligent and efficient extraction technique required to filter appropriate and opt literature from the growing body of biomedical literature repository. In this research work, new combination of model is proposed in order to find the new insights in applying the combination of algorithm on biological data set. The information in the biomedical field is the basic information for healthy living. National Center for Biotechnology Information (NCBI)’s PubMed is the major source of peer-reviewed biomedical documents for researchers and health practitioners in the field of health-related management. In this paper, abstracts available in PubMed database is used for experimentation. In recent years, deep learning-based neural approach models provide an efficient way to create an end-to-end model that can accurately measure classification labels. This research work is a systematic analysis of performance of the supervised learning models such as Naïve Bayes (NB), support vector machine (SVM) and long short-term memory (LSTM) by implementing on textual medical data. The novelty in this work is the process of incorporating certain topic modelling techniques after the pre-processing phase to automatically label the documents. Topic modelling is a useful technique in increasing the efficiency and improves the ability of researchers to interpret biological information. So, the classification algorithms thus proposed are implemented in combination with popular topic modelling algorithms such as latent Dirichlet algorithm (LDA) and non-negative matrix factorization (NMF). The final performance of the combination of algorithms is also analysed and is found that SVM with NMF outperforms the other models.
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    A STUDY ON MACHINE LEARNING-BASED APPROACHES FOR PM2.5 PREDICTION
    (Springer Link, 2022-01-17) Santhana Lakshmi, V; Vijaya, M S
    Clean air and water is the fundamental need of humans. But people are exposed to polluted air produced due to several reasons such as combustion of fossil fuels, industrial discharge, dust and smoke which generates aerosols. Aerosols are tiny droplets or solid particles such as dust and smoke that floats in the atmosphere. The size of the aerosol also called as particulate matter ranges from 0.001–10 μm which when inhaled by human affects the respiratory organs. Air pollution affects the health of 9% of the people every year. It is observed as the most important risk factor that affects human health. There is a need for an efficient mechanism to forecast the quality of air to save the life of the people. Statistical methods and numerical model methods are largely employed for the predicting the value of PM2.5. Machine learning is an application of artificial intelligence that gives a system capability to learn automatically from the data, and hence, it can be applied for the successful prediction of air quality. In this paper, various machine learning methods available to predict the particulate matter 2.5 from time series data are discussed.
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    PHYTOSTABILIZATION OF METAL MINE TAILINGS—A GREEN REMEDIATION TECHNOLOGY
    (Elsevier, 2022) Lavanya, Muthusamy; Manikandan, Rajendran; Kavitha, Ramamoorthy; Mathiyazhagan, Narayanan; Sabariswaran, Kandasamy
    Mine tailings are the main source of environmental pollution due to the containing high concentration of heavy metals in the tailings, which cause a serious health threat to humans and animals. The toxic metals in mines are rapidly releasing into the nearby groundwater and agricultural soils causing various environmental and health problems in the mining area. Therefore, mine tailings need suitable techniques to reduce the release of heavy metals and their remediation technology. To date, numerous physical and chemical techniques have been applied for the remediation of heavy metals in contaminated water and soils, these techniques are expensive and applicable on small scale. Phytoremediation is one of the most promising, cost-effective, and eco-friendly technology for the removal of heavy metals from the polluted environment. In this chapter, we emphasize the environmental and health impacts of mine tailings and a potential method of removal and reduction of leaching toxic metals in mine tailings via green remediation technology.
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    STUDYING THE EFFECTIVENESS OF COMMUNITY DETECTION ALGORITHMS USING SOCIAL NETWORKS
    (Springer Link, 2022-11-01) Kiruthika, R; Vijaya, M S
    Social network analysis is a significant area of research for analyzing the interconnection between the people within network. Community detection is one of the most important applications in SNA. The main motive of CD is to discover the collection of node that are tightly correlated within the network and weakly correlated to another network for partitioning the network to form the group of communities. The aim of this work is to detect communities from undirected disjoint social networks in which it is implemented on lesmis and email-Eu-core-department-labels networks. Effective partitioning and detection of the network are the primary factors for implementing this work by using Girvan–Newman, greedy modularity maximization, and Kernighan–Lin bipartition CD algorithms. The effectiveness of these CD algorithms is analyzed with respect to ground-truth communities based on measures such as recall, normalized mutual information score, precision, and F1-score. Experimental results show that the greedy modularity maximization algorithm provides best results for CD on email-Eu-core-department-labels network with respect to corresponding ground-truth communities.
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    BIOSENSORS BASED ON METAL-ORGANIC FRAMEWORK (MOF): PAVING THE WAY TO POINT-OF-CARE DIAGNOSIS
    (Elsevier, 2022) Sushma, Dave; Jone Kirubavathy, S
    Metal-organic frameworks (MOFs) are recent versatile materials that have found application in gas storage, electrochemical sensors, drug delivery vehicles, and so on. MOFs have become an exotic class of material candidates due to their limitless combination of metal ions and organic ligands. This provides a wide range of inherent functionalities. The infinite crystalline lattice, porosity, and high surface area of MOFs have made them an important tool to sense various analytes of interest. The pore size of MOFs can also be tuned by employing organic ligands with many functional groups. The promising qualities of MOFs have extended its application in the detection of various contaminates in edible products and also as biosensors. This chapter discusses MOFs as biosensors and examines quantitative detection including the specific association of an analyte with an MOF and the utilization of both primitive and synthetic MOFs for occasional visual detection and direct in vitro or in vivo estimation.
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    THERMODYNAMIC ANALYSIS OF LITHIUM-ION BATTERY STORAGE SYSTEM
    (Elsevier, 2022) Nithya, C; Gopukumar, S
    The most promising energy storage systems are lithium-ion batteries (LIBs) owing to its high energy and power density. The electrochemical lithium storage in LIB is investigated in terms of thermodynamic functions such as free energy, entropy, enthalpy and heat capacities. These thermodynamic functions are influenced by various factors such as temperature, porosity, defects in electrode materials, solvation of Li+ ions by electrolyte solvents, double layer formation between electrode/electrolyte, phase transition during cycling and coexistence of Li intercalation reaction in single-phase and multiphase etc. Herein, we have analyzed the thermodynamics of overall electrochemical lithium storage and this analysis helpful to explore the stable electrode and electrolyte materials for next generation of LIBs and beyond.
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    ANTIMICROBIAL ACTIVITY OF COPPER NANOMATERIALS: CURRENT STATUS AND FUTURE PERSPECTIVES
    (Elsevier, 2022) Bhuvaneshwari, V; Nirmal Kumar, Ramasamy; Idhaya Kumar, S; Kalaivani, S; Vaidehi, D; Karthik Kumar, D
    Copper (Cu) is an essential element in governing the health of major organisms such as plants, animals, and human beings. Recently eco-friendly problem-solving methods have drawn considerable attention as they are simple and viable, alternative to numerous physical and chemical methods. A wider application and utilization of metal oxide nanoparticles (NPs) have gained momentum as an eco-friendly approach to solve numerous problems. Among the NPs, copper nanoparticles (CuNPs) or copper oxide nanoparticles (CuONPs) have a specialized role, and their use in nanobiotechnology as fertilizers to improve nutrition in the soil for sustainable crop development is also important. Cu-based agro-chemicals have traditionally been used in agriculture to maintain the nutrition and health status of the plants. Biological synthesis of CuNPs/CuONPs using plant extracts and microbial extracts, which showed better antimicrobial potential in inhibiting the growth of plant bacterial and fungal pathogens, was discussed in this chapter. The mechanistic impact of CuNPs/CuONPs against the pathogenic microorganisms of crops was also discussed.
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    NANOTUBULAR DEVICE EFFECT, SUPER CELL EFFECTIVENESS, HIRSHFELD ENERGY ANALYSIS AND BIOMEDICINAL EFFICACY OF 2-FLUORO-5-NITRO-ANILINE (2F5NA) CRYSTALS
    (Wiley, 2022-06-15) Flora, G; Munikumari, A; Sheeba, M; Jemma, Hermelin Jesy Diaz; Senthil Kannan, K; Ponrathy, T; Muthu Sheeba, M; Joshua Steve Abishek, B
    The effective mechanism for the growth of 2-fluoro-5-nitro-aniline (2F5NA) crystals is by the traditional cost-effective solution growth method; the crystallinesample is monoclinic with P2 1 /n space group; the XRD data reveal the crystaldata of 2F5NA; the nanotubular, super cell, Hirshfeld analysis, and biomedicalutility of 2F5NA crystalline samples are well portrayed using suitable softwaresfor the better outcome, super cell lattice, as well as the device with nanotube andwith acidic, peptide link are well portrayed with the internal, external, shaping indexing, curvedness effect, patching, and 3 × 3 × 3 order of the lattice are alsodiscussed. The titled organic crystalline specimen is a well and effective utility inbio as antifungal and antidiabetic are properly delineated. The interaction energyby HF/B3LYP is given in the tabular representation for energy in kJ/mol; ORTEPof crystal for the molecular way and unit cell by ORTEP are presented for a properand clear picture of the crystal confirmation. The novelty is mainly for bio medicinaluse (antibacterial) and enhanced electronic sectors; the material is havingnanotube device use mainly the predominant use of the sample.
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    BIOLOGICAL, ELECTRONIC-FILTER, INFLUX AND THEORETICAL PRACTICALITIES OF 2-CHLORO-6-NITROANILINE (2C6NA) CRYSTALS FOR BIOMEDICAL AND MICROELECTRONICS TASKS
    (Wiley, 2022-06-15) Maria Sumathi, B; Maalmarugan, J; Ganesan, H; Saravanan, P; Patel, R P; Sheeba, M; Flora, G; Senthil Kannan, K
    The effect and impact of biomedical, electronic, and computational analysis are more on 2C6NA crystals and are based on aniline by means of the anticancer,antibacterial, antifungal, the structural effectiveness by computational and influxof 3.4679 microns influx data. The efficacy is by HeLa cell lines; for the cervicalcancer and the E. coli, B. subtilis, K. pneumoniae, A. niger, and A. terreus andA. flavus. DPPH, FRAP, and antidiabetic utility of the 2C6NA nanocrystallinespecimen are portrayed with conversion of milling with 43-nm scaling and the structure effectiveness by means of software and optoelectronic filter utility is alsodiscussed. The crystal is grown by evaporation method and properly milled to nmscalings. Aniline plays a major impact for bioactivity and in influx datum. Theleading value of filter is applied to electronic functions and other NLO use, as wellas biocompatibility of the titled crystal sample and is reported for these studies asa novel work and in the future can be preceded for harboring process.
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    BIOMEDICAL AND ELECTRONIC TUNE-UPS OF 2C4NA NANOCRYSTALLINE SAMPLE
    (Wiley, 2022-06-15) Maalmarugan, J; Egbert Selwin Rose, A; Anbarasan, P; Poorani, R; Aarthi, N; Ganesan, H; Senthil Kannan, K; Flora, G
    2-chloro-4-nitroaniline (2C4NA) crystalline sample is grown by a solution growth method; it is milled to nanoform of 46 nm. The computational effect of the moleculararrangement of 2C4NA is shown with the ORTEP diagram and unit cell, andthe nanotubular form, di-peptide linkage, super cell lattice are portrayed in a wellmanner for 2C4NA crystals. The interactions for the finger print effect of 2C4NAare well portrayed. The 2C4NA have superior extent for anti-diabetic as a resultof the aniline existence and have augments in inhibition as the value of the concentrationenhances and the IC-50 value as 37.9 for macro and in nanoform, it is30.3. Also, the AD-nm variations have good efficiency, whereas the dimension of the test decreases from 266 to 46 nm. The 2C4NA crystals are used in filter applicationsalso as the data are represented and concluded with the inferences andreported with the utilities for electronic and pharma utilities and good FL datain nanoscaling and is used in anti-cancer and anti-inflammatory proviso and intribology too.