Optimizing citrus disease detection: a transferable convolutional neural network model enhanced with the fruitfly optimization algorithm
Bacteria; Citrus fruit disease detection; Enhanced fruitfly optimization algorithm; Image analysis; Transferrable convolutional neural network
Fungal, bacterial, and viral diseases significantly threaten citrus production and quality worldwide, prompting producers to explore technological solutions to mitigate the financial impact of these diseases. Image analysis techniques have emerged as powerful tools for detecting citrus diseases by differentiating between healthy and diseased specimens through the extraction of discriminative features from input images. This paper introduces a valuable dataset comprising 953 color images of orange leaves from the species Citrus sinensis (L.) Osbeck, which serves to train, evaluate, and compare various algorithms aimed at identifying abnormalities in citrus fruits. The development of automated detection systems is crucial for reducing economic losses in citrus production, with this research focusing on twelve specific diseases and nutrient deficiencies. We propose a novel approach to citrus plant disease detection utilizing a hyper-parameter tuned transferrable convolutional neural network (TCNN) model, referred to as the enhanced fruitfly optimization algorithm (EFOA)-TCNN model. This model optimizes the parameters of TCNN using the EFOA and enhances architectural design by incorporating three convolutional layers alongside an energy layer instead of a traditional pooling layer. Experimental results demonstrate that the proposed EFOA-TCNN model outperforms existing state-of-the-art methods, achieving a sensitivity of 0.975 and an accuracy of 0.995. 2025, Institute of Advanced Engineering and Science. All rights reserved.
Lingaraju, Anoop Ganadalu; Shankaregowda, Asha Mangala; Sathiyamurthy, Babu Kumar; Channegowda, Santhrupth Budanoor; Jalapur, Shruti; Chennakeshava, Chaitra Palahalli
IAES International Journal of Artificial Intelligence;Volume;14;Issue;4;pp.3201-3213
Institute of Advanced Engineering and Science
https://doi.org/10.11591/ijai.v14.i4.pp3201-3213 https://www.scopus.com/pages/publications/105014374362?origin=resultslist
Lingaraju A.G., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST University, Bengaluru, India; Shankaregowda A.M., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST University, Bengaluru, India; Sathiyamurthy B.K., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST University, Bengaluru, India; Channegowda S.B., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST University, Bengaluru, India; Jalapur S., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST University, Bengaluru, India; Chennakeshava C.P., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST University, Bengaluru, India
Detection and analysis of android malwares using hybrid dual Path bi-LSTM Kepler dynamic graph convolutional network
Android application; Benign; Dynamic graph; Malicious; Selected features; Syntax tree
In past decade, the android malware threats have been rapidly increasing with the widespread usage of internet applications. In respect of security purpose, there are several machine learning techniques attempted to detect the malwares effectively, but failed to achieve the accurate detection due to increasing number of features, more time consumption decreases in detection efficiency. To overcome these limitations, in this research work an innovative Hybrid dual path Bidirectional long short-term memory Kepler dynamic graph Convolutional Network (HBKCN) is proposed to analyze and detect android malwares effectively. First, the augmented abstract syntax tree is applied for pre-processing and extracts the string function from each malware. Second, the adaptive aphid ant optimization is utilized to choose the most appropriate features and remove irrelevant features. Finally, the proposed HBKCN classifies benign and malware apps based on their specifications. Four benchmark datasets, namely Drebin, VirusShare, Malgenome -215, and MaMaDroid datasets, are employed to estimate the effectiveness of the technique. The result demonstrates that the HBKCN technique achieved excellent performance with respect to a few important metrics compared to existing methods. Moreover, detection accuracies of 99.2%, 99.1%,99.8% and 99.8% are achieved for the considered datasets, respectively. Also, the computation time is greatly reduced, illustrating the efficiency of the proposed model in identifying android malwares. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.
Lingayya S.; Kulkarni P.; Salins R.D.; Uppoor S.; Gurudas V.R.
International Journal of Machine Learning and Cybernetics
Springer Science and Business Media Deutschland GmbH
Lingayya S., Department of Computer Science and Engineering, Shri Madhwa Vadiraja Institute of Technology and Management, Karnataka, Bantakal, 574115, India; Kulkarni P., Department of Computer Science and Engineering, School of Engineering, Dayananda Sagar University, Ramanagara Dt., Bengaluru,, 562112, India; Salins R.D., Department of Aerospace, Honeywell Technology Solutions Lab Pvt. Ltd, Bellandur, Bangalore, 560103, India; Uppoor S., Infosys Ltd, Karnataka, Mangalore, 574153, India; Gurudas V.R., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST University, Karnataka, Bangalore, India
Lingayya, Sadananda; Kulkarni, Praveen; Salins, Rohan Don; Uppoor, Shruthi; Gurudas, V.R.
International Journal of Machine Learning and Cybernetics;Volume;16;Issue;2;Article No.;117833;pp.835-853
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1007/s13042-024-02303-3 https://www.scopus.com/pages/publications/85201560496?origin=resultslist
Combined Enzymatic Action of Pseudomonas psedoalcaligenes Protease and Stutzerimonas xanthomarina Amylase in Textile Blood Stain Removal
Amylase; Blood Stain; Crude Enzyme; Detergent Powder; Protease
Blood is a body fluid. When it contacts oxygen, it can clot, forming a dark solid substance. In accidents involving wounds, blood is released and can stain clothing when absorbed by fabrics. Removing blood stains from fabrics is challenging with various commercial detergents. This study aimed to determine the efficacy of commercially available detergent powder supplemented with amylase and protease enzymes in removing bloodstains from textiles. Stutzerimonas xanthomarina and Pseudomonas pseudoalcaligenes were isolated from soil and examined for their enzymatic activity. The results demonstrated that these enzymes can effectively remove bloodstains from cotton fabric, with maximum activity observed at 50% saturation with ammonium sulfate and specific activity of protease at 93.45 2.17 U/mg and amylase at 36.28 1.36 U/mg. This study suggests that the addition of enzymes to chemical detergents can enhance their surface impact and improve their effectiveness. These findings indicate a potential for the development of more efficient detergents. The Author(s) 2025.
Journal of Pure and Applied Microbiology;Volume;19;Issue;4;Article No.;10637;pp.3011-3021
Journal of Pure and Applied Microbiology
All Open Access; Gold Open Access; Green Open Access
https://doi.org/10.22207/JPAM.19.4.43 https://www.scopus.com/pages/publications/105026176509?origin=resultslist
Linsha Mali A., Department of Life Sciences, CHRIST University, Karnataka, Bengaluru, India; Joseph B.V., Department of Life Sciences, CHRIST University, Karnataka, Bengaluru, India
Carbon dots derived from frankincense soot for ratiometric and colorimetric detection of lead (II)
carbon dots; detection of lead; frankincense soot; photoluminescence; TD-DFT; UV light LED chip
We report a simple one-pot hydrothermal synthesis of carbon dots from frankincense soot. Carbon dots prepared from frankincense (FI-CDs) have narrow size distribution with an average size of 1.80 nm. FI-CDs emit intense blue fluorescence without additional surface functionalization or modification. A negative surface charge was observed for FI-CDs, indicating the abundance of epoxy, carboxylic acid, and hydroxyl functionalities that accounts for their stability. A theoretical investigation of the FI-CDs attached to oxygen-rich functional groups is incorporated in this study. The characteristics of FI-CDs signify arm-chair orientation, which is confirmed by comparing the indirect bandgap of FI-CDs with the bandgap obtained from Tauc plots. Also, we demonstrate that the FI-CDs are promising fluoroprobes for the ratiometric detection of Pb2+ ions (detection limit of 0.12 ?M). The addition of Pb2+ to FI-CD solution quenched the fluorescence intensity, which is observable under illumination by UV light LED chips. We demonstrate a smartphone-assisted quantification of the fluorescence intensity change providing an efficient strategy for the colorimetric sensing of Pb2+ in real-life samples. 2022 IOP Publishing Ltd.
Lisa John V.; Fasila P.M.; Chaithra K.P.; Vinod T.P.
ISSN: 9574484; PubMed ID: 36049475; CODEN: NNOTE
https://doi.org/10.1088/1361-6528/ac8e76 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138458505&doi=10.1088%2f1361-6528%2fac8e76&partnerID=40&md5=bd7f5294eb43822f617b9771d77f855f
Lisa John V., Department of Chemistry, CHRIST (Deemed to be University), Bangalore, 560029, India; Fasila P.M., Department of Chemistry, Sir Syed College, Taliparamba, Kerala, Kannur, 670142, India; Chaithra K.P., Department of Chemistry, CHRIST (Deemed to be University), Bangalore, 560029, India; Vinod T.P., Department of Chemistry, CHRIST (Deemed to be University), Bangalore, 560029, India
Amine functionalized carbon quantum dots from paper precursors for selective binding and fluorescent labelling applications
amine-modified CQDs; Carbon quantum dots; DNA binding; DNA cleavage; Modified carbon quantum dots; Photoluminescence
We report a novel synthesis route for preparing carbon quantum dots (CQDs) of customized surface functionality from readily available precursors. The synthetic strategy is based on the chemical modification of paper precursors prior to preparing CQDs from them. The pre-synthesis modification of paper precursors with (3-Aminopropyl) triethoxy silane (APTES) enabled us to synthesize CQDs with amine functional groups on the surface. The silane coupling via condensation between the ethoxy group of APTES and the cellulose hydroxyl group on the paper resulted in the tethering of amine groups on the paper substrates, which are retained as surface-bound species during the synthesis of CQDs from the modified paper. Amine functionalization on the surface of CQDs helped us use them in applications such as DNA binding. We analyzed the interaction of CQDs with calf thymus DNA (CT-DNA), and the results imply their propensity as an efficient biological probe. The synthetic strategy presented here can also be extended to other functional groups. 2022 Elsevier Inc.
Lisa John V.; Joy F.; Jose Kollannoor A.; Joseph K.; Nair Y.; Vinod T.P.
Journal of Colloid and Interface Science, Vol-617, pp. 730-744.
ISSN: 219797; PubMed ID: 35316786; CODEN: JCISA
https://doi.org/10.1016/j.jcis.2022.03.070 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126569826&doi=10.1016%2fj.jcis.2022.03.070&partnerID=40&md5=70be38f1e4d7d192d173c5534de1ffca
Lisa John V., Department of Chemistry, CHRIST (Deemed to be University), Hosur Road, Karnataka, Bangalore, 560029, India; Joy F., Department of Chemistry, CHRIST (Deemed to be University), Hosur Road, Karnataka, Bangalore, 560029, India; Jose Kollannoor A., Department of Chemistry, CHRIST (Deemed to be University), Hosur Road, Karnataka, Bangalore, 560029, India; Joseph K., Indian Institute of Space Science and Technology, Kerala, Thiruvananthapuram, 695547, India; Nair Y., Department of Chemistry, CHRIST (Deemed to be University), Hosur Road, Karnataka, Bangalore, 560029, India; Vinod T.P., Department of Chemistry, CHRIST (Deemed to be University), Hosur Road, Karnataka, Bangalore, 560029, India
Further Discussion on the Significance of Quartic Autocatalysis on the Dynamics of Water Conveying 47nm Alumina and 29nm Cupric Nanoparticles
29nm CuOwater; 47nm Al 2O 3water; Brownian motion; Heat transfer; Nanofluid; Thermophoresis
Improvement of product performance, efficiency, and reliability is a major concern of experts, scientists, and technologists dealing with the dynamics of water conveying nanoparticles on objects with nonuniform thickness either coated or sprayed with the catalyst. However, little is known on the significance of quartic autocatalysis as it affects the dynamics of water conveying alumina and cupric nanoparticles. In this study, comparative analysis between the dynamics of water conveying 29nm CuO and 47nm Al 2O 3 on an upper horizontal surface of a paraboloid of revolution is modeled and presented. In the transport phenomena, migration of nanoparticles due to temperature gradient, the haphazard motion of nanoparticles, and diffusion of motile microorganisms were incorporated into the mathematical models. Due to the inherent nature of the thermophysical properties of the two nanofluids, viscosity, density, thermal radiation, and heat capacity of the two nanofluids were incorporated in the mathematical model. The nonlinear partial differential equations that model the transport phenomenon were transformed, non-dimensionalized and parameterized. The corresponding boundary value problems were converted to an initial value problem using the method of superposition and solved numerically. The concentration of the catalyst increases significantly with buoyancy at a larger magnitude of space-dependent internal heat source in the flow of 29nm CuOwater nanofluid. Negligible migration of nanoparticles due to temperature gradient decreases the concentration of the fluid throughout the domain. 2020, King Fahd University of Petroleum & Minerals.
Liu H.; Animasaun I.L.; Shah N.A.; Koriko O.K.; Mahanthesh B.
Arabian Journal for Science and Engineering, Vol-45, No. 7, pp. 5977-6004.
Liu H., College of Mechanical and Electronic Engineering, Northwest Agriculture and Forestry University, Yangling, 712100, Shaanxi, China; Animasaun I.L., Department of Mathematical Sciences, Fluid Dynamics and Survey Research Group, Federal University of Technology, Akure, Nigeria; Shah N.A., Informetrics Research Group, Ton Duc Thang University, Ho Chi Minh City, Viet Nam, Faculty of Mathematics & Statistics, Ton Duc Thang University, Ho Chi Minh City, Viet Nam; Koriko O.K., Department of Mathematical Sciences, Federal University of Technology, Akure, Nigeria; Mahanthesh B., Department of Mathematics, CHRIST (Deemed to be University), Bengaluru, 560029, Karnataka, India
Ochratoxin A as an alarming health threat for livestock and human: A review on molecular interactions, mechanism of toxicity, detection, detoxification, and dietary prophylaxis
Biotransformation; Detoxification; Dietary prophylaxis; Ochratoxin; Toxicokinetics
Ochratoxin A (OTA) is a toxic metabolite produced by Aspergillus and Penicillium fungi commonly found in raw plant sources and other feeds. This review comprises an extensive evaluation of the origin and proprieties of OTA, toxicokinetics, biotransformation, and toxicodynamics of ochratoxins. In in vitro and in vivo studies, the compatibility of OTA with oxidative stress is observed through the production of free radicals, resulting in genotoxicity and carcinogenicity. The OTA leads to nephrotoxicity as the chief target organ is the kidney. Other OTA excretion and absorption rates are observed, and the routes of elimination include faeces, urine, and breast milk. The alternations in the Phe moiety of OTA are the precursor for the amino acid alternation, bringing about Phe-hydroxylase and Phe-tRNA synthase, resulting in the complete dysfunction of cellular metabolism. Biodetoxification using specific microorganisms decreased the DNA damage, lipid peroxidation, and cytotoxicity. This review addressed the ability of antioxidants and the dietary components as prophylactic measures to encounter toxicity and demonstrated their capability to counteract the chronic exposure through supplementation as feed additives. 2022 Elsevier Ltd
Liu W.-C.; Pushparaj K.; Meyyazhagan A.; Arumugam V.A.; Pappuswamy M.; Bhotla H.K.; Baskaran R.; Issara U.; Balasubramanian B.; Mousavi Khaneghah A.
ISSN: 410101; PubMed ID: 35452686; CODEN: TOXIA
https://doi.org/10.1016/j.toxicon.2022.04.012 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129290299&doi=10.1016%2fj.toxicon.2022.04.012&partnerID=40&md5=ff8e2421597dafd652765e0b6d37f796
Liu W.-C., Department of Animal Science, College of Coastal Agricultural Sciences, Guangdong Ocean University, Zhanjiang, 524088, China; Pushparaj K., Department of Zoology, School of Biosciences, Avinashilingam Institute for Home Science and Higher Education for Women, Tamil Nadu, Coimbatore, 641 043, India; Meyyazhagan A., Department of Life Science, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560076, India; Arumugam V.A., Department of Human Genetics and Molecular Biology, Bharathiar University, Tamil Nadu, Coimbatore, 641 046, India; Pappuswamy M., Department of Life Science, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560076, India; Bhotla H.K., Department of Animal Science, College of Coastal Agricultural Sciences, Guangdong Ocean University, Zhanjiang, 524088, China; Baskaran R., Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan; Issara U., Division of Food Science and Technology Management, Faculty of Science and Technology, Rajamangala University of Technology Thanyaburi, Pathum Thani, 12110, Thailand; Balasubramanian B., Department of Food Science and Biotechnology, College of Life Science, Sejong University, Seoul, 05006, South Korea; Mousavi Khaneghah A., Department of Food Science and Nutrition, Faculty of Food Engineering, University of Campinas (UNICAMP), S Paulo, Campinas, Brazil
Machine Learning Based Spam E-Mail Detection Using Logistic Regression Algorithm
E-Mail; Logistic Regression; Machine Learning; Nae Bayes; Spam detection; Support Vector Machines
The rise of spam mail, or junk mail, has emerged as a significant nuisance in the modern digital landscape. This surge not only inundates user's email inboxes but also exposes them to security threats, including malicious content and phishing attempts. To tackle this escalating problem, the proposed machine learning-based strategy that employs Logistic Regression for accurate spam mail prediction. This research is creating an effective and precise spam classification model that effectively discerns between legitimate and spam emails. To achieve this, we harness a meticulously labeled dataset of emails, each classified as either spam or non-spam. This model is to apply preprocessing techniques to extract pertinent features from the email content, encompassing word frequencies, email header data, and other pertinent textual attributes. The choice of Logistic Regression as the foundational classification algorithm is rooted in its simplicity, ease of interpretation, and appropriateness for binary classification tasks. To process train the model using the annotated dataset, refining its hyper parameters to optimize its performance. By incorporating feature engineering and dimensionality reduction methodologies, bolster the model's capacity to generalize effectively to unseen data. Our evaluation methodology encompasses rigorous experiments and comprehensive performance contrasts with other well-regarded machine learning algorithms tailored for spam classification. The assessment criteria encompass accuracy, precision, recall, and the F1 score, offering a holistic appraisal of the model's efficacy. Furthermore, we scrutinize the model's resilience against diverse forms of spam emails, in addition to its capacity to generalize to new data instances. This model is to findings conclusively demonstrated that our Logistic Regression-driven spam mail prediction model achieves a competitive performance standing when juxtaposed with cutting-edge methodologies. The model adeptly identifies and sieves out spam emails, thereby cultivating a more trustworthy and secure email environment for users. The interpretability of the model lends valuable insights into the pivotal features contributing to spam detection, thereby aiding in the identification of emerging spam patterns. 2023 IEEE.
Livia Shreenithi S.A.; Yougandar S.V.; Jayapandian N.
3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023
Institute of Electrical and Electronics Engineers Inc.
Livia Shreenithi S.A., Christ (Deemed to Be University), Department of Cse, Bangalore, India; Yougandar S.V., Christ (Deemed to Be University), Department of Cse, Bangalore, India; Jayapandian N., Christ (Deemed to Be University), Department of Cse, Bangalore, India
An exploration into body image experiences and views among the third gender
Bioconversion of chicken feather waste into feather hydrolysate by multifaceted keratinolytic Bacillus tropicus LS27 and new insights into its antioxidant and plant growth-promoting properties
Antioxidants; Chicken feather; Feather hydrolysate; Heavy metal; Keratin; Metal tolerance; Plant growth
Abstract: Keratin, the main structural constituent of feathers, contains a lot of valuable amino acids which are potential bioactive compounds as well. Since conventional methods are not efficient enough to achieve complete removal of chicken feather waste, biological mode of feather degradation is one of the most appropriate ways to utilize feathers, thereby reducing wastes as well as generating value-added products from feathers. This study was focussed on valorizing chicken feather into feather hydrolysate (FH) containing bioactive compounds for plant growth promotion. Keratinolytic bacteria capable of degrading chicken feathers were isolated from the poultry waste dumping site of Russell Market, Shivajinagar, Bangalore, Karnataka, India. The isolated bacteria was identified as Bacillus tropicus LS 27. A minimal media with chicken feather as the sole source of carbon and nitrogen was prepared and inoculated with Bacillus tropicus LS 27 [5% (v/v)]. Degradation of keratin protein by bacteria caused the solubilization of amino acids which was confirmed by high-performance liquid chromatography (HPLC) analysis where an appreciable amount of amino acids like cysteine, valine, isoleucine, proline, lysine, methionine, and phenylalanine was detected. The Fourier transform infrared spectroscopy (FTIR) analysis of hydrolysed chicken feathers showed C=0 stretching, S-H bond stretching, and formation of carboxylic acid groups indicating effective degradation of chicken feathers. Scanning electron microscope (SEM) images revealed the degradation pattern of feathers showing complete degradation of barbs and barbules with a portion of rachis remaining. Feather hydrolysate was further explored for its antioxidant activity using DPPH scavenging assay, and the value was found to be 1.5 mg/mL. The bacterial cells when screened for heavy metal tolerance showed significant metal tolerance to lead (Pb) and chromium (Cr). Since Bacillus tropicus LS27 showed indole-3-acetic acid (IAA), siderophore, and ammonia production, the prepared feather hydrolysate along with the bacterial cells were used as soil amendment for plant growth studies over Spinacia oleracea L. The study revealed that plants supplemented with 20% (v/v) FH showed elevated plant growth, therefore proving to be optimum for the support of plant growth. Graphical abstract: [Figure not available: see fulltext.] 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Liya S.M., Department of Life Sciences, CHRIST (Deemed to be University), Hosur Road, Karnataka, Bengaluru, 560029, India; Umesh M., Department of Life Sciences, CHRIST (Deemed to be University), Hosur Road, Karnataka, Bengaluru, 560029, India
Optimized production of keratinolytic proteases from Bacillus tropicus LS27 and its application as a sustainable alternative for dehairing, destaining and metal recovery
Bacillus tropicus; Dehairing; Destaining; Keratinase; Metal recovery; Optimization; Protease
The present study describes the isolation and characterization of Bacillus tropicus LS27 capable of keratinolytic protease production from Russell Market, Shivajinagar, Bangalore, Karnataka, with its diverse application. The ability of this strain to hydrolyze chicken feathers and skim milk was used to assess its keratinolytic and proteolytic properties. The strain identification was done using biochemical and molecular characterization using the 16S rRNA sequencing method. Further a sequential and systematic optimization of the factors affecting the keratinase production was done by initially sorting out the most influential factors (NaCl concentration, pH, inoculum level and incubation period in this study) through one factor at a time approach followed by central composite design based response surface methodology to enhance the keratinase production. Under optimized levels of NaCl (0.55 g/L), pH (7.35), inoculum level (5%) and incubation period (84 h), the keratinase production was enhanced from 41.62 U/mL to 401.67 9.23 U/mL (9.65 fold increase) that corresponds to a feather degradation of 32.67 1.36% was achieved. With regard to the cost effectiveness of application studies, the crude enzyme extracted from the optimized medium was tested for its potential dehairing, destaining and metal recovery properties. Complete dehairing was achieved within 48 h of treatment with crude enzyme without any visible damage to the collagen layer of goat skin. In destaining studies, combination of crude enzyme and detergent solution [1 mL detergent solution (5 mg/mL) and 1 mL crude enzyme] was found to be most effective in removing blood stains from cotton cloth. Silver recovery from used X-ray films was achieved within 6 min of treatment with crude enzyme maintained at 40 C. 2023 Elsevier Inc.
Liya S.M.; Umesh M.; Nag A.; Chinnathambi A.; Alharbi S.A.; Jhanani G.K.; Shanmugam S.; Brindhadevi K.
ISSN: 139351; PubMed ID: 36639016; CODEN: ENVRA
https://doi.org/10.1016/j.envres.2023.115283 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146445028&doi=10.1016%2fj.envres.2023.115283&partnerID=40&md5=62e555f71d486d4ce092b92bc30f1894
Liya S.M., Department of Life Sciences, CHRIST (Deemed to be University), Karnataka, Bangalore, India; Umesh M., Department of Life Sciences, CHRIST (Deemed to be University), Karnataka, Bangalore, India; Nag A., Department of Life Sciences, CHRIST (Deemed to be University), Karnataka, Bangalore, India; Chinnathambi A., Department of Botany and Microbiology, College of Science, King Saud University, PO Box -2455, Riyadh, 11451, Saudi Arabia; Alharbi S.A., Department of Botany and Microbiology, College of Science, King Saud University, PO Box -2455, Riyadh, 11451, Saudi Arabia; Jhanani G.K., Center for Transdisciplinary Research (CFTR), Department of Pharmacology, Saveetha Dental College, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, India; Shanmugam S., Chair of Biosystems Engineering, Institute of Forestry and Engineering, Estonian University of Life Sciences, Kreutzwaldi 56, Tartu, 51014, Estonia; Brindhadevi K., University Centre for Research & Development, Department of Chemistry, Chandigarh University, Mohali, India
Abstract: Keratin, the main structural constituent of feathers, contains a lot of valuable amino acids which are potential bioactive compounds as well. Since conventional methods are not efficient enough to achieve complete removal of chicken feather waste, biological mode of feather degradation is one of the most appropriate ways to utilize feathers, thereby reducing wastes as well as generating value-added products from feathers. This study was focussed on valorizing chicken feather into feather hydrolysate (FH) containing bioactive compounds for plant growth promotion. Keratinolytic bacteria capable of degrading chicken feathers were isolated from the poultry waste dumping site of Russell Market, Shivajinagar, Bangalore, Karnataka, India. The isolated bacteria was identified as Bacillus tropicus LS 27. A minimal media with chicken feather as the sole source of carbon and nitrogen was prepared and inoculated with Bacillus tropicus LS 27 [5% (v/v)]. Degradation of keratin protein by bacteria caused the solubilization of amino acids which was confirmed by high-performance liquid chromatography (HPLC) analysis where an appreciable amount of amino acids like cysteine, valine, isoleucine, proline, lysine, methionine, and phenylalanine was detected. The Fourier transform infrared spectroscopy (FTIR) analysis of hydrolysed chicken feathers showed C=0 stretching, S-H bond stretching, and formation of carboxylic acid groups indicating effective degradation of chicken feathers. Scanning electron microscope (SEM) images revealed the degradation pattern of feathers showing complete degradation of barbs and barbules with a portion of rachis remaining. Feather hydrolysate was further explored for its antioxidant activity using DPPH scavenging assay, and the value was found to be 1.5 mg/mL. The bacterial cells when screened for heavy metal tolerance showed significant metal tolerance to lead (Pb) and chromium (Cr). Since Bacillus tropicus LS27 showed indole-3-acetic acid (IAA), siderophore, and ammonia production, the prepared feather hydrolysate along with the bacterial cells were used as soil amendment for plant growth studies over Spinacia oleracea L. The study revealed that plants supplemented with 20% (v/v) FH showed elevated plant growth, therefore proving to be optimum for the support of plant growth. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.
Biomass Conversion and Biorefinery;Volume;15;Issue;21;pp.28063-28073
https://doi.org/10.1007/s13399-023-04664-1 https://www.scopus.com/pages/publications/85165582629?origin=resultslist
Early Sepsis Prediction using Hybrid LightGBM and LSTM Model
Hybrid Model; LightGBM; LSTM; MIMIC-III Dataset; Sepsis Prediction; Streamlit
Sepsis is a critical organ malfunction that results from an abnormal response of the body to infection and might be lethal. The early detection of sepsis is essential for the patient's life. However, the traditional clinical diagnostic systems are not capable of analyzing the complicated changes in the patient's vitals over time. Therefore, a hybrid predictive framework that merges Light Gradient Boosting Machine (LightGBM) and Long Short-Term Memory (LSTM) networks for fast and accurate sepsis detection in real-time using freely accessible MIMIC-III data, has been proposed in this research. Using LightGBM, the nonlinear relationships among the features are learnt very fast and efficient, while the LSTM gives the temporal dependencies in the sequence of the patient vital signs. The combined output of the two models is said to be more sensitive and robust than that of the single models. A Streamlit-based clinical dashboard is being provided, allowing for real-time predictions and visualization for healthcare professionals. The proposed system has shown a considerable increase in the accuracy of early sepsis detection and offers a non-restricted method for AI-assisted ICU monitoring. 2025 IEEE.
Lizzy Angelin Alphonsa, G.; Mary Jasmine, E.; Rajavel, Rajkumar
International Conference on NexGen Networks and Cybernetics, IC2NC 2025 - Proceedings;pp.794-800
https://doi.org/10.1109/IC2NC67409.2025.11376300 https://www.scopus.com/pages/publications/105035598002?origin=resultslist
Lizzy Angelin Alphonsa G., Christ University, Department of AI, Ml & Data Science, Bangalore, India; Mary Jasmine E., Christ University, Department of AI, Ml & Data Science, Bangalore, India; Rajavel R., Christ University, Department of AI, Ml & Data Science, Bangalore, India
STABILITY IN CHAOS: IMPACT OF MONETARY, FISCAL, AND FIRM CHARACTERISTICS ON INVESTOR SENTIMENT IN ASIAN EMERGING MARKETS
emerging markets; firm characteristics; Investor sentiment; monetary policies; volatility
This study investigates the impact of firm characteristics, monetary policies, and fiscal policies on investor sentiment, specifically focusing on market volatility and trading volume in six Asian emerging markets during the pre-pandemic and pandemic periods. Using panel data regression on a sample of 5,619 firms between 2015 and 2023, this study analyses the distinct roles of firm-specific factors and macroeconomic policies in shaping market behaviour during periods of economic instability. The findings reveal that firm characteristics such as capital structure and payout policies consistently drive both volatility and trading volume. Monetary policies, particularly interest rates and money supply, showed heightened significance during the pandemic, while fiscal policies, though largely insignificant pre-pandemic, became more relevant during the crisis. The study's results provide critical insights for policymakers and investors on the dynamic interplay between firm-level and macroeconomic factors during crisis periods, emphasising the need for coordinated policy responses. 2024, Universiti Malaysia Sarawak. All rights reserved.
International Journal of Business and Society, Vol-25, No. 3, pp. 1145-1164.
https://doi.org/10.33736/ijbs.8573.2024 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215441739&doi=10.33736%2fijbs.8573.2024&partnerID=40&md5=1e3b666b4d0f787016e4007efc001129
Loang O.K., Universiti Malaya, Malaysia; Candra S., BINUS University, Indonesia; Naveenan R.V., Christ University, India
Hybrid Model Integrating Firefly Algorithm and Gradient Boosted Trees for Stronger Financial Fraud Detection
Class imbalance; F1 score; Financial fraud; Firefly Algorithm; Gradient Boosted Trees; Metaheuristic optimization; Precision; Real-time systems; Recall
Financial fraud poses a critical threat, with complex attack patterns overwhelming traditional rule-based detection methods. This project proposes a hybrid model integrating the Firefly Algorithm (FA) for hyperparameter optimization with Gradient Boosted Trees (GBTs) to enhance fraud detection. Evaluated on an imbalanced financial dataset, the FA-GBT framework achieved significant performance gains. ROC-AUC increased by 5.08% to 0.9883, demonstrating superior discrimination. Critically, Precision rose by 13.38%, substantially reducing false positives/false alarms and operational costs. The optimization also reduced training time by 64%(from 9.8 seconds to 3.5 seconds). This supports a robust, high-confidence, and efficient strategy for deployment in real-time transaction systems. 2025 IEEE.
Lobo, Nethan Noel; Rajesh, R.; Radhalakshmi, K.N.
Proceedings of the 9th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2025;pp.1964-1971
https://doi.org/10.1109/ICECA66444.2025.11383302 https://www.scopus.com/pages/publications/105034900857?origin=resultslist
Lobo N.N., Christ (Deemed to be University), Department of Statistics and Data Science, Karnataka, Bengaluru, 560029, India; Rajesh R., Christ (Deemed to be University), Department of Statistics and Data Science, Karnataka, Bengaluru, 560029, India; Radhalakshmi K.N., Christ (Deemed to be University), Department of Statistics and Data Science, Karnataka, Bengaluru, 560029, India
Exploratory analysis of legal case citation data using node embedding
Graph embedding; Legal citation network; Network analysis; Node embed-ding, Node2vec
Legal case citation network is primary tool to understand mutable landscape of the legal domain. These networks are also used to study legal knowledge transfer, similar precedents and inter-relationship among laws of a judiciary. These networks are often very huge and complex due to the multidimensional texture of this domain. In recent years, network embedding using deep learning emerges as a promising breakthrough for analyzing networks. This paper presents a novel approach of learning vector representation for a legal case based on its citation context in the network using node2vec algorithm. These vector embedding are further used in understanding similarities between cases. Paper highlights that the tSNE reduced representation of the obtained vectors facilitates visual exploration and provides insights into the complex citation network. Suitability of node embedding for application of machine learning algorithm is demonstrated by clustering the node vectors for finding similar cases. ICIC International 2019.
ICIC Express Letters, Vol-13, No. 10, pp. 883-889.
https://doi.org/10.24507/icicel.13.10.883 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072955918&doi=10.24507%2ficicel.13.10.883&partnerID=40&md5=38f6f4a293acd4e6eb63fd2bd7b514aa
Lodha S., Department of Computer Science, CHRIST (Deemed to Be University), Bangalore, Karnataka, 560029, India; Wagh R., Department of Computer Science, CHRIST (Deemed to Be University), Bangalore, Karnataka, 560029, India
Heat and Mass Transport in Casson Nanofluid Flow over a 3-D Riga Plate with Cattaneo-Christov Double Flux: A Computational Modeling through Analytical Method
3-D flow; Casson nanofluid; CatteneoChristov model; HAM; Riga plate
This work examines the non-Newtonian Cassonnanofluids three-dimensional flow and heat and mass transmission properties over a Riga plate. The Buongiorno nanofluid model, which is included in the present model, includes thermo-migration and random movement of nanoparticles. It also took into account the CattaneoChristov double flux processes in the mass and heat equations. The non-Newtonian Casson fluid model and the boundary layer approximation are included in the modeling of nonlinear partial differential systems. The homotopy technique was used to analytically solve the systems governing equations. To examine the impact of dimensionless parameters on velocities, concentrations, temperatures, local Nusselt number, skin friction, and local Sherwood number, a parametric analysis was carried out. The velocity profile is augmented in this study as the size of the modified Hartmann number increases. The greater thermal radiative enhances the heat transport rate. When the mass relaxation parameter is used, the mass flux values start to decrease. 2023 by the authors.
Loganathan K.; Eswaramoorthi S.; Chinnasamy P.; Jain R.; Sivasakthivel R.; Ali R.; Devi N.N.
https://doi.org/10.3390/sym15030725 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85152668598&doi=10.3390%2fsym15030725&partnerID=40&md5=2e0c062ceb06297aaa3dd881443654c1
Loganathan K., Department of Mathematics and Statistics, Manipal University Jaipur, Rajasthan, Jaipur, 303007, India; Eswaramoorthi S., Department of Mathematics, Dr. N.G.P. Arts and Science College, Tamil Nadu, Coimbatore, 641035, India; Chinnasamy P., Department of Computer Science and Engineering, MLR Institute of Technology, Telangana, Hyderabad, 500043, India; Jain R., Department of Mathematics and Statistics, Manipal University Jaipur, Rajasthan, Jaipur, 303007, India; Sivasakthivel R., Department of Computer Science, School of Sciences, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560029, India; Ali R., Department of Mathematics, College of Science and Arts, King Khalid University, Muhayil, 61413, Saudi Arabia; Devi N.N., Department of Science and Humanities, Faculty of Engineering, Karpagam Academy of Higher Education, Tamil Nadu, Coimbatore, 641021, India
Assembly of discrete and oligomeric structures of organotin double-decker silsesquioxanes: Inherent stability studies
Double-decker silsesquioxane (DDSQ), a type of incompletely condensed silsesquioxane, has been used as a molecular precursor for synthesizing new organotin discrete and oligomeric compounds. The equimolar reaction between DDSQ tetrasilanol (DDSQ-4OH) and Ph2SnCl2 in the presence of triethylamine leads to obtaining discrete [Ph4Sn2O4(DDSQ)(THF)2] (1). The change of sterically bulky aryl Ph2SnCl2 precursor to linear alkyl nBu2SnCl2 led to the isolation of oligomeric [nBu4Sn2O4(DDSQ)] (2). The structures of compounds 1 and 2 have been demonstrated using single-crystal X-ray diffraction measurements. Indeed, the formation of oligomeric organotin DDSQ compound (2) was determined using GPC and MALDI-TOF mass spectroscopy. In compound 1, the geometry of the tin atom is five-coordinated trigonal bipyramidal by two phenyl groups, two Si-O from DDSQ and one tetrahydrofuran. Compound 2 contains four coordinated two peripheral tin atoms and two five-coordinated central tin atoms, in which, the fifth coordinating oxo groups in the central tin atoms create the bridge between two different DDSQ units that leads to the formation of oligomeric structure. Density functional theory calculations on organotin DDSQs infer that the obtained lattice energy for compound 1 is far higher than for the case of compound 2, which indicates that the crystal of compound 1 is better stabilized in its crystal lattice with stronger close packing via intermolecular interactions between discrete molecules with coordinated THF compared to the crystal of compound 2. The greater stability arises mainly due to the sterically bulkier phenyl groups attached to the tin centers in compound 1, which provide accessibility for accommodating the THF molecule per tin via Sn-THF bonding, while contrarily the smaller n-butyl groups aid the polymerization of the four repeating units of [SnSi4O7] or two Sn2O4(DDSQ) through ?-oxo groups. Both compounds 1 and 2 were chosen to be promising precursors for the synthesis of ceramic tin silicates. The thermolysis of 2 at 1000 C afforded the mixture of crystalline SnSiO4 and SiO2 but the same mixture was only formed by thermolysis of 1 at relatively higher temperature (1500 C), which infers that compound 1 is more stable than compound 2 that is in good synergy with theoretical lattice energy. The Royal Society of Chemistry and the Centre National de la Recherche Scientifique.
Loganathan P.; Pillai R.S.; Jeevananthan V.; David E.; Palanisami N.; Bhuvanesh N.S.P.; Shanmugan S.
New Journal of Chemistry, Vol-45, No. 43, pp. 20144-20154.
https://doi.org/10.1039/d1nj03128g https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119263516&doi=10.1039%2fd1nj03128g&partnerID=40&md5=3a7e18ac4be8c6528e07ead8dd79c281
Loganathan P., Department of Chemistry, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Tamil Nadu, Kattankulathur, 603203, India; Pillai R.S., Department of Chemistry, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Tamil Nadu, Kattankulathur, 603203, India, Department of Chemistry, Christ University, Karnataka, Bangalore, 56029, India; Jeevananthan V., Department of Chemistry, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Tamil Nadu, Kattankulathur, 603203, India; David E., Centre for Functional Materials, Department of Chemistry, School of Advanced Sciences, Vellore Institute of Technology, Tamil Nadu, Vellore, 632014, India; Palanisami N., Centre for Functional Materials, Department of Chemistry, School of Advanced Sciences, Vellore Institute of Technology, Tamil Nadu, Vellore, 632014, India; Bhuvanesh N.S.P., X-ray Diffraction Laboratory, Department of Chemistry, Texas AandM University, College Station, 77842, TX, United States; Shanmugan S., Department of Chemistry, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Tamil Nadu, Kattankulathur, 603203, India
A site-isolated Lewis acidic aluminium and Brsted basic amine sites in the dimeric silsesquioxane cage as a reusable homogeneous bifunctional catalyst for one-pot tandem deacetalization/deketalization-Knoevenagel condensation reactions
The development of multifunctional catalysts for one-pot tandem reactions is significantly required to attain multiple sequential transformations in a single reactor, which would considerably decrease the number of manipulations demanded for chemical manufacturing in industries. Herein, dimeric silsesquioxane Al-POSS-NH2 (2), a homogenous bifunctional acid-base catalyst containing environmentally friendly robust silica and high chemical and thermal stabilities, permanent catalytic activity, and reusability, was synthesized by the reaction of trisilanol aminopropyl hexaisobutyl-POSS (1) with trimethylaluminium. Al-POSS-NH2 was successfully used as a bifunctional catalyst for one-pot tandem reactions because of the synergism and effective compartmentalization between Lewis acidic aluminium and Brsted basic amine sites (>10.0 in the dimeric silsesquioxane cage, which was confirmed by DFT and QTAIM studies. Subsequently, different acetals were tested to obtain their corresponding benzylidene malononitrile derivatives using Al-POSS-NH2 for the one-pot tandem deacetalization-Knoevenagel condensation reactions and showed high efficiency (>90%) under optimized conditions (DMF, 0.3 mol% catalyst loading and 80 C) with different reaction times. Furthermore, the bifunctional Al-POSS-NH2 catalyst was separated from the reaction mixture via the precipitation method by adding acetonitrile into the reaction mixture and reusing it for five consecutive cycles without losing activity considerably, thus providing the inherent advantage over traditional homogeneous catalysts. In a one-pot tandem deketalization-Knoevenagel condensation reaction for various ketals, the reaction condition was slightly modified by increasing the catalyst loading (0.6 mol%) and reaction time (16 to 24 hours) to acquire better conversion and yield of their desired products. Finally, the present study suggests that the bifunctional POSS might facilitate the rapid development of environmentally friendly and economically feasible catalysts for multistep reactions. 2023 The Royal Society of Chemistry.
Loganathan P.; Pillai R.S.; Jennifer G A.; Varathan E.; Kesavan M.; Shanmugan S.
New Journal of Chemistry, Vol-47, No. 17, pp. 8439-8450.
https://doi.org/10.1039/d3nj00276d https://www.scopus.com/inward/record.uri?eid=2-s2.0-85152697506&doi=10.1039%2fd3nj00276d&partnerID=40&md5=3c66f257b33355b1c97adb04dacc69ff
Loganathan P., Department of Chemistry, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Tamil Nadu, Kattankulathur, 603203, India; Pillai R.S., Department of Chemistry, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Tamil Nadu, Kattankulathur, 603203, India, Department of Chemistry, Christ University, Karnataka, Bangalore, 56029, India; Jennifer G A., Department of Chemistry, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Tamil Nadu, Kattankulathur, 603203, India; Varathan E., Department of Chemistry, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Tamil Nadu, Kattankulathur, 603203, India; Kesavan M., Interdisciplinary Institute of Indian System of Medicine (IIISM), SRM Institute of Science and Technology, Tamil Nadu, Kattankulathur, 603203, India; Shanmugan S., Department of Chemistry, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Tamil Nadu, Kattankulathur, 603203, India