Development of Biocompatible Barium peroxide/Pluronic F127/L-ornithine Composite for Enriched Antimicrobial, Antioxidant and Anticancer Potential: An in vitro Study
Anti-oxidant; Anticancer activities; Antimicrobial; Barium peroxide; L-ornithine; Pluronic F 127
Osteosarcoma (MG-63) is a type of bone cancer affects mostly adolescents and young adults. Disease-causing microorganisms like Bacillus subtilis, Staphylococcus aureus, Escherichia coli, Klebsiella pneumoniae and Candida albicans pose serious illness in humans. There is a need to develop multifunctional composite to combat cancer and other most common disease caused by disease causing microorganisms. In this context, BaO2 and pluronic F127, L-Ornithine coated BaO2 (BaO2-PF127-LO) composite have been prepared and characterized by XRD, FTIR, UV-Vis, SEM, HRTEM, EDAX, and XPS analytical techniques. BaO2 and BaO2-PF127-LO were orthorhombic crystalline structure and the crystallite size was found as 32nm for BaO2 and 26nm for modified BaO2 PL studies revealed the green emission observed at 506nm for BaO2-PF127-LO composite which is absent in the case of bare BaO2. Antimicrobial activity of BaO2 and BaO2-PF127-LO was investigated. MTT assay was performed to determine the anticancer potential while the DPPH free radical scavenging assay was carried out to determine the antioxidant potential. The experiment study revealed that the BaO2-PF127-LO exhibited enhanced antimicrobial, antioxidant, and anticancer activity and low toxicity when compared to pristine BaO2. The experimental results revealed that the BaO2-PF127-LO composite holds promising potential for biomedical applications. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
Almaimani, Riyad A.; Ibrahim, Ibrahim Abdel Aziz; Almaimani, Ghassan; Almasmoum, Hussain Ali; Ghaith, Mazen M.; Bamagous, Ghazi A.; Arulselvan, Palanisamy; Thangavelu, Indumathi
Journal of Inorganic and Organometallic Polymers and Materials;Volume;35;Issue;5;pp.3454-3470
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1007/s10904-024-03467-9 https://www.scopus.com/pages/publications/85209652518?origin=resultslist
Almaimani R.A., Department of Biochemistry, Faculty of Medicine, Umm Al-Qura University, Al Abdeyah, Makkah, 24381, Saudi Arabia; Ibrahim I.A.A., Department of Pharmacology and Toxicology, Faculty of Medicine, Umm Al-Qura University, P.O.Box 13578, Al-Abidiyah, Makkah, 21955, Saudi Arabia; Almaimani G., Department of surgery, Faculty of Medicine, Umm Al-Qura University, PO Box 7607, Al Abdeyah, Makkah, Saudi Arabia; Almasmoum H.A., Department of Clinical Laboratory Sciences, Faculty of Applied Medical Sciences, Umm Al-Qura University, Makkah, Saudi Arabia; Ghaith M.M., Department of Clinical Laboratory Sciences, Faculty of Applied Medical Sciences, Umm Al-Qura University, Makkah, Saudi Arabia; Bamagous G.A., Department of Pharmacology and Toxicology, Faculty of Medicine, Umm Al-Qura University, P.O.Box 13578, Al-Abidiyah, Makkah, 21955, Saudi Arabia; Arulselvan P., Department of Chemistry, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, 602105, India; Thangavelu I., Department of Chemistry, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India
Optimized green synthesis of ZnO nanoparticles: evaluation of structural, morphological, vibrational and optical properties
In this study, leaf extracts of Aloe vera (AV), Azadirachta indica (AI), and Amaranthus dubius (AD) were used to synthesize zinc oxide nanoparticles utilizing a simple green synthesis process. The structural, optical, band energy, size, surface area, and shape of as-prepared nanoparticles were studied using analytical techniques. The hexagonal phase was revealed by XRD studies for all three samples: AV-ZnO, AI-ZnO, and AD-ZnO, with crystallite sizes of 35.8nm, 30.83nm, and 33.1nm, respectively. The UVVisible spectra of AV-ZnO, AI-ZnO, and AD-ZnO exhibit the characteristic absorption in the range of 200 to 450nm, and the band gap energy was found to be 3.10eV, 3.12eV, and 3.07eV, respectively. FESEM and TEM studies revealed that the ZnO NPs are rod-shaped with a roughly spherical appearance. EDAX analysis confirmed the presence of zinc and oxygen and indicates that the formed product is a pure phase of ZnO NPs. Increased antibacterial activity was noted for AV-ZnO, AI-ZnO, and AD-ZnO against gram-negative (Klebsiella pneumonia, Shigella dysenteriae), gram positive (Staphylococcus aureus, and Bacillus) bacterial strain. 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Almarhoon Z.M.; Indumathi T.; Kumar E.R.
Journal of Materials Science: Materials in Electronics, Vol-33, No. 30, pp. 23659-23672.
Almarhoon Z.M., Department of Chemistry, College of Science, King Saud University, P.O. Box 2455, Riyadh, 11451, Saudi Arabia; Indumathi T., Department of Chemistry, CHRIST (Deemed to Be University), Karnataka, Bangalore, 560029, India; Kumar E.R., Department of Physics, KPR Institute of Engineering and Technology, Tamil Nadu, Coimbatore, 641407, India
Structural modification of electrophilic group substituted phenyldiazenyl derivatives for antitubercular application
Antitubercular activity; DFT study; GCRD; Phenyldiazenyl derivatives; Photophysical properties
In the present work, four electrophilic group substitute phenyldiazenyl derivatives were synthesized using an electrophilic substitution reaction. The physicochemical analysis was carried out using FT-IR, 1H NMR, and HR-MS data. The photophysical studies were carried out using theoretical methods. Density functional theory was employed to illustrate the electronic and optical characteristics of the synthesized compounds. The HOMO-LUMO energies were theoretically computed in different solvents using Gaussian 09W software and results are compared with the experimental values. The molecule PT4 shows highest bandgap of 4.497eV. Further, the global chemical reactivity descriptors were used to determined nature of chemical reactivity. The anti-tubercular activity was evaluated using invitro and molecular docking techniques and results reveal that barbituric acid coupled with phenyldiazenyl displayed excellent anti-tubercular activity compared with the standard Gentamycin. 2024 Indian Chemical Society
Almehizia A.A.; Naglah A.M.; El-Mowafi S.A.; Lobo P.L.; Patil P.; Naik L.
Journal of the Indian Chemical Society, Vol-101, No. 9
https://doi.org/10.1016/j.jics.2024.101228 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197089208&doi=10.1016%2fj.jics.2024.101228&partnerID=40&md5=40f65d972f2218cc25553f3e55c4774b
Almehizia A.A., Department of Pharmaceutical Chemistry, College of Pharmacy, King Saud University, P.O. Box 2457, Riyadh, 11451, Saudi Arabia; Naglah A.M., Department of Pharmaceutical Chemistry, College of Pharmacy, King Saud University, P.O. Box 2457, Riyadh, 11451, Saudi Arabia; El-Mowafi S.A., Peptide Chemistry Department, National Research Centre, Cairo, Dokki, 12622, Egypt; Lobo P.L., Department of Chemistry, Maharani's Science College for Women, Maharani Cluster University, Karnataka, Bengaluru, 560 001, India; Patil P., Department of Studies in Chemistry, Davangere University, Shivagangothri, Karnataka, Davanagere, 577 007, India; Naik L., Department of Physics and Electronics, CHRIST (Deemed to be University), Bengaluru, 560029, India
AI and Big Data Analytics for Management of Nutraceuticals
Artificial intelligence (AI); Big data analytics; Data mining; Nutraceuticals management; Nutritional data analysis; Personalized nutrition; Predictive analytics; Quality and safety
This chapter explores the transformative impact of artificial intelligence (AI) and Big Data Analytics on nutraceutical management. As consumer demand for health-focused products such as supplements, functional foods, and herbal extracts grows, ensuring quality, safety, regulatory compliance, and personalization remains challenging. AI enhances formulation and quality assurance through predictive modeling and computer vision, while analyzing individual health data for personalized nutrition. Concurrently, Big Data leverages vast datasets from clinical trials to consumer feedback to forecast market trends, segment consumers, and guide R&D. The integration of AI and Big Data fosters deeper insights, driving innovation, safety, and tailored solutions. However, challenges like data privacy, standards, and ethics persist. Future developments may include AI-driven discovery of bioactives, real-time supply chain monitoring, and blockchain-based transparency. Embracing these innovations is essential for advancing safer, more effective, and personalized nutraceuticals that promote public health. Springer Nature Switzerland AG 2026.
Almoselhy, Rania I. M.; Usmani, Afreen; Rajendran, Rajakumari; Thomas, Sabu
Handbook of Nutraceuticals: Science, Technology and Engineering;pp.1177-1193
https://doi.org/10.1007/978-3-031-98791-5_50 https://www.scopus.com/pages/publications/105032508153?origin=resultslist
Almoselhy R.I.M., Oils and Fats Research Department, Food Technology Research Institute, Agricultural Research Center, Giza, Egypt; Usmani A., Department of Pharmacology, MESCO Institute of Pharmacy, Amroha, India; Rajendran R., The Oxford College of Pharmacy, Karnataka, Bengaluru, India; Thomas S., School of Nanoscience and Nanotechnology, Mahatma Gandhi University, Kerala, Kottayam, India, Department of Physics and Electronics, Christ University, Bangalore, India, Center of Excellence in Polymeric Materials for Medical Practice Devices, Department of Chemical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand, TrEST Research Park, Kerala, Trivandrum, India
Analyses of the Power Flow through Distributed Generator based on Unsynchronized Measurements
Distributed generator; Microgrid; Non-conventional energy sources; Power flow
Based on measurements taken from the main substation and the connections between distributed generators and micro-grids that are not in sync, this study suggests a new way to look at the load flow of distributed generation. The conclusions are based on data from a distribution generatora's Load Flow Analysis that was not in sync. Distributed generation is what this approach is based on. Creating a strong communication system and using measurement data from the past are two ways to make this happen. This objective may be achieved with the use of previously gathered measurements. The time-tested backward-forward sweep method is the method of choice for analyzing power flow using unsynchronized data. This is the preferred approach. The angles of synchronization are likely to be unknowns that must be estimated. On a smart grid system with a large number of distributed generation and microgrids, a range of mathematical computations are conducted to verify the correctness of performance predictions produced by the suggested theory. The classic backward-forward sweep was shown to be the most effective method for analyzing power flow based on data that was not synchronized in many instances. This is the strategy that is presently being recommended. Because the angles of synchronization are presumed to be unknown, a mathematical equation must be devised to determine them. The Authors, published by EDP Sciences, 2024.
Almoussawi Z.A.; Raju V.V.R.; Venkatasubramanian R.; Hariharan R.; Mundher Adnan M.
https://doi.org/10.1051/e3sconf/202456411019 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203821737&doi=10.1051%2fe3sconf%2f202456411019&partnerID=40&md5=a6d3b4c7156bacde298e36dee73a1c0e
Almoussawi Z.A., College of Islamic Science, Ahl Al Bayt University, karbala, Iraq; Raju V.V.R., Department of EEE, GRIET, Bachupally, Telangana, Hyderabad, India; Venkatasubramanian R., New Prince Shri Bhavani College of Engineering and Technology, Chennai, India; Hariharan R., School of Business and Management, CHRIST(Deemed to Be University), Bangalore, India; Mundher Adnan M., Department of Computers Techniques Engineering, College of Technical Engineering, The Islamic University, Najaf, Iraq, Department of Computers Techniques Engineering, College of Technical Engineering, The Islamic University of Al Diwaniyah, Al Diwaniyah, Iraq, Department of Computers Techniques Engineering, College of Technical Engineering, The Islamic University of Babylon, Babylon, Iraq
Development of CaO/Chitosan/Dopamine NanoparticlesAntibacterial, Anticancer, andAntioxidant Activities
biomedical applications; biopolymers and renewable polymers; surfaces and interfaces
Infectious diseases and cancer are two significant groups of diseases attributed to the major death around the globe. There is a need to develop innovative strategies to treat antibiotic resistance bacteria and cancer effectively. In this context, the present work focused on development of calcium oxide (CaO) and CaO modified with chitosan and dopamine nanocomposites (CaOCsDop) as potential antibacterial, anticancer, and antioxidant agents. The prepared nanoparticles were characterized using various characterization techniques. FTIR revealed the functional groups of prepared samples indicating the successful preparation of nanoparticles. XRD revealed the fcc cubic nature of CaO nanoparticles and the crystallite size was found to be 23 nm for CaOCsDOP and 31 nm for CaO nanoparticles. DLS results confirmed the mean particle hydrodynamic size was found as nm for 231.90 CaO and 189.90 nm for CaOCsDOP nanocomposite. The disk diffusion assay was carried out against common pathogenic bacterial strains as Pseudomonas aeruginosa, Klebsiella pneumoniae, Vibrio cholerae, Escherichia coli, and Shigella dysenteriae. MTT assay was carried out to determine the anticancer activity against MOLT-4 cell line, a human acute lymphoblastic leukemia model. The results indicated that CaOCsDOP nanocomposites exhibited enhanced antibacterial and anticancer activities compared with bare CaO nanoparticles, making them a promising multifunctional agent in biomedical applications. 2025 Wiley Periodicals LLC.
Alothaim, Abdulaziz S.; Suresh, Mickymaray; ALFaleh, Faleh A.; Alsowayeh, Noorah; Suresh, Kaviya; Thangavelu, Indumathi; Arulselvan, Palanisamy
Journal of Applied Polymer Science;Volume;142;Issue;16;Article No.;e56754;
https://doi.org/10.1002/app.56754 https://www.scopus.com/pages/publications/86000737358?origin=resultslist
Alothaim A.S., Department of Biology, College of Science Al-Zulfi, Majmaah University, Saudi Arabia; Suresh M., Department of Biology, College of Science Al-Zulfi, Majmaah University, Saudi Arabia; ALFaleh F.A., Department of Biology, College of Science Al-Zulfi, Majmaah University, Saudi Arabia; Alsowayeh N., Department of Biology, College of Science Al-Zulfi, Majmaah University, Saudi Arabia; Suresh K., Department of Pharmaceutics, Sri Ramachandra Faculty of Pharmacy, Sri Ramachandra Institute of Higher Education and Research (DU), Chennai, India; Thangavelu I., Department of Chemistry, CHRIST (Deemed to Be University), Karnataka, Bangalore, India; Arulselvan P., Department of Chemistry, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, India
Enhancing Malware Detection Through Hybrid Deep Learning Techniques
BiGRU-CNN; Cybersecurity; Deep Learning; Malware Detection
The detection of malware needs superior methods than basic signature detection because it remains vital to cybersecurity. This research examines malware classification through the deep learning approach by analyzing Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU) and develops a new BiGRU + CNN hybrid model. The main purpose is to achieve better detection performance through reduced numbers of false alarms. The research employs executable file feature data while implementing preprocessing methods together with fivefold cross-validation validation to establish strong model reliability. Experimental findings show CNN along with LSTM and GRU attains excellent recall values yet produces elevated erroneous positive predictions. The proposed BiGRU + CNN model delivers superiority over single-model architecture as it reaches 96.06% accuracy alongside 96.13% precision and 99.92% recall and 97.99% F1-score. The obtained results show that this integration has better malware detection capabilities thereby demonstrating its potential for cybersecurity applications. 2025 IEEE.
Alphine, P.J.; Alapatt, Bosco Paul; George, Jossy P
Proceedings of 6th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2025;pp.478-483
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/ICICV64824.2025.11085589 https://www.scopus.com/pages/publications/105013072560?origin=resultslist
Alphine P.J., CHRIST University, School of Sciences, India; Alapatt B.P., CHRIST University, School of Sciences, India; George J.P., CHRIST University, School of Sciences, India
Theory of planned behavior in predicting the construction of eco-friendly houses
Attitude; Behavioral intention; Confirmatory factor analysis; Correlation; Eco-friendly house; Perceived behavioral control; Structural equation modeling; Subjective norm
Purpose: The present study aimed to explore the applicability of theory of planned behavior in construction of eco-friendly houses. Design/methodology/approach: Study utilized cross-sectional correlational research design, collected data from 269 adult house owners of Kerala, India, with the help of a self-report measures namely, attitude towards eco-friendly house construction, subjective norm, perceived behavioral control, behavioral intention to build eco-friendly houses, check list of eco-friendly house and socio-demographic data sheet. Descriptive statistics, Karl Pearson product moment correlation, confirmatory factor analysis and mediation analysis with the help of AMOS were used to describe the distribution of study variables and to test the research hypotheses and proposed model. Findings: Study revealed that behavioral intention to build eco-friendly house was the immediate and strongest predictor of actual behavior of constructing an eco-friendly house. Behavioral intention mediated the relationship of attitudinal variables, normative variables and control variables with the behavior of constructing eco-friendly houses. Research limitations/implications: The results vouched the applicability of theory of planned behavior as a comprehensive model in explaining the behavior of eco-friendly house construction. Practical implications: Results of the study iterates the utility of attitudinal, normative and control factors in enhancing the choice of constructing eco-friendly houses. The results can be applied to develop a marketing tool to enhance the behavior of choosing or constructing eco-friendly houses in the population. Originality/value: Role of conventional concrete construction in climate crisis is unquestioned, and adopting eco-friendly architecture is a potential solution to the impending doom of climate crisis. Behavioral changes play a significant role in the success of global actions to curb the climate crisis. Present study discusses the role of psychological variables in constructing eco-friendly houses. 2022, Emerald Publishing Limited.
Management of Environmental Quality: An International Journal, Vol-33, No. 4, pp. 938-954.
https://doi.org/10.1108/MEQ-10-2021-0249 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125926259&doi=10.1108%2fMEQ-10-2021-0249&partnerID=40&md5=cedc96e281a431ac1231b79ff1c5949d
Alphonsa Jose K., Department of Psychology, Christ University, Bangalore, India; Sia S.K., Department of Applied Psychology, Pondicherry University, Puducherry, India
A multi-scale and rotation-invariant phase pattern (MRIPP) and a stack of restricted Boltzmann machine (RBM) with preprocessing for facial expression classification
Classification; Emotion; Feature; Pattern; Texton
In facial expression recognition applications, the classification accuracy decreases because of the blur, illumination and localization problems in images. Therefore, a robust emotion recognition technique is needed. In this work, a Multi-scale and Rotation-Invariant Phase Pattern (MRIPP) is proposed. The MRIPP extracts the features from facial images, and the extracted patterns are blur-insensitive, rotation-invariant and robust. The performance of classification algorithms like Fisher faces, Support Vector Machine (SVM), Extreme Learning Machine (ELM), Convolutional Neural Network (CNN) and Deep Neural Network (DNN) are analyzed. In order to reduce the time for classification, an OPTICS-based pre-processing of the features is proposed that creates a non-redundant and compressed training set to classify the test set. Ten-fold cross validation is used in experimental analysis and the performance metric classification accuracy is used. The proposed approach has been evaluated with six datasets Japanese Female Facial Expression (JAFFE), Cohn Kanade (CK +), Multi- media Understanding Group (MUG), Static Facial Expressions in the Wild (SFEW), Oulu-Chinese Academy of Science, Institute of Automation (Oulu-CASIA) and ManMachine Interaction (MMI) datasets to meet a classification accuracy of 98.2%, 97.5%, 95.6%, 35.5%, 87.7% and 82.4% for seven class emotion detection using a stack of Restricted Boltzmann Machines(RBM), which is high when compared to other latest methods. 2020, Springer-Verlag GmbH Germany, part of Springer Nature.
Alphonse A.S.; Shankar K.; Jeyasheela Rakkini M.J.; Ananthakrishnan S.; Athisayamani S.; Robert Singh A.; Gobi R.
Journal of Ambient Intelligence and Humanized Computing, Vol-12, No. 3, pp. 3447-3463.
Springer Science and Business Media Deutschland GmbH
Alphonse A.S., Department of Information Technology, Ponjesly College of Engineering, Nagercoil, India; Shankar K., Department of Computer Applications, Alagappa University, Karaikudi, India; Jeyasheela Rakkini M.J., School of Computing, Sastra Deemed To Be University, Thanjavur, India; Ananthakrishnan S., School of Computing, Sastra Deemed To Be University, Thanjavur, India; Athisayamani S., School of Computing, Sastra Deemed To Be University, Thanjavur, India; Robert Singh A., School of Computing, Kalasalingam Academy of Research and Education, Anand Nagar, India; Gobi R., Department of Computer Science, Christ University, Bengalore, India
Synthesis, characterization and photophysical studies of a novel schiff base bearing 1, 2, 4-Triazole scaffold
Electronic state dipole moment; MLR analysis; Solvent polarity methods; TD-DFT calculations; Triazolyl schiff base
A novel Schiff base derivative containing 1, 2, 4-triazole nucleus (TMPIMP) was synthesized from 4- [1,2,4] triazol-1-ylmethyl-phenylamine and salicylaldehyde in the presence of glacial acetic acid in an ethanolic medium. The synthesized compound was characterized by 1H-NMR, IR and UV spectral analysis. The excitation and emission spectra of triazolyl methyl phenyl imino methyl phenol (abbreviated as TMPIMP) were recorded in various solvents to investigate their solvatochromic behaviour. Dipole moments of the two electronic states of TMPIMP were calculated from solvatochromic spectral shifts. These were correlated with refractive index (?) and dielectric constant (?) of various solvents. Theoretical calculations were performed to estimate the excited state dipole moment on the basis of different solvent correlation methods, like the Bilot-Kawski, Bakhshiev, Lippert-Mataga, Kawski-Chamma-Viallet and Reichardt methods. The dipole moment in the excited state was found to be higher than that in the ground state due to a substantial redistribution of electron densities and charges. Using a multiple regression analysis, the solvent-solute interactions were determined by means of Kamlet Taft parameters (?, ?, ??). Computational studies were performed by Gaussian 09 W software using a time-dependent density functional theory (TD-DFT) in order to calculate the atomic charges and frontier molecular orbital energies in the solvent phase. The calculations indicated that the dipole moment of the molecule in an excited state is much higher than that in a ground state. The chemical stability of TMPIMP was determined by means of chemical hardness (?) using HOMO-LUMO energies. The reactive centers in the molecule were also identified by molecular electrostatic potential (MESP) 3D plots as a result of TD-DFT computational analysis. 2016 Elsevier B.V. All rights reserved.
Journal of Molecular Structure, Vol-1113, pp. 60-69.
https://doi.org/10.1016/j.molstruc.2016.02.036 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84958786984&doi=10.1016%2fj.molstruc.2016.02.036&partnerID=40&md5=bb3a5da5d1b1425a8aef038b5c0a8d77
Alphonse R., Department of Chemistry, Christ University, Hosur Road, Bangalore, 560029, India; Varghese A., Department of Chemistry, Christ University, Hosur Road, Bangalore, 560029, India; George L., Department of Chemistry, Christ University, Hosur Road, Bangalore, 560029, India
Estimation of ground state and excited state dipole moments of a novel Schiff base derivative containing 1, 2, 4-triazole nucleus by solvatochromic method
Dipole moment; Solvatochromic effect; Solvent polarity correlation method; TD-DFT calculation
A novel schiff base derivative containing 1, 2, 4-triazole moiety (NBTMPA) has been synthesized from 4- [1, 2, 4] triazol-1-ylmethyl-phenylamine and 4-nitrobenzaldehyde in the presence of glacial acetic acid in an ethanolic medium. The absorbance and fluorescence spectra of (4-nitro-benzylidene)-(4- [1, 2, 4] triazol-1-ylmethyl-phenyl)-amine (NBTMPA) were recorded in various solvents to investigate their solvatochromic behaviour. Dipole moments of the two electronic states of NBTMPA were calculated from solvatochromic spectral shifts. These were correlated with the refractive index (n) and dielectric constant (?) of various solvents. Theoretical calculations were performed to estimate the excited state dipole moment on the basis of different solvent correlation methods, like the Bilot-Kawski, Bakhshiev, Lippert-Mataga, Kawski-Chamma-Viallet and Reichardt methods. The dipole moment in the excited state was found to be higher than that in the ground state due to a substantial redistribution of electron densities and charges. Using a multiple regression analysis, the solvent-solute interactions were determined by means of Kamlet Taft parameters (?, ?, ??). Computational studies were performed by Gaussian 09 W software using a time-dependent density functional theory (TDDFT) in order to calculate the atomic charges and frontier molecular orbital energies in the solvent phase. The calculations indicated that the dipole moment of the molecule in an excited state is much higher than that in a ground state. The chemical stability of NBTMPA was determined by means of chemical hardness (?) using HOMO-LUMO energies. The reactive centres in the molecule were also identified by molecular electrostatic potential (MESP) 3D plots as a result of a TDDFT computational analysis. 2015 Elsevier B.V.
Alphonse R.; Varghese A.; George L.; Nizam A.
Journal of Molecular Liquids, Vol-215, pp. 387-395.
https://doi.org/10.1016/j.molliq.2015.12.050 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84952342514&doi=10.1016%2fj.molliq.2015.12.050&partnerID=40&md5=f044afc4a8461b2cbff00885ebbd2c2e
Alphonse R., Department of Chemistry, Christ University, Hosur Road, Bangalore, 560029, India; Varghese A., Department of Chemistry, Christ University, Hosur Road, Bangalore, 560029, India; George L., Department of Chemistry, Christ University, Hosur Road, Bangalore, 560029, India; Nizam A., Department of Chemistry, Christ University, Hosur Road, Bangalore, 560029, India
Synthesis and fluorescence studies of heterocyclic compounds
Thermal analysis of a radiative nanofluid over a stretching/shrinking cylinder with viscous dissipation
Convective condition; Nanofluid; Stretching/Shrinking Cylinder; Thermal radiation; Viscous dissipation
This study explores the impact of thermal radiation and viscous dissipation on the stagnation point flow of a copperwater nanofluid across a convective stretching/shrinking cylinder. The copper suspension in the base fluid water enables the fluid to conduct more heat by increasing its thermal conductivity. The mathematical model that governs the flow of Cu-H2O nanofluid is formulated by the system of partial differential equations (PDEs) which are then subjected to transformation by introducing suitable similarity variables so the system is transformed to the Ordinary Differential Equations (ODEs). These equations have been solved numerically via the bvp4c package in MATLAB. The outcomes have been signified graphically in the form of heat transfer rate, temperature, skin friction and velocity which are dependent on the concerning flow parameters. For each of these result, dual solutions have been produced which are conditional on the shrinking of cylinder. These results declare that the skin friction increases for the shrinking cylinder and decreases for the stretching cylinder whereas an opposite trend is seen for the rate of heat transfer. Similarly, heat transfer is found to be decreasing for the increase in both Biot and Eckert number. Meanwhile, the existence of greater values of curvature parameter causes to enhance both first and second solution of velocity as well as the temperature is augmenting with the increase in Eckert number and volume fraction of nano particles. 2022 Elsevier B.V.
Alqahtani A.M.; Riaz Khan M.; Akkurt N.; Puneeth V.; Alhowaity A.; Hamam H.
https://doi.org/10.1016/j.cplett.2022.140133 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140485626&doi=10.1016%2fj.cplett.2022.140133&partnerID=40&md5=5eac979d0b7aabb24e4f41ebcb3954f9
Alqahtani A.M., Department of Mathematical Sciences, College of Science, Princess Nourah bint Abdulrahman University, P. O. Box 84428, Riyadh, 11671, Saudi Arabia; Riaz Khan M., Department of Mathematics, Quaid-i-Azam University, 45320, Islamabad, 44000, Pakistan; Akkurt N., Munzur University, Department of Mechanical Engineering 62000, Tunceli, Turkey; Puneeth V., Department of Computational Sciences, CHRIST (Deemed to be University), Ghaziabad, 201003, India; Alhowaity A., Department of Mathematics, College of Science and Arts at Alkamil, University of Jeddah, Jeddah, Saudi Arabia; Hamam H., Mathematics Department, Umm Al-Qura University, Makkah, Saudi Arabia
Knots of the umbilical cord: Incidence, diagnosis, and management
diagnosis; management; pathophysiology; ultrasound; umbilical cord knot
Knot(s) of the umbilical cord have received emphasis because the clinical assessments and sonographic literature show a crucial role in fetal outcomes. The true umbilical cord knot could be a knot in a singleton pregnancy or an entanglement of two umbilical cords in monoamniotic twins. Clinical manifestations are almost silent, which can raise clinical challenges. They worsen outcomes, and the pathology can be easily missed during prenatal visits because ultrasonographers do not pay attention to the cord during an obstetric ultrasound scan. However, most medical centers now have ultrasound machines that improve fetal assessment. The umbilical cord should be routinely evaluated during a fetal assessment, and suspicion of an umbilical cord knot can be more frequently diagnosed and is detected only incidentally. Clinical outcome is usually good but depends on the knot's characteristics and if it is tight or loose. In this review, we discuss pathophysiology, the theories on formation, the main risk factors, ultrasound signs and findings, different opinions in the management, and features of pregnancy outcomes feature. 2024 International Federation of Gynecology and Obstetrics.
AlQasem M.; Meyyazhagan A.; Tsibizova V.; Clerici G.; Arduini M.; Khader M.; M.Alkarabsheh A.; DiRenzo G.C.
International Journal of Gynecology and Obstetrics, Vol-166, No. 2, pp. 559-566.
ISSN: 207292; PubMed ID: 38264935; CODEN: IJGOA
https://doi.org/10.1002/ijgo.15373 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183028804&doi=10.1002%2fijgo.15373&partnerID=40&md5=51c8831c2fcc58becad513a2d25f268e
AlQasem M., Centre of Perinatal and Reproductive Medicine, University of Perugia, Perugia, Italy, Department of Obstetrics and Gynecology, Faculty of Medicine, Mutah University, Al-Karak, Jordan; Meyyazhagan A., Centre of Perinatal and Reproductive Medicine, University of Perugia, Perugia, Italy, Department of Life Sciences, CHRIST (Deemed to be University), Karnataka, Bengaluru, India, PREIS International School, Firenze, Italy; Tsibizova V., PREIS International School, Firenze, Italy, CEMER, European Centre for Medical Research, Perugia, Italy; Clerici G., Centre of Perinatal and Reproductive Medicine, University of Perugia, Perugia, Italy, CEMER, European Centre for Medical Research, Perugia, Italy; Arduini M., Centre of Perinatal and Reproductive Medicine, University of Perugia, Perugia, Italy; Khader M., Department of Obstetrics and Gynecology, Faculty of Medicine, Mutah University, Al-Karak, Jordan; M.Alkarabsheh A., Department of Obstetrics and Gynecology, Faculty of Medicine, Mutah University, Al-Karak, Jordan; DiRenzo G.C., Centre of Perinatal and Reproductive Medicine, University of Perugia, Perugia, Italy, PREIS International School, Firenze, Italy, Department of Obstetrics, Gynecology and Perinatology, IE Sechenov First State University, Moscow, Russian Federation
Stability and bifurcation analysis of a fractional-order preypredator model with ratio-dependent functional response
bifurcation; chaos; fractional-order; preypredator model; stability
This paper explores the dynamics of a fractional preypredator system with a ratio-dependent functional response with memory and hereditary effects in predatorprey interactions. The model is developed by the Caputo fractional derivative, and the existence, uniqueness, positivity, and boundedness of solutions are proven to satisfy biological reality. Stability conditions for local and global stability of both predator-free and coexistence equilibria are proven through linearization and Lyapunov function techniques. The fractional order is used as a bifurcation parameter, and the appearance of Hopf bifurcations is analytically explained with demonstration of the influence of memory on oscillations. To examine discrete-time dynamics, the piecewise constant argument is used to derive a discrete counterpart of the fractional model. The discrete model indicates a wide range of rich complex oscillatory phenomena, including period-doubling and NeimarkSacker bifurcations, leading to periodic, quasiperiodic, and chaotic oscillations. Numerical computations, including bifurcation diagrams, phase portraits, and Lyapunov exponents, verify the analytical results and describe the routes of transition to chaos. A comparative analysis to compare integer- and fractional-order cases indicates that memory effects enhance dynamical richness and sensitivity to parameters. The study provides a unified framework relating continuous fractional dynamics and their discrete implementations and provides additional insight into how memory and discretization interact to modify stability and bifurcation in ecological models. 2026 the Author(s),
Alraddadi, Ibrahim; Perumal, Ramesh; Ahmed, Rizwan; Khan, Jawad; Lee, Youngmoon
AIMS Mathematics;Volume;11;Issue;1;pp.1412-1448
American Institute of Mathematical Sciences
https://doi.org/10.3934/math.2026060 https://www.scopus.com/pages/publications/105028597124?origin=resultslist
Alraddadi I., Department of Mathematics, Faculty of Science, Islamic University of Madinah, Madinah, Saudi Arabia; Perumal R., Department of Mathematics, Madanapalle Institute of Technology & Science (MITS), Deemed to be University, Madanapalle,, Andhra, Pradesh, India; Ahmed R., Department of Mathematics, Air University Multan Campus, Multan, 60001, Pakistan; Khan J., School of Computing, Gachon University, Seongnam, 13120, South Korea; Lee Y., Department of Robotics, Hanyang University, Ansan, 15588, South Korea, Department of Applied AI, Hanyang University, Ansan, 15588, South Korea
Early Identification and Detection of Driver Drowsiness by Hybrid Machine Learning
accidents; Driver drowsiness; facial expression; machine learning
Drunkenness or exhaustion is a leading cause of car accidents, with severe implications for road safety. More fatal accidents could be avoided if fatigued drivers were warned ahead of time. Several drowsiness detection technologies to monitor for signs of inattention while driving and notifying the driver can be adopted. Sensors in self-driving cars must detect if a driver is sleepy, angry, or experiencing extreme changes in their emotions, such as anger. These sensors must constantly monitor the driver's facial expressions and detect facial landmarks in order to extract the driver's state of expression presentation and determine whether they are driving safely. As soon as the system detects such changes, it takes control of the vehicle, immediately slows it down, and alerts the driver by sounding an alarm to make them aware of the situation. The proposed system will be integrated with the vehicle's electronics, tracking the vehicle's statistics and providing more accurate results. In this paper, we have implemented real-time image segmentation and drowsiness using machine learning methodologies. In the proposed work, an emotion detection method based on Support Vector Machines (SVM) has been implemented using facial expressions. The algorithm was tested under variable luminance conditions and outperformed current research in terms of accuracy. We have achieved 83.25 % to detect the facial expression change. 2013 IEEE.
Altameem A.; Kumar A.; Poonia R.C.; Kumar S.; Saudagar A.K.J.
https://doi.org/10.1109/ACCESS.2021.3131601 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120584518&doi=10.1109%2fACCESS.2021.3131601&partnerID=40&md5=d3db07c29350cac4cecb25a868493db6
Altameem A., Department of Computer Science, College of Applied Studies, King Saud University, Riyadh, 11495, Saudi Arabia; Kumar A., Department of Computer Science and Engineering, Swami Keshvanand Institute of Technology Management and Gramothan, Rajasthan, Jaipur, 302017, India; Poonia R.C., Department of Computer Science, Christ (Deemed to Be University), Karnataka, Bengaluru, 560029, India; Kumar S., Department of Computer Science and Engineering, Christ (Deemed to Be University), Karnataka, Bengaluru, 560074, India; Saudagar A.K.J., Information Systems Department, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia
Plant Identification Using Fitness-Based Position Update in Whale Optimization Algorithm
Bag-of-features; Feature optimization; Nature-inspired algorithm; Plant leaf classification; Swarm intelligence
Since the beginning of time, humans have relied on plants for food, energy, and medicine. Plants are recognized by leaf, flower, or fruit and linked to their suitable cluster. Classification methods are used to extract and select traits that are helpful in identifying a plant. In plant leaf image categorization, each plant is assigned a label according to its classification. The purpose of classifying plant leaf images is to enable farmers to recognize plants, leading to the management of plants in several aspects. This study aims to present a modified whale optimization algorithm and categorizes plant leaf images into classes. This modified algorithm works on different sets of plant leaves. The proposed algorithm examines several benchmark functions with adequate performance. On ten plant leaf images, this classification method was validated. The proposed model calculates precision, recall, F-measurement, and accuracy for ten different plant leaf image datasets and compares these parameters with other existing algorithms. Based on experimental data, it is observed that the accuracy of the proposed method outperforms the accuracy of different algorithms under consideration and improves accuracy by 5%. 2022 Tech Science Press. All rights reserved.
Altameem A.; Kumar S.; Poonia R.C.; Saudagar A.K.J.
Computers, Materials and Continua, Vol-71, No. 2, pp. 4719-4736.
https://doi.org/10.32604/cmc.2022.022177 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122756654&doi=10.32604%2fcmc.2022.022177&partnerID=40&md5=cf7737cdb69b7e6cf74c9d7fb5276c2a
Altameem A., Department of Computer Science, College of Applied Studies, King Saud University, Riyadh, 11495, Saudi Arabia; Kumar S., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, 560074, India; Poonia R.C., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, 560029, India; Saudagar A.K.J., Information Systems Department, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia
Breast Cancer Detection in Mammography Images Using Deep Convolutional Neural Networks and Fuzzy Ensemble Modeling Techniques
breast cancer; deep CNN; DenseNet121; ensemble; fuzzy; Gompertzfunction; Inception V4; ResNet-164; VGG-11
Breast cancer has evolved as the most lethal illness impacting women all over the globe. Breast cancer may be detected early, which reduces mortality and increases the chances of a full recovery. Researchers all around the world are working on breast cancer screening tools based on medical imaging. Deep learning approaches have piqued the attention of many in the medical imaging field due to their rapid growth. In this research, mammography pictures were utilized to detect breast cancer. We have used four mammography imaging datasets with a similar number of 1145 normal, benign, and malignant pictures using various deep CNN (Inception V4, ResNet-164, VGG-11, and DenseNet121) models as base classifiers. The proposed technique employs an ensemble approach in which the Gompertz function is used to build fuzzy rankings of the base classification techniques, and the decision scores of the base models are adaptively combined to construct final predictions. The proposed fuzzy ensemble techniques outperform each individual transfer learning methodology as well as multiple advanced ensemble strategies (Weighted Average, Sugeno Integral) with reference to prediction and accuracy. The suggested Inception V4 ensemble model with fuzzy rank based Gompertz function has a 99.32% accuracy rate. We believe that the suggested approach will be of tremendous value to healthcare practitioners in identifying breast cancer patients early on, perhaps leading to an immediate diagnosis. 2022 by the authors.
Altameem A.; Mahanty C.; Poonia R.C.; Saudagar A.K.J.; Kumar R.
Multidisciplinary Digital Publishing Institute (MDPI)
All Open Access; Gold Open Access; Green Open Access
https://doi.org/10.3390/diagnostics12081812 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137404869&doi=10.3390%2fdiagnostics12081812&partnerID=40&md5=eccc4042fccd07e514cff67c3508b976
Altameem A., Department of Computer Science and Engineering, College of Applied Studies and Community Services, King Saud University, Riyadh, 11533, Saudi Arabia; Mahanty C., Department of Computer Science and Engineering, GIET University, Odisha 765022, India; Poonia R.C., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, 560029, India; Saudagar A.K.J., Information Systems Department, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia; Kumar R., Department of Computer Science and Engineering, GIET University, Odisha 765022, India
Improvement of Automatic Glioma Brain Tumor Detection Using Deep Convolutional Neural Networks
convolutional neurol networks; deep learning; higher grade gliomas; image segmentation; lower grade gliomas; voxel
This article introduces automatic brain tumor detection from a magnetic resonance image (MRI). It provides novel algorithms for extracting patches and segmentation trained with Convolutional Neural Network (CNN)'s to identify brain tumors. Further, this study provides deep learning and image segmentation with CNN algorithms. This contribution proposed two similar segmentation algorithms: one for the Higher Grade Gliomas (HGG) and the other for the Lower Grade Gliomas (LGG) for the brain tumor patients. The proposed algorithms (Intensity normalization, Patch extraction, Selecting the best patch, segmentation of HGG, and Segmentation of LGG) identify the gliomas and detect the stage of the tumor as per taking the MRI as input and segmented tumor from the MRIs and elaborated the four algorithms to detect HGG, and segmentation to detect the LGG works with CNN. The segmentation algorithm is compared with different existing algorithms and performs the automatic identification reasonably with high accuracy as per epochs generated with accuracy and loss curves. This article also described how transfer learning has helped extract the image and resolution of the image and increase the segmentation accuracy in the case of LGG patients. Copyright 2022, Mary Ann Liebert, Inc., publishers 2022.
Altameem A.; Mallikarjuna B.; Saudagar A.K.J.; Sharma M.; Poonia R.C.
Journal of Computational Biology, Vol-29, No. 6, pp. 530-544.
ISSN: 10665277; PubMed ID: 35235381; CODEN: JCOBE
https://doi.org/10.1089/cmb.2021.0280 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131665529&doi=10.1089%2fcmb.2021.0280&partnerID=40&md5=86242a97b59a579c63394dfb7f07852c
Altameem A., Department of Computer Science and Engineering, College of Applied Studies and Community Services, King Saud University, Riyadh, Saudi Arabia; Mallikarjuna B., School of Computing Science and Engineering, Galgotias University, Greater Noida, India; Saudagar A.K.J., Information Systems Department, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia; Sharma M., School of Computing Science and Engineering, Galgotias University, Greater Noida, India; Poonia R.C., Department of Computer Science, CHRIST (Deemed to Be University), Bangalore, India
A Hybrid AES with a Chaotic Map-Based Biometric Authentication Framework for IoT and Industry 4.0
Biometric Authentication Framework; biometric data; fingerprint authentication; Hybrid AES (Advanced Encryption Standard) with Chaotic Map Encryption; Industry 4.0; Internet of Things; privacy; security
The Internet of Things (IoT) is being applied in multiple domains, including smart homes and energy management. This work aims to tighten security in IoTs using fingerprint authentications and avoid unauthorized access to systems for safeguarding user privacy. Captured fingerprints can jeopardize the security and privacy of personal information. To solve privacy- and security-related problems in IoT-based environments, Biometric Authentication Frameworks (BAFs) are proposed to enable authentications in IoTs coupled with fingerprint authentications on edge consumer devices and to ensure biometric security in transmissions and databases. The Honeywell Advanced Encryption Security-Cryptography Measure (HAES-CM) scheme combined with Hybrid Advanced Encryption Standards with Chaotic Map Encryptions is proposed. BAFs enable private and secure communications between Industry 4.0s edge devices and IoT. This works suggested schemes evaluations with other encryption methods reveal that the suggested HAES-CM encryption strategy outperforms others in terms of processing speeds. 2023 by the authors.
Altameem A.; P P.; T S.; Poonia R.C.; Saudagar A.K.J.
https://doi.org/10.3390/systems11010028 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146813957&doi=10.3390%2fsystems11010028&partnerID=40&md5=94f51ebc4f2867c823a7bde1187741e0
Altameem A., Department of Computer Science and Engineering, College of Applied Studies and Community Services, King Saud University, Riyadh, 11533, Saudi Arabia; P P., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, 560029, India; T S., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, 560029, India; Poonia R.C., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, 560029, India; Saudagar A.K.J., Information Systems Department, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia