Construction of a waste-derived graphite electrode integrated IL/Ni-MOF flowers/Co3O4 NDs for specific enrichment and signal amplification to detect aspartame
Aspartame; Co3O4 NDs; Food samples; Graphite electrode; Ionic liquid; Ni-MOF; Sensor
A novel and cost-efficient electrochemical sensor was designed by immobilizing IL/Ni-MOF/Co3O4 nanodiamonds on the graphite (GE) electrode, marking the first application for the detection of aspartame. The graphite electrode was extracted and recycled from discharged batteries to serve as a working electrode. The nanocomposite features unique Co3O4 nanodiamonds, generated using Coriandrum sativum seed extract, alongside Ni-metal organic framework (MOF), which were synthesized through a solvothermal method. The conductivity and stability of the electrochemical sensor were enhanced through the incorporation of the ionic liquid (IL) ([BMIM][MeSO4]. The phytochemical profile of Coriandrum sativum seed extract, analyzed by GC-MS, identified key compounds involved in the synthesis of Co3O4 nanodiamonds. A comprehensive characterization of the nanocomposite was performed using UV-Vis, FTIR, DLS, Zeta potential, XRD, XPS, FE-SEM, TEM, optical profilometry, and AFM to confirm the structural and elemental modifications. Electrochemical characterization of the bare and modified electrodes was conducted through cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). The GE/IL/Ni-MOF/Co3O4 nanodiamonds modified electrode displayed enhanced electroanalytical performance for aspartame detection, characterized by signal amplification at +7.0 V. Quantitative analysis by Differential Pulse Voltammetry (DPV) and Square Wave Voltammetry (SWV) revealed a linear detection range of 315 M for aspartame. A comparison of SWV and DPV revealed superior analytical performance for SWV, with limit of detection (LOD) and limit of quantification (LOQ) values of 1.02 M and 3.1 M (R2 = 0.993) compared to 1.81 M and 5.5 M (R2 = 0.986) for DPV. This study reveals the excellent adsorption capabilities of Ni-MOF and Co3O4 nanodiamonds (Co3O4 NDs), attributed to their high porosity and large surface area, paving the way for the development of affordable sensing devices for artificial sweeteners. 2024 Elsevier B.V.
Colloids and Surfaces A: Physicochemical and Engineering Aspects, Vol-705
https://doi.org/10.1016/j.colsurfa.2024.135648 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207878721&doi=10.1016%2fj.colsurfa.2024.135648&partnerID=40&md5=fc8805b64b793c28a0a2dd57f5b60613
Manuel M., Department of Chemistry, CHRIST University, Bengaluru, 560 029, India; Kanchi S., Department of Chemistry, CHRIST University, Bengaluru, 560 029, India
Ni-Co MOF Flowers/ZnO NRs Mediated Electrochemical Sensor for Rapid and Ultrasensitive Detection of Neotame in Food Samples
electrochemical sensor; food samples; neotame; Ni-Co MOF; ZnO NRs
This study focuses on the bioreduction of waste-derived graphite rods into reduced graphene oxide(rGO), followed by the fabrication with Ni-Co metal-organic flowers and Zinc oxide nanorods(ZnO NRs) using Nafion, for sensitive detection of neotame. The Ni-Co metal-organic flowers and ZnO NRs were synthesized using solvothermal synthesis and Azadirachta indica leaf extract, respectively. Additionally, Nafion polymer enhances the stability and conductivity of the nanocomposite. The nanocomposite was characterized using UVvis, Fourier transform infrared spectorscopy, X-ray diffraction, Raman spectroscopy, Dynamic light scattering, X-ray photoelectron spectroscopy, Field-emission scanning electron microscopy, Energy-dispersive X-ray analsysis, Transmission electron microscopy, and Atomic force microscopy. The electrochemical studies were carried out using Electrochemical impedance spectroscopy and Cyclic voltammetry. The modified electrode (rGO/Nafion/Ni-Co MOF/ZnO NRs) demonstrated improved electrochemical activity (34.01 ?A) for neotame with an enhanced peak current at +0.73 V. The LOD and LOQ values were calculated and found to be 0.32 and 0.99 ?M with a recovery (%) ranging from 94.50 to 101.34%. The outcome of this study identifies the morphological and electrochemical factors as major contributors to the adsorption affinities and catalytical activities, with promising possibilities for the design of electrochemical sensing of artificial sweeteners. 2024 The Electrochemical Society (ECS). Published on behalf of ECS by IOP Publishing Limited. All rights, including for text and data mining, AI training, and similar technologies, are reserved.
Journal of the Electrochemical Society, Vol-171, No. 12
https://doi.org/10.1149/1945-7111/ada0ed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85213705687&doi=10.1149%2f1945-7111%2fada0ed&partnerID=40&md5=b7f418b20d813a690369ceddf0ac43a7
Experimenting with scalability of Beacon controller in software defined network
In traditional network, a developer cannot develop software programs to control the behavior of the network switches due to closed vendor specific configuration scripts. In order to bring out innovations and to make the switches programmable a new network architecture must be developed. This led to a new concept of Software Defined Networking(SDN). In Software defined networking architecture, the control plane is detached from the data plane of a switch. The controller is implemented using the control plane which takes the heavy lift of all the requests of the network. Few of the controllers used in SDN are Floodlight, Ryu, Beacon, Open Daylight etc. In this paper, authors are evaluating the performance of Beacon controller using scalability parameter on network emulation tool Mininet and IPERF. The experiments are performed on multiple scenarios of topology size range from 50 to 1000 nodes and further analyzing the controller performance. BEIESP.
International Journal of Recent Technology and Engineering, Vol-7, No. 5, pp. 550-555.
Blue Eyes Intelligence Engineering and Sciences Publication
Manuel T., Christ (Deemed To Be University), Bangalore, Karnataka, India; Goswami B.H., Christ (Deemed To Be University), Bangalore, Karnataka, India
Blockchain and the Evolving Internal Audit Function
Audit practices; Blockchain technology; Internal audit; Risk management; Smart contract
Blockchain Technology indicates a transformative era for internal audit practices in the evolving digital finance and operations landscape. This research explores the internal audit function in a Blockchain-driven world, emphasizing the changing perspectives and methodologies necessitated by this disruptive technology. With its foundational principles of transparency, immutability, and decentralization, Blockchain presents challenges and opportunities for internal auditors. The paper delves into how Blockchain is poised to redefine traditional audit practices, moving towards more real-time and continuous auditing techniques. It examines the implications of Blockchain for risk assessment, fraud detection, and compliance, highlighting the shift towards proactive rather than reactive audit strategies. Furthermore, the research examines Blockchains opportunities and challenges to the internal audit function. This study provides insights into integrating Blockchain Technology in internal auditing through a comprehensive secondary data analysis. It proposes a roadmap for auditors to adapt and thrive in this new era. The findings underscore the importance of embracing technological advancements, advocating for a dynamic approach to audit practices that aligns with the complexities of a blockchain-driven world. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
Studies in Systems, Decision and Control;Volume;555;pp.1209-1215
Springer Science and Business Media Deutschland GmbH
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1007/978-3-031-67890-5_108 https://www.scopus.com/pages/publications/85210149322?origin=resultslist
Manuel A., School of Commerce, Finance and Accountancy, Christ University, Karnataka, Banglore, 560 029, India; Arumugam S.K., School of Commerce, Finance and Accountancy, Christ University, Karnataka, Banglore, 560 029, India
Biocompatibility and toxicity of nanomaterials in the designing of tools for the diagnosis of infectious diseases
Biocompatibility; Infectious diseases; Nanomaterials; Toxicity
Nanomaterials have revolutionized the landscape of infectious disease diagnostics by offering unparalleled advantages in terms of sensitivity, specificity, and rapidity. However, their integration into diagnostic tools necessitates a profound understanding of their biocompatibility and toxicity profiles to ensure both diagnostic accuracy and patient life. The successful translation of these nanomaterials into practical diagnostic tools hinges on a thorough understanding of their biocompatibility and toxicity. Biocompatibility, a fundamental characteristic of nanomaterials refers to their ability to coexist with biological systems without causing harm or triggering immune responses. This chapter unfolds the pivotal role of biocompatibility assessment, examining the compatibility of nanomaterials with biological systems. In vitro and in vivo evaluation methods, and adherence to regulatory standards, are emphasized as essential components of biocompatibility analysis. Simultaneously, the exploration of nanotoxicity and its hazardous effect highlights the significance of establishing safe exposure limits. Toxicity is a pressing concern when dealing with nanomaterials. The chapter explores the factors that contribute to nanomaterials toxicity, including size-dependent effects, surface modifications, and the route of exposure. It also delves into the mechanisms by which nanomaterials can exert toxicity, such as reactive oxygen species, reaction with surface expanded group, and penetration into the cell. To mitigate the potential risk associated with nanomaterials, the chapter discusses strategies for improving biocompatibility. Finally, it gives a glance into the various tools prepared highlighting the successful integration of biocompatible nanotoxicity of nanomaterials into infectious disease diagnostics. Ultimately, this chapter emphasizes the need for comprehensive biocompatibility and toxicity evaluation as integral components of designing effective and safe diagnostic tools thereby contributing to the advancement of healthcare through cutting-edge nanotechnology. 2025 Scrivener Publishing LLC. All rights reserved.
Nano-Biosensor Technologies for Diagnosis of Infectious Diseases;pp.449-468
https://doi.org/10.1002/9781394287697.ch15 https://www.scopus.com/pages/publications/105010120402?origin=resultslist
Manuel M., Department of Chemistry, CHRIST (Deemed to be University), Bengaluru, India
Novel biogenic CNS@AgNPs hybrid nanostructures for electrochemical detection of sucralose: Experimental and in silico strategies
Carbon nanospheres; DFT; Electrochemical sensor; Monte Carlo simulation; Silver nanoparticles; Sucralose
Carbon, the most abundant and versatile element, has played a significant role in scientific innovations, forming the backbone of material science and nanotechnology. This study presents the first reported simultaneous photogenic synthesis of carbon nanospheres (CNS) integrated with silver nanoparticles (CNS@AgNPs) using Coriander sativum seed extract for sucralose detection. The CNS@AgNPs formation, mediated by oleic acid from the extract, was confirmed with GCMS analysis. The morphology of the CNS@AgNPs was characterized using SEM, TEM, XPS, XRD, Raman, BET, Diffuse Reflectance Spectroscopy (DRS), and Thermogravimetric analysis (TGA). The fabricated GE/Nafion/CNS@AgNPs electrode demonstrated an intense oxidation peak current at +0.7V, with Differential Pulse Voltammetry (DPV) showing a linear response from 2.0 to 14?M, with a LOD and LOQ of 0.2?M and 0.62?M (R2=0.998), respectively. The Density Functional Theory (DFT) studies revealed key mechanistic insights, including the methanol cleavage energy (~3.135נ103eV) and HOMO-LUMO differences between neutral sucralose and its cationic form. Monte Carlo (MC) simulations confirmed favourable adsorption energy (?52.739kcal/mol) with specific binding interactions (3.3578.653 influencing electron transfer pathways. This eco-friendly approach presents the potential of sustainable materials for developing efficient electrochemical sensors for detecting artificial sweeteners in real samples. 2025 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Manuel, Manju; Bisetty, Krishna; Kanchi, Suvardhan
Diamond and Related Materials;Volume;160;Issue;;Article No.;112960;
https://doi.org/10.1016/j.diamond.2025.112960 https://www.scopus.com/pages/publications/105019741627?origin=resultslist
Manuel M., Department of Chemistry, CHRIST University, Bengaluru, 560029, India, Centre for Renewable Energy and Environmental Sustainability, CHRIST University, Bengaluru, 560 029, India; Bisetty K., Department of Chemistry, Faculty of Applied Sciences, Durban University of Technology, Durban, 4000, South Africa; Kanchi S., Department of Chemistry, CHRIST University, Bengaluru, 560029, India, Centre for Renewable Energy and Environmental Sustainability, CHRIST University, Bengaluru, 560 029, India
Colloids and Surfaces A: Physicochemical and Engineering Aspects;Volume;705;Issue;;Article No.;135648;
https://doi.org/10.1016/j.colsurfa.2024.135648 https://www.scopus.com/pages/publications/85207878721?origin=resultslist
Detection and management of sucralose pollution: innovations in sustainable monitoring, mitigation, and energy utilization
catalyst; environmental nanotechnology; Environmental pollution; mitigation; sucralose
Sucralose, a widely utilized artificial sweetener, has emerged as a potential environmental contaminant, persisting in aquatic environments and causing ecological risks. The use of suitable methods and sustainable materials can be applied for the detection of sucralose in wastewater, sludge, and other waste matrices Using sustainable materials and novel techniques for discerning sucralose. Leveraging waste-derived components, particularly in electrochemical sensing systems with competent nanomaterials can be a promising avenue for sucralose detection. Moreover, integrating waste-derived carbon materials like activated carbon or carbon black as electrodes enhances sensor sensitivity by augmenting organic molecule adsorption capacities, thus amplifying detection capabilities. Furthermore, waste-derived substrates such as cellulose fibers or agricultural residues can be engineered to serve as substrates or immobilization matrices in enzyme-based sensors for sucralose detection. While the electrochemical systems current output may not directly serve energy applications, its potential utilization for energy generation or storage, through technologies like electrochemical capacitors or batteries, underscores the broader sustainability objectives by repurposing energy from the detection process for additional beneficial applications. The amalgamation of sustainable methodologies in sucralose pollution detection and management not only contributes to environmental preservation but also aligns with the broader trajectory toward sustainable resource utilization and conservation. 2026 Elsevier Inc. All rights reserved.
Waste Derived Catalysts: Sustainable Materials for Energy and Environment;pp.345-358
https://doi.org/10.1016/B978-0-443-32958-6.00024-8 https://www.scopus.com/pages/publications/105032956736?origin=resultslist
Manuel M., Department of Chemistry, Christ University, Karnataka, Bengaluru, India; Kanchi S., Department of Chemistry, Christ University, Karnataka, Bengaluru, India
Exploring a GE/Nafion/Co-MOF nanosheets/CuO NPs/GOx powered electrochemical biosensor for ultrasensitive detection of rebaudioside A
Rebaudioside A (Reb A) is a natural, non-nutritive sweetener highly prevalent in the global sweetener market and widely preferred by consumers. In this study, an advanced electrochemical biosensor was developed for sensing Reb A, using a modified graphite rod electrode extracted from discharged ZnC batteries. The electrode was fabricated using a layer-by-layer strategy with Nafion, Co-MOF nanosheets, CuO NPs, and glucose oxidase (GOx) enzyme. The nanomaterials were characterized by UV-vis, FTIR, DLS, zeta potential measurements, XRD, Raman, SEM, TEM, EDS, and XPS techniques. Electrochemical characterization via Cyclic Voltammetry (CV) and Electrochemical Impedance Spectroscopy (EIS) revealed a significant enhancement in electrical conductivity and increased electroactive surface area. The designed biosensor exhibited a sharp oxidation peak at 0.16 V due to ester bond cleavage in Reb A, which was further amplified in the presence of GOx, resulting from hydroxyl oxidation and hydrogen peroxide generation. Differential pulse voltammetry (DPV) demonstrated a linear response over a concentration range of 2.014 M (R2 = 0.993) with a limit of detection (LOD) of 0.23 M. The sensor displayed excellent analytical performance, with repeatability, reproducibility (RSD = 3.9%), and stability. Additionally, recovery studies confirmed its accuracy, ranging from 97% to 98.17%. Further, the molecular docking studies confirmed strong Reb AGOx interactions (?7.26 kcal mol?1), supporting the biosensor's specificity. The developed biosensor demonstrates excellent analytical performance, making it highly suitable for routine laboratory analysis of sweeteners in complex food matrices. This journal is The Royal Society of Chemistry, 2026
Manuel, Manju; Kanchi, Suvardhan; Losetty, Venkatramana
Nanoscale Advances;Volume;8;Issue;1;pp.167-180
All Open Access; Gold Open Access; Green Open Access
https://doi.org/10.1039/d5na00591d https://www.scopus.com/pages/publications/105022739012?origin=resultslist
Manuel M., Department of Chemistry, CHRIST University, Bengaluru, 560 029, India, Centre for Renewable Energy and Environmental Sustainability, CHRIST University, Bengaluru, 560 029, India; Kanchi S., Department of Chemistry, CHRIST University, Bengaluru, 560 029, India, Centre for Renewable Energy and Environmental Sustainability, CHRIST University, Bengaluru, 560 029, India; Losetty V., Department of Chemistry, Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, 600 062, India
A hybrid algorithm for face recognition using PCA, LDA and ANN
Face recognition is an evolving technique in the field of digital device security. The two procedures Principal Component Analysis and Linear Discriminant Analysis (LDA) are standard methodologies commonly used for feature extraction and dimension reduction techniques extensively used in the recognition of face system. This paper discourse, PCA trailed through a feed forward neural network (FFNN) called PCA-neural network and LDA trailed through feed forward neural network as LDA-neural network are considered for development of hybrid face recognition algorithm. In the current research work, a hybrid model of face recognition is presented with the integration of PCA, LDA, and FFNN. The proposed system experimental results indicate better performance compared to the state of the art literature methods. IAEME Publication.
International Journal of Mechanical Engineering and Technology, Vol-9, No. 3, pp. 85-93.
Manukumar N.M., Department of Computer Science, Christ University, Hosur Road, Bengaluru, India; Hiremath M., Department of Computer Science, Christ University, Hosur Road, Bengaluru, India
A Big Data Analysis Using Fuzzy Deep Convolution Network Based Model for Heart Disease Classification
Big data analysis; Cleveland heart disease UCI datasets; Fuzzy deep convolution network; Traffic data
Heart disease is a serious disease that causes sudden death among 80% of the people around the world. The traditional models performed predictive analytics using machine learning techniques to make a better decision. For better decision making in heart disease prediction, the big data analysis shows the great opportunities to predict the future health status from health parameters and provide best outcomes. However, the traditional decision making models had traffic data or contained noise and uncertainty was unpredictable as the data ambiguity emerged. In order to overcome such an issue, the big data is used to ensure the medical service which is mostly needed in a timely manner and for accurate diagnosis. The pre-processing of the medical data acquired from Cleveland heart disease UCI datasets has a vast number of attributes which helps to predict the heart disease. The data are contaminated with the noise and some of the data are missing, so the pre-processing using Min max Normalization is performed to remove contaminated noise acquired in the data which is taken from the UCI repository dataset. The proposed Fuzzy Deep Convolution Network (FDCN) permits the input features for fuzzification process that uses transformed features. The fuzzification process eliminates the redundant or irrelevant fuzzified features and overcomes the system complexity problems. The proposed FDCN obtains accuracy of 95.56 % and 92 % of F-score shows better results when compared with the existing KNN-DT, Naive Bayes, and Random Forest algorithms. 2021,International Journal of Intelligent Engineering and Systems. All Rights Reserved.
International Journal of Intelligent Engineering and Systems, Vol-14, No. 2, pp. 147-156.
https://doi.org/10.22266/ijies2021.0430.13 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102800710&doi=10.22266%2fijies2021.0430.13&partnerID=40&md5=ee869cba3e7bb10f4f63c58a2ee387bb
Manur M., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Hyderabad, India; Pani A.K., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Hyderabad, India; Kumar P., Department of Computer Science and Technology, Motihari College of Engineering, Bihar, India
A prediction technique for heart disease based on long short term memory recurrent neural network
Bi-directional long short term memory; Conditional random field; Heart disease prediction; Nae bayes; Support vector machine
In recent years, heart disease is one of the leading cause of death for both women and men. So, heart disease prediction is considered as a significant part in the clinical data analysis. Standard data mining techniques like Support Vector Machine (SVM), Naive Bayes and other machine learning techniques used in the earlier research for heart disease prediction. These methods are not sufficient for effective heart disease prediction due to insufficient test data. In this research, Bi-directional Long Short Term Memory with Conditional Random Field (BiLSTM-CRF) has been proposed to increase the efficiency of heart disease prediction. The input medical data were analyzed in a bidirectional manner for effective analysis, and CRF provided the linear relationship between the features. The BiLSTMCRF method has been tested on the Cleveland dataset to analyze the performance and compared with existing methods. The results showed that the proposed BiLSTM-CRF outperformed the existing methods in heart disease prediction. The average accuracy of the proposed BiLSTM-CRF is 90.04%, which is higher than the existing methods. 2020 by the authors.
International Journal of Intelligent Engineering and Systems, Vol-13, No. 2, pp. 31-39.
https://doi.org/10.22266/ijies2020.0430.04 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092497346&doi=10.22266%2fijies2020.0430.04&partnerID=40&md5=5aa662fa28fb9e0554cf56220c5b94bf
Manur M., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bengaluru, India; Pani A.K., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bengaluru, India; Kumar P., Department of Computer Science and Technology, Motihari College of Engineering, Bihar, India
Machine Learning based Candidate Recommendation System using Bayesian Model
Artificial Intelligence; Bayesian Technique; Decision Tree; Job Seeker; Machine Learning; Service Learning
Online websites that recommend books, music, movies, and other media are becoming increasingly prevalent because of collaborative filtering. This online websites are using many algorithms to provide the better recommendation to attract the customer. Bayesian statistics, which is based on Bayes' theorem, is a technique for data analysis in which observable data are used to update the parameters of a statistical model. To discuss a strategy called item-based collaborative filtering, which bases predictions on the similarities between the said objects. This uses Machine Learning based Candidate Recommendation System which uses Bayesian Model database to assess the proposed method. The actual results show that for collaborative filtering which is based on correlation, the Bayesian techniques we have proposed outperform traditional algorithms. Also discuss a technique for improving prediction accuracy that combines the Simple Bayesian Classifier with user- and item-based collaborative filtering. The user-based recommendation is then applied to the matrix once the user-item rating matrix has been filled out with pseudo-scores produced by the item-based filter. This model is demonstrated that the combined approach outperforms the individual collaborative recommendation approach. The creation of UI based web application will help Students to manage achievement details. Job seekers and admin will be given a separately formatted version of the application where, students can upload and view their certificate, wherein admin can access student achievement details categorized by different parameters. This proposed model is developed under the service learning scheme to benefit both job seeker and recruiter. 2023 IEEE.
Manuval G.G.; George T.T.; Aby B.P.; Mathew M.; Chandran A.S.; Jayapandian N.
Proceedings of the 2023 2nd International Conference on Electronics and Renewable Systems, ICEARS 2023, pp. 1172-1178.
Institute of Electrical and Electronics Engineers Inc.
Manuval G.G., Christ (Deemed to Be University), Department of CSE, India; George T.T., Christ (Deemed to Be University), Department of CSE, India; Aby B.P., Christ (Deemed to Be University), Department of CSE, India; Mathew M., Christ (Deemed to Be University), Department of CSE, India; Chandran A.S., Christ (Deemed to Be University), Department of CSE, India; Jayapandian N., Christ (Deemed to Be University), Department of CSE, India
A stochastic propagation model to the energy dependent rapid temporal behaviour of Cygnus X-1 as observed by AstroSat in the hard state
accretion, accretion discs; black hole physics -X-rays: binaries; X-rays: individual: Cygnus X-1.
We report the results from analysis of six observations of Cygnus X-1 by Large Area X-ray Proportional Counter (LAXPC) and Soft X-ray Telescope (SXT) onboard AstroSat, when the source was in the hard spectral state as revealed by the broad-band spectra. The spectra obtained from all the observations can be described by a single-temperature Comptonizing region with disc and reflection components. The event mode data from LAXPC provides unprecedented energy dependent fractional root mean square (rms) and time-lag at different frequencies which we fit with empirical functions.We invoke a fluctuation propagation model for a simple geometry of a truncated disc with a hot inner region. Unlike other propagation models, the hard X-ray emission (>4 keV) is assumed to be from the hot inner disc by a single-temperature thermal Comptonization process. The fluctuations first cause a variation in the temperature of the truncated disc and then the temperature of the inner disc after a frequency dependent time delay.We find that the model can explain the energy dependent rms and time-lag at different frequencies. 2019 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society.
Maqbool B.; Mudambi S.P.; Misra R.; Yadav J.S.; Gudennavar S.B.; Bubbly S.G.; Rao A.; Jogadand S.; Patil M.K.; Bhattacharyya S.; Singh K.P.
Monthly Notices of the Royal Astronomical Society, Vol-486, No. 2, pp. 2964-2975.
All Open Access; Bronze Open Access; Green Open Access
https://doi.org/10.1093/mnras/stz930 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068007710&doi=10.1093%2fmnras%2fstz930&partnerID=40&md5=0c653dc51b826d5e57a0fb410d2aaa12
Maqbool B., Inter-University Centre for Astronomy and Astrophysics, Ganeshkind, Pune, 411007, India; Mudambi S.P., Department of Physics and Electronics, CHRIST (Deemed to Be University), Hosur Road, Bengaluru, 560029, India; Misra R., Inter-University Centre for Astronomy and Astrophysics, Ganeshkind, Pune, 411007, India; Yadav J.S., Tata Institute of Fundamental Research, Homi Bhabha Road, Mumbai, 400005, India; Gudennavar S.B., Department of Physics and Electronics, CHRIST (Deemed to Be University), Hosur Road, Bengaluru, 560029, India; Bubbly S.G., Department of Physics and Electronics, CHRIST (Deemed to Be University), Hosur Road, Bengaluru, 560029, India; Rao A., Department of Physics and Astronomy, Faculty of Physical Sciences and Engineering, University of Southampton, Southampton, SO17 1BJ, United Kingdom; Jogadand S., Swami Ramanand Teerth Marathwada University, Nanded, 431 606, India; Patil M.K., Swami Ramanand Teerth Marathwada University, Nanded, 431 606, India; Bhattacharyya S., Tata Institute of Fundamental Research, Homi Bhabha Road, Mumbai, 400005, India; Singh K.P., Indian Institute of Science Education and Research Mohali, Knowledge City, Sector 81 SAS Nagar, Manauli, 140306, India
Surveillance and security: integrating video, audio, and sensor data
anomaly detection; big data analytics; cybersecurity; Data fusion; intelligent surveillance; predictive analytics
This period of rapid technological development offers enormous prospects for enhancement in security and surveillance systems through integration of sensor, audio, and video data. Conventional surveillance systems, that rely primarily on video feeds, have been undergoing the process of incorporating audio and sensor data to provide monitoring solutions that are more comprehensive and accurate. In addition to this the research provides an overview of the possibility of strengthening security in a number of settings. It also deals with the strategy involved in the integration of multimodal data as well as challenges associated with this process. This expansion is crucial for the deployment of these integrated systems. Moreover, in the application of machine learning (ML) and artificial intelligence (AI) technologies in making sense of such vast volumes of data as output by the respective systems, the detection of threats with higher precision and speed has improved. Complex sensor, audio, and video pattern analysis are theoretically possible using models of AI and ML. This study culminates in the presentation of a list of prospective future research subjects. It is meant to integrate technology innovation with social aspects such as privacy and security systems. Among them are the development of lightweight algorithms, ethical frameworks, and integration solutions that are efficient. 2026 Elsevier Inc. All rights reserved.
Marakkar, Sheheen; Thakur, Shubham Kumar
Multimodal Learning Using Heterogeneous Data;pp.229-238
https://doi.org/10.1016/B978-0-443-27528-9.00020-6 https://www.scopus.com/pages/publications/105032925264?origin=resultslist
Marakkar S., CHRIST University, Bengaluru, India; Thakur S.K., CHRIST University, Bengaluru, India
BSLnO: Multi-agent based distributed intrusion detection system using Bat Sea Lion Optimization-based hybrid deep learning approach
deep maxout network; deep residual network; intrusion detection system; multi-agent system; Sea Lion Optimization
Intrusion detection system (IDS) is a robust model that plays an essential role in dealing with intrusion detection, especially in detecting abnormal anomalies and unknown attacks. The major challenges faced by IDS are the computation time required for analysis, and the exchange of a huge amount of data from one division of the network to another. For the sake of tackling such limitations, this probe proposes a multi-agent enabled IDS for detecting intrusions using the Bat Sea Lion Optimization (BSLnO) algorithm. The proposed strategy consists of five phases, namely pre-processor agent, reducer agent, augmentation agent, classifier agent, and decision agent. Initially, input data is subjected to pre-processor agent, where pre-processing is carried out using data normalization and missing value imputation. Thereafter, the pre-processed result is fed up to the reducer agent, where dimension reduction is carried out using mutual information. The third step is data augmentation in which the dimensionality of data is enhanced. After that, the augmented result is subjected to classifier agent to classify intrusions or malicious activities present in the network based on hybrid deep learning strategies, namely deep maxout network and deep residual network. A developed BSLnO is implemented by incorporating Bat Algorithm (BA) and Sea Lion Optimization (SLnO) algorithm to train the hybrid classifier. The proposed scheme has acquired a higher precision of 0.936, recall of 0.904, and F-measure of 0.920. 2022 John Wiley & Sons Ltd.
International Journal of Adaptive Control and Signal Processing, Vol-36, No. 8, pp. 1909-1930.
https://doi.org/10.1002/acs.3427 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130487135&doi=10.1002%2facs.3427&partnerID=40&md5=7b881140e224fb28bcd894c2e3978a00
Maram B., Department of Computer Science and Engineering, Chitkara University Institute of Engineering and Technology, Himachal Pradesh, Chitkara University, India; Mandala J., Department of Computer Science and Engineering, Christ (Deemed to be) University, Bengaluru, India; Satish A.R., School of Computer Science and Engineering, VIT-AP University, Andhra Pradesh, Vijayawada, India
A critical analysis of the zimbabwean political leadership in the practice of justice
Political leadership is a fundamental philosophical issue influencing governance newlineof states and the success of every state is directly or indirectly linked to the newlineleadership ideology of its political leaders. This study investigated the nature and newlinecharacter of political leaders in Zimbabwe by assessing their political philosophy in the context of the major historical events since 1890 masked by three eras namely the pre-colonial era, (characterised by little political activities) followed by the colonial era (dominated by socio-political system that weakened the African culture and governance) and lastly, the post-independence era (characterised by failure to uphold the constitution by the ruling party leadership). newlineThe scope of this study is based on benchmarking the concepts of the Zimbabwean political philosophy with political ideas from renown philosophers such as Plato, Aristotle and Gandhi. More specifically, the Gandhian philosophy was selected and conceptually applied to the Zimbabwean political situation in an attempt to develop an ideal political philosophy because of its illustrious wisdom with regards to good governance principles. A hermeneutics and newlinephilosophical analytic models were used to interpret relevant literature and leadership responsibilities as provided for in the Zimbabwe Constitution. Study findings revealed challenges in the current political leadership that calls for developing a new Zimbabwean political leadership philosophy. These include but not limited to partisan politics, political violence, unbridled favouritism and nepotism, ethnicity in leadership, state capture of key institutions, negative use of power by the state, political violence, poor electoral systems flouted with impunity and rampant corruption. These political challenges militate against democratic principles of common good that fight human oppression, repression and suppression.
Kachappilly, Kurian and Kurasha, Jameson
A critical analysis of the zimbabwean political leadership in the practice of justice /
Implementation of AI-Assisted Tools in Foreign Language Training
AI-assisted language training tool; communication skills; English as a foreign language; information technologies in linguistics; language training
The article discusses didactic issues of implementing of AI-assisted tools into foreign language training of linguistics students. The purpose of the study evaluate the potential and analyze the case of integrating an AI-assisted tool into a foreign language course. Methodologically the study rests on two didactic approaches, namely a competence-based approach and a personal activity approach. The analysis of the functional scope of AI-assisted tools used for academic linguistic and practical linguistic purposes is carried out. A brief comparative analysis of the available options with an account of essential functional characteristics is given. Based on the needs-analysis the choice of a particular platforms for the training process is reasoned. A set of teaching principles was observed to master the target skills in the experiment. To estimate the efficiency of the training process and development of the target skills, a questionnaire was compiled and offered to students. The analysis of students performance as regards the focus communicative interaction skills was carried out. Challenges in combining traditional and AI-assisted tools in the educational process are analyzed; the ways to overcome them are recommended. The study suggests that the use of AI-assisted tools in foreign language training of linguistics students can be based on a combined approach aimed at improving the quality of individual work and further consolidation of target skills in contact time. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
Marchenko, Tatiana; Vartanova, Liana; Prakasha, G.S.; Moshchenko, Oksana
Lecture Notes in Networks and Systems;Volume;1616 LNNS;pp.314-322
https://doi.org/10.1007/978-3-032-04365-8_31 https://www.scopus.com/pages/publications/105019512997?origin=resultslist
Marchenko T., North-Caucasus Federal University, 1 Pushkin Street, Stavropol, 355017, Russian Federation; Vartanova L., North-Caucasus Federal University, 1 Pushkin Street, Stavropol, 355017, Russian Federation; Prakasha G.S., School of Education, Christ University, Bengaluru, India; Moshchenko O., Financial University Under the Government of the Russian Federation, Moscow, Russian Federation
Social networking- A comparative study on the new google + project and facebook /
This research project set out to study the new Google + Project, a new social network by Google. The dissertation looks at the social network as compared to already existing social networks especially Facebook, what the sight possesses that other social networks may not have and how successful the site will be in the years to come and if there is a need for a new social network after what is already there. The researcher went out to find out from users of the social networks, how popular Google plus and Facebook are and how frequently the sites are and what possible future the sites have. The researcher found out that Google plus isnt as popular as Facebook just yet but in years to come if they can learn from the mistakes that the already existing social network have made and develop on that to make the site more user friendly.