Robust Deep Learning Empowered Real Time Object Detection for Unmanned Aerial Vehicles based Surveillance Applications
computer vision; deep learning; image processing; object detection; Surveillance; unmanned aerial vehicles
Surveillance is a major stream of research in the field of Unmanned Aerial Vehicles (UAV), which focuses on the observation of a person, group of people, buildings, infrastructure, etc. With the integration of real time images and video processing approaches such as machine learning, deep learning, and computer vision, the UAV possesses several advantages such as enhanced safety, cheap, rapid response, and effective coverage facility. In this aspect, this study designs robust deep learning based real time object detection (RDL-RTOD) technique for UAV surveillance applications. The proposed RDL-RTOD technique encompasses a two-stage process namely object detection and objects classification. For detecting objects, YOLO-v2 with ResNet-152 technique is used and generates a bounding box for every object. In addition, the classification of detected objects takes place using optimal kernel extreme learning machine (OKELM). In addition, fruit fly optimization (FFO) algorithm is applied for tuning the weight parameter of the KELM model and thereby boosts the classification performance. A series of simulations were carried out on the benchmark dataset and the results are examined under various aspects. The experimental results highlighted the supremacy of the RDL-RTOD technique over the recent approaches in terms of several performance measures. 2022 River Publishers.
Ranjith C.P.; Hardas B.M.; Khaja Mohideen M.S.; Nijil Raj N.; Robert N.R.; Mohan P.
Journal of Mobile Multimedia, Vol-19, No. 2, pp. 451-476.
Ranjith C.P., Faculty in Information Technology Department, University of Technology and Applied Sciences, Shinas, Oman; Hardas B.M., Electronics Engineirng Dept, Shri Ramdeobaba College of Enginering and Management, Nagpur, India; Khaja Mohideen M.S., Department of Information Technology, University of Technology and Applied Science, Salalah, Oman; Nijil Raj N., Department of Computer Science and Engineering Younus College of Engineering and Technology, Kollam, India; Robert N.R., Department of Computer Science, Christ University, Bangalore, India; Mohan P., School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India
Predicting Stock Market Movements Through Multisource Data Fusion Graphs: An Approach Employing Graph Convolutional Neural Network
Data fusion; Gated Recurrent Unit; Graph Convolution Neural Network; Multi-source data; Multi-source data fusion graphs; Stock market trends; Stock trend prediction
The stock market plays an important role in the capital market, and investigating price fluctuations in the stock market has consistently been a prominent subject for researchers. The application of soft computing techniques to predict and categorize stock market movements is a significant research challenge that has gathered considerable attention from researchers. Although several studies highlight the significance of incorporating information from two sources in stock movement prediction, the potential of advanced graphical techniques for modeling and analyzing multi-source data remains an unattended research area. This study aims to address this gap by introducing a novel model that utilizes multi-source data fusion graphs to predict future market movements. The primary challenge involves establishing a model that can effectively gather the relationships among various data sources and employ this understanding to improve prediction performance. Compared to several existing methods relying only on historical data or sentiment data, which show limited predictive power and lack generality, the proposed approach seeks to overcome these limitations. The proposed model integrates various information sources, including historical prices, news data, Twitter data, and technical indicators for predicting future stock market trends. This presented method involves constructing a subgraph map for each data type to capture events from both rising and falling markets. Then, a Gated Recurrent Unit (GRU) is employed to aggregate the subgraph nodes. These aggregated nodes are then integrated with a Graph Convolutional Neural Network (GCNN) to classify the multi-source graph, therefore achieving stock market trend prediction effectively. To further validate its effectiveness, the presented model is applied to Indian stock market data, demonstrating its feasibility in fusing multi-source stock data and establishing its suitability for effectively predicting stock market movements. 2024 Seventh Sense Research Group
International Journal of Engineering Trends and Technology, Vol-72, No. 6, pp. 8-18.
https://doi.org/10.14445/22315381/IJETT-V72I6P102 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197208553&doi=10.14445%2f22315381%2fIJETT-V72I6P102&partnerID=40&md5=d7dcd751e80fb1aabcd2c949abf825a9
Ranjith J., School of Business and Management, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Chandar S.K., School of Business and Management, CHRIST (Deemed to be University), Karnataka, Bengaluru, India
Enhancing Stock Market Trend Prediction Using Explainable Artificial Intelligence and Multi-source Data
Explainable artificial intelligence; Machine learning; Multi-source data; Sentiment analysis; Stock market prediction; Technical indicators
Determining the trend of the stock market is a complex task influenced by numerous factors like fundamental variables, company performance, investor behavior, sentiments expressed in social media, etc. Although machine learning models support predicting stock market trends using historical or social media data, reliance on a single data source poses a serious challenge. This study introduces a novel Explainable artificial intelligence (XAI) to address a binary classification problem wherein the objective is to predict the trend of the stock market, utilizing an integration of multiple data sources. The dataset includes trading data, news and Twitter sentiment, and technical indicators. Sentiment analysis and the Natural Language Toolkit are utilized to extract the qualitative information from social media data. Technical indicators, or quantitative characteristics, are therefore generated from trade data. The technical indicators are fused with the stock sentiment features to predict the future stock market trend. Finally, a machine learning model is employed for upward or downward stock trend predictions. The proposed model in this study incorporates XAI to interpret the results. The presented model is evaluated using five bank stocks, and the results are promising, outperforming other models by reporting a mean accuracy of 90.14%. Additionally, the proposed model is explainable, exposing the rationale behind the classifier and furnishing a complete set of interpretations for the attained outcomes. 2024, American Scientific Publishing Group (ASPG). All rights reserved.
Fusion: Practice and Applications, Vol-16, No. 2, pp. 178-189.
American Scientific Publishing Group (ASPG)
https://doi.org/10.54216/FPA.160211 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201629045&doi=10.54216%2fFPA.160211&partnerID=40&md5=0c98cd96375a5917d38668bb4cb6923c
Harnessing Medical Databases and Data Mining in the Big Data Era: Advancements and Applications in Healthcare
In the contemporary period of Big Data, the healthcare industry is witnessing a transformative paradigm shift, propelled by the convergence of medical databases and data mining technology. This research paper delves into the multifaceted application of this synergy, offering a comprehensive overview of its implications and opportunities. With the exponential growth of healthcare data, the utilisation of medical databases serves as the bedrock for data mining techniques, fostering critical advancements in diagnosis, treatment, and patient care. Through this research, we explore the integration of electronic health records, genomic data, and clinical databases, unveiling new dimensions of predictive analytics, patient profiling, and disease monitoring. Moreover, we assess the ethical and privacy concerns entailed in this data-rich landscape, emphasising the need for robust governance and security measures. Our paper encapsulates the evolving landscape of health care, demonstrating the immense potential and the ethical responsibilities accompanying this groundbreaking merger of technology and medicine in the period of Big Data. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Ranjith Kumar G.; Nassa V.K.; Manikandan K.; Suribabu P.; Pandian P.M.; Acharjee P.B.
Smart Innovation, Systems and Technologies, Vol-390, pp. 153-165.
Springer Science and Business Media Deutschland GmbH
Ranjith Kumar G., School of Computer Science and Artificial Intelligence, SR University, Telangana, Warangal, India; Nassa V.K., Department of Information Communication Technology (ICT), Tecnia Institute of Advanced Studies(Delhi), Affiliated With Guru Gobind Singh Indraprastha University, Delhi, India; Manikandan K., Department of Pharmaceutical Analysis, SRM College of Pharmacy, SRM Institute of Science and Technology, Kattankulathur, Kanchipuram, SRM Nagar, 603203, India; Suribabu P., Computer Science and Engineering, JNTU Hyderabad, Hyderabad, India; Pandian P.M., Department of Chemistry, Saveetha Engineering College, Chennai, Thandalam, India; Acharjee P.B., Computer Science, CHRIST University Pune Lavasa Campus, Lavasa, India
Green marketing: Exploring concepts, strategies, and future trends
Green marketing research is becoming more and more well-liked in academia and business. Both companies and customers today recognize the value of eco-friendly products due to increased awareness of how companies respond to various factors contributing to environmental degradation. One should understand the meaning, opportunities, and threats associated with green marketing to harness the benefits of green marketing. This book chapter aims to explore various aspects of green marketing, including its evolution throughout the years, opportunities, threats, the future of green marketing, etc. To sum up, this chapter aims to gain an in-depth understanding of green marketing and how companies could use it to their advantage. Successful implementation will be possible only if associated threats are carefully analyzed and understood. Therefore, a part of this chapter will be dedicated to understanding the threats associated with green marketing strategies. 2023, IGI Global. All rights reserved.
Achieving Economic Growth and Welfare Through Green Consumerism, pp. 1-22.
Ranjith N., CHRIST University (Deemed), India; Preethi D., CHRIST University (Deemed), India; Prasath S.K., CHRIST University (Deemed), India
An analytical model for a TFET with an n-doped channel operating in accumulation and inversion modes
Analytical model; Band-to-band tunneling (BTBT); Poissons equation; Simulations; Technology computer-aided design (TCAD); Tunnel field-effect transistor (TFET)
The tunnel field-effect transistor (TFET) is an ambipolar device that conducts current with the channel in both accumulation and inversion modes. Analytical expressions for the channel potential and current in a TFET with an n-doped channel when operating in the accumulation and inversion modes are proposed herein. The potential model is derived by solving the two-dimensional (2D) Poisson equation using the superposition principle while considering the charges present in the channel due to electron or hole accumulation along with the depletion charges. An expression for the tunneling current corresponding to the maximum tunneling probability is also derived. The tunneling current is obtained by analytically calculating the minimum tunneling length in a TFET when operating in the accumulation or inversion mode. The results of the proposed potential model is compared with technology computer-aided design (TCAD) simulations for TFET with various dimensions, revealing good agreement. The potential and current in an n-type TFET (nTFET) obtained using the proposed models are also analyzed. 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Journal of Computational Electronics, Vol-20, No. 3, pp. 1125-1136.
https://doi.org/10.1007/s10825-021-01683-x https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104732940&doi=10.1007%2fs10825-021-01683-x&partnerID=40&md5=46a0626aa0cff684bec82dde18f310f7
Ranjith R., Department of Electronics and Communication Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bengaluru, India; Suja K.J., Department of Electronics and Communication Engineering, National Institute of Technology Calicut, Kozhikode, India; Komaragiri R.S., Department of Electronics and Communication Engineering, Bennett University, Greater Noida, India
Analytical modeling of reconfigurable transistors
A functionally enhanced transistor is a potential candidate for further advancing electronics and Moore's law beyond the classical scaling. This chapter discusses these kinds of multifunctional transistors called reconfigurable field-effect transistor (RFET) and reconfigurable tunnel field-effect transistor (RTFET). The RFET works on the principle of Schottky barrier tunneling, and the RTFET works on the principle of band-to-band tunneling. Both devices can be configured as an n-type and p-type device based on the biasing. This chapter explains the working and performance comparison of RFET and RTFET in detail with the help of technology computer-aided design (TCAD) simulations. Further, the potential and current models of a single-gated RFET and double-gated RTFET are presented in this chapter. The presented analytical models are compared and verified with TCAD simulations. The potential in the channel regions of RFET and RTFET is modeled by solving a two-dimensional (2D) Poisson's equation. Because the working principle of both devices is different, two different formulas are utilized for modeling the current in the device. The current model for the RFET is developed by integrating Landauer's formula, whereas the current model for RTFET is obtained by integrating band-to-band generation rate over the tunneling volume. The procedure, technique, and assumptions followed to obtain the potential and current models of RFET and RTFET are detailed in this chapter. 2022 selection and editorial matter, Ashish Raman, Deep Shekhar and Naveen Kumar; individual chapters, the contributors.
Sub-Micron Semiconductor Devices: Design and Applications, pp. 355-372.
Ranjith R., Department of Electronics and Communication Engineering, School of Engineering and Technology CHRIST (Deemed to be University), Bangalore, India; Suja K.J., Department of Electronics and Communication Engineering, National Institute of Technology Calicut, Kozhikode, India; Komaragiri R.S., Department of Electronics and Communication Engineering, School of Engineering and Applied Science Bennett University, Greater Noida, India
A sum signed graph S = (G, f, and#963;) is a signed graph of the underlying graph G where f : V (G) and#8722;and#8594; {1, 2, . . . , | V (G) |} is a bijective function and and#963; : E(G) and#8722;and#8594; {+, and#8722;} is newlinea mapping such that and#963;(uv) = +, whenever f(u) + f(v) and#8804; n and and#963;(uv) = and#8722;, whenever f(u) + f(v) gt n. The minimum number of negative and positive edges among all the sum signed labelings of G is known as rna and rna complement number respectively. The maximum number of positive edges among all the sum signed labelings of G is known as adhika number. The set X and#8838; V (G) is said to be a s - dominating of a signed graph whenever X is a dominating set and there exists exactly s number of negative edges between X and its complement. The minimum cardinality of such a dominating set over all signed graphs of the graph G is called an s - domination number. newlineIn the present study, we initiate the study of a new labeling in signed graphs namely, newlinesum signed labeling. The characteristics of sum signed graphs and the bound of rna number of in terms of the number of vertices in the underlying graph are explored by examining the rna number of different graphs. The properties of signed graphs such as negating and balancing is analyzed. The relation between rna number and rna complement number is established. The connection of sum signed labeling with parity signed labeling and cordial labeling is discussed. The absolute cordial condition for graphs satisfying sum signed labeling is examined. The concept of s - domination was also introduced during this period of study. The s domination in both the positive and negative homogeneous signed graph is investigated for each value of s. The properties of s domination in sum signed graphs are also analyzed. The s - domination number for specifc values of s is investigated for various graphs. The maximum value of s for a graph for which the s - domination will exist is discussed.
Ionic strength and phase systems influence nanotubular material functionality
chromonic liquid crystals (CLCs); Ionic strength; Nanofibers; Thiacyanine dyes
We synthesized novel thiacyanine chromonic liquid crystals (CLCs) and structurally characterized using NMR and mass spectrometry. The impact of distinct substitution at the para position of aromatic counter anions, aliphatic counter ion chain length, and varied spacer parity of thiacyanine dyes on CLC formation is investigated. Liquid crystal properties of the synthesized dyes are characterized by polarizing optical microscopy (POM) and X-ray diffraction (XRD) studies. Dyes exhibit nematic (N), lamellar (L?), columnar rectangular (Colr), and columnar oblique (Colob) CLCs at different concentrations in the water. Electronic absorption spectra reveal Scheibe aggregation in all the dyes. Cylicvoltametry studies confirm redox behaviour in TC-1a and TC-5e dyes. Chromonic LCs hybrid nano-materials are synthesized using solgel method. Scanning electron microscopy employed to confirm nano tubular fiber structure of the hybrid nanomaterilals. 2024 Elsevier B.V.
Ranjitha B.S.; Kumar Behera P.; Ammathnadu Sudhakar A.; Hegde G.; Singh S.; Shanker G.
https://doi.org/10.1016/j.molliq.2024.124339 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187205260&doi=10.1016%2fj.molliq.2024.124339&partnerID=40&md5=c8ebdabab87b5e2731588d0fff31ab9e
Ranjitha B.S., Department of Chemistry, Bangalore University, Jnana Bharathi Campus, Bengaluru, 560056, India; Kumar Behera P., Department of Chemistry, Indian Institute of Technology Guwahati, Assam, Guwahati, 781039, India; Ammathnadu Sudhakar A., Department of Chemistry, Indian Institute of Technology Guwahati, Assam, Guwahati, 781039, India; Hegde G., Centre for Advanced Research and Development (CARD), CHRIST (Deemed to be University), Hosur Road, Bengaluru, 560029, India; Singh S., Crystal Growth Centre, AcTech Campus, Anna University, Tamil Nadu, Chennai, 600025, India, Centre for Energy Storage Technologies, AcTech Campus, Anna University, Tamil Nadu, Chennai, 600025, India; Shanker G., Department of Chemistry, Bangalore University, Jnana Bharathi Campus, Bengaluru, 560056, India
Impact of terminal group on azobenzene liquid crystal dimers for photo-responsive optical storage devices
Optical storage device; Photoisomerization; Photoresposive LC dimers; Self-assembled H aggregates
Three new series of photo responsive dimers bearing different terminal functional groups (-CN, -COOEt, -OMe) and variable aliphatic spacers have been synthesized and investigated in detail. The molecular structures of the new materials were proved using different spectroscopic techniques and their liquid crystal self-assembly was characterized using differential scanning calorimetry (DSC), polarized light microscope (POM) and X-ray diffraction (XRD). Moreover, their photo switching behaviour was investigated in details in solution. The results revealed that, almost all members of the dimeric materials are mesomorphic exhibit either nematic or both smectic A and nematic phases. Under UV illumination the materials show interesting photo-switching properties, reaching a photo-stationary state in 40 s and a thermal back-relaxation time of approximately 35 h in solution. Finally, a fabricated device authenticates the potential of the reported materials for optical storage devices. 2023 Elsevier B.V.
Ranjitha B.S.; Sandhya Kumari D.; Shetty A.; Shanker G.; Alaasar M.; Pashameah R.; Hegde G.
https://doi.org/10.1016/j.molliq.2023.121985 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85159564184&doi=10.1016%2fj.molliq.2023.121985&partnerID=40&md5=3d046e54b485c332bf3dbeba88e0829c
Ranjitha B.S., Department of Chemistry, Bangalore University, Jnana Bharathi Campus, Bengaluru, 560056, India; Sandhya Kumari D., Department of Chemistry, Bangalore University, Jnana Bharathi Campus, Bengaluru, 560056, India; Shetty A., Department of Chemistry, CHRIST (Deemed to be University), Hosur Road, Bengaluru, 560029, India, Centre for Advanced Research and Development (CARD), CHRIST (Deemed to be University), Hosur Road, Bengaluru, 560029, India; Shanker G., Department of Chemistry, Bangalore University, Jnana Bharathi Campus, Bengaluru, 560056, India; Alaasar M., Department of Chemistry, Faculty of Science, Cairo University, Giza, 12613, Egypt; Pashameah R., Department of of Chemistry, Faculty of Applied Science, Umm Al-Qura University, Makkah, 24230, Saudi Arabia; Hegde G., Centre for Advanced Research and Development (CARD), CHRIST (Deemed to be University), Hosur Road, Bengaluru, 560029, India
Women's empowerment within the tourism industry: Risk assessment and mitigation strategies for solo women travellers
Travelling is essential for maintaining one's happiness and mental wellness. Tourism is a job providing industry and huge contributions to economic growth, and many studies have been undertaken to determine who is attracted to tourism. Surprisingly, women take a major stand in the tourism industry. The purpose of this study is to examinefemale solo travelers' risk assessment and their mitigation strategies by investigating online travel blogs. Therefore, the study aims at assessment of risk such as physical risk, destination-specific risk, and financial risk faced by solo women travellers by considering and analyz- ing online women travellers' blog narratives. These narratives from online travel blogs pertaining to Indian solo female travellers are considered in this study. Hence, this assessment gives an opportunity to know how to stay safe in uncertain situations and also mitigation strategies to stay alert. The result of the study indicates the various risks faced by solo female travel and mitigation strategies for promotion of more female travellers. 2023, IGI Global. All rights reserved.
Women's Empowerment Within the Tourism Industry, pp. 221-233.
Ranjitha R., BNM Institute of Technology, India; Mathew S., CHRIST University (Deemed), India
Fluorescent chiral nematogens derived from naturally occurring moieties
chiral dimers; cholesterol; Fluorescent nematogens; thermochromism; vanillin
We discuss the synthesis and characterisation of new non-symmetric liquid crystal dimers of cholesteryl n-(4-formyl-2-methoxyphenoxy)alkanoates by tethering cholesterol and vanillin. Spectroscopic characterisation such as IR, 1H and 13C NMR used to confirm the molecular structures. Mesophase properties are assessed using polarising optical microscopy (POM) and differentially scanning calorimeter (DSC). All the dimers in the series display liquid crystalline phase particularly cholesteric and smectic A* phases. Additionally, a UV-Vis study showed that the molecules self-assemble into H-aggregates with strong fluorescence emission. DFT studies provide insights into molecular geometry, electronic structure, and energy states, enabling detailed estimations of chemical behaviour and interactions. 2025 Informa UK Limited, trading as Taylor & Francis Group.
Ranjitha, Bommanahalli Shivalingegowda; Chongliu, Kiamo; Rabari, Mahima; Prajapati, Ashishkumar Kantilal; Shanker, Govindaswamy
Liquid Crystals;Volume;52;Issue;12;pp.1070-1081
https://doi.org/10.1080/02678292.2025.2560465 https://www.scopus.com/pages/publications/105016823079?origin=resultslist
Ranjitha B.S., Department of Chemistry, Bangalore University, Bengaluru, India; Chongliu K., Department of Chemistry, CHRIST (Deemed to be University), Bengaluru, India; Rabari M., Department of Chemistry, Faculty of Science, The Maharaja Sayajirao University of Baroda, Vadodara, India; Prajapati A.K., Department of Chemistry, Faculty of Science, The Maharaja Sayajirao University of Baroda, Vadodara, India; Shanker G., Department of Chemistry, Bangalore University, Bengaluru, India
Case Analysis of Evolving and Successful Practices in Water Governance in Asia
community engagement; stakeholders; water governance; water management; water policy
Water governance is a critical issue across Asia, where countries face escalating challenges such as water scarcity, pollution, and inefficient management systems. This paper contributes to the field by offering a comparative analysis of innovative and successful governance practices from multiple Asian countries, including Pakistan, Turkey, Kyrgyzstan, and Jordan. Unlike prior studies that focus on isolated national or local contexts, this research synthesizes diverse governance models to highlight scalable innovations and transferable strategies. Key insights include the effectiveness of decentralized governance frameworks, inclusive stakeholder engagement, and data-driven policy integration. These practices are evaluated for their potential to inform context-specific water management solutions that are both sustainable and resilient. The study also emphasizes the alignment of governance strategies with the United Nations Sustainable Development Goals (SDGs), particularly SDG 6 (Clean Water and Sanitation), SDG 12 (Responsible Consumption and Production), and SDG 13 (Climate Action). By bridging theoretical insights with practical applications, the research addresses critical gaps in the literature and provides actionable frameworks for policymakers to strengthen water security across the region. 2025 Policy Studies Organization.
World Water Policy;Volume;11;Issue;4;pp.984-999
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1002/wwp2.70040 https://www.scopus.com/pages/publications/105019961082?origin=resultslist
RanjithaGrace A., School of Architecture, CHRIST (Deemed to be University), Bangalore, India; Suseelan A., School of Architecture, CHRIST (Deemed to be University), Bangalore, India
Facile fabrication of 3D-?-Fe2O3@2D-g-C3N4 heterojunction composite materials: Effect of iron oxide loading on the electrochemical performance
Carbon materials; Cyclic voltammetry; Graphitic carbon nitride (g-C3N4); Hydrothermal synthesis method; Supercapacitor electrode devices; ?-Fe2O3 nanospheres
Designing heterojunction nanocomposites is crucial for optimizing supercapacitor electrodes. This study addresses the challenge by introducing a facile synthesis method for creating 3D-?-Fe2O3@2D-g-C3N4 heterojunctions through a bulk carbon nitride-assisted hydrothermal process. During this process, the growth of ferric oxide particles coincides with the exfoliation and deposition of carbon nitride, leading to simultaneous structural changes in both iron oxide and carbon nitride phases. The resulting composite's properties strongly correlate with the iron oxide loading. Comprehensive characterization using XRD, FTIR, SEM-EDAX, XPS and TEM identified three distinct structures for ?-Fe2O3/g-C3N4 composites based on iron oxide loading: low, medium, and high. The medium-loaded sample demonstrated superior electrochemical performance, attributed to better interfacial contact and the formation of 3D-Fe2O3@2D-g-C3N4 heterojunctions. This composite, with an optimized 22 wt% iron oxide loading, exhibited a maximum specific capacitance of 925.1 Fg?1 at 5 mVs?1 and 498.6 Fg?1 at 6 Ag?1 in charge-discharge analysis, with stable performance over 2000 cycles. Overall, this research presents an enhanced hydrothermal method for facile preparation of effective ?-Fe2O3/g-C3N4 heterojunction materials. 2024 Elsevier B.V.
Ranjithkumar R.; Lakshmanan P.; Palanisami N.; Devendran P.; Sudhahar S.; Nallamuthu N.; Thrimurthulu G.; Kim I.T.; Krishna Kumar M.
Inorganic Chemistry Communications, Vol-165
https://doi.org/10.1016/j.inoche.2024.112553 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192789068&doi=10.1016%2fj.inoche.2024.112553&partnerID=40&md5=ac4fd647ff93ff4d5424d3eca233b90a
Ranjithkumar R., Department of Physics, International Research Centre, Kalasalingam Academy of Research and Education, Tamil Nadu, Krishnankoil 626 126, India, Department of Chemistry and Chemical Engineering, Education and Research Center for Smart Energy and Materials, Inha University, Incheon, 22212, South Korea, Department of Chemical and Biological Engineering, Gachon University, Gyeonggi-do, Seongnam-si, 13120, South Korea; Lakshmanan P., Department of Chemistry and Chemical Engineering, Education and Research Center for Smart Energy and Materials, Inha University, Incheon, 22212, South Korea; Palanisami N., Centre for Functional Materials, Vellore Institute of Technology, Tamil Nadu, Vellore, 632 014, India; Devendran P., Department of Physics, International Research Centre, Kalasalingam Academy of Research and Education, Tamil Nadu, Krishnankoil 626 126, India; Sudhahar S., Department of Physics, Alagappa University, Tamil Nadu, Karaikudi, 630 003, India; Nallamuthu N., Dayananda Sagar Academy of Technology and Management, Bangalore, 560 082, India; Thrimurthulu G., Inorganic and Physical Chemistry Division, Indian Institute of Chemical Technology, Telangana, Hyderabad, 500007, India; Kim I.T., Department of Chemical and Biological Engineering, Gachon University, Gyeonggi-do, Seongnam-si, 13120, South Korea; Krishna Kumar M., Department of Physics and Electronics, CHRIST (Deemed to be University), Karnataka, Bengaluru 560 029, India
Contextual Embedding Fusion for Quick-Commerce Reviews: Rating, 5-Class Classification, and Sentiment Analysis Across Classical ML, DL, and Transformer Models
Contextual embedding fusion; E-commerce analytics; Quick-commerce; Rating prediction; Sentiment analysis; Transformer models
Quick-commerce platforms depend on accurate reading of customer feedback to steer pricing and service quality. We study a dataset of delivery-agent reviews, and build two rating predictors and a text-only classifier suite. On the rating regression task (1-5), we benchmark seven models (Ridge, Lasso, ElasticNet, SVR-RBF, Gradient Boosting, Random Forest, XGBoost). The best test performance is achieved by RandomForestRegressor with MAE = 0.527, RMSE = 0.973, and R2 = 0.555; cross-validation places XGBoost and Gradient Boosting close behind. For 5-class rating classification, we compare seven TF-IDF baselines (LogReg, LinearSVC, two SGD variants, RidgeClassifier, ComplementNB, XGBClassifier) and find SGD (hinge) strongest (Accuracy = 0.817, Macro-F1 = 0.389), yet mid-rating classes (2-4) remain difficult. To address label ordinality, we fine-tune DistilBERT with ordinal heads: CORAL and CORN. These achieve Accuracy = 0.837/0.819 and Macro-F1 = 0.439/0.441, improving Macro-F1 by +5-6 points over TF-IDF baselines, mainly by rescuing classes 2-4. We further map regression outputs to 5 classes via optimized cutpoints, raising Macro-F1 from 0.329 (rounding) to 0.380. Paired, fold-wise comparisons using Wilcoxon signed-rank and bootstrap 95% CIs show consistent gains; with 5 folds the discretized p = 0.0625 indicates all folds favor the better model, suggesting statistical significance with more folds. Overall, ordinal transformers and learned thresholds provide measurable, reproducible improvements for short, noisy e-commerce reviews, delivering higher fidelity on mid-range ratings while preserving high accuracy. 2025 IEEE.
Ranka, Monica; Malhotra, Amit; Hada, Bhupendra Singh; Jana, Samiran; Maheshwari, Abhishek; Basha, Md Shaik Amzad
Proceedings of the 2025 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2025;pp.1197-1202
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/AISummit66170.2025.11411126 https://www.scopus.com/pages/publications/105035324628?origin=resultslist
Ranka M., Dayananda Sagar College of Arts, Science and Commerce, Bengaluru, India; Malhotra A., Christ (Deemed to be University), School of Commerce, Finance and Accountancy, Ghaziabad, India; Hada B.S., Christ (Deemed to be University), School of Commerce, Finance and Accountancy, Delhi-NCR, India; Jana S., Soil Institute of Business Design, Haryana, India; Maheshwari A., Christ (Deemed to be University), School of Commerce, Finance and Accountancy, Ghaziabad, India; Basha M.S.A., Christ Academy Institute for Advanced Studies, Gitam School of Business (Deemed to be University), Gandhi Institute of Technology and Managemet, Bangalore, India
ArcGAN: Generative Adversarial Networks for 3D Architectural Image Generation
Albedo; BiGAN; Conditional image generation; FineGAN; GAN inversion; Generative adversarial networks; MixNMatch; StyleGAN; Unsupervised 3D shape reconstruction
Due to advancements in infrastructural modulations, architectural design is one of the most peculiar and tedious processes. As the technology evolves to the next phase, using some latest techniques like generative adversarial networks, creating a hybrid architectural design from old and new models is possible with maximum accuracy. Training the model with appropriate samples makes it evident that the designing phase will be simple for even a layman by including proper parameters such as material description, structural engineering, etc. This research paper suggests a hybrid model for an architectural design using generative adversarial networks. For example, merging Romes architectural style with Italys will accurately and precisely recover the pixel-level structure of 3D forms without needing a 2D viewpoint or 3D annotations from a real 2D-generated image. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Ranpara D.; Raghul V.; Devan A.M.; Shukla S.
Lecture Notes in Electrical Engineering, Vol-1189 LNEE, pp. 75-85.
Ranpara D., Christ (Deemed to Be University), Karnataka, Bangalore, India; Raghul V., Christ (Deemed to Be University), Karnataka, Bangalore, India; Devan A.M., Christ (Deemed to Be University), Karnataka, Bangalore, India; Shukla S., Christ (Deemed to Be University), Karnataka, Bangalore, India
Melatonin seed priming enhances early seedling tolerance to individual and combined abiotic stresses in rice (Oryza sativa L.)
Combined stress; Drought; Melatonin; Priming; Rice
Combined drought and salinity pose a critical constraint to rice (Oryza sativa L.) seedling establishment, often exerting a greater impact than individual stresses such as drought, salinity, or heavy metals. In this study, we evaluated the efficacy of melatonin seed priming (50M) in mitigating the combined effects of drought-salinity stress along with individual drought, salinity, and chromium (Cr) stress during the seedling stage in the indica variety IR64, and validated the findings across three major Indian cultivars (MTU1001, KMP-225 and Jyothi). Melatonin priming significantly mitigated stress-induced root and shoot inhibition. Under combined drought and salinity stress, melatonin-primed shoots (4.17cm) were significantly higher and maintained lower oxidative stress markers, with reduced malondialdehyde and hydrogen peroxide, compared to hydro-primed controls, indicating improved membrane stability and antioxidant protection. Under individual drought, salinity, and chromium stress, melatonin-primed seedlings showed significantly enhanced root and shoot lengths compared to hydro-primed controls, supporting better early growth and resource allocation. Spectral profiling revealed higher chlorophyll content and normalized difference vegetation index values in melatonin-primed seedlings under salinity and chromium stress, indicating improved pigment stability and seedling vigor. Across all genotypes, melatonin conferred consistent benefits, indicating its potential as a low-cost and potentially scalable strategy. These findings highlight melatonin seed priming as a promising approach for improving rice resilience, with significant potential for enhancing crop establishment in stress-prone, resource-limited agroecosystems. Akadiai KiadZrt. 2026.
https://doi.org/10.1007/s42976-026-00803-2 https://www.scopus.com/pages/publications/105036171898?origin=resultslist
Ransi S., Department of Life Sciences, Christ University, Bangalore, India; Thomas T., Department of Life Sciences, Christ University, Bangalore, India, Centre for Landscapes, Wildlife and Marine Ecology (CLIME), Department of Life Sciences, Christ University, Bangalore, India
Finite Element Analysis of Hybrid Skin Sandwich Composite
Aluminium alloy; ANSYS Mechanical APDL; Areca fibre; Sandwich structured composite
Sandwich structured composite is a particular classification in composite materials. This type of structure has been mainly used in recent studies because of its high specific strength, low density, and stiffness. It is increasingly more commonly employed in structural designs due to its features and performance. The sandwich composites used in this investigation are made of aluminium alloys and areca fibre. The sandwich composites face sheet comes in a variety of thicknesses. The adhesive skin layer is also varied to investigate the effect of using natural fibre. The sandwich composite is subjected to 3 point bend test. The modal analysis is investigated using the finite element method. The 3D model of sandwich composites is modelled using solid works 2020. Using Altair Hyper Works, the boundary conditions and meshing is carried out. ANSYS Mechanical APDL is used to analyse the sandwich composites. This investigation analyses the behaviour of composite sandwich beams. 2022, Books and Journals Private Ltd.. All rights reserved.
Rao A.; Niranjana S.J.; Shivakumar S.; Kiran K.; Sharathchandra; Naresh H.; Sampath H.P.
Journal of Mines, Metals and Fuels, Vol-70, No. 8, pp. 188-199.
All Open Access; Hybrid Gold Open Access
https://doi.org/10.18311/jmmf/2022/32041 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85170033899&doi=10.18311%2fjmmf%2f2022%2f32041&partnerID=40&md5=018954b5ef17f4434ec02be2d6cf4354
Rao A., Department of Mechanical and Automobile Engineering, CHRIST University, Bangalore, 560074, India; Niranjana S.J., Department of Mechanical and Automobile Engineering, CHRIST University, Bangalore, 560074, India; Shivakumar S., Department of Mechanical and Automobile Engineering, CHRIST University, Bangalore, 560074, India; Kiran K., Department of Mechanical and Automobile Engineering, CHRIST University, Bangalore, 560074, India; Sharathchandra, Department of Mechanical Engineering, NMAM Institute of Technology, Karnataka, Nitte, 574110, India; Naresh H., Department of Mechanical Engineering, Siddaganga Institute of Technology, Karnataka, Tumkur, 572103, India; Sampath H.P., Department of Mechanical Engineering, VTU, Karnataka, Bangalore, India
An outlook on zero-dimensional nanocarbons as components of DSSC
Carbon dots; Counter electrode; Dye-sensitized solar cells; Photoanodes; Sensitizers
Solar energy is an abundant source of energy, and harnessing the suns radiation with an efficient solar cell can be a promising technology for a limitless supply of sustainable energy. The amount of solar power that reaches the earth is beyond the worlds energy consumption. But, the main cause for minimal usage of the suns energy is the complicated technology, restricted band gap, high-temperature instability, and high cost of production. Likewise, the usage of space and infrastructure required for the installation of solar cells is yet another reason for limited usage. Upon comparing the emerging photovoltaics, DSSC (dye-sensitized solar cells) can be a solution for the drawbacks faced by the older generation solar cells which has greater future scope as an energy harvester. Rapid technological growth over the years, usage of affordable materials, and capability of working efficiently in low lighting conditions make DSSC a commercially viable and potent solar energy harvester. Furthermore, its efficiency can be improved with the inclusion of low-dimensional nanocarbons in various components of DSSC. Therefore, this review describes the mechanisms of improving the performance of zero-dimensional nanocarbons and their application in components of DSSC alternative to conventional materials. The significant impact of surface functionalization of low-dimensional nanocarbon on the performance of dye-sensitized solar cells is also discussed. Graphical abstract: (Figure presented.) The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
Biomass Conversion and Biorefinery, Vol-14, No. 8, pp. 9023-9045.
Rao A.A., Department of Physics and Electronics, Christ University, Karnataka, Bengaluru, 560029, India; Joseph A.P., Department of Media Studies, Christ University, Karnataka, Bengaluru, 560029, India; Balachandran M., Department of Physics and Electronics, Christ University, Karnataka, Bengaluru, 560029, India
Enhanced visible light harvesting in dye-sensitized solar cells through incorporation of solution-processable silver plasmons and anthracite-derived graphene quantum dots
Anthracite coal; Dye-sensitized solar cells; Graphene quantum dots; Linear sweep voltammetry; Open circuit voltage decay; Surface plasmon resonance
The major setback for the enhanced performance of DSSC is the narrow absorption window and the interfacial exciton recombination. Therefore, in this work, the photovoltaic performance of dye-sensitized solar cells has been improved by the synergistic effect of anthracite-derived graphene quantum dots and silver plasmons. GQD and Ag coupled photoanodes were fabricated by a facile solution processable process under room temperature. The as-fabricated DSSC TiO2/Ag/GQD (TAG) exhibited an enhanced power conversion efficiency of 10.5 % with a current density of 22.40 mAcm?2 measured under solar irradiation of 100 mWcm?2 with AM 1.5G. An enhancement surpassing 30.5 % was obtained for the champion cell when compared to the pristine TiO2 based DSSC. Furthermore, this study emphasizes developing a cutting-edge approach for the high-quality use of fossil fuel-derived graphene quantum dots in energy conversion systems, thereby encouraging the green conversion of fossil fuels and broadening the potential of anthracite coal's utilization in energy conversion applications. 2024 Elsevier Ltd
Rao A.A.; Narendhiran S.; Balachandran M.
https://doi.org/10.1016/j.mtphys.2024.101512 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198256901&doi=10.1016%2fj.mtphys.2024.101512&partnerID=40&md5=4bd0620f3064ce302c8d7a7b1996503c
Rao A.A., Department of Physics and Electronics, Christ (Deemed to be University), Karnataka, Bengaluru, 560029, India; Narendhiran S., Department of Physics and Electronics, Christ (Deemed to be University), Karnataka, Bengaluru, 560029, India; Balachandran M., Department of Physics and Electronics, Christ (Deemed to be University), Karnataka, Bengaluru, 560029, India