IoT based hybrid patient health monitoring system
ECG Sensor; Internet of Things; Patient Monitoring; Pulse Sensor; Temperature Sensor; Thing speak
According to the data released by the World Health Organization (WHO). Cardiovascular diseases (CVDs) are still the leading thread worldwide. Every year around 17.9 million fatalities worldwide are attributed to CVDs, making up 31% of overall deceases. Heart attacks and strokes are the main causes account for most of these deaths, and a large fraction happen so early in the age group of under 70. Check-ups are essential to monitor the healthcondition of the elderly people, which generates a considerable challenge to the existing medical field. Subsequently,It's becoming more crucial to diagnose diseases quickly and accurately with an affordable cost. The World's population growth need for a smart and affordable healthcare solutions to reduce the medical costs. Thus, the development of an effective health monitoring system that can quickly identify irregularities in health and provide precise diagnoses based on gathered data is imperative. New developments in cloud computing and mobile technologies have led to the development of a number of cloud-based healthcare products and services. These cloud systems allow for the automatic collection and transmission of medical data to providers from any place, allowing for the network-based delivery of patient feedback. This project's goal is to use the ThingSpeak IoT platform to create and implement an Internet of Things-based patient health monitoring system in order to meet these goals. 2026 Author(s).
Chithra, R. Baby; Neethu, P.S.; Patil, Kavita Avinash; Anju, M.I.; Joy, Salna; Partheeban
AIP Conference Proceedings;Volume;3345;Issue;1;Article No.;20244;
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1063/5.0298976 https://www.scopus.com/pages/publications/105028292982?origin=resultslist
Chithra R.B., New Horizon College of Engineering, Karnataka, Bangalore, India; Neethu P.S., School of Engineering and Technology, CHRIST University, Karnataka, Bangalore, India; Patil K.A., M S Ramaiah Institute of Technology, Karnataka, Bangalore, India; Anju M.I., SRM Institute of Science and Technology, Vadapalani Campus, Tamilnadu, Chennai, India; Joy S., New Horizon College of Engineering, Karnataka, Bangalore, India; Partheeban, School of Computing Science & Engineering (SCSE), Galgotias University, Greater Noida, India
IoT Enabled Energy Optimization Through an Intelligent Home Automation
The benefit of IoT devices is that they allow for automation; nevertheless, billions of connected devices connected with one another waste a substantial amount of energy. IoT systems will have difficulty in wide adoption if the energy requirements are not adequately managed. This study proposes a solution for IoT devices to regulate their energy consumption. Both hardware and software aspects are taken into consideration. Using a mobile computer or smartphone with Internet connectivity to interact with actual scenarios has grown more prevalent as technology has advanced over the years. An intelligent home automation system based on android applications has been developed to save electricity and human energy. This study aims to create comprehensive Energy optimization through intelligent home automation utilizing widely available mobile applications and Wi-Fi technologies. The devices are turned on and off using Wi-Fi. Intelligent home, in the area of electronics, automation is the most purposely misused term. Numerous technological revolutions have occurred as a result of this demand for automation. These were more essential than any other technologies due to their ease of use. These can be used in place of household current switches, resulting in sparks and, in rare instances, such as fires. A unique energy optimization system was developed to control household appliances while taking advantage of Wi-Fi benefits. 2023, Bentham Books imprint.
Chitra Kiran N.; Viswanatha Rao J.; Aurelia S.; Skanda M.G.; Lakshminarayana M.
Pragmatic Internet of Everything (IOE) for Smart Cities: 360-Degree Perspective, pp. 108-124.
Chitra Kiran N., Department of Electronics and Communication Engineering, Alliance University, Karnataka, Bengaluru, India; Viswanatha Rao J., VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, India; Aurelia S., Department of Computer Science, CHRIST University, Karnataka, Bengaluru, India; Skanda M.G., Department of Industrial & Production Engineering, JSS S&T University, SJCE, Mysore, India; Lakshminarayana M., Department of Medical Electronics Engineering, M S Ramaiah Institute Of Technology, Karnataka, Bengaluru, India
SARIMA-Random Forest Framework for Forecasting Anthracnose Severity in Bottle Gourd Under Variable Transplanting Dates
artificial intelligence; climate-resilient disease management; disease forecasting; epidemiological modelling; machine learning; time series modelling
Anthracnose, caused by Colletotrichum lagenarium, is an economically important disease affecting bottle gourd. This study aimed to evaluate the influence of transplanting time and weather parameters on anthracnose progression and to develop a forecasting framework using statistical and machine-learning models. Field experiments were conducted during the monsoon seasons of 2023 and 2024, with four transplanting dates: 1 June, 15 June, 1 July and 15 July. Disease severity was assessed weekly on leaves and fruits along with concurrent recording of weather data. Correlation and regression analyses revealed minimum temperature as the most influential weather variables, particularly during early transplanting dates. The regression models yielded the highest explanatory power for 1 June fruits (R2 = 0.675), while later transplanting dates showed reduced disease pressure and lower model accuracy. To capture seasonal trends and short-term predictability, Seasonal Autoregressive Integrated Moving Average (SARIMA) models with configuration (1,1,1) (1,1,1) [15] were applied. These models effectively forecasted disease progression, especially for July transplanting with lower mean squared errors (MSE < 200). Time series decomposition showed strong seasonal and trend components in early sowings, while cross-correlation analysis confirmed a 13-week lag between weather triggers and disease expression. This study emphasises the importance of transplanting time in disease development and demonstrates the potential of combining SARIMA and random forest models for developing weather-based early warning systems. These findings contribute to climate-resilient crop protection strategies and can aid in timely decision-making for anthracnose management in bottle gourd and related cucurbits. 2025 British Society for Plant Pathology.
Chittaragi, Amoghavarsha; Patil, Balanagouda; Manjesh, M.; Sridhar, S.; Hurakadli, Manjunath S.; Praveenakumar, R.; Dharamveer, S. Duhan; Kumar, Anil; Kumar, Rakesh; Mohan, Man; Kasniya, Pawan Kumar
Plant Pathology;Volume;75;Issue;1;Article No.;e70093;
https://doi.org/10.1111/ppa.70093 https://www.scopus.com/pages/publications/105021523092?origin=resultslist
Chittaragi A., Department of Plant Pathology, College of Agriculture, Chaudhary Charan Singh Haryana Agricultural University, Haryana, Hisar, India, ICAR-KVK, Chintamani, University of Agricultural Sciences, Karnataka, Bangalore, India; Patil B., Department of Plant Pathology, Keladi Shivappa Nayaka University of Agricultural and Horticultural Sciences, Karnataka, Shivamogga, India; Manjesh M., Department of Life Sciences, Christ University, Bengaluru, India; Sridhar S., Centre for Climate Resilient Agriculture, Keladi Shivappa Nayaka University of Agricultural and Horticultural Sciences, Karnataka, Shivamogga, India; Hurakadli M.S., Department of Plant Pathology, College of Agriculture, Chaudhary Charan Singh Haryana Agricultural University, Haryana, Hisar, India; Praveenakumar R., ICAR-KVK, Chintamani, University of Agricultural Sciences, Karnataka, Bangalore, India; Dharamveer S.D., Department of Vegetable Sciences, Chaudhary Charan Singh Haryana Agricultural University, Haryana, Hisar, India; Kumar A., Department of Nematology, College of Agriculture, Chaudhary Charan Singh Haryana Agricultural University, Haryana, Hisar, India; Kumar R., Department of Plant Pathology, College of Agriculture, Chaudhary Charan Singh Haryana Agricultural University, Haryana, Hisar, India; Mohan M., Department of Plant Pathology, College of Agriculture, Chaudhary Charan Singh Haryana Agricultural University, Haryana, Hisar, India; Kasniya P.K., Department of Plant Pathology, College of Agriculture, Chaudhary Charan Singh Haryana Agricultural University, Haryana, Hisar, India
Structure and mechanism of zinc-finger nucleases-mediated genome editing in plants
Crop plants; Endonuclease enzyme; Genome editing; Zinc-finger nucleases
The chapter provides a comprehensive exploration of zinc-finger nucleases (ZFNs), intricate molecular tools designed to integrate genes of interest into target sites, thereby instigating both phenotypic and genotypic transformations. The focal point of this genetic manipulation lies in the activity of the FokI endonuclease enzyme, which, through the formation of homodimers, orchestrates precise cleavage at integration sites. Leveraging the wealth of genomic information across diverse organisms, the chapter elucidates the mechanistic underpinnings of ZFNs. A particular emphasis is placed on the modular assembly of ZFNs, unravelling the formation of ??? motifs and explicating the nuanced mechanisms governing their actions. The application of this methodology in plant engineering holds paramount significance, particularly in the realm of augmenting stress tolerance and nutritional value. The chapter systematically examines the gene of interest for various plants, including tobacco, soybean, Arabidopsis thaliana, rice, and wheat, elucidating the corresponding ZFN mechanisms. The amalgamation of sophisticated genetic tools with a detailed understanding of plant genomes presents a promising avenue for tailoring crops to meet diverse agricultural challenges. This work not only contributes to the fundamental understanding of ZFNs but also underscores their practical implications in advancing crop improvement strategies for sustainable agriculture. CAB International 2025. All rights reserved.
Chivukula, Jahnavi; Ponnachan, Blessy; Kumar, Dipanshu; Nagella, Praveen
Genome Editing for Crop Improvement: Theory and Methodology;pp.35-46
https://doi.org/10.1079/9781800622517.0003 https://www.scopus.com/pages/publications/105003982530?origin=resultslist
Chivukula J., Department of Life Sciences, School of Sciences, CHRIST (Deemed to be University), Bangalore, India; Ponnachan B., Department of Life Sciences, School of Sciences, CHRIST (Deemed to be University), Bangalore, India; Kumar D., Department of Life Sciences, School of Sciences, CHRIST (Deemed to be University), Bangalore, India; Nagella P., Department of Life Sciences, School of Sciences, CHRIST (Deemed to be University), Bangalore, India
Pneumonia classification from chest X-rays using significant feature selection and machine learning
Feature selection; Machine learning; Pneumonia; Radiomics; Wavelet features; X-ray
The chest X-ray images of normal lungs differ only subtly from those of lungs with pneumonia, making image-based diagnosis highly challenging. To address this issue, we developed a machine learning (ML)-based, lightweight, end-to-end Python package that processes chest X-ray images, implements robust feature selection methods, and classifies the images using various algorithms. While many studies have focused on improving classification accuracy using newer methods, few have addressed the interpretability of the extracted features or the growing computational demands of complex models. We used four publicly available datasets and extracted first-order, textural, and transform-based radiomic features to test our package. Features were selected using the Shapley additive explanations (SHAP) combined with recursive feature elimination (RFE) and stability selection algorithms. Our final solution contains a method that extracts a finite set of features identified by stability selection and feeds them as inputs into classical ML algorithms. Our model achieved 98% accuracy on the primary dataset, and 97%1, 96%2, and 94%2% accuracy on the other three datasets. Our approach is fast, self-contained, and requires only an ideal set of features, making it suitable for resource-constrained clinical environments. This is an open access article under the CC BY-SA license. https://creativecommons.org/licenses/by-sa/4.0/
Chodagam, Yugandhar; Hiremath, Manjunatha
IAES International Journal of Artificial Intelligence;Volume;15;Issue;1;pp.592-603
Institute of Advanced Engineering and Science
https://doi.org/10.11591/ijai.v15.i1.pp592-603 https://www.scopus.com/pages/publications/105031500747?origin=resultslist
Chodagam Y., Department of Computer Science, Christ University, Bengaluru, India; Hiremath M., Department of Computer Science, Christ University, Bengaluru, India
HCI Authentication to Prevent Internal Threats in Cloud Computing
Biometric authentication; Cloud computing; FIDO; HCI; Multi-authentication
Cloud computing reduces physical resources and simplifies common management tasks. Over the past decade, cloud computing has become an important IT (information technology) industry, driving cost savings, flexibility, convenience, and scalability. Despite these advantages, many government organizations and companies are still cautious about using cloud computing. They continue to believe that the threats inherent in cloud computing technology are greater and deadly than traditional technologies. Cloud computing security threats typically include insider attacks, malware attacks, information leaks and losses, distributed denial of service, and application programming interface vulnerability attacks. Technical security improvements for virtual networks are actively researched, and many are working hard. But defending against internal attackers is more than just a technical solution but a complement to manuals and company policy. In reality, however, there are cases of damage by internal attackers, and the damage is getting bigger. Technically malicious internal attackers can relatively easily manipulate the control system and cause malfunctions. This paper provides comprehensive information about security threats in cloud computing, shows the severity of attacks by insiders, analyzes the latest authentication technologies for humancomputer interaction, and identifies the pros and cons. This shows how HCI (humancomputer interaction) technology can be applied to cloud computing management servers. The result is an innovative security certification model that can be applied. 2020, Springer Nature Switzerland AG.
EAI/Springer Innovations in Communication and Computing, pp. 119-135.
Springer Science and Business Media Deutschland GmbH
Choe H., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India; Aurelia S., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India
Advancing Tibetan Text-to-Speech: Challenges and Innovations
FastPitch; Fine-tuned machine learning models; HiFi-GAN; Phonetic adaptation; Text to audio processing; Tibetan Text-to-Speech
This initiative aims to develop a platform for Tibetan Text-to-Speech (TTS) technology, addressing the significant demand for this technology for the Tibetan language. The main objective of this project is to create a system that is capable of converting text into natural and good quality speech. Through the compilation of Tibetan text-audio datasets, the project meets the increasing demand for technology that preserves oral traditions and allows Tibetans to communicate with other people interested in the language. The process includes the gathering of varied Tibetan text and audio samples, such as news articles, followed by processing of data through cleaning processes and statistical analysis. A benchmark dataset is created to enable the testing of models. The lack of certain resources for Tibetan TTS is addressed by the development of pre-trained machine learning models specific to acoustic modeling, using the adapted FastPitch model for waveform synthesis through the HiFi-GAN vocoder. The existing models were?further trained utilizing features particular to Tibetan phonetics and tonalities. The TTS approach is a key strategy for improving digital accessibility for Tibetan speakers and for safeguarding their cultural heritage; it finds applications in media, education, and communication, thus helping to preserve the Tibetan language in the digital era. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
Choedon, Tenzin; Nagaraju, Shamanth; Rekha, V.
Lecture Notes in Networks and Systems;Volume;1737 LNNS;pp.182-192
https://doi.org/10.1007/978-981-95-5082-1_18 https://www.scopus.com/pages/publications/105028258192?origin=resultslist
Choedon T., Department of Computer Science and Engineering, School of Technology and Engineering, Christ University, Bangalore, India; Nagaraju S., Christ University, Bangalore, India; Rekha V., Christ University, Bangalore, India
Drought PredictionA Comparative Analysis of Supervised Machine Learning Techniques
Artificial neural network; Decision tree; K-nearest neighbors; Machine learning; Random forest; Supervised; Support vector machine
Drought is a natural phenomenon that puts many lives at risk. Over the last decades, the suicide rate of farmers in the agriculture sector has increased due to drought. Water shortage affects 40% of the world's population and is not to be taken lightly. Therefore, prediction of drought places a significant role in saving millions of lives on this planet. In this research work, six different supervised machine learning (SML) models namely support vector machine (SVM), K-nearest neighbor (KNN), decision tree (DT), convolutional neural networks (CNNs), long short-term memory (LSTM), and recurrent neural networks (RNNs) are compared and analyzed. Three dimensionality reduction techniques principal component analysis (PCA), linear discriminant analysis (LDA), and random forest (RF) are applied to enhance the performance of the SML models. During the experimental process, it is observed that RNN model yielded better accuracy of 88.97% with 11.26% performance enhancement using RF dimensionality reduction technique. The dataset has been modeled using RNN in such a way that each pattern is reliant on the preceding ones. Despite the greater dataset, the RNN model size did not expand, and the weights are observed to be shared between time steps. RNN also employed its internal memory to process the arbitrary series of inputs, which helped it outperform other SML models. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Choesang T.; Ryntathiang S.; Jacob B.A.; Krishnan B.; Kokatnoor S.A.
Smart Innovation, Systems and Technologies, Vol-351, pp. 295-307.
Choesang T., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India; Ryntathiang S., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India; Jacob B.A., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India; Krishnan B., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India; Kokatnoor S.A., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India
Victimizing Cyberbullying Mental Illness Through Social Media
With the exponential increase of technologies and the growth of social media users, bullying takes different methods to reach its targets. Cyberbullying has been emerging lately in the form of bullying through voice memos, videos, and most frequently in the form of text messages. Bullies might use the rich and expansive environment that social networks offer to target their victims with their attacks. Cyberbullying has an especially negative impact on younger generations, who value social affirmation above everything. Studies have found a clear link between cyberbullying and suicidal ideation, particularly in teens. This concerning trend needs the development of efficient ways for identifying and combating cyberbullying, thereby protecting young lives. Many techniques can be used to identify the bullies linguistic patterns and create a detection model that will automatically identify instances of cyberbullying and whether it can lead to the happening of suicide or not. This project proposes a hybrid model of BiLSTM and EmoBERTa for detecting cyberbullying and checking the possibilities of it leading to the happenings of suicide. The dataset was run on different models and the proposed model yields the best average performance. By putting such detecting mechanisms in place, we can make the Internet safer. Early detection of cyberbullying enables intervention, which protects vulnerable people and may avoid disasters. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Smart Innovation, Systems and Technologies;Volume;413 SIST;pp.291-303
https://doi.org/10.1007/978-981-97-7717-4_20 https://www.scopus.com/pages/publications/86000439282?origin=resultslist
Choesang T., Christ (Deemed to Be University), Karnataka, Bangalore, India; Gokulapriya R., Christ (Deemed to Be University), Karnataka, Bangalore, India
Novel magnetic nanocomposites and their environmental applications
Environmental contamination by numerous emerging pollutants including pharmaceuticals, microplastics, and pesticides residues is one of the greatest problems facing the world today. The release of these pollutants into the air, water, and soil causes serious threat to plants and animals. These contaminants enter the food chain through contaminated agricultural produce and animals, posing a threat to human health. Therefore, there is an urgent need to develop novel methods to detect, degrade, and remove toxic environmental pollutants. Recently, nanomaterials have been widely used in various applications as catalysts, sensors, and adsorbents due to their unique outstanding properties. This chapter, therefore, focuses on the recent application of magnetic nanoparticles and their respective nanocomposites as degradation catalysts, adsorbents, and electrochemical sensors for detection and removal of environmental pollutants. 2024 Elsevier Ltd. All rights reserved.
Chokkareddy R.; Kilele J.C.; Kanchi S.; Redhi G.G.; Kabane B.; Katari N.K.
Magnetic Nanoparticles and Polymer Nanocomposites: Fundamentals and Biological, Environmental and Energy Applications, pp. 403-414.
Chokkareddy R., Department of Chemistry, Durban University of Technology, Durban, South Africa, Department of Chemistry, Aditya College of Engineering, Andhra Pradesh, Surampalem, India; Kilele J.C., Department of Chemistry, Durban University of Technology, Durban, South Africa; Kanchi S., Department of Chemistry, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Redhi G.G., Department of Chemistry, Durban University of Technology, Durban, South Africa; Kabane B., Department of Chemistry, Durban University of Technology, Durban, South Africa; Katari N.K., Department of Chemistry, School of Science, GITAM Deemed to be University, Telangana, Hyderabad, India
Recent Enzyme Discovery: Engineering Strategies for Biocatalysis and Its Applications
Direct evaluation; Protein engineering; Supercritical fluids; Biocatalysis
Biocatalysis has been growing over the past 30years because of the developments in many technical fields. Due to their biocompatibility, selectivity, and specificity in material, ability to continue operating under temperature and pH conditions, and relatively high interaction, biocatalysts have become significant replacements for conventional catalytic reactions. Several methods, such as enzyme structural improvements (such as protein engineering, direct evaluation), engineering approaches (such as electrode materials, supercritical fluid extraction), and sensory preservation (such as encapsulation, CLEAS), have been developed, which together are dominant instruments in the improvement of biotransformation and the synthesis of new products. This chapter summarizes the benefits of applying a performance-based approach to biocatalyst invention and engineering in cell culture for enhancing their development in critical modules and separation. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
Chokkareddy, Rajasekhar; Adireddy, Ramesh; Prasanthi, Rayudu; Kanchi, Suvardhan; Bathinapatlad, Ayyappa; Redhi, Gan G.
Nanotechnology in the Life Sciences;Volume;Part F1218;pp.233-253
Springer Science and Business Media B.V.
https://doi.org/10.1007/978-3-032-08576-4_10 https://www.scopus.com/pages/publications/105026572345?origin=resultslist
Chokkareddy R., Department of Chemistry, Durban University of Technology, Durban, South Africa, Department of Chemistry, Aditya University, Andhra Pradesh, Surampalem, India; Adireddy R., Department of EEE, Aditya University, Andhra Pradesh, Surampalem, India; Prasanthi R., Department of ECE, Aditya University, Andhra Pradesh, Surampalem, India, Electronics and Communications Engineering Department, JNTUK, Kakinada, India; Kanchi S., Department of Chemistry, CHRIST (Deemed to be University), Bengaluru, India; Bathinapatlad A., Department of Chemistry, CMR Institute of Technology, Bengaluru, India, Centre of Excellence-Sensors & Nanoelectronics, CMR Institute of Technology, Bengaluru, India; Redhi G.G., Department of Chemistry, Durban University of Technology, Durban, South Africa
Sustainable Nanomaterials for Treatment and Diagnosis of Infectious Diseases
The book focuses on the design and novel synthetic routes of sustainable nanomaterials in diagnosing and treating infectious diseases offering potential benefits in terms of efficiency, biocompatibility, and environmental impact. The fifteen chapters in this book provide a comprehensive exploration of how sustainable nanotechnology can revolutionize infectious disease management and bridge the gap between the fundamental principles of nanotechnology and their practical applications in combating infectious diseases. Subjects covered include: the rise of multidrug-resistant pathogens and the limitations of existing therapies; the challenges of infectious disease management including the rise of multidrug-resistant pathogens and the limitations of existing therapies; nano-pharmacology and pharmacotherapeutics in the treatment of infectious diseases; the advancements in nanomaterial-based drug delivery systems, vaccines, and diagnostic tools, and the future of personalized medicine; nanotheranostic mechanisms outlining how nanomaterials can be engineered to simultaneously diagnose and treat infections; nano drug delivery systems that contribute to enhancing the efficacy and precision of treatment modalities; biocompatibility and toxicity of nanomaterials in the diagnosis and treatment of infectious diseases; regulatory perspectives of nanomaterials ensuring they meet safety and efficacy standards; green synthesis of reduced graphene oxide, carbon dots, and its composites for infectious diseases and biosurfactants; nanomaterials of polymeric design underscoring their potential to create more effective and sustainable therapeutic options; how nanomaterials can be engineered to simultaneously diagnose and treat infections. Audience The book targets nanotechnology researchers, scientists, and healthcare professionals interested in understanding nanotechnologys critical role in sustainable healthcare solutions. 2025 Scrivener Publishing LLC.
Chokkareddy, Rajasekhar; Kanchi, Suvardhan; Redhi, Gan G.
Sustainable Nanomaterials for Treatment and Diagnosis of Infectious Diseases;pp.1-441
https://doi.org/10.1002/9781394200559 https://www.scopus.com/pages/publications/85217992785?origin=resultslist
Chokkareddy R., Department of Chemistry, Aditya University, Andhra Pradesh, India, Department of Chemistry, Durban University of Technology, Durban, South Africa; Kanchi S., Department of Chemistry, CHRIST University, Bengaluru, India; Redhi G.G., Department of Chemistry, Durban University of Technology, Durban, South Africa
Green synthesis of Cobalt Oxide nanoparticles with in-vitro cytotoxicity assessment using pomegranate (Punica granatumL.) seed oil: A promising approach for antimicrobial and anticancer applications
anticancer cytotoxicity; antimicrobial; cobalt oxide nanoparticles; green synthesis; Punica granatum seed oil
Green synthesis of nanoparticles and their pharmacological implementation have gained importance in the field of nanotechnology. This study primarily aims to explore the use of Punica granatum L. seed oil as a reducing agent for the synthesis of cobalt nanoparticles, making it both economically and pharmacologically valuable. Gas chromatography-mass spectroscopy analysis was carried out to study the active metabolites present in P. granatum seed oil. The green synthesis of cobalt nanoparticles was established based on the color change of the reaction mixture from dark green to light green. These particles showed a ?max at 279.88 nm for UV-visible spectrometry analysis. Furthermore, X-ray Diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), Field Emission Scanning Electron Microscope (FE SEM) and Dynamic Light Scattering (DLS) were performed to confirm the nature of these nanoparticles. The pharmacological potential of these cobalt oxide nanoparticles was tested against microbial pathogens. The results suggest that these nanoparticles exhibited significant activity against various human bacterial and fungal pathogens. Additionally, in in vitro cytotoxicity analysis, demonstrated that CoONPs selectively targeted MCF-7 cancer cells with a significant IC50 value compared to non-cancerous cells (L929). In conclusion, this study demonstrated that green synthesized CoONPs using P. granatum show significant potential against eukaryotic cancer cells and microbial pathogens. Furthermore, this study has implications for medical research centers and pharmaceutical industries in addressing modern challenges such as increasing antibiotic resistance in communities. 2024 Horizon e-Publishing Group. All rights reserved.
Plant Science Today, Vol-11, No. 2, pp. 221-232.
https://doi.org/10.14719/pst.3014 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191861268&doi=10.14719%2fpst.3014&partnerID=40&md5=4ed00611d58d4975de3e1b02cb85b857
Chole P.B., Department of Life Sciences, CHRIST (Deemed to be University), Bangalore, 560 029, India; Bt M., Department of Life Sciences, CHRIST (Deemed to be University), Bangalore, 560 029, India
Nutritional, biochemical and antioxidant activities of edible and non-edible parts of Punica granatum L.
antioxidative properties; nutritional; phytochemical; Punica granatum L
In the present study, the nutritional profiling and antioxidant analysis of the different parts of Punica granatum was done. In the nutritional profiling, different percentages of moisture content was found in the flower (9.63%), leaf (9.17%), peel (5.34%), root (8.08%), stem (4.09%) and fruit (3.54%). Ash content was recorded higher in the stem (30%/g) followed by root, leaf, flower, fruit and peel. Also, the major and minor elements like nitrogen, potassium, calcium, phosphorus, magnesium, sulphur, zinc, copper, manganese and iron were analysed in different parts of P. granatum. The fruit recorded the highest amount of nitrogen (5.710.01%) and phosphorus (5.710.01%) whereas peel was recorded with more potassium (0.990.01%). The phytochemical quantification showed the major content of carbohydrates in the flower (317.96 mg/g) and leaf (315.62 mg/g). The protein was recorded higher in fruit (69 mg/g) and proline in root (19.54 mg/g). The TPC was recorded more in the peel (240.72 g/g) followed by the flower (223.05 g/g). P. granatum peel was recorded with maximum flavonoid content (873.13 g/g) and had a higher antioxidative response in comparison to other plant parts of P. granatum. 2024, Indian journals. All rights reserved.
Medicinal Plants, Vol-16, No. 1, pp. 182-190.
Chole P.B., Department of Life Sciences, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India; Manjunath B.T., Department of Life Sciences, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India
A Study on Nutritional, Biochemical and Pharmacological Property of Punica grantum L.
Biologically active components present in different medicinal plants which protects the human from diseases and allow health benefits. In the present study, the nutritional, biochemical and newlinepharmacological analysis of the different parts of Punica granatum var Bhagwa was done. In the nutritional profiling, dry moisture content was found high in the flower (9.63%) followed by leaf, peel, root, stem and fruit. Ash content was recorded higher in the stem (30%/gm) followed by root, leaf, flower, fruit and peel. Also, the macro and microelements present in different parts of newlineP. granatum var Bhagwa were analyzed. The fruit recorded the highest amount of nitrogen and phosphorus whereas the peel was recorded with more potassium. The phytochemical newlinequantification showed the major content of carbohydrates in the flower (317.96 mg/g) and leaf (315.62 mg/g). The protein in fruit (69 mg/g) and proline in root (19.54 mg/g) were recorded. P. granatum peel was recorded with maximum phenolic and flavonoid content. It showed a high antioxidative response in comparison to other plant parts. This study also aims to explore the use of P. granatum seed oil as a reducing agent for the synthesis of cobalt nanoparticles. These cobalt particles showed a and#955;max at 279.88 nm for UV-visible spectrometry analysis. Furthermore, X-ray Diffraction, Fourier Transform Infrared Spectroscopy, Field Emission Scanning Electron Microscope and Dynamic Light Scattering were performed to confirm the nature of these nanoparticles. The pharmacological potential of these cobalt oxide nanoparticles was tested against microbial pathogens. The results suggest that these nanoparticles exhibited significant newlineactivity against various human bacterial and fungal pathogens. Additionally, in vitro cytotoxicity analysis of CoONPs had targeted MCF-7 cancer cells with a significant IC50 value compared to non-cancerous cells (L929). This study concluded that Bhagwa variety of P.
Decoding Big Data: The Essential Elements Shaping Business Intelligence
Big Data Analytics; Business Intelligence(BI); Competitive Advantage; Modern Enterprises
In today's Business Intelligence (BI) world, Big Data Analytics integration has become critical, transforming company strategy and decision-making processes. This study investigates the complex influence of Big Data on business intelligence, focusing on important drivers of this transition. It investigates how Big Data's improved data processing capabilities, integration of advanced analytics techniques such as machine learning, and real-time data insights enable businesses to make more informed decisions and achieve a competitive advantage. Furthermore, the paper emphasizes the importance of personalized consumer insights, operational savings, and strategic benefits obtained from predictive analytics when adopting Big Data for BI. 2024 IEEE.
Chopra D.; Shekhar S.; Christopher A.; Vinay M.; Kavitha S.
8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/CSITSS64042.2024.10816944 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85216927014&doi=10.1109%2fCSITSS64042.2024.10816944&partnerID=40&md5=12614261a431a40ec7c979dfce6eaf3c
Chopra D., Christ University, Department of Computer Science, Bangalore, India; Shekhar S., Christ University, Department of Computer Science, Bangalore, India; Christopher A., Christ University, Department of Computer Science, Bangalore, India; Vinay M., Christ University, Department of Computer Science, Bangalore, India; Kavitha S., Christ University, Department of Computer Science, Bangalore, India
Multi-Agent Deep Reinforcement Learning for Hybrid Motion Planning in Dynamic Environments
Autonomous agents; Distributed algorithms; Dynamic obstacles; Multi-agent systems; Trajectory optimization
This research presents a novel approach to addressing the challenges of gesture forecasting in impenetrable and dynamic atmospheres by integrating a hybrid algorithm within a multi-agent system framework. Traditional methods such as Force-based motion planning (FMP) & deep reinforcement learning (RL) often struggle to handle complex scenarios involving multiple autonomous agents due to their inherent limitations. To overcome these challenges, we propose a hybrid algorithm that seamlessly combines the strengths of RL and FMP while leveraging the coordination capabilities of a multi-agent system. By integrating this hybrid algorithm into a multi-agent framework, we demonstrate its effectiveness in enabling multiple agents to navigate densely populated environments with dynamic obstacles. Through extensive simulation studies, we illustrate the superior performance of our approach compared to traditional methods, achieving higher success rates and improved efficiency in scenarios involving simultaneous motion planning for multiple agents. A hybrid motion planning algorithm is also introduced in this very research. Performance Comparison of Hybrid Algorithm, Deep RL, and FMP are also discussed in the result section. This research paves the way for the development of robust and scalable solutions for motion planning in real-world applications such as collaborative robotics, autonomous vehicle fleets, and intelligent transportation systems. 2024 IEEE.
Chopra J.; Avhad P.; Ekatpure S.R.; Rallapalli M.; Kumari S.; Upreti K.
2024 International Conference on Emerging Trends in Networks and Computer Communications, ETNCC 2024 - Proceedings, pp. 235-241.
Chopra J., SEAS, GWU, Washington, DC, United States; Avhad P., Computer Engineering, Veermata Jijabai Technological Inst., Mumbai, India; Ekatpure S.R., Industrial Engineering, Kulicke and Soffa, PA, United States; Rallapalli M., CSE - AI, JAIN University, Bengaluru, India; Kumari S., HSBC, Maharashtra, India; Upreti K., Computer Science Engineering, CHRIST University, Delhi Ncr, India
Central Bank Digital Currencies (CBDC) as Catalysts for Financial Inclusion and Economic Expansion in Emerging Asian Economies
This chapter examines the potential of Central Bank Digital Currencies (CBDCs) to enhance financial inclusion and economic growth in emerging economies. Unlike decentralized cryptocurrencies, CBDCs are government- backed digital currencies that offer secure and regulated alternatives to traditional money. The chapter explores how CBDCs can improve access to financial services, reduce transaction costs, and promote financial stability, particularly in countries with limited banking infrastructure. It also discusses the challenges and risks associated with CBDCs, such as financial stability concerns and the displacement of commercial banks. Through case studies from China and India, the chapter highlights how well- designed CBDCs can act as a catalyst for inclusive economic development, while emphasising the need for robust regulatory frameworks for successful implementation. 2026 by IGI Global Scientific Publishing. All rights reserved.
Chopra, Akarshi; Dhaundiyal, Pankaj; Kataria, Abhinav
Monetary Dynamics and Socio-Economic Development in Emerging Economies;pp.81-115
https://doi.org/10.4018/979-8-3693-9765-7.ch004 https://www.scopus.com/pages/publications/105017782930?origin=resultslist
Chopra A., Christ University, India; Dhaundiyal P., Christ University, India; Kataria A., Christ University, India
Algorithmic Trading and Machine Learning: An Empirical Study of Stock Price Prediction in India
Algorithmic Trading; Financial Markets; Lasso Regression; Long-Short Term Memory; Stock Price Prediction
This research paper uses historical data from Ambuja Cement to compare nine machine learning algorithms for algorithmic trading in the Indian stock market. The algorithms applied include SVM, Linear Regression, Decision Tree, K-NN, Ridge Regression, Lasso Regression, Bayesian Ridge Regression, Random Forest, Elastic Net Regression, XGBoost, and reinforcement learning. MSE, MAE, and R2 are metrics used to evaluate predictive performance. The findings show that ensemble approaches and regularized regressions outperform simpler models, emphasizing the importance of model complexity and feature selection. Reinforcement Learning has the potential for optimizing tactics through constant adaptation. The study provides valuable insights on enhancing algorithmic trading in emerging Indian markets. 2025 IEEE.
Chopra, Darshan; Singh, Aditya; Jayapriya, J.; Vinay, M.
Proceedings of the 3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025;
https://doi.org/10.1109/IITCEE64140.2025.10915273 https://www.scopus.com/pages/publications/105001579741?origin=resultslist
Chopra D., Christ University, Department of Computer Science, Karnataka, Bangalore, India; Singh A., Christ University, Department of Computer Science, Karnataka, Bangalore, India; Jayapriya J., Christ University, Department of Computer Science, Karnataka, Bangalore, India; Vinay M., Christ University, Department of Computer Science, Karnataka, Bangalore, India
Smart Intelligence Perspective and Integrating AI with the Power System
AI-driven applications; Artificial Intelligence (AI); Contemporary research; Edge computing; Energy frameworks; Environmental conservation; Foresight modelling; Microgrid Management; Quantum computing
The confluence of Artificial Intelligence (AI) and energy frameworks has become a focal point in contemporary research, driven by the imperative to innovate and advance our energy systems. The book chapter delves into the nuanced relationship between AI and these systems, highlighting its opportunities for improved performance, resilience, and environmental conservation. Central to this exploration is the elucidation of AIs transformative potential in energy dynamics, spanning areas such as machine learning, deep learning, neural architectures, and foresight modelling. The chapter portrays diverse AI-driven applications, emphasising their transformative capabilities in steering energy strategies. Insights into the multifaceted advantages of integrating AI into energy frameworks are presented, stressing augmented stability, amplified efficiency, fiscal savings, and forward-thinking outage resolutions. Based on real-world examples, the research highlights the value of integrating AI into energy strategies. It also identifies new paradigms in the evolving energy landscape that will shape our energy future. This underscores AIs crucial role in driving energy transformations and calls for collaboration among academia, decision-makers, and industry leaders to create a greener, more efficient energy path. The importance of data analytics in managing power system data, enabling insights into consumption trends, and assisting in making educated decisions is also covered in this chapter. In order to ensure a responsible and secure implementation, the ethical and privacy issues associated with AI deployment in the power sector are also addressed. Furthermore, the chapter elaborates on the prospective trajectory of AI within Power Systems, elucidating its involvement in quantum computing, edge computing, and the incorporation of IoT to facilitate Microgrid Management. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
https://doi.org/10.1007/978-3-031-89459-6_1 https://www.scopus.com/pages/publications/105018013423?origin=resultslist
Chopra G., Christ University, School of Sciences, Pune, Lavasa, India; Jacob L., Christ University, School of Sciences, Pune, Lavasa, India