A novel framework for cloud-based analytic of massive and multi-structured healthcare images for real-time insights
The dependency of healthcare industry on the information and communication technology newline(ICT) domain is consistently on the rise in order to conceptualize and provide1537608 newlinesophisticated services to various newlinestakeholders including patients, newlinecaregivers, support service providers, medical practitioners, and experts. There are a variety of decisive advancements in the diagnosis, medication and surgical processes, medical electronics, instruments and equipment, healthcare-centric robots, a bevy of cloud-based healthcare software solutions, medical data hubs, etc. One direct offshoot of all these developments is that the amount of multi-structured data is exponentially growing. There is a litany of support and expert systems in order to lessen the doctors workloads. However the brewing challenges and new-generation requirements include the real-time processing of medical data to extract real-time insights and decision-enablement, the substantial enhancements in appropriate and accurate processing and understanding of various and overlapped symptoms towards correct and strategically sound decisions, the real-time analytics of medical data, the empowerment of medical devices to assist surgeons and specialists in performing their tasks in an assured manner, etc. newlineThe Problem Description - Medical imaging is one of the fundamental and most important areas of the healthcare system. This needs accuracy in processing and producing best results for further diagnosis and action. There are various factors impelling medical imaging like patient preparation, different scanning modalities, the scanner used to capture the image and various algorithms adopted for processing the captured images.
A Novel Framework for Harnessing AI for Evidence-Based Policymaking in E-Governance Using Smart Contracts
Artificial Intelligence (AI); Citizen engagement; Data analysis; E-governance; Evidence-based policymaking; Personalized services; Prediction
Harnessing AI for evidence-based policymaking in e-governance has the potential to revolutionize the way governments formulate and implement policies. By leveraging AI technologies, governments can analyze vast amounts of data, extract valuable insights, and make informed decisions based on evidence. This chapter explores the various ways in which AI can be employed in e-governance to facilitate evidence-based policymaking. It discusses the use of AI algorithms for data analysis and prediction, enabling governments to identify patterns, trends, and emerging issues from diverse data sources. Moreover, AI-powered tools can enhance citizen engagement and participation, by facilitating data-driven decision-making processes and providing personalized services. Additionally, AI can assist in policy evaluation and impact assessment, by automating the collection and analysis of data, thus enabling governments to measure the effectiveness of their policies in real-time. Furthermore, AI can contribute to enhancing transparency and accountability in e-governance, by automating processes such as fraud detection and risk assessment. Despite the immense potential, the adoption of AI in e-governance must address challenges such as data privacy, algorithmic bias, and ethical considerations. This chapter concludes by emphasizing the importance of building trust, ensuring fairness, and promoting responsible AI practices to maximize the benefits of AI in evidence-based policymaking for e-governance. The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
Upreti K.; Verma A.; Mittal S.; Vats P.; Haque M.; Ali S.
Communications in Computer and Information Science, Vol-1921 CCIS, pp. 231-240.
Springer Science and Business Media Deutschland GmbH
Upreti K., Department of Computer Science, CHRIST (Deemed to be University), Delhi NCR, Ghaziabad, India; Verma A., Dr. Akhilesh Das Gupta Institute of Technology and Management, Delhi, India; Mittal S., Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, Rajpura, India; Vats P., SCSE, Manipal University Jaipur, Rajasthan, Jaipur, India; Haque M., Dr. D.Y. Patil Vidyapeeths Centre for Online Learning, Dr. D.Y. Patil Vidyapeeth, Pune (Deemed to be University), Pune, India; Ali S., Faculty of Digital Business, Lithan Academy (eduCLaaS Pte Ltd.), Singapore, Singapore
A Novel Framework for Integrating Machine Learning in CSR to Accelerate Sustainability in the Indian Automobile Sector
Corporate Social Responsibility (CSR); Environmental Impact; Green Technology; Indian Automobile Sector; Machine Learning; Resource Optimization; Supply Chain Transparency; Sustainability
This paper aims at determining the suitability of using machine learning in CSR in enhancing sustainability of the Indian automobile industry. Prominent automobile companies are known to be major sources of environmental pollution together with wastage of various natural resources. There is a challenge of incorporating sustainability policies in the sector due to the rising regulation and consumers' awareness. Machine learning contains new approaches to managing resources more effectively, minimizing emissions and providing transparency of goods to clients. This study scrutinizes the previous literature, outlines the machine learning-based framework system of CSR activities, and validates the applicability of the system using case studies and qualitative data. The results show that learning from data can improve sustainability at an extensive scale and that the changes are sustainable and financially advantageous. 2025 IEEE.
Yogi, Kottala Sri; Dankan Gowda, V.; Roshani, Puja; Shankar, Ch; Kumar, G L N Sravan; Murthy, C. Ravindra
Proceedings of the 7th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2025;pp.836-843
Institute of Electrical and Electronics Engineers Inc.
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1109/ICIDCA66325.2025.11280471 https://www.scopus.com/pages/publications/105031444553?origin=resultslist
Yogi K.S., Symbiosis Institute of Business Management Hyderabad Symbiosis International University, Department of Operations, Pune, India; Dankan Gowda V., Bms Institute of Technology and Management, Department of Electronics and Communication Engineering, Karnataka, Bangalore, India; Roshani P., Christ University, Department of Commerce, Karnataka, Bangalore, India; Shankar C., Gitam (Deemed to Be) University, Gitam School of Business, Hyderabad, India; Kumar G.L.N.S., Malla Reddy College of Engineering and Technology [ M R C e T], Department of Mba, Telangana, Hyderabad, India; Murthy C.R., Mohan Babu University, School of Engineering, Department of Ece, Andrapradesh, Tirupati, India
A novel free space communication system using nonlinear InGaAsP microsystem resonators for enabling power-control toward smart cities
Demand response; Free space communication (FSO); Metering infrastructure; Microring resonator (MRR); Smart grid (SG)
Nowadays, the smart grid has demonstrated a great ability to make life easier and more comfortable given recent advances. This paper studies the above issue from the perspective of two important and very useful smart grid applications, i.e., the advanced metering infrastructure and demand response using the instrumentality of a set of well-known scheduling algorithms, e.g., best-channel quality indicator, log rule, round robin, and exponentialproportional fairness to validate the performance. To increase the data transmission bandwidth, a new concept of optical wireless communication known as free-space optical communication (FSO) system based on microring resonator (MRR) with the ability to deliver up to gigabit (line of sight) transmission per second is proposed for the two studied smart grid applications. The range between 374.7 and 374.79THz frequency band was chosen for the generation of 10 successive-carriers with a free spectral range of 8.87GHz. The ten multi-carriers were produced through drop port of the MRR. The results show up to 10 times bandwidth improvement over the radius as large as 600m and maintain receive power higher than the minimum threshold (? 20dBm) at the controller/users, so the overall system is still able to detect the FSO signal and extract the original data without detection. 2019, Springer Science+Business Media, LLC, part of Springer Nature.
Sivakumar P.; Nagaraju R.; Samanta D.; Sivaram M.; Hindia M.N.; Amiri I.S.
Wireless Networks, Vol-26, No. 4, pp. 2317-2328.
https://doi.org/10.1007/s11276-019-02075-7 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068323115&doi=10.1007%2fs11276-019-02075-7&partnerID=40&md5=ca2b2f6ea0abbd030a0ca89a2955ca53
Sivakumar P., Department of EEE, PSG College of Technology, Coimbatore, India; Nagaraju R., Department of Computer Science and Engineering, Sreyas Institute of Engineering and Technology, Hyderabad, India; Samanta D., Department of CSE, Christ (Deemed to be University), Bangalore, India; Sivaram M., Department of CSE, Lebanese French University, Erbil, Kurdistan Region, Iraq; Hindia M.N., Department Wireless Communication Centre, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Skudai, Johor, Malaysia; Amiri I.S., Computational Optics Research Group, Advanced Institute of Materials Science, Ton Duc Thang University, Ho Chi Minh City, Viet Nam, Faculty of Applied Sciences, Ton Duc Thang University, Ho Chi Minh City, Viet Nam
A Novel Fuzzy-Based Thresholding Approach for Blood Vessel Segmentation from Fundus Image
blood vessel segmentation; fundus images; fuzzy thresholding; IsoData; mean-c; thresholding techniques
Retinal vessel segmentation is a vital part of pathological analysis in Fundus imaging. The automatic detection of blood vessels resolves several issues in the manual segmentation process. Most unsupervised segmentation methods depend on conventional thresholding techniques for final vessel extraction. It may lead to the loss of some vessel pixels, leading to inaccurate analysis of retinal diseases. In this work, we incorporate fuzzy concepts into two threshold-based vessel detection methods, namely mean-c thresholding and Iso-Data thresholding, which results in a mask consisting of membership values rather than binary values. The two fuzzy-based thresholding algorithms are applied independently on each image, and the resultant membership image (mask) is fused to get a single membership mask. The fusion is performed using fuzzy union operation. Experiments are carried out with Fundus images from DRIVE, STARE and CHASE_DB1 databases.ses. The proposed fusion framework gives a 3%, 6%, and 5% increase in sensitivity compared to traditional thresholding methods when applied to the DRIVE, STARE, and CHASE_DB1 databases, respectively. The accuracy obtained for the datasets is 96.02%, 94.57%, and 94.34%, respectively. 2023 by the authors.
Wahid F.F.; Raju G.; Joseph S.M.; Swain D.; Das O.P.; Acharya B.
Journal of Advances in Information Technology, Vol-14, No. 2, pp. 185-192.
https://doi.org/10.12720/jait.14.2.185-192 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85151568217&doi=10.12720%2fjait.14.2.185-192&partnerID=40&md5=8d30ad9663ef551e5bd0f8eb0007b4e3
Wahid F.F., Department of Information Technology, Kannur University, Kerala, India; Raju G., CHRIST University, Bengaluru, India; Joseph S.M., Mahatma Gandhi College, Iritty, Kerala, Kannur, India; Swain D., Department of Computer Science and Engineering, Pandit Deendayal Energy University, Gandhinagar, India; Das O.P., GSK India Global Services Private Limited, Bangalore, India; Acharya B., Department of Computer Engineering-AI & BD, Marwadi University, Rajkot, India
A Novel Generative Adversarial Network-Based Approach for Automated Brain Tumour Segmentation
autoencoder; brain MRI; deep learning; generative adversarial learning; tumour segmentation
Background: Medical image segmentation is more complicated and demanding than ordinary image segmentation due to the density of medical pictures. A brain tumour is the most common cause of high mortality. Objectives: Extraction of tumorous cells is particularly difficult due to the differences between tumorous and non-tumorous cells. In ordinary convolutional neural networks, local background information is restricted. As a result, previous deep learning algorithms in medical imaging have struggled to detect anomalies in diverse cells. Methods: As a solution to this challenge, a deep convolutional generative adversarial network for tumour segmentation from brain Magnetic resonance Imaging (MRI) images is proposed. A generator and a discriminator are the two networks that make up the proposed model. This network focuses on tumour localisation, noise-related issues, and social class disparities. Results: Dice Score Coefficient (DSC), Peak Signal to Noise Ratio (PSNR), and Structural Index Similarity (SSIM) are all generally 0.894, 62.084 dB, and 0.88912, respectively. The models accuracy has improved to 97 percent, and its loss has reduced to 0.012. Conclusions: Experiments reveal that the proposed approach may successfully segment tumorous and benign tissues. As a result, a novel brain tumour segmentation approach has been created. 2023 by the authors.
Sille R.; Choudhury T.; Sharma A.; Chauhan P.; Tomar R.; Sharma D.
All Open Access; Gold Open Access; Green Open Access
https://doi.org/10.3390/medicina59010119 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146771972&doi=10.3390%2fmedicina59010119&partnerID=40&md5=2be2250c4cfaf823c6d410dc8969b672
Sille R., School of Computer Science, University of Petroleum and Energy Studies (UPES), Dehradun, 248007, India; Choudhury T., School of Computer Science, University of Petroleum and Energy Studies (UPES), Dehradun, 248007, India; Sharma A., School of Computer Science, University of Petroleum and Energy Studies (UPES), Dehradun, 248007, India; Chauhan P., School of Computer Science, University of Petroleum and Energy Studies (UPES), Dehradun, 248007, India; Tomar R., Persistent Systems, India, 411057, India; Sharma D., School of Business and Management, CHRIST University, Bangalore, 560074, India
A Novel Georouting Potency based Optimum Spider Monkey Approach for Avoiding Congestion in Energy Efficient Mobile Ad-hoc Network
Energy efficiency; Energy management; Knowledge-based algorithm; Mobile ad hoc network; Routing overhead; Spider monkey optimization
Mobile Ad-hoc Network (MANET) is one of the recent fields in wireless communication that involves a large number of wireless nodes, which could be changed arbitrarily with the ability to link or exit the system anytime. Nevertheless, network congestion and energy management is a major problem in MANET. Consequently, the infrastructure of a network changes frequently which results in data loss and communication overheads. Therefore, in this paper, a novel Georouting Potency based Optimum Spider Monkey algorithm has been proposed for energy management and network congestion. The proposed technique in MANET is implemented using Network Simulator2 platform and the proposed outcomes show that the node energy, overload, and delay are minimized by increasing the quantity of packets transmitted through the network. Moreover, the delay in routing overhead and congestion is decreased by the proposed protocol. Consequently, the energy management is enhanced based on constraints of delay, energy consumption, and routing overhead of the nodes. Thus the effectiveness of the proposed protocol is enhanced by selecting the optimal path within the network, decreasing the consumption of energy, and congestion avoidance. Sequentially, the performance of the proposed routing algorithm is compared to existing protocols in terms of end-to-end delay, throughput, Packet Delivery Ratio, energy consumption, etc. Thus the result shows that the lifetime of the nodes have been enhanced by a high 98% of throughput ratio, less 0.01% of energy consumption, and congestion avoidance using the proposed network. 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Wireless Personal Communications, Vol-127, No. 2, pp. 1157-1186.
Boddu N., Computer Science and Engineering, KL (Deemed to be University), Vaddeswaram, Andhra Pradesh, Guntur, 522502, India; Boba V., Department of Computer Science and Engineering, TKR College of Engineering and Technology, Medbowli, Meerpet, Saroornagar, Telangana, Hyderabad, 500097, India; Vatambeti R., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University, Kengeri Campus, Bangalore, 560074, India
A Novel Hybrid Ensemble Architecture for Stroke Risk Prediction Using Healthcare Data
Classification; Ensemble Learning; Feature Selection; Healthcare Analytics; Hybrid Models; Machine Learning; PCA; Stacking; Stroke Prediction
Stroke is the reason for an alarming number of disabilities worldwide, further emphasising the critical need for early and accurate prediction of risks to inform clinical management. This paper presents a novel hybrid ensemble architecture that leverages the superiority of multiple machine learning models for stroke health risk prediction using health data. In this novel hybridisation, decision tree classifiers belonging to the Random Forest and XGBoost families are effectively combined with support vector machines and a shallow neural network within a Stacked ensemble strategy that uses a hard vote technique. To improve model generalizability and avoid overfitting, feature selection and dimensionality reduction methods like Recursive Feature Elimination (RFE) and Principal Component Analysis (PCA) have been included expertly without compromising performance. After extensive training and testing on a real-world health repository covering a broad range of demographic, lifestyle, and clinical features, the model obtained an outstanding F1-score of 0.9427 and an exemplary ROC-AUC value of 0.9872, much higher than the performance of the individual models. Statistical significance was assessed using the Friedman and Wilcoxon signed-rank test. The model is a strong candidate for incorporation into clinical decision support systems and is fully deployable and EHR-compatible. The Author(s) 2026.
Ghosh, Tushar; George, Jossy; Chanti, S.
Lecture Notes in Networks and Systems;Volume;1929 LNNS;pp.44-55
All Open Access; Hybrid Gold Open Access
https://doi.org/10.1007/978-3-032-22911-3_4 https://www.scopus.com/pages/publications/105039307403?origin=resultslist
Ghosh T., Christ (Deemed to Be University), Bangalore, India; George J., Christ (Deemed to Be University), Bangalore, India; Chanti S., Christ (Deemed to Be University), Bangalore, India
A Novel Hybrid Model for Time Series Forecasting Using Artificial Neural Network and Autoregressive Integrated Moving Average Models
ANN; ARIMA; Combination of forecasts; Hybrid model
Enhancing forecast accuracy while using time series is a potential area of research. Evidences exist in the literature to show that hybrid models can significantly improve the forecasting performance, as they combine the exclusive strengths of different models. This paper presents a novel hybrid model by combining forecasts from Autoregressive Integrated Moving Average (ARIMA) and artificial neural network (ANN) models with suitable weights, thereby improving the forecast accuracy. The methodology employs appropriate error metrics to construct the weights. The paper further demonstrates the efficiency of the proposed methodology through an empirical study, based on two real-world time series data sets. Thus, the new methodology can be used for enhancing the forecast accuracy in a number of fields of research. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Thangarajan B.; Nagaraja M.S.; Dhandra B.V.
Trends in Mathematics, Vol-Part F2357, pp. 747-754.
Thangarajan B., CHRIST (Deemed to be University), Bengaluru, India; Nagaraja M.S., CHRIST (Deemed to be University), Bengaluru, India; Dhandra B.V., CHRIST (Deemed to be University), Bengaluru, India
A novel image compression method using wavelet coefficients and Huffman coding
Bits per pixel; Compression ratio; Integer wavelet transform; Medical imaging; Visual quality
Compressing medical images to reduce their size while maintaining their clinical and diagnostic information is crucial. Because medical images can be large and demand a lot of storage and transmission capacity, effective compression methods aid medical institutions in better storing and transmitting medical images, reducing costs, speeding up data transfer, and simplifying managing image databases. However, it is essential to note that image compression in medical imaging can also introduce drawbacks, such as loss of information and poor output image quality. Therefore, a suitable compression algorithm and parameter must be chosen to balance file size and visual fidelity. This paper suggests an effective image compression method employing the Discrete Wavelet Transform (DWT), followed by a reduction operation and Huffman coding to produce a mere lossless encoding to transmit the images over a channel. The extracted DWT coefficients are mapped to the nearest integral value. All four sub-bands of DWT are joined, and then a window of 3 3 is selected for reduction operation by choosing the origin as the pivot element. The Huffman coding algorithm is used to compress the processed image. The pivot origin element is used in the reversible reduction while uncompressing the image. When sending compressed data across an unreliable route, the window size and pivot element selection keep the compressed data secure. Standard measures such as bits per pixel (BPP) and compression ratio (CR) are used to assess the suggested approach. The efficiency of the suggested course of action is supported by the research's findings, which use a peak signal-to-noise ratio (PSNR) of 54.66 dB. 2023 The Authors
Thomas S.; Krishna A.; Govind S.; Sahu A.K.
Journal of Engineering Research (Kuwait)
https://doi.org/10.1016/j.jer.2023.08.015 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200512552&doi=10.1016%2fj.jer.2023.08.015&partnerID=40&md5=66fa56a22b84e485e741fc67c65654ae
Thomas S., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bangalore, India, Department of Computer Science, Rajagiri College of Social Sciences (Autonomous), Kerala, Kalamassery, India; Krishna A., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bangalore, India; Govind S., Department of Computer Science, Rajagiri College of Social Sciences (Autonomous), Kerala, Kalamassery, India; Sahu A.K., Amrita School of Computing, Amrita Vishwa Vidyapeetham, Andhra Pradesh, Amaravati, India
Thomas, Shiju; Krishna, Addapalli; Govind, Sabeen; Sahu, Aditya Kumar
Journal of Engineering Research (Kuwait);Volume;13;Issue;1;pp.361-370
https://doi.org/10.1016/j.jer.2023.08.015 https://www.scopus.com/pages/publications/105001062458?origin=resultslist
A Novel Investigation on p-GaN GATE with and without AlGaN Back Barrier for AlGaN/GaN High Electron Mobility Transistors
Ambient Temperature; Back Barrier; E-mode; p-GaN Gate; Silicon Carbide Transconductance; Threshold Voltage
p-GaN layers are relatively mature and controllable, making p-GaN HEMTs the leading structure that is most likely to be commercialized. The analysis of the gate design parameters, such as transconductance, breakdown voltage, threshold voltage, Johnson Figure of Merit (JFOM), and gate turn-on current of p-GaN devices, which determine the on-state characteristics, needs to be investigated. The AlGaN barrier, p-GaN gate, GaN, AlGaN back barrier, and SiC substrate constitute the structure of p-GaN, which is operated in the E-mode. The use of an AlGaN back barrier reduces the punch-through current. Silicon carbide (SiC) is used as a substrate to have lower lattice mismatch with the nitride layer. The transfer characteristics, transconductance, threshold voltage, breakdown voltage, and JFOM are analyzed. The device demonstrates a positive threshold voltage that varies linearly with changes in ambient temperature. In addition, the device featuring an AlGaN back barrier shows a higher breakdown voltage of 105 V, in contrast to the device lacking a back barrier. 2025, Society for Communication and Computer Technologies. All rights reserved.
Arunraja, A.; Kishorekumar, R.; Sangeetha, D.; Dharmaprakash, R.; Bharaty, K.S.; Balamanikandan, A.
Journal of VLSI Circuits and Systems;Volume;7;Issue;1;pp.167-175
Society for Communication and Computer Technologies
https://doi.org/10.31838/JVCS/07.01.18 https://www.scopus.com/pages/publications/105015373949?origin=resultslist
Arunraja A., Department of Electronics and Communication Engineering, Christ University, Karnataka, Bengaluru, India; Kishorekumar R., Department of Electronics and Communication Engineering, Christ University, Karnataka, Bengaluru, India; Sangeetha D., Department of Computer Science and Applications, Dr. Vishwanath Karad MIT World Peace University, Pune, India; Dharmaprakash R., Department of Elecrical and Electronics Engineering, Panimalar Engineering College, Chennai, India; Bharaty K.S., Department of Electrical and Electronics Engineering, School of Engineering, Mohan Babu University, Tirupathi, India; Balamanikandan A., Department of Electronics and Communication Engineering, School of Engineering, Mohan Babu University, Tirupathi, India
A novel laccase-based biocatalyst for selective electro-oxidation of 2-thiophene methanol
2-thiophene carboxaldehyde; Carbon fiber paper electrode; Immobilization; Laccase; Poly (3-aminobenzoic acid); TEMPO
An effective biocatalyst was fabricated for TEMPO-mediated electrooxidation of 2-thiophene methanol. Laccase obtained from Trametes versicolor was covalently immobilized onto electrochemically polymerized ortho-amino benzoic acid (PABA) layer on carbon fiber paper (CFP) electrode. The composite material was characterized by Fourier transformed infrared (FTIR) spectroscopy, X-ray photoelectron spectroscopy (XPS), Optical profilometry (OP), and scanning electron microscopy (SEM). Electrochemical parameters were studied using cyclic voltammetry (CV). Moreover, the developed biocatalyst (Lac-PABA/CFP) was used for selective conversion of 2-thiophene methanol to 2-thiophene carboxaldehyde using 2,2,6,6-Tetramethyl-1-piperidinyloxy, free radical (TEMPO) as a mediator. The formation of the product was confirmed via FTIR, GCMS, 1HNMR and 13CNMR. The enzyme activity of free and immobilized laccase was studied using 2, 2?-Azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) substrate at optimal conditions. Computational In silico analysis also suggested the presence of active sites (T2/T3 trimeric sites-copper ions) in laccase (PDB id: 1KYA's) interacting amino acid residues with the TEMPO and 2-thiophene methanol. Additionally, molecular dynamics simulations revealed that 2-thiophene methanol as compared to TEMPO is more stable (better RMSD, RMSF) in interacting with laccase specifically having strong interaction residues at Asp206, Glu242, Gly262, Gln293, and Glu302. Furthermore, the proposed strategy was confirmed by assessing the various interactions using computational tools. This work would be highly beneficial to develop an electrocatalyst for effective synthesis of 2-thiophene carboxaldehyde, a common intermediate in pharmaceutical, agrochemical, dye, fertilizer and chemical industries. 2021
Benny L.; Cherian A.R.; Varghese A.; Sangwan N.; Avti P.K.; Hegde G.
https://doi.org/10.1016/j.mcat.2021.111999 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119287683&doi=10.1016%2fj.mcat.2021.111999&partnerID=40&md5=eb1538da8a533179a641f58f36e74f93
Benny L., Department of Chemistry, CHRIST (Deemed to be University), Hosur Road, Bengaluru, 560029, India; Cherian A.R., Department of Chemistry, CHRIST (Deemed to be University), Hosur Road, Bengaluru, 560029, India; Varghese A., Department of Chemistry, CHRIST (Deemed to be University), Hosur Road, Bengaluru, 560029, India; Sangwan N., Department of Biophysics, Post Graduate Institute of Medical Education and Research, Chandigarh, 160012, India; Avti P.K., Department of Biophysics, Post Graduate Institute of Medical Education and Research, Chandigarh, 160012, India; Hegde G., Department of Chemistry, CHRIST (Deemed to be University), Hosur Road, Bengaluru, 560029, India, Center for Advanced Research and Development (CARD), CHRIST (Deemed to be University), Hosur Road, Bengaluru, 560029, India
A novel launch power determination strategy for physical layer impairment-aware (PLI-A) lightpath provisioning in mixed-line-rate (MLR) optical networks
launch power; mixed line rate; optical networks; PLIs; WDM
In mixed-line-rate (MLR) networks, various data rates, on varied wavelengths, exist on a fiber. In MLR networks, end-to-end lightpaths can be established with the desired line rate; requiring advanced modulation formats for higher data rates. However, along the route, the signals experience different physical layer impairments (PLIs), and their quality also worsens. The transmission signal quality is affected by the launch power, which must be high for lesser noise at the receiver, and must also be low, such that the PLIs do not start to distort the signal. Further, higher launch power also disrupts the existing lightpath and its neighbours. We propose a weighted strategy for provisioning PLI-aware (PLI-A) lightpaths in MLR networks. Through the simulations, we compare and demonstrate that the proposed strategy demonstrates better performances than our previously proposed algorithm (i.e. PLI-Average (PLI-A)), and existing approaches. 2016 IEEE.
Proceedings of the 2016 IEEE International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2016, pp. 617-622.
Presses Polytechniques Et Universitaires Romandes
https://doi.org/10.1109/WiSPNET.2016.7566207 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992088979&doi=10.1109%2fWiSPNET.2016.7566207&partnerID=40&md5=afd412227a6076319b61e0539241988f
Iyer S., Christ University, Faculty of Engineering, Department of ECE, Mysore Road, Bangalore, 560060, India; Singh S.P., Division of ECE, Netaji Subhas Institute of Technology, Sector-3, Dwarka, New Delhi, 110078, India
A Novel Machine Learning Approach for Tuberculosis Detection using Volatile Organic Compounds
1D-CNN; Breath Biopsy; Classification; Machine Learning; MDR-TB; Non-invasive Diagnosis; Random Forest; Tuberculosis (TB); Volatile Organic Compounds (VOCs); XGBoost
For world health, better TB diagnosis is still absolutely necessary. Using VOC Atlas, this study assesses a few machine learning techniques for categorizing breath samples depending on volatile organic compound (VOC) profiles. We created a machine learning pipeline and tried out four different models: Random Forest, XGBoost, Multi-Layer Perceptron (MLP), and a 1D-Convolutional Neural Network (1D-CNN). There were 1,500 patient profiles in the dataset spanning three groups: healthy people, drug-sensitive TB cases, and multidrug-resistant TB cases. Using VOC biomarker patterns found in VOC Atlas and prior TB research, these profiles were developed. While XGBoost stood out by reaching 100% accuracy, our studies revealed that most models performed rather well. This implies that gradient boosting-based ensemble models can adequately grasp the complex patterns found in breath data. One major caveat is that we have not tested these models on real clinical breath samples to validate them. Testing these models with actual patient samples in clinical settings would be the next reasonable step. All told, this research provides a strong basis for creating non-invasive ways to detect illnesses. 2025 IEEE.
Proceedings of the 4th International Conference on Intelligent Computing, Information and Control Systems, ICOIICS 2025;pp.1612-1618
https://doi.org/10.1109/ICOIICS67115.2025.11390373 https://www.scopus.com/pages/publications/105036734426?origin=resultslist
Vishal B.G., CHRIST (Deemed to be University), Bangalore, India; George J., CHRIST (Deemed to be University), Bangalore, India; Chanti S., CHRIST (Deemed to be University), Bangalore, India
A Novel Machine Learning Ensemble Approach for Corrupt Data Packet Identification
Corrupt Data Packet Detection; Ensemble Learning; Security; Stacking
In contemporary network infrastructures, ensuring the fidelity of data transmission is paramount for robust communication and security. The intrusion of corrupted data packets can severely degrade network efficiency, resulting in critical data loss, exploitable security gaps, and suboptimal resource allocation. This paper indicates the significantly increase detection accuracy and system resilience by synergistically using the predictive capability of many machine learning paradigms especially. This paper employs sophisticated feature engineering to extract discriminative attributes from network packet headers and payloads, followed by a refined ensemble learning strategy that leverages both stacking and boosting techniques for optimal classification performance. Compared to conventional single-model techniques, evaluated on real-world network traffic datasets our model shows a significant increase in key performance measures. Here a pioneering hybrid machine learning ensemble framework designed for the precise identification and mitigation of corrupted data packets. Notably, the ensemble framework excels in minimizing false positives, enabling real-time packet analysis and bolstering network security. This study contributes to the evolution of intelligent, adaptive network defense mechanisms, providing a scalable and high-performance solution for safeguarding data integrity and mitigating the deleterious effects of corrupted data packets in modern, high-throughput communication environments. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
Poonia, Ramesh Chandra; Rathore, Vishal Singh; Kumar, Ajay
Lecture Notes in Networks and Systems;Volume;1749 LNNS;pp.314-325
https://doi.org/10.1007/978-3-032-13003-7_30 https://www.scopus.com/pages/publications/105028291127?origin=resultslist
Poonia R.C., Department of Computer Science, CHIRST University, Karnataka, Bangalore, India; Rathore V.S., Department of Computer Science and Engineering, JECRC University, Rajasthan, Jaipur, India; Kumar A., Department of Computer Science and Engineering, JECRC University, Rajasthan, Jaipur, India
A Novel Machine Learning-Based Prediction Method for Early Detection and Diagnosis of Congenital Heart Disease Using ECG Signal Processing
classification; congenital heart disease; healthcare; internet of medical things; prediction
Congenital heart disease (CHD) represents a multifaceted medical condition that requires early detection and diagnosis for effective management, given its diverse presentations and subtle symptoms that manifest from birth. This research article introduces a groundbreaking healthcare application, the Machine Learning-based Congenital Heart Disease Prediction Method (ML-CHDPM), tailored to address these challenges and expedite the timely identification and classification of CHD in pregnant women. The ML-CHDPM model leverages state-of-the-art machine learning techniques to categorize CHD cases, taking into account pertinent clinical and demographic factors. Trained on a comprehensive dataset, the model captures intricate patterns and relationships, resulting in precise predictions and classifications. The evaluation of the models performance encompasses sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve. Remarkably, the findings underscore the ML-CHDPMs superiority across six pivotal metrics: accuracy, precision, recall, specificity, false positive rate (FPR), and false negative rate (FNR). The method achieves an average accuracy rate of 94.28%, precision of 87.54%, recall rate of 96.25%, specificity rate of 91.74%, FPR of 8.26%, and FNR of 3.75%. These outcomes distinctly demonstrate the ML-CHDPMs effectiveness in reliably predicting and classifying CHD cases. This research marks a significant stride toward early detection and diagnosis, harnessing advanced machine learning techniques within the realm of ECG signal processing, specifically tailored to pregnant women. 2024 by the authors.
Pachiyannan P.; Alsulami M.; Alsadie D.; Saudagar A.K.J.; AlKhathami M.; Poonia R.C.
Multidisciplinary Digital Publishing Institute (MDPI)
https://doi.org/10.3390/technologies12010004 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183410549&doi=10.3390%2ftechnologies12010004&partnerID=40&md5=72e38b95cc5ee341a8eaa309ecd216ab
Pachiyannan P., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, 560029, India; Alsulami M., Department of Software Engineering, Umm Al-Qura University, Makkah, 21961, Saudi Arabia; Alsadie D., Department of Computer Science and Artificial Intelligence, Umm Al-Qura University, Makkah, 21961, Saudi Arabia; Saudagar A.K.J., Information Systems Department, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia; AlKhathami M., Information Systems Department, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia; Poonia R.C., Department of Computer Science, CHRIST (Deemed to be University), Delhi-NCR, 201003, India
A novel map matching algorithm for real-time location using low frequency floating trajectory data
clusters; confidence level; Euclidean distance; GAGAN; GPS trajectory; Kalman filter; map matching; perpendicular distance; signal frequency
The continuous enhancement of technologies and modern well-equipped infrastructures are necessary for easy life. Road accident and missing vehicle ratio are very challenging in preventing misshapenness because these are continually increasing due to traffic hazards. The single way to protect human life from such type of conditions that is more reliable navigation services such as correct location tracking of vehicles on the road network. The real-time location tracking methods fully depends on the map matching algorithms, which also compute a reliable path on the road network. A smart vehicle can provide more reliable tracking services during or before any misshaping using proposed map matching algorithm. This work contributes to ensure correct location for necessary action during misshaping, alert accident zone and communicate messages without wasting valuable time. The proposed approach is validated on the real tracking data and is compared against poor GPS service. Copyright 2023 Inderscience Enterprises Ltd.
International Journal of Advanced Intelligence Paradigms, Vol-24, No. 45750, pp. 442-455.
https://doi.org/10.1504/IJAIP.2023.129188 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153758955&doi=10.1504%2fIJAIP.2023.129188&partnerID=40&md5=36461fbcaa00d6e784ba341e31df7368
Sharma K.P., Amity Institute of Information Technology, Amity University, Rajasthan, Jaipur, India; Poonia R.C., Department of Computer Science, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India; Sunda S., Space Applications Centre, Indian Space Research Organisation, Ahmedabad, India
A novel mathematical investigation of carbon emissions, economic growth, carbon taxation and renewable energy dynamics: stability analysis and forecasting
02.30.Hq 02.60.Lj; 04.20.Ex; auto-regressive integrated moving average; boundedness; carbon emission; carbon tax; GDP; Mathematical modelling; renewable energy consumption
The main cause of global warming is carbon dioxide (CO2) emissions, acting as a significant greenhouse gas. These emissions stem from various sources and significantly contribute to climate change. Fortunately, we have countermeasures like carbon taxes to curb CO2 output. Carbon taxes incentivise a reduction in CO2 production and a shift towards cleaner energy sources by placing a cost on emissions. This paper investigates the interplay between carbon tax policy, carbon emissions, economic output (GDP) and renewable energy consumption. A system of differential equations is constructed to model these relationships based on a comprehensive literature review. Parameter estimation based on real-world data yielded successful fits for the variables. However, the fit for the carbon tax equation is less conclusive, suggesting a more complex relationship with carbon emissions. Stability analysis and the boundedness of the system are carried out. Auto-regressive integrated moving average (ARIMA) forecasting is employed to predict future trends. The results suggest a projected increase in GDP and renewable energy consumption over the next ten years, indicating a potential for a cleaner energy transition. Furthermore, the forecasts anticipate a rise in carbon tax implementation. This analysis emphasises how important carbon taxes are for cutting emissions and advancing renewable energy. Results indicate that carbon taxes can promote decarbonisation and economic growth, despite the complicated link between them and CO2 emissions. Both GDP growth and the use of renewable energy are anticipated to increase. However, policies must be improved to combat climate change effectively. Future studies should improve parameters and investigate other relevant elements to promote a low-carbon future. Indian Academy of Sciences 2025.
Namboothiri, V S Manasi; Veeresha, P.; Sherly, K.
Pramana - Journal of Physics;Volume;99;Issue;4;Article No.;155;
https://doi.org/10.1007/s12043-025-03027-x https://www.scopus.com/pages/publications/105022684473?origin=resultslist
Namboothiri V.S.M., Centre for Mathematical Needs, Department of Mathematics, CHRIST University, Bengaluru, 560 029, India; Veeresha P., Centre for Mathematical Needs, Department of Mathematics, CHRIST University, Bengaluru, 560 029, India; Sherly K., Centre for Mathematical Needs, Department of Mathematics, CHRIST University, Bengaluru, 560 029, India
A novel method for crowd detection and altering social distance using OIT and methods thereof /
Patent Number: 202111019009, Applicant: Dr. Purvi Pareek. The Invention Discloses a Novel Process and Method for Continuously Monitoring public places. The Primary Factors related to density and distance are collected using Sensors and fed to Database Module. The Machine Learning Module analyses the Inputs and sends alerts to the Communication Module. The alert message is activated in loud speaker to ensure social distancing. This Invention ensures safety of Public from spread of air borne diseases.
https://drive.google.com/file/d/1sjBBX-40wpqU9zWv9NfKEz0CU6rGyzf6/view