An improved AI-driven Data Analytics model for Modern Healthcare Environment
Approach; Improve; Powerful; Progressed; Statistics
AI-driven statistics analytics is a swiftly advancing and impactful era that is transforming the face of healthcare. By leveraging the energy of AI computing and gadget studying, healthcare organizations can speedy gain insights from their huge datasets, offering a greater comprehensive and personalized approach to hospital therapy and populace health management. This paper explores the advantages of AI-driven statistics analytics in healthcare settings, masking key benefits along with progressed analysis and treatment, better-affected person effects, and financial savings. Moreover, this paper addresses the main challenges associated with AI-pushed analytics and offers potential solutions to enhance accuracy and relevance. In the long run, statistics analytics powered by way of AI gives powerful opportunities to improve healthcare outcomes, and its use is expected to expand within the coming years. 2024 IEEE.
Patil S.C.; Takkalapally P.; Kasula B.Y.; Logeshwaran J.
2024 15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024
Institute of Electrical and Electronics Engineers Inc.
Patil S.C., United States; Takkalapally P., Lewis University, Department of Engineering, Computing and Mathematical Sciences, United States; Kasula B.Y., University Of The Cumberlands, Dept of Information Technology, Williamsburg, KY, United States; Logeshwaran J., CHRIST (Deemed to be University), Department of Computer Science, Karnataka, Bengaluru, 560029, India
An Improved Alternative Method of Imputation for Missing Data in Survey Sampling
bias; imputation; mean squared error (MSE); percent relative efficiency (PRE); Population mean
In the present paper, a new and improved method of ratio type imputation and corresponding point estimator to estimate the finite population mean is proposed in case of missing data problem. It has been shown that this estimator utilizes the readily available auxiliary information efficiently and gives better results than the ratio and mean methods of imputation; furthermore, its efficiency is also compared with the regression method of imputation and some other imputation methods, discussed in this article, using four real data sets. A simulation study is carried out to verify theoretical outcomes, and suitable recommendations are made. 2022 NSP Natural Sciences Publishing Cor.
Singh G.N.; Jaiswal A.K.; Singh C.; Usman M.
Journal of Statistics Applications and Probability, Vol-11, No. 2, pp. 535-543.
https://doi.org/10.18576/jsap/110214 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127098817&doi=10.18576%2fjsap%2f110214&partnerID=40&md5=ce39c25679078f273d2a8f42b798a1b5
Singh G.N., Indian Institute of Technology (Indian School of Mines), Dhanbad, 826004, India; Jaiswal A.K., Indian Institute of Technology (Indian School of Mines), Dhanbad, 826004, India; Singh C., Department of Statistics, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Usman M., Indian Institute of Technology (Indian School of Mines), Dhanbad, 826004, India
An Improved and Efficient YOLOv4 Method for Object Detection in Video Streaming
Object detection; Optimized inferencing; YOLOV4
As object detection has gained popularity in recent years, there are many object detection algorithms available in today's world. Yet the algorithm with better accuracy and better speed is considered vital for critical applications. Therefore, in this article, the use of the YOLOV4 object detection algorithm is combined with improved and efficient inference methods. The YOLOV4 state-of-the-art algorithm is 12% faster compared to its previous version, YOLOV3, and twice as faster compared to the EfficientDet algorithm in the Tesla V100 GPU. However, the algorithm has lacked performance on an average machine and on single-board machines like Jetson Nano and Jetson TX2. In this research, we examine the performance of inferencing in several frameworks and propose a framework that effectively uses hardware to optimize the network while consuming less than 30% of the hardware of other frameworks. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Lecture Notes in Networks and Systems, Vol-462, pp. 305-316.
Springer Science and Business Media Deutschland GmbH
Hussain J., Department of Computer Science and Engineering, Christ (Deemed to be University), Bangalore, India; Prathap B.R., Department of Computer Science and Engineering, Christ (Deemed to be University), Bangalore, India; Sharma A., Department of Computer Science and Engineering, Christ (Deemed to be University), Bangalore, India
An Improved Artificial Intelligence based Service Quality to Increase Customer Satisfaction and Customer Loyalty in Banking Sector
AISAQUAL Banking; BANQUAL; SERQUAL; Service; Tangibility
This study clarifies and determines how service quality affects customer loyalty and reliability. The support of quality in the open and private financial sphere and understanding of its connection to customer loyalty and conduct goal Utilizing an upgraded SERVQUAL (BANQUAL) tool with 26 items, the review was conducted among 802 bank customers. The social goal battery was used to estimate the clients' expected conduct. The expert used a seven-point Likert scale to assess the standard and saw service quality (implementation), as well as the social expectations of the clients. The most reliable tool to quantify the conceptualization of the differentiation score is the BANQUAL instrument. It is used to evaluate gaps in service between assumptions and perceptions of service quality. The SERVQUAL instrument is modified to make it suitable in the banking industry. Questions on parking at the bank, the variety of things and programmes available, and the banks' genuine efforts to address customer grievances are added to the instrument (Responsiveness). The writing audit was sufficiently compiled from many sources, reflecting both an Indian and foreign environment. The postulation included several hypotheses then examined using structural equation modelling. To meet the exploration goals, the views were tested using the products AMOS and SISS. The data were analysed using corroborative and explorative element research to confirm the BANQUAL instrument's dependability and legitimacy of the financial business execution and service quality aspects. The resulting CFA model value exhibits excellent psychometric qualities. Professional businesses and clients increasingly use artificial intelligence support specialists (AISA) for management. However, no measure measuring the support quality can fully capture the essential factors affecting AISA service quality. By developing a scale for evaluating the quality of AISA service, this study seeks to solve this deficiency(AISAQUAL). 2023 IEEE.
Shunmugasundaram M.; Ganesh K.S.; Gnanapragash T.J.; John E.P.; Raj J.P.; Smitha V.
2nd International Conference on Sustainable Computing and Data Communication Systems, ICSCDS 2023 - Proceedings, pp. 433-439.
https://doi.org/10.1109/ICSCDS56580.2023.10105048 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85159146276&doi=10.1109%2fICSCDS56580.2023.10105048&partnerID=40&md5=30ff5b06fbd8e93c1499d6803d5caab5
Shunmugasundaram M., Scad College of Eng. & Tech, Tamilnadu, Tirunelveli, India; Ganesh K.S., Lead College of Management, Kerala, Palakkad, India; Gnanapragash T.J., Christ Deemed to Be University, School of Business & Management, Bangalore, India; John E.P., Srm Institute of Science and Technology, Chennai, India; Raj J.P., Christ University, School of Business and Management, India; Smitha V., Lead College of Management, Kerala, Palakkad, India
An improved atom search optimization algorithm based on ranking strategy and sine cosine algorithm for epileptic seizure detection
Atom search optimization (ASO); Electroencephalogram (EEG) signals; Epileptic seizure detection; KruskalWallis test; Sine cosine algorithm (SCA); Wavelet packet transform (WPT)
Epilepsy is a serious neurological disorder that remains difficult to detect with high accuracy. Automated seizure detection using EEG signals has gained increasing attention, and optimization algorithms are often applied to improve system performance. Atom Search Optimization (ASO) has strong global search ability but frequently suffers from premature convergence and limited local search efficiency. To address these issues, this study proposes a hybrid algorithm that combines ASO with the SineCosine Algorithm (SCA) and a ranking strategy (RSHASOSCA). ASO provides effective global exploration, SCA enhances local exploitation, and the ranking strategy stabilizes convergence, together creating a more balanced and reliable search process. The method was evaluated on the CHB-MIT scalp EEG dataset. Features were extracted using Wavelet Packet Transform (WPT) and refined with the KruskalWallis test (p ? 0.001). Comparative experiments against twelve established optimization algorithms showed that the RSHASOSCA framework achieved superior performance. When applied with an SVM classifier, it reached 99.13% accuracy and an AUC of 1. These findings highlight the value of integrating ASO, SCA, and ranking strategy, and demonstrate the potential of the proposed framework for reliable and efficient seizure detection in clinical practice. The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.
Swain, Kaliprasanna; Tak, Tan Kuan; Upreti, Kamal; Kshirsagar, Pravin R.; Krishnan, Sivaneasan Bala; Poonia, Ramesh Chandra; Mohapatra, Sumant Kumar; Nayak, Soumya Ranjan
Signal, Image and Video Processing;Volume;19;Issue;15;Article No.;1315;
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1007/s11760-025-04879-x https://www.scopus.com/pages/publications/105019701064?origin=resultslist
Swain K., Department of ETC, Trident Academy of Technology, Odisha, Bhubaneswar, 751024, India; Tak T.K., Singapore Institute of Technology, Singapore, Singapore; Upreti K., CHRIST University, Delhi NCR, Ghaziabad, India; Kshirsagar P.R., J D College of Engineering and Management, Maharashtra, Nagpur, India; Krishnan S.B., Singapore Institute of Technology, Singapore, Singapore; Poonia R.C., CHRIST University, Delhi NCR, Ghaziabad, India; Mohapatra S.K., Department of CSE (AI & ML), Dayananda Sagar University School of Engineering, Karnataka, Bengaluru, 562112, India; Nayak S.R., School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT) Deemed to be University, Odisha, Bhubaneswar, 751024, India
An Improved Combined Adaptive Outline for Contrast Enhancement of Blood Vessels
Adaptive histogram equalization (AHE); Brightness preserving bi-histogram equalization (BBHE); Contrast enhancement; Histogram equalization (HE)
Appropriate vascular segmentation is dependent on effective picture pre-processing techniques that improve the contrast of the blood vessels, reduce noise, eliminate non-uniform illumination, highlight thin vessels, and retain background texture. These techniques are necessary for accurate vessel segmentation. Here, both the edge- and texture-smoothed data from the vessel probability map are used in the derivation of the adaptive optimal q-order in the G-L mask. The smooth information is not affected, the textures are maintained, and the contrast of the blood vessels is enhanced, thanks to the proposed filter. In addition to sharpening the focus on the vessels themselves, a Gaussian curve fitting is used to contrast stretch the entire image. Retinal fundus images processed with cerebral DSA are subjected to both qualitative and quantitative assessments of contrast enhancement. Quantitative performance indicators are tabulated and compared to other approaches to show how well this technique works for improving medical images everywhere. The suggested filter is easy to implement, flexible enough to adapt to different images, and effective at increasing both vessel contrast and overall image contrast. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.
Warrier G.S.; Mahapatra H.; Khuntia M.; Rajesh M.; Vincent R.; Pathak A.
https://doi.org/10.1007/s42979-023-02069-5 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85169662274&doi=10.1007%2fs42979-023-02069-5&partnerID=40&md5=22aed3dbd0bfad5b3673a1e1bdec3f24
Warrier G.S., Department of Computer Science, Christ(Deemed to be University), Karnataka, Bangalore, 560 073, India; Mahapatra H., Department of Endocrinology, IMS & SUM Hospital, SOA University, Bhubaneswar, India, Sevayan Diabetes Centre, Puri, India; Khuntia M., Department of Rural Management, KIIT University, Bhubaneswar, India; Rajesh M., School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India; Vincent R., School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India; Pathak A., Department of Computer Science, Shobhit Institute of Engineering & Technology (Deemed to-be University), Meerut, India
An improved compocasting technique for uniformly dispersed multi-walled carbon nanotube in AA2219 Alloy Melt
Alloys; Aluminium; Casting; Compocasting; Composites; MWCNT; Nanocomposites; Nanotube
Technology transfer for economic bulk production is the greatest challenge of the era. Production of high strength lightweight materials with nanocarbon reinforcement has attained its importance among the researchers. Property enhancement with multi-walled carbon nanotube (MWCNT) reinforcement is reported by all researchers. But effective utilization of its property remains a challenge even though it is the strongest material in the world. Achieving homogeneous dispersion especially in molten metal is a complex task. To address the same, a new approach was tried which could trigger de-bundling and make a uniform dispersion. Various metallurgical and mechanical characterizations were done. Grain refinement and the structure were studied with an optical microscope, MWCNT dispersion and structural damage was studied using field emission scanning microscope, Phase change and reactions during casting was done with XRD scan. The method remarkably facilitated 23.7% and 69.75% improvement in hardness and ultimate compressive strength respectively with the addition of MWCNT. Faculty of Mechanical Engineering, Belgrade.
Thomas S.; Keshavamurthy R.; Kumar P.G.S.; Tambrallimath V.; Prasanna G.D.
FME Transactions, Vol-48, No. 3, pp. 581-587.
https://doi.org/10.5937/fme2003581T https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087778546&doi=10.5937%2ffme2003581T&partnerID=40&md5=06e6157cff1f4b8fbcc74232ed402c67
Thomas S., Department of Mechanical and Automobile Engineering, CHRIST (Deemed to be University), Bangalore, India; Keshavamurthy R., Department of Mechnaical Engineering, Dayananda Sagar College of Engineering, India; Kumar P.G.S., Department of Mechanical and Automobile Engineering, CHRIST (Deemed to be University), Bangalore, India; Tambrallimath V., Department of Automobile Engineering, Dayananda Sagar College of Engineering, India; Prasanna G.D., Department of Studies in Physics, Davanegere Univeristy, India
An Improved Deep Learning Framework for Energy Management in Low-Energy Building Integrated Photovoltaics (LE-BIPV)
Building; Deep Learning; Energy; Neural Networks; Photovoltaics; Solar
The possibility of incorporating photovoltaics (PV) as part of building design has opened a new approach to energy generation from sustainable resources. An effective method to facilitate the good operation of these systems would be efficient energy-level management. The existing Energy Management of LE-BIPV employs a conventional control strategy, which is inconvenient for operation and fails to properly deal with nonlinearity in the PV system. The proposed model aims to provide a new deep-learning framework for the energy management of LE-BIPV. The proposed neural network framework can learn the intricate relationships between PV generation and battery storage and enable accurate energy management predictions. This proposed deep learning framework can substantially upgrade the global energy control of building-integrated PV systems in low-energy buildings. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
Logeshwaran, J.; Yadav, Satya Prakash; Altrjman, Chadi; Al-Turjman, Fadi
https://doi.org/10.1007/978-3-031-98997-1_45 https://www.scopus.com/pages/publications/105039102369?origin=resultslist
Logeshwaran J., Department of Computer Science, Christ University, Karnataka, Bengaluru, India; Yadav S.P., School of Computer Science Engineering and Technology (SCSET), Bennett University, U.P, Greater Noida, India; Altrjman C., Department of Chemical Engineering, Waterloo University, Waterloo, ON, Canada; Al-Turjman F., Artificial Intelligence, Software, and Information Systems Engineering Departments, Research Center for AI and IoT, AI and Robotics Institute, Near East University, Mersin, Turkey
An improved frequent pattern tree: the child structured frequent pattern tree CSFP-tree
CSFP-tree; CSFP-tree mining; Data mining; FP-tree; Frequent itemset mining; Improved FP-tree
Frequent itemsets are itemsets that occur frequently in a dataset. Frequent itemset mining extracts specific itemsets with supports higher than or equal to a minimum support threshold. Many mining methods have been proposed but Apriori and FP-growth are still regarded as two prominent algorithms. The performance of the frequent itemset mining depends on many factors; one of them is searching the nodes while constructing the tree. This paper introduces a new prefix-tree structure called child structured frequent pattern tree (CSFP-tree), an FP-tree attached with a child search subtree to each node. The experimental results reveal that the CSFP-tree is superior to the FP-tree and its new variations for any kind of datasets. 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
Pattern Analysis and Applications, Vol-26, No. 2, pp. 437-454.
Jamsheela O., EMEA College of Arts and Science, Kerala, Kondotty, India; Raju G., Christ University Yeshwantpur Campus, Bengaluru, India
An Improved Image Up-Scaling Technique using Optimize Filter and Iterative Gradient Method
corner averaging; high resolution; low resolution; optimized filter component
In numerous realtime applications, image upscaling often relies on several polynomial techniques to reduce computational complexity. However, in high-resolution (HR) images, such polynomial interpolation can lead to blurring artifacts due to edge degradation. Similarly, various edge-directed and learning-based systems can cause similar blurring effects in high-frequency images. To mitigate these issues, directional filtering is employed post corner averaging interpolation, involving two passes to complete the corner average process. The initial step in low-resolution (LR) picture interpolation involves corner pixel refinement after averaging interpolation. A directional filter is then applied to preserve the edges of the interpolated image. This process yields two distinct outputs: the base image and the detail image. Furthermore, an additional cuckoo-optimized filter is implemented on the base image, focusing on texture features and boundary edges to recover neighboring boundary edges. Additionally, a Laplacian filter is utilized to enhance intra-region information within the detailed image. To minimize reconstruction errors, an iterative gradient approach combines the optimally filtered image with the sharpened detail image, generating an enhanced HR image. Empirical data supports the effectiveness of the proposed algorithm, indicating superior performance compared to state-of-the-art methods in terms of both visual appeal and measured parameters. The proposed method's superiority is demonstrated experimentally across multiple image datasets, with higher PSNR, SSIM, and FSIM values indicating better image degradation reduction, improved edge preservation, and superior restoration capabilities, particularly when upscaling High-Frequency regions of images. 2023 IEEE.
Poonia R.C.; Upreti K.; Hundekari S.; Dadhich P.; Malik K.; Kapoor A.
3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023
Poonia R.C., CHRIST(Deemed to Be University), Dept. of Computer Science, Ghaziabad, Delhi NCR, India; Upreti K., CHRIST(Deemed to Be University), Dept. of Computer Science, Ghaziabad, Delhi NCR, India; Hundekari S., MIT ADT University Rajbaug Loni Kalbhor, Pune, India; Dadhich P., School of Computer Application, Manav Rachna International Institute of Research and Studies, India; Malik K., School of Law, CHRIST(Deemed to Be University), Ghaziabad, Delhi NCR, India; Kapoor A., Maharaja Surajmal Institute of Technology, Dept. of Computer Science, Delhi, India
An improved LSTM based thermal prediction and control algorithm for battery management system in hybrid electric vehicles
Adaptive learning; Battery; Electric vehicles; LSTM; Optimization; Reliability; Thermal management
Effective thermal management of lithium-ion batteries is critical for ensuring safety, longevity, and optimal performance in Hybrid Electric Vehicles (HEV). This research proposes an improved Long Short-Term Memory (LSTM) based thermal prediction and control algorithm for Battery Management Systems (BMS) to enhance temperature regulation accuracy and computational efficiency. The proposed model integrates an optimized LSTM network with attention mechanisms to capture long-term dependencies in thermal dynamics while reducing prediction latency. A multi-physics-based thermal model is employed to generate high-fidelity training data, accounting for electrochemical-thermal coupling effects. The algorithm incorporates adaptive learning rates and dropout regularization to mitigate overfitting and improve generalization under varying load conditions. A model predictive control framework is designed to leverage real-time LSTM predictions for proactive cooling strategy optimization, minimizing energy consumption while maintaining safe operating temperatures. The proposed model reached RMSE of Heat generation rate of 1.08 W/mA3, Entropy coefficient Error of 0.024 mV/K, Thermal conductivity of 0.626 w/mK, Latency of 28 ms, Cooling energy Consumption of 314.61 kWh and Temperature deviation of 3.34 AC. The proposed solution offers a computationally efficient, scalable framework for next-generation BMS, enhancing battery reliability and vehicle efficiency. 2025 Elsevier Ltd
Prabu, P.; Ganeshkumar, P.; Krishnan, Prabhakar; Dhanasekaran, S.; Jiang, Weiwei; Logeshwaran, J.
International Communications in Heat and Mass Transfer;Volume;169;Issue;;Article No.;109930;
https://doi.org/10.1016/j.icheatmasstransfer.2025.109930 https://www.scopus.com/pages/publications/105020670519?origin=resultslist
Prabu P., Department of Information Systems, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11442, Saudi Arabia; Ganeshkumar P., Department of Computer Science, College of Computer and Information Sciences, Al Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia; Krishnan P., Center for Cyber Security and Networks, Amrita Vishwa Vidyapeetham, Amritapuri Campus, Kollam, India; Dhanasekaran S., Department of Electronics and Communication Engineering, Sri Eshwar College of Engineering, Tamil Nadu, Coimbatore, 641202, India; Jiang W., School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, 100876, China; Logeshwaran J., Department of Computer Science, Christ University, Karnataka, Bengaluru, 560029, India
An Improved Security Framework for Vulnerable Intrusions in High Dense Fog Networks
Cloud computing; Fog computing; High dense fog networks; Network security; Security framework; Vulnerable intrusions
This work presents an advanced security model to prevent vulnerable invasions through highly dense fog networks. Advantages: One of the strengths that fog computing has revealed is how beneficial its native design can be, but on the other hand, one critical aspect to keep in mind is that it brings several fresh security subjects because of its extremely dynamic and decentralized nature. The framework uses anomaly detection and secure communication protocols to identify the potential intrusion into a network. This also includes clustering fog nodes and more frequent network updates to improve security across the whole network. We evaluate our framework by implementing it in simulation experiments. We show that communication among existing trusted peers can be enhanced, whereas non-trusted sources entering the network cannot conduct attacks. Generally, this framework provides an attractive approach to improve fog networks security and renders them more attack-resistant. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Srivastava, Durgesh; Cynthia, C.; Vivek Kumar, M.; Gupta, Rishi; Garg, Amit
Lecture Notes in Networks and Systems;Volume;1278;pp.445-459
https://doi.org/10.1007/978-981-96-2703-5_30 https://www.scopus.com/pages/publications/105006915447?origin=resultslist
Srivastava D., Institute of Engineering and Technology, Chitkara University, Chitkara University, Punjab, Rajpura, India; Cynthia C., Department of Computer Science, Christ University, Karnataka, Bengaluru, India; Vivek Kumar M., Department of Electronics and Communication Engineering, Sri Eshwar College of Engineering, Tamil Nadu, Coimbatore, India; Gupta R., Department of Computer Science and Engineering, Manipal University Jaipur, Rajasthan, Jaipur, India; Garg A., Department of Computer Science and Engineering, Manipal University Jaipur, Rajasthan, Jaipur, India
An improved web caching system with locally normalized user intervals
Locally normalized intervals; pre-fetching; proxy; web cache
Caching is one of the most promising areas in the field of future internet architecture like Information-centric Networking, Software Defined Networking, and IoT. In Web caching, most of the web content is readily available across the network, even if the webserver is not reachable. Several existing traditional caching methods and cache replacement strategies are evaluated based on the metrics like hit ratio and byte hit Ratio. However, these metrics have not been improved over the period because of the traditional caching policies. So, in this paper, we have used an intelligent function like locally normalized intervals of page visit, website duration, users' interest between user groups is proposed. These intervals are combined with multiple distance metrics like Manhattan, squared Euclidean, and 3-,4-,5-norm Minkowski. In order to obtain significant common user navigation patterns, the clustering relation between the users using different intervals and distances is thoroughly analyzed. These patterns are successfully coupled with greedy web cache replacement strategies to improve the efficiency of the proposed web cache system. Particularly for improving the caching metrics more, we used an AI-based intelligent approach like Random Forest classifier to boost the prefetch buffer performance and achieves the maximum hit rate of 0.89, 0.90, and byte hit rate of 0.87, 0.89 for Greedy Dual Size Frequency and Weighted Greedy Dual Size Frequency algorithms, respectively. Our experiments show good hit/byte hit rates than the frequently used algorithms like least recently used and least frequently used. 2013 IEEE.
Vaithinathan K.; Pernabas J.B.; Parthiban L.; Shrestha B.; Joshi G.P.; Moon H.
https://doi.org/10.1109/ACCESS.2021.3103804 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113251855&doi=10.1109%2fACCESS.2021.3103804&partnerID=40&md5=7c7ad1f86343b42869b4d8b09bd9b1c9
Vaithinathan K., Department of Computer Engineering, Karaikal Polytechnic College, Karaikal, 609609, India; Pernabas J.B., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to Be University), Kengeri Campus, Karnataka, Bangalore, 560074, India; Parthiban L., Department of Computer Science and Engineering, Community College, Pondicherry University, Puducherry, 605014, India; Shrestha B., Department of Information Contents, Kwangwoon University, Seoul, 01897, South Korea; Joshi G.P., Department of Computer Science and Engineering, Sejong University, Seoul, 05006, South Korea; Moon H., Department of Computer Science and Engineering, Sejong University, Seoul, 05006, South Korea
An improvised grid resource allocation and classfication through regression
prediction; Regression; Resource monitoring
The resource allocation is one of the important mechanisms of grid computing, which helps to assign the available resources very efficiently. The one of the issue of grid computing is fixing the target nodes during the grid job execution. In existing method, resource monitored data are collected from grid then jobs are allocated to the resources based on available data, through regression algorithm. In this method total execution time of an application and run time of jobs should be high. The proposed method mitigate running time by classify the resources in the data collected from grid based on dwell time using novel classification algorithm. It reduces the jobs run time and fit the best available resources to the jobs in the computational grid. 2017 IEEE.
Krishnakumar A.; Kumaran T.S.; Balamurugan M.
2017 Conference on Emerging Devices and Smart Systems, ICEDSS 2017, pp. 129-132.
https://doi.org/10.1109/ICEDSS.2017.8073672 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85039908223&doi=10.1109%2fICEDSS.2017.8073672&partnerID=40&md5=555d1467932b2db43559537b45db0ece
Krishnakumar A., Dept of CSE, ACS College of Engineering, India; Kumaran T.S., Dept of CSE, ACS College of Engineering, India; Balamurugan M., Dept of CSE, Christ Unitversity, Faculty of Engineering, India
An Improvised Mechanism for Optimizing Fault Detection for Big Data Analytics Environment
In the applications of fault detection, the inputs are the data reflected from health state of the observed system. A major challenge to finding errors is the nonlinear relationship between the data. Big data has other drawbacks, and the volume and speed with which it is generated are reflected in the data streams themselves. In this paper, we develop a deep learning model that aims to provide fault detection in big data analytics engine. This investigation develops an approach for fault detection in large datasets using unsupervised learning. In this research, an unsupervised method of learning is developed specifically for the task of classifying large datasets. To discover regular textual patterns in large datasets, this research use data visualization methods. In this virtual environment, we employ an unsupervised learning method of machine learning that does not require human oversight. Instead, the system should be allowed some leeway to work and find things on its own. The unsupervised learning approach utilizes data that has not been tagged. In contrast to supervised learning, this approach can handle complex tasks. 2024, Ismail Saritas. All rights reserved.
Paulin M.S.; Subramani K.; Muralidharan P.; Maruthamuthu; Kumar R.S.
International Journal of Intelligent Systems and Applications in Engineering, Vol-12, No. 9s, pp. 427-433.
Paulin M.S., Business and Management, Christ (Deemed to be University), Kengeri campus, Bangalore, India; Subramani K., Business and Management, Christ (Deemed to be University), Kengeri campus, Bangalore, India; Muralidharan P., Business and Management, Christ (Deemed to be University), Kengeri campus, Bangalore, India; Maruthamuthu, Madanapalle Institute of Technology & Science, Angallu, Madanapalle, India; Kumar R.S., Sathak College of Engineering & Technology, Tamilnadu, Ramnad, India
An in vitro slow-growth callus conservation strategy for several medicinal plants using response surface methodology and machine learning
Antioxidant capacity; Citric acid; In vitro conservation; Methyl jasmonate; Plant cell culture; Response surface methodology
Background: In vitro culture of callus is an effective method for conserving the genetic resources of economically important crops. However, continuous subculturing is a costly and labor-intensive process. Therefore, establishing an efficient in vitro long-term conservation system applicable to various plant species is required. In this study, calli derived from five medicinal plant species, Camellia japonica (Cj), Centella asiatica (Ca), Ligusticum afficinale (Lo), Panax ginseng (Pg), and Sageratia thea (St) were used to optimize storage conditions and establish a suitable in vitro conservation strategy. Calli cultures were maintained on the appropriate culture medium at 5C for 120 days. Cell viability and regrowth rate were assessed during the storage period, and correlations between growth and antioxidant traits were examined. Subsequently, pretreatment optimization using sucrose (39%), MeJA (0-200 M), and CTR (020mg/L) was performed using RSM, and the effects of pretreatment and storage temperature on callus conservation were evaluated. In addition, machine learning models such as GRNN, MLP, RF, SVR, and XGBoost were applied to the experimental data. Results: The findings demonstrated that, in comparison to Ca and St, Lo, Pg, and Cj exhibited noticeably higher antioxidant capacity while maintaining high cell viability and regrowth rates. Interestingly, Ca and St drastically decreased viability and regrowth after 60 days, whereas Lo, Pg, and Cj maintained viability and regeneration for up to 90 days. Both TPC and AC (measured by FRAP assay) showed a high positive correlation with cell viability and growth rate, according to correlation analysis. RSM predicted that the optimal pretreatment medium for enhancing antioxidant capacity was the species-specific proliferation medium supplemented with 3% sucrose, 135 M MeJA, and 20mg/L CTR, while the highest TSSC was achieved using the species-specific proliferation medium supplemented with 9% sucrose and 200 M MeJA. When the storage temperature was set to 15C following the antioxidant-enhancing pretreatment derived from the RSM optimization, all five species showed improved cell viability and regrowth rates, among the storage methods. Among the ML models tested, XGBoost demonstrated the most stable and accurate predictive performance for both viability and regrowth during in vitro conservation. SHAP-based analysis of the XGBoost model, focusing on regrowth rate, revealed that storage duration was the most influential factor affecting regrowth prediction, followed by storage temperature, while pretreatment conditions showed secondary but meaningful contributions. Conclusions: This study demonstrates that long-term callus conservation is closely associated with AC and TPC. Medium supplemented with sucrose 3%, 135 M MeJA, and 20mg/L CTR, followed by storage at 15C, significantly improved viability and regrowth, and calli could be maintained up to 120 days without subculturing. This approach provides an efficient and broadly applicable in vitro strategy for the conservation of diverse plant genetic resources. The Author(s) 2026.
Kim, Ye-Won; Lee, Han-Sol; Murthy, Hosakatte Niranjana; Lee, Hyoshin; Park, So-Young
BMC Biotechnology;Volume;26;Issue;1;Article No.;49;
All Open Access; Gold Open Access; Green Open Access
https://doi.org/10.1186/s12896-026-01129-x https://www.scopus.com/pages/publications/105036335427?origin=resultslist
Kim Y.-W., Department of Horticultural Science, Division of Animal, Horticultural and Food Sciences, Chungbuk National University, Cheongju, 28644, South Korea; Lee H.-S., Department of Horticultural Science, Division of Animal, Horticultural and Food Sciences, Chungbuk National University, Cheongju, 28644, South Korea; Murthy H.N., Department of Horticultural Science, Division of Animal, Horticultural and Food Sciences, Chungbuk National University, Cheongju, 28644, South Korea, Department of Life Sciences, Christ University, Bengaluru, 560029, India; Lee H., Department of Horticultural Science, Division of Animal, Horticultural and Food Sciences, Chungbuk National University, Cheongju, 28644, South Korea; Park S.-Y., Department of Horticultural Science, Division of Animal, Horticultural and Food Sciences, Chungbuk National University, Cheongju, 28644, South Korea
An In-Depth Analysis of Collision Avoidance Path Planning Algorithms in Autonomous Vehicles
artificial intelligence; autonomous surface vehicle; collision avoidance; dynamic environment; Path planning; unmanned vehicle
Path planning is a way to define the motion of an autonomous surface vehicle (ASV) in any existing obstacle environment to enable the vehicle's movement by setting directions to avoid that can react to the obstacles in the vehicle's path. A good, planned path perceives the environment to the extent of uncertainty and tries to build or adapt its change in the path of motion. Efficient path planning algorithms are needed to alleviate deficiencies, that are to be modified using the deterministic path that leads the ASV to reach a goal or a desired location while finding optimal solution has become a challenge in the field of optimization along with a collision-free path, making path planning a critical thinker. The traditional algorithms have a lot of training and computation, making it difficult in a realistic environment. This review paper explores the different techniques available in path planning and collision avoidance of ASV in a dynamic environment. The objective of good path planning and collision avoidance for a dynamic environment is compared effectively with the existing obstacles movement of different vehicles. Different path planning technical approaches are compared with their performance and collision avoidance for unmanned vehicles in marine environments by early researchers. This paper gives us a clear idea for developing an effective path planning technique to overcome marine accidents in the dynamic ocean environment while choosing the shortest, obstacle-free path for Autonomous Surface Vehicles that can reduce risk and enhance the safety of unmanned vehicle movement in a harsh ocean environment. 2024 Bentham Science Publishers.
Recent Advances in Computer Science and Communications, Vol-17, No. 8, pp. 62-72.
Daniel K.L., Amity Institute of Information Technology, Amity University Rajasthan, Jaipur, 303002, India; Poonia R.C., Department of Computer Science, Christ (Deemed to be University), NCR, Delhi, 201003, India
An in-Depth Analysis on the Cumulative Effect of Co and Sintering Temperatures on the Formaldehyde Sensing Attributes of NiO
Doping; Gas sensor; Nanostructures; NiO; XPS
In-depth studies are availing to explore and utilize the sensing attributes of p-type NiO nanostructures. However, the surface functionalization of NiO using Co for gas sensing along with varying temperature profile is a novel attempt till date. The research succeeded in synthesizing pure and substituted NiO via co-precipitation route and assessed the sensing capability of the samples by testing with 10 different target gases. The Co doped NiO sintered at 500C exhibited promising sensing performance within a concentration range of 1100ppm, notably achieving a high response of 7817 for 100ppm HCHO at room temperature. The proposed sensor demonstrated rapid response and recovery times (9s and 8s), and it successfully passed stability tests conducted over a 30-day period and repeatability tests consisting of eight cycles. The work paved a way to the implication of the prepared sensor as a breath analyzer to detect lung cancer due to its appreciable formaldehyde sensing characteristics. Graphical Abstract: (Figure presented.) The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
John R.A.B., Department of Physics and Electronics, Christ (Deemed to be) University, Karnataka, Bengaluru, India; Vijayan K., School of Advanced Sciences, Vellore Institute of Technology, Tamil Nadu, Vellore, 632 014, India, Centre for Condensed Matter Physics, National Taiwan University, Taipei, Taiwan; Joshi H., School of Advanced Sciences, Vellore Institute of Technology, Tamil Nadu, Vellore, 632 014, India
An in-depth investigation into financial literacy levels in Indian households
In a complex financial world, lack of awareness complicates money management and savings. Emphasizing financial literacy is vital for informed decision-making. This study explores global financial illiteracy, advocating international initiatives. In India, it assesses disparities and government activities and reviews tax-saving and mobile banking. Gaps include limited studies on Indian households, necessitating gender-specific analyses and research on education's impact. The methodology outlines justification, operational definitions, and data collection techniques. With ANOVA and descriptive statistics on 285 respondents, the study reveals demographic analysis, indicating higher financial literacy with age and a gender gap. Education. positively correlates with financial literacy. Recommendations include interventions like financial seminars, collaboration with regulators, and destigmatizing money talks at home to enhance financial literacy and bridge gaps. 2024 by IGI Global. All rights reserved.
Patil A.; Niranjan L.R.; Patavardhan R.; Jain V.
Contemporary Research and Practices for Promoting Financial Literacy and Sustainability, pp. 113-146.
Patil A., Christ University, India; Niranjan L.R., Christ University, India; Patavardhan R., Christ University, India; Jain V., Christ University, India
An in-silico pharmacophore-based molecular docking study to evaluate the inhibitory potentials of novel fungal triterpenoid Astrakurkurone analogues against a hypothetical mutated main protease of SARS-CoV-2 virus
COVID 19; MD Simulation; Phytochemicals; Principal component analysis; ZINC compounds
Background: The main protease is an important structural protein of SARS-CoV-2, essential for its survivability inside a human host. Considering current vaccines' limitations and the absence of approved therapeutic targets, Mpro may be regarded as the potential candidate drug target. Novel fungal phytocompound Astrakurkurone may be studied as the potential Mpro inhibitor, considering its medicinal properties reported elsewhere. Methods: In silico molecular docking was performed with Astrakurkurone and its twenty pharmacophore-based analogues against the native Mpro protein. A hypothetical Mpro was also constructed with seven mutations and targeted by Astrakurkurone and its analogues. Furthermore, multiple parameters such as statistical analysis (Principal Component Analysis), pharmacophore alignment, and drug likeness evaluation were performed to understand the mechanism of protein-ligand molecular interaction. Finally, molecular dynamic simulation was done for the top-ranking ligands to validate the result. Result: We identified twenty Astrakurkurone analogues through pharmacophore screening methodology. Among these twenty compounds, two analogues namely, ZINC89341287 and ZINC12128321 showed the highest inhibitory potentials against native and our hypothetical mutant Mpro, respectively (?7.7 and ?7.3 kcal mol?1) when compared with the control drug Telaprevir (?5.9 and ?6.0 kcal mol?1). Finally, we observed that functional groups of ligands namely two aromatic and one acceptor groups were responsible for the residual interaction with the target proteins. The molecular dynamic simulation further revealed that these compounds could make a stable complex with their respective protein targets in the near-native physiological condition. Conclusion: To conclude, Astrakurkurone analogues ZINC89341287 and ZINC12128321 can be potential therapeutic agents against the highly infectious SARS-CoV-2 virus. 2022 Elsevier Ltd
Nag A.; Dasgupta A.; Sengupta S.; Lai T.K.; Acharya K.
Computers in Biology and Medicine, Vol-152
All Open Access; Bronze Open Access; Green Open Access
ISSN: 104825; PubMed ID: 36565483; CODEN: CBMDA
https://doi.org/10.1016/j.compbiomed.2022.106433 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144556537&doi=10.1016%2fj.compbiomed.2022.106433&partnerID=40&md5=f0cb22523215711163b43da252e6540c
Nag A., Department of Life Sciences, CHRIST (Deemed to be University), Karnataka, Bangalore, India; Dasgupta A., Department of Botany, University of Calcutta, West Bengal, Kolkata, India; Sengupta S., Department of Life Sciences, CHRIST (Deemed to be University), Karnataka, Bangalore, India; Lai T.K., Department of Chemistry, Vidyasagar Metropolitan College, West Bengal, Kolkata, India; Acharya K., Department of Botany, University of Calcutta, West Bengal, Kolkata, India