Enhanced Design and Performance Analysis of a Seven-Level Multilevel Inverter for High-Power Applications
The structure and performance analysis of a seven-level multilevel inverter is discussed in this study. Due to their capacity to get around the drawbacks of traditional two-level inverters, like high voltage stress on power devices and harmonic distortion, multilevel inverters have attracted a lot of attention lately. Multiple voltage levels can be produced by the seven-level multilevel inverter which is being proposed because it uses a sequential arrangement of power sources and capacitors. The design methodology involves selecting appropriate power devices and capacitance values to achieve the desired voltage levels while minimizing losses and ensuring reliable operation. Total harmonic distortion (THD), inverter efficiency, and voltage stress on power devices are all considered as part of the performance analysis. In comparison to conventional two-level inverters, simulation results indicate that the proposed seven-level multilevel inverter offers lower THD, increased efficiency, and reduced voltage stress. This research contributes to the advancement of multilevel inverter technology and its potential applications in various power conversion systems. 2023 IEEE.
Parimalasundar E.; Jayakumar S.; Nagajothi P.; Chandrika V.S.; Devaraju T.; Suresh K.
Proceedings of the 2023 2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023, pp. 1862-1868.
Institute of Electrical and Electronics Engineers Inc.
Parimalasundar E., Mohan Babu University (Erst while Sree Vidyanikethan Engineering College), Electrical and Electronics Engineering, Tirupati, India; Jayakumar S., Sri Sairam College of Engineering, Electronics and Communication Engineering, Bengaluru, India; Nagajothi P., S.A. Engineering College, Electrical and Electronics Engineering, Chennai, India; Chandrika V.S., Kpr Institute of Engineering and Technology, Electrical and Electronics Engineering, Coimbatore, India; Devaraju T., Mohan Babu University (Erst while Sree Vidyanikethan Engineering College), Electrical and Electronics Engineering, Tirupati, India; Suresh K., Christ University, Electrical and Electronics Engineering, Bangalore, India
Enhanced Detection of Malicious URLs Using Supervised Machine Learning Models
F1 score; Feature Extraction; Phishing; Precision; Real-time protection; Recall; URL Detection
This paper deliberates on URL phishing, one important subset of cyber threats. Most modern-day deceptive practices have shifted to the digital space due to the vast scope of information available on the internet. URL phishing is a dishonest practice that includes masquerading harmful links as legitimate links to trick users into sharing their private data. Detection of URL phishing is extremely challenging, hence most of these attacks go undetected until it is too late for the victim. Automatic blacklist that rely heavily on user-generated reports to monitor internet links have been repeatedly proven ineffective time and again. Along with failing to identify newly listed phishing sites, these systems also tend to mistake harmless links for phishing traps. This paper proposes the application of classification techniques of practical machine learning, specifically analysing the patterns and behaviours of URLs to detect phishing websites accurately. Leveraging the properties of Decision Trees, Random Forests, Logistic Regression, SVM, and Light GBM, we were able to come up with a detection model, which precisely calculates accuracy, precision, recall, as well as F1 score to evaluate the validity of URL classification. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
Anu, P.; Saseekala, M.; Subhashini, A.; Aarthee, S.; Kalyani, N.
Communications in Computer and Information Science;Volume;2845 CCIS;pp.407-419
Springer Science and Business Media Deutschland GmbH
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1007/978-3-032-20907-8_34 https://www.scopus.com/pages/publications/105038901940?origin=resultslist
Anu P., School of Computing, SASTRA Deemed to Be University, Thanjavur, 613402, India; Saseekala M., Faculty of Computer Applications, School of Business and Management, CHRIST University, Bangalore, India; Subhashini A., Department of Software Systems, PSG College of Arts and Science, Coimbatore, 641014, India; Aarthee S., School of Computing, SASTRA Deemed to Be University, Thanjavur, 613402, India; Kalyani N., School of Computing, SASTRA Deemed to Be University, Thanjavur, 613402, India
Enhanced dielectric and supercapacitive properties of spherical like Sr doped Sm2O3@CoO triple oxide nanostructures
Dielectric properties; Morphology; Nanostructures; Optical; Srx:Sm2O3@CoO; Supercapacitors
Integrating the hybrid nanostructures exhibiting enhanced storage and electrical properties requires tuning of composition of constituents. To address this issue, we prepared Sr2+ nanoparticles (NPs) decorated over Sm2O3@CoO nanostructures (NS) by chemical precipitation. The structure integrity of the composite was determined by analytical tools. Based on the strongest peak of X-ray diffraction (XRD), crystallite size of the nanoparticles was determined to be 26.14 nm, indicating a mixed phase of monoclinic and tetragonal crystal formation. FESEM revealed a spherical-like morphology with a homogeneous distribution of microstructures with average sizes ranging from 68 nm to 60 nm. The optical absorptivity revealed a redshift in absorption bands centred at 337.0 nm, 343.9 nm, and 353.0 nm in UV-region. The optical band gap of NS was found to be in the range of 3.38 eV to 3.15 eV, and the BET surface area of Sr15%:Sm2O3@CoO was found to be 458469 cm2/g with a corresponding pore size of 13.17 nm. All Sr-doped Sm2O3@CoO NS exhibited higher ionic conductivity and dielectric constant than undoped material. In an aqueous KOH electrolyte, the NS showed a specific capacity of 234.2C/g (65.1mAh/g) demonstrating the material as potential candidate in energy storage and dielectrics. 2022 Elsevier Ltd
Adimule V.; Batakurki S.; Bhat V.S.; Yallur B.C.; Hegde G.; Bathula C.
https://doi.org/10.1016/j.est.2022.106318 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143808393&doi=10.1016%2fj.est.2022.106318&partnerID=40&md5=8ad2436cdae3d6f27b9529e3f3ed70f4
Adimule V., Department of Chemistry, Angadi Institute of Technology and Management (AITM), Savagaon Road, Karnataka, Belagavi, 590009, India; Batakurki S., Department of Chemistry, MS Ramaiah University of Applied Sciences, MSR nagar, New BEL Road, Karnataka, Bangalore, 560054, India; Bhat V.S., Centre for Nano-Materials and Displays (CND), B.M.S. College of Engineering, Bull temple road, Basavanagudi, Bangalore, 560 019, India; Yallur B.C., Department of Chemistry, M S Ramaiah Institute of Technology, Karnataka, Bangalore, 560054, India; Hegde G., Centre for Advanced Research and Development (CARD), CHRIST (Deemed-to-be University), Hosur Rd, Bhavani Nagar, S.G. Palya, Karnataka, Bangalore, 560029, India, Department of Chemistry, CHRIST (Deemed-to-be University), Hosur Rd, Bhavani Nagar, S.G. Palya, Karnataka, Bangalore, 560029, India; Bathula C., Division of Electronics and Electrical Engineering, Dongguk University, Seoul, 04620, South Korea
Enhanced Digital Image Watermarking Using 3-Level Discrete Wavelet Transform (DWT)
Discrete Wavelet Transform (DWT); Watermark and Statistical measure
This study compares the algorithm's performance to that of the DWT level 1 and level 2 techniques while proposing a digital picture watermarking technology using a 3-step Discrete Wavelet Transform (DWT). The suggested method uses alpha blending to overlay a multibit watermark into the frequency subband of the lower cover image. The watermark's appearance is controlled by the blending scale. For uniformity, watermark extraction uses the same scale factor. The 3-stage DWT approach is superior because the algorithm performs well for various scaling factors that are obtained in relation to statistical characteristics connected to the Mean Square Error (MSE) and Peak Signal-to-Noise Ratio (PSNR). 2025 IEEE.
2025 7th International Conference on Information Systems and Computer Networks, ISCON 2025;
https://doi.org/10.1109/ISCON65210.2025.11341523 https://www.scopus.com/pages/publications/105033350155?origin=resultslist
Neetha S.S., Christ University, Department of Computer Science, Bangalore, India; Sah S., School of Computing Science and Engineering, Sharda University, India; Deepa S., Christ University, Department of Computer Science, Bangalore, India
Enhanced Edge Computing Model by using Data Combs for Big Data in Metaverse
Augmented Reality; Big data Processing; Data Hive Architecture; Edge computing; Metaverse; Virtual Reality
The Metaverse is a huge project undertaken by Facebook in order to bring the world closer together and help people live out their dreams. Even handicapped can travel across the world. People can visit any place and would be safe in the comfort of their homes. Meta (Previously Facebook) plans to execute this by using a combination of AR and VR (Augmented Reality and Virtual Reality). Facebook aims to bring this technology to the people soon. However, a big factor in this idea that needs to be accounted for is the amount of data generation that will take place. Many Computer Science professors and scientists believe that the amount of data Meta is going to generate in one day would almost be equal to the amount of data Instagram/Facebook would have generated in their entire lifetime. This will push the entire data generation by at least 30%, if not more. Using traditional methods such as cloud computing might seem to become a shortcoming in the near future. This is because the servers might not be able to handle such large amounts of data. The solution to this problem should be a system that is designed specifically for handling data that is extremely large. A system that is not only secure, resilient and robust but also must be able to handle multiple requests and connections at once and yet not slow down when the number of requests increases gradually over time. In this model, a solution called the DHA (Data Hive Architecture) is provided. These DHAs are made up of multiple subunits called Data Combs and those are further broken down into data cells. These are small units of memory which can process big data extremely fast. When information is requested from a client (Example: A Data Warehouse) that is stored in multiple edges across the world, then these Data Combs rearrange the data cells within them on the basis of the requested criteria. This article aims to explain this concept of data combs and its usage in the Metaverse. 2023 IEEE.
IDCIoT 2023 - International Conference on Intelligent Data Communication Technologies and Internet of Things, Proceedings, pp. 249-255.
Mohan T.L.R., Christ (Deemed to Be University), School of Engineering and Technology, India; Jayapandian N., Christ (Deemed to Be University), School of Engineering and Technology, India
Enhanced electrical properties of CuO:CoO decorated with Sm2O3 nanostructure for high-performance supercapacitor
CuO:CoO; Electrical properties; Energy storage; Nanostructures; Samarium oxide; Super capacitor
In the present investigation, we have synthesized samarium (Sm) nanoparticles (NPs) and anchored them onto the surface of CuO:CoO nanostructure (NS) by utilizing a simple chemical precipitation method. Nanostructures (NS) were characterized utilizing powdered X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), X-ray photoelectron spectroscopy (XPS), scanning electron spectroscopy (SEM), transmission electron spectroscopy (TEM), UVvisible spectroscopy (UVVis), and BrunauerEmmettTeller (BET) studies. Resulting Smx CuO: CoO (x = 1%, 5%, 10%, and 12%) NS were investigated for their anomalous electrical and supercapacitive behavior. NS energy storage performance was experimentally determined using cyclic voltammetry (CV), galvanostatic chargedischarge (GCD), and electrochemical impedance spectroscopy (EIS). Sm10%CuO:CoO exhibited better electrochemical response than other samples and showed a maximum specific capacitance of 283.6F/g at 0.25A/g in KOH electrolyte. However, contrary to our expectation, NS displayed rectifying nature in I-V, intercalative nature in C-V, and polaronic permittivity in all concentrations of Sm2O3 doping as compared with undoped CuO:CoO NS. The outstanding properties of Smx CuO:CoO NS are attributed to the synergy of high charge mobility of Sm NPs, leading to significant variation in dielectric permittivity, currentvoltage (I-V) response, capacitancevoltage (C-V) behavior, with the formation of Sm3+ ionic cluster. The clusters lead to a change in dipole moment creating a strong local electric field. Additionally, a CR2032 type symmetric supercapacitor cell was fabricated using Sm10%CuO:CoO, which exhibited a maximum specific capacitance of 67.4F/g at 0.1A/g. The cell was also subjected to 5000 GCD cycles where it retained 96.3% Coulombic efficiency. Graphical Abstract: [Figure not available: see fulltext.] 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Adimule V.; Bhat V.S.; Joshi R.; Batakurki S.; Hegde G.; Yallur B.C.
Journal of Solid State Electrochemistry, Vol-27, No. 2, pp. 511-529.
https://doi.org/10.1007/s10008-024-06007-0 https://www.scopus.com/pages/publications/85198440265?origin=resultslist
Adimule V., Department of Chemistry, Angadi Institute of Technology and Management (AITM), Savagaon Road, Karnataka, Belagavi, 590009, India; Bhat V.S., Centre for Nano-Materials and Displays (CND), B.M.S. College of Engineering, Bull Temple Road, Basavanagudi, Bangalore, 560 019, India; Joshi R., Department of Physics, School of Physical Sciences, Central University of Karnataka, Aland Road, Karnataka, Kadaganchi, Gulbarga, 585311, India; Batakurki S., Department of Chemistry, M. S. Ramaiah University of Applied Sciences, Bangalore, 560054, India; Hegde G., Centre for Adavanced Research and Development (CARD), CHRIST (Deemed to be University), Hosur Rd, Bhavani Nagar, S.G. Palya, Karnataka, Bangalore, 29, India, Department of Chemistry, CHRIST (Deemed to be University), Hosur Rd, Bhavani Nagar, S.G. Palya, Karnataka, Bangalore, 29, India; Yallur B.C., Department of Chemistry, M S Ramaiah Institute of Technology, Karnataka, Bangalore, 560054, India
Enhanced encryption technique for secure iot data transmission
Cipher; Cryptography; Network Security; Public Key Server; Security Attack
Internet of things is the latest booming innovation in the current period, which lets the physical entity to process and intervene with the virtual entities. As all the entities relate to each other, it generates load of data, which lacks proper security and privacy standards. Cryptography is one of the domains of Network Security, which is one such mechanism that helps the data transmission process to be secure enough over the wireless or wired channel and along with that, it provides authenticity, confidentiality, integrity of data and prevents repudiation. In this paper, we have proposed an alternate enhanced cryptographic solution combing the characteristic of symmetric, asymmetric encryption algorithms and Public Key Server. Here, the key pairs of end points (Users Device and IoT device) are generated using Elliptic Curve Cryptography and the respective public keys are registered in Public Key Server along with their unique MAC address. Thereafter, both the ends will agree on one common private secret key, which will be the base for further cryptographic process using AES algorithm. This model can be called as multi-phase protection mechanism. It will make the process of data transmission secure enough that no intermediate can tamper the data. Copyright 2019 Institute of Advanced Engineering and Science. All rights reserved.
International Journal of Electrical and Computer Engineering, Vol-9, No. 5, pp. 3732-3738.
Institute of Advanced Engineering and Science
All Open Access; Gold Open Access; Green Open Access
https://doi.org/10.11591/ijece.v9i5.pp3732-3738 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067336335&doi=10.11591%2fijece.v9i5.pp3732-3738&partnerID=40&md5=9475dc3e73e49ea68f6a32c7473a622a
Bhandari R., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, 560029, India; Kirubanand V.B., Faculty of Computer Science Department, CHRIST (Deemed to be University), India
Enhanced Energy Efficient Routing for Wireless Sensor Network Using Extended Power Efficient Gathering in Sensor Information Systems (E-PEGASIS) Protocol
E-PEGASIS; Energy efficient routing; LEACH; PEGASIS; Wireless Sensor Networks
Recent technological advancement in wireless communication and sensors made Wireless Sensor Networks (WSNs) as one of the demanding platforms in the current scenario. In WSN, tiny sensor nodes are collecting and monitoring the biological data or physical data or environmental data and transmitted to the base station (BS) through gateway routers. These data can be accessed anywhere and anytime. Usually sensor nodes have restrained battery power which creates the rigorous lifetime duration issues in WSN. Sensor nodes can communicate with each other using various routing protocols. Data transmission devours more amounts of energy and power. So, energy preservation is an important factor in WSN. There are plenty of researches going on in designing less energy consuming protocols for data transmission which helps to increase the lifetime of WSN. In this paper we have proposed Extended Power Efficient Gathering in Sensor Information Systems (E-PEGASIS) protocol for enhanced energy efficient data transmission based on PEGASIS protocol. In this proposed method average distance between the sensor nodes are considered as the criterion for chaining and fix the outermost node's radio range value the base station. Later it chains the related nodes available in the radio range. Consequently, the chained node checks their distance with the next nearest end node to go on with the chaining procedure which will enhance the performance of data transmission between the sensor node and the base station. The simulation of the proposed work shows that lifetime of the network is increased when comparing to the LEACH and PEGASIS protocol. 2021 The Authors. Published by Elsevier B.V.
Procedia Computer Science, Vol-194, pp. 89-101.
https://doi.org/10.1016/j.procs.2021.10.062 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121778596&doi=10.1016%2fj.procs.2021.10.062&partnerID=40&md5=f2da3288aea63661266b0c1720e426a7
Sadhana S., Department of Computer Science and Engineering, KalaignarKarunanidhi Institute of Technology, Coimbatore, India; Sivaraman E., Department of Computer Science and Engineering, PES University, Bangalore, India; Daniel D., Department of Computer Science and Engineering, Christ University, Bangalore, India
Enhanced Energy-Efficient Routing for Wireless Sensor Network Using Extended Power-Efficient Gathering in Sensor Information Systems (E-PEGASIS) Protocol
E-PEGASIS; Energy-efficient routing; LEACH; PEGASIS; Wireless sensor networks
Recent technological advancements in wireless communication and sensors made Wireless Sensor Networks (WSNs) as one of the demanding platforms in the current scenario. In WSN, tiny sensor nodes are collecting and monitoring the biological data or physical data or environmental data and transmits to the Base Station (BS) through gateway routers. These data can be accessed anywhere and anytime. Usually, sensor nodes have restrained battery power which creates the rigorous lifetime duration issues in WSN. Sensor nodes can transmit the data with each other using various routing protocols. Data transmission devours more amounts of energy and power. So, energy preservation is an important factor in WSN. There are plenty of researches going on in designing less energy consuming protocols for data transmission which helps to increase the lifetime of WSN. In this manuscript, we have proposed Extended Power-Efficient Gathering in Sensor Information Systems (E-PEGASIS) protocol for enhanced energy-efficient data transmission based on PEGASIS protocol. In this proposed method, the average distance between the sensor nodes is considered as the criterion for chaining and fixing the outermost nodes radio range value to the base station. Later it chains the related nodes available in the radio range. Consequently, the chained node checks their distance with the next nearest end node to go on with the chaining procedure which will enhance the performance of data transmission amid the base station and sensor node. The simulation of the proposed work shows that lifetime of the network is increased when compared to the LEACH and PEGASIS protocol. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Smart Innovation, Systems and Technologies, Vol-235, pp. 159-171.
Sadhana S., KalaignarKarunanidhi Institute of Technology, Coimbatore, India; Sivaraman E., PES University, Bangalore, India; Daniel D., Christ (Deemed To Be University), Bangalore, India
Enhanced Geographical Information System Architecture for Geospatial Data
Singh M.; Agarwal S.; Ajay Prasanna Y.; Jayapandian N.; Kanmani P.
New Trends in Computational Vision and Bio-Inspired Computing - Selected Works Presented at the ICCVBIC 2018, pp. 37-44.
Singh M., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Kengeri Campus, Bangalore, India; Agarwal S., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Kengeri Campus, Bangalore, India; Ajay Prasanna Y., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Kengeri Campus, Bangalore, India; Jayapandian N., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Kengeri Campus, Bangalore, India; Kanmani P., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Kengeri Campus, Bangalore, India
Enhanced Horse Optimization Algorithm Based Intelligent Query Optimization in Crowdsourcing Systems
Crowdsourcing; EHOA-IQOCSS; Location-based services; Query optimization
Crowdsourcing is a strategy of collecting information and knowledge from an abundant range of individuals over the Internet in order to solve cognitive or intelligence intensive challenges. Query optimization is the process of yielding an optimized query based upon the cost and latency for a given location based query. In this view, this article introduces an Enhanced Horse Optimization Algorithm based Intelligent Query Optimization in Crowdsourcing Systems (EHOA-IQOCSS) model. The presented EHOA-IQOCSS model mainly based on the enhanced version of HOA using chaotic concepts. The proposed model plans to accomplish a better trade-off between latency and cost in the query optimization process along with answer quality. The EHOA-IQOCSS is used to compute the Location-Based Services (LBS) namely K-Nearest Neighbor (KNN) and range queries, where the Space and Point of Interest (POI) can be obtained by the conviction level computation. The comparative study stated the betterment of the EHOA-IQOCSS model over recent methods. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Renukadevi M.; Anita E.A.M.; Geetha D.M.
Communications in Computer and Information Science, Vol-1797 CCIS, pp. 234-249.
Renukadevi M., Department of Electronics andCommunication Engineering, Sri Krishna College of Engineering and Technology, Tamil Nadu, Coimbatore, India; Anita E.A.M., Department of CSE, School of Engineering and Technology, CHRIST University, Bengaluru, India; Geetha D.M., Department of Electronics andCommunication Engineering, Sri Krishna College of Engineering and Technology, Tamil Nadu, Coimbatore, India
Enhanced Image Classification using Transfer Learning with ResNet50-V2: A Case Study on Wildlife Recognition
CNN; CycleGAN; Model Selection; ResNet50-V2; Transfer Learning; wildlife classification
This study explores the application of transfer learning using the ResNet50-V2 architecture for accurate classification of Arctic wildlife species, including Arctic foxes, polar bears, and walruses. Transfer learning leverages pretrained networks to enhance performance in new tasks with limited labeled data, reducing the need for extensive data collection and computational resources. In this work, we utilized a dataset of 1000 labeled images across the three species and applied ResNet50-V2, pre-trained on ImageNet, as a feature extractor. The model achieved high accuracy, with training and validation accuracies nearing 99% and 95-97%, respectively, though minor overfitting was observed. This indicates the model's strong ability to generalize across the dataset while benefiting from pre-trained weights on diverse, non-related images. Additionally it compares with models like SSD and CycleGAN, emphasizing its capability to generalize well, handle small datasets, and mitigate overfitting. We discuss model architecture, data preprocessing, and the experimental results, focusing on improvements achievable through regularization techniques to counteract overfitting. This study demonstrates the effectiveness of transfer learning for wildlife classification, providing insights into optimizing CNNs for ecological and conservation applications. 2025 IEEE.
Chandan, N.; Manimekala, B.; Siva Balan, R.V.
6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025 - Proceedings;pp.1565-1570
https://doi.org/10.1109/ICMCSI64620.2025.10883359 https://www.scopus.com/pages/publications/105000007871?origin=resultslist
Chandan N., Department of Computer Science, CHRIST (Deemed to be University), Yeshwanthpur Campus, Karnataka, Bangalore, 560073, India; Manimekala B., Department of Computer Science, CHRIST (Deemed to be University), Yeshwanthpur Campus, Karnataka, Bangalore, 560073, India; Siva Balan R.V., Department of Computer Science, CHRIST (Deemed to be University), Yeshwanthpur Campus, Karnataka, Bangalore, 560073, India
Enhanced image encryption using fractional-order chaotic systems and neural network-based optimization for secure multimedia applications
Fractional-order chaotic systems; Image encryption; Logistic map; Neural network optimization
The rapid expansion of multimedia data in fields like healthcare and finance necessitates robust image encryption to protect sensitive content. Conventional chaotic encryption, based on integer-order systems, is hindered by restricted key spaces (e.g., and suboptimal parameter choices, exposing vulnerabilities. This work introduces an innovative encryption method that merges a fractional-order chaotic Logistic map with neural network optimization to overcome these shortcomings and enhance security. Utilizing the Grunwald-Letnikov derivative, the fractional-order Logistic map produces a complex, unpredictable sequence for encryption. A feedforward neural network fine-tunes parameters (,), elevating the Lyapunov exponent from 0.5032 to 0.6540, signifying heightened chaos. This integration harnesses fractional-order memory effects and neural network adaptability, surpassing traditional integer-order encryption constraints. The method achieves a key space of, entropy of 7.9962, and horizontal correlation of 0.0028. Parameter sensitivity tests show significant output variation with minor changes. Security analysis yields NPCR at 99.60% and UACI at 33.45%. Neural network training achieves a low mean squared error of 0.0032912 by epoch 100, with high correlation. Encryption of 256256 images in 0.21 seconds and 720p video at 41.67 fps (0.024 s/frame) supports real-time applications. By combining fractional-order chaos with machine learning, this approach delivers superior image encryption, addressing integer-order system limitations. It provides a scalable framework for secure multimedia communications. Future efforts will extend the technique to color images and video, incorporating advanced machine learning for greater resilience. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026.
P, Bhuvaneswari; V, Thirunavukkarasu; J, Loveline Zeema; NV, Poornima; Karnan, Lokanayaki
Multimedia Tools and Applications;Volume;85;Issue;6;Article No.;534;
https://doi.org/10.1007/s11042-026-21701-z https://www.scopus.com/pages/publications/105039884419?origin=resultslist
P B., Department of Machine Learning, Yazhli Global Multidisciplinary Research Organization(YGMRO), Tamilnadu, Tirupur, India; V T., Department of Computer Science, Christ (Deemed to be University), Karnataka, Bengaluru, India; J L.Z., Department of Computer Science, Christ (Deemed to be University), Karnataka, Bengaluru, India; NV P., Symbiosis Centre for Management Studies, Symbiosis International (Deemed University), Karnataka, Bengaluru, India; Karnan L., School of Computer Science and Engineering, RV University, Karnataka, Bengaluru, India
Enhanced Jaya Optimization Algorithm with Deep Learning Assisted Oral Cancer Diagnosis on IoT Healthcare Systems
Computer-assisted diagnoses; Deep learning; Healthcare; Internet of Things; Jaya Optimization Algorithm; Medical imaging; Oral cancer
Recently, healthcare systems integrate the power of deep learning (DL) models with the connectivity and data processing capabilities of the Internet of Things (IoT) to enhance the early recognition and diagnosis of disease. Oral cancer diagnosis comprises the detection of cancerous or pre-cancerous abrasions in the oral cavity. Timely identification is essential for successful treatment and enhanced prognosis. Here is an overview of the key aspects of oral cancer diagnosis. One potential benefit of utilizing DL for oral cancer detection is that it analyses huge counts of data fast and accurately, and it could not need clear programming of the rules for recognizing abnormalities. This can create the procedure of detecting oral cancer more effective and efficient. Thus, the study presents an Enhanced Jaya Optimization Algorithm with Deep Learning Based Oral Cancer Classification (EJOADL-OCC) method. The presented EJOADL-OCC method aims to classify and detect the existence of oral cancer accurately and effectively. To accomplish this, the presented EJOADL-OCC method initially exploits median filtering for the noise elimination. Next, the feature vector generation process is performed by the residual network (ResNetv2) model with EJOA as a hyperparameter optimizer. For accurate classification of oral cancer, a continuously restricted Boltzmann machine with a deep belief network (CRBM-DBN) model. The simulated validation of the EJOADL-OCC algorithm is tested by the series of simulations and the outcome demonstrates its supremacy over present DL approaches. 2024, American Scientific Publishing Group (ASPG). All rights reserved.
Rajkumar R.; Valluru D.; Siva Satya Sreedhar P.; Ramshankar N.; Sujatha S.; Somasundaram R.; Sudha M.; Navaneethan S.
Journal of Intelligent Systems and Internet of Things, Vol-11, No. 2, pp. 97-110.
American Scientific Publishing Group (ASPG)
https://doi.org/10.54216/JISIoT.110209 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189005327&doi=10.54216%2fJISIoT.110209&partnerID=40&md5=8e7251f6e1c2adb87150893aa151d51a
Rajkumar R., Department of ECE, Vel Tech Rangarajan Dr. Saguthala R&D institute of Science and Technology, Tamil Nadu, Chennai, India; Valluru D., Department of IT, MLRITM Engineering College, Hyderabad, India; Siva Satya Sreedhar P., Department of Information Technology, Seshadri Rao Gudlavalleru Engineering College, Krishna district, Andhra Pradesh, Gudlavalleru, India; Ramshankar N., Department of Computer Science and Engineering, R. M. D. Engineering College, Kavaraipettai, Tamil Nadu, Tiruvallur, India; Sujatha S., Department of Electronics and communication Engineering, Christ University, School of Engineering and Technology, Bangalore, India; Somasundaram R., Management Studies Department, Kongu Engineering College, Erode, 638060, India; Sudha M., Department of ECE, Paavai Engineering College (Autonomous), Tamilnadu, Namakkal, India; Navaneethan S., Department of ECE, Saveetha Engineering College, Tamil Nadu, Chennai, India
Enhanced Learning in IoT-Based Intelligent Plant Irrigation System for Optimal Growth and Water Management
ANN; CNN; Machine Learning; Precision Agriculture; Smart Irrigation; SVM
This research looked at the transformative potential of cutting-edge machine learning algorithms in various areas of precision agriculture, with an emphasis on enhancing smart irrigation systems for onion farming. Using a vast sensor network and real-time monitoring, we investigated the performance of CNN, ANN, and SVM, three well-known machine learning algorithms. After extensive testing and investigation, our results reveal that CNN beats ANN and SVM in terms of outstanding accuracy in predicting plant water requirements. Because of CNN's superior predictive powers, our intelligent irrigation system maintains perfect soil conditions, resulting in increased agricultural yields and resource savings. The study's findings have important implications for modern agriculture, paving the way for data-driven, sustainable agricultural methods that address global concerns such as food security and environmental sustainability. As we approach the era of smart agriculture, our research demonstrates how technology has the potential to alter crop farming and aid in the development of a more resilient and successful agricultural industry. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Singh J.; Shelke N.A.; Hasan D.S.; Sajid M.; Alsahlanee A.T.R.; Upreti K.
Lecture Notes in Networks and Systems, Vol-1050 LNNS, pp. 231-240.
Singh J., School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India; Shelke N.A., School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India; Hasan D.S., Department of Computer Science and Information Technology, University of Salahaddin-Erbil, Erbil, Iraq; Sajid M., Department of Computer Science, Aligarh Muslim University, Aligarh, India; Alsahlanee A.T.R., Development and Continuous Education Center, University of Thi-Qar, Thi-Qar, Iraq; Upreti K., Department of Computer Science, CHRIST (Deemed to Be University), Ghaziabad, India
Enhanced Level Brain Tumor Identification Using CNN, VGG16 and ResNet Models
Brain Tumor; CNN; Deep Learning; MRI Classification; RESNET; VGG16
The comprehension of brain growths is significantly improved through the identification and categorization of these disorders. Still, their discovery is relatively grueling due to their variability in terms of position, shape, and size. Fortunately, deep literacy has revolutionized the field and significantly improved recognition, prediction, and opinion in various healthcare areas, including brain excrescences. The main goal of this study is to thoroughly review exploration that utilizes CNN, VGG16, and RESNET infrastructures to classify brain excrescences using MRI images. The performance of these models varied significantly, with CNN, VGG16, and RESNET achieving an emotional delicacy of 99.6. Additionally, ResNet and VGG16 achieved rigor of 92.4 and 89.7 independently. Likewise, the visualization of the decision-making processes of these models has provided valuable insight into the features they prioritize. By incorporating these models into their practice, healthcare professionals have the opportunity to enhance their individual capabilities, eventually leading to improved patient outcomes. 2024 IEEE.
Proceedings of InC4 2024 - 2024 IEEE International Conference on Contemporary Computing and Communications
Sambandam R.K., Christ University, Dept. of Computer Science and Engineering, Bengaluru, India; Jyoshna M., Christ University, Dept. of Computer Science and Engineering, Bengaluru, India
Enhanced light harvesting in DSSCs using carbon dots derived from Alstonia venenata
This study presents a novel co-sensitization strategy utilizing carbon dots derived from Alstonia venenata in combination with the N719 dye to enhance the light-harvesting efficiency of dye-sensitized solar cells (DSSCs). The carbon dots were synthesized via a hydrothermal process using an aqueous extract of Alstonia venenata leaves, resulting in a material with broad absorption characteristics. These synthesized carbon dots were then drop-cast onto an N719-sensitized photoanode, leading to improved carrier generation and enhanced device performance. The selection of Alstonia venenata as a precursor is based on its rich phytochemical composition, which contains alkaloids, flavonoids, terpenoids and phenolic compounds that act as efficient carbon precursors and surface passivation agents. Upon carbonisation, these biomolecules yield functionally active carbon dots that can improve electron transport, minimise charge recombination and enhance dye anchoring at the TiO2 surface. Carbon dots have demonstrated significant potential as co-sensitizers, offering a highly effective approach to increasing DSSC efficiency. Their strong binding affinity further facilitates efficient photoinduced electron transfer to the photoanode, contributing to improved device functionality. In this research, TiO2 was employed as the photoanode, while N719 dye and carbon-dot-modified N719 served as sensitizers. Iodolyte HI-30 acted as the electrolyte, and Platisol T/sp functioned as the counter electrode. The unmodified DSSC exhibited a power conversion efficiency of 5.2%, which was enhanced to 6.0% with the incorporation of carbon dots as co-sensitizers. The significant efficiency improvement achieved through this co-sensitization strategy underscores the unique capabilities of carbon dots derived from Alstonia venenata, making this approach a promising advancement toward the development of cost-effective and high-performance DSSCs. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026.
Ninan, Gisa Grace; Varghese, Meera; Dean, Sowmya; Manoj, B.
Journal of Materials Science: Materials in Electronics;Volume;37;Issue;3;Article No.;253;
https://doi.org/10.1007/s10854-026-16676-4 https://www.scopus.com/pages/publications/105028368800?origin=resultslist
Ninan G.G., Department of Physical Sciences and Mathematics, St. Francis de Sales College (Autonomous), Electronics City, Karnataka, Bengaluru, 560100, India; Varghese M., Department of Physical Sciences and Mathematics, St. Francis de Sales College (Autonomous), Electronics City, Karnataka, Bengaluru, 560100, India, Department of Physics and Electronics, Christ University, Karnataka, Bengaluru, 560029, India; Dean S., Kuwait Institute for Scientific Research, P.O. Box 24885, Safat, Kuwait City, 13109, Kuwait; Manoj B., Department of Physics and Electronics, Christ University, Karnataka, Bengaluru, 560029, India
Enhanced Light Scattering Using a Two-Dimensional Quasicrystal-Decorated 3D-Printed Nature-Inspired Bio-photonic Architecture
A number of strategies have been exploited so far to trap photons inside living cells to obtain high-contrast imaging. Also, launching light inside biological materials is technically challenging. Using photon confinement in a three-dimensional (3D)-printed biomimetic architecture in the presence of a localized surface plasmon resonance (LSPR) promoter can overcome some of these issues. This work compares optical confinement in natural and 3D-printed photonic architectures, namely, fish scale, in the presence of atomically thin Al70Co10Fe5Ni10Cu5 quasicrystals (QCs). Due to their wideband LSPR response, the QCs work as photon scattering hotspots. The architecture acts as an additive source of excitation for the two-dimensional (2D) QCs via total internal reflection (TIR). The computational analysis describes the surface plasmon-based scattering property of 2D QCs. The 3D-printed fish scale's image contrast with the 2D Al70Co10Fe5Ni10Cu5 QC has been compared with other 2D materials (graphene, h-BN, and MoS2) and outperforms them. The present study conceptually presents a new approach for obtaining high-quality imaging of biological imaging, even using high-energy photons. 2023 American Chemical Society.
Kumbhakar P.; Pramanik A.; Mishra S.S.; Tromer R.; Biswas K.; Dasgupta A.; Galvao D.S.; Tiwary C.S.
Journal of Physical Chemistry C, Vol-127, No. 20, pp. 9779-9786.
https://doi.org/10.1021/acs.jpcc.3c00513 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85160735552&doi=10.1021%2facs.jpcc.3c00513&partnerID=40&md5=5733e0f1514538bf53a250f1496e0fac
Kumbhakar P., Department of Metallurgical and Materials Engineering, Indian Institute of Technology Kharagpur, Kharagpur, 721302, India, Department of Physics and Electronics, CHRIST (Deemed to Be University), Bangalore, 560029, India; Pramanik A., Department of Metallurgical and Materials Engineering, Indian Institute of Technology Kharagpur, Kharagpur, 721302, India; Mishra S.S., Department of Materials Science and Engineering, Indian Institute of Technology Kanpur, Kanpur, 208016, India; Tromer R., Applied Physics Department, University of Campinas, Campinas, 13083-872, Brazil; Biswas K., Department of Materials Science and Engineering, Indian Institute of Technology Kanpur, Kanpur, 208016, India; Dasgupta A., Physical Metallurgy Division, Materials Metallurgy Group, Indira Gandhi Center for Atomic Research, HBNI, Kalpakkam, 603102, India; Galvao D.S., Applied Physics Department, University of Campinas, Campinas, 13083-872, Brazil; Tiwary C.S., Department of Metallurgical and Materials Engineering, Indian Institute of Technology Kharagpur, Kharagpur, 721302, India
Enhanced Lumpy Cattle Skin Disease Prognosis via Deep Learning Methods
Deep Learning; ECLAT Algorithm; Lesk Algorithm; Lumpy skin disease; Prediction
Animal illness is growing in importance. Identification of the illness is important since various diseases may affect different animals, and immediate guidance will be provided. Cows with lumpy skin issues are caused by the Neethling infection. The affection of these diseases causes lasting injury to the cattle's skin. Reduced Poor growth, reversal, milk production, gravidity, and, in severe cases, mortality are the most common adverse consequences of the illness. We developed a deep learning-based architecture that can predict or recognize disease. A deep literacy system is required to identify the microorganism causing the lumpy skin disease. This system collects diverse cattle electronic medical records and uses data analysis to create an intelligent diagnosis system for cattle diseases. It involves text preprocessing to enhance data quality, and the ECLAT algorithm correlates disease names with probabilities, providing tailored treatment plans. The system ensures timely disease treatment, reducing herders' losses and promoting scientific intelligence in animal husbandry. 2024 IEEE.
Lamani M.R.; Padmaja K.; Shilpa B.L.; Asha V.
2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024
https://doi.org/10.1109/NMITCON62075.2024.10699233 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207421713&doi=10.1109%2fNMITCON62075.2024.10699233&partnerID=40&md5=47605afd71b949cca44a50a97f997dda
Lamani M.R., CHRIST(Deemed to be University), Dept of Cse, Bangalore, India; Padmaja K., Gsssietw, Dept of Ise, Mysore, India; Shilpa B.L., Atmece, Dept of Cse, Mysore, India; Asha V., Rns Institute of Technology, Dept of Cse, Bangalore, India
Enhanced mechanical properties of CNT/Graphene reinforced PLA-based composites fabricated via fused deposition modelling
CNT; Fused deposition modelling; Graphene; PLA; Polymer composite
This study addresses the mechanical properties of polylactic acid (PLA) composite materials reinforced by CNTs and graphene, produced using the Fused Deposition Modeling process. The collaborative impact of graphene and CNTs upon the primary mechanical attributes including UTS, yield strength, modulus of elasticity, and impact resistance has been investigated. Three distinct CNT's weight percentages of 0.5, 1, and 1.5 have been fabricated under constant graphene content at 0.5 wt%. These findings revealed that the UTS of pure PLA were 28 MPa, whereas for the composite with 1.5 wt% CNT and 0.5 wt% graphene, it was raised to 48 MPa. From 2.6 GPa to 4 GPa the young's modulus enhancement is seen and the yield strength enhancement is seen up to 28 MPa for the composite from 20 MPa of pure PLA. The impact strength was greatly enhanced from 1.2 J for pure PLA to as high as 4 J for the composite comprising 1.5 wt percentage CNT and 0.5 wt percentage graphene. 2025 The Author(s)
Kumar, G S Pradeep; Keshavamurthy, R.; Panigrahi, Sajna P; Thomas, Shijo; R, Thejaraju; Hebbar, Gurumoorthy S; Prasad, C. Durga; Aden, Adem Abdirkadir
Results in Engineering;Volume;25;Issue;;Article No.;104472;
https://doi.org/10.1016/j.rineng.2025.104472 https://www.scopus.com/pages/publications/85218891316?origin=resultslist
Kumar G.S.P., Department of Mechanical and Automobile Engineering, CHRIST University, Karnataka, Bengaluru, 560074, India; Keshavamurthy R., Department of Mechanical Engineering, Dayananda Sagar College of Engineering, Karnataka, Bengaluru, 560078, India; Panigrahi S.P., Department of Mechanical and Automobile Engineering, CHRIST University, Karnataka, Bengaluru, 560074, India; Thomas S., Department of Mechanical and Automobile Engineering, CHRIST University, Karnataka, Bengaluru, 560074, India; R T., Department of Mechanical and Automobile Engineering, CHRIST University, Karnataka, Bengaluru, 560074, India; Hebbar G.S., Department of Mechanical and Automobile Engineering, CHRIST University, Karnataka, Bengaluru, 560074, India; Prasad C.D., Department of Mechanical Engineering, RV Institute of Technology and Management, Karnataka, Bengaluru, 560076, India; Aden A.A., Department of Mechanical Engineering, Bulehora University, Oromia, Ethiopia