Enhanced mother optimization algorithm-based optimal reconfiguration to accommodate emerging electric vehicle demand
Dynamic leaning techniques; Electrical distribution system; Loadability enhancement; Loss reduction; Mother optimization algorithm; Multi-objective optimization
Radial configuration and high x/r ratio branches in electrical distribution systems (EDSs) result in significant power losses and diminished stability margins. Optimal network reconfiguration (ONR) is a highly flexible solution methodology for addressing these challenges. The identification of optimal branches or tie lines to modify their on/off status in relation to multiple objectives under radial constraints constitutes a complex optimization challenge. This paper presents a novel variant of the mother optimization algorithm (MOA) that incorporates dynamic learning techniques for the optimal placement and sizing of electric vehicle (EV) charging stations to enhance distribution system loadability. The proposed modifications enhanced the overall performance of the algorithm by improving the exploration and exploitation characteristics. This leads to superior global best results and faster convergence than with other competitive algorithms when addressing complex optimization problems. In addition, an enhanced mother optimization algorithm (EMOA) is employed to address the ONR problem in 7-, 12-, 33-, 69-, and 118-bus IEEE radial systems. The losses are reduced by 44.15%, 30.07%, 33.87%, 55.72%, and 33.04% when compared to the base case across the respective test systems. Moreover, the loadability is increased in the 33-bus and 69-bus configurations by 208.75% and 177.07% for the base and optimal configurations, respectively. The results indicate the appropriateness of the ONR for enhancing the loadability to accommodate the rising penetration levels of electric vehicles (EVs) in support of sustainability. The Author(s) 2025.
K, Radha Rani; P, Sobha Rani; K, Swarnasri; Janamala, Varaprasad
Journal of King Saud University - Engineering Sciences;Volume;37;Issue;3;Article No.;7;
All Open Access; Gold Open Access; Green Open Access
https://doi.org/10.1007/s44444-025-00008-8 https://www.scopus.com/pages/publications/105007998259?origin=resultslist
K R.R., R.V.R & J.C. College of Engineering, Chowdavaram, Andhra Pradesh, Guntur, 522019, India; P S.R., Lakireddy Bali Reddy College of Engineering (Autonomous), Jawaharlal Nehru Technological University, Kakinada (JNTUK), East Godavari, Andhra Pradesh, Kakinada, 533003, India; K S., R.V.R & J.C. College of Engineering, Chowdavaram, Andhra Pradesh, Guntur, 522019, India; Janamala V., School of Engineering and Technology, Christ University, Karnataka, Bangalore, 560074, India
Enhanced Multi-Model Approach for Motion and Violence Detection using Deep Learning Methods Using Open World Video Game Dataset
3DCNN; Attention Mechanism; Deep Learning; IoT; MobilenetSSD; YOLOv8
For today's environment, it is extremely important to understand hostility and motion in a variety of contexts, particularly where accidents are concerned. There's also a high safety risk in public places if there is no proper identification of suspicious activities that occur fast and cannot be accurately observed through traditional surveillance systems that rely on constant human monitoring. Although deep learning algorithms have proven useful for detecting anomalies such as fraud recently, there has been little research on real-time crime detection because of issues related privacy when using live data sets. To tackle the key problem of motion and violence detection with current deep learning methods, this work exploits the Open World Game Dataset which provides realistic activities. The reliance on only one technique undermined the previous models' accuracy while this study comes up with various models to raise the detection precision and real-time processing capability. This work applies MobileNet SSD, YOLOv8 (You Only Look Once), and SSD (Single Shot MultiBox Detector) techniques to create a more accurate movement detection system. To identify violent or illegal behavior from videos, 3D convolutional neural networks (3DCNN) will be used alongside attention approaches. A diverse inexpensive training environment that enables simulating. 2024 IEEE.
1st International Conference on Pioneering Developments in Computer Science and Digital Technologies, IC2SDT 2024 - Proceedings, pp. 355-360.
Institute of Electrical and Electronics Engineers Inc.
Agarwal V., Coumputer Science and Engineering, Christ(Deemed To Be University), Banglore, India; Singh M., Coumputer Science and Engineering, Christ(Deemed To Be University), Banglore, India; Prathap B.R., Coumputer Science and Engineering, Christ(Deemed To Be University), Banglore, India
Enhanced photocatalytic activity of La3+ doped bicrystalline titania prepared via combustion method for the degradation of cationic dye under solar illumination
Combustion synthesis; Degradation of cationic dye; La3+ doped titania; Photocatalysis; Titania
La3+ doped TiO2 photocatalysts were successfully synthesized by combustion method in the presence of urea and were characterized by various physico-chemical techniques. Further, the photocatalytic performance of the synthesized catalysts was monitored by photocatalytic degradation of synthetic cationic dye-Methylene Blue (MB) under solar illumination. The bicrystalline phase of anatase and rutile was confirmed by X-ray diffraction analysis. Moreover, the transformation from anatase to rutile phase proceeds at a slower rate in the La3+ doped TiO2 catalysts. Effective separation of charge carriers, a synergistic effect in the bicrystalline framework of anatase and rutile, smaller crystallite size, and higher concentration of surface adsorbed hydroxyl groups helped these catalysts to show improved activity for the dye degradation. Copyright 2018 BCREC Group. All rights reserved.
Bulletin of Chemical Reaction Engineering and Catalysis, Vol-13, No. 1, pp. 119-126.
https://doi.org/10.9767/bcrec.13.1.1427.119-126 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041186449&doi=10.9767%2fbcrec.13.1.1427.119-126&partnerID=40&md5=f2eaeec4287940c6ec361d25efceac7c
Nair R.R., Department of Chemistry, Christ University, Bangalore, 560029, Karnataka, India; Mohan M.K., Department of Sciences and Humanities, Christ University, Bangalore, 560074, Karnataka, India; Sunajadevi K.R., Department of Chemistry, Christ University, Bangalore, 560029, Karnataka, India
Enhanced Pneumonia Detection from Chest X-rays Using Machine Learning and Deep Neural Architectures
Artificial intelligence; Chest X-rays; Fuzzy opponent histogram filter; Machine learning; Pneumonia
Pneumonia is a major worldwide health concern, particularly for vulnerable groups such as babies and the elderly. Despite advances in medical imaging, diagnosing pneumonia using a chest X-ray remains difficult, due to the subtle presentation of symptoms and the variety in picture interpretation. This study utilizes modern machine learning can improve the accuracy and speed of diagnosing pneumonia using chest X-ray images. Utilizing a comprehensive dataset from the Kaggle online repository, consisting of over 5,000 annotated images, we evaluate the efficacy of various machine learning models including deep convolutional neural networks (CNN) and ensemble learning techniques. Our findings indicate that models like the Fuzzy opponent histogram filter combined with Logistic model trees (LMT) achieved the highest accuracy at 96.97%, while the deep learning-based Lenet (CNN) with LMT closely followed at 95.85%. The study aims to improve diagnostic precision, reduce interpretation discrepancies, and facilitate faster clinical decision-making by identifying the most effective machine learning approaches for real-world applications in healthcare settings. 2025 Kamal Upreti, Anju Singh, Divakar Singh, Preety Shoran, Uma Shankar, Meenakshi Yadav and Rituraj Jain.
Upreti, Kamal; Singh, Anju; Singh, Divakar; Shoran, Preety; Shankar, Uma; Yadav, Meenakshi; Jain, Rituraj
ARO-The Scientific Journal of Koya University;Volume;13;Issue;1;Article No.;12174;pp.215-226
https://doi.org/10.14500/aro.12174 https://www.scopus.com/pages/publications/105011745503?origin=resultslist
Upreti K., Department of Computer Science, CHRIST (Deemed to be University), Delhi NCR Campus, Ghaziabad, India; Singh A., Department of Computer Science and Engineering, Lakshmi Narain College of Technology, Kalchuri Nagar, Raisen Road, Madhya Pradesh, Bhopal, India; Singh D., Department of Computer Science and Engineering, university Institute of Technology, Barkatullah University, Madhya Pradesh, Bhopal, India; Shoran P., Department of Computer Science, CHRIST (Deemed to be University), Delhi NCR Campus, Ghaziabad, India; Shankar U., Department of Management, Faculty of Management and Social Sciences, Qaiwan International University, Kurdistan, Sulaimanyah, Iraq; Yadav M., Department of Information Technology, Galgotias College of Engineering and Technology, Greater Noida, India; Jain R., Department of Information Technology, Marwadi University, Gujarat, Rajkot, India
Enhanced Postoperative Brain MRI Segmentation with Automated Skull Removal and Resection Cavity Analysis
Cavity Segmentation; Enhanced U-Net; Post-Operative Brain MRI; Skull Removal; VGG16 Backbone
Brain tumors present a significant medical challenge, often necessitating surgical intervention for treatment. In the context of postoperative brain MRI, the primary focus is on the resection cavity, the void that remains in the brain following tumor removal surgery. Precise segmentation of this resection cavity is crucial for a comprehensive assessment of surgical efficacy, aiding healthcare professionals in evaluating the success of tumor removal. Automatically segmenting surgical cavities in post-operative brain MRI images is a complex task due to challenges such as image artifacts, tissue reorganization, and variations in appearance. Existing state-of-the-art techniques, mainly based on Convolutional Neural Networks (CNNs), particularly U-Net models, encounter difficulties when handling these complexities. The intricate nature of these images, coupled with limited annotated data, highlights the need for advanced automated segmentation models to accurately assess resection cavities and improve patient care. In this context, this study introduces a two-stage architecture for resection cavity segmentation, featuring two innovative models. The first is an automatic skull removal model that separates brain tissue from the skull image before input into the cavity segmentation model. The second is an automated postoperative resection cavity segmentation model customized for resected brain areas. The proposed resection cavity segmentation model is an enhanced U-Net model with a pre-trained VGG16 backbone. Trained on publicly available post-operative datasets, it undergoes preprocessing by the proposed skull removal model to enhance precision and accuracy. This segmentation model achieves a Dice coefficient value of 0.96, surpassing state-of-the-art techniques like ResUNet, Attention U-Net, U-Net++, and U-Net. (2024) Sobha Xavier P., Sathish P. K. and Raju G.
Journal of Computer Science, Vol-20, No. 5, pp. 585-593.
All Open Access; Hybrid Gold Open Access
https://doi.org/10.3844/JCSSP.2024.585.593 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85194284446&doi=10.3844%2fJCSSP.2024.585.593&partnerID=40&md5=dd8a104fd6768b13e9b519e68bbde065
Sobha X.P., Department of Computer Science, Christ (Deemed to be University), Karnataka, Bengaluru, India; Sathish P.K., Department of Computer Science, Christ (Deemed to be University), Karnataka, Bengaluru, India; Raju G., Department of Computer Science, Christ (Deemed to be University), Karnataka, Bengaluru, India
Enhanced power quality control of a photo voltaic power plant integrated with multiple electric vehicle
Distribution side generation (DSG); DQ control; electric vehicle; multicarrier SVPWM; photo-voltaic (PV)
As there is a great need for high-quality electricity on the distribution side, distribution side generation (DSG) has become increasingly important. The increased weight of EVs on the distribution side is the cause of this. There are numerous power quality mitigation techniques employed to address this type of issue, but many of the solutions suggest the usage of a separate device, such as an active power filter. But while construction the DSG the solution to this problem may be addressed using the proposed solution in this paper. Power quality (PQ) problems are being caused by the grids integration of Photo-Voltaic (PV) and its application to all connected loads. With the aid of Direct Quardrature (DQ) controller and Multicarrier Space Vector Pulse Width Modulation (SVPWM) technology, the overall power quality disturbance is decreased. A Simulink model for the PV-EV-Grid system was built to measure voltage and current Total Harmonic Distortion (THD) percentages under linear, non-linear, and Plug in Hybrid Vehicle (PHEV) load situations. The model shows that the THD values are well within the IEEE 519. Indian Academy of Sciences 2024.
Sadhana - Academy Proceedings in Engineering Sciences, Vol-49, No. 4
Harish B.N., Department of Electrical and Electronics Engineering, School Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India; Surendra U., Department of Electrical and Electronics Engineering, School Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India
Enhanced Process Model and Analysis of Risk Integration in Software effort estimation
Multi-Layer Perceptron model; Process Model; Risk score; Risk-integrated Effort estimation
The development of software within the estimated effort is remaining as a challenging task. The process of effort estimation is a critical activity in a software project, where effort estimates are utilized to arrive at the schedule, resources, and cost. Though many software effort estimation techniques exist, effort overrun occurs in a project. Identification of risks and their consideration in software lifecycle activities play a significant role in the successful execution of a software project. It would be required to account for uncertainty and the key factors that contribute to it. This study focuses on the need to include project risk score in the software effort estimation process to arrive at better effort estimates. This paper depicts the standard and enhanced process frameworks for estimation of software development efforts. A multi-layer perceptron model was built and the results indicated the relevance of considering project risk score in the effort estimation process. The usage of an enhanced gradient boosting technique for predictive modelling revealed a decrease in standard deviation of the residuals, thus indicating a better fit for the effort estimation model through integration of risks. 2019 IEEE.
Ramakrishnan N.; Girijamma H.A.; Balachandran K.
Proceedings of the 2nd International Conference on Smart Systems and Inventive Technology, ICSSIT 2019, pp. 419-422.
https://doi.org/10.1109/ICSSIT46314.2019.8987841 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080052336&doi=10.1109%2fICSSIT46314.2019.8987841&partnerID=40&md5=88b4cf789002804c1549ddca2c1f1ccb
Ramakrishnan N., CHRIST Institute of Management, CHRIST (Deemed to Be University), Bengaluru, India; Girijamma H.A., RNS Institute of Technology, Computer Science and Engineering Department, Bengaluru, India; Balachandran K., CHRIST (Deemed to Be University), Computer Science and Engineering Department, Bengaluru, India
Enhanced radial basis function neural network for tomato plant disease leaf image segmentation
Enhanced radial basis function; Feature extraction; Modified sunflower optimization; Segmentation; Tomato leaf disease
Primary crop losses in agriculture are due to leaf diseases, which farmers cannot identify early. If the diseases are not detected early and correctly, then the farmer will have to undergo huge losses. Therefore, in the field of agriculture, the detection of leaf diseases in tomato crops plays a vital role. Recent advances in computer vision and deep learning techniques have made disease prediction easy in agriculture. Tomato crop front side leaf images are considered for research due to their high exposure to diseases. The image segmentation process assumes a significant role in identifying disease affected areas on tomato leaf images. Therefore, this paper develops an efficient tomato crop leaf disease segmentation model using an enhanced radial basis function neural network (ERBFNN). The proposed ERBFNN is enhanced using the modified sunflower optimization (MSFO) algorithm. Initially, the noise present in the images is removed by a Gaussian filter followed by CLAHE (contrast-limited adaptive histogram equalization) based on contrast enhancement and un-sharp masking. Then, color features are extracted from each leaf image and given to the segmentation stage to segment the disease portion of the input image. The performance of the proposed ERBFNN approach is estimated using different metrics such as accuracy, Jaccard coefficient (JC), Dice's coefficient (DC), precision, recall, F-Measure, sensitivity, specificity, and mean intersection over union (MIoU) and are compared with existing state-of-the-art methods of radial basis function (RBF), fuzzy c-means (FCM), and region growing (RG). The experimental results show that the proposed ERBFNN segmentation model outperformed with an accuracy of 98.92% compared to existing state-of-the-art methods like RBFNN, FCM, and RG, as well as previous research work. 2022 Elsevier B.V.
https://doi.org/10.1016/j.ecoinf.2022.101752 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135888868&doi=10.1016%2fj.ecoinf.2022.101752&partnerID=40&md5=fae568c73ea3f425f0a62d0a0e3c1e1b
Patil M.A., Research Scholar, Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India, Assistant Professor, Department of Information Technology, Vasavi College of Engineering, Ibrahimbagh, Telangana, Hyderabad, 500031, India; M M., Associate Professor, Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India
Enhanced Random Forest-Based Model forFlood Detection andClassification
Bag-of-visual-words; Flood detection; Natural disaster; Random Forest algorithm
Flooding is one of the most devastating natural disasters globally, causing extensive damage to infrastructure, the environment, and human lives. With increasing occurrences due to climate change, accurate classification and analysis of flood imagery are essential for early detection, damage assessment, and post-disaster recovery. Reliable flood classification systems are critical for early warning, resource allocation, and mitigation efforts, helping to minimize the impact on affected regions. Remote sensing and computer vision techniques, including the Bag-of-Visual-Words (BOV) model, offer powerful tools for interpreting flood images by categorizing and identifying flooded regions across vast and complex terrains. This paper presents a modification of the standard Random Forest algorithm to enhance the accuracy of image classification within a Bag-of-Visual-Words (BOV) model. The modified Random Forest achieves better adaptability and performance across flood image datasets by introducing flexibility in parameter tuning through custom hyperparameters and automatic grid search. This modification addresses challenges in balancing efficiency and accuracy for classifying high-dimensional image data sets. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
Arora, Suchita; Kumar, Sunil; Kumar, Sandeep
Smart Innovation, Systems and Technologies;Volume;122 SIST;pp.799-807
Springer Science and Business Media Deutschland GmbH
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1007/978-981-96-6250-0_55 https://www.scopus.com/pages/publications/105023102818?origin=resultslist
Arora S., Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University Rajasthan, Jaipur, India, Department of Computer Science and Engineering, Poornima University, Rajasthan, Jaipur, 303905, India; Kumar S., Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University Rajasthan, Jaipur, India; Kumar S., Department of AI, ML and Data Science, Christ University, Bangalore, India
Enhanced Secure Technique for Detecting Cyber Attacks Using Artificial Intelligence and Optimal IoT
The Internet of Things (IoT) is a broad term that refers to the collection of information about all of the items that are linked to the Internet. It supervises and controls the functions from a distance, without the need for human interaction. It has the ability to react to the environment either immediately or via its previous experiences. In a similar vein, robots may learn from their experiences in the environment that is relevant to their applications and respond appropriately without the need for human interaction. A greater number of sensors are being distributed across the environment in order to collect and evaluate the essential information. They are gaining ground in a variety of industries, ranging from the industrial environment to the smart home. Sensors are assisting in the monitoring and collection of data from all of the real-time devices that are reliant on all of the different types of fundamental necessities to the most advanced settings available. This research study was primarily concerned with increasing the efficiency of the sensing and network layers of the Internet of Things to increase cyber security. Due to the fact that sensors are resource-constrained devices, it is vital to provide a method for reacting, analysing, and transmitting data collected from the sensors to the base station as efficient as possible. Resource requirements, such as energy, computational power, and storage, vary depending on the kind of sensing devices and communication technologies that are utilised to link real-world objects together. Sensor networks' physical and media access control layers, as well as their applications in diverse geographical and temporal domains, are distinct from one another. Transmission coverage range, energy consumption, and communication technologies differ depending on the application requirements, ranging from low constraints to high resource enrich gadgets. This has a direct impact on the performance of the massive Internet of Things environment, as well as the overall network lifetime of the environment. Identifying and communicating matching items in a massively dispersed Internet of Things environment is critical in terms of spatial identification and communication. 2022 Anand Kumar et al.
Kumar A.; Rahmath M.; Raju Y.; Reddy Vulapula S.; Prathap B.R.; Hassan M.M.; Mohamed M.A.; Asakipaam S.A.
Security and Communication Networks, Vol-2022
Kumar A., Cambridge Institute of Technology, North Campus, Karnataka, Bangalore, 562110, India; Rahmath M., Department of Computer Science, Prince Sattam Bin Abdulaziz University, Wadi Ad-Dawasir, Saudi Arabia; Raju Y., Department of Computer Science and Engineering, Vignana Bharathi Institute of Technology, Telangana, Hyderabad, India; Reddy Vulapula S., Department of IT, Vignana Bharathi Institute of Technology, Hyderabad, India; Prathap B.R., Department of Computer Science and Engineering, Christ University, Bangalore, India; Hassan M.M., Department of Biology, College of Science, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia; Mohamed M.A., Department of Medical Instruments Engineering Techniques, AI-Turath University, Baghdad, 10021, Iraq, Department of Medical Instruments Engineering Techniques, Al-Farahidi University, Baghdad, 10021, Iraq; Asakipaam S.A., Department of Electrical and Electronics Engineering, Tamale Technical University, Tamale, Ghana
Enhanced Security for Large-Scale 6G Cloud Computing: A Novel Approach to Identity based Encryption Key Generation
Cloud Computing; Cloud Security; Equality Test; Identity-Based Encryption; Large Scale 6G
Cloud computing and 6G networks are in high demand at present due to their appealing features as well as the security of data stored in the cloud. There are various challenging methods that are computationally complicated that can be used in cloud security. Identity-based encryption (IBE) is the most widely used techniques for protecting data transmitted over the cloud. To prevent a malicious attack, it is an access policy that restricts access to legible data to only authorized users. The four stages of IBE are setup, key extraction or generation, decryption and encryption. Key generation is a necessary and time-consuming phase in the creation of a security key. The creation of uncrackable and non-derivable secure keys is a difficult computational and decisional task. In order to prevent user identities from being leaked, even if an opponent or attacker manages to encrypted material or to decode the key this study presents an advanced identity-based encryption technique with an equality test. The results of the experiments demonstrate that the proposed algorithm encrypts and decrypts data faster than the efficient selective-ID secure IBE strategy, a competitive approach. The proposed method's ability to conceal the identity of the user by utilizing the Lagrange coefficient, which is constituted of a polynomial interpolation function, is one of its most significant aspects. 2023 The Authors. Published by AnaPub Publications. This is an open access article under the CC BY-NC-ND license. (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Rathinam G.; Balamurugan M.; Arulkumar V.; Kumaresan M.; Annamalai S.; Bhuvana J.
Journal of Machine and Computing, Vol-3, No. 2, pp. 80-91.
Rathinam G., Information and communication Engineering, College of Engineering, University of Buraimi, Al Buraimi, Oman; Balamurugan M., Department of CSE, CHRIST(Deemed to be University), Bengaluru, 560074, India; Arulkumar V., School of Computer Science and Engineering, Vellore Institute of Technology, TamilNadu, Vellore, India; Kumaresan M., Department of Computer Science and Engineering, Jain Deemed-to-be University, Karnataka, India; Annamalai S., Department of Computer Science and Engineering, Jain Deemed-to-be University, Karnataka, India; Bhuvana J., Department of CSIT, Jain(Deemed to be University), Bengaluru, 560069, India
Enhanced Security in Payment Gateways Through Face Detection: An Advanced Approach Using DenseNet 121- BiLSTM Models
Facial Recognition (FR); Field Programmable Gate Arrays (FPGA); Payment System
Because it is one of the most promising applications of image analysis, face recognition has been the subject of intense research and development for many decades. Many modern identification and verification requirements have found a potential new home with the introduction of face recognition (FR) technology. Facial recognition is just one of numerous uses for biometric pattern recognition algorithms. Sequencing is essential for many tasks, including as feature extraction, model training, and preprocessing. Eliminating background noise and obtaining dense vertical edges are part of the preprocessing procedures. Facial feature extraction will be employed to extract features after feature extraction. Use attributes cautiously when training a Desnet121-BiLSTM model. In every respect, the suggested method outperforms two state-of-the-art algorithms, Desnet121 and BiLSTM. An accuracy rating of 97.19% was indicative of a considerable improvement in the figures. 2024 IEEE.
Gowsic K.; Sowmiya M.; Nirmala Devi M.; Chauhan A.; Arul Mary Rexy V.; Singh D.
1st International Conference on Electronics, Computing, Communication and Control Technology, ICECCC 2024
Gowsic K., Mahendra Engineering College (Autonomous), Department of Computer Science and Engineering, Namakkal, India; Sowmiya M., Mahendra Engineering College (Autonomous), Department of Computer Science and Engineering, Namakkal, India; Nirmala Devi M., School of Sciences, CHRIST (Deemed to be University), Department of Life Sciences, Karnataka, Bengaluru, India; Chauhan A., St. Martin's Engineering College, Department of English, Secunderabad, India; Arul Mary Rexy V., Saveetha College of Liberal Arts and Sciences, SIMATS, Department of Commerce, Kuthambakkam, India; Singh D., School of Management Sciences, Department of Applied Science, Lucknow, India
Enhanced Sensing Performance of an Ammonia Gas Sensor Based on Ag-Decorated ZnO Nanorods / Polyaniline Nanocomposite
Ammonia Gas Sensor; Nanocomposites; Polyaniline; Silver; Zinc Oxide
The development of low-cost ammonia sensors with high sensitivity and selectivity has gained considerable interest. Though the response of these sensors at room temperature is low and needs enhancement. In the present study high sensitivity ammonia gas sensors based on nanocomposite films of polyaniline (PANI) and with varying ZnO concentrations were synthesized and investigated. With a loading of 10 at% ZnO, the gas sensing response of 59 % was obtained for 120 ppm NH3 gas. The gas response was further enhanced by decorating the ZnO nanorods with different concentrations of silver (Ag) nanoparticles. The Ag-decorated ZnO nanorods were embedded in the PANi matrix using the in-situ oxidative polymerization technique. It was shown that PANi ZnO, p-n junction, and the introduction of porosity in nanocomposite act synergistically in increasing the resistance caused by the deprotonation of PANi by NH3. Among various compositions studied, 2 % loading of Ag in ZnO embedded in PANi matrix, thin films were found to be highly selective and sensitive towards NH3 gas at room temperature with a chemiresistive response of 70 % at 120 ppm and a recovery time of less than 120 s. The selectivity of the nanocomposite was also studied towards various reducing and oxidizing gasses. 2023 Wiley-VCH GmbH.
Karmakar N.; Jain S.; Fernandes R.; Shah A.; Patil U.; Shimpi N.; Kothari D.
https://doi.org/10.1002/slct.202204284 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85159103067&doi=10.1002%2fslct.202204284&partnerID=40&md5=269e076553baf48ffe494fef12fae68b
Karmakar N., Dwarkadas. J. Sanghvi College of Engineering, Vile Parle, Mumbai, 400 056, India; Jain S., Department of Chemistry, Jai Hind College, Mumbai, 400020, India; Fernandes R., Department of Physics and Electronics, Christ University, Bengaluru, 560029, India; Shah A., Department of Chemistry, University of Mumbai, Santacruz, Mumbai, 400098, India; Patil U., Wilson College, Chowpatty, Mumbai, 400007, India; Shimpi N., Department of Chemistry, University of Mumbai, Santacruz, Mumbai, 400098, India; Kothari D., Department of Physics, University of Mumbai Santacruz, Mumbai, 400098, India
Enhanced Social Media Profile Authenticity Detection Using Machine Learning Models and Artificial Neural Networks
ANN; fake profile classification; Fake profile detection; GAN; Machine Learning
Fake engagement is one of the main issues with online networks or ONSs, which are used to artificially boost an account's popularity, this study examines the effectiveness of seven sophisticated Machine Learning Algorithms, Random Forest Classifier, Decision Tree Classifier, XGBoost, LightGBM, Extra Trees Classifier, and SVM, and got 93% accuracy in Decision Tree Classifier. In order to solve overfitting issues and improve model resilience, the paper proposes Generative Adversarial Networks (GANs) and uses K-Fold Cross-Validation. Furthermore, design a Gan-ANN model that combines Batch Normalization and Artificial Neural Networks (ANN) with GAN-generated synthetic data is investigated. The enhanced dataset seeks to strengthen model performance and generalization when combined with cutting-edge modeling methods. This study aims to improve model scalability, predictive accuracy, and dependability across different machine learning paradigms. 2023 IEEE.
Mahanta B.; Nair A.M.; Alapatt B.P.; George J.P.
3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023
Mahanta B., Christ (Deemed to Be University), India; Nair A.M., Christ (Deemed to Be University), India; Alapatt B.P., Christ (Deemed to Be University), India; George J.P., Christ (Deemed to Be University), India
Enhanced Spam Detection in Short Message Service using Hybrid Techniques
Convolutional Neural Network (CNN); Machine Learning; Nae Bayes; SMS Spam
Receiving unwanted text messages, or SMS spam, costs consumers time and money and poses a security concern. To address this issue, we can deploy a system that recognizes and automatically filters out undesirable messages. This method, a testament to the advancement in technology, employs machine learning algorithms that gain knowledge from a pool of communications classified as spam or not. Managing various message contents and languages is one of the system's unique challenges. Notwithstanding these challenges, the approach may be effective in reducing unsolicited communications, improving the security of people's mobile devices and saving them time and money. To address this issue, a variety of machine learning approaches have been employed, ranging from more modern deep learning methods like Convolutional Neural Networks (CNNs) to more traditional ones like Naive Bayes. It is common practice to assess the effectiveness of SMS spam classifiers using measures like as F1-score, precision, and recall. All things considered, SMS spam classification is crucial for protecting the security and privacy of mobile phone users and has useful applications in everyday situations. Grenze Scientific Society, 2025.
Raj, E. Babu; Barona, R.; Mary, N. Ansgar; George, Shiju
16th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2025;Volume;1;pp.4203-4209
https://www.scopus.com/pages/publications/105034072843?origin=resultslist
Raj E.B., Christ University, Bengaluru, Bengaluru, India; Barona R., St. Xavier's Catholic College of Engineering, IT, Chunkankadai, India; Mary N.A., St. Xavier's Catholic College of Engineering, IT, Chunkankadai, India; George S., Christ University, Bengaluru, Bengaluru, India
Enhanced Stock Market Prediction Using Hybrid LSTM Ensemble
Stock market value prediction is the activity of predicting future market values so as to increase gain and profit. It aids in forming important financial decisions which help make smart and informed investments. The challenges in stock market predictions come due to the high volatility of the market due to current and past performances. The slightest variation in current news, trend or performance will impact the market drastically. Existing models fall short in computation cost and time, thereby making them less reliable for large datasets on a real-time basis. Studies have shown that a hybrid model performs better than a stand-alone model. Ensemble models tend to give improved results in terms of accuracy and computational efficiency. This study is focused on creating a better yielding model in terms of stock market value prediction using technical analysis, and it is done by creating an ensemble of long short-term memory (LSTM) model. It analyzes the results of individual LSTM models in predicting stock prices and creates an ensemble model in an effort to improve the overall performance of the prediction. The proposed model is evaluated on real-world data of 4 companies from Yahoo Finance. The study has shown that the ensemble has performed better than the stacked LSTM model by the following percentages: 21.86% for the Tesla dataset, 22.87% for the Amazon dataset, 4.09% for Nifty Bank and 20.94% for the Tata dataset. The models implementation has been justified by the above results. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Lecture Notes on Data Engineering and Communications Technologies, Vol-114, pp. 49-61.
Roy R.P., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bengaluru, India; Thiruthuvanathan M.M., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bengaluru, India
Enhanced supercapacitors and LPG sensing performance of reduced graphene oxide/cobalt chromate pigments for energy storage applications
CoCr2O4; Composite; rGo; Supercapacitors
It is imperative that an initial inquiry be conducted as soon as possible since the production of monolayer of carbon atoms (rGO) composites is the root cause of their poor performance in supercapacitor and LPG sensors. Here, an effort is undertaken to construct a cobalt chromate pigments-reduced graphene oxide (CoCr2O4/rGO) by solution combustion method for the supercapacitor and LPG sensor. The proposed method is efficient and easy in terms of its application to the production of CoCr2O4/rGO polycrystalline composite on a wide scale. Within the scope of this work is an investigation into the improved supercapacitor and LPG sensing behaviour of CoCr2O4/rGO polycrystalline composite. We have implemented a simple method that has been identified for mass-producing reduced graphene oxide. The Solution combustion technique was used, and it was successful in achieving this goal for the very first time. X-ray diffraction technique is used analyse crystallinity, phase, and structural investigation. The nature of gas sensing behaviour with a step function of LPG gas at 500 ppb was studied at room temperature for rGO, The CoCr2O4 pigments and 0.5CoCr2O4+0.5rGo polycrystalline composite samples. The gas response is maximum for 0.5CoCr2O4+0.5rGo polycrystalline composite in the order of 97% in compare with the reduced graphene oxide sample which shows the lowest sensitivity in the order of 26% on exposure of liquified petroleum gas (LPG). The recorded response and recovery times of 0.5CoCr2O4+0.5rGo polycrystalline composite is found to be 40 s and 52 s respectively in comparison to the rGO sample about 58 and 74 s respectively. By adding rGo to the material, the cyclic voltammetry (CV) findings demonstrate improved current density and area of CV loop with increased scan rate. In three-electrode reveals the system, a CoCr2O4-rGo material exhibits a specific capacitance of 226 F/g. Thus, the results reveals that rGo is contributing significantly to the enhancement of a supercapacitor's performance of CoCr2O4. 2023 Elsevier Ltd and Techna Group S.r.l.
Angadi V J.; Pasha A.; Molahalli V.; K Pattar V.; Kumar A.; Hegde G.; Prakash C.; Pandit B.; Sehgal S.; Ubaidullah M.
Ceramics International, Vol-49, No. 22, pp. 35333-35339.
https://doi.org/10.1016/j.ceramint.2023.08.206 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85168727030&doi=10.1016%2fj.ceramint.2023.08.206&partnerID=40&md5=0706908f337c6f5ea33ffb463718aea5
Angadi V J., Department of Physics, P. C. Jabin Science College, Karnataka, Hubballi, 580031, India; Pasha A., Department of Physics, Ghousia College of Engineering, Karnataka, Ramanagara, India; Molahalli V., Department of Chemistry, CHRIST (Deemed to be University), Bangalore, 560029, India; K Pattar V., Education Technology Unit, JNCASR, Jakkur, Karnataka, Bengaluru, 560064, India; Kumar A., Department of Chemistry, GLA University, Mathura, 281406, India; Hegde G., Department of Chemistry, CHRIST (Deemed to be University), Bangalore, 560029, India, Centre for Advanced Research and Development (CARD), CHRIST (Deemed to be University), Bangalore, 560029, India; Prakash C., School of Mechanical Engineering, Lovely Professional University, Punjab, Phagwara, 144411, India; Pandit B., Department of Materials Science and Engineering and Chemical Engineering, Universidad Carlos III de Madrid, Avenida de la Universidad 30, Legan, Madrid, 28911, Spain; Sehgal S., Division of Research Innovation, Uttaranchal University, Dehradun, India; Ubaidullah M., Department of Chemistry, College of Science, King Saud University, P.O. Box 2455, Riyadh, 11451, Saudi Arabia
Enhanced technique for detection and prevention of phishing on websites
CORS; Heuristic function; Phishing; Public Repository; Python
Phishing is a kind of assault where cyber criminals trap individuals to gain access to someone's private data like credit card details, passwords, account details, etc. The false e-mails look shockingly genuine and even the Web pages where clients are requested to enter their data may look legitimate. Forgery of a website is a sort of online assault where the phishing person builds a duplicate of a true authorized site, with the objective of misguiding a client by fishing out data that could be utilized to dupe or instigate different assaults upon the victim. In this paper, a new technique is developed using the combination of CORS, Public Repository technique and Heuristic functions. This technique allows only authorized Domain to replicate the original website. Copyright 2019 American Scientific Publishers All rights reserved.
Journal of Computational and Theoretical Nanoscience, Vol-16, No. 45813, pp. 2614-2618.
https://doi.org/10.1166/jctn.2019.7939 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068539770&doi=10.1166%2fjctn.2019.7939&partnerID=40&md5=01e9814df9416be868351f1b8c6d310e
Prashanth Kumar P., Department of Computer Science, CHRIST (Deemed to be University), Karnataka, 560029, India; Vinay M., Department of Computer Science, CHRIST (Deemed to be University), Karnataka, 560029, India
Enhanced transport, dielectric and magnetic properties of Ni-doped (YFeO3)0.5(BaTiO3)0.5 perovskite for NTC thermistor and multifunctional applications
The solid-state reaction method was successfully employed to synthesize the environmentally friendly polycrystalline perovskite (Y0.5Ni0.5FeO3)0.5(BaTiO3)0.5. X-ray diffraction (XRD) analysis, complemented by Rietveld refinement, confirms its multiphase crystalline structure, comprising two cubic and one orthorhombic phase. Field-emission scanning electron microscopy (FE-SEM) reveals a well-defined surface morphology, while energy-dispersive spectroscopy (EDS) and elemental mapping validate the homogeneous distribution of constituent elements. Raman and FTIR spectroscopy further confirm the vibrational and atomic structural integrity of the material. Dielectric studies indicate a high dielectric constant (?338 at 100 Hz, room temperature), with strong frequency and temperature-dependent relaxation effects. Impedance spectroscopy reveals non-Debye relaxation behaviour, NTCR characteristics and impedance in the megaohm range at lower temperatures. AC conductivity results align well with Jonscher's power law. The thermistor coefficient (?) reaches 4778.61 at 450 C, demonstrating excellent potential for thermistor applications. Magnetic studies confirm a prominent ferromagnetic response at room temperature, with a saturation magnetization of 3.654 emu g?1 and coercive field of 196.4 Oe. These combined properties make (Y0.5Ni0.5FeO3)0.5(BaTiO3)0.5 a promising candidate for multifunctional applications. 2025 RSC.
Chouhan, Harshavardhan; Prasad, Saurabh; Parida, B.N.; Parida, R.K.
Materials Advances;Volume;6;Issue;14;pp.4778-4794
https://doi.org/10.1039/d4ma01233j https://www.scopus.com/pages/publications/105008530203?origin=resultslist
Chouhan H., Department of Physics, Central Institute of Technology Kokrajhar, Deemed to be University, MoE, Govt. of India, BTR, Assam, 783370, India; Prasad S., Department of Physics, Central Institute of Technology Kokrajhar, Deemed to be University, MoE, Govt. of India, BTR, Assam, 783370, India; Parida B.N., Department of Physics, Central Institute of Technology Kokrajhar, Deemed to be University, MoE, Govt. of India, BTR, Assam, 783370, India; Parida R.K., Department of Physics, Siksha O Anusandhan (Deemed to be University), Khandigiri, Odisha, Bhubaneswar, 751030, India
Enhanced visible light harvesting in dye-sensitized solar cells through incorporation of solution-processable silver plasmons and anthracite-derived graphene quantum dots
Anthracite coal; Dye-sensitized solar cells; Graphene quantum dots; Linear sweep voltammetry; Open circuit voltage decay; Surface plasmon resonance
The major setback for the enhanced performance of DSSC is the narrow absorption window and the interfacial exciton recombination. Therefore, in this work, the photovoltaic performance of dye-sensitized solar cells has been improved by the synergistic effect of anthracite-derived graphene quantum dots and silver plasmons. GQD and Ag coupled photoanodes were fabricated by a facile solution processable process under room temperature. The as-fabricated DSSC TiO2/Ag/GQD (TAG) exhibited an enhanced power conversion efficiency of 10.5 % with a current density of 22.40 mAcm?2 measured under solar irradiation of 100 mWcm?2 with AM 1.5G. An enhancement surpassing 30.5 % was obtained for the champion cell when compared to the pristine TiO2 based DSSC. Furthermore, this study emphasizes developing a cutting-edge approach for the high-quality use of fossil fuel-derived graphene quantum dots in energy conversion systems, thereby encouraging the green conversion of fossil fuels and broadening the potential of anthracite coal's utilization in energy conversion applications. 2024 Elsevier Ltd
Rao A.A.; Narendhiran S.; Balachandran M.
https://doi.org/10.1016/j.mtphys.2024.101512 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198256901&doi=10.1016%2fj.mtphys.2024.101512&partnerID=40&md5=4bd0620f3064ce302c8d7a7b1996503c
Rao A.A., Department of Physics and Electronics, Christ (Deemed to be University), Karnataka, Bengaluru, 560029, India; Narendhiran S., Department of Physics and Electronics, Christ (Deemed to be University), Karnataka, Bengaluru, 560029, India; Balachandran M., Department of Physics and Electronics, Christ (Deemed to be University), Karnataka, Bengaluru, 560029, India