Efficient handwritten character recognition of modi script using wavelet transform and svd
Feature extraction method; Handwritten character recognition; Image processing; MODI script; Wavelet transform
MODI script has historical importance as it was used for writing the Marathi language, until 1950. Due to the complex nature of the script, the character recognition of MODI script is still in infancy. The implementation of more efficient methods at the various stages of the character recognition process will increase the accuracy of the process. In this paper, we present a hybrid method called WT-SVD (Wavelet Transform-Singular Value Decomposition), for the character recognition of MODI script. The WT-SVD method is a combination of singular value decomposition and wavelet transform, which is used for the feature extraction. Euclidean distance method is used for the classification. The experiment is conducted using Symlets and Biorthogonal wavelets, and the results are compared. The method using Biorthogonal wavelet feature extraction achieved the highest accuracy The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021.
Lecture Notes in Networks and Systems, Vol-132, pp. 227-233.
Joseph S., CHRIST (Deemed to Be University), Bengaluru, India, Carmel College of Arts Science and Commerce for Women, Nuvem, Goa, India; George J., CHRIST (Deemed to Be University), Bengaluru, India
Efficient hydrogen evolution reaction performance of Ni substituted WS2 nanoflakes
Hydrogen evolution reaction; Transition metal dichalcogenides; Two dimensional materials; WS2
We have investigated the structural, optical and electrocatalytic hydrogen evolution reaction (HER) performance of pristine, Co and Ni substituted WS2 nanoflakes synthesised by facile hydrothermal method. The XRD pattern confirms the formation of hexagonal WS2 for both pristine and substituted WS2 nanoflakes. The FESEM images validate the flake-like structure for both pristine and substituted WS2. In addition, we have also analysed the Raman and UV-Vis absorbance spectra of the samples. The electrocatalytic studies reveal that the nickel-substituted WS2 (Ni-WS2) nanoflakes show superior hydrogen evolution (HER) performance compared to cobalt-substituted WS2 (Co-WS2) nanoflakes. Hence, we have varied the Ni concentration and investigated the dependence of Ni content on the electrocatalytic performance. It is found that the electrocatalytic performance of the Ni-WS2 nanoflakes increases with an increase in Ni content owing to the modified edge structures. Thus, our studies suggest Ni substitution in WS2 nanostructures can boost electrocatalytic HER performance. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.
Joseph A.; Chacko L.; Sanal K.C.; Pineda-Aguilar N.; Jasna M.; Antony A.; Aneesh P.M.
Applied Physics A: Materials Science and Processing, Vol-130, No. 12
Springer Science and Business Media Deutschland GmbH
Joseph A., Department of Physics, Central University of Kerala, Tejaswini Hills, Kerala, Periye, Kasaragod, 671320, India; Chacko L., Department of Physics and Electronics, CHRIST (Deemed to be University), Hosur Road, Bangalore, 560029, India; Sanal K.C., Facultad de Ciencias Quicas, Universidad Automa de Nuevo Leon, San Nicolas de los Garza, Mexico; Pineda-Aguilar N., Centro de Investigaci en Materiales Avanzados, S.C. (CIMAV), Subsede Monterrey, Parque de Investigaci e Innovaci on Tecnolica, Apodaca, Mexico; Jasna M., Department of Physics, Cochin University of Science and Technology, Kerala, Kochi, 682022, India, Microelectronics Unit, Faculty of Information Technology and Electrical Engineering, University of Oulu, FI-90014, Oulu, Finland; Antony A., Department of Physics, Cochin University of Science and Technology, Kerala, Kochi, 682022, India; Aneesh P.M., Department of Physics, Central University of Kerala, Tejaswini Hills, Kerala, Periye, Kasaragod, 671320, India
Efficient Integration of Photovoltaic Cells with Multiport Converter for Enhanced Energy Harvesting
Battery; Efficiency; Multiport converter; PV Cell
This research work presents a novel approach for the efficient integration of photovoltaic (PV) cells with a multiport converter to enhance energy harvesting in renewable energy systems. The proposed system combines the advantages of PV technology with the flexibility and scalability of multiport converters, enabling improved power extraction and utilization from solar energy sources. The integration is achieved by employing a multi-input multi-output (MIMO) control strategy, which optimally distributes power among multiple energy storage systems and loads. A comprehensive modeling and analysis of the PV cell characteristics and the multiport converter are conducted to identify the optimal operating conditions. Furthermore, a power management algorithm is developed to dynamically regulate the power flow and maximize the energy harvesting efficiency. The proposed approach demonstrates superior performance compared to traditional single-input single-output converters, achieving higher energy yields and enabling effective integration of PV cells in diverse applications. Simulation results validate the effectiveness of the proposed approach, showcasing its potential to significantly enhance energy harvesting from photovoltaic sources and contribute to the development of sustainable and reliable renewable energy systems. 2023 IEEE.
Parimalasundar E.; Jayakumar S.; Chandrika V.S.; Muthukaruppasamy S.; Devaraju T.; Suresh K.
Proceedings of the 5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023, pp. 1590-1595.
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; Chandrika V.S., Kpr Institute of Engineering and Technology, Electrical and Electronics Engineering, Coimbatore, India; Muthukaruppasamy S., Velammal Institute of Technology, Electrical and Electronics Engineering, Chennai, 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
Efficient Intrusion Detection through Class Balancing and Feature Selection: A Case Study with SVM
Bot-IoT dataset; Class imbalance; Feature selection; Intrusion Detection Systems (IDS); Random under sampling (RUS); SelectKBest; Support Vector Machine (SVM)
Intrusion Detection Systems are of paramount importance in network security. However, in real-world scenarios, they always suffer from the challenge of class imbalance, which is dominated by normal traffic. This paper presents a novel approach to enhancing the performance of IDS by proposing a hybrid of the Random Under sampling technique with the univariate feature selection technique, SelectKBest, for handling both problems of class imbalance and high dimensionality. This model was hence tried on the Bot-IoT dataset, which is a real-world IoT network traffic representation. The SVM classifier, which has been trained with the resampled and feature-selected data, showcased 95% balanced accuracy for both normal and malicious traffic detection. The combination of RUS and SelectKBest, apart from reducing overfitting, ensured the retention of the most relevant features and thereby made the IDS model robust. It can practically enhance the performance of IDS in an imbalanced and high-dimensional dataset by providing a balanced, efficient, and precise detecting mechanism. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Fernandes, Melsa Magdalene; Pereira, Sharlene Anna; Anita, EA Mary; Sowmya, T.
Lecture Notes in Networks and Systems;Volume;1296 LNNS;pp.1-9
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1007/978-981-96-3314-2_1 https://www.scopus.com/pages/publications/105009242266?origin=resultslist
Fernandes M.M., Department of CSE, Christ University, Bangalore Kengeri Campus Kanmanike, Karnataka, Bangalore, India; Pereira S.A., Department of CSE, Christ University, Bangalore Kengeri Campus Kanmanike, Karnataka, Bangalore, India; Anita E.A.M., Department of CSE, Christ University, Bangalore Kengeri Campus Kanmanike, Karnataka, Bangalore, India; Sowmya T., Department of Computer Science and Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Karnataka, Manipal, India
Efficient lipophilicity prediction of molecules employing deep-learning models
Convolutional neural network; Deep learning; Drug design; Lipophilicity; Long-short term memory; Multi-layer perceptron
Lipophilicity, expressed as logP, is a significant physiochemical property and is an indicator of absorption, distribution, metabolism and elimination characteristics of drugs used in medication. It is one of the major deciding factors of the fate of a molecule to be a successful drug. Mol2vec is a convenient and unsupervised machine learning technique which produces high-dimensional vector representations of molecules and its molecular substructures. The work described here aims to simplify prediction of logP values with high-degree of accuracy by using Deep Learning (DL) models paired with Mol2vec. The work described in this paper empirically demonstrates that by using the described DL models paired with Mol2vec, one can achieve results which are much better than the conventional ML techniques as well as more complex and recent algorithms like Message-passing Neural Networks (MPNN), Graph Convolution (GC) and Spatial Graph embedding (C-SGEN). Our RMSE (Root Mean Square Error) scores from the ensemble model is one of the best reported so far in literature. The methods elaborated in this paper are simple, yet effective in predicting logP values to a great degree of accuracy due to the use of Mol2vec and standard TensorFlow operators. The models employed here can be coded and maintained with much more ease compared to the techniques of MPNN, C-SGEN or GC. 2021 Elsevier B.V.
Chemometrics and Intelligent Laboratory Systems, Vol-213
https://doi.org/10.1016/j.chemolab.2021.104309 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104961606&doi=10.1016%2fj.chemolab.2021.104309&partnerID=40&md5=f4c3a6a54a3a7c8a1d49ff40cdab3da5
Datta R., Department of Chemistry, CHRIST (Deemed to be University), Hosur Road, Bengaluru, 560029, India; Das D., Intel, Bengaluru, 560103, India; Das S., Sprinklr India Pvt Ltd, Yemalur, Bengaluru, 560037, India
Efficient Load Balancing and Resource Allocation in Networked Sensing SystemsAn Algorithmic Study
AI algorithms; energy efficiency; Load balancing; machine learning; performance optimization; resource allocation
In the current environment, data generation and data transmission are increasing exponentially in day-to-day life. These exponentially growing data might create heavy traffic when transmitted between systems. Also, this affects many functionalities like configuration of networked systems, system and routing configuration parameters, load managing factors of network devices, etc. A dynamic traffic control mechanism needs to be adopted with the help of load-balancing algorithms and efficient resource allocation mechanisms to deal with heavy data traffic. Load balancing algorithms in networked sensing systems aim to distribute the workload evenly among sensor nodes to optimize network performance and energy efficiency and prolong the network lifetime. Resource allocation mechanisms in a networked sensing system involve allocating and distributing network resources efficiently, such as energy, bandwidth, processing power, etc., to optimize performance and increase the networks lifetime. To achieve efficient resource allocation with a balanced load, notable works have been done in optimization and machine learning. The work gives a scientific analysis of traditional and Artificial Intelligence algorithms from a centralized and distributed perspective. Researchers can take this analysis forward when deciding on algorithms based on their application and infrastructural needs. 2025 Scrivener Publishing LLC.
Krishnasamy, Lalitha; Vetriveeran, Divya; Sambandam, Rakoth Kandan; Jenefa, J.
https://doi.org/10.1002/9781394310890.ch6 https://www.scopus.com/pages/publications/85219153943?origin=resultslist
Krishnasamy L., Department of Artificial Intelligence & Data Science, Nandha Engineering College, Tamil Nadu, Erode, India; Vetriveeran D., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Kengeri Campus, Bengaluru, India; Sambandam R.K., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Kengeri Campus, Bengaluru, India; Jenefa J., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Kengeri Campus, Bengaluru, India
Efficient Lung Cancer Classification on Multi level Convolution Neural Network using Histopathological Images
Cancer Detection; CNN; computer aided diagnosis; Deep Learning; Histopathological Images; image Classification; machine learning
Lung cancer can be detected by lung nodules, which are a key sign. An early diagnosis enhances the likelihood that the patient will survive by enabling the appropriate therapy to start. To reduce the responsibility of radiologists' difficult and time-consuming labour of finding and categorising malignancy in Computed Tomography (CT) images, researchers have created CAD (computer-assisted diagnosis) systems. The likelihood and kind of malignancy are commonly determined by pathologists using histopathological images of biopsy specimens taken from potentially sick areas of the lungs. To categorise lung nodule malignancy, we recommend employing a four-level convolutional neural network (ML-CNN). From lung nodule CT scan images, multiple scales are extracted. ML-CNN's employs four CNNs network model structure. After the result of the last pooling layer has been flattened to a vector with a single dimension for each level, the vectors are concatenated. These four ML-CNNs will help our model perform better. The ML-CNN model can recognise and classify different forms of lung cancer with reasonable accuracy. The 25000 images employed in the ML-CNN model have been separated into three categories: training, validation, and testing. Three distinct tissue types were assessed and training and validation took up within 80% and 15% of the total time and 5% for testing, respectively. The histopathological images included the following tissue type's 1.Benign tissue 2. Large cell carcinoma 3.squamous cell carcinoma. The proposed model demonstrated superior performance on both the training set, achieving an accuracy of 78%, and the validation set, achieving an accuracy of 89.6% by the end of the evaluation 2023 IEEE.
Ramesh M.; Maheswaran S.; Theivanayaki S.; Kodeeswari K.; Krishnasamy L.; Sriram N.
2023 14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023
https://doi.org/10.1109/ICCCNT56998.2023.10307852 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179839559&doi=10.1109%2fICCCNT56998.2023.10307852&partnerID=40&md5=e0adb3b1e5b898cf056da480adc4a970
Ramesh M., Kongu Engineering College, Department of Electronics and Communication Engineering, Erode, India; Maheswaran S., Kongu Engineering College, Department of Electronics and Communication Engineering, Erode, India; Theivanayaki S., Excel Engineering College, Department of Electronics and Communication Engineering, Namakkal, India; Kodeeswari K., Excel Engineering College, Department of Electronics and Communication Engineering, Namakkal, India; Krishnasamy L., Christ University, Department of Computer Science and Engineering, Bengaluru, India; Sriram N., Kongu Engineering College, Department of Electronics and Communication Engineering, Erode, India
Efficient management of feed resources using data mining techniques /
Feed is the largest input in any livestock enterprise and the rapid increase in feed prices and shortage of feed resources has been one of the major constraints for farmers, livestock industries, planners and the policy makers. This calls for prudent management of available resources and application of computing techniques can be one of the possible potential approaches. India is endowed with a wide range of feed resources varying widely in their composition and utility for different livestock species. Clustering of feed resource into different groups based on the composition can help in better feed management. To evaluate and to suggest a best technique for clustering feed resources, we have evaluated three clustering techniques viz. K-means, spectral k-means and auto spectral on two different data sets containing 236 and 106 feed resources with major constituents like crude protein, crude fiber ash, fat etc., .
Efficient Method for Personality Prediction using Hybrid Method of Convolutional Neural Network and LSTM
Correlation Based Feature Subset; Personality Prediction; Social Network
Users' contributions and the emotions conveyed in status updates may prove invaluable to studies of human behavior and character. A number of other research have taken a similar approach, and the field itself is still growing. The goal of this proposed is to create a technique for deducing a user's personality traits based on their social media profiles. Among the many customer services now available on SNSs are media and recommendations of user involvement. The need to give internet users with more specialized and customized services that meet their specific requirements, which sometimes depend heavily on the users' inner personalities, is significant. However, there hasn't been much work done on the psychological analysis that's needed to deduce the user's inner nature from their outward activities. In this instance, LSTM-CNN was fed pre-processed and vectorized text documents. SNF is used for feature extraction. The proposed method employs CFS for the purpose of Feature Selection. Finally, LSTM-CNN was used to train the model. While CNN is good at extracting features that are independent of time, LSTM is better at capturing long-term dependencies. combination of features for personality prediction, the LSTM-CNN model is superior to the individual models. 2023 IEEE.
Sujatha N.; Pramod S.; Bhatla S.; Thulasimani T.; Kant R.; Chauhan A.
2023 4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023 - Proceedings, pp. 959-964.
Sujatha N., B. M. S. College of Engineering, Department of Mathematics, Karnataka, Bengaluru, India; Pramod S., Nitte Meenakshi Institute of Technology Yelahanka Bangalore, Department of Mathematics, Karnataka, India; Bhatla S., Graphic Era Hill University, Department of Computer Science and Engineering, Dehradun, India; Thulasimani T., Bannari Amman Institute of Technology Sathyamangalam, Department of Mathematics, Tamilnadu, Erode, India; Kant R., Shoolini University, Cse Department, Himachal Pradesh, Solan, India; Chauhan A., Christ (Deemed to Be University), Department of Life Sciences, Karnataka, Bengaluru, India
Efficient Method for Tomato Leaf Disease Detection and Classification based on Hybrid Model of CNN and Extreme Learning Machine
Extreme Learning Machine (ELM); Grey Level Co-occurence Matrix; K-Means Algorithm
Through India, most people make a living through agriculture or a related industry. Crops and other agricultural output suffer significant quality and quantity losses when plant diseases are present. The solution to preventing losses in the harvest and quantity of agricultural products is the detection of these illnesses. Improving classification accuracy while decreasing computational time is the primary focus of the suggested method for identifying leaf disease in tomato plant. Pests and illnesses wipe off thousands of tons of tomatoes in India's harvest every year. The agricultural industry is in danger from tomato leaf disease, which generates substantial losses for producers. Scientists and engineers can improve their models for detecting tomato leaf diseases if they have a better understanding of how algorithms learn to identify them. This proposed approaches a unique method for detecting diseases on tomato leaves using a five-step procedure that begins with image preprocessing and ends with feature extraction, feature selection, and model classification. Preprocessing is done to improve image quality. That improved K-Means picture segmentation technique proposes segmentation as a key intermediate step. The GLCM feature extraction approach is then used to extract relevant features from the segmented image. Relief feature selection is used to get rid of the categorization results. finally, classification techniques such as CNN and ELM are used to categorize infected leaves. The proposed approach to outperforms other two models such as CNN and ELM. 2023 IEEE.
Chaturvedi A.; Yagnasree S.; Chhabra G.; Chidambara Rajan P.; Chauhan A.; Maranan R.
2023 4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023 - Proceedings, pp. 1179-1184.
https://doi.org/10.1109/ICESC57686.2023.10193102 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85168308909&doi=10.1109%2fICESC57686.2023.10193102&partnerID=40&md5=08c0d015be371abbdda4a5eb115cac80
Chaturvedi A., Gla University, Department of Electronics and Communication Engineering, Uttar Pradesh, Mathura, India; Yagnasree S., Lovely Professional University, Department of Electronics & Communication Engineering, Punjab, Phagwara, India; Chhabra G., Graphic Era Hill University, Department of Computer Science and Engineering, Dehradun, India; Chidambara Rajan P., Dr. N. G. P. Arts and Science College, Department of Biotechnology, Tamilnadu, Coimbatore, India; Chauhan A., Christ (Deemed to Be University), Department of Life Sciences, Karnataka, Bengaluru, India; Maranan R., Saveetha School of Engineering, Simats, Department of Research and Innovation, Tamil Nadu, Chennai, India
Efficient Mitosis Segmentation and Detection in Breast Cancer Histopathological Images Using YOLOv5 Model
breast cancer; deep learning; histopathological images; mitosis cells; segmentation; YOLO-v5
Mitosis count serves as a critical biomarker in breast cancer research, aiding in the prediction of aggressiveness, prognosis, and grade of the disease. However, accurately identifying mitotic cells amidst shape and stain variations, while distinguishing them from similar objects like lymphocytes and cells with dense nuclei, presents a significant challenge. Traditional machine learning methods have struggled with this task, particularly in detecting small mitotic cells, leading to high inter-rater variability among pathologists. In recent years, the rise in deep learning has reduced the subjectivity of mitosis detection. However, Deep Learning models face challenges with segmenting and classifying mitosis due to its intricate morphological variations, cellular heterogeneity, and overlapping structures. In response to these challenges, this study presents an Intelligent Mitosis Segmentation and Detection in Breast Cancer Histopathological Images Using Deep Learning (IMSD-BCHIDL) Model. The purpose of the IMSD-BCHIDL technique is to segment and classify mitosis in the histopathological images. To accomplish this, the IMSD-BCHIDL technique mainly employs YOLO-v5 model, which proficiently segments and classifies the mitosis cells. In addition, InceptionV3 is applied as a backbone network for the YOLO-v5 model, which helps in capturing extensive contextual details from the input image and results in improved detection tasks. For demonstrating the greater solution of the IMSD-BCHIDL method of the IMSD-BCHIDL technique, a wide range of experimental analyses is made. The simulation values portrayed the improved solution of the IMSD-BCHIDL system with other recent DL models. 2024 by the authors.
Journal of Advances in Information Technology, Vol-15, No. 10, pp. 1184-1192.
Lijo J., Department of Computer Science, Christ University, Karnataka, Bengaluru, India, School of Computer Applications, Dayananda Sagar University, Karnataka, Bengaluru, India; Saleema J.S., Department of Statistics and Data Science, Christ University, Karnataka, Bengaluru, India
Efficient Multilingual Language Detection Using Machine Learning Algorithms
FastText; Long Short-Term Memory; Natural Language Processing; Recurrent Neural Networks; Support Vector Machines
Natural Language Processing (NLP) is one of the important technologies in recent days, because language detection this NLP is play a vital role. This research focuses on detecting languages using various machine learning algorithms. FastText, Recurrent Neural Networks (RNN), Support Vector Machines (SVM) algorithms are used for this experiment. The following datasets are used to take this result that is Europarl and Tatoeba. The proposed method is to preprocess, train, and test these models. Evaluation is done by measuring precision, recall, and F1 score of the three algorithms. Results show that RNN provides precision close perfect or near-perfect results in both bilingual and multilingual datasets. SVM performs with high precision and recall, but less than RNN. Its performance slightly decreases as the dataset increases. On the other hand, FastText, although fast and efficient, drops significantly in performance as the dataset grows, especially with the inclusion of a third language. It provides an all-inclusive methodology that has pinned the strengths and weaknesses of each algorithm, providing valuable insight into which one best fit real-world language detection task: RNN with their ability to handle complex sequences, SVM for large-scale high-dimensional sparse features, and FastText for simpler, smaller dataset. 2025 IEEE.
Josemon, Felix; Anil, Anakha; Jayapandian, N.
2025 World Skills Conference on Universal Data Analytics and Sciences, WorldSUAS 2025;
https://doi.org/10.1109/WorldSUAS66815.2025.11199239 https://www.scopus.com/pages/publications/105022265928?origin=resultslist
Josemon F., Christ University, Department of Computer Science and Engineering, Kengeri Campus, Bangalore, India; Anil A., Christ University, Department of AI, ML and Data Science, Kengeri Campus, Bangalore, India; Jayapandian N., Christ University, Department of Computer Science and Engineering, Kengeri Campus, Bangalore, India
Efficient multipath model based cross layer routing techniques for Gauss Markov movable node management in MANET
This research unveils an innovative cross-layer routing methodology tailored for managing Gauss Markov mobile nodes within MANETs. The primary focus deceits cutting-edge inspiring network performance through the efficient utilization of resources and the steadfast maintenance of mobile node connectivity. Central to this model is the implementation of joint optimization, which takes into account both node mobility patterns and resource allocation dynamics to pinpoint the most favorable data transmission pathway. Incorporating multipath routing, the methodology enables the simultaneous exploration of multiple transmission routes, thereby fortifying the network against potential link failures and disruptions. By embracing a cross-layer approach, it seamlessly integrates functionalities across network, and steering layers, thereby amplifying the complete system efficacy. Comprehensive simulations conducted reveal the superior performance of this approach compared to existing techniques, particularly in terms of network throughput, latency reduction, and augmentation of packet delivery ratios. Such findings underscore the immense potential of this methodology across a spectrum of MANET applications that demand streamlined and dependable data transmission mechanisms. 2024 Author(s).
Musa A.I.A.; Sivakumar V.; Chandrasekharan N.; Priya T.M.; Kanna R.R.
AIP Conference Proceedings, Vol-3161, No. 1
https://doi.org/10.1063/5.0229502 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204024285&doi=10.1063%2f5.0229502&partnerID=40&md5=5da5eb5f70ea4a9594cc93c69ccadb45
Musa A.I.A., Department of Computer Science, College of Computer, Qassim University, Riyadh, Saudi Arabia; Sivakumar V., School of Computing, Faculty of Computing Engineering and Technology, Asia Pacific University of Technology and Innovation, Kuala Lumpur, Malaysia; Chandrasekharan N., School of Computing, Faculty of Computing Engineering and Technology, Asia Pacific University of Technology and Innovation, Kuala Lumpur, Malaysia; Priya T.M., Department of Computer Science, CHRIST (Deemed to Be University), Bangalore, India; Kanna R.R., Department of Computer Science, CHRIST (Deemed to Be University), Bangalore, India
Efficient neighbour feedback based trusted multi authenticated node routing model for secure data transmission
Authenticated routing mode; MANET; Mobile ad hoc network; Neighbour feedback
The Mobile Ad Hoc Network (MANET) is a network that does not have a fixed infrastruc-ture. Migratory routes and related hosts that are connected via wireless networks self-configure it. Routers and hosts are free to wander, and nodes can change the topology fast and unexpectedly. In emergencies, such as natural/human disasters, armed conflicts, and emergencies, the lowest configuration will ensure ad hoc network applicability. Due to the rapidly rising cellular service requirements and deployment demands, mobile ad-hoc networks have been established in numerous places in recent decades. These applications include topics such as environmental surveillance and others. The underlying routing protocol in a given context has a significant impact on the ad hoc network deployment power. To satisfy the needs of the service level and efficiently meet the deployment requirements, developing a practical and secure MANET routing protocol is a critical task. However, owing to the intrinsic characteristics of ad hoc networks, such as frequent topology changes, open wireless media and limited resources, developing a safe routing protocol is difficult. Therefore, it is vital to develop stable and dependable routing protocols for MANET to provide a better packet delivery relationship, fewer delays, and lower overheads. Because the stability of nodes along this trail is variable, the route discovered cannot be trusted. This paper proposes an efficient Neighbour Feedback-based Trusted Multi Authenticated Node (NFbTMAN) Routing Model. The proposed model is compared to traditional models, and the findings reveal that the proposed model is superior in terms of data security. 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Bondada P.; Samanta D.; Chaudhry S.A.; Zikria Y.B.; Ishmanov F.
Sustainability (Switzerland), Vol-13, No. 23
https://doi.org/10.3390/su132313296 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120906906&doi=10.3390%2fsu132313296&partnerID=40&md5=8d82a0fc52d78d6668857de26c9c3c38
Bondada P., Dayananda Sagar Research Foundation, University of Mysore (UoM), Mysuru, 570005, India; Samanta D., Dayananda Sagar Research Foundation, University of Mysore (UoM), Mysuru, 570005, India, Department of Computer Science, CHRIST (Deemed to be) University, Bangalore, 560029, India; Chaudhry S.A., Department of Computer Engineering, Faculty of Engineering and Architecture, Istanbul Gelisim University, Istanbul, 34310, Turkey; Zikria Y.B., Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38541, South Korea; Ishmanov F., Department of Electronics and Communication Engineering, Kwangwoon University, Seoul, 01897, South Korea
EFFICIENT NON-DEGRADABLE WASTE PROCESSING TECHNOLOGIES INTEGRATED WITH MANETS FOR SUSTAINABLE WASTE MANAGEMENT MODELS
circular economy; integrated technologies; MANETs (Mobile Ad-hoc Networks); non-degradable waste; real-time monitoring; sustainable waste management
In order to handle the growing amount of non-biodegradable trash, creative and sustainable solutions are becoming more and more necessary as the global waste management challenge grows. To create a complete and sustainable waste management model, this investigation suggests a revolutionary approach that combines Mobile Ad-hoc Networks (MANETs) with effective non-degradable waste processing technology. Utilising cutting-edge waste processing technology that can efficiently handle non-biodegradable materials including plastic, e-waste, and other persistent pollutants is the main goal of this. With the goal of reducing their negative effects on the environment and advancing the concepts of circular economy, these technologies include sophisticated sorting systems, chemical treatments, and recycling procedures. Furthermore, the efficiency and real-time monitoring of waste processing processes are improved by the incorporation of MANETs into the waste management paradigm. MANETs enable smooth data transmission and communication between the central control centres, waste processing units, and monitoring sensors that make up the waste management system. Because of this connectedness, waste processing activities can be dynamically optimised, facilitating prompt resource allocation and decision-making. In addition to addressing the environmental issues raised by non-biodegradable garbage, the suggested paradigm advances the creation of intelligent and networked waste management systems. Because MANETs are used, the system is scalable and adaptable, making it appropriate for a variety of urban and rural areas. The model incorporates the Ant Colony Optimisation (ACO) algorithm for resource allocation. The integration of ACO optimises resource allocation, contributing to the reduction of environmental footprints associated with waste processing. The interconnectedness facilitated by MANETs, in conjunction with ACO, enables dynamic optimisation of waste processing operations, ensuring prompt resource allocation and decision-making. This investigation envisions a sustainable waste management model that minimises pollution, promotes resource recovery, and establishes a robust framework for addressing the growing challenges of non-degradable waste on a global scale by combining cutting-edge waste processing technologies with a strong communication infrastructure. The results of the investigation have a significant impact on waste management procedures by encouraging a more ecologically friendly and sustainable way to deal with non-biodegradable garbage. 2024, Scibulcom Ltd. All rights reserved.
Ganesh V.N.; Prathapa Reddy S.L.; Banumathi S.; Acharjee P.B.; Basha H.A.; Kumar S.S.; Rajaram A.
Journal of Environmental Protection and Ecology, Vol-25, No. 2, pp. 440-448.
Ganesh V.N., Department of Civil Engineering, PSG Institute of Technology and Applied Research, Coimbatore, India; Prathapa Reddy S.L., Department of Electronics and Communication Engineering, KSRM College of Engineering, Andhra Pradesh, Kadapa, India; Banumathi S., Department of Electrical and Electronics Engineering, M. Kumarasamy College of Engineering, Karur, India; Acharjee P.B., CHRIST University Pune, Lavasa Campus, India; Basha H.A., Department of Computer Science, Saveeth College of Liberal Arts and Sciences, Saveetha Institute of Medical and Technical Sciences, Chennai, India; Kumar S.S., Department of Information Technology, Sri Ramakrishna Mission Vidyalaya College of Arts and Science (Autonomous), Periyanaickenpalayam, Coimbatore, India; Rajaram A., Department of Electronics and Communication Engineering, E.G.S. Pillay Engineering College, Tamil Nadu, Nagapattinam, 611 002, India
Efficient one-pot green synthesis of carboxymethyl cellulose/folic acid embedded ultrafine CeO2 nanocomposite and its superior multi-drug resistant antibacterial activity and anticancer activity
Antibacterial activity; Biocompatibility; Carboxymethyl cellulose; CeO2; Folic acid
Due to the prevalence of drug-resistant bacteria and the ongoing shortage of novel antibiotics as well as the challenge of treating breast cancer, the therapeutic and clinical sectors are consistently seeking effective nanomedicines. The incorporation of metal oxide nanoparticles with biological macromolecules and an organic compound emerges as a promising strategy to enhance breast cancer treatment and antibacterial activity against drug-resistant bacteria in various biomedical applications. This study aims to synthesize a unique nanocomposite consisting of CeO2 embedded with folic acid and carboxymethyl cellulose (CFC NC) via a green precipitation method using Moringa oleifera. Various spectroscopic and microscopic analyses are utilized to decipher the physicochemical characteristics of CFC NC and active phytocompounds of Moringa oleifera. Antibacterial study against MRSA (Methicillin-resistant Staphylococcus aureus) demonstrated a higher activity (95.6%) for CFC NC compared to its counterparts. The impact is attributed to reactive oxygen species (ROS), which induces a strong photo-oxidative stress, leading to the destruction of bacteria. The minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of CFC NC are determined as 600g/mL and 1000g/mL, respectively. The anticancer activity against breast cancer cell resulted in the IC50 concentration of 10.8?g/mL and 8.2?g/mL for CeO2 and CFC NC respectively.The biocompatibility test was conducted against fibroblast cells and found 85% of the cells viable, with less toxicity. Therefore, the newly synthesized CFC NC has potential applications in healthcare and industry, enhancing human health conditions. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.
Boopathi T.S.; Rajiv A.; Patel T.S.G.M.; Bareja L.; Salmen S.H.; Aljawdah H.M.; Arulselvan P.; Suriyaprakash J.; Thangavelu I.
ISSN: 16157591; PubMed ID: 39503917; CODEN: BBEIB
Boopathi T.S., Department of Chemistry, Amrita School of Physical Sciences, Amrita Vishwa Vidyapeetham, Coimbatore, 641112, India, Functional Materials Laboratory, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, 641112, India; Rajiv A., Department of Physics, Faculty of Sciences, JAIN (Deemed-to-Be University), Karnataka, Bangalore, 562112, India; Patel T.S.G.M., Department of Community Medicine, Parul University, Gujarat, PO Limda, Tal. Waghodia, Vadodara, 391760, India; Bareja L., Centre of Research Impact and Outcome, Chitkara University, Punjab, Rajpura, 140417, India; Salmen S.H., Department of Botany and Microbiology, College of Science, King Saud University, PO Box-2455, Riyadh, 11451, Saudi Arabia; Aljawdah H.M., Department of Zoology, College of Science, King Saud University, PO Box-2455, Riyadh, 11451, Saudi Arabia; Arulselvan P., Department of Chemistry, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Tamil Nadu, Chennai, 602 105, India; Suriyaprakash J., Guangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of Matter, Guangdong Provincial Key Laboratory of Nanophotonic Functional Materials and Devices, School of Information and Optoelectronic Science and Engineering, South China Normal University, Guangzhou, 510006, China; Thangavelu I., Department of Chemistry, CHRIST (Deemed to Be University), Karnataka, Bangalore, 560029, India
Boopathi, Thalakulam Shanmugam; Rajiv, Asha; Patel, T.S. Geetika Madan; Bareja, Lakshay; Salmen, Saleh H.; Aljawdah, Hossam M.; Arulselvan, Palanisamy; Suriyaprakash, Jagadeesh; Thangavelu, Indumathi
Bioprocess and Biosystems Engineering;Volume;48;Issue;1;pp.121-131
https://doi.org/10.1007/s00449-024-03097-y https://www.scopus.com/pages/publications/85208816933?origin=resultslist
Efficient Pathfinding in a Maze to overcome Challenges in Robotics and AI Using Breadth-First Search
Breadth-First Search; Dijkstras; Pathfinding; Robotic Applications; Unweighted Graphs
Efficient pathfinding in a maze is a key obstacle in robotics, computer science, and artificial intelligence. The article is proposing a strategy using the Breadth-First Search (BFS) algorithm to establish the shortest path for a robot navigating from the top-left to the bottom-right corner of a maze depicted as a two-dimensional grid. The maze comprises open pathways and obstructions, signified by 0 and 1, respectively. The robot's permissible actions include up, down, left, and right, restricted by the boundaries of the grid and the position of obstacles. BFS, an approach well-suited for unweighted graphs, sequentially examines all available routes, ensuring that the first observed path to the goal is the shortest. A visited set removes redundant cell visits, reducing infinite loops and inefficient processing. The algorithm's efficiency is dramatically upgraded by harnessing a queue structure to maintain live routes and their associated steps. This approach assures effectiveness and extensiveness for grid-based navigation problems, making it especially appropriate for real-world robotic applications where minimizing traversal cost is critical. Additionally, the paper discusses the algorithm's execution, complexities, and potential upgrades for larger grids or dynamic environments. Experimental results demonstrate BFS's resilience and efficacy in solving pathfinding challenges in various maze configurations. This work contributes to developing stable navigation techniques, integral to advancing autonomous robotic navigation and related fields. 2025 IEEE.
Biju, Abiya; Gayathri, M.P.; Jayapandian, N.; Chris, Alan; Thaleeparambil, Noeal Rajeev
2025 IEEE 14th International Conference on Communication Systems and Network Technologies, CSNT 2025;pp.727-732
https://doi.org/10.1109/CSNT64827.2025.10968383 https://www.scopus.com/pages/publications/105004548317?origin=resultslist
Biju A., Department of CSE, Christ University, Kengeri Campus, Bangalore, India; Gayathri M.P., Department of CSE, Christ University, Kengeri Campus, Bangalore, India; Jayapandian N., Department of CSE, Christ University, Kengeri Campus, Bangalore, India; Chris A., Department of Computer Science and Engineering, Christ University, Kengeri Campus, Bangalore, India; Thaleeparambil N.R., Department of Computer Science and Engineering, Christ University, Kengeri Campus, Bangalore, India
Efficient Photocatalytic Degradation of Methylene Blue From Aqueous Solution Using Hybrid Biomass-Derived Nanostructured Carbon-TiO2 Photocatalyst
biomass; biowaste-derived materials; coffee leaves; dye degradation; methylene blue remediation; photocatalysis
Industrial dye usage results in substantial wastewater discharge, posing environmental and health hazards. Hence, developing efficient, sustainable, and cost-effective treatment technologies is crucial. Photocatalysis using TiO? has emerged as a promising approach for dye degradation. This study explores the photocatalytic removal of methylene blue (MB), a model dye pollutant, using a composite of biomass-derived carbon nanoparticles (CNPs) and nanosized TiO? under UV light. The CNPs were synthesized via one-step pyrolysis from waste coffee leaves, offering a sustainable carbon source. The resulting CNPs (CL-10) and the TiO?-CNP composite (PC@CL-10) were thoroughly characterized using advanced techniques. Incorporating carbon significantly reduces the band gap of TiO? from ?3.2eV to 2.90eV, enhancing photocatalytic activity. Degradation studies under varying catalyst doses, dye concentrations, and pH levels demonstrate effective MB removal under UV irradiation. Photocatalytic experiments revealed up to 99% degradation of MB under UV light, while tests conducted in the dark showed negligible activity, confirming the light-dependent efficiency. Kinetic analysis indicated that intra-particle diffusion (IPD) governs the dye degradation process. Moreover, recyclability tests over seven cycles showed consistent performance with minimal decline, highlighting the catalyst's stability and reusability. These findings suggest that PC@CL-10 is a highly effective, low-cost photocatalyst with strong potential for large-scale wastewater treatment applications. 2025 The Author(s). Chemistry A European Journal published by Wiley-VCH GmbH.
Sharma, Aman; Shivanna, Jyothi Mannekote; Gupta, Navneet Kumar; Menezes, Prashanth W.; Hegde, Gurumurthy
Chemistry - A European Journal;Volume;31;Issue;44;Article No.;e202501564;
All Open Access; Green Open Access; Hybrid Gold Open Access
https://doi.org/10.1002/chem.202501564 https://www.scopus.com/pages/publications/105011042710?origin=resultslist
Sharma A., Department of Chemistry, Christ University, Bengaluru, 560029, India, Centre for Advanced Research and Development (CARD), Christ University, Bengaluru, 560029, India; Shivanna J.M., Nitte (Deemed to be University), Nitte Meenakshi Institute of Technology (NMIT), Department of Chemistry, Bengaluru, 560064, India; Gupta N.K., Centre for Sustainable Technologies, Indian Institute of Science, Gulmohar Marg, Mathikere, Bengaluru, 560012, India; Menezes P.W., Department of Material Chemistry for Catalysis, Helmholtz- Zentrum f Materialien und Energie, Albert-Einstein-Str. 15, Berlin, 12489, Germany, Department of Chemistry, Technical Univerisity of Berlin, Stra des 17 Juni 135. Sekr. C2, Berlin, 10623, Germany; Hegde G., Department of Chemistry, Christ University, Bengaluru, 560029, India, Centre for Advanced Research and Development (CARD), Christ University, Bengaluru, 560029, India
Efficient Power Conversion in Single-Phase Grid-Connected PV Systems through a Nine-Level Inverter
Efficiency; multilevel inverter; pulse width modulation; reduced switches; THD
In this paper, a novel nine-level inverter-based method for achieving efficient power conversion in single-phase grid-connected photovoltaic (PV) systems is proposed. The traditional two-level inverter has poor power quality and a high harmonic content. By using fewer power switches and adding more voltage levels, the proposed nine-level inverter gets around these restrictions, improving power conversion efficiency and lowering total harmonic distortion (THD). The effectiveness of the indicated technique for accomplishing better power quality and greater overall system efficiency is demonstrated by the simulation findings. A promising approach to improving the efficiency of single-phase grid-connected PV systems is the suggested nine-level inverter. 2023 IEEE.
Parimalasundar E.; Cheepati K.R.; Kamalkumar V.; Suresh K.; Devaraju T.; Kumar B.H.
2023 2nd International Conference on Trends in Electrical, Electronics and Computer Engineering, TEECCON 2023, pp. 1-6.
https://doi.org/10.1109/TEECCON59234.2023.10335776 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85181532747&doi=10.1109%2fTEECCON59234.2023.10335776&partnerID=40&md5=4fdfea49f1ac8e13cc3e67f88db17c2c
Parimalasundar E., Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College), Electrical and Electronics Engineering, Tirupati, India; Cheepati K.R., Kandula Sreenivasa Reddy Memorial College of Engineering, Electrical and Electronics Engineering, Kadapa, India; Kamalkumar V., KPR Institute of Engineering and Technology Coimbatore, Electrical and Electronics Engineering, Coimbatore, India; Suresh K., Christ University, Electrical and Electronics Engineering, Bangalore, India; Devaraju T., Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College), Electrical and Electronics Engineering, Tirupati, India; Kumar B.H., Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College), Electrical and Electronics Engineering, Tirupati, India