Hybrid Deep Learning-Based Potato andTomato Leaf Disease Classification
Crop disease; Hybrid model; MobileNet; OCNN-OLSTM; ResNet50
Predicting potato and tomato leaf disease is vital to global food security and economic stability. Potatoes and tomatoes are among the most important staple crops, providing essential nutrition to millions worldwide. However, many tomato and potato leaf diseases can seriously reduce food productivity and yields. We are proposing a hybrid deep learning model that combines optimized CNN (OCNN) and optimized LSTM (OLSTM). The weight values of LSTM and CNN models are optimized using the modified raindrop optimization (MRDO) algorithm and the modified shark smell optimization (MSSO) algorithm, respectively. The OCNN model is used to extract potato leaf image features and then fed into the OLSTM model, which handles data sequences and captures temporal dependencies from the extracted features. Precision, recall, F1-score, and accuracy metrics are used to analyze the output of the proposed OCNN-OLSTM model. The experimental performance is compared without optimizing the CNN-LSTM model, individual CNN and LSTM models, and existing MobileNet and ResNet50 models. The presented model results are compared with existing available work. We have received an accuracy of 99.25% potato and 99.31% for tomato. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Patil M.A.; Manohar M.; Laxuman C.; Parane K.; Dodamani B.M.; Sunkad G.
Lecture Notes in Networks and Systems, Vol-891, pp. 157-174.
Springer Science and Business Media Deutschland GmbH
Patil M.A., Department of Computer Science and Engineering, Christ (Deemed to be University) School of Engineering and Technology, Kengeri, Karnataka, Bengaluru, 560074, India, Department of Computer Science and Engineering, G. Narayanamma Institute of Technology and Science, Shaikpet, Telangana, Hyderabad, 500104, India; Manohar M., Department of Computer Science and Engineering, Christ (Deemed to be University) School of Engineering and Technology, Kengeri, Karnataka, Bengaluru, 560074, India; Laxuman C., Zonal Agricultural Research Station, UAS-R, Karnataka, Kalaburagi, 585101, India; Parane K., NITK Surathkal, Karnataka, Surathkal, India; Dodamani B.M., Zonal Agricultural Research Station, UAS-R, Karnataka, Kalaburagi, 585101, India; Sunkad G., University of Agricultural Sciences, Karnataka, Raichur, 584104, India
Hybrid fruit-fly optimization algorithm with k-means for text document clustering
Fruit-fly optimization algorithm; K-means; Machine learning; Metaheuristic algorithms; Text document clustering
The fast-growing Internet results in massive amounts of text data. Due to the large volume of the unstructured format of text data, extracting relevant information and its analysis becomes very challenging. Text document clustering is a text-mining process that partitions the set of text-based documents into mutually exclusive clusters in such a way that documents within the same group are similar to each other, while documents from different clusters differ based on the content. One of the biggest challenges in text clustering is partitioning the collection of text data by measuring the relevance of the content in the documents. Addressing this issue, in this work a hybrid swarm intelligence algorithm with a K-means algorithm is proposed for text clustering. First, the hybrid fruit-fly optimization algorithm is tested on ten unconstrained CEC2019 benchmark functions. Next, the proposed method is evaluated on six standard benchmark text datasets. The experimental evaluation on the unconstrained functions, as well as on text-based documents, indicated that the proposed approach is robust and superior to other state-of-the-art methods. 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Bezdan T.; Stoean C.; Naamany A.A.; Bacanin N.; Rashid T.A.; Zivkovic M.; Venkatachalam K.
https://doi.org/10.3390/math9161929 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112764015&doi=10.3390%2fmath9161929&partnerID=40&md5=fb2f46fae7238409adba829db8cb7e27
Bezdan T., Faculty of Informatics and Computing, Singidunum University, Danijelova 32, Belgrade, 11010, Serbia; Stoean C., Human Language Technology Research Center, University of Bucharest, Bucharest, 010014, Romania; Naamany A.A., Department for Mathematics and Computer Science, Modern College of Business and Science, Muscat, 113, Oman; Bacanin N., Faculty of Informatics and Computing, Singidunum University, Danijelova 32, Belgrade, 11010, Serbia; Rashid T.A., Computer Science and Engineering Department, University of Kurdistan Hewler, Erbil, 44001, Iraq; Zivkovic M., Faculty of Informatics and Computing, Singidunum University, Danijelova 32, Belgrade, 11010, Serbia; Venkatachalam K., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, 560029, India
Hybrid HOG-SVM encrypted face detection and recognition model
68M25; DES3; Face landmark estimation; HOG; RSA; SVM
Security plays a major role in an individuals life to win this world with highly secure and authentic lifestyle with the digital equipments. The paper proposed an encryption based secure face detection and recognition model which can be implemented in daily life to generate a more robust and efficient security bubble around the world. The most crucial problem encountered during face recognition is due to the variation in face direction of an individual, the model solves the mentioned pose variation problem. The proposed model takes the help of face recognition library to recognize the face and use HOG (Histogram of Oriented Gradients) & SVM for checking the face authentication by performing an image match, the model also applies the concept of HOG to generate the encoded features from the image. The system is divided into two modules first is to detect a face and then match the detected face from the authentic persons dataset available. The system uses the concept of OpenCV library for giving a support system for the real time image. For data encryption, proposed model used the concept of DES3 and RSA algorithm. The proposed model gets 83.33% accuracy while tested for three different image types and states that the RSA algorithm performs encryption in less computational time. 2022 Taru Publications.
Sharma S.; Raja L.; Bhatnagar V.; Sharma D.; Bhagirath S.N.; Poonia R.C.
Journal of Discrete Mathematical Sciences and Cryptography, Vol-25, No. 1, pp. 205-218.
Sharma S., Department of Computers Applications, Manipal University Jaipur, Rajasthan, Jaipur, 303007, India; Raja L., Department of Computers Applications, Manipal University Jaipur, Rajasthan, Jaipur, 303007, India; Bhatnagar V., Department of Computers Applications, Manipal University Jaipur, Rajasthan, Jaipur, 303007, India; Sharma D., Department of Computers Applications, Manipal University Jaipur, Rajasthan, Jaipur, 303007, India; Bhagirath S.N., Department of Computers Applications, Manipal University Jaipur, Rajasthan, Jaipur, 303007, India; Poonia R.C., Department of Computer Science, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India
Hybrid homomorphic-asymmetric lightweight cryptosystem for securing smart devices: A review
The Internet of Things (IoT) has emerged as a new concept in information and communication technology, and its structure depends on smart device communications. It was evolving as a significant factor of the Internet and made the interconnection of huge devices likely, accumulating huge amounts of information through innovative technologies. Thus, the requirement for IoT security is more significant. Scalable services and applications are susceptible to information leakage and attacks, demanding higher privacy and security. Cryptography is a technique to secure data integrity, confidentiality, authentication, and network access control. Owing to several limitations of IoT devices, the classical cryptographic protocols are not appropriate for all IoT smart devices like smart cities, smart homes and so forth. Consequently, researchers have introduced numerous lightweight cryptographic (LWC) protocols and algorithms for IoT security. Numerous solutions are available in the research field regarding security using cryptographic algorithms in IoT environments; however, such solutions have not attained satisfactory outcomes. So, finding a solution by examining the recent issues is open research. This article investigates the various LWC protocols for IoT devices and provides a reasonable enquiry into existing ubiquitous ciphers. Furthermore, the article appraises various recently presented lightweight (LW) block ciphers and hybrid homomorphic LWC regarding security. In addition, this article assists in comprehending the significance of security features and progression in cryptographic algorithms. Finally, the article reports on the necessary changes and recommends upcoming research focuses. Also, this article assists in realizing the importance of security and progressions in cryptographic algorithms. 2023 John Wiley & Sons, Ltd.
Transactions on Emerging Telecommunications Technologies, Vol-35, No. 1
https://doi.org/10.1002/ett.4866 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179313097&doi=10.1002%2fett.4866&partnerID=40&md5=b415405c214a1880f1d8cb1e18f2989a
Mathews R., Department of Computer Science, Christ University, Karnataka, Bangalore, India, Department of Computer Science and Engineering, St. Mary's College, Kerala, Sulthan Bathery, India; Jose D.V., Department of Computer Science, Christ University, Karnataka, Bangalore, India
Hybrid Intrusion Detection Technique for Internet of Things
The rapid expansion and integration of Internet of Things (IoT) applications in newlinevarious aspects of daily life has significantly surprised and impacted contemporary society. The most crucial keyword concerning these applications is security, specifically, in the enormous amount of data generated every second, and how it is used. These applications are vulnerable to various attacks, which could result in an unthinkable catastrophe if not managed and controlled with sufficient foresight. Growing concerns about data security in the expanding IoT landscape are driven by factors such as increased vulnerability of devices to viruses, susceptibility to denial-of-service attacks, and heightened risk of intrusion attempts. To prevent such occurrences, stronger precautions should be taken, enabling system developers and manufacturers of IoT devices to enhance their approaches to better security mitigation. It is essential to identify all potential threats and vulnerabilities that are created explicitly for IoT infrastructures. It is believed that to lessen potential dangers, there is a need for more significant research on security attacks. Security difficulties have been found and must be dealt with, so they may be avoided. Further research must address security challenges in IoT-based environments, particularly for suppliers and consumers, to gradually raise the reliability of IoT applications. Although many conventional methods are still used, there might be superior options for devices with limited resources. Artificial intelligence plays a significant role in this issue. newlineThis research first tries to comprehend how machine learning methods relate to attack newlinedetection. The effects of different machine learning techniques are evaluated using the newlineUNSW-NB 15 dataset. Additionally, it has been found that each model performs worse overall, mainly when security issues are present. As a result, real-time datasets and Deep Learning (DL) algorithms for intrusion detection in the IoT need to be prioritized.
Hybrid Model Using Interacted-ARIMA andANN Models forEfficient Forecasting
INTARIMA; Interacted lagged variables; Interaction; Time series
When two models applied to the same dataset produce two different sets of forecasts, it is a good practice to combine the forecasts rather than using the better one and discarding the other. Alternatively, the models can also be combined to have a hybrid model to obtain better forecasts than the individual forecasts. In this paper, an efficient hybrid model with interacted ARIMA (INTARIMA) and ANN models is proposed for forecasting. Whenever interactions among the lagged variables exist, the INTARIMA model performs better than the traditional ARIMA model. This is validated through simulation studies. The proposed hybrid model combines forecasts obtained through the INTARIMA model from the dataset, and those through the ANN model from the residuals of INTARIMA, and produces better forecasts than the individual models. The quality of the forecasts is evaluated using three error metrics viz., Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). Empirical results from the application of the proposed model on the real dataset - lynx - suggest that the proposed hybrid model gives superior forecasts than either of the individual models when applied separately. The methodology is replicable to any dataset having interactions among the lagged variables.. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol-14078 LNAI, pp. 747-756.
Baskaran T., CHRIST (Deemed to be University), Hosur Road, Karnataka, Bengaluru, 560029, India; John N., CHRIST (Deemed to be University), Hosur Road, Karnataka, Bengaluru, 560029, India; Dhandra B.V., CHRIST (Deemed to be University), Hosur Road, Karnataka, Bengaluru, 560029, India
Hybrid models for intraday stock price forecasting based on artificial neural networks and metaheuristic algorithms
Artificial bee colony algorithm; Artificial neural network; Genetic algorithm; Hit rate; Intraday stock prediction; Nature inspired algorithm; Stock market prediction
Stock market prediction is one of the critical issues in fiscal market. It is important issue for the traders and investors. Artificial Neural Networks (ANNs) associated with nature inspired algorithms are playing an increasingly vital role in many areas including medical field, security systems and stock market. Several prediction models have been developed by researchers to forecast stock market trend. However, few studies have focused on improving stock market prediction accuracy especially when utilizing artificial neural networks to perform the analysis. This paper proposed nine new integrated models for forecasting intraday stock price based on the potential of three ANNs, Back Propagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN), Time Delay Neural Network (TDNN) and nature inspired algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC).The developed models were named as GA-BPNN, PSO-BPNN, ABC-BPNN, GA-RBFNN, PSO-RBFNN, ABC-RBFNN, GA-TDNN, PSO-TDNN and ABC-TDNN. Nature inspired algorithms are employed for optimizing the parameters of ANNs. Technical indicators calculated from historical data are fed as input to developed models. Proposed hybrid models validated on four datasets representing different sectors in NSE. Four statistical metrics, Root Mean Square Error (RMSE), Hit Rate (HR), Error Rate (ER) and prediction accuracy were utilized to gauge the performance of the developed models. Results proved that the PSO-BPNN model yielded the highest prediction accuracy in estimating intraday stock price. The other models, GA-BPNN, ABC-BPNN, GA-RBFNN, PSO-RBFNN, ABC-RBFNN, GA-TDNN, PSO-TDNN and ABC-TDNN produced lower performance with mean prediction accuracy of 97.24%, 98.37%, 84.01%, 85.15%, 84.01%, 83.87%, 89.95% and 78.61% respectively. 2021
Pattern Recognition Letters, Vol-147, pp. 124-133.
https://doi.org/10.1016/j.patrec.2021.03.030 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105116690&doi=10.1016%2fj.patrec.2021.03.030&partnerID=40&md5=393862b59147e7a09b83488a2b5bfc53
S K.C., School of Business & Management, CHRIST (Deemed to be University), Bangalore, India
Hybrid nanofluid flow over a vertical rotating plate in the presence of hall current, nonlinear convection and heat absorption
Hall current; Hybrid nanofluids; Laplace transforms method; Nonlinear convection; Unsteady flow
An exact analysis has been carried out to study a problem of the nonlinear convective flow of hybrid nanoliquids over a vertical rotating plate with Hall current and heat absorption. Three different fluids namely CuAl2O3H2Ohybrid nanofluid, Al2O3H2O nanofluid and H2O basefluids are considered in the analysis. The simulation of the flow was carried out using the appropriate values of the empirical shape factor for five different particle shapes (i.e., sphere, hexahedron, tetrahedron, column and lamina). The governing PDEs with the corresponding boundary conditions are non-dimensionalised with the appropriate dimensionless variables and solved analytically by using LTM (Laplace transform technique). This investigation discusses the effects of governing parameters on velocity and temperature fields in addition to the rate of heat transfer. The numeric data of the density, thermal conductivity, dynamic viscosity, specific heat, Prandtl number and Nusselt number for twelve different hybrid nanofluids at 300 K is presented. The temperature profile of hybrid nanoliquid is larger than nanoliquid for same volume fraction of nanoparticles. Also, the glycerin-based nanoliquid has a high rate of heat transfer than engine oil, ethylene glycol and water-based nanoliquids in order. 2018 by American Scientific Publishers All rights reserved.
Journal of Nanofluids, Vol-7, No. 6, pp. 1138-1148.
https://doi.org/10.1166/jon.2018.1550 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047875321&doi=10.1166%2fjon.2018.1550&partnerID=40&md5=338b35bb41715d3a6cbd8d030c128194
Amala S., Department of Mathematics, CHRIST (Deemed to be University), Bengaluru, 560029, India; Mahanthesh B., Department of Mathematics, CHRIST (Deemed to be University), Bengaluru, 560029, India
Hybrid optimization for efficient 6G IoT traffic management and multi-routing strategy
Air pollution monitoring; Deep reinforcement learning; Dynamic cluster head selection; IoT; Mobile ad-hoc networking; Quantum entanglement and mobility metric; Quantum genetic algorithm; Quantum-inspired clustering algorithm
Efficient traffic management solutions in 6G communication systems face challenges as the scale of the Internet of Things (IoT) grows. This paper aims to yield an all-inclusive framework ensuring reliable air pollution monitoring throughout smart cities, capitalizing on leading-edge techniques to encourage large coverage, high-accuracy data, and scalability. Dynamic sensors deployed to mobile ad-hoc pieces of fire networking sensors adapt to ambient changes. To address this issue, we proposed the Quantum-inspired Clustering Algorithm (QCA) and Quantum Entanglement and Mobility Metric (MoM) to enhance the efficiency and stability of clustering. Improved the sustainability and durability of the network by incorporating Dynamic CH selection employing Deep Reinforcement Learning (DRL). Data was successfully routed using a hybrid Quantum Genetic Algorithm and Ant Colony Optimization (QGA-ACO) approach. Simulation results were implemented using the ns-3 simulation tool, and the proposed model outperformed the traditional methods in deployment coverage (95%), cluster stability index (0.97), and CH selection efficiency (95%). This work is expected to study the 6G communication systems as a key enabler for IoT applications and as the title legible name explains, the solutions smartly done in a practical and scalable way gives a systematic approach towards solving the IoT traffic, and multi-routing challenges that are intended to be addressed in 6G era delivering a robust IoT ecosystem in securing the process. The Author(s) 2024.
Logeshwaran J.; Patel S.K.; Kumar O.P.; Al-Zahrani F.A.
Logeshwaran J., Department of Computer Science, Christ University, Karnataka, Bengaluru, 560029, India; Patel S.K., Department of Computer Engineering, Marwadi University, Rajkot, 360003, India; Kumar O.P., Department of Electronics and Communication Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India; Al-Zahrani F.A., Computer Engineering Department, Umm Al-Qura University, Mecca, 24381, Saudi Arabia
Hybrid Renewable Road Side Charging Station with I2V Communication Functionality
Energy management system; Road side charging stations; Road side unit; Vehicle communication
The faster adoption of Renewable-based Energy Sources for charging Electric Vehicles is highly required. The paper proposes a novel strategy of design and developing a hybrid Road Side Unit (RSU) that would be easy to install and provides easy access to Electric Vehicle charging. The system inculcates Infrastructure to Vehicle (I2V) communication framework enabling communication between the Infrastructure and the Vehicle to identify the nearest charging station based on the availability. The communication framework is based on Wi-Fi communication and enables bidirectional communication between the Vehicle and the Infrastructure as well. The modelling and development of the RSU, and the active power flow regulation from the RSU to the Charging Station is also developed, using a Fuzzy Controller. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Mathew A.; Babu R.; Philip M.T.; Joseph G.T.; Parag Jose C.; Haneesh K.M.
Lecture Notes in Mechanical Engineering, pp. 741-750.
Mathew A., School of Engineering & Technology, CHRIST (Deemed to be University), Bangalore, India; Babu R., School of Engineering & Technology, CHRIST (Deemed to be University), Bangalore, India; Philip M.T., School of Engineering & Technology, CHRIST (Deemed to be University), Bangalore, India; Joseph G.T., School of Engineering & Technology, CHRIST (Deemed to be University), Bangalore, India; Parag Jose C., School of Engineering & Technology, CHRIST (Deemed to be University), Bangalore, India; Haneesh K.M., School of Engineering & Technology, CHRIST (Deemed to be University), Bangalore, India
Hybrid scheme image compression using DWT and SVD
DCT; DWT; Image compression; SVD; Wavelet filters
Image compression is process of reducing data size to represent an image by removing redundant data. Hybrid scheme image compression is combination of methods performed in order or as an amalgam to form a new technique. In this paper, we proposed a new approach to compress the image by collaborating Discrete Wavelet Transformation (DWT) and Singular Value Decomposition (SVD). Image is decomposed into wavelets using DWT and approximate wavelet is subsequently transformed into four bands. Different wavelet filters are implemented for transformation namely Haar, Daubechies, Biorthogonal and Coiflets. Apart from approximate image, SVD is applied on remaining wavelets (Horizontal, Vertical and Diagonal Details) at each decomposition level. On reconstruction, various singular values are selected depending on the level transformation. The performance of the proposed method is compared and evaluated with SVD, DCT-SVD and DWT-DCT-SVD. Evaluation is carried out based on Compression Ratio (CR), Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) index. From the experimental results, it is observed that proposed method yields better MSE, PSNR and SSIM compared to state-of-the-art methods. 2017, Institute of Advanced Scientific Research, Inc. All rights reserved.
Journal of Advanced Research in Dynamical and Control Systems, Vol-2018, pp. 225-233.
Institute of Advanced Scientific Research, Inc.
Thinley T., Department of Computer Science, Christ University, Bengaluru, India; Praveen Kumar P.U., Department of Computer Science, Christ University, Bengaluru, India
Hybrid short term load forecasting using ARIMA-SVM
Autoregressive Integrated Moving Average (ARIMA); Mean Absolute Percentage Error (MAPE); Outliers; Percentage Error (PE); Short Term Load Forecasting (STLF); Support Vector Machines (SVM)
In order to perform a stable and reliable operation of the power system network, short term load forecasting is vital. High forecasting accuracy and speed are the two most important requirements of short-term load forecasting. It is important to analyze the load characteristics and to identify the main factors affecting the load. ARIMA method is most commonly used, as it predict the load purely based on the historical loads and no other assumptions are considered. Therefore there is a need for Outlier detection and correction method as the prediction is based on historical data, the historical data may contain some abnormal or missing values called outliers. Also the load demand is influenced by several other external factors such as temperature, day of the week etc., the Artificial Intelligence techniques will incorporate these external factors which improves the accuracy further. In this paper a hybrid model ARIMA-SVM is used to predict the hourly demand. ARIMA is used to predict the demand after correcting the outliers using Percentage Error (PE) method and its deviation is corrected using SVM. Main objective of this method is to reduce the Mean Absolute percentage Error (MAPE) by introducing a hybrid method employing with outlier detection technique. The historical load data of 2014-2015 from a utility system of southern region is taken for the study. It is observed that the MAPE error got reduced and its convergence speed increased. 2017 IEEE.
2017 Innovations in Power and Advanced Computing Technologies, i-PACT 2017, Vol-2017-January, pp. 1-7.
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/IPACT.2017.8245060 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045948276&doi=10.1109%2fIPACT.2017.8245060&partnerID=40&md5=c83ecea07cf835108c5811faf7e4756e
Karthika S., Department of Electrical and Electronics Engineering, Christ University, Faculty of Engineering, Bangalore, India; Margaret V., Department of Electrical and Electronics Engineering, Christ University, Faculty of Engineering, Bangalore, India; Balaraman K., Power Research and Development Consultants Pvt. Ltd, Bengaluru, India
Hybrid shuffled frog leaping and improved biogeography-based optimization algorithm for energy stability and network lifetime maximization in wireless sensor networks
energy stability; improved biogeography-based optimization algorithm (IBBOA); network lifetime; shuffled frog leaping algorithm; wireless sensor networks (WSNs)
Wireless sensor networks are significantly used for data sensing and aggregating dusts from a remote area environment in order to utilize them in a diversified number of engineering applications. The data transfer among the sensor nodes is attained through the inclusion of energy efficient routing protocols. These energy efficient routing necessitates optimal cluster head selection procedure for handling the challenge of energy consumption to extend the stability and lifetime in the sensor networks. The implementation of energy efficient routing is still complicated even when the process of clustering is enhanced through the cluster head selection. The majority of the existing cluster head selection schemes suffer from the issues of poor selection accuracy, increased computation, and duplicate nodes' selection. In this paper, hybrid shuffled frog leaping and improved biogeography-based optimization algorithm (HSFLBOA) for optimal cluster head selection is proposed for resolving issues that are common in cluster head selection schemes. This proposed HSFLBOA used the objective function that used the parameters of node energy, data packet transmission delay, cluster traffic density, and internode distance in the cluster. The simulation results of the proposed HSFLBOA is determined to be significant in achieving superior throughput and network energy compared to benchmarked metaheuristic optimal cluster head schemes. 2021 John Wiley & Sons Ltd.
Tamilarasan N.; Lenin S.B.; Jayapandian N.; Subramanian P.
International Journal of Communication Systems, Vol-34, No. 4
https://doi.org/10.1002/dac.4722 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099228351&doi=10.1002%2fdac.4722&partnerID=40&md5=f37574f1ba83124e8be6c4f6286f948b
Tamilarasan N., Department of ECE, Sri Indu College of Engineering and Technology, Hyderabad, India; Lenin S.B., Department of ECE, Sri Manakula Vinayagar Engineering College, Puducherry, India; Jayapandian N., Department of Computer Science & Engineering, Christ University, Bengaluru, India; Subramanian P., Department of CSE, Sri Indu College of Engineering and Technology, Hyderabad, India
Hybrid sparse and block-based compressive sensing algorithm for industry based applications
block compressive sensing; image reconstruction; MRI image; sparse compressive sensing
Image reconstructions are a challenging task in MRI images. The performance of the MRI image can be measure by following parameters like mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM). Compromising the above parameters and reconstructing the MRI image leads to false diagnosing. To avoid the false diagnosis, we have combined sparse based compressive sensing and block-based compressive sensing algorithm, and we introduced the hybrid sparse and block-based compressive sensing algorithm (HSBCS). In compressive stage, however, image reconstruction performance is decreased, hence, in the image reconstruction module, we have introduced convex relaxation algorithm. This proposed algorithm is obtained by relaxing some of the constraints of the original problem and meanwhile extending the objective function to the larger space. The performance is compared with the existing algorithm, block-based compressive sensing algorithm (BCS), BCS based on discrete wavelet transform (DWT), and sparse based compress-sensing algorithm (SCS). The experimentation is carried out using BRATS dataset, and the performance of image compression HSBCS evaluated based on SSIM, and PSNR, which attained 56.19 dB, and 0.9812. Copyright 2024 Inderscience Enterprises Ltd.
Sekar R.; Manikanda Devarajan N.; Ravi G.; Rajasekaran B.; Chidambaram S.
International Journal of Enterprise Network Management, Vol-15, No. 1, pp. 44-59.
https://doi.org/10.1504/IJENM.2024.137432 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188825482&doi=10.1504%2fIJENM.2024.137432&partnerID=40&md5=89e2914413d30418a3d630431a9c7d9f
Sekar R., Department of ECE, Presidency University, Bangalore, India; Manikanda Devarajan N., Department of ECE, Malla Reddy Engineering College, Telangana, Secunderabad, India; Ravi G., Department of ECE, Sona College of Technology, Salem, India; Rajasekaran B., Department of ECE, Vinayaka Missions Kirupananda Variyar Engineering College, Salem, India; Chidambaram S., Department of ECE, Christ University, Bangalore, India
Hybrid Subset Feature Selection and Importance Framework
Artificial Intelligence; Feature Importance; Feature Selection; feature-selectionpy; Stock Market; supervised learning
Feature selection algorithms are used in high-dimensional data to remove noise, reduce model overfitting, training and inference time, and get the importance of features. Features subset selection is choosing the subset with the best performance. This research provides a Hybrid subset feature selection and importance (HSFSI) framework that provides a pipeline with customization for choosing feature selection algorithms. The authors propose a hybrid algorithm in the HSFSI framework to select the best possible subset using an efficient exhaustive search. The framework is tested using the Bombay stock exchange IT index's companies' data collected quarterly for 16 years consisting of 71 financial ratios. The experimental results demonstrate that models created using 12 features chosen by the proposed algorithm outperform models with all features with up to 6% accuracy. The importance-based ranks of all features are generated using the framework calculated using 13 implemented feature selection techniques. All selected feature subsets are cross-validated using prediction models such as support vector machine, logistic regression, KNeighbors classffier, random forest, and deep neural network. The HSFSI framework is available as an open-source Python software package named ''feature-selectionpy'' available at GitHub and Python package index. 2023 IEEE.
Proceedings of IEEE InC4 2023 - 2023 IEEE International Conference on Contemporary Computing and Communications
Chaudhari C., CHRIST (Deemed to Be University), Dept. of Commerce, Bangalore, India; Purswani G., CHRIST (Deemed to Be University), Dept. of Commerce, Bangalore, India
Hybridization of Texture Features for Identification of Bi-Lingual Scripts from Camera Images at Wordlevel
In this paper, hybrid texture features are proposed for identification of scripts of bi-lingual camera images for a combination of 10 Indian scripts with Roman scripts. Initially, the input gray-scale picture is changed over into an LBP image, then GLCM and HOG features are extracted from the LBP image named as LBGLCM and LBHOG. These two feature sets are combined to form a potential feature set and are submitted to KNN and SVM classifiers for identification of scripts from the bilingual camera images. In all 77,000-word images from 11 scripts each contributing 7000-word images. The experimental results have shown the identification accuracy as 71.83 and 71.62% for LBGLCM, 79.21 and 91.09% for LBHOG, and 84.48 and 95.59% for combined features called CF, respectively for KNN and SVM. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Lecture Notes in Electrical Engineering, Vol-967, pp. 113-124.
Mallappa S., Department of P.G.Studies and Research in Computer Science, Gulbarga University, Gulbarga, India; Dhandra B.V., Department of Statistics, Christ (Deemed to Be University), Bengaluru, India; Mukarambi G., School of Computer Science, Department of Computer Science, Central University of Karnataka, Gulbarga, India
Hycons Renewable Private Limited: decision to accept or reject an equity investment
Financial analysis/forecasting; Management accounting/corporate finance; Small businesses
Learning outcomes: This study will help students determine the economic value of a firm particularly in case of a small business. The crux of the case is to help students estimate an enterprise value for a company and figure the actual worth of the company to aid in decision-making. Case overview/Synopsis: This case is about a decision dilemma faced by Shashi Hegde, Director, Hycons Renewable Private Ltd, a company ventured into the production of Bio-CNG. It is about a recent proposal received by the firm from APL Ltd for equity investment with 40% stake in the firm. The case reflects the dilemma faced by small businesses to choose between investment or loss of control. Accepting the proposal will bring in additional funds, whereas the Board loss control on the firm. The case revolves around this dilemma. To help Hegde in this task, he seeks advice from his CFO and his confidant Kumar. Complexity academic level: This case is most appropriate for a core finance class for both under-graduate and graduate programs. Supplementary materials: Teaching notes are available for educators only. Subject code: CSS 1: Accounting and Finance. 2022, Emerald Publishing Limited.
Emerald Emerging Markets Case Studies, Vol-12, No. 2, pp. 1-25.
https://doi.org/10.1108/EEMCS-05-2021-0161 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127769330&doi=10.1108%2fEEMCS-05-2021-0161&partnerID=40&md5=cff21a330d2960e9e7d2d72dba446044
Ramdas R., School of Business and Management, CHRIST (Deemed to be University), Bangalore, India; Shankar N., School of Business and Management, CHRIST (Deemed to be University), Bangalore, India
Hycons Renewable Private Limited: Viable Biogas Production from Paddy Straws: A Capital Budgeting Decision
Capital budgeting; funding techniques; net present value; sensitivity analysis
The case revolves around the decision to be taken by Mr Sashikant Hegde, Managing Director, Hycons Renewable Private Ltd, on the project viability of a business proposal. The proposal was to start a manufacturing plant in Punjab to produce compressed biogas using paddy straws. Hegde firmly believed that the company would do well considering the growth of the CNG market in India, as the oil and natural gas sector in India is among the top 10 core industries in the country and plays an important role in the existence of other important sectors as well. The proposal would benefit Hycons to establish its presence in northern India, but the project viability and funding of the investment remained an unanswered question. 2023 Lahore University of Management Sciences.
Shankar N., School of Business and Management, CHRIST University, Karnataka, Bangalore, India; Ramdas R., School of Business and Management, CHRIST University, Karnataka, Bangalore, India
Hydrogen Sulfide-Induced Activatable Photodynamic Therapy Adjunct to Disruption of Subcellular Glycolysis in Cancer Cells by a Fluorescence-SERS Bimodal Iridium Metal-Organic Hybrid
activatable PDT; ATP reduction; clinical validation; disrupting glycolysis; enhanced apoptosis; H2S depletion; stimuli-responsive activation
The practical application of photodynamic therapy (PDT) demands targeted and activatable photosensitizers to mitigate off-target phototoxicity common in always on photosensitizers during light exposure. Herein, a cyclometalated iridium complex-based activatable photodynamic molecular hybrid, Cy-Ir-7-nitrobenzofurazan (NBD), is demonstrated as a biomedicine for molecular precision. This design integrates a hydrogen sulfide (H2S)-responsive NBD unit with a hydroxy-appended iridium complex, Cy-Ir-OH. In normal physiological conditions, the electron-rich Ir metal center exerts electron transfer to the NBD unit, quenches the excited state dynamics, and establishes a PDT-off state. Upon exposure to H2S, Cy-Ir-NBD activates into the potent photosensitizer Cy-Ir-OH through nucleophilic substitution. This mechanism ensures exceptional specificity, enabling targeted phototherapy in H2S-rich cancer cells. Additionally, we observed that Cy-Ir-NBD-induced H2S depletion disrupts S-sulfhydration of the glyceraldehyde-3-phosphate dehydrogenase enzyme, impairing glycolysis and ATP production in the cellular milieu. This sequential therapeutic process of Cy-Ir-NBD is governed by the positively charged central iridium ion that ensures mitochondria-mediated apoptosis in cancer cells. Dual-modality SERS and fluorescence imaging validate apoptotic events, highlighting Cy-Ir-NBD as an advanced theranostic molecular entity for activatable PDT. Finally, as a proof of concept, clinical assessment is evaluated with the blood samples of breast cancer patients and healthy volunteers, based on their H2S overexpression capability through SERS and fluorescence, revealing Cy-Ir-NBD to be a promising predictor for PDT activation in advanced cancer phototherapy. 2024 American Chemical Society.
Shamjith S.; Murali V.P.; Joesph M.M.; Fathima T.S.; Chandana R.; Jayarajan R.O.; Maiti K.K.
ACS Applied Materials and Interfaces, Vol-16, No. 21, pp. 27114-27126.
https://doi.org/10.1021/acsami.4c02761 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193550318&doi=10.1021%2facsami.4c02761&partnerID=40&md5=a3c98ef71695e183c55fbf07f4d32c64
Shamjith S., Chemical Sciences & Technology Division (CSTD), Organic Chemistry Section, CSIR-National Institute for Interdisciplinary Science & Technology (CSIR-NIIST), Industrial Estate, Pappanamcode, Kerala, Thiruvananthapuram, 695019, India, Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India; Murali V.P., Chemical Sciences & Technology Division (CSTD), Organic Chemistry Section, CSIR-National Institute for Interdisciplinary Science & Technology (CSIR-NIIST), Industrial Estate, Pappanamcode, Kerala, Thiruvananthapuram, 695019, India; Joesph M.M., Department of Life Sciences, Christ University, Bangalore, 560029, India; Fathima T.S., Chemical Sciences & Technology Division (CSTD), Organic Chemistry Section, CSIR-National Institute for Interdisciplinary Science & Technology (CSIR-NIIST), Industrial Estate, Pappanamcode, Kerala, Thiruvananthapuram, 695019, India; Chandana R., Chemical Sciences & Technology Division (CSTD), Organic Chemistry Section, CSIR-National Institute for Interdisciplinary Science & Technology (CSIR-NIIST), Industrial Estate, Pappanamcode, Kerala, Thiruvananthapuram, 695019, India, Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India; Jayarajan R.O., Chemical Sciences & Technology Division (CSTD), Organic Chemistry Section, CSIR-National Institute for Interdisciplinary Science & Technology (CSIR-NIIST), Industrial Estate, Pappanamcode, Kerala, Thiruvananthapuram, 695019, India, Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India; Maiti K.K., Chemical Sciences & Technology Division (CSTD), Organic Chemistry Section, CSIR-National Institute for Interdisciplinary Science & Technology (CSIR-NIIST), Industrial Estate, Pappanamcode, Kerala, Thiruvananthapuram, 695019, India, Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
Hydrogen Sulfide: A new warrior in assisting seed germination during adverse environmental conditions
Abiotic stress; Donor; Hydrogen sulfide; NaHS; Rice; Seed germination
Seed, being a truly static period of the plant's existence, is exposed to a variety of biotic and abiotic shocks during dormancy that causes many cellular alterations. To improve its germination and vigor, the seed industry employs a variety of invigoration techniques, which are commonly referred to as seed priming procedures. The treatment with an exogenous H2S donor such as sodium hydrosulfide (NaHS) has been proven to improve seed germination. The H2S molecule is not only a key contributor to the signal transduction pathway meant for the sensation of seed exposure to various biotic and abiotic stresses but also contribute toward the alleviation of different abiotic stress. Although it was initially recognized as a toxic molecule, later its identification as a third gaseous transmitter molecule unveiled its potential role in seed germination, root development, and opening of stomata. Its involvement in cross talks with several other molecules, including plant hormones, also guides numerous physiological responses in the seeds, such as regulation of gene expression and enzymatic activities, which contribute to reliving various biological and non-biological stresses. However, the other metabolic pathways that could be implicated in the dynamics of the germination process when H2S is used are unclear. These pathways possibly may contribute to the seed germinability process with improved performance and stress tolerance. The present review briefly addresses the signaling and physiological impact of H2S in improving seed germination on exposure to various stresses. Graphical abstract: [Figure not available: see fulltext.] 2022, The Author(s), under exclusive licence to Springer Nature B.V.
Sharma P.; Meyyazhagan A.; Easwaran M.; Sharma M.M.M.; Mehta S.; Pandey V.; Liu W.-C.; Kamyab H.; Balasubramanian B.; Baskaran R.; Kleme J.J.; Mesbah M.; Chelliapan S.
Plant Growth Regulation, Vol-98, No. 3, pp. 401-420.
Springer Science and Business Media B.V.
Sharma P., Department of Microbiology, CCS Haryana Agricultural University, Haryana, Hisar, 125004, India; Meyyazhagan A., Department of Life Science, CHRIST (Deemed to Be University), Karnataka, Bengaluru, 560076, India; Easwaran M., Nutritional Improvement of Crops, International Centre for Genetic Engineering and Biotechnology, Aruna Asaf Ali Marg, New Delhi, 110067, India; Sharma M.M.M., Department of Agriculture and Life Industry, Kangwon National University, Gangwon-DO, Chuncheon, 24341, South Korea; Mehta S., School of Agricultural Sciences, K.R. Mangalam University, Haryana, Sohna Rural, 122103, India; Pandey V., School of Comparative Indic Studies & Tribal Science, Kalinga Institute of Social Sciences (KISS-DU), Odisha, Bhubaneswar, 751024, India; Liu W.-C., Department of Animal Science, College of Agriculture, Guangdong Ocean University, Guangdong, Zhanjiang, 524088, China; Kamyab H., Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, Kuala Lumpur, 54100, Malaysia, Department of Biomaterials, Saveetha Dental College and Hospital, Saveetha Institute of Medical and Technical Sciences, Chennai, 600 077, India; Balasubramanian B., Department of Food Science and Biotechnology, College of Life Sciences, Sejong University, Seoul, 05006, South Korea; Baskaran R., Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan; Kleme J.J., Sustainable Process Integration Laboratory (SPIL), Faculty of Mechanical Engineering, NETME Centre, Brno University of Technology - VUT Brno, Technick2896/2, 616 00, Brno, Czech Republic; Mesbah M., Engineering Department, Razak Faculty of Technology and Informatics, Universiti Teknologi Malaysia, Jln sultan Yahya Petra, Kuala Lumpur, 54100, Malaysia; Chelliapan S., Engineering Department, Razak Faculty of Technology and Informatics, Universiti Teknologi Malaysia, Jln sultan Yahya Petra, Kuala Lumpur, 54100, Malaysia