Hybrid Approach for Multi-Classification of News Documents Using Artificial Intelligence
BERT; Deep Learning; Machine Learning; Natural Language Processing; Text Classification
In the context of news articles, text classification is essential for organizing and retrieving useful information from massive amounts of textual data. Effectively categorizing news titles has gotten more challenging due to the development of online news outlets and the ongoing production of news. A multi-text classification technique primarily targeted at news titles is shown. The suggested approach automates the classification of news titles into predetermined classes or subjects by combining deep learning approaches and natural language processing (NLP) algorithms. Data preprocessing, which includes text normalization, tokenization, and feature extraction, is the first step in the procedure. This prepares the raw news titles for deep learning models. 2024 IEEE.
Proceedings - 2024 5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024, pp. 466-473.
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/ICICV62344.2024.00079 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193255348&doi=10.1109%2fICICV62344.2024.00079&partnerID=40&md5=b7e7cd69387a5bf8266796b9fd4c193d
Nagendra N., Christ (Deemed to Be) University, Department of Computer Science., Bengaluru, India; Chandra J., Christ (Deemed to Be) University, Department of Computer Science., Bengaluru, India
Hybrid Approach for Predicting Heart Disease Using Optimization Clustering and Image Processing
Centroid clustering; Coronary artery blockage; Genetic algorithm; Heart disease; Image processing; Predictive model
Heart disease (cardiovascular disease) is one of the core issues prevalent in this generation. Every year, millions of people die due to various heart diseases. The problem occurs due to hereditary or changes in life styles. Various data mining techniques are used in order to predict heart diseases. Data mining increases the accuracy, precision, and sensitivity of the model being used. In the proposed hybrid approach for predicting heart disease using optimization clustering and image processing (Hy-OCIP) model, a hybrid approach is used to predict heart diseases with the help of optimization, clustering, and image processing. After the heart image is being processed, centroid clustering is used for clustering the processed imaged into a set of chromosomes for optimization. The optimization method used for our model is genetic algorithm. The same methods are performed for both, a healthy and a heart patient. As a result, the model used in this research is able to distinguish between a normal patient and a heart patient by a hybrid combination of image processing, clustering, and optimization. 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Paul N.K.; Harsha K.G.; Kumar P.; Philip S.; George J.P.
Smart Innovation, Systems and Technologies, Vol-195, pp. 355-362.
Springer Science and Business Media Deutschland GmbH
Paul N.K., CHRIST (Deemed to be University), Lavasa, Maharashtra, India; Harsha K.G., CHRIST (Deemed to be University), Lavasa, Maharashtra, India; Kumar P., CHRIST (Deemed to be University), Lavasa, Maharashtra, India; Philip S., CHRIST (Deemed to be University), Lavasa, Maharashtra, India; George J.P., CHRIST (Deemed to be University), Lavasa, Maharashtra, India
Hybrid Approach to Document Anomaly Detection: An Application to Facilitate RPA in Title Insurance
Anomaly detection; autoencoder; dimensionality reduction; one-class support vector machine (OSVM); robotic process automation; term frequency inverse document frequency (TF-IDF); title insurance
Anomaly detection (AD) is an important aspect of various domains and title insurance (TI) is no exception. Robotic process automation (RPA) is taking over manual tasks in TI business processes, but it has its limitations without the support of artificial intelligence (AI) and machine learning (ML). With increasing data dimensionality and in composite population scenarios, the complexity of detecting anomalies increases and AD in automated document management systems (ADMS) is the least explored domain. Deep learning, being the fastest maturing technology can be combined along with traditional anomaly detectors to facilitate and improve the RPAs in TI. We present a hybrid model for AD, using autoencoders (AE) and a one-class support vector machine (OSVM). In the present study, OSVM receives input features representing real-time documents from the TI business, orchestrated and with dimensions reduced by AE. The results obtained from multiple experiments are comparable with traditional methods and within a business acceptable range, regarding accuracy and performance. 2020, Institute of Automation, Chinese Academy of Sciences and Springer-Verlag GmbH Germany, part of Springer Nature.
International Journal of Automation and Computing, Vol-18, No. 1, pp. 55-72.
Guha A., Data Science Department, CHRIST (Deemed to be University), Bangalore, 560029, India, First American India Private Ltd., Bangalore, 560038, India; Samanta D., Computer Science Department, CHRIST (Deemed to be University), Bangalore, 560029, India
Hybrid approach: Naive bayes and sentiment VADER for analyzing sentiment of mobile unboxing video comments
Mobile unboxing; Naive bayes; Sentiment analysis; Sentiment VADER; YouTube
Revolution in social media has attracted the users towards video sharing sites like YouTube. It is the most popular social media site where people view, share and interact by commenting on the videos. There are various types of videos that are shared by the users like songs, movie trailers, news, entertainment etc. Nowadays the most trending videos is the unboxing videos and in particular unboxing of mobile phones which gets more views, likes/dislikes and comments. Analyzing the comments of the mobile unboxing videos provides the opinion of the viewers towards the mobile phone. Studying the sentiment expressed in these comments show if the mobile phone is getting positive or negative feedback. A Hybrid approach combining the lexicon approach Sentiment VADER and machine learning algorithm Naive Bayes is applied on the comments to predict the sentiment. Sentiment VADER has a good impact on the Naive Bayes classifier in predicting the sentiment of the comment. The classifier achieves an accuracy of 79.78% and F1 score of 83.72%. Copyright 2019 Institute of Advanced Engineering and Science. All rights reserved.
International Journal of Electrical and Computer Engineering, Vol-9, No. 5, pp. 4452-4459.
Institute of Advanced Engineering and Science
https://doi.org/10.11591/ijece.v9i5.pp4452-459 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073443739&doi=10.11591%2fijece.v9i5.pp4452-459&partnerID=40&md5=5c020646eb319fe3d8a7869880490c30
Chaithra V.D., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India
Hybrid architecture of digital filter for multi-standard transceivers
Digital filter; Digital signal processing; FPGA; Hardware reduction; Low power design; Multi-rate decimation filter
This paper addresses on three different architectures of digital decimation filter design of a multi-standard RF transceivers. Instead of using single stage decimation filter network, the filters are implemented in multiple stages using FPGA to optimize the area and power. The proposed decimation filter architectures reflect the considerable reduction in area & power consumption without degradation of performance. First, the types of decimation filter architectures are tested and implemented using conventional binary number system. Then the two different encoding schemes i. e. Canonic Signed Digit (CSD) and Minimum Signed Digit (MSD) are used for filter coefficients and then the architecture performances are tested using FPGA. The results of CSD and MSD based architectures show a considerable reduction in the area & power against the conventional number system based filter design implementation. The implementation results reflect that considerable reduction in area of 25. 64% and power reduction of 16. 45% are achieved using hybrid architecture. Research India Publications.
International Journal of Applied Engineering Research, Vol-9, No. 21, pp. 11583-11600.
Latha R., Department of Electronics and Communication Engineering, Christ University, Bangalore, 560 060, India; Vanathi P.T., Department of Electronics and Communication Engineering, P. S. G. College of Technology, Coimbatore, 641 004, India
Hybrid architecture of Multiwalled carbon nanotubes/nickel sulphide/polypyrrole electrodes for supercapacitor
Asymmetric supercapacitors; Nickel sulphides; Polypyrrole; Rate capability; Specific capacitance
A hybrid electrode structure consisting of amino functionalised multi-walled carbon nanotube, nickel sulphide, and polypyrrole is successfully synthesized using a two-step synthesis such as hydrothermal and in-situ polymerization method. The resulting MWCNT/NiS/PPy composite exhibits a distinct tube-in-tube morphology with excellent stratification. The combination of different components and the unique structure of the composite contribute to its impressive specific capacitance of 1755 F g?1 at 3 A g?1. The prepared ternary composite enables ample exposure of numerous active sites while improving structural stability, ultimately leading to enhanced energy storage capabilities. They do this by combining the advantages of constituent components, a hierarchical assembly approach, and an integrated composite structure. Furthermore, even after undergoing 10,000 charge-discharge cycles, the supercapacitor retains more than 97% of columbic efficiency. An asymmetric coin cell was fabricated using MWCNT/NiS/PPy//AC device which delivered an energy density and power density of 33.12 Wh Kg?1 and 6750 W kg?1 respectively. These findings highlight the exceptional potential of the fabricated device for future applications in hybrid energy storage systems. 2024 Elsevier Ltd
Baby A.; Vigneswaran J.; Jose S.P.; Davis D.; PB S.
https://doi.org/10.1016/j.mtsust.2024.100727 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187197694&doi=10.1016%2fj.mtsust.2024.100727&partnerID=40&md5=c5c65df761afd3c5be63aaf2e858168c
Baby A., Department of Chemistry, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560029, India; Vigneswaran J., Nano Laboratory, School of Physics, Madurai Kamaraj University, Madurai, 625021, India; Jose S.P., Nano Laboratory, School of Physics, Madurai Kamaraj University, Madurai, 625021, India; Davis D., Department of Chemistry, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560029, India; PB S., Department of Chemistry, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560029, India
Hybrid area explorationbased mobility-assisted localization with sectored antenna in wireless sensor networks
area exploration; directional antennas; localization; mobile anchor node; wireless sensor networks
In common practice, sensor nodes are randomly deployed in wireless sensor network (WSN); hence, location information of sensor node is crucial in WSN applications. Localization of sensor nodes performed using a fast area exploration mechanism facilitates precise location-based sensing and communication. In the proposed localization scheme, the mobile anchor (MA) nodes integrated with localization and directional antenna modules are employed to assist in localizing the static nodes. The use of directional antennas evades trilateration or multilateration techniques for localizing static nodes thereby resulting in lower communication and computational overhead. To facilitate faster area coverage, in this paper, we propose a hybrid of max-gain and cost-utilitybased frontier (HMF) area exploration method for MA node's mobility. The simulations for the proposed HMF area explorationbased localization scheme are carried out in the Cooja simulator. The paper also proposes additional enhancements to the Cooja simulator to provide directional and sectored antenna support. This additional support allows the user with the flexibility to feed radiation pattern of any antenna obtained either from simulated data of the antenna design simulator, ie, high frequency structure simulator (HFSS) or measured data of the vector network analyzer (VNA). The simulation results show that the proposed localization scheme exhibits minimal delay, energy consumption, and communication overhead compared with other area explorationbased localization schemes. The proof of concept for the proposed localization scheme is implemented using Berkeley motes and customized MA nodes mounted with indigenously designed radio frequency (RF) switch feed network and sectored antenna. 2019 John Wiley & Sons, Ltd.
Nagaraju S.; Sreejith V.; Gudino L.J.; Kadam B.V.; Ramesha C.K.; Rodrigues J.
International Journal of Communication Systems, Vol-33, No. 4
https://doi.org/10.1002/dac.4220 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075072203&doi=10.1002%2fdac.4220&partnerID=40&md5=61968c757457e2a41b6b7e3df513f78c
Nagaraju S., Department of Computer Science and Information Systems, BITS Pilani, K. K. Birla Goa Campus, Sancoale, India; Sreejith V., Department of Computer Science and Engineering, Presidency University, Bangalore, India; Gudino L.J., Department of Computer Science and Information Systems, BITS Pilani, Pilani, India; Kadam B.V., Department of Electrical and Electronics Engineering, BITS Pilani, K. K. Birla Goa campus, Sancoale, India; Ramesha C.K., Department of Electrical and Electronics Engineering, BITS Pilani, K. K. Birla Goa campus, Sancoale, India; Rodrigues J., Department of Electrical and Electronics Engineering, Christ University, Bangalore, India
Hybrid Bacterial Foraging Optimization with Sparse Autoencoder for Energy Systems
deep learning; energy systems; Internet of things; machine learning; metaheuristics; state of charge estimation
The Internet of Things (IoT) technologies has gained significant interest in the design of smart grids (SGs). The increasing amount of distributed generations, maturity of existing grid infrastructures, and demand network transformation have received maximum attention. An essential energy storing model mostly the electrical energy stored methods are developing as the diagnoses for its procedure was becoming further compelling. The dynamic electrical energy stored model using Electric Vehicles (EVs) is comparatively standard because of its excellent electrical property and flexibility however the chance of damage to its battery was there in event of overcharging or deep discharging and its mass penetration deeply influences the grids. This paper offers a new Hybridization of Bacterial foraging optimization with Sparse Autoencoder (HBFOA-SAE) model for IoT Enabled energy systems. The proposed HBFOA-SAE model majorly intends to effectually estimate the state of charge (SOC) values in the IoT based energy system. To accomplish this, the SAE technique was executed to proper determination of the SOC values in the energy systems. Next, for improving the performance of the SOC estimation process, the HBFOA is employed. In addition, the HBFOA technique is derived by the integration of the hill climbing (HC) concepts with the BFOA to improve the overall efficiency. For ensuring better outcomes for the HBFOA-SAE model, a comprehensive set of simulations were performed and the outcomes are inspected under several aspects. The experimental results reported the supremacy of the HBFOA-SAE model over the recent state of art approaches. 2023 CRL Publishing. All rights reserved.
Josephine H.V.L.; Vedaiyan R.; Xavier V.M.A.; Winston J.J.; Jegatheesan A.; Lakshmi D.; Raj J.S.
Computer Systems Science and Engineering, Vol-45, No. 1, pp. 701-714.
All Open Access; Hybrid Gold Open Access
Josephine H.V.L., School of Business and Management, Christ University, Bangalore, 560029, India; Vedaiyan R., Department of Computer Science and IT, School of Sciences, Jain Deemed to be University, Bangalore, 560027, India; Xavier V.M.A., Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, 641114, India; Winston J.J., College of Computer Studies, University of Technology Bahrain, 18041, Bahrain; Jegatheesan A., Institute of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, 602105, India; Lakshmi D., Department of Computer Science and Engineering, Panimalar Institute of Technology, Chennai, 600123, India; Raj J.S., Department of Information Science & Engineering, CMR Institute of Technology, Bengaluru, 560037, India
Hybrid Bayesian and modified grey PROMETHEE-AL model-based trust estimation technique for thwarting malicious and selfish nodes in MANETs
Bayesian bestworst method (BBWM); Cooperation; Grey Theory; Mobile ad hoc networks (MANETs); The preference ranking organization method for enrichment of evaluations (PROMETHEE)
Cooperation among mobile nodes during the routing process is indispensable for attaining reliable data delivery between the source and destination nodes in the Mobile ad hoc networks (MANETs). This cooperation between mobile nodes sustains the performance of the network especially when they are been deployed for handling an emergency scenario like forest fire, flooding, and military vehicle monitoring. In specific, the criteria considered for determining the cooperation degree of mobile nodes attributed towards the routing proves is dynamic and uncertain. In this paper, Hybrid Bayesian, and Modified Grey PROMETHEE-AL Model-based Trust Estimation (MGPALTE) technique is proposed for thwarting Malicious and Selfish Nodes for enforcing cooperation between the mobile nodes in MANETs. It specifically utilized Bayesian BestWorst Method method for generating the set of weights related to objective group criteria. It is also used for aggregating the judgements of cooperation determined during indirect monitoring process. Moreover, Grey theory is integrated with the classical PROMETHEE for improving its efficacy in terms of accuracy with respect to ranking of mobile nodes participating in the process of routing. This proposed MGPALTE technique isolated the malicious mobile nodes from the routing path depending on the threshold of detection. The simulation results of the proposed MGPALTE technique confirmed better packer delivery rate of 19.21%, improved throughput of 22.38%, minimized delay of 23.19%, and reduced end-to-end delay of 21.36%, better than the competitive cooperation enforcement strategies with different number of mobile nodes in the network. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.
Suresh J.; Sahayaraj J.M.; Rajakumar B.; Jayapandian N.
Wireless Networks, Vol-30, No. 3, pp. 1697-1718.
Suresh J., Department of Computer Science and Engineering, CARE College of Engineering, Tamil Nadu, Trichy, 620009, India; Sahayaraj J.M., Department of Electronics and Communication Engineering, Sri Indu College of Engineering and Technology, TS, Hyderabad, 501510, India; Rajakumar B., Department of Artificial Intelligence, Saveetha School of Engineering, Thandalam, Chennai, 602 105, India; Jayapandian N., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Kengeri Campus, Bangalore, India
Hybrid Bidirectional GRU Approach for Crop Yield Prediction and Climate Change Impact Assessment in Agriculture
Analysis of Variance (ANOVA); Crop Yield (CY); Pearson Correlation Coefficient (PCC); Pesticides Tonnes (PT); Rain Fall (RF)
The impacts of climate change induced by humans will be felt most acutely by the agriculture sector due to its extreme dependence on weather. To ensure a steady supply of food, it is necessary to study and anticipate the effects of climate change on agricultural output. The impact of climate change on agricultural yield predictions is examined in this study using a novel methodology. In the proposed model, preprocessing, feature extraction, and training are the main processes. Data pretreatment guarantees quality by cleaning and normalising the data, while the PCC is utilised for feature selection. The model utilises AM and BiGRU for usage with large datasets. Using word vectors, the word embedding layer improves contextual awareness. Experiment findings show that the model is accurate to within 98.31% and can withstand a wide range of climate conditions. Current state-of-the-art methods are vastly outperformed by it, with performance measures like as R2 = 0.921%, MAE = 0.127%, and RMSE = 0.158%. These findings show that agricultural strategists and lawmakers can use AM-BiGRU to assess the effects of climate change and build a more resilient food system. 2025 IEEE.
Monika, M.; Kumar, Arun; Kelwade, Kamlesh; John, Tegil J; Krishna Prasad, S.; Nerlekar, Tanaya
Proceedings of 8th International Conference on Computing Methodologies and Communication, ICCMC 2025;pp.1458-1463
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1109/ICCMC65190.2025.11140711 https://www.scopus.com/pages/publications/105016903358?origin=resultslist
Monika M., Cambridge Institute of Technology, Department of Mca, Karnataka, Bangalore, India; Kumar A., Medicaps University, Department of Computer Science and Engineering, Madhya Pradesh, Indore, India; Kelwade K., Anjuman College of Engineering and Technology, Department of Computer Science and Engineering, Maharashtra, Nagpur, India; John T.J., Christ University, Department of Computer Science, Karnataka, Bangalore, India; Krishna Prasad S., Nitte (Deemed to Be University), Nmam Institute of Technology, Nitte, Department of Mechanical Engineering, Udupi, India; Nerlekar T., Dr. D. Y. Patil Institute of Technology, Department of Civil Engineering, Pimpri, Maharashtra, Pune, India
Hybrid bimetallic sulfide (FeCoS)-doped conductive polymer as efficient oxygen evolution reaction electrocatalyst for direct seawater electrolysis
Anion exchange membrane water electrolyzer; Electrolysis; FeCoS; Oxygen evolution reaction; Polyaniline; Seawater electrolysis; Transition metal sulfide
Seawater electrolysis is critical for sustainable hydrogen production, especially in regions facing freshwater scarcity. However, chloride ions compete through parasitic reactions, such as the chlorine evolution reaction, creating a serious challenge that reduces catalytic activity and durability. Herein, a hybrid electrocatalyst composed of FeCoS embedded in a polyaniline matrix (FCS-PANI) is synthesized using a simple hydrothermal method. This fabricated composite combines the benefits of the high catalytic activity of FeCoS and the corrosion resistance of the conductive polymer (PANI). Structural analysis establishes the formation of a uniform nanocomposite with strong metalsulfur and metalnitrogen interactions. Advanced oxygen evolution reaction (OER) performance with a low overpotential of 327?mV at 30?mA?cm?2 and a Tafel slope of 38.67?mV dec?1 is achieved through electrochemical testing in alkaline seawater. High stability, low degradation (0.3?mV?h?1) over 500?h of operation, and 99.97% hydrogen purity are observed upon integration into an anion exchange membrane water electrolyzer (AEMWE), indicating its practical potential for seawater electrolysis. 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Kyaw, Aye Myint Myat; Han, Youri; Yang, Juchan; Kim, Suyeon; Wang, Hao; Manh, Tu Le; Matthew, Sobin; Li, Oi Lun
Journal of Power Sources;Volume;684;Issue;;Article No.;240423;
https://doi.org/10.1016/j.jpowsour.2026.240423 https://www.scopus.com/pages/publications/105039704338?origin=resultslist
Kyaw A.M.M., Department of Materials Science and Engineering, Pusan National University, South Korea; Han Y., Department of Materials Science and Engineering, Pusan National University, South Korea; Yang J., Hydrogen Materials Research Center, Energy & Environmental Materials Research Division, Korea Institute of Materials Science (KIMS), Changwon, 51508, South Korea; Kim S., Department of Materials Science and Engineering, Pusan National University, South Korea; Wang H., Beijing Key Laboratory of Solid State Battery and Energy Storage Process, Institute of Process Engineering Chinese Academy of Sciences, Beijing, 100190, China; Manh T.L., Faculty of Material Science and Engineering, Phenikaa University, Hanoi, Viet Nam; Matthew S., Centre for Renewable Energy and Environmental Sustainability, Christ University, Bangalore, 560 029, India; Li O.L., Department of Materials Science and Engineering, Pusan National University, South Korea, Institute of Materials Technology, Pusan National University, South Korea
Hybrid botnet detection using ensemble approach
Botnet; C&C; Ensemble; Hybrid Botnets; P2P
Botnets are one of the most threatening cyber-attacks available today. This paper proposes a hybrid system which can effectively detect the presence of C&C, P2P and hybrid botnets in the network. The powerful machine learning algorithms like BayesNet, IBk, KStar, J48 and Random Tree have been deployed for detecting these malwares. The performance and accuracy of the individual classifiers are compared with the ensemble approach. Labelled dataset of botnet logs were collected from the Malware Facility. Secured data was collected from Christ university network and the combined dataset is tested using virtual test bed. The performance of the algorithms is studied in this paper. Ensemble approach out performed individual classifiers. 2005 ongoing JATIT & LLS.
Journal of Theoretical and Applied Information Technology, Vol-95, No. 8, pp. 1646-1654.
Samson F., Department of Computer Science, Christ University, Bengaluru, India; Vaidehi V., Department of Computer Science, Christ University, Bengaluru, India
Hybrid CMNV2: DeepFake faces classification and recognition using deep learning methods
Classification; Deep learning; Deep neural network; Detection; Hybrid CMNV2 model; Transfer learning
Deepfake detection has become a critical component of digital forensics and security, as manipulated images and videos increasingly threaten trust in visual media. However, existing methods often struggle with robustness under post-processing operations such as JPEG compression, Gaussian blur, scaling, and filtering, and with the growing diversity and realism of face image modification (FIM) forgeries. This work proposes CMNV2, a hybrid architecture that integrates MobileNetV2 with a custom CAFFE block to enhance feature extraction and classification accuracy. By adding five additional layers to a pre-trained structure, the model demonstrates superior resilience against complex real-world conditions and achieves 99.10% accuracy across multiple datasets, outperforming 13 baseline CNN models. The study trained and tested CMNV2 on 5,000 images (real and deepfake faces), using a combination of deep neural networks (DNNs), transfer learning (TL), and deep learning (DL) techniques. Compared to 13 CNN-based architectures, the proposed model achieved superior performance across some important evaluation metrics, including accuracy, precision, recall, F1-score, error rate, and computational efficiency. These results highlight hybrid CMNV2 as a robust and efficient solution for deepfake face detection and classification, with potential applications in security, healthcare, and education. 2025
Kumar, B. Anil; Misra, Neeraj Kumar; Pathak, Nirupma; Ahmadpour, Seyed-Sajad; Krishnamoorthy, Murugaperumal; Shukla, Dhirendra Kumar; Patidar, Mukesh; Hakimi, Musawer
Results in Engineering;Volume;28;Issue;;Article No.;107513;
All Open Access; Gold Open Access; Green Open Access
https://doi.org/10.1016/j.rineng.2025.107513 https://www.scopus.com/pages/publications/105018590292?origin=resultslist
Kumar B.A., Department of Computer Science and Engineering, Vignan's Foundation for Science, Technology and Research (VFSTR), (Deemed to be University), Off-campus Hyderabad, Telangana, India; Misra N.K., School of Electronics Engineering, VIT-AP University, Andhra Pradesh, Amaravathi, 522237, India; Pathak N., Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Green Fields, Guntur District, Andhra Pradesh, Vaddeswaram, 522502, India; Ahmadpour S.-S., Department of Computer Engineering, Faculty of Engineering and Natural Science, Istanbul Atlas University, Istanbul, Turkey; Krishnamoorthy M., Department of Electrical and Electronics Engineering, Vardhaman College of Engineering, Hyderabad, 501218, India; Shukla D.K., School of Computer Science, Galgotias University, Greater Noida, India; Patidar M., Department of Computer Science & Engineering, Parul Institute of Engineering & Technology (Parul University) Vadodara, India; Hakimi M., Department of Computer Science, Samangan University, Samangan Province, Northeast Aybak, Afghanistan
Hybrid Computational Intelligence: Challenges and Applications
Hybrid Computational Intelligence: Challenges and Utilities is a comprehensive resource that begins with the basics and main components of computational intelligence. It brings together many different aspects of the current research on HCI technologies, such as neural networks, support vector machines, fuzzy logic and evolutionary computation, while also covering a wide range of applications and implementation issues, from pattern recognition and system modeling, to intelligent control problems and biomedical applications. The book also explores the most widely used applications of hybrid computation as well as the history of their development. Each individual methodology provides hybrid systems with complementary reasoning and searching methods which allow the use of domain knowledge and empirical data to solve complex problems. 2020 Elsevier Inc.
Bhattacharyya S.; Snl V.; Gupta D.; Khanna A.
Hybrid Computational Intelligence: Challenges and Applications, pp. 1-231.
Bhattacharyya S., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India; Snl V., Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czech Republic; Gupta D., Department of Computer Science and Engineering, Maharaja Agrasen Institute of Technology, Guru Gobind Singh Indraprastha University, Delhi, India; Khanna A., Department of Computer Science and Engineering, Maharaja Agrasen Institute of Technology (MAIT), New Delhi, India
Hybrid Convolutional Neural Network and Extreme Learning Machine for Kidney Stone Detection
Convolutional Neural Network (CNN); Extreme Learning Machine (ELM)
When it comes to diagnosing structural abnormalities including cysts, stones, cancer, congenital malformations, swelling, blocking of urine flow, etc., ultrasound imaging plays a key role in the medical sector. Kidney detection is tough due to the presence of speckle noise and low contrast in ultrasound pictures. This study presents the design and implementation of a system for extracting kidney structures from ultrasound pictures for use in medical procedures such as punctures. To begin, a restored input image is used as a starting point. After that, a Gabor filter is used to lessen the impact of the speckle noise and refine the final image. Improving image quality with histogram equalization. Cell segmentation and area based segmentation were chosen as the two segmentation methods to compare in this investigation. When extracting renal regions, the region-based segmentation is applied to obtain optimal results. Finally, this study refines the segmentation and clip off just the kidney area and training the model by using CNN-ELM model. This method produces an accuracy of about 98.5%, which outperforms CNN and ELM models. 2023 IEEE.
Pandiya Rajan G.; Sapra P.; Mary S.S.C.; Chauhan A.; Parte S.A.; Nishant N.
Proceedings of the 2023 2nd International Conference on Electronics and Renewable Systems, ICEARS 2023, pp. 936-942.
Pandiya Rajan G., Kpr Institute of Engineering & Technology, Department of Computer Science and Engineering (AIML), Tamilnadu, Coimbatore, India; Sapra P., Rayat Bahara University, Computer Science and Engineering, Punjab, Mohali, India; Mary S.S.C., Panimalar Engineering College, Department of Information Technology, Tamilnadu, Chennai, India; Chauhan A., Christ (Deemed to Be University), Department of Life Sciences, Karnataka, Bengaluru, India; Parte S.A., Mits, Department of Computer Science and Engineering, Madhya Pradesh, Gwalior, India; Nishant N., Babu Banarasi das University, School of Engineering, Department of Computer Science and Engineering, Uttar Pradesh, Lucknow, India
Hybrid cryptography security in public cloud using TwoFish and ECC algorithm
Cloud computing; Cryptography; Elliptic curve Cryptography; Private key; Public key; TwoFish
Cloud computing is a structure for rendering service to the user for free or paid basis through internet facility where we can access to a bulk of shared resources which results in saving managing cost and time for large companies, The data which are stored in the data center may incur various security, damage and threat issues which may result in data leakage, insecure interface and inside attacks. This paper will demonstrate the implementation of hybrid cryptography security in public cloud by a combination of Elliptical Curve Cryptography and TwoFish algorithm, which provides an innovative solution to enhance the security features of the cloud so that we can improve the service thus results in increasing the trust overthe technology. 2019 Institute of Advanced Engineering and Science.
International Journal of Electrical and Computer Engineering, Vol-9, No. 4, pp. 2578-2584.
https://doi.org/10.11591/ijece.v9i4.pp2578-2584 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85066294736&doi=10.11591%2fijece.v9i4.pp2578-2584&partnerID=40&md5=20806d60011ffe74082530a944376ce7
Siva Sankaran P., Department of Computer Science, Christ (Deemed to Be University), Bangalore, 560029, India; Kirubanand V.B., Department of Computer Science, Christ (Deemed to Be University), Bangalore, 560029, India
Hybrid Deep Learning Based GRU Model for Classifying the Lung Cancer from CT Scan Images
computerized tomography; convolutional network; gated recurrent unit; karhunenloeve; lung cancer detection; otsu thresholding
Lung cancer is a potentially fatal condition, posing significant challenges for early detection and treatment within the healthcare domain. Despite extensive efforts, the etiology and cure of cancer remain elusive. However, early detection offers hope for effective treatment. This study explores the application of image processing techniques, including noise reduction, feature extraction, and identification of cancerous regions within the lung, augmented by patient medical history data. Leveraging machine learning and image processing, this research presents a methodology for precise lung cancer categorization and prognosis. While computed tomography (CT) scans are a cornerstone of medical imaging, diagnosing cancer solely through CT scans remains challenging even for seasoned medical professionals. The emergence of computer-assisted diagnostics has revolutionized cancer detection and diagnosis. This study utilizes lung images from the Lung Image Database Consortium (LIDC-IDRI) and evaluates various image preprocessing filters such as median, Gaussian, Wiener, Otsu, and rough body area filters. Subsequently, feature extraction employs the Karhunen-Loeve (KL) methodology, followed by lung tumor classification using a hybrid model comprising a One-Dimensional Convolutional Neural Network (1D-CNN) and a Gated Recurrent Unit (GRU). Experimental findings demonstrate that the proposed model achieves a sensitivity of 99.14%, specificity of 90.00%, F -measure of 95.24%, and accuracy of 95%. 2024 IEEE.
Gondkar R.R.; Gondkar S.R.; Kavitha S.; Siva Balan R.V.
3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024
Gondkar R.R., Christ (Deemed-to-be-University), Department Of Computer Science, Bangalore, India; Gondkar S.R., Bms Institute Of Technology And Management, Department Of Electronic And Communication Engineering, Bangalore, India; Kavitha S., Nitte Meenakshi Institute Of Technology, Department Of Mca, Bangalore, India; Siva Balan R.V., Christ (Deemed-to-be-University), Department Of Computer Science, Bangalore, India
Hybrid Deep Learning Cloud Intrusion Detection
-CNN; Cloud Security; Intrusion detection; k-NN DDoS
The scalability and flexibility that cloud computing provides, organisations can readily adapt their resources to meet demand without having to make significant upfront expenditures in hardware infrastructure. Three main types of computing services are provided to people worldwide via the Internet. Increased performance and resource access are two benefits that come with using cloud computing, but there is also an increased chance of attack. As a result of this research, intrusion detection systems that can process massive amounts of data packets, analyse them, and produce reports using knowledge and behaviour analysis were created. Convolution Neural Network Algorithm encrypts data as it's being transmitted end-to-end and is stored in the cloud, providing an extra degree of security. Data protection in the cloud is improved by intrusion detection. This study uses a model to show how data is encrypted and decrypted, of an algorithm and describes the defences against attacks. When assessing the performance of the suggested system, it's critical to consider the time and memory needed to encrypt and decrypt big text files. Additionally, the security of the cloud has been investigated and contrasted with various encoding techniques now in use. 2024 IEEE.
Karnan L.; Mahalakshmi S.B.; Thirunavukkarasu V.
2024 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024 - Proceedings
https://doi.org/10.1109/INCOS59338.2024.10527729 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85194048134&doi=10.1109%2fINCOS59338.2024.10527729&partnerID=40&md5=5a550f81f2ae52e2af0c65a23b4a11fd
Karnan L., Christ University, Department of Computer Science, Karnataka, Bangalore, 560029, India; Mahalakshmi S.B., Coimbatore Institute of Technology, Department of Artificial Intelligence & Machine Learning, Tamil Nadu, Coimbatore, 641014, India; Thirunavukkarasu V., Christ University, Department of Computer Science, Karnataka, Bangalore, 560029, India
Hybrid Deep Learning Framework for Continuous Blood Glucose Monitoring and Gestational Diabetes Risk Prediction
Continuous glucose monitoring; deep learning; gestational diabetes prediction; interpretive dynamic smoothing; maternal care; multi-sensor data integration; scenarios
Gestational diabetes mellitus (GDM) affects almost 10%-12% of pregnancies worldwide, threatening maternal and fetal life. Continuous glucose monitoring (CGM) forms the backbone of managing GDM, and the current methodologies largely disregard physiological and behavioral factors, thereby greatly reducing accuracy and clinical interpretability. Methods: A hybrid deep learning framework was developed by fusing CGM with multi-sensing modality data, including heart rate, activity levels, sleep patterns, and dietary intake. For data preprocessing, Kalman filtering was applied for temporal alignment, adaptive normalization provided outlier handling and imputation, while the CNN-BiLSTM backbone with attention was harnessed for feature extraction. A Multi-Task Attention Fusion Network (MTAFN) was used to predict glucose values and classify GDM risk simultaneously, while SHAP and dynamic smoothing contributed to interpretability sets. Results: The framework was validated on an extended OhioT1DM dataset with adaptations for pregnancy. It reached a glucose prediction RMSE of 9.8 mg/dL and a GDM risk classification accuracy of 93%. Compared to competitive approaches, the present solution attained a 25% better accuracy on interpretability and an improvement in sensitivity and specificity of about 4-6% across various physiological conditions. Discussion: The use of multi-sensing data increased prediction robustness by capturing complex physiological dependencies. The SHAP-based interpretability justified the predictions through a physiological lens. With an attention mechanism for feature weighting, it was possible to identify crucial variables like meal intake and nighttime variability in the workflow sets. Conclusion: The hybrid framework proposed here is reliable for clinically interpretable continuous glucose monitoring and GDM risk predictions. Its application with high reliability can lead to integrating it within clinical protocols for real-time maternal care sets. 2026, The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Chanumolu, Kiran Kumar; G., Muni Nagamani; G., Muni Hemalatha
Current Diabetes Reviews;Volume;22;Issue;8;
https://doi.org/10.2174/0115733998380389251111041618 https://www.scopus.com/pages/publications/105037044320?origin=resultslist
Chanumolu K.K., Department of Computer Science and Engineering, Koneru Lakshmaiah Educational Foundation, Deemed to be University, Green Fields, Vaddeswaram, Andhra Pradesh, 522237, India; G. M.N., Department of Computer Science and Engineering, Andhra Loyola Institute of Engineering and Technology, Andhra Pradesh, Vijayawada, 520008, India; G. M.H., Department of Chemistry, Mohan Babu University, Sree Sainath Nagar, Chittoor (District), Andhra Pradesh, Tirupati, 517102, India
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.
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