Hybrid Digital Control and Single-Stage Converter Architecture for IoT and Photovoltaic Grid Applications
DC-DC Buck Converter; Digital PWM; Photovoltaic Integration; Renewable Energy Systems; Universal Solar Converter; Zero Current Detector
This work presents a hybrid power conversion strategy that merges high-efficiency digital DC-DC techniques with a universal single-stage DC-DC/AC converter for renewable and low-power applications. From the first concept, a digitally controlled buck converter employing a two-step digital PWM, Adaptive Window ADC, and Self-Tracking Zero Current Detector ensures ultra-low power consumption, minimized output ripple, and improved efficiency, making it suitable for IoT and energy-constrained devices. From the second, a dual-leg single-stage converter architecture enables seamless photovoltaic integration with both DC and AC grids, reducing redundancy through shared semiconductor structures and optimized protection circuits. By unifying these approaches, the proposed system enhances power density, conversion flexibility, and grid adaptability while ensuring reduced switching losses, minimized EMI, and stable operation under varying loads. Simulation and experimental validation confirm efficiency above 91% for low-power DC applications and up to 97% for PV-based grid operation, demonstrating strong potential in sustainable smart energy systems. 2025 IEEE.
Jayakumar, S.; Reddy Cheepati, Kumar; Praveen Kumar, D.; Sita, Hareesh; Arunraja, A.; Venkatapathi, K.
Proceedings of the 6th International Conference on Smart Electronics and Communication, ICOSEC 2025;pp.787-792
Institute of Electrical and Electronics Engineers Inc.
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1109/ICOSEC67334.2025.11459802 https://www.scopus.com/pages/publications/105037132070?origin=resultslist
Jayakumar S., Sri Sairam College Of Engineering, Bengaluru, India; Reddy Cheepati K., Ksrm College Of Engineering, Electrical And Electronics Engineering, Kadapa, India; Praveen Kumar D., Sri Venkateswara College Of Engineering, Electrical And Electronics Engineering, Tirupati, India; Sita H., Mother Theresa Institute Of Engineering And Technology, Electrical And Electronics Engineering, Palamaner, India; Arunraja A., Christ Deemed To Be University, Bangalore, India; Venkatapathi K., Sri Venkatesa Perumal College Of Engineering And Technology, Electrical And Electronics Engineering, Puttur, India
Hybrid EconometricMachine Learning Models for High- Dimensional Data: Robust Approaches to Anomaly Detection and Inference
This chapter, according to the authors, looks at making modeling robust and interpretable when faced with complex, irregular, and contaminated data environments. As empirical research continues to move towards larger datasets having numerous variables and high dependency among variables, along with instances of irregular data, traditional models of analysis can no longer be very effective. The chapter looks at the possibility of having a comprehensive modeling approach by integrating concepts of robustness like bounded sensitivity and stability analysis, along with flexible modeling at the analytical and computational levels. A major focus is on finding influential data points, rare observations, and structural breaks during the modeling process and not after the process is complete. 2026 by IGI Global Scientific Publishing. All rights reserved.
Robust Methods for Anomaly Detection in Econometrics;pp.165-208
https://doi.org/10.4018/979-8-3373-8297-5.ch006 https://www.scopus.com/pages/publications/105031551896?origin=resultslist
Jain L., Christ University, India; Chopra P., Christ University, India
Hybrid feature optimization and radial basis function networks for cardiovascular disease prediction
Cardiovascular disease prediction; Harris Hawks Search; Hybrid feature optimization; Metaheuristic feature selection and machine learning; Radial basis function networks
The study addresses the critical challenge of accurately predicting cardiovascular disease (CVD), a leading cause of mortality worldwide, where early diagnosis is crucial for effective intervention. Traditional models often struggle with high-dimensional data, imbalanced classes, and nonlinear feature interactions, limiting prediction reliability. Motivated by these gaps, this research proposes a hybrid methodology integrating Harris Hawks Search (HHS) for feature optimization with Radial Basis Function Networks (RBFN) to enhance CVD risk assessment. The HHS algorithm efficiently selects key predictive features such as chest pain type and number of vessels, reducing dimensionality while preserving vital information. Trained on optimized features, the RBFN classifier achieved superior performance with 92.1% accuracy, high sensitivity, and specificity, surpassing conventional models like Logistic Regression (81.2%) and Random Forest (86.7%). Ablation studies confirm each component's contribution, with significant gains validated statistically (p < 0.05). The hybrid model also offers computational efficiency with training times around 31.7 s. Future work aims to validate this approach on diverse, larger datasets and integrate it into real-time clinical decision support systems, advancing personalized, interpretable, and efficient cardiovascular healthcare tools. 2026 Elsevier Ltd
Savitha, S.; Rajiv Kannan, A.; Logeswaran, K.
Biomedical Signal Processing and Control;Volume;117;Issue;;Article No.;109606;
https://doi.org/10.1016/j.bspc.2026.109606 https://www.scopus.com/pages/publications/105027733541?origin=resultslist
Savitha S., Department of CSE, K.S.R. College of Engineering, Tamil Nadu, Tiruchengode, 637215, India; Rajiv Kannan A., Department of CSE, K.S.R. College of Engineering, Tamil Nadu, Tiruchengode, 637215, India; Logeswaran K., Department of AI and Data Science Engineering, School of Engineering and Technology, CHRIST University, Kengeri, Karnataka, Bangalore, 560074, India
Hybrid fractional thermoelasticmachine learning (KNN, CNN and SVM classifier) framework for heat and mass transfer: A computational mechanics approach
AtanganaBaleanu operator; CattaneoVernotte law; CNN; Computational mechanics; Fractional models; Heat and mass transfer; Hybrid framework; KNN; Machine learning; SVM; Thermoelastic simulations
The main goal of this study is to create a single fractional thermoelasticmachine learning framework that can accurately model how heat and stress move through skin tissue over time and automatically sort thermal regimes into safe and dangerous ones. The proposed method combines the AtanganaBaleanu fractional operator with the CattaneoVernotte heat flux law and data-driven classifiers (KNN, SVM, and CNN), and Laplace Transforms techniques to derive generalized thermoelastic formulations capable of capturing finite-speed thermal propagation, memory effects, and nonlocal stress relaxation. This connects strict analytical modeling with smart thermal safety prediction. Closed-form expressions for temperature, displacement, dilation, and stress fields are obtained in the Laplace domain and numerically inverted to evaluate transient responses under thermal shock. All fractional thermoelastic simulations and Laplace inversions were executed in MATLAB R2023a, whereas the machine-learning models (KNN, SVM, CNN) were implemented in Python 3.10 using scikit-learn and TensorFlow. To extend the predictive capacity of the analytical models, simulation-derived datasets are used to train three machine learning classifiersK-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Convolutional Neural Network (CNN). Comparative analyses through confusion matrices, dispersion maps, ROC curves, residual maps, and bar charts demonstrate that CNN achieves superior nonlinear feature extraction and generalization, SVM provides stable global decision boundaries, and KNN efficiently identifies localized thermalmechanical anomalies. The AB fractional model is shown to suppress temperature overshoot and reduce stress concentration relative to CV, offering safer predictions for biological tissues. The combined fractionalML framework enables rapid classification of safe and risky heating regimes, with potential applications in hyperthermia therapy, burn injury prevention, dermatological laser treatments, and thermal hotspot detection in engineered composites. This study establishes a unified pathway where fractional thermoelastic modeling, deep learning, and classical machine learning synergistically addresses complex biomedical and material thermal interactions. A synthetic dataset generated from fractional ABCV thermoelastic simulations was used for training the ML classifiers. 2026 Elsevier Ltd
Seema; Singhal, Abhinav; Saeed, Abdulkafi Mohammed; Nirwal, Sonal; Chaudhary, Anjali
International Communications in Heat and Mass Transfer;Volume;172;Issue;;Article No.;110623;
https://doi.org/10.1016/j.icheatmasstransfer.2026.110623 https://www.scopus.com/pages/publications/105028654555?origin=resultslist
Seema, Christ University, Bengaluru, 560029, India; Singhal A., Christ University, Bengaluru, 560029, India; Saeed A.M., Department of Mathematics, College of Science, Qassim University, Buraydah, 51452, Saudi Arabia; Nirwal S., Centre for Mathematical Needs, Department of Mathematics, CHRIST (Deemed to be University), Bengaluru, India; Chaudhary A., Department of Management, College of Business Administration, Princess Nourah bint Abdulrahman University, P.O.Box 84428, Riyadh, 11671, Saudi Arabia
Hybrid fractional thermoelasticmachine learning framework for heat and mass transfer in skin tissue: Enhanced simulations using AtanganaBaleanu, CattaneoVernotte models, and KNNSVM classifiers
AtanganaBaleanu operator; Bioheat transfer; Biomedical applications; CattaneoVernotte law; Fractional models; Heat and mass transfer; Hybrid simulations; KNN; Laplace transform; Machine learning; SVM; Thermal stress; Thermoelasticity
This study presents a hybrid computational framework that couples advanced fractional thermoelastic modeling with machine-learning-based safety classification for heat and mass transfer in skin tissue. The classical CattaneoVernotte (CV) non-Fourier heat conduction law is extended through the AtanganaBaleanu (AB) fractional operator to capture memory-driven thermal responses, finite thermal wave propagation, and nonlocal biological effects more accurately than traditional Fourier-type formulations. Closed-form expressions are derived using Laplace transforms and inverted numerically to obtain transient temperature, displacement, dilation, and stress fields within the tissue. The AB fractional model demonstrates smoother thermal evolution, reduced overshoot, and lower stress concentrations relative to the CV model, reflecting improved biomedical safety margins during rapid thermal exposure. To enable real-time risk assessment, synthetic datasets generated from the thermoelastic simulations are used to train Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) classifiers. The ML models reliably distinguish safe and risky thermal regimes, with SVM offering superior generalization and KNN capturing localized variations. The novelty of this work lies in directly integrating fractional physics-based modeling with machine-learning classification for thermal safety diagnosticsestablishing a unified paradigm for predictive biomedical heat transfer. The framework advances thermal therapy planning, burn-injury prevention, implant design, and smart clinical monitoring. While the current study is based on idealized geometry and simulated data, future extensions will incorporate in-vivo tissue characteristics, complex skin layers, and deep learning models to further enhance clinical applicability. 2025 Elsevier Ltd
Singhal, Abhinav; Seema; Saeed, Abdulkafi Mohammed; Tiwari, Rakhi; Chaudhary, Anjali
International Communications in Heat and Mass Transfer;Volume;171;Issue;;Article No.;110074;
https://doi.org/10.1016/j.icheatmasstransfer.2025.110074 https://www.scopus.com/pages/publications/105021951870?origin=resultslist
Singhal A., Christ University, Bengaluru, 560029, India; Seema, Christ University, Bengaluru, 560029, India; Saeed A.M., Department of Mathematics, College of Science, Qassim University, Buraydah, 51452, Saudi Arabia; Tiwari R., University Department of Mathematics, Babasaheb Bhimrao Ambedkar Bihar University, Muzaffarpur, India; Chaudhary A., Department of Management, College of Business Administration, Princess Nourah bint Abdulrahman University, P.O.Box 84428, Riyadh, 11671, Saudi Arabia
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 GNN-Driven Framework for Intelligent Malware Detection and Cryptojacking Prevention in Heterogeneous Cloud Environments
Cloud security; Cryptojacking; Graph neural networks; Machine learning; Malware detection; Network security
Cloud environments are increasingly targeted by cryptojackers who use the computers processing capabilities for mining cryptocurrency without authorization. This research aims to enhance the security features that protect against cyber attackers by implementing deep learning techniques that help to detect anomalous behaviors in the cloud through analysis of data from typical system transactions. The hybrid HGCN-SIEM Fusion architecture for cryptojacking prevention and malware detection incorporates four types of Graph Neural Network (GNN) approaches: GCN, GAT, GIN, and GraphSAGE. The proposed technique achieves superior malware detection accuracy compared to all baseline models. After experiments on the standard SoK cryptojacking malware dataset, GAT and GraphSAGE demonstrated an accuracy average of 97.5%, GCN and GIN achieved similar accuracy, with an average score of 95.5%. The HGCN-SIEM model outperforms with an optimum accuracy of 98.8%, ensures low latency, and provides a well-balanced mix of rapid attack detection and the best utilization of the network bandwidth. SHA-256 is used to hash all process, instance, and event identifiers to protect privacy and ensure distinct, impenetrable node representations. Graph sampling, edge pruning, and adaptive batching are used to manage computational scalability in heterogeneous cloud networks, which reduces latency, increases throughput, and optimizes resource utilization during inference. This research work points out those GNN architectures that combine different node types that are extremely useful for security monitoring and malware detection in various network settings, demonstrating reliability and practicality in cybersecurity contexts. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
Aarthee, S.; Palanimeera, J.; Anu, P.; Saseekala, M.; Subhashini, A.
Lecture Notes in Networks and Systems;Volume;1937 LNNS;pp.326-344
Springer Science and Business Media Deutschland GmbH
https://doi.org/10.1007/978-3-032-23577-0_25 https://www.scopus.com/pages/publications/105038475745?origin=resultslist
Aarthee S., School of Computing, SASTRA Deemed to be University, Tamil Nadu, Thanjavur, India; Palanimeera J., Sathyabama Institute of Science and Technology, Tamil Nadu, Chennai, India; Anu P., School of Computing, SASTRA Deemed to be University, Tamil Nadu, Thanjavur, India; Saseekala M., School of Business and Management, CHRIST (Deemed to be University), Bengaluru, India; Subhashini A., Department of Software Systems, PSG College of Arts and Science, Tamil Nadu, Coimbatore, 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 Machine Learning Approach for Gait Type Classification Using Pose-Based Feature Extraction
Fuzzy C Means; Gait Analysis; Random Forest; Rehabilitation; Scissor Gait; Support Vector Machine; Waddling Gait
Gait analysis is essential for the diagnosis of neuromuscular and musculoskeletal disorders. Traditional methods are vulnerable and lead to inconsistency as they rely on subjective assessments. An angle-based approach which uses advanced machine learning techniques have been used address this. Extracted joint angle measurements have been extracted from the video data using computer vision methods. The characteristics used in this research were used to train a hybrid model of a Random Forest classifier and a Fuzzy C-Means clustering algorithm. Random Forest model was used as it is stable and capable of dealing with intricate nonlinear relationships and Fuzzy C-Means was used as it can manage ambiguity in the data as well as overlapping class distributions. The results showed that the Random Forest classifier has a classification accuracy of 94.62%, which is better than the other models in distinguishing between normal and abnormal gait patterns. Fuzzy C-Means also shows high accuracy is capable of clustering various forms of gait and extracting detailed features in gait dynamics. Results suggest that integrating joint angle analysis with machine learning methods provides a credible tool for gait analysis, which can aid clinicians in the early detection and treatment of gait related disorders. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
Srivastava, Pulkit; Singhal, Prateek; Singh, Madan
Lecture Notes in Networks and Systems;Volume;1601 LNNS;pp.522-534
https://doi.org/10.1007/978-3-032-03527-1_41 https://www.scopus.com/pages/publications/105021218990?origin=resultslist
Srivastava P., Department of Computer Science, School of Sciences, Christ (Deemed to be University) Delhi-NCR, Uttar Pradesh, Ghaziabad, India; Singhal P., Department of Computer Science, School of Sciences, Christ (Deemed to be University) Delhi-NCR, Uttar Pradesh, Ghaziabad, India; Singh M., Department of Computer Science, School of Sciences, Christ (Deemed to be University) Delhi-NCR, Uttar Pradesh, Ghaziabad, India
Hybrid Machine Learning Models for Crowdfunding Success Prediction
Crowdfunding; Decision Tree; Fundraising Success; LightGBM; Logistic Regression; Machine learning; Random Forest; XGBoost
Crowdfunding has become a viable option for founders and entrepreneurs as an alternative source of funding, where individuals can access a large pool of supporters to fill the funding gap. There are a variety of reasons why it is difficult to predict if crowdfunding project will be successful. As such, types of projects, duration of the campaign, target funding goal, and overall supporter activity are constantly changing. This research, therefore, aims to explore, the use of machine learning for predictive models that quantitatively leverage the historical records of projects on kickstarter, in order to find the success probability. In order to analyze the predictive ability for campaign success, was used a combination of machine learning models - Logistic Regression, Random Forest, XGBoost, LightGBM, and Decision Trees - the models that had the greatest precision were Decision Tree (99.91% acc), and LightGBM (99.90% acc) hence why they were selected. In addition, this research demonstrates how feature selection coupled with ensemble learning can significantly increase predictive potential by providing valuable information for campaign builders, platform operators, and investors who are undertaking crowdfunding projects. These findings indicate that predictive modelling can support campaign design, promote investor trust, and enhance credibility for crowdfunding platforms, through uncovering fraud. Additional measures measuring social media interaction or sentiment analysis could be incorporated to provide information for better predictive models. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
Yadav, Mahi; Manish, Maitreya; Jain, Yash; Srivastava, Roli; Singhal, Prateek
Lecture Notes in Networks and Systems;Volume;1855 LNNS;pp.102-112
https://doi.org/10.1007/978-3-032-18910-3_9 https://www.scopus.com/pages/publications/105040511840?origin=resultslist
Yadav M., School of Commerce, Christ (Deemed to be University), Delhi, India; Manish M., School of Commerce, Christ (Deemed to be University), Delhi, India; Jain Y., School of Commerce, Christ (Deemed to be University), Delhi, India; Srivastava R., School of Commerce, Christ (Deemed to be University), Delhi, India; Singhal P., Department Computer Science, School of Science, Christ (Deemed to be University), Delhi, India
Hybrid Mobile-Spinalnet with feature extraction for brain tumor detection using MRI images
Brain tumor; Fractional Calculus; Fuzzy C-Means; Hybrid model; MobileNet; SpinalNet; TransUNet
Brain tumors are deadly and can hinder the normal functioning of the human body. Generally, surgical methods are preferred for treating brain tumors. Early and accurate detection remains a problem, due to the complexity of tumor shapes and poor generalization to diverse tumor types. To address this, a hybrid Mobile-SpinalNet is established in this paper for the detection of brain tumors with Magnetic Resonance Imaging (MRI) images. This system involves seven stages, including input image acquisition, image preprocessing, skill stripping, tumor segmentation, data augmentation, feature extraction, and brain tumor detection. Initially, image acquisition is carried out, and then the input image is preprocessed by using a Mean filter. Subsequently, the skull stripping is performed using Fuzzy C-Means (FCM). After that, by using the TransUNet, the tumor region is isolated in the segmentation module. Furthermore, the data augmentation is carried out, and then the feature mining takes place in the feature extraction phase to excerpt features such as Speeded Up Robust Features (SURF), Oriented Fast and Rotated BRIEF (ORB), Fuzzy Local Binary Pattern (FLBP) and statistical features. At last, the brain tumor is identified by employing a hybrid Mobile-SpinalNet. This framework fuses the MobileNet and SpinalNet depending on regression modeling with applied Fractional Calculus (FC). The Mobile-SpinalNet is validated for its efficacy by comparing it to other techniques, and it showed better performance with a precision of 0.953, accuracy of 0.943, and recall of 0.970. 2025 Elsevier Ltd
Vadivu, P. Balashanmuga; Prabhakar, Telagarapu; Mandala, Jyothi; Chandra Sekhar, Kattamuri Venkata
Biomedical Signal Processing and Control;Volume;112;Issue;;Article No.;108571;
https://doi.org/10.1016/j.bspc.2025.108571 https://www.scopus.com/pages/publications/105013884056?origin=resultslist
Vadivu P.B., Department of ECE, Mahendra Engineering College, Tamil Nadu, Namakkal, India; Prabhakar T., Department of ECE, GMR Institute of Technology, GMR Nagar, Andhra Pradesh, Rajam, India; Mandala J., Department of Computer science and engineering, CHRIST (Deemed to be University), Banglore, India; Chandra Sekhar K.V., Department of Artificial Intelligence & Machine Learning, Aditya Institute of Technology and Management, Srikakulam, Tekkali, India
Hybrid Model Integrating Firefly Algorithm and Gradient Boosted Trees for Stronger Financial Fraud Detection
Class imbalance; F1 score; Financial fraud; Firefly Algorithm; Gradient Boosted Trees; Metaheuristic optimization; Precision; Real-time systems; Recall
Financial fraud poses a critical threat, with complex attack patterns overwhelming traditional rule-based detection methods. This project proposes a hybrid model integrating the Firefly Algorithm (FA) for hyperparameter optimization with Gradient Boosted Trees (GBTs) to enhance fraud detection. Evaluated on an imbalanced financial dataset, the FA-GBT framework achieved significant performance gains. ROC-AUC increased by 5.08% to 0.9883, demonstrating superior discrimination. Critically, Precision rose by 13.38%, substantially reducing false positives/false alarms and operational costs. The optimization also reduced training time by 64%(from 9.8 seconds to 3.5 seconds). This supports a robust, high-confidence, and efficient strategy for deployment in real-time transaction systems. 2025 IEEE.
Lobo, Nethan Noel; Rajesh, R.; Radhalakshmi, K.N.
Proceedings of the 9th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2025;pp.1964-1971
https://doi.org/10.1109/ICECA66444.2025.11383302 https://www.scopus.com/pages/publications/105034900857?origin=resultslist
Lobo N.N., Christ (Deemed to be University), Department of Statistics and Data Science, Karnataka, Bengaluru, 560029, India; Rajesh R., Christ (Deemed to be University), Department of Statistics and Data Science, Karnataka, Bengaluru, 560029, India; Radhalakshmi K.N., Christ (Deemed to be University), Department of Statistics and Data Science, Karnataka, Bengaluru, 560029, India
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 Quantum Network with Snow Geese-Elk Herd Optimization for Smart Load Shedding in Grids with Electric Vehicles and Photovoltaic Systems
Demand response potential; Energy consumption patterns; Inflexible building load; Load criticality; Photovoltaic output; Real-time load flexibility
The increasing penetration of variable renewable energy and the growth of electric vehicles (EV) have created an urge for more sophisticated load management methods to ensure grid stability. Conventional load shedding (LS) methods are typically not equipped to manage the unpredictability brought about by these modern additions to the grid. This study introduces an innovative smart load-shedding strategy that uses a hybrid optimization model. At its core is a Quantum Neural Network (QNN), which enables intelligent and data-based load prioritization by evaluating factors such as load criticality, energy usage, responsiveness to demand, and operational flexibility. The required LS amount is calculated through a combined use of Snow Geese Optimization (SGO) and the Elk Herd Optimizer (EHO), with specific attention given to the flexibility offered by EVs to address the variability in photovoltaic (PV) power generation. Testing has been performed on the IEEE 33-bus network reveal a notable decrease in total load demand by around 33%, contributing to improved grid stability, with voltage levels staying close to 0.99 p.u. Additionally, the average load across the network buses dropped by roughly 52%. This hybrid approach not only ensures better performance but also achieves quicker convergence compared to existing optimization methods. The proposed intelligent LS method presents an effective strategy for preserving grid stability amid growing integration of renewables and EV by incorporating QNN with SGO and EHO while accounting for EV adaptability. The Author(s), under exclusive licence to Shiraz University 2025.
Iranian Journal of Science and Technology - Transactions of Electrical Engineering;
https://doi.org/10.1007/s40998-025-00925-z https://www.scopus.com/pages/publications/105019073241?origin=resultslist
Srinivas C., Department of Electrical and Electronics Engineering, School of Engineering and Technology, CHRIST Deemed to be University, Kengeri Campus, Banglore, India; Margaret V., Department of Electrical and Electronics Engineering, School of Engineering and Technology, CHRIST Deemed to be University, Kengeri Campus, Banglore, India
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