Browse Items (14421 total)
Sort by:
-
A Hybrid Edge-Cloud Computing Approach for Energy-Efficient Surveillance Using Deep Reinforcement Learning
This paper explores the novel application of Deep Reinforcement Learning (DRL) in designing a more efficient, scalable, and distributed surveillance architecture, which addresses concerns such as data storage limitations, latency, event detection accuracy, and significant energy consumption in cloud data centers. The proposed architecture employs edge and cloud computing to optimize video data processing and energy usage. The study further investigates the energy consumption patterns of such a system in detail. The implementation leverages machine learning models to identify optimal policies based on system interactions. The proposed solution is tested over an extensive period, resulting in a system capable of reducing latency, enhancing event detection accuracy, and minimizing energy consumption. 2023 IEEE. -
A hybrid ensemble framework with particle swarm optimization for network anomaly detection
The increasing complexity of cyber threats necessitates the development of a robust and adaptive Intrusion Detection System (IDS) capable of safeguarding network infrastructures. Traditional IDS approaches often struggle to detect sophisticated attacks due to their reliance on predefined patterns. We propose an adaptive particle swarm optimization (PSO)-optimized ensemble learning framework tailored to address these challenges in modern IDS applications. Our approach leverages the NSL-KDD and CICIDS datasets to ensure the IDS is trained and evaluated on data reflecting current network behaviours and threat landscapes. We evaluate multiple machine learning models, including Decision Trees (DT), Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), Random Forests (RF), and an ensemble of these models for both binary and multi-class classification tasks. By incorporating adaptive mechanisms within the PSO algorithm, our framework dynamically adjusts hyperparameters during optimization, enhancing model robustness and convergence speed. The proposed framework is also benchmarked against state-of-the-art IDS approaches, including ASRL and PSOGSA. Empirical evaluations demonstrate that the ensemble model achieves superior detection accuracy and reduced false positive rates, thereby advancing the efficacy of intrusion detection methodologies. The Author(s) 2025. -
A Hybrid Ensemble Model Combining Machine Learning and Fuzzy Logic for Robust Stress Level Detection
Accurate stress detection remains challenging because stress manifests differently across diverse populations. This research presents a novel hybrid approach that combines four machine learning classifiers - XGBoost, Random Forest, Support Vector Machine, and Logistic Regression - with an enhanced fuzzy logic system through weighted voting. The study employed a comprehensive dataset comprising over 1,100 samples containing more than 45 features that capture psychological, academic, social, physical, and environmental stress indicators. The results demonstrate that the ensemble achieves 87.27% accuracy with statistically significant improvements over individual models (p0.001). The approach maintains strong performance when 10% noise is injected or missing data is simulated, and provides well-calibrated probability estimates that could support clinical decision-making. 2025 IEEE. -
A Hybrid Evolutionary Deep Learning Model Integrating Multi-Modal Data for Optimizing Ovarian Cancer Diagnosis
This research intends to enhance ovarian cancer detection by the combination of state-of-the-art machine learning algorithms with extensive multi-modal datasets. The Convolutional Neural Network (CNN), K-Nearest Neighbors (KNN), and VGG16 models were thoroughly assessed, displaying remarkable precision, recall, F1 scores, and overall accuracy. Notably, VGG16 emerged as a strong performance with a precision of 0.97, recall of 0.96, F1 score of 0.97, and accuracy reaching 98.65%. The addition of confusion matrices enables a thorough insight on each model's classification performance. Leveraging multiple datasets, spanning CT and MRI scans with demographic and biographical facts, promotes the holistic knowledge of ovarian cancer features. While the suggested Hybrid Evolutionary Deep Learning Model was not deployed in this work, the results underscore the potential for its development in future research. These discoveries signify a huge leap forward in early detection capabilities and individualized treatment techniques for ovarian cancer patients. As technology and medicine combine, this study tracks a road for breakthrough diagnostic approaches, empowering clinicians and encouraging favourable results in the continuing struggle against ovarian cancer. 2024 IEEE. -
A Hybrid FinTech Fraud Detection Model Integrating Multiscale Entropy and Transformer-GAT Techniques
Credit card fraud has become a major concern in the FinTech industry due to the rapid growth of digital payment platforms and the increasing sophistication of fraudulent activities. Accurate and timely detection of fraud is essential to minimize financial losses and maintain trust in FinTech services. This study presents a hybrid deep learning framework for credit card fraud detection using the 2023 Credit Card Fraud Detection Dataset. The proposed approach with data preprocessing, which includes handling missing values, removing duplicate entries, and encoding categorical features to ensure clean and structured input for modeling. Normalization is applied to scale features uniformly, preventing bias from varying magnitudes and improving model convergence. Multiscale Entropic (MSE) analysis is employed for feature extraction, capturing both short- and long-term temporal patterns within transaction sequences, enhancing the representation of complex transactional behaviors. The extracted features are then processed using a Transformer-GAT classifier, which combines the attention mechanism of Transformers with Graph Attention Networks (GAT) to learn complex inter-transaction dependencies and graph-based relationships. This hybrid architecture enables the model to capture both local and global patterns, improving fraud detection performance. On the training dataset, the model achieved outstanding results with 98.65% accuracy, 98.70% precision, 98.50% recall, and an F1-score of 98.60 %, demonstrating a strong balance between correctly identifying fraudulent transactions and minimizing false alarms. The approach offers significant advantages for FinTech applications, including robust handling of imbalanced data, effective detection of subtle fraud patterns, and strong generalization to unseen transactions. 2025 IEEE. -
A Hybrid Framework for Detecting Hallucinations in Large Language Model Outputs
With Large Language Models (LLMs) continuously growing, they are on a path to replace the search engines soon. No matter how powerful they get, there is a certain level of uncertainty because they tend to hallucinate. Hallucination here refers to generate factually incorrect data, this can include making up names, generating false links and fabricating stories. This makes it extremely difficult to trust large language models. Existing papers provide solutions which are either not monetarily feasible or lack capabilities to build a robust hallucination detector. This paper aims to build a low resource hallucination detector which combines multiple heuristic signals like semantic similarity, self-consistency, external grounding via Wikipedia, NER overlap, flexible numerical check and a quantized LLM Falcon-7b. This eliminates the need to train the model from scratch. Upon evaluating with an input dataset of 50 questions the model was able to achieve an accuracy of 88%. 2025 IEEE. -
A Hybrid Genetic Algorithm and Large Language Model Approach for Agricultural Products Price Optimization
This paper introduces a hybrid approach, based on Genetic Algorithm (GA) and Large Language Models (LLMs), namely Mixtral 8x7B, to optimize pricing strategies for agricultural products. The method processes real-time market data, using Machine Learning (ML) techniques to generate competitive and profitable price recommendations. GAs allow for adaptive optimization, while LLMs capture complex trends in the market, making this approach more precise with respect to the pricing strategy. Case studies related to onions and tomatoes illustrate the efficiency of the optimization process. The outcome shows that the optimized prices achieve a fitness score of 0.915 (onions) and a competitive index of 0.89 (onions) compared to the market averages. Compared to traditional methods, the proposed hybrid model provides a better approach towards decision making through multi-objective optimization and real-time data analysis. This research contributes to improved profitability for farmers by adopting sustainable pricing strategies and agricultural market efficiency. 2025 IEEE. -
A hybrid GNNvanilla vision transformer model for IoT-based soil and crop forecasting
In this work, we propose a Graph?Neural Network (GNN) and Vanilla Transformer-based hybrid model for IoT driven soil and crop prediction. Conventional forecasting approaches are unable to model complicated spatial and temporal inter-dependencies and are not?very effective. The given paper solves this problem by using GNNs to learn the spatial relationships among the IoT sensor nodes and vanilla transformer model to?learn the temporal dependencies in crop and weather data. Vanilla vision transformer is able to recover missing contextual information during training. It is trained on data from IoT sensors that monitor soil moisture, temperature, humidity and a variety of other environmental factors as?well as historical crop yield and weather related information. The hybrid model can enable the real-time accurate prediction for crop?yield production and soil health status, which enables a smarter agriculture decision. The experimental results show that the proposed work achieves the lowest root mean square error (RMSE 2.1) and the highest crop accuracy (92%) for?short-term and long-term forecasts. Bharati Vidyapeeth's Institute of Computer Applications and Management 2025. -
A Hybrid Grayscale Image Scrambling Framework Using Block Minimization and Arnold Transform
Image disarranging is the process of randomly rearranging picture elements to make the visibility unreadable and break the link among neighboring elements. Pixel values often don't change while they are being scrambled. There has been a slew of proposed image encryption techniques recently. The two steps that most image encryption algorithms go through are confusion and diffusion. Using a scrambling technique, the pixel positions are permuted during the confusion phase, and an inverse-able function is used to modify the pixel values during the diffusion phase. A good scrambling method practically eliminates the high relationships between adjacent pixels in a picture. In the proposed scheme, XOR based minimization operator is applied on blocks of images followed by Arnold Transform. The suggested design is assessed using a matrix comprising the Structured Similarity Index and the Peak Signal to Noise Ratio. The computed PSNR value less than 10 indicates the input image and scrambled image has high variation. The SSIM value nearer to 0 indicates no similarity in the structure of the input image and scrambled image. 2024 IEEE. -
A Hybrid Intrusion Detection System for detecting Cross-layer DoS attacks in IoT
The Internet of Things (IoT) is critically prone to Denial of Service (DoS) attacks at multiple layers. If designed carefully, intrusion detection systems (IDS) can detect these attacks effectively. In the proposed study, we develop a Hybrid IDS to detect Cross-Layer DoS attacks in IoT. The proposed Cross-Layer system reduces the false positive rate considerably than a single IDS. The IDS is designed by ensembling multiple machine learning techniques to avoid overfitting or underfitting. The Hybrid IDS works in two stages, the first stage for detection of the attack occurrence (Anomaly detection) followed by a second stage to classify the attack types (Signature of the attacks). The output of the first stage is Correctly Detected Samples (CDS), which are again tested by the second stage to get Correctly Classified Samples (CCS). Another unique aspect of the proposed study is the dataset generation for different attacks considered. Rather than using the existing dataset, we have developed a trace file in NetSim Simulator by designing an attack environment. At the same time, during the feature selection process, a novel and efficient technique is applied to select the best feature set along with the critical component (CF). Simulation results accurately detect CDS of up to 95% and CCS of up to 96% with a weighted average F1 score. The testing time of the proposed model is also considerably lower than that of individual models, which makes the system efficient and lightweight. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
A hybrid level set based approach for surface water delineation using landsat-8 multispectral images
The detection and delineation of surface water is a crucial step in change detection studies on water bodies using satellite images. Single band methods, spectral index methods, classification using machine learning and spectral un-mixing methods are the widely used strategies for surface water mapping from multi-spectral images. Level set theory based algorithms have been successfully employed in image segmentation problems and are proven to be effective. This study presents a hybrid level set theory based segmentation algorithm which is a combination of edge based and region based approaches to detect and delineate surface water bodies in Landsat 8 images. Level set algorithms were applied in combination with Modified Normalized Difference Water Index (MNDWI) to further improve the delineation accuracy. Robustness of the proposed approach was established by successfully applying the algorithm to delineate water bodies of different sizes, ranging from 0.5 km2 to 298 km2 in surface area. The proposed algorithm was also compared with established machine learning based delineation methods and found to be faster than the algorithms those produced comparable delineation outputs. As the ground truth was not available for accuracy measurement, the output image of the proposed method was compared with the outputs of the machine learning algorithms using Pearsons correlation co-efficient, Structural Similarity Index (SSIM) and Dice Similarity Index. The proposed algorithm was subsequently applied to multi-temporal Landsat data for water body change detection and analysis. 2021, International Association of Engineers. All rights reserved. -
A hybrid level shifted carrier-based PWM technique for modular multilevel converters
This paper presents a hybrid level shifted carrier-based pulse width modulation (HLSC-PWM) technique for modular multilevel converters (MMCs). The concept of the proposed HLSC-PWM method is developed by combining the principles of phase disposition PWM (PD-PWM), phase opposition disposition PWM (POD-PWM), and alternate phase opposition disposition PWM (APOD-PWM) methods. The main aim of the proposed HLSC-PWM method is to generate an output voltage with half-wave and quarter-wave symmetries. The generated symmetrical PWM output voltage based on the proposed HLSC-PWM method provides less total harmonic distortion (THD) and enhances the DC-Link voltage utilization (DCLVU). A generalized mathematical model is formulated to develop a single HLSC for MMC with an N number of submodules (SMs) per arm. Theoretical analysis of DCLVU for the proposed method is described. The functionality and performance of the HLSC-PWM method are carried out on a three-phase five-level MMC in MATLAB/Simulink. A hardware prototype of a single-phase five-level MMC is designed for experimental validation. The proposed HLSC-PWM method is implemented on an Altera/Cyclone I series (EP1C12Q240C8N) field-programmable gate array (FPGA), simulation and experimental results are presented. 2021 The Authors. IET Power Electronics published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology -
A Hybrid Machine Learning Model (NB-SVM) for Cardiovascular Disease Prediction
One of the leading causes of death is heart disease. The prediction of cardiovascular disease remains as a significant challenge in the clinical data analysis domain. Although predicting cardiac disease with a high degree of accuracy is highly challenging, it is possible with Machine Learning (ML) approaches. The implementation of an effective ML system can minimize the need for additional medical testing, minimize human intervention, and predict cardiovascular diseases with high accuracy. This type of assessment can reduce the disease's severity and mortality rate. Only a few studies show how machine learning techniques might forecast cardiac disease. This study presents a method for improving cardiovascular disease prediction accuracy using Machine Learning (ML) technologies. Various feature combinations and many known classification techniques are used to develop various cardio vascular disease prediction models. The proposed hybrid Machine Learning (ML) prediction model for heart disease leverages a higher degree of performance and accuracy. 2023 IEEE. -
A hybrid multi-optimizer approach using CNN and GB for accurate prediction of citrus fruit diseases
Efficient prediction of citrus fruit diseases is essential for maintaining orchard health and productivity. Traditional diagnostic methods, often relying on manual inspection, are labor-intensive and prone to inaccuracies. Deep learning techniques, especially Convolutional Neural Networks (CNNs), offer an automated and accurate alternative. This study introduces a novel model integrating CNN with Gradient Boosting (GB) and optimized using the Nesterov-Accelerated Adaptive Moment Estimation (Nadam) optimizer to enhance prediction accuracy. The model employs a custom CNN architecture combined with GB, leveraging Nadam for faster convergence and improved performance. Trained on a dataset of 3,000 citrus fruit images sourced from Kaggle, the model follows a structured process of preprocessing, feature extraction, integration of GB with CNN, and optimal prediction. Comparative analysis using metrics such as accuracy, precision, F1 score, and recall demonstrates the model's effectiveness, achieving an accuracy of 98.03% and precision of 98.04%. This robust approach addresses limitations of traditional methods by enabling automated feature extraction and reliable disease prediction. The proposed CNN-GB-Nadam model significantly enhances efficiency and reliability, providing a valuable tool for protecting citrus fruit health and improving orchard management practices. The Author(s) 2025. -
A hybrid scheme of image compression employing wavelets and 2D-PCA
In this paper, we have presented a method of compressing 2D grey-scale images employing wavelets and two-dimensional principal component analysis (2D-PCA). Principal component analysis (PCA) is an already established technique for image compression which primarily aims at exploiting inter pixel redundancies present in the image, while wavelet is a tool widely used in multi-resolution image processing. In the proposed method the image is subjected to a multi-resolution decomposition using wavelet. Subsequently, 2D-PCA is applied on the set of detail images at each level of resolution. The compressed form of the image is constituted by representative pairs of principal components and projection vectors from each level of resolution along with the approximate image at the coarsest resolution. The proposed method requires relatively few number of principal components (of varied dimension) to produce improved compression ratio with acceptable peak signal to noise ratio (PSNR). The method has been implemented and tested on a set of real 2D grey-scale images and the results have been assessed on both qualitative and quantitative basis by measuring parameters like compression ratio (CR), PSNR, structural similarity index measurement (SSIM) and the overall performance is found to be satisfactory. Copyright 2017 Inderscience Enterprises Ltd. -
A hybrid semantic algorithm for web image retrieval incorporating ontology classification and user-driven query expansion
There is always a need to increase the overall relevance of results in Web search systems. Most existing web search systems are query-driven and give the least preferences to the users needs. Specifically, mining images from the Web are a highly cumbersome task as there are so many homonyms and canonically synonymous terms. An ideal Web image recommendation system must understand the needs of the user. A system that facilitates modeling of homonymous and synonymous ontologies that understands the users need for images is proposed. A Hybrid Semantic Algorithm that computes the semantic similarity using APMI is proposed. The system also classifies the ontologies using SVM and facilitates a homonym lookup directory for classifying the semantically related homonymous ontologies. The users intentions are dynamically captured by presenting images based on the initial OntoPath and recording the user click. Strategic expansion of OntoPath based on the users choice increases the recommendation relevance. An overall accuracy of 95.09% is achieved by the proposed system. 2018, Springer Nature Singapore Pte Ltd. -
A Hybrid Stacked Ensemble Model for Heart Disease Prediction
Cardiovascular Diseases (CVDs), especially heart attacks, are resulting in high rates of death worldwide, which highlights the need for early prediction systems. This paper deals with advanced ML and DL methods to predict heart attacks with a pre-processed clinical dataset. 6 models were used: a Hybrid Stacked Model combined with Logistic Regression, Random Forest, and XGBoost using a neural meta-learner; CNN with LSTM, BiGRU, and dense layers; an RNN with BiLSTM; and an XGBoost method using deep feature representations. Data preprocessing involved feature scaling and class balancing with the help of SMOTE. Model performance is being measured by Accuracy, Precision, Recall, and F1-Score. Hybrid Stacked Model had the highest accuracy (94.24%) and F1-score (94.12%), while CNN + LSTM had the best recall (95.96%), to reduce false negatives. XGBoost with deep features demonstrated competitive accuracy (91.22%) and transparency. These results point to the efficiency of hybrid and sequential deep learning models in cardiovascular risk prediction. In the future, research will be focused on real-time patient data integration, federated learning for privacy, and personalized health promotion using IoT-based monitoring. 2025 IEEE. -
A hybrid technique linked FOPID for a nonlinear system based on closed-loop settling time of plant
Wind and hydroelectric systems are more cost-effective and environmentally beneficial. A hybrid technique is proposed for the fractional-order proportional-integral-derivative (FOPID) controller to regulate the wind and hydro system. The proposed hybrid technique combines the feedback-artificial-tree (FAT), and atomic-orbital-search (AOS); together known as FAT-AOS approach. The proposed technique is utilized to decide the optimum controller parameters, and it guarantees system constancy in large disturbances using less computation and overshoot by restraining the parameter variation. The FAT is used to predict the optimum gain parameter of FOPID, and minimizing the system error is accomplished with the AOS approach. The performance metrics are peak time, rise time, settling time, and peak overshoot, are analyzed. The performance of the proposed method is done in the MATLAB platform. The simulation result of proposed approach for the rise time as 0.001 sec, settling time is 0.012 sec, and the overshoot percentage is 0.02 %. By comparing the existing methods, like Ant lion optimizer (ALO), Salp swarm algorithm (SSA), Particle swarm optimization (PSO), the proposed approach rise time and settling time overshoot, is less. The comparison proves that the proposed system delivers improved outcome than existing systems. 2024 -
A hybridized semantic trust-based framework for personalized web page recommendation
The World Wide Web is constantly evolving and is the most dynamic information repository in the world that has ever existed. Since the information on the web is changing continuously and owing to the presence of a large number of similar web pages, it is very challenging to retrieve the most relevant information. With a large number of malicious and fake web pages, it is required to retrieve Web Pages that are trustworthy. Personalization of the recommendation of web pages is certainly necessary to estimate the user interests for suggesting web pages as per their choices. Moreover, the Web is tending towards a more organized Semantic Web which primarily requires semantic techniques for recommending the Web Pages. In this paper, a framework for personalized web page recommendation based on a hybridized strategy is proposed. Web Pages are recommended based on the user query by analyzing the Web Usage Data of the users. An array of strategies is intelligently integrated together to achieve an efficient Web Page Recommendation system. Latent Semantic Analysis is applied to the User-Term Matrix and the Term-Frequency Matrix that are built from the Web Usage Information to form a Term Prioritization Vector. Further, techniques like Latent Dirichlet Allocation for Topic-based Segregation of the URLs and Normalized Pointwise Mutual Information strategies are used for recommending web pages based on users queries. The Personalization is achieved by prioritizing the Web pages based on the Prioritization Vector. Also, a unique methodology is incorporated into the system to retrieve trustworthy websites. An overall Accuracy of 0.84 is achieved which is better than the existing strategies. 2018 Informa UK Limited, trading as Taylor & Francis Group. -
A Implementation of Integration of AI and IOT Along with Metaverse Technology in the Field of Healthcare Industry
In the evolving panorama of healthcare, the appearance of Metaverse technology emerges as a transformative pressure, redefining traditional paradigms of healthcare shipping and education. This systematic assessment delves into the multifaceted impact of Metaverse technology, encapsulating their role in revolutionizing healthcare through modern-day academic frameworks, patient care interventions, and groundbreaking enhancements in medical imaging. Through an in-depth assessment of present-day literature, this observe illuminates the Metaverse's potential to facilitate immersive mastering tales, allow far flung interventions, and enhance the pleasant of scientific diagnostics and treatment making plans with its 3 -dimensional virtual environments. The findings underscore a burgeoning growth in Metaverse packages inner healthcare, highlighting its capability to noticeably beautify healthcare outcomes, affected person engagement, and expert abilities. Consequently, this evaluate advocates for the prolonged integration of Metaverse generation in healthcare, urging stakeholders to embody the ones enhancements and adapt to the following digital transformation in healthcare services and education. 2024 IEEE.