Browse Items (14421 total)
Sort by:
-
A heavy metal tolerant Thiopseudomonas alkaliphila strain as a potential plant growth promoter isolated from Bengaluru region
Thiopseudomonas alkaliphila, a Pseudomonadaceae has diverse environmental role that has not been much explored. Current study highlights, the isolated strain from industrial sites of Bengaluru with heavy metal tolerance against lead, chromium and cadmium. The antibiotic susceptibility test (AST) and minimum inhibitory concentration (MIC) showed sensitive against all the antibiotics used in the study. Subsequently, 16s rRNA analysis established and closely related to T. alkaliphila D2441 strain, whole genome was submitted, GenBank SRA database accession number is as follows PRJNA1258058. The unravelling of genetic determinants analyzed for heavy metals, antibiotic resistance and plant growth promoting traits were compared with related strains. A single chromosome with 2,400,551 bp length, average GC ratio 49.44 % and with 1941 protein-encoding genes (PEGs), the strain can bioremediate different heavy metals (354 genes/proteins), along with an aptitude as plant growth promoting rhizobacteria (PGPR) evidenced by genes showcasing tolerance against adverse environmental conditions under stress for phytohormones, plant nutrient acquisition, heat and shock chaperones, siderophore etc. The study highlights, T. alkaliphila as a non-pathogenic, potential heavy metal remediator with potential activity for PGPR traits at genetic levels. 2025 -
A heuristic analysis of equity and equality in the institutionalisation of property rights: The Baliraja water distribution experiment, India
Natural resource management perceived as a search for institutions that can ensure simultaneous fulfilment of three goals: productivity (or efficiency), sustainability and equity. In this article, we study the implications of pursuing the goal of equity in the management of surface water resources for irrigation with a heuristic model incorporating a Leontief-type fixed production function. The analysis has been carried out in the backdrop of the Baliraja water distribution experiment in India. One suggestion is that the allocating tradable water rights over water, a common property natural resource, can be used as an instrument to improve equity. Unfortunately, advocating the use of water distribution as an instrument of poverty alleviation is fraught with implicit assumptions about the rural economy and uncertain outcomes. It is important for planners to understand that the concepts of equity and equality are applicable to inputs and outputs or outcomes. We attempt to understand the implications of equality in water distribution on social welfare with a simple heuristic analysis. Theoretical analysis shows the possible outcomes of such a policy and also intended to raise pertinent questions and hypotheses in studying the effectiveness of irrigation and watershed initiatives where rights over water have been redistributed equally. Copyright 2009 Inderscience Enterprises Ltd. -
A Heuristic Approach to Resolve Priority-Driven Unbalanced Transportation Problem (PUTP)
This research addresses the priority-driven unbalanced transportation Problem (PUTP), characterized by a situation where the overall demand surpasses the available supply. We propose the Max-flow Min-cost Priority-driven Unbalanced Transportation Problem (MMPUTP) as a heuristic approach to handle this issue effectively. The strategy of MMPUTP focuses on optimizing resource allocation and reducing costs, making it highly effective in fulfilling high priority needs in a cost-efficient manner. Through a comparison with Vogel's Approximation Method (VAM) over different sets of problems ranging in size from 5?5 to 50?50, the effectiveness of the MMPUTP algorithm is evident. The findings underscore the significance of choosing the right algorithm based on the size and complexity of the problem set in the context of the Priority-driven Unbalanced Transportation Problem, with MMPUTP proving to be a flexible and reliable option in various situations. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
A Heuristic Model For Personalised Risk Assesment of PCOS
According to WHO 8-13% of women are affected by Polycystic Ovary Syndrome (PCOS) out of which 70% women remain undiagnosed, it is a common endocrine disorder necessitating early diagnosis for timely intervention. In this paper a heuristic model is developed for PCOS prediction, by combining XGBoost and Random Forest through stacking techniques. Class imbalance was addressed using Random Oversampling. Cross-validation demonstrated the meta-model's superior accuracy compared to individual XGBoost and Random Forest models, highlighting its potential for reliable PCOS prediction. It is observed that the best possible results that the meta-model was able to provide was a score of 93.5% which was acquired in the 4th sample, the lowest score was 87.90% attained in the 2nd sample. To finalise the results, the mean accuracy was calculated which is 90.98% with a standard deviation of 1.96. deterministic model offers reproducible results and interpretability, aiding clinical decision-making. Future research could explore additional biomarkers and probabilistic techniques for personalized risk assessment. 2024 IEEE. -
A High-Capacity Reversible Data Hiding on Grayscale Images Using Threshold-Based Clustering
In the digital age, safeguarding confidential information from unauthorized access is critical. Data transmission occurs in various formats, including text, images, and videos, and while advancements have enhanced the efficiency and convenience of these processes, vulnerabilities in communication channels remain a concern. The proposed scheme secures communication by hiding secret data within a carrier image, visually making it undetectable to potential attackers. Steganography is challenging when transmitting huge quantities of secret data because there is a negotiation between data capacity and imperceptibility. The mastered framework uses grayscale images as a medium for the conveyance of high-capacity secret data. First, the threshold value is computed from the histogram and the statistical mode value. The threshold calculated in the initial step will be a crucial data embedding parameter in the proposed scheme. Then the carrier image is interpolated to increase its size to double. The expanded image is then segmented into 2X2 blocks, the block undergoes a segmentation function to decide whether the block value is above the threshold or below the threshold. Finally, the secret data is embedded into the pixel block based on the threshold value of the block. The secret data and the input image are extracted separately on the receiving end. Standard metrics, Peak Signal to Noise Ratio (PSNR), and Structure Similarity Index Metric (SSIM) are used to evaluate the efficiency of the proposed scheme. The values above 42 and 36 obtained for PSNR for low payload and high payload show the effectiveness of the mastered scheme. Statistical measures, such as mean, standard deviation, and entropy, are used to validate the efficiency of the scheme in ensuring data integrity and security during transmission. 2025 IEEE. -
A high-efficiency poly-input boost DCDC converter for energy storage and electric vehicle applications
This research paper introduces an avant-garde poly-input DCDC converter (PIDC) meticulously engineered for cutting-edge energy storage and electric vehicle (EV) applications. The pioneering converter synergizes two primary power sourcessolar energy and fuel cellswith an auxiliary backup source, an energy storage device battery (ESDB). The PIDC showcases a remarkable enhancement in conversion efficiency, achieving up to 96% compared to the conventional 8590% efficiency of traditional converters. This substantial improvement is attained through an advanced control strategy, rigorously validated via MATLAB/Simulink simulations and real-time experimentation on a 100 W test bench model. Simulation results reveal that the PIDC sustains stable operation and superior efficiency across diverse load conditions, with a peak efficiency of 96% when the ESDB is disengaged and an efficiency spectrum of 9195% during battery charging and discharging phases. Additionally, the integration of solar power curtails dependence on fuel cells by up to 40%, thereby augmenting overall system efficiency and sustainability. The PIDCs adaptability and enhanced performance render it highly suitable for a wide array of applications, including poly-input DCDC conversion, energy storage management, and EV power systems. This innovative paradigm in power conversion and management is poised to significantly elevate the efficiency and reliability of energy storage and utilization in contemporary electric vehicles and renewable energy infrastructures. The Author(s) 2024. -
A highly effective curcumin analogue as naked eye colorimetric and fluorescent sensor for sensitive and selective detection of Hg2+ ions and its application on test strips and real sample analysis
A thiophene appended curcumin-based colorimetric and fluorescent receptor (TAA) for selective recognition of Hg2+ ions was synthesized and characterized using 1H NMR, 13C NMR and LC-MS spectroscopic techniques. TAA facilitates detection of Hg2+ by a naked-eye color change from yellow to colorless in visible light, and fluorescence turn-off in UV light (365 nm). The observed fluorescence quenching is due to the chelation-enhanced fluorescence quenching (CHEQ). TAA exhibited excellent selectivity and sensitivity toward Hg2+ ions, even in the presence of competing cations. The binding constant (Ka) for Hg2+ ions was found to be 3.4 105 M?1, indicating a strong binding affinity. The binding mechanism was elucidated using DFT calculations and supported by LC-MS and FT-IR studies. TAA forms a 1 : 1 complex with Hg2+ ions, as confirmed by Job's plot analysis. Additionally, the colorimetric limit of detection was found to be 0.67 ?M, while the fluorometric limit of detection was found to be 0.24 ?M, which demonstrates the high sensitivity of TAA towards Hg2+. Furthermore, TAA probe exhibited successful detection of Hg2+ ions in real water samples. Also, it can serve as an effective on-site detection tool for mercury ions by a simple test strip method that requires no additional instrumentation. 2025 The Royal Society of Chemistry. -
A homotopy-based computational scheme for two-dimensional fractional cable equation
In this paper, we examine the time-dependent two-dimensional cable equation of fractional order in terms of the Caputo fractional derivative. This cable equation plays a vital role in diverse areas of electrophysiology and modeling neuronal dynamics. This paper conveys a precise semi-analytical method called the q-homotopy analysis transform method to solve the fractional cable equation. The proposed method is based on the conjunction of the q-homotopy analysis method and Laplace transform. We explained the uniqueness of the solution produced by the suggested method with the help of Banach's fixed-point theory. The results obtained through the considered method are in the form of a series solution, and they converge rapidly. The obtained outcomes were in good agreement with the exact solution and are discussed through the 3D plots and graphs that express the physical representation of the considered equation. It shows that the proposed technique used here is reliable, well-organized and effective in analyzing the considered non-homogeneous fractional differential equations arising in various branches of science and engineering. 2024 World Scientific Publishing Company. -
A Hybrid AES with a Chaotic Map-Based Biometric Authentication Framework for IoT and Industry 4.0
The Internet of Things (IoT) is being applied in multiple domains, including smart homes and energy management. This work aims to tighten security in IoTs using fingerprint authentications and avoid unauthorized access to systems for safeguarding user privacy. Captured fingerprints can jeopardize the security and privacy of personal information. To solve privacy- and security-related problems in IoT-based environments, Biometric Authentication Frameworks (BAFs) are proposed to enable authentications in IoTs coupled with fingerprint authentications on edge consumer devices and to ensure biometric security in transmissions and databases. The Honeywell Advanced Encryption Security-Cryptography Measure (HAES-CM) scheme combined with Hybrid Advanced Encryption Standards with Chaotic Map Encryptions is proposed. BAFs enable private and secure communications between Industry 4.0s edge devices and IoT. This works suggested schemes evaluations with other encryption methods reveal that the suggested HAES-CM encryption strategy outperforms others in terms of processing speeds. 2023 by the authors. -
A hybrid algorithm for face recognition using PCA, LDA and ANN
Face recognition is an evolving technique in the field of digital device security. The two procedures Principal Component Analysis and Linear Discriminant Analysis (LDA) are standard methodologies commonly used for feature extraction and dimension reduction techniques extensively used in the recognition of face system. This paper discourse, PCA trailed through a feed forward neural network (FFNN) called PCA-neural network and LDA trailed through feed forward neural network as LDA-neural network are considered for development of hybrid face recognition algorithm. In the current research work, a hybrid model of face recognition is presented with the integration of PCA, LDA, and FFNN. The proposed system experimental results indicate better performance compared to the state of the art literature methods. IAEME Publication. -
A Hybrid Approach Against Black Hole Attackers Using Dynamic Threshold Value and Node Credibility
Detecting black hole attackers is tedious in Vehicular Ad Hoc Networks due to vehicles' high mobility. The main consequence faced because of these attackers is an increase in the number of dropped packets which converts secure and fastest paths to compromised ones. Since these attackers can act individually and collaboratively as a group, early detection of these attackers must be feasible to preserve the network's performance. The majority of current methods rely on predetermined threshold and trust score values, which are ineffective in accurately identifying black hole attackers. Hence, this paper proposes a hybrid approach using dynamic threshold value and node credibility for early detection of black hole attackers. RSUs periodically compute the dynamic threshold value and categorize the vehicles into categories 1, 2, and 3. Vehicles classified as Category 1 are legitimate, whereas Category 3 vehicles are attackers. Vehicles in Category 2 are suspicious, requiring further analysis using node credibility values to identify attackers. It is protected against single, multiple, and collaborative black hole attackers. The NS2 simulation results demonstrate that the suggested method is optimal concerning PDR, Throughput, Delay, and Packet Loss Ratio compared to recent techniques. Since the proposed scheme efficiently identifies the attackers, it has 89.67% PDR, which is higher when compared to other schemes. 2013 IEEE. -
A hybrid approach for COVID-19 detection using biogeography-based optimization and deep learning
The COVID-19 pandemic has created a major challenge for countries all over the world and has placed tremendous pressure on their public health care services. An early diagnosis of COVID-19 may reduce the impact of the coronavirus. To achieve this objective, modern computation methods, such as deep learning, may be applied. In this study, a computational model involving deep learning and biogeography-based optimization (BBO) for early detection and management of COVID-19 is introduced. Specifically, BBO is used for the layer selection process in the proposed convolutional neural network (CNN). The computational model accepts images, such as CT scans, X-rays, positron emission tomography, lung ultrasound, and magnetic resonance imaging, as inputs. In the comparative analysis, the proposed deep learning model CNN is compared with other existing models, namely, VGG16, InceptionV3, ResNet50, and MobileNet. In the fitness function formation, classification accuracy is considered to enhance the prediction capability of the proposed model. Experimental results demonstrate that the proposed model outperforms InceptionV3 and ResNet50. 2022 Tech Science Press. All rights reserved. -
A HYBRID APPROACH FOR LANDMARK DETECTION OF 3D FACES FOR FORENSIC INVESTIGATION
Facial landmark detection is a key technology in many forensic applications, such as facial identification and facial reconstruction. However, the accuracy of facial landmark detection is often limited in 3D face images due to the challenges of occlusion, illumination, and pose variations. This paper proposes a hybrid approach for landmark detection of 3D faces for forensic investigation. A hybrid method of edge contour detection and Harris corner detection is proposed for feature extraction in face images for forensic investigation. Edge contour detection is used to detect the boundaries of the face, while Harris corner detection is used to detect the corners. The advantage of using a hybrid method of edge contour detection and Harris corner detection for feature extraction in face images is that it can capture both global and local features of the face. Edge contour detection can capture global features, such as the overall shape and outline of the face, while Harris corner detection can capture local features, such as the corners of the mouth, nose and eyes which are vital for facial reconstruction. Experimental results show that the proposed method outperforms existing landmark detection algorithms in terms of time complexity and minimum loss. 2023 Little Lion Scientific. -
A Hybrid Approach for Predictive Maintenance Monitoring of Aircraft Engines
The realm of aircraft maintenance involves predictive maintenance, which utilizes historical data and machine parts' performance to anticipate the need for maintenance activities. The primary focus of this paper is to delve into the application of predictive maintenance of aircraft gas turbine engines. Our methodology involves assigning a randomly chosen deterioration value and monitoring the change in flow and efficiency over time. By carefully analyzing these factors, we can deduce whether the engines are at fault and whether their condition will deteriorate further. The ultimate objective is to identify potential engine malfunctions early to prevent future accidents. Recent years have witnessed the emergence of multiple machine learning and deep learning algorithms to predict the Remaining Useful Life (RUL) of engines. The precision and accuracy of these algorithms in assessing the performance of aircraft engines are pretty promising. We have incorporated a hybrid model on various time series cycles to enhance their efficacy further. Employing data collected from 21 sensors, we can predict the remaining useful life of the turbine engines with greater precision and accuracy. 2024 IEEE. -
A Hybrid Clustering Approach for Enhanced Classification Efficiency in Data Analytics
Clustering is a fundamental technique in data analytics that groups data points with similar characteristics into clusters. It is crucial for uncovering hidden patterns, trends, and structures in datasets. Clustering reduces the complexity of large datasets by summarizing data into representative clusters. This simplification makes it easier to analyze and interpret data, especially when dealing with high-dimensional datasets. By identifying meaningful groups, clustering provides actionable insights that supports decision-making. For instance, businesses can make concrete decisions about product recommendations, pricing strategies, or resource allocation based on cluster analysis. The approach described in the paper offers an efficient method for combining K-means and Gaussian Mixture Model (GMM) clustering techniques. The method combines two wellknown clustering techniques, K-means and GMM, to leverage their respective strengths. K-means is known for its simplicity and efficiency, while GMM can model complex data distributions with varying covariance structures. Instead of directly integrating the results of K-means and GMM, the approach uses a simplified averaging technique to converge the cluster labels obtained independently from both methods. This suggests that the method may involve assigning weights to the cluster labels obtained from K-means and GMM and then averaging them to obtain final cluster assignments. Overall, this approach presents a promising direction for combining K-means and GMM clustering techniques, offering a streamlined integration process that simplifies the consideration of varying covariance types in GMM. The effectiveness of the method is evaluated through empirical studies and comparisons with existing clustering approaches. 2025 IEEE. -
A hybrid crypto-compression model for secure brain mri image transmission
Medical image encryption is a major issue in healthcare applications where memory, energy, and computational resources are constrained. The modern technological architecture of digital healthcare systems is, in fact, insufficient to handle both the current and future requirements for data. Security has been raised to the highest priority. By meeting these conditions, the hybrid crypto-compression technique introduced in this study can be used for securing the transfer of healthcare images. The approach consists of two components. In order to construct a cutting-edge generative lossy compression system, we first combine generative adversarial networks (GANs) with oearned compression. As a result, the second phase might address this problem by using highly effective picture cryptography techniques. A randomly generated public key is subjected to the DNA technique. In this application, pseudo-random bits are produced by using a logistic chaotic map algorithm. During the substitution process, an additional layer of security is provided to boost the techniques fault resilience. Our proposed system and security investigations show that the method provides trustworthy and long-lasting encryption and several multidimensional aspects that have been discovered in various public health and healthcare issues. As a result, the recommended hybrid crypto-compression technique may significantly reduce a photos size and remain safe enough to be used for medical image encryption. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. -
A Hybrid Deep Learning and Ensemble Framework for Real-Time Cyclone Path and Intensity Prediction in Disaster-Prone Regions
Predicting the path and strength of cyclones involves significant issues in meteorology, as mistakes can greatly affect disaster management and evacuation strategies. Current models frequently encounter difficulties in achieving accurate real-time forecasting, particularly in representing complicated spatial-temporal dynamics of cyclones. The proposed study presents an innovative hybrid architecture combining deep learning and ensemble methods, using convolutional layers, LSTM units, and a gradient boosting meta-learner to improve prediction efficiency. The system was trained and verified utilising multi-year cyclone datasets obtained from Kaggle, which included atmospheric and oceanic factors. The model architecture attained exceptional accuracy, with a track error of 28 km, a mean absolute error (MAE) of 3.2 hPa for pressure, 4.5 km/h for wind speed, and a root mean square error (RMSE) of 35.4 km. The suggested approach consistently outperformed baseline models, including ConvLSTM, GRU, and XGBoost, across all critical criteria. The deployment in real-time was enabled by a containerised, low-latency API that can integrate with disaster early warning systems. This research enhances cyclone forecasting by offering a scalable, precise, and operationally feasible solution for disaster-prone areas, demonstrating practical superiority over current methodologies. The results highlight the capability of hybrid AI models to improve the accuracy and dependability of meteorological forecasts. 2025 IEEE. -
A hybrid deep learning and quantum computing approach for optimized encryption algorithms in secure communications
As online dangers get worse, there is a greater need for strong encryption methods to protect private conversations. Utilizing the strengths of both deep learning and quantum computing, this study suggests a new mixed method for improving the security of communication systems by making encryption algorithms work better. When it comes to keeping up with new online threats, traditional security methods often fall behind. Deep learning techniques could be a good way to improve encryption algorithms because they let the system learn and change to new attack methods. In the meantime, quantum computing offers unmatched computing power that can completely change how cryptography works by using quantum events like superposition and entanglement. Our suggested method combines the flexibility of deep learning with the computing power of quantum computing to get around the problems with current encryption methods. This will make safe communication systems more resistant to attacks from smart people. Through tests and models, we show that our mixed approach works better and more effectively than current encryption methods. This shows that it has the ability to solve the growing safety problems in a world that is becoming more and more linked. 2024, Taru Publications. All rights reserved. -
A Hybrid Deep Learning Model Using U-Net and Vision Transformer for Artificial Intelligence Powered Cervical Stenosis Diagnosis
This study presents a deep learning-based approach for the classification of cervical stenosis using MRI spine images, integrating multiple phases such as preprocessing, segmentation, feature extraction, and classification. A U-Net-based segmentation model effectively delineates key anatomical structures, including the spinal canal, intervertebral discs (IVDs), and neural foramen, improving feature extraction and classification accuracy. Furthermore, ResNet-50 is employed for feature map generation, leveraging deep hierarchical representations to extract meaningful spatial patterns from MRI slices. For classification, a Vision Transformer (ViT)-based model is utilized, taking advantage of its self-attention mechanism to capture both local and global dependencies within MRI images. Unlike conventional CNN-based models, ViT processes MRI scans as patches, enabling a more context-aware analysis of stenotic regions. The model is trained using an 80%20% train-test split and evaluated using standard performance metrics, achieving an accuracy of 92.60%, precision of 90.16%, recall of 95.43%, and an F1-score of 91.56%. These results indicate that the ViT model outperforms traditional CNN-based classifiers in cervical stenosis detection, ensuring higher sensitivity and specificity in real-world clinical applications. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
A Hybrid Deep-ensemble Decision-Support Framework for Reliable Early Breast Cancer Detection: A Cross-validated Outcome Analysis
OBJECTIVE The necessity to diagnose breast cancer early and correctly is the need to minimize the diagnostic uncertainty and unwarranted clinical procedures. This paper assesses the reliability of a hybrid deep-ensemble decision-support model in terms of diagnostic reliability, stability of outcome, and translational feasibility of the model via structured clinical data to detect early breast cancer. METHODS The Wisconsin Diagnostic Breast Cancer dataset which consisted of 569 cases of benign and malignant tumors was analyzed retrospectively. The framework proposed combines the deep learning of latent representations with stacked classification, ensemble-based feature selection, and stacked classification. Performance evaluation was performed based on sensitivity, specificity, accuracy, F1-score, and area under the curve (AUC) performed using stratified 10-fold cross-validation. The statistical stability across folds and the comparison with baseline models were determined with the help of non-parametric tests (p<0.05). RESULTS The model had good diagnostic performance with an accuracy of between 91.2-100 (Mean 96), Sensitivity of 76.2-100, good specificity value, and AUC 0.973-1.000. Variability in performance between folds was low, and statistically significant enhancement as compared to baseline classifiers were present. CONCLUSION The hybrid deep-ensemble model is highly diagnostic, has robust discriminative ability, and ultimately remains stable, which demonstrates the methodological robustness and diagnostic reliability of the proposed framework as a proof-of-concept decision-support model for early breast cancer detection, with potential translational relevance subject to further external clinical validation. 2026, Turkish Society for Radiation Oncology.