Bag Technique Research Articles

Novel Ensemble Models Based on the Split‐Point Sampling and Node Attribute Subsampling Classifier for Groundwater Potential Mapping

AbstractGroundwater potential maps are crucial tools for effectively managing water resources, particularly in agriculturally focused countries such as Vietnam. However, creating these maps is a challenging task that requires reliable data and methods. In this study, we integrated the Split‐Point Sampling and Node Attribute Subsampling Classifier (SPAARC) with the Bagging (B), MultiBoostAB (MBAB), and Random Subspace (RSS) ensemble learning techniques and developed three ensemble models: B‐SPAARC, MBAB‐SPAARC, and RSS‐SPAARC. We selected 13 geoenvironmental factors based on their availability, relevance, and association with groundwater potential in the Sesan River basin of Vietnam. We assessed the models' performance using various metrics such as area under the curve (AUC), accuracy, sensitivity, specificity, and RMSE. The findings indicated that the ensemble models performed better than the single SPAARC model in mapping groundwater potential. The MBAB‐SPAARC model demonstrated the highest accuracy with an AUC value of 0.891, followed by B‐SPAARC (AUC = 0.844), RSS‐SPAARC (AUC = 0.871), and the single SPAARC (AUC = 0.853) models. The results also highlighted that elevation, rainfall, land use/cover, and altitude were the most significant factors for mapping groundwater potential in the Sesan River basin. The innovative ensemble models and reliable potential maps developed in this study assist water resource managers in planning water usage based on the benefits and costs for various users and in devising sustainable strategies for using, protecting, and managing groundwater.

Chronic Obstructive Pulmonary Disease Diagnosis with Bagging Ensemble Learning and ANN Classifiers

Chronic Obstructive Pulmonary Disease (COPD) is a persistent respiratory disease that poses a significant threat to global human health with elevated incidence and mortality rates. Timely recognition and diagnosis of COPD play a pivotal role in efficiently managing and treating the condition. The incorporation of deep learning technologies into healthcare has significant potential to enhance diagnostics and treatment outcomes. This study proposes an innovative deep-learning approach along with an ensemble technique to address the imperative need for an effective predictive model in COPD disease classification, particularly in situations with limited available data. This was achieved by leveraging the ensemble bagging technique and incorporating ANN as a classifier within this framework. Training and evaluation of the proposed ensemble ANN model were performed on a dataset comprising a variety of attributes, including demographic information, medical history, diagnostic measurements, and pollution exposures. Data were collected from people aged 18 to 60 originating from Pakistan, encompassing patients, attendants, hospital staff, faculty, and students. The effectiveness of the model in classifying COPD was measured using F1 score, recall, precision, and accuracy. The evaluation of the model produced notable results, as it achieved a 90% F1 score, 96% recall, 84% precision, and 89% accuracy in identifying the presence of COPD in individuals. Furthermore, this study carried out a comparative analysis between a standalone ANN model and the proposed ensemble ANN model which revealed that the proposed Ensemble ANN model outperforms existing methods, particularly in scenarios with limited sample size. This research provides substantial contributions to healthcare technology, as it presents an efficient tool for COPD prediction, facilitates early intervention, and significantly increases the overall standard of patient care.

I will bag in the bagging zone. Demonstration of bagging technique of right and left lower lobes.

I will bag in the bagging zone. Demonstration of bagging technique of right and left lower lobes.

Unveiling the nexus between atmospheric visibility, remotely sensed pollutants, and climatic variables across diverse topographies: A data-driven exploration empowered by artificial intelligence

Deteriorating visual range (VR) can cause challenges for the transportation sector, resulting in economic and life losses. Air pollutants, smoke, fog, and many meteorological parameters such as air temperature (T), relative humidity (RH), wind speed (WS), and wind direction (WD) can contribute to light extinction and degrade VR. Advancements in geospatial technologies have triggered artificial intelligence to analyze and model the relationships among environmental and climatological parameters. This paper aims to assess the potential of supervised machine learning models for the parameterization of VR over Pakistan's diverse topography by utilizing meteorological parameters and some pollutants. The daily data from 2005 to 2020 of VR, T, RH, WS, WD, Aerosol Optical Depth (AOD), Nitrogen dioxide (NOx), Sulfate, Sulfur dioxide (SOx), and Dust were acquired. Ten machine learning models, including Random Forest (RF), Extreme Gradient Boosting (XGB), Artificial Neural Networks (ANN), Support Vector Machine (SVM), Decision Trees (DT), Gradient Boosting Machine (GBM), Causal, Unbiased, Binned, and Intermittent, Search, and Tree (CUBIST), Multi-Layer Perceptron (MLP), Multivariate Adaptive Regression Splines (MARS), and K-Nearest Neighbor (KNN) were gauged for VR estimation. We also coupled the Bagged Extreme Gradient Boosting (BG-XG) model by combining XGB and bagging technique. BG-XG performed better than the rest of the models, with coefficients of determination of 0.90 for the training and 0.70 to 0.90 for the validation set. Degradation in the VR was highly dependent on the changes in RH followed by SOx and dust associated with anthropogenic emissions. RH, SO4, and SO2 emerged as the most important parameters for the VR decline. Proposed model parameters can be helpful in accurate VR projections and improving severe weather alerts, including analyzing and managing air pollution. This work will also be helpful to improve aviation and transportation safety for pilots, drivers, and automated vehicles to minimize low-visibility accidents.

Aging behaviour assessment of cellulosic fibres in alkaline media: A green technology approach in construction materials

With the increasing demand for buildings, the utilisation of green materials represents a novel approach toward achieving sustainable development goals. Cellulose-based materials, such as jute, exemplify eco-friendly substances for civil engineering applications. The present study aims at studying the aging behavior of jute fibers in an alkaline environment. For this purpose, three alkali environments (NaOH, KOH, Ca(OH)2) with three different concentrations were selected. Samples were treated at three different periods (7 d, 14 d, and 28 d) to assess the impact of treatment on the fiber properties. This study introduces a pioneering concept of the alkali treatment of jute-based fibres to evaluate tensile strength and weight loss, marking the first of its kind. This approach incorporates various characterisation techniques, including Differential Scanning Calorimetry (DSC), Thermo-Gravimetric Analysis (TGA), and Scanning Electron Microscopy (SEM) to assess the treatment process. In our experimental procedures, we evaluated key parameters such as the type of alkali, alkali concentration, and curing time for their influence on tensile strength. Subsequently, we applied Response Surface Methodology (RSM) in conjunction with bagging techniques to develop mathematical models based on the acquired data. Results demonstrated varied tensile strengths based on alkali type, concentration, and treatment duration. Fibres treated with 15 g/L concentration of NaOH exhibited an increase in tensile strength from 7 days (79.66 MPa) to 28 days (225.05 MPa). The highest tensile strength of jute fibres treated with KOH was observed with 15 g/L concentration at 14 days (237.58 MPa). For Ca(OH)₂ treatments, the highest tensile strength was observed with 30 g/L concentration at 14 days (111.28 MPa). It was also observed that prolonged exposure to very high alkali concentrations could adversely affect the tensile strength, as observed in the case of 30 g/L concentration of NaOH at 28 days (84.73 MPa). Among the various alkali materials tested, NaOH and KOH demonstrated greater effectiveness compared to Ca(OH)2. According to RSM analysis, the time of sample curing was found to be the most significant factor in tensile strength, with a P-value of 0.009. On average, the tensile strength changes by 54 %, 52 %, and 35 % for NaOH, KOH, and Ca(OH)₂, respectively, when the curing time shifts from 7 days to 28 days.

Bioconcentration capacity of moss Leskea angustata Tayl., for heavy metals and its application in the atmospheric biomonitoring of a metropolitan area

In this work, the bioconcentration capacity of heavy metals Cd, Cu, Pb and Zn in the moss Leskea angustata Tayl., exposed under laboratory conditions was evaluated, and subsequently the usefulness of moss for the heavy metals' biomonitoring associated with suspended particulate matter in air of the Toluca Valley Metropolitan Area was determined by means of the moss bag technique. To achieve the aims, bioassays were carried out in the laboratory with the moss, the exposure for a period of 6 months of the moss bags in the sites of the Automatic Atmospheric Monitoring Network of the Government of the State of Mexico and their subsequent analysis through of elemental and structural characterization techniques. The results of the bioassays on the moss L. angustata, demonstrate that this moss has adequate bioconcentration capabilities to be used as a biomonitor of heavy metals in polluted areas. The structural characterization of the moss L. angustata, shows that this moss has good adsorption characteristics (presence of functional groups, surface area of 19.87 m2 g−1, average pore diameter equal to 2.86 nm and a total pore volume of 0.014218 cm3 g−1). The significant correlations between Cd–Zn–Pb and between Pb and Zn with PM2.5, as well as a strong correlation between Pb and Zn with PM10, allow us to establish that these three metals have their main origin in the domestic combustion of wood and liquid petroleum gas (LP gas), as well as emissions from agricultural burning in the study area. Cu has a different origin than other metals and PM10 and PM2.5. The findings demonstrate a strong association between anthropogenic activities - such as domestic wood and LP gas combustion, agricultural burning emissions, vehicular emissions, tire wear, and industrial activities - and the detection, entrapment, and accumulation of several heavy metals by L. angustata moss.

Detection of Malicious URLs using Machine Learning based on Lexical Features

As the digital world evolves, the risk of valuable information being exposed to unauthorized parties is increasing. One common vulnerability is the use of malicious Uniform Resource Locator (URL), which are fraudulent links spread across various platforms such as social media and emails. Traditional methods of identifying these URLs, such as blacklisting and heuristic search, rely heavily on syntax or keyword matching, but struggle to keep up with the evolving tactics of cyber attackers. Hence, this paper proposes a solution for detecting malicious URLs and their types based on lexical features. Lexical features in a URL refer to the components that convey semantic and lexical meaning. These can include domain names, path lengths, special characters, and other elements that can be analyzed for patterns or anomalies. In our proposed method, we use 23 different lexical features that focus on the semantic and lexical meaning of the URLs. An Exploratory Data Analysis (EDA) is used to filter the most important lexical features that effectively contribute to predictions. With these carefully curated features, we address the problem as a multi-classification task, aiming to assess the performance of three distinct classifiers: Random Forest, which currently stands as the domain's best solution and a pure bagging technique, as well as XG Boost and Light GBM, both of which utilize boosting techniques. With the proposed method, we could achieve over 93\% accuracy for all the three classifiers while Random Forest achieving the best performance.

Novel pH-responsive hybrid hydrogels for controlled delivery of curcumin: Overcoming conventional constraints and enhancing cytotoxicity in MCF-7 cells

Traditional drug delivery techniques often have drawbacks such inadequate bioavailability, quick clearance, and non-specific targeting, which may result in unfavorable side effects. Hybrid nanocarriers composed of nanoclay have the potential to be a viable solution to these problems. The goal of this work was to design a biocompatible hybrid nanocarrier that would release curcumin under regulated conditions to specific locations, especially cancer cells. FTIR, XRD, and FESEM were used to effectively construct and evaluate our polyvinyl pyrrolidone/polyethylene glycol/montmorillonite (PVP/PEG/MMT) hybrid hydrogels, verifying their synthesis and intended features. Zeta potential and DLS studies showed a suitable size distribution (∼300 nm) and high stability. The curcumin encapsulation efficiency and release profile were assessed using the use of UV spectrophotometry and the dialysis bag technique. The findings showed a gradual and continuous release that was highly impacted by the acidic environment (which mimicked the characteristics of cancer cells), suggesting pH-responsive activity. The MTT test was used in vitro cytotoxicity experiments to show that curcumin-loaded nanocarriers were more effective against MCF-7 cancer cells than curcumin in solution. Increased rates of necrosis and apoptosis in cancer cells treated with the nanocarrier formulation were further validated by flowcytometry. According to these results, PVP/PEG/MMT hybrid hydrogels have a lot of potential as a cutting-edge drug delivery strategy for curcumin. They may be able to overcome some of the drawbacks of traditional techniques and improve the effectiveness of treatment against cancer cells.

Experimental investigation of the performances of thick CFRP, GFRP, and KFRP composite plates under ballistic impact

Abstract This study discusses the experimental results of the ballistic impact on thick-layered carbon fibre-reinforced polymer (CFRP), glass-based FRP (GFRP), and Kevlar-based FRP (KFRP) composite plates. Ten 10 mm plates of each type were manufactured via the hand lay-up and vacuum bagging techniques to produce plates with a thickness of 2 mm each, which were bonded together using epoxy adhesive and hot pressed at 75°C to obtain thick layered plates with a total thickness of 20 mm each. The specimen plates were tested according to the NIJ 0108.01 standard in a special firing room using a 9 mm G2 Elite handgun with a full metal jacketed round nose 9 mm × 19 mm Mu1-TJ bullets moving at a velocity of approximately 358 m/s. The results indicated that no total penetration occurred on any of the plates. The bullets were destroyed by impact, and the penetration depths of the CFRP, GFRP, and KFRP plates were 11.10, 9.95, and 22.22%, respectively. The layered structures in these plates appear to have changed the penetration behaviour compared with previous studies. In this study, delamination was the primary failure mode.

Ballistic behaviour of hybride carbon/basalt fiber reinforced epoxy-hBN composite

Ballistic behaviours of hybrid composite armors were investigated through experiments. The effects of hexagonal boron nitride (h-BN) nanopowders and number of layers on ballistic performance were examined. Four types of armors were manufactured by hand lay-up and vacuum bagging technique: 60 layers of fabric (30 layers carbon and 30 layers basalt fabrics) with 0% h-BN (1-A) and 1% h-BN (1-B), also 100 layers of fabric (50 layers carbon and 50 layers basalt fabrics) with 0% h-BN (2-A) and 1% h-BN (2-B) with epoxy resin. Ballistic impact tests were performed on the armors using a 9 mm full metal jacket projectile. The densities of the ballistic plates are 1.53, 1.56, 1.61 and 1.65 respectively. After three shots to each plate, the average hole depths were 5.55 mm on the 1-A coded plate, 4.34 mm on the 1-B armor, 4.68 mm on the 2-A plate, and 4.69 mm on the 2-B armor. All of the armors were able to confront for the velocities between [Formula: see text] m/s successfully. However, the h-BN showed a significant influence on the overall ballistic performance of composite armors. It has been found that the penetration depth decreases with the addition of h-BN. Also SEM-EDS mapping and XRD analysis were used to characterize the hybrid composites.

Short-Term Load Forecasting Based on Optimized Random Forest and Optimal Feature Selection

Short-term load forecasting (STLF) plays a vital role in ensuring the safe, efficient, and economical operation of power systems. Accurate load forecasting provides numerous benefits for power suppliers, such as cost reduction, increased reliability, and informed decision-making. However, STLF is a complex task due to various factors, including non-linear trends, multiple seasonality, variable variance, and significant random interruptions in electricity demand time series. To address these challenges, advanced techniques and models are required. This study focuses on the development of an efficient short-term power load forecasting model using the random forest (RF) algorithm. RF combines regression trees through bagging and random subspace techniques to improve prediction accuracy and reduce model variability. The algorithm constructs a forest of trees using bootstrap samples and selects random feature subsets at each node to enhance diversity. Hyperparameters such as the number of trees, minimum sample leaf size, and maximum features for each split are tuned to optimize forecasting results. The proposed model was tested using historical hourly load data from four transformer substations supplying different campus areas of the University of Beira Interior, Portugal. The training data were from January 2018 to December 2021, while the data from 2022 were used for testing. The results demonstrate the effectiveness of the RF model in forecasting short-term hourly and one day ahead load and its potential to enhance decision-making processes in smart grid operations.

Predicting the mechanical properties of plastic concrete: An optimization method by using genetic programming and ensemble learners

This study presents a comparative analysis of individual and ensemble learning algorithms (ELAs) to predict the compressive strength (CS) and flexural strength (FS) of plastic concrete. Multilayer perceptron neuron network (MLPNN), Support vector machine (SVM), random forest (RF), and decision tree (DT) were used as base learners, which were then combined with bagging and Adaboost methods to improve the predictive performance. In addition, gene expression programming (GEP) was used to develop computational equations that can be used to predict the CS and FS of plastic concrete. An extensive database containing 357 and 125 data points was obtained from the literature, and the eight most impactful ingredients were used in the model’s development. The accuracy of all models was assessed using several statistical measures, including an error matrix, Akaike information criterion (AIC), K-fold cross-validation, and other external validation equations. Furthermore, sensitivity and SHAP analysis were performed to evaluate input variables' relative significance and impact on the anticipated CS and FS. Based on statistical measures and other validation criteria, GEP outpaces all other individual models, whereas, in ELAs, the SVR ensemble with Adaboost and RF modified with the Bagging technique demonstrated superior performance. SHapley Additive exPlanations (SHAP) and sensitivity analysis reveal that plastic, cement, water, and the age of the specimens have the highest influence, while superplasticizer has the lowest impact, which is consistent with experimental studies. Moreover, GUI and GEP-based simple mathematical correlation can enhance the practical scope of this study and be an effective tool for the pre-mix design of plastic concrete.

Optimizing E-commerce Inventory to prevent Stock Outs using the Random Forest Algorithm Approach

This research investigates the effectiveness of the Random Forest algorithm in optimizing e-commerce inventory management. In a digital business that continues to grow, inventory management is crucial for smooth operations and customer satisfaction. The Random Forest algorithm, a development of the CART method by applying bagging techniques and random feature selection, was tested to predict inventory. An experimental design is used to test the algorithm's performance algorithms performance, using data relevant to the observed inventory variables. The analysis involves evaluating the performance of algorithms in predicting and preventing stockouts. The results show that the Random Forest algorithm provides more accurate inventory predictions than traditional methods. Comparison with linear and rule-based regression reveals higher accuracy, making this algorithm a promising choice for e-commerce inventory management. These findings imply that the Random Forest Algorithm can be an effective solution in overcoming the complexity and fluctuations of digital markets. Practical recommendations include a deep understanding of the data, engagement of trained human resources, and training strategies for optimal use of these algorithms. This research also contributes to the literature by expanding understanding of the application of the Random Forest algorithm in various contexts, including forest basal area prediction, supply chain management, and backorder prediction. In conclusion, the Random Forest algorithm has great potential to improve e-commerce inventory management, opening up opportunities for broader application in the digital business world. Proactive adoption of these algorithms can have a positive impact on operational efficiency, customer satisfaction, and a company's competitiveness in an ever-evolving market.

Influence of multi‐walled carbon nanotubes on mechanical characteristics of glass fiber reinforced polymer composites: An experimental and analytical approach

AbstractAs a consequence of their magnificent performance like mechanical, electrical and chemical properties, multiwalled carbon nanotubes (MWCNTs) are widely used as a secondary reinforcement in composite field. It has been developed by arc discharging process under atmospheric pressure. Subsequently, MWCNTs doped nano‐composite were developed through hand lay‐up and followed by vacuum bagging techniques. Quasi – isotropic symmetrical laminate of eight layers (0/90)/(±45)/(±45)/(0/90)//(0/90)/(±45)/(±45)/(0/90) were fabricated under room temperature. To fabricate the composite laminates, purified MWCNTs were homogeneously dispersed in glass fiber reinforced epoxy with 0.5%, 1.25%, and 2 wt% loading. Tensile strength, tensile modulus, strain to failure and fracture behavior of unfilled and MWCNTs doped composite laminates were evaluated. Field emission scanning electron microscope (FE‐SEM) was employed to evaluate the structural and morphological characteristics of advanced nano‐composites. Reinforcement effect is found to be more pronounced in 1.25% MWCNTs embedded glass fiber reinforced polymer. This reinforcement effect was corroborated by tensile fractography which depicted by hackle region. Results indicated that tensile strength of 1.25 wt% nano‐composite increased by 47.36% with respect to 0.5 wt% MWCNT doped composites.Highlights Development of multi‐walled carbon nanotubes (MWCNTs) by arcing process. Fabrication of MWCNTs doped glass fiber reinforced polymer (GFRP) composites. Mechanical characterization of nano‐composites. Modeling of nano‐composites by Halpin‐Tsai equation. Fractrography of nano‐composites in details.

Optimizing Phishing Threat Detection: A Comprehensive Study of Advanced Bagging Techniques and Optimization Algorithms in Machine Learning

Optimizing Phishing Threat Detection: A Comprehensive Study of Advanced Bagging Techniques and Optimization Algorithms in Machine Learning

Aeschynomene overseeding and nitrogen fertilization effects in bahiagrass litter decomposition

AbstractProductivity of tropical and subtropical grasslands is often limited by inadequate nitrogen (N) supply. Aeschynomene (Aeschynomene americana L.) is a warm‐season legume that may contribute as N input in grazing systems, but there is limited information about aeschynomene litter decomposition and N dynamics on pastures. The objective of this study was to quantify the effect of overseeding aeschynomene or applying N fertilizer to existing bahiagrass (Paspalum notatum Flüggé) on litter disappearance and decomposition rates. The study was conducted in Ona, FL, from October 2019 to May 2020 and October 2020 to May 2021. Treatments were the split‐plot arrangement of bahiagrass‐aeschynomene or bahiagrass monoculture (main plot) and N fertilization level, (0 [control] or 60 kg N ha−1) as subplot, distributed in a randomized complete block design with four replicates. The nylon bag technique was used to estimate organic matter (OM), N and carbon (C) disappearance, and lignin concentration for 184 days of incubation. Bahiagrass‐aeschynomene had greater OM disappearance (from 330 to 356 g kg−1 OM) and was driven by C disappearance. Nitrogen fertilization did not impact litter decomposition responses, except for C:N ratio. Overseeding aeschynomene is an effective management practice to increase OM decomposition and optimize nutrient cycling in warm‐season pastures.

A novel Bagged-CNN architecture for short-term wind power forecasting

ABSTRACT Wind power forecasting plays a pivotal role in ensuring the dependable integration of wind energy into power grids. This research introduces a novel Bagged-CNN architecture tailored for short-term wind power forecasting. This architecture harnesses the combined strengths of the Bagging ensemble technique and Convolutional Neural Networks (CNN) to enhance prediction accuracy and robustness. Our approach demonstrates superior forecasting capabilities by exploiting the spatial and temporal features captured by CNNs. To validate our proposed architecture, we have conducted a comprehensive evaluation across three distinct wind farms, each characterized by unique geographical and climatic conditions. We systematically compared the performance of our Bagged-CNN model against several standalone machine-learning algorithms commonly employed in wind power forecasting. The hyperparameters of our Bagged-CNN model are meticulously optimized using the GRID search algorithm, which systematically explores a predefined parameter space. Notably, our predictions shed light on the substantial impact of training data period length on wind power prediction accuracy. Our research underscores the potential of coupling Bagging and CNN techniques for short-term wind power forecasting. The Bagged-CNN architecture, as proposed, serves as a robust framework for capturing intricate spatial and temporal patterns in wind power data, thereby facilitating more precise and dependable predictions.

Life cycle assessment of sugarcane bagasse based natural composites prepared via vacuum bagging technique

The demand for lightweight materials has been constantly mounting in aerospace and other structural applications with focus towards sustainable and green engineering. In aerospace applications, various industrial regulations have stepped towards carbon composites replacement owing to its inability to repair and recycle which has envisaged to bio-based composites depending on the manufacturing processes. This paper focuses on life cycle analysis of bagasse based biocomposites which are predominant by-product from sugarcanes. The sugarcane bagasse biocomposites were prepared through vacuum bagging technique and their emission and energy consumptions were analyzed. The sugarcane bagasse biocomposites showcased overall energy consumption of ∼178 MJ and ∼30 kgCO2Eq. global warming potential (GWP) with chemical treatment, pesticides, and vacuum bagging as major processes. The energy consumption of biocomposites was ∼5 times less than carbon composites procured via same route illustrating sugarcane bagasse as more environmentally friendly but requiring additional research to understand the transition towards bio-based materials.

A bagging approach for improved predictive accuracy of intradialytic hypotension during hemodialysis treatment

The primary objective of this study is to enhance the prediction accuracy of intradialytic hypotension in patients undergoing hemodialysis. A significant challenge in this context arises from the nature of the data derived from the monitoring devices and exhibits an extreme class imbalance problem. Traditional predictive models often display a bias towards the majority class, compromising the accuracy of minority class predictions. Therefore, we introduce a method called UnderXGBoost. This novel methodology combines the under-sampling, bagging, and XGBoost techniques to balance the dataset and improve predictive accuracy for the minority class. This method is characterized by its straightforward implementation and training efficiency. Empirical validation in a real-world dataset confirms the superior performance of UnderXGBoost compared to existing models in predicting intradialytic hypotension. Furthermore, our approach demonstrates versatility, allowing XGBoost to be substituted with other classifiers and still producing promising results. Sensitivity analysis was performed to assess the model’s robustness, reinforce its reliability, and indicate its applicability to a broader range of medical scenarios facing similar challenges of data imbalance. Our model aims to enable medical professionals to provide preemptive treatments more effectively, thereby improving patient care and prognosis. This study contributes a novel and effective solution to a critical issue in medical prediction, thus broadening the application spectrum of predictive modeling in the healthcare domain.

The Investigation of KH560 Coupling Agent with Carbon Fiber One Step Dipping Method Compared with Functionalized Multi Walled Carbon Nanotubes

Interlaminar and interfacial properties of composites are important parameters for any application, extensive research has been out bursting to enhance the above properties using nanotubes (NTs) as an alternate solution to minimize delamination of laminates. This research focused on significance of functionalized engineering materials for better efficiency in industries, especially stealth technology. Silane-coupling agent “3-glycidyletheroxypropyl-tri methoxy silane, KH-560” is one module among five modules designed in this work. Five modules are considered as (M1, M2, M3, M4 & M5) with (0.5wt% of AMINE, CARBOXYL, multi wall carbon nano tubes (MWCNTs), 5% KH-560 treated carbon laminate and finally with base carbon fiber). PAN-based 12k carbon fiber as base fiber with functionalized and non-functionalized nanocomposite are incorporated as fillers in the epoxy laminates fabricated using vacuum bagging technique. Findings indicated 5% KH-560 silane treated carbon fiber (module M5) showed best results such as properties with 39%,55% and 20% in “tensile, flexural and interlaminar shear strength (ILSS)” respectively. FESEM analysis is conducted to understand morphology of the laminates.