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Comparative analysis of machine learning algorithms for predicting student success and enhancing their educational outcomes
The primary objective of this study is to predict the performance of students and evaluate the efficacy of various machine learning algorithms in predicting student success based on their marks and grades (academic factors). Through a comprehensive review of literature and experimentation, this research compares the performance of different machine learning models, including but not limited to decision trees, random forests, support vector machines, logistic regression, and neural networks. The evaluation metrics considered in this comparative analysis include accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC). Fourteen experiments have been performed and preliminary results suggest that performances of students on the basis of academic factors might be predictable and by understanding the strengths and weaknesses of student's educational outcomes and foster student achievement can be improved. Through extensive experimentation and comparative analysis, XGBoost(ExtremeGradient Boosting) and AdaBoost demonstrated as the most effective predictive models to analyze the students' performance. 2025 Author(s). -
Comparative Analysis of Machine Learning Algorithms for Effective Crop Recommendation
The global call for sustainable farming necessitates a move away from traditional crop selection methods. These conventional approaches, often relying on farmer intuition, are imprecise and scale poorly in the face of complex environmental variables. Machine Learning (ML) models offer a robust, data-driven solution. By analyzing multifaceted data-spanning soil chemistry, weather patterns, precipitation trends, and historical yield performance-ML models can significantly enhance decision-making, optimize resource utilization, and improve overall crop outcomes. This paper delivers an extensive comparative review of key ML algorithms employed for crop recommendation, including Decision Trees (DT), Random Forests (RF), Support Vector Machines (SVM), k-Nearest Neighbors (KNN), and Artificial Neural Networks (ANN). We also explore the critical role of Explainable AI (XAI) in building model transparency. Our study evaluates these models on the metrics of accuracy, interpretability, and computational overhead. The research also investigates hybrid methods that integrate deep learning with conventional ML to enhance predictive power. Our comparative findings highlight the strengths and weaknesses of each model, concluding that ANN and XAI-based approaches demonstrate the highest accuracy and adaptability for diverse agricultural conditions. We also identify significant challenges, such as data imbalances and the absence of real-time data, and discuss future trends like the integration of IoT, remote sensing, and federated learning, which will be key to making precision farming scalable and accessible. 2025 IEEE. -
Comparative analysis of Histogram Equalization techniques
Histogram Equalization (HE) is one of the techniques which is used for Image enhancement. This paper shows the comparative studies of Global Histogram Equalization, Local Histogram Equalization and Fast Quadratic Dynamic Histogram Equalization based on the execution time, mean squared error and Peak Signal to Noise Ratio (PSNR). This paper shows the experimental results for these three methods with graphical representation. 2014 IEEE. -
Comparative Analysis of GANs and Diffusion Models for Hyperspectral Image Classification
Hyperspectral imaging, which is obtained across numerous spectral bands, presents difficulties in classification due to its high dimensionality and intricate nature. This study provides a comparison of Generative Adversarial Networks (GANs) and Diffusion models regarding the classification of the Indian Pines, Pavia University, and Salinas Datasets, utilizing Multi-Layer Perceptron and Random Forest classifiers. The findings indicate the GANs combined with Random Forest outperform Diffusion models, attaining accuracies of 88%, 96% and 95% respectively. This approach may not outperform the top models, such as HTD-2D-3D-PCNN, but is simpler in structure and more computational efficient. Key recommendations would be real-time processing, edge device optimization, and applications customized to agriculture and urban planning. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Comparative Analysis of Disaster Recovery, Encryption, and Database Migration Methods in Cloud Environments
This research conducts comparative analysis and performance evaluation on disaster recovery approaches, encryption strategies, and database migration methods in cloud environments. The study highlights deeper technical insights encryption techniques and demonstrates superior performance compared to the other encryption methods in securing non-data files. This approach enhances protection against insider threats while avoiding reliance on existing Oracle wallet features, ultimately leading to a reduction in licensing expenses. This study also evaluates various database migration solutions, specifically AWS DMS, Google DMS, Azure DMS, and IBMS. Notably, IBMS stands out for its proficiency in producing cross-region data copies while achieving a 75% reduction in infrastructure costs. A comparative analysis was conducted on various disaster recovery strategies, including Standard DR, Pilot Light, Warm Standby, Hot Standby, Semi Replication, and DDI. Among these, the DDI is being observed as noteworthy since it excels in decision making capabilities and auto replication role switching advantages of standby databases. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Comparative Analysis of Digital Business Models
This paper discusses the comparative analysis of different attributes of Google and Facebook business model and their novel features for handling innovative business framework. We have compared Google and Facebook business model on different key attributes and also discussed the statistical analysis of business models using Google business analytics platform. We have argued performance analysis of these models. One important point which we discuss and analyze in this paper is that a business model is not about just building revenue generating machine, but it is indeed more than that. It explores the strategy and business approaches of both the models of revenue generating line of attacks. Our research contributes a considerate understanding of Google and Facebook architectural model and its influence on business framework. Statistical enactment and results are analyzed, precisely when big data and media are applied. This paper also provides better understanding of the digital marketplace for both of the platforms and its earning methodology. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. -
Comparative Analysis of Different Machine Learning Prediction Models for Seasonal Rainfall and Crop Production in Cultivation
Agriculture is one of the strengths of India, from the last few years, gradually the agriculture growth is going downwards in other side the population growth is upwards. Reason for agricultural downward growth depends on so many parameters. The rainfall is one of the main parameters which affects the crop yield. Because of this, the farmers are also facing the loss. If they know this information in prior, the farmers can plan accordingly the type of crop suited for the particular season and it helps the farmer to get good profit out of it. Machine learning scientific and statistical methods are used for predicting the rain fall and crop yield. Kharif and Rabi are two seasons taken for analysis. The regressor predicting models are constructed to predict the seasonal rainfall and crop yield. This study primarily focuses on seasonal crop production prediction, which is dependent on rainfall. The different types of machine learning regression method are used to achieve better results. The performance of comparison models is evaluated using different metrics. Finally, the linear regression and Bayesian linear regression models comparatively produce the best result in terms of accuracy for rainfall prediction. The boosted decision tree regression model is achieving the better result for crop prediction. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Comparative Analysis of CPI prediction for India using Statistical methods and Neural Networks
Inflation is one of the main issues affecting the world economy right now, necessitating the accurate inflation prediction for the development of tools and policies by the monetary authorities to prevent extreme price volatility. Expectations of inflation influence many financial and economic actions, and this dependence motivates economists to develop techniques for precise inflation forecasting. Nearly everyone in the economy is impacted by inflation, including lending institutions, stock brokers, and corporate financial officials. In many cases, inflation determines whether a firm will accept a particular project or if banks will make a particular loan. These different economic actors can modify their financial portfolios, strategic goals, and upcoming investments if they are able to forecast changes in inflation rates. The multiple interaction economic components that depend on inflation will be better understood by economic agents operating in a business context if inflation forecasting accuracy is improved. There are numerous techniques to forecast inflation ranging from basic statistical methods to complex neural network methods. Therefore, this paper employs LSTM model to train and analyze the Consumer Price Index (CPI) indicators to obtain inflation-related prediction results. The experimental results on historical data show that the statistical model has good performance in predicting India's inflation rate compared to deep learning methods in case of smaller dataset. 2023 IEEE. -
Comparative Analysis of Composite Column Capacity Estimation in International Codes
The primary function of a building, bridge or any structural system is to transmit loads safely from the superstructure to the foundation. The columns play a critical role in this function, and any inaccuracies in load prediction can lead to catastrophic damage. Hence, the evaluation of a column's strength assumes considerable importance. Upon this premise, this research aimed to investigate the strength prediction of M40 grade concrete columns subjected to uniaxial compression loading. The experimental loading capacities of various columns were compared with the evaluated loads as per the Indian Standard code (IS 456:2000), British Standard (BS 8110-1:1997), American Concrete Institute (ACI 318-14) and European Standard (EN 1994-1-1 (2004)). It was observed that the partial safety factors and design philosophies in these codes were different. The experimental results suggested that the load-carrying capacities experimentally determined of the tested columns compare well with the capacities recommended by the IS code and the BS code for columns. In contrast, the other two codes have vastly different column capacity assessments due to higher partial safety factors. It is concluded that all four codes have evolved based on different design philosophies and, hence, have varying partial safety factors. Thus, a direct code comparison is not appropriate. The Authors, published by EDP Sciences, 2025. -
Comparative Analysis of Classification Models Using Various Feature Sets
Feature selection is a fundamental step in Machine Learning (ML) that involves choosing some input data that would enhance the model performance. The model is able to run faster using lesser computational resources while giving reasonable results. Hence, feature selection as important as selection of a good model. In this chapter the aim is to analyze how the performance of different multiclass classification algorithms is affected on different features. The algorithms used are K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), and Convolutional Neural Network (CNN) on the CIFAR-10 dataset. To obtain the new dataset with modified features, we use dimension reduction methods on the original dataset. The new dataset is at least 500x smaller, and we have noticed that in the best case scenarios reducing dimensions reduces the accuracy score only marginally. The SVM is the most consistent among the experimented models. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Comparative analysis of carrier material efficiency in the encapsulation of flavor bioactives from Decalepis hamiltonii extract by using spray-drying and freeze-drying
An aqueous extract from the tuberous roots of Decalepis hamiltonii was encapsulated by spray-drying and freeze-drying for food applications. The study aimed to identify suitable carrier materials among sodium caseinate, maltodextrin, and gum acacia, used alone and in blends, to understand their collective effect during encapsulation. The physicochemical characteristics of freeze-dried and spray-dried samples revealed differences of 14%20% in 2-hydroxy-4-methoxy benzaldehyde, 12%40% in phenolic content, and 7%40% in flavonoid content in the dried powders. Similarly, the methanol extracts of freeze-dried encapsulated samples demonstrated good antioxidant potential compared with those of spray-dried encapsulated powder. Among the carrier materials used, sodium caseinate showed good retention of bioactives and a flavor metabolite (2-hydroxy-4-methoxybenzaldehyde), which was quantified by high-performance liquid chromatography (encapsulation efficiency 82%; yield 40 w/w) and confirmed by1H nuclear magnetic resonance (NMR). However, in this study considering flavor retention and powder yield (encapsulation efficiency 74% and 59 w/w), maltodextrin in combination with sodium caseinate (MS) was observed to be the best carrier material for spray-drying. These "maltodextrinsodium caseinate" microcapsules are stable and show 70% retention of flavor metabolite after 3 months of storage at room temperature, with the microbial load remaining within acceptable limits. The particle size of the carrier materials ranges from 11.1 to 17.6 m. Thus, the current study suggests that a carrier material mixture (sodium caseinate and maltodextrin) can be used as a prospective material for encapsulating Decalepis hamiltonii bioactives with flavor metabolites and may be useful in food formulations. 2025 by the author(s). -
Comparative Analysis of Banking StocksBSE BANKEX vs. NEPSE
Equities may also be termed as shareholder's equity that make the holder an owner of corporate equity and empower him to vote in the annual general meeting of the company. Equity, both in its common and preferred form, is used by investors to understand risk and reward patterns in order to identify and minimize losses, and equally, to maximize gains. Relative to the risk-return trade off, this paper seeks to examine the Indian and Nepali banking equities performance. Currently the banking industry holds a large part of the GDP of the two trading partners; in Nepal it accounts for 18% and in India it accounts for 7.7%. Nepal is a very import oriented economy and the most part of this import money is made through remittances while India has diverse industries that constitute its economy. The empirical analysis is based on the five selected commercial banks; three banks from BSE Bankex of India and two banks from NEPSE Banking Sub-Index of Nepal based on market capitalization. Employing the historical data of five years (from April 2017 to March 2022), it can use Mean, Standard Deviation, Correlation, Regression, and ANOVA to make analysis. Analysis reveals that Indian banking equities exhibit better returns than the Nepali equities over the comparable period. Annualized returns help identify benchmark banks including ICICI Bank and NIC Asia Bank. However, there is more risk in Indian equities because they offer the capacity of higher returns. Thus, the Nepali banking equities have lower risk but produce only mediocre returns with several banks even negative returns. Indian banks provide much better investment opportunities and higher returns even though they are more risky. It helps investors determine profitable equities based on thorough risk-return assessments for equities. 2026 selection and editorial matter, Dr. Harold Andrew Patrick and Dr. Ravichandran Krishnamoorthy; individual chapters, the contributors. -
Comparative analysis between 36 nm and 47 nm aluminawater nanofluid flows in the presence of Hall effect
White crystalline powder (aluminum oxide- Al 2 O 3 ) and water are the products often formed after the heating of aluminum hydroxide. In this report, boundary layer flow of two different nanofluids (i.e., 36nm Al 2 O 3 -water and 47nm Al 2 O 3 -water) over an upper horizontal surface of a paraboloid of revolution under the influence of magnetic field is presented. The combined influence of magnetic field strength, electric current density, electric charge, electron collision time, and the mass of an electron in the flows are considered in the governing equations. Three-dimensional transport phenomenon was considered due to the influence of the Lorentz force (F?) along the z-direction as in the case of Hall currents. In this study, the dynamic viscosity and density of the nanofluids are assumed to vary with the volume fraction ?. The dimensional governing equations were non-dimensionalization and parametrization using similarity variables. The corresponding boundary value problem was transformed into initial value problem using the method of superposition and solved numerically using fourth-order RungeKutta method with shooting technique (RK4SM). Magnetic field parameter is seen to have dual effects on the cross-flow velocity profiles of both nanofluids. The maximum cross-flow velocity is attained within the fluid domain when 36nm nanoparticles alumina is used. The cross-flow velocity gradient at the wall increases with magnetic field parameter (M) and also increases significantly with Hall parameter at larger values of M. 2018, Akadiai Kiad Budapest, Hungary. -
Comparative analysis and suggestion of architectures for reduction of road accidents
As Road Accidents are increasing all over the world, it is very important to save peoples lives. With the advancement in technology we can make use of various real time sensors and technology to save peoples lives. This paper focuses on comparing various architectures which consists of various real time sensors like Eye blink sensor, Alcohol sensor, Speed sensor, load sensor, tilt and turning sensor and various technologies like GPS, GSM. After comparison paper suggests which architecture should be used in the vehicle based on certain attributes. For E.g. If the car always travels outside the city then this paper suggests the architecture which has Eye blink sensor, Speed Sensor GPS and GSM. IAEME Publication. -
Comparative Analysis and Development of Recommendations for the Use of Machine Learning Methods to Identify Network Traffic Anomalies in the Development of a Subsystem for User Behavioral Analysis
This article discusses various machine learning methods in order to conduct a more effective analysis of user network traffic using a subsystem for analyzing user behavior and detecting network anomalies, since there is a need to evaluate big data. The methods and techniques used to detect network anomalies are analyzed. In analyzing the methods and technologies used to detect network anomalies, a classification of anomaly detection methods is proposed. To solve these problems, different algorithms can be used, differing in specificity and, as a result, efficiency. The classification of machine learning methods for detecting network anomalies is considered separately, since machine learning algorithms will be the most effective for the task. Various criteria for evaluating the effectiveness of machine learning models in solving the problem of network traffic profiling are considered. In accordance with the specifics of the tasks of user recognition and network anomaly detection, the most appropriate criteria for evaluating the effectiveness of machine learning models have been selected: AUC ROC the area under the error curve. Four stages of the subsystem for analyzing user behavior and detecting network anomalies are highlighted. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Compact substrate integrated waveguide power divider with slot-loaded ground plane for dual-band applications
In this paper, a novel design of compact substrate integrated waveguide (SIW) dual-band power divider is proposed. The dual-band operation of the power divider is obtained by exploiting the loading of slots on the ground plane. The electric-dipole nature of these slots allows the power divider to exhibit a passband below the cutoff frequency of the SIW. An in-depth description of the proposed power divider, supported by detailed parametric analysis over the operating frequency bands is reported. Design examples are illustrated to achieve different operating frequency bands. To validate the design studies, a prototype of the dual-band power divider operating at 4.7 GHz and 11.7 GHz is designed, fabricated and tested. The measurement results are found to be in good agreement with the simulation results. 2018 IEEE. -
Compact Polarization Insensitive Triple-Band Bandstop Frequency Selective Surface forTerahertz Applications
In this article, a compact triple-band bandstop frequency selective surface (FSS) is designed for terahertz applications. The proposed FSS is a single-layer structure. The unit cell consists of cross-dipoles featuring three distinct arms. The designed FSS offers three bandstop responses centered at 0.34, 0.39, and 0.43 THz with 10-dB stopband bandwidths of 12, 5, and 14GHz, respectively. The filter operation is elucidated using surface current densities and an equivalent circuit model. The unit cell exhibits a surface area of 0.374?00.374?0, where ?0 is free space wavelength at lower resonance frequency. The proposed FSS exhibits excellent stability in incidence angles up to 40? and 80? for TE and TM polarization, respectively. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Compact out-of-phase wideband substrate integrated waveguide based power divider loaded by slots for Ku and K band applications
In this paper a novel Substrate Integrated Waveguide (SIW) based single layer ground-loaded compact wideband out-of phase equal power divider is proposed . The wide-band and out-of-phase operation of the proposed power divider is obtained by creating defects in the ground plane with rectangular slots. The Defected Ground Structure (DGS) allows the power divider to exhibit a wide passband. The obtained passband is 11.5 GHz wide considering the return loss better than -10dB. Compactness in the proposed design is attributed to the dispersion characteristic of the slow-wave. The proposed design working in the passband from 14.5 GHz to 26 GHz is fabricated and tested. The size of the proposed design is 0.57 ?2g excluding feed lines. Here ?g is the guided wavelength at free space. The measured amplitude imbalance of (01) dB is obtained within the passband. The measured and simulated results are compared and found with in good agreement. 2019 IEEE. -
Compact LoRa Patch Antenna Optimization Using Dual Random Starfish Aggregation Coupled Transformer Network for Vital Sign Detection in Breast Cancer WBANs
The rapid advancement of Wireless Body Area Networks (WBANs) has created a growing demand for compact, efficient, and reliable antenna systems to support continuous health monitoring, particularly for breast cancer applications. Recent methods, including CPW-fed patch antennas, artificial neural network (ANN)-driven models, and wearable textile antennas, have improved antenna design automation and flexibility. However, challenges such as signal distortion from body proximity, gain reduction under bending, Specific Absorption Rate (SAR) compliance, and lack of adaptive tuning continue to limit practical deployment. To overcome these limitations, this study presents a compact LoRa patch antenna optimized using a novel Dual Random Starfish Aggregation Coupled Transformer Network (Dual-Ran-SACTN) framework. This system combines the Starfish Optimization Algorithm (SFOA), a Random-Coupled Neural Network (RCCN), and a Dual-Aggregation Transformer Network (DuAT) to enhance convergence speed and learning efficiency. The antenna, designed in CST Microwave Studio, measures only 80נ60mm2 (0.23??נ0.17??), offering a lightweight and wearable structure for continuous vital sign monitoring. The proposed model exhibits a bidirectional radiation pattern in the E-plane and an omnidirectional pattern in the H-plane, achieving a peak gain of 2.12dBi and a high radiation efficiency of 99.8% at 868MHz. Additionally, the design maintains low SAR and stable performance under bending, making it robust for wearable WBAN applications. This work offers a real-time, energy-efficient solution for intelligent breast cancer monitoring through adaptive antenna optimization. This model supports practical applications such as continuous breast cancer monitoring, wearable health diagnostics, and real-time WBAN-based physiological signal tracking. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
Compact Dual-Band Millimeter Wave MIMO Antenna for Wireless Communication Systems
The article presents the compact dual-band MIMO antenna resonating at 27.5 and 32 GHz. The radiating structure is a rose-shape with elliptical slots and a horizontal slit to achieve the above resonances. The MIMO antenna dimension is 6.2 0 mm2, where an edge-to-edge distance of 1.82 mm separates radiating elements. The ground plane has simple slits to suppress the mutual coupling. The simulation results of the MIMO antenna is validated through measured and diversity parameter results. 2024 IEEE.