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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. -
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 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 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 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 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 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 Machine Learning Models in Predicting Academic Outcomes: Insights and Implications for Educational Data Analytics
In the evolving landscape of educational research, the predictive analysis of student performance using data science has garnered significant interest. This study investigates the influence of diverse factors on academic outcomes, ranging from personal demographics to socioeconomic conditions, to enhance educational strategies and support mechanisms. We employed a diverse ml models to analyze a information containing academic records and socioeconomic information. The models tested include Logistic Regression, Random Forest (RF), Gradient Boosting (GB), Support Vector Machines (SVC), K-Nearest Neighbors (KNN), Gaussian Naive Bayes, and Decision Trees. The process involved comprehensive data preprocessing, exploratory analysis, model training, and evaluation based on metrics such as precision, recall, accuracy, and F1 score. The results indicate that ensemble methods, specifically RF and GB, demonstrate superior efficacy in accurately predicting categories of student performance such as 'Enrolled,' 'Graduated,' and 'Dropped Out.' These models excelled in handling the complex interplay of varied predictors affecting student success. The results further underline the potential of advanced ensemble ML techniques in significantly outperforming the prediction accuracy in the academic domain, hence facilitating the tailoring of educational interventions to foster improved engagement and better outcomes for students. This has provided a comparative analysis of the methods that guide the future application of predictive analytics in education. 2024 IEEE. -
Comparative Analysis of Maize Leaf Disease Detection using Convolutional Neural Networks
Worldwide, maize is a significant cereal crop for crop productivity, identifying diseases in the plant's leaves is essential to raise a good crop. Deep learning methods that have been used in recent years to precisely identify and categorize these serious diseases, offering a non-destructive and effective way to find maize leaf ailments. In order to detect maize leaf disease, this paper suggests using three well-liked deep learning models: VGG16, Inception V3, and EfficientNet. The models were trained and assessed using a datasets of 4000 images of three distinct maize leaf diseases and a healthy class. All three models had high accuracy rates, according to the results, though EfficientNet outperformed the other two models. The suggested method can detect and track diseases in maize crops with high accuracy and can be applied practically. It can accurately classify various diseases. The study also demonstrates that deep learning models can offer a trustworthy and effective solution for detecting crop diseases, which can aid in lowering crop losses, raising crop yields, and enhancing food security. 2023 IEEE. -
Comparative Analysis of Non-Destructive Silkworm Cocoon Sex Classification using Machine Learning Models Based on X-Ray and Camera Images
Silk production plays a vital role in global economies, with sericulture heavily dependent on efficient seed production processes. Traditional methods involve manually cutting cocoons to classify silkworm sex, which leads to silk damage, labor intensiveness, and potential inaccuracies. In response, non-destructive technologies like X-ray and camera imaging have emerged, enabling sex classification without cocoon damage, thereby enhancing efficiency and reducing manual errors. This study undertakes a comparative analysis of X-ray and camera imaging methods for silkworm sex classification. X-ray imaging demonstrates superior efficiency in extracting detailed features from silkworm pupae, crucial for accurate classification. In contrast, camera imaging excels in the rapid and cost-effective classification of silkworms based on extracted features. The results reveal significant findings: using X-ray imaging model achieves 97.1% accuracy for FC1 and 96.3% accuracy for FC2, employing ensemble learning technique like AdaBoost. Meanwhile, camera imaging achieves an accuracy above 98% for both FC1 and FC2 using XGBoost, showcasing its effectiveness in real-time classification scenarios. Computational time analysis indicates that X-ray imaging is faster in feature extraction, while camera imaging consumes less memory during classification. These findings underscore the practical advantages of non-destructive imaging technologies and machine learning in revolutionizing sericulture practices. By enhancing productivity and sustainability through accurate sex classification of silkworms, these methods contribute significantly to the growth and efficiency of the silk industry. 2024 IEEE. -
COMPARATIVE ANALYSIS OF ORGINAL MOVIES AND ITS REMAKES IN INDIA
The remake is a phenomenon both well-known and immediately recognizable but in India it is not theoretically analyzed. However, by analyzing these remakes, we can understand how these films reflect some specific cultural differences between one state and other State in India. Here researcher has taken four original and its remake films to understand the phenomena of remake. The highly intensed watching the films has helped researcher to understand the difference of films original and remakes. Researcher took one Tamil Movie and its remake in Hindi and also a Malayalam Movie and its remake in Tamil. Films are Tamil Singam to Hindi Singam and Malayalam Manichithrathazhu to Tamil Chandramukhi. All the changes made in movies are on the basis of the cultural differences between the regions where the film is introduced. As researcher have done two Tamil movies he came to know that Tamil Industry possess one culture even though it is a remade they try to change it according to their culture. The basic element of films are the audience, a film is made according to them. So that film Industry sticks to the culture of the audiences. -
Comparative analysis of original movies and its remakes in India /
The remake is a phenomenon both well-known and immediately recognizable but in India it is not theoretically analyzed. However, by analyzing these remakes, we can understand how these films reflect some specific cultural differences between one state and other State in India. Here researcher has taken four original and its remake films to understand the phenomena of remake. The highly intensed watching the films has helped researcher to understand the difference of films original and remakes. Researcher took one Tamil Movie and its remake in Hindi and also a Malayalam Movie and its remake in Tamil. Films are Tamil Singam to Hindi Singam and Malayalam Manichithrathazhu to Tamil Chandramukhi. -
Comparative Analysis of Phytochemicals and Antioxidant Potential of Ethanol Leaf Extracts of Psidium guajava and Syzygium jambos
Background: Plant-based drugs for various human ailments are becoming very important in the current domain of therapeutics. Aim: Psidium guajava and Syzygium jambos are two such plant species known for their medicinal properties in traditional systems of medicine like Ayurveda. Methods: Phytochemical analysis including GCMS, and antioxidant studies (DPPH) was carried out for both plant extracts. Results: Comparative phytochemical analyses of ethanol extracts of both these plants have shown the existence of bioactive components like tannins, polyphenols, alkaloids, flavonoids and terpenoids. These phytochemicals were quantified and the ethanol extracts were subjected to GCMS analysis which showed the presence of cis-?-farnesene, cis-calamenene, copaene, humulene, caryophyllene, phytol, neophytadiene, n-hexadecanoic acid etc, many of which possess diverse properties like antimicrobial, antibiofilm, antioxidant and anti-inflammatory. DPPH and reducing power assays revealed the excellent radical scavenging activity of the extracts. Conclusion: Among the two plants under the current study, S. jambos extract showed better results when compared to P. guajava concerning the antioxidant potential and the quantity of flavonoids, alkaloids, polyphenols and tannins present in the plant samples. 2024, Informatics Publishing Limited. All rights reserved. -
Comparative Analysis of Prediction Algorithms for Heart Diseases
Cardiovascular diseases (CVDs) are the leading source of demises universally: More individuals perish yearly from heart disease than due to any other reason. An estimated 17.9 million humans died from CVDs in 2016, constituting 31% of all global deaths. [1] Such high rates of death due to heart diseases have to cease. This idea can be accelerated by the prediction of risk of CVDs. If a person can be medicated much earlier, before they have any symptoms that can be far more beneficial in averting sickness. The paper strives to communicate this issue of heart diseases employing various prediction models and optimizing them for better outcomes. The accuracy of each algorithm guides to a relative enquiry of these prediction models, forming a solid base for further research, finer prognosis and detection of diabetes. 2021, Springer Nature Singapore Pte Ltd. -
Comparative Analysis of Predictive Models for Customer Churn Prediction in the Telecommunication Industry
To determine the best model for churn prediction in the telecom industry, this paper compares 11 machine learning algorithms namely Logistic Regression, Support Vector Machine, Random Forest, Decision Tree, XGBoost, LightGBM, Cat Boost, AdaBoost, Extra Trees, Deep Neural Network, and Hybrid Model (MLPClassifier). It also aims to pinpoint the top three factors that lead to customer churn and conducts customer segmentation to identify vulnerable groups. The results indicate that the Logistic Regression model performs the best, with an F1 score of 0.6215, 81.76% accuracy, 68.95% precision, and 56.57% recall. The top three attributes that cause churn are found to be tenure, Internet Service Fiber optic, and Internet Service DSL; conversely, the top three models in this article that perform the best are Logistic Regression, Deep Neural Network, and AdaBoost. The K means algorithm is applied to establish and analyze four different customer clusters. This study has effectively identified customers that are at risk of churn and may be utilized to develop and execute strategies that lower customer attrition. 2024 IEEE. -
Comparative analysis of rural consumers purchase behavior towards mobile phone in Karnataka
Indian urban market is getting saturated for many products. Thus, due to success of brands like Chik shampoo, Project Shakti, LG, Dabur, HLL (then2005), many marketers are now expanding their product offerings to rural markets as well. Also, since major part of India living in villages (around 70%) are now more improved due to increased literacy, TV penetration and improved affordability is a reason for marketers to expand. Of the research conducted on rural India, majority was either on understanding rural consumers on price, quality, brand, function and style or comparing rural consumers over urban consumers on buying behavior. This research focused on comparing rural consumers of two different districts on age, brand and opinion leaders role on influencing the rural preference towards mobile phone. The research focused on understanding the buying behavior of two villages, Keelara and Alekere of Mandya and two villages, Araleri and medahatti of Kolar with reference to mobile phone. 2019 SERSC. -
Comparative Analysis of State-of-the-Art Face Recognition Models: FaceNet, ArcFace, and OpenFace Using Image Classification Metrics
In recent years, facial recognition has emerged as a key technological advancement with numerous useful applications in numerous industries. FaceNet, ArcFace, and OpenFace are three widely used techniques for facial identification. In this study, we examined the accuracy, speed, and capacity to manage variations in face expression, illumination, and occlusion of these three approaches over a period of five years, from 2018 to 2023. According to our findings, FaceNet is more accurate than ArcFace and OpenFace, even under difficult circumstances like shifting lighting and facial occlusion. Also, during the previous five years, FaceNet has shown a significant improvement in performance. Even while ArcFace and OpenFace have made significant strides, they still lag behind FaceNet in terms of accuracy. Therefore, based on our findings, we conclude that FaceNet is the most effective method for facial recognition and is well-suited for use in high-stakes applications where accuracy is crucial. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Comparative analysis of Tata - JLR and Volkswagen - Skoda Merger /
This paper focuses on the strategies adopted by Volkswagen and Tata to rebuild the image of Skoda and Jaguar respectively. Through this paper, the researcher wants to find out how change in ownership and how change in PR strategies, can rebuild the image of a brand. Both Skoda and Jaguar were facing a downhill when it came to sales and were on the verge of shutting down. That is when the change in ownership took place. -
Comparative Analysis of The Internet of Things (IOT) in the Health Sector
The Internet of Things (IoT) technology is still the main target of the discussion since it now has a significant influence on the healthcare industry. The majority of researchers who use technologies are professors and specialists. It aids in obtaining accurate study results so that rural areas may utilize technologies as well. It offers appropriate financial gains that are substantial. Services at a reasonable cost. Today, it is crucial to advance both the therapy and pharmaceutical sectors of medicine. The level of technology aids in conducting appropriate investigation appropriate solutions. The IoT is being utilized to improve the wearable electronic technologies that are applied to provide smart healthcare services in several different methods. They can survive as a result of it. According to research, IOT in the administration of wheelchairs, mobile healthcare solutions, as well as other variables has favourably affected the improvement of healthcare services. 2023 IEEE. -
Comparative Analysis of Various Ensemble Approaches for Web Page Classification
The amount of data available on web pages is enormous, and extracting the relevant information and classifying them is an important task. Web page classification finds applications in web content filtering, maintaining and expanding web directories, building efficient crawlers, etc. Machine Learning methods known for their well-established classification approaches have proved to be effective in web page classification. The present work uses ensemble methods like Bagging Meta Estimator, Random Forest, Adaptive boosting, Gradient Tree boosting, Extreme Gradient boosting and stacking to improve single classifiers results. One dataset is manually created to classify web pages into IoT projects and non-IoT projects. Another publicly available dataset is used to classify publications- and conference-related web pages. The advantage of the Ensemble methods over single classifiers has been validated, and various parameters to tune the Ensemble classifiers have been presented and analysed, with accuracy being the metric for performance. Features like learning rate, number of estimators, and maximum number of features have been tuned besides other parameters, and a comparison has been presented. 2023 Scrivener Publishing LLC.