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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. -
Comparative Analysis Study of 43-point and 27-point Buyoff Stations for Stressed Mirror Polishing (SMP) Metrology
As a collaborative effort within the Thirty Meter Telescope (TMT) project, India is committed to supplying 84 polished segments for the primary mirror, employing the innovative Stressed Mirror Polishing (SMP) technology obtained from Coherent Inc., USA. SMP allows for the efficient polishing of highly aspheric non-axisymmetrical glass blanks at an accelerated rate. India-TMT (I-TMT) successfully applied SMP to qualify three glass roundels at Coherent's facility in Richmond, CA. The study focuses on a comparative analysis of Buyoff Stations (BOS) used in the SMP process. It contrasts results from the 43-point hydraulic-based BOS at Coherent with simulated outcomes from the 27-point whiffletree-based BOS at I-TMT. This analysis assesses efficacy and performance differences between the two BOS configurations, involving a comprehensive examination of a 1520mm diameter polished glass roundel. The study integrates Finite Element Method (FEM) simulations with experimental data, providing insights into the efficiency of the respective BOS setups. 2024 SPIE. -
Comparative efficiency analysis of RF power amplifiers with fixed bias and envelope tracking bias
RF power amplifier (RF PA) finds its application in almost all the areas of electronics, mobile communication being identified as a major area. The paper performs a comparative efficiency analysis of RF power amplifiers operating with a fixed bias and an envelope tracking bias. Simulations are performed using Keysight advanced design system (ADS) tool. A class a RF PA operating at a 12 dB gain is fixed for the work. 16 QAM LTE signal operating at 5 MHz input frequency, with a peak to average power ratio (PAPR) of 6.0 dB is used as input signal. An envelope simulation at 2.5 GHz is performed on the RF power amplifier. Simulation result shows an improvement of 12% in power added efficiency (PAE) at 6 dB back-off and 6.422% in mean PAE while using envelope tracking power amplifiers, compared to RF PA with fixed supply. Envelope tracking power amplifiers reduced AM/AM distortions also by a factor of 0.248. The results obtained are much better than that obtained using a conventional RF PA with fixed bias. RF PA being the most power dissipative block in a mobile handset, improving its efficiency contributes directly to a great improvement in the battery lifetime of mobile phones. The major challenges faced by envelope tracking PA (ETPA) designers in achieving this efficiency improvement is also delineated in the paper. 2024 Institute of Advanced Engineering and Science. All rights reserved. -
Comparative electrochemical investigation for scheelite structured metals tungstate (MWO4 (M = Ni, Cu and Co)) nanocubes for high dense supercapacitors application
Scheelite structured metal tungstate MWO4 (M = Ni, Cu and Co) nanocubes were synthesized through the chemical reflux for supercapacitors application and ceyltrimethylammonium bromide (C-TAB) as surfactant. In X-ray diffraction (XRD) result are fit with relevant JCPDS cards, synthesized materials are closely matched with monoclinic and triclinic crystal phase corresponding to NiWO4, CoWO4 and CuWO4 with Scheelite type structure. To resist the growth of the particles and succeeding nanocubes morphology were achieving by using PEG-400 and C-TAB act as a surfactant. The prepared modified electrodes were examined electrochemical analysis after successive coating of working material in empty Ni foil. From the galvanostatic charge-discharge (GCD) comparative analysis, fast ions movements are interacts through the aqueous electrolyte medium with nanocubes NiWO4 electrode are achieving specific capacitance of 1185 Fg?1 at 0.5 Ag?1 and cyclic stability 93.084 % (retentivity) formerly compare to CuWO4 and CoWO4 electrodes. 2023 -
Comparative experimental study of base line and thermal barrier coated four stroke four cylinder diesel fueled engine with low heat rejection
The depletion of conventional fuel source at a fast rate and increasing of environment pollution motivated extensive research in energy efficient engine design. In the present work, experimental investigations were carried out on a four-stroke four-cylinder diesel-fuelled Base Line Engine (BLE) by conducting a normal load test and measuring the required Brake Thermal Efficiency (BThE) and Specific Fuel Consumption (SFC) in a 100 HP dyno facility. A six-gas Analyser was used for the measurement of Unburnt Hydrocarbons (UBHC), Carbon monoxide (CO), Carbon dioxide (CO2), free Oxygen (O2), Nitrogen oxides (NOx), Sulphur oxides (SOx) and a smoke meter was used to measure smoke opacity. Low Heat Rejection (LHR) engine was realized by coating the crown of the aluminium alloy piston with the most popular Thermal Barrier Coating (TBC) material, namely 8%Yttria Partially Stabilized Zirconia (8YPSZ), after coating qualification on research pistons, specifically fabricated to retain the piston material specification, and the geometry of the crown contour. A normal load test was conducted on LHR engine to evaluate the performance as well as to determine the concentration of pollutants. A ~30% improvement in BThE and ~35% improvement in SFC was exhibited by the LHR engine at all loads studied (7 to 64%). While UBHC level showed an increase, the CO, CO2 and O2 contents as revealed in the emission test showed a mixed response (high and low) for an LHR engine. Compared with BLE, NOx and smoke level in LHR engine emission showed an increasing trend with the load. On comparing BLE and LHR engine test results, value addition to the BLE in terms of reduced fuel consumption and pollutants was observed. Universiti Malaysia Pahang, Malaysia.