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Comparative Analysis of Predictive Models to Detect Alzheimers Disease
Alzheimers disease is the most common type of dementia, often affecting people above the age of 60, as all the brain connections and cells themselves start to die, affecting motor, speech and memory, slowing eating away a person once it sets out as it is a non-curable disease as of now. But an early and easy diagnosis may help slow down the process and start treatment, so it is essential to diagnose it quickly. But this disease needs a number of tests and time to determine the diagnosis, and time is of the essence. Various Machine Learning (ML) algorithms are being applied nowadays, with newer methods being trialed every day for the detection of Alzheimers more consistently and easily, but it is essential to apply the most accurate of models and require only the optimum number, and cost efficient tests for reliable diagnosis so this horrid disease could be started the treatment for as soon as possible. This paper is presenting its arguments for various methods of prediction of Alzheimers to improve efficiency of detection, a comparison of models taking into consideration the costs, the accuracy and the true benefit of the test for early tackling of this illness. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
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 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 on containers based on kubernetes, Docker swarm, open shift and Mesos
Cloud Computing has become increasingly common to use cloud computing solutions for Big Data processing because of their vast variety of computer resources and ability to extend over multiple cloud platforms. The rapid growth of the Internet of Things (IoT) concept has sparked this development. A traditional approach of cloud organisation is virtualization, which uses virtual computers and containers. It is impossible to overestimate the importance of lightweight cloud infrastructure for microservices. Many academics have proposed container-based virtualized computing services as a result of this. Container technology has risen in prominence as a viable alternative to traditional virtual machines in recent years. There is need to exploit high-level services such as orchestration. In this paper, we compare performance of various container orchestrators like Kubernetes, Dockswarm, Openshift and Mesos. 2025 IEEE. -
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 Evaluation of Curcumin Derivatives Loaded 3D Printable Chitosan/Gelatin Hydrogels: Release Behaviour, Antimicrobial, Antioxidant, and Immunomodulatory Properties
The development of multifunctional scaffolds with improved mechanical strength, swelling resistance, antibacterial activity and cytocompatibility is crucial for tissue engineering. In this study, chitosangelatin (CH GT) scaffolds were reinforced with curcumin (Cur), nano-curcumin (nCur), and PLGA-encapsulated curcumin (PLGA_Cur) to enhance physicochemical and biological properties. SEM micrographs confirmed uniform, interconnected pores with reduced pore wall disruption upon Cur incorporation. Mechanical testing revealed that the highest tensile strength and tensile modulus for CH GT nCur were observed at 34kPa and 58kPa, respectively. Swelling studies showed a significant reduction in equilibrium swelling ratio from ~ 675% (CH GT) to ~ 340% (CH GT_nCur), correlating with enhanced hydrogen bonding and physical crosslinking. Antibacterial assays indicated significant inhibition against S. aureus (~ 94%) and E. coli (~ 92%) for CH GT_nCur. Cytocompatibility tests showed > 85% cell viability across all formulations, with CH GT_nCur supporting superior cell attachment and cell migration capabilities compared to controls. Cur release from CH GT Cur and CH GT nCur hydrogel scaffolds resulted in antioxidant activity; however it was slightly impeded by rapid release. In the PLGA-based system, antioxidant activity is enhanced with sustained release. CH GT Cur and CH GT nCur enhanced M2 macrophage polarization (p < 0.001) compared to CH GT Cur hydrogels, which successfully decreased inflammation and oxidative stress. Notably, despite a delayed M2 response, the PLGA-encapsulated Cur system (CH GT PLGA_Cur) demonstrated sustained decrease of ROS levels and iNOS expression, suggesting extended anti-inflammatory effect. These results demonstrate the promise of CH GT-based hydrogels, particularly the PLGA_Cur system, for oxidative stress management and regulated immunomodulation in therapeutic settings. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
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. -
Comparative optimization studies (Isp 4 vs isp 3 vs isp 2 media) of mangrovian streptomyces pluripotens anukcjv1 for its ?-amylase production and geographical correlation of mangrovian actinomycetes strains
Streptomyces pluripotens ANUKCJV1 was isolated from Coringa Mangroves which was located along the South Indian Delta. The Current work which was in continuation to our previously reported work which suggests that Streptomyces pluripotens ANUKCJV1 was the potential strain and the same has been subjected to comparative optimization studies in the current work by employing three media: ISP 4; ISP 3; ISP 2 media for enhanced ?-Amylase Production. Physico-Chemical variables viz Incubation period, PH, Temperature, Carbon and Nitrogen sources with respect to three different media (ISP 4, ISP 3 and ISP 2) were tested and cumulative analysis of three different media for differential bioactivity of ?-Amylase was done. Results suggest that ISP 4 found to be the best medium with cumulative value of 24.2 U/mL, where as the cumulative value of ISP 3 and ISP 2 were 19.3 U/mL and 19.4 U/mL respectively. Peptone as Nitrogen source of ISP 4 found to be the favourite Individual variable among all with production value of 8.0 U/mL. Geographical correlation with respect to number of Actinomycetes strains and ?-Amylase Bioactivity depicts that Distant geographical soil samples from the shore found to be favourable for higher number of Actinomycetes strains: A1 soil samples (~ 500 m)-33 %; A2 samples (~ 400 m)-22 %. With regard to ?-Amylase Bioactivity, A5 samples (~ 100 m) analysed to be the potential geographical bioactive zone for ?-Amylase Production. From the study it can be concluded that since ISP 4 found to be the favourite medium of the potential strain, by employing the same large scale exploration of the Streptomyces pluripotens ANUKCJV1 of the Coringa Mangroves may be done to tap the industrial benefits of ?-Amylase. EM International. -
Comparative Performance Analysis of Clustering Algorithms for Scalable and Reliable Vehicular Ad-Hoc Networks (VANETs)
Vehicular Ad-Hoc Networks (VANETs), widely used in intelligent transport systems, require effective clustering techniques to maintain network stability, reduce network latency and enhance communication efficiency. This research presents an in-depth analysis of three widely used clustering algorithms: K-Means, Spectral, and Leiden. Efficacy is assessed across different vehicle densities and speeds. The study focuses on examining four primary factors: the modularity of cluster formation, silhouette score, throughput, packet delay and cluster head change rate. The results obtained from the tests indicate that K-Means always sends more data & has the quickest packet delivery which generates the best-shaped clusters to elect CH. This is the best choice for networks with a varying number of cars that change speeds. Leiden does well when there are a lot of cars on the road. It stays stable but changes for huge graphs. Spectral clustering always does worse, with longer delays, less data getting through, and cluster heads that change too much. These findings show that selecting the right algorithm matters when building VANETs that can grow and stay reliable. The study concludes that K-Means is the best choice for cluster formation & electing CH where there is a need for quick responses and lots of data flow. Leiden works well in packed networks that need balanced performance. Spectral clustering does not work efficiently when keeping the network running in real-life vehicle situations at higher density & speed. 2025 IEEE. -
Comparative Performance Analysis of Deep Learning Models in Cervical Cancer Detection
Cervical cancer one of the four most common malignancies worldwide and poses a significant threat, particularly in resource-constrained regions. Automated diagnostic approaches, leveraging colposcope image analysis, hold great promise in curbing the impact of this disease. In this paper, we deploy a range of deep learning methods, including DenseNet 121, ResNet 50, AlexNet and VGG 16 to classify the cervical intraepithelial neoplasia. Our methodology is deployed on a dataset sourced from a Cancer Research institute in India. The current experiment aims to establish the execution of the state-of-the-art pretrained frameworks in deep learning. This will be a baseline experiment for researcher who aim to develop further deep learning models for cervical cancer diagnosis. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Comparative Performance Analysis of Machine Learning and Deep Learning Techniques in Pneumonia Detection: A Study
Pneumonia is a bacterial or viral infection that inflames the air sacs in one or both lungs. It is a severe life-threatening disease, making it increasingly necessary to develop accurate and reliable artificial intelligence diagnosis models and take early action. This paper evaluates and compares various Machine Learning and Deep Learning models for pneumonia detection using chest X-rays. Six machine learning models -Logistic Regression, KNN, Decision Tree, Random Forest, Naive Bayes, and Support Vector Machines - and three deep learning models - CNN, VGG16, and ResNet - were created and compared with each other. The results exhibit how just the model choice can significantly affect the quality and inerrancy of the final diagnostic tool. 2023 IEEE. -
Comparative Performance Analysis of Segmentation Methods in Cervigram Images
One of the most common cancers of the lower female reproductive tract is cervical cancer and it is a major contributor of mortality in developing nations. Screening tests include image analysis of pap smear and colposcope pictures. In image analysis, machine learning techniques can be employed to analyze and interpret images of the cervix through segmentation and extraction of characteristics for the classification of cervix images. K-means algorithm and Gaussian mixture model are popular segmentation algorithms used in cervix region-of-interest extraction. In the context of deep network learning, segmentation means the use of deep convolution networks to accurately identify different objects or regions in an image. R-CNN and Deeplab architectures are among the most frequently employed models in deep learning for automated cervix image processing. In this paper, we have systematically reviewed machine and deep learning models popularly employed in cervical cancer identification through colposcope images. Four carefully chosen models were deployed, and their performance was comparatively analyzed. This research can be a foundation for scientists looking to develop new models for the classification and segmentation of cervical cancer. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Comparative Performance Evaluation of GEO, MEO, and LEO Satellite Networks under Traffic Attacks
This paper presents a comparative evaluation of geostationary (GEO), medium-earth orbit (MEO), and low-earth orbit (LEO) satellite constellations under realistic traffic attack models. We use OMNeT++ v6.1 with the INET-4.5 framework for simulation and Python for analysis. Key performance metrics include end-to-end latency, throughput, packet delivery ratio (PDR), and resource utilization measured under normal and attack conditions. Our results indicate that MEO yields the highest throughput and resource utilization, while LEO offers the lowest latency. We provide a clear description of the simulation conditions, attack models, and statistical methods used to evaluate resilience under degraded operation. 2025 IEEE. -
Comparative Performance of LSTM and ARIMA for the Short-Term Prediction of Bitcoin Prices
This research assesses the prediction of Bitcoin prices using the autoregressive integrated moving average (ARIMA) and long-short-term memory (LSTM) models. We forecast the price of Bitcoin for the following day using the static forecast method, with and without re-estimating the forecast model at each step. We take two different training and test samples into consideration for the cross-validation of forecast findings. In the first training sample, ARIMA outperforms LSTM, but in the second training sample, LSTM exceeds ARIMA. Additionally, in the two test-sample forecast periods, LSTM with model re-estimation at each step surpasses ARIMA. Comparing LSTM to ARIMA, the forecasts were much closer to the actual historical prices. As opposed to ARIMA, which could only track the trend of Bitcoin prices, the LSTM model was able to predict both the direction and the value during the specified time period. This research exhibits LSTM's persistent capacity for fluctuating Bitcoin price prediction despite the sophistication of ARIMA. 2023, University of Wollongong. All rights reserved. -
Comparative Study Analysis on News Articles Categorization using LSA and NMF Approaches
Due to exponentially growing news articles every day, most of their important data goes unnoticed. It is important to come up with the ability to automatically analyse these articles and segregate them based on the context and related to their particular domain. This paper applies topic modelling which is one of the most growing unsupervised machine learning fields on a million headlines articles in order to produce topics to describe the context of the news article. There are various generative models but we specifically focusing on the non-negative matrix factorization (NMF) and Latent Semantic Analysis (LSA) for implementing and evaluating news dataset. Furthermore, the findings reveal that both NMF and LSA are useful topic modelling tools and classification frameworks, but based on the experimental results the LSA model performed well to identify the hidden data with better mean coherence values and also consumes lesser execution time than NMF. 2022 IEEE.