Browse Items (16481 total)
Sort by:
-
-
Comparative Study of Product Liability and Data Confidentiality in Case of Intermediaries with Special Reference to India and The European Union
Technology has played a major role in human development. The advent and invention of wheel and fire changed the coverage of human society. On a similar note in 90 s a technology called internet was developed and it changed all rules of the game. This technology removed all hindrances of place and time. It created faceless market place wherein; consumer not only have huge choices and varieties but also, they can create goods and services on their own. This was the origin of Electronic Business and it gave birth to new breed of middleman / intermediaries to facilitate it. These intermediaries are application provider, ISP, network service provider etc. The mantras of success were wide choices and data. But this mantra created a new legal challenge of data handling and liability for defects in goods and services. Researcher has studied and analysed all dimensions of intermediaries newlineand how they handled the two new legal challenge of data confidentiality and newlineproduct liability. In addition, researcher has examined the legal framework of India and compared it with legal framework of European Union and finally concluded on the coverage and effectiveness of Indian legal structure and what India learn and implement from European Union. This thesis mainly focusing on generic business model used by intermediaries. Issues like IPR, industry specific domain like financial systems and medical domain are excluded. Researcher followed the doctrinal research methodology to understand the evolution of intermediaries, product liability, data confidentiality in India by various primary resources like the Indian Laws i.e., Consumer newlineProtection Act, 2019, Indian Contract Act, 1872, Information Technology Act, 2000 and other various statutes. This thesis compares Indian legal framework with European Union and test the hypothesis of coverage and effectiveness of Indian legal structure with European Union. -
Comparative study of phytoremediation of chromium contaminated soil by Amaranthus viridis in the presence of different chelating agents
Chromium is a harmful heavy metal to the environment due to the toxicity induced by it to plants and other living organisms. High concentration of Cr in soil poses severe toxicological problems ecosystem. Phytoremediation using different plants is an economical and environment-friendly method for removing Cr from soil. The addition of chelating agents augments the phytoex-traction using plants.The present study aimed to augment the Cr phytoremediation capacity of Amaranthus virdis, a predomi-nant plant species in the Cr-contaminated open dumpsites of Bangalore.. Phytoextraction of Cr by Amaranthus viridis was studied in the presence of different chelating agents viz. ethylenediaminetetraacetic acid (EDTA), citric acid (CA), growth pro-moting hormone-indoleacetic acid (IAA) and NPK fertiliser. A. viridis grown under different concentrations (5, 10 and 20 mg/Kg) of Cr were treated with 0.5g EDTA/Kg of soil, 0.5g CA/Kg of soil, 1mg IAA/Kg of soil and NPK (125 mg of nitrogen, 45 mg of phosphorous and 156 mg of potassium per Kg of soil). Results indicated that CA, at 10 mg/kg Cr supply, induced the highest uptake (up to 29.25 g/plant). Furthermore, the study revealed that CA amendment induced maximum Cr uptake in A. viridis at all levels of Cr supply as compared to other amendments. This was due to the increased solubility of Cr in the presence of citric acid and the amelioration of oxidative stress due to Cr to plants by citric acid. This study inferred that the non-hyperaccumulating plant, A. virdis could be used as a phytoremediator for Cr in the presence of citric acid in the places where it is grown abundantly. Author (s). Publishing rights @ ANSF. -
Comparative Study of Graph Theory for Network System
The historical background of how graph theory emerged into world and gradually gained importance in different fields of study is very well stated in many books and articles. Some of the most important applications of graph theory can be seen in the field network theory. Its significance can be seen in some of the complex network systems in the field of biological system, ecological system, social systems as well as technological systems. In this paper, the basic concepts of graph theory in terms of network theory have been provided. The various network models like star network model, ring network model, and mesh network model have been presented along with their graphical representation. We have tried to establish the link between the models with the existing concepts in graph theory. Also, many application-based examples that links graph theory with network theory have been looked upon. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Comparative study of Breakdown Phenomena and Viscosity in Liquid Dielectrics
Liquid dielectrics are extensively used in electrical apparatus which are operating in distribution and transmission systems. The function of electrical equipment strongly depends on the conditions of liquid dielectric. Liquid dielectrics used are the most expensive components in power system apparatus like transformers and circuit breakers. A failure of these equipment would causes a heavy loss to the electrical industry and also utilities. Insulation failures are the leading cause of transformer failures and thus the liquid dielectrics plays a major role in the safe operation of transformers. One of the main causes for the failure of transformers is due to the presence of moisture. In this work, the life of insulating medium is estimated by comparing the Breakdown strength and Viscosity of different pure oils with that of the contaminated oils (which contains moisture) and also finding the alternative for mineral oil. vegetable oils which are reliable, cost-effective and environmental friendly even when they are contaminated. 2019 IEEE. -
Comparative study of benchmarking models for higher education institutions
Benchmarking is a systematic and ongoing process of assessing an organisations business processes against those of business process leaders to obtain data that will enable the firm to take corrective action to enhance performance (Pattison, 1993). Eight benchmarking models, namely the European Foundation for Quality Management (EFQM) excellence model, American Productivity and Quality Centre (APQC) consortium framework, Commonwealth Higher Education Management Service (CHEMS) model, Mckinnon model, Henderson-Smart et al. model, educational development efficiency (EDE) model, Tee benchmarking model, and fourth generation balanced scorecard method are being studied, analysed, evaluated and compared. While most models effectiveness depends on the cooperation and participation of benchmarking partners, few depends on secondary data are an exception. Most benchmarking models lack the implementation and are fluid and flexible models. This comparative benchmarking study helps an institution understand which benchmarking model needs to be used, as the study details each models essential features, advantages, and limitations. Copyright 2025 Inderscience Enterprises Ltd. -
Comparative Study of AI Models for Automated Tuberculosis Detection Using Image Processing Techniques
Tuberculosis is a critical global health issue, particularly in resource-limited regions where early and accurate diagnosis is important and is in need so that the treatment is effective and the control transmission is controlled. The known diagnostic methods, such as sputum smear microscopy and nucleic acid amplification tests are costly, time-consuming, and require trained professionals. Due this in some cases it is inaccessible in many regions. Deep learning-based automated TB detection offers a promising alternative by enhancing diagnostic efficiency through medical imaging analysis. This study presents a comparative evaluation of five deep learning models, InceptionResNetV2, DenseNet, VGG16, ANN, and a custom CNN, trained on a dataset of 3,008 chest radiograph images, evenly distributed between TB-positive and normal cases. The dataset underwent advanced preprocessing techniques, pixel normalization, and data augmentation. The hyperparameter tuning process was applied, which optimized the learning rates, dropout rates, convolutional filter sizes, and batch sizes to enhance model performance. The models were assessed using accuracy, precision, recall, F1-score, sensitivity, specificity.. Experimental results indicated that the custom CNN achieved the highest classification accuracy (99.51). The superior performance of the custom CNN over other models is attributed to optimized feature extraction, effective preprocessing, and structured hyperparameter tuning. A comparative analysis with previous studies highlights how this approach mitigates dataset limitations and improves model interpretability, and the potential of AI-driven TB detection, enhancing future diagnostic efficiency by improving model generalizability and deployment in real-world healthcare settings. 2025 IEEE. -
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. -
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 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 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 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 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 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 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 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 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 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 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.