Browse Items (9794 total)
Sort by:
-
Cipher Block Chaining Support Vector Machine for Secured Decentralized Cloud Enabled Intelligent IoT Architecture
The growth of internet era leads to a major transformation in a storage of data and accessing the applications. One such new trend that promises the endurance is the Cloud computing. Computing resources offered by the Cloud includes the servers, networks, storage, and applications, all as services. With the advent of Cloud, a single application is delivered as a metered service to numerous users, via an Application Programming Interface (API) accessible over the network. The services offered via the Cloud are such as the infrastructure, software, platform, database and web services. The main motivation of this application model is to provide computationally secure key generation to protect the data via encryption. This key generation in the cryptography process falls into three categories in this research work. In the first part, SVM based encryption service model is constructed for which the key generation is from the conventional encryption operation mode with some improvements. To make the process more complex, the optimization techniques are taken into account for the key generation in descendant two methods application model that acts computationally more secure specifically for Cloud environment. The results of security analysis confirm the effectiveness of the proposed application model withstands potentially against various attacks such as Chosen Cipher Attack, Chosen Plain text Attack indistinguishable attacks for files. In case of images, it resists well against statistical and differential attacks. Comparative Analysis shows evidence of the efficiency of the developed pioneering application model quality and strength compared with that of the existing services. 2013 IEEE. -
Methodologies and Applications of Computational Statistics for Machine Intelligence
With the field of computational statistics growing rapidly, there is a need for capturing the advances and assessing their impact. Advances in simulation and graphical analysis also add to the pace of the statistical analytics field. Computational statistics play a key role in financial applications, particularly risk management and derivative pricing, biological applications including bioinformatics and computational biology, and computer network security applications that touch the lives of people. With high impacting areas such as these, it becomes important to dig deeper into the subject and explore the key areas and their progress in the recent past. Methodologies and Applications of Computational Statistics for Machine Intelligence serves as a guide to the applications of new advances in computational statistics. This text holds an accumulation of the thoughts of multiple experts together, keeping the focus on core computational statistics that apply to all domains. Covering topics including artificial intelligence, deep learning, and trend analysis, this book is an ideal resource for statisticians, computer scientists, mathematicians, lecturers, tutors, researchers, academic and corporate libraries, practitioners, professionals, students, and academicians. 2021, IGI Global. All rights reserved. -
A Novel Approach for Web Mining Taxonomy for High-Performance Computing
Web mining is a central part of data analysis. The fetching and discovering knowledge from the different web data in data mining mechanism is more important nowadays. Web usage mining customs data mining practice for the investigation of custom decoration from different data storages. In this article paper, introducing a new approach for web mining taxonomy for high-performance computing. The primary motivation of this research is on the data collection in different real-time web servers for implementation and analysis. This article is focussed the WebLog Expert lite 9.3 tools for our study. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Trends in Terahertz Biomedical Applications
Terahertz radiation has drawn enormous attention in recent times due to its various application possibilities. This chapter reviews various emerging trends and well-established technologies in Terahertz biomedical. Due to its extraordinary sensing capabilities, non-invasive, non-ionizing properties, sensitive instrumentations for spectroscopy and imaging, Terahertz has found various biomedical sensing applications from biomolecules, proteins to cells and tissues. This chapter highlights terahertz device engineering, system technologies, range of materials, aiming at various biomedical applications. It also includes emerging topics such as terahertz biomedical imaging, pattern recognition and tomographic reconstruction by machine learning and artificial intelligence, for possible biomedical imaging applications. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Tunable graphene nanopatch antenna design for on-chip integrated terahertz detector arrays with potential application in cancer imaging
Aim: Further to our reports on chip-integrable uncooled terahertz microbolometer arrays, compatible with medium-scale semiconductor device fabrication processes, the possibility of the development of chip-integrable medical device is proposed here. Methods: The concept of graphene-based nanopatch antennas with design optimization by the finite element method (FEM) is explored. The high-frequency structure simulator (HFSS) utilized fine FEM solver for analyzing empirical mode decomposition preprocessing and for modeling and simulating graphene antennas. Results: Graphene nanopatch antennas exhibited tunable features with varying patch dimensions and dependence on substrate material permittivity. Conclusion: This work implements reconfigurable graphene nanopatch antenna compatible with terahertz microbolometer arrays. This design concept further develops on-chip medical devices for possible screening of cancer cell with terahertz image processing. 2021 Future Medicine Ltd. -
Optimized Tree Strategy with Principal Component Analysis Using Feature Selection-Based Classification for Newborn Infant's Jaundice Symptoms
One of the most important and difficult research fields is newborn jaundice grading. The mitotic count is an important component in determining the severity of newborn jaundice. The use of principal component analysis (PCA) feature selection and an optimal tree strategy classifier to produce automatic mitotic detection in histopathology images and grading is given. This study makes use of real-time and benchmark datasets, as well as specific approaches for detecting jaundice in newborn newborns. According to research, the quality of the feature may have a negative impact on categorization performance. Additionally, compressing the classification method for exclusive main properties can result in a classification performance bottleneck. As a result, identifying appropriate characteristics for training the classifier is required. By combining a feature selection method with a classification model, this is possible. The major outcomes of this study revealed that image processing techniques are critical for predicting neonatal hyperbilirubinemia. Image processing is a method of translating analogue images to digital formats and manipulating them. The primary goal of medical image processing is to collect information useful for disease detection, diagnosis, monitoring, and therapy. Image datasets can be used to validate the performance of newborn jaundice detection. When compared to conventional approaches, it offers results that are accurate, quick, and time efficient. Accuracy, sensitivity, and specificity, which are common performance indicators, were also predictive. 2021 Debabrata Samanta et al. -
Distributed Feedback Laser (DFB) for Signal Power Amplitude Level Improvement in Long Spectral Band
This study outlines the distributed feedback laser for signal power amplitude level improvement in the long spectral band of 1550 nm wavelength within supporting pumped wavelength of 1480 nm. The bias and modulation peak currents based distributed feedback laser are varied in order to test the signal power level, peak signal amplitude variations after the fiber-optic channel and light detectors. The signal power level, peak signal amplitude is measured against spectral wavelength and time bit period variations. The study emphasis the signal power level, peak signal amplitude are enhanced for the best selection values of both a bias current at 45 mA and modulation peak current at 0.5 mA. 2023 Walter de Gruyter GmbH. All rights reserved. -
A Brief Review onDifferent Machine Learning-Based Intrusion Detection Systems
In the contemporary cybersecurity landscape, the proliferation of complex and sophisticated cyber threats necessitates the development of robust Intrusion Detection Systems (IDS) for safeguarding network infrastructures. These threats make it more challenging to maintain the communitys availability, integrity, and confidentiality. To ensure a secure network, community administrators should implement multiple intrusion detection systems (IDS) to monitor and detect unauthorized and malicious activities. An intrusion detection system examines the networks traffic by analyzing data flowing through computers to identify potential security threats or malicious activities. It alerts administrators when suspicious activities are detected. IDS generally performs two types of malicious activity detection: misuse or signature-based detection, which entails collecting and comparing information to a database of known attack signatures, and anomaly detection, which detects any behavior that differs from the standard activity and assumes it to be malicious. The proposed paper offers an overview of how different Machine Learning Algorithms like Random forest, k - Nearest Neighbor, Decision tree, Support Vector Machine, Naive Bayes, and K- means are used for IDS and how these algorithms perform on different well-known datasets, and Their accuracy and performance are evaluated and compared, providing valuable insights for future work. kNN shows an accuracy of 90.925% for Denial of Service Attacks and 98.244% for User To Root attacks. The SVM algorithm shows an accuracy of 93.051% for Probe attacks and 80.385% accuracy for remote-to-local attacks. According to our implementation, these two algorithms work better than the others. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Optical Character Recognition (OCR) based Vehicle's License Plate Recognition System Using Python and OpenCV
License Platform Detection is a computer technology that enables us to identify digital images on the platform automatically. Different operations are covered in this system, such as imaging, number pad locations, alphanumeric character truncation and OCR. The final objective of the system is to construct and create efficient image processing procedures and techniques to position a licensing platter on the Open Computer View Library picture. It was used and implemented the K-NN algorithm and python programming language. The technology can be used in different industries such as security, highway speed detection, lighting violations, manuscript documents, automatic charging system, etc. Auto plate recognition is an integrated technology which identifies the auto licence plate. Auto plate auto recognition. Multiple applications include complex safety systems, public spaces, parking and urban traffic control. Automatic Vehicle License Plate Recognition (AVLPR) has undesirable aspects because of many effects, such as light and speed. This work presents an alternative technique to leverage free software for the implementation of AVLPR systems including Python and the Open Computer Vision (openCV). 2021 IEEE. -
Comparison of Machine Learning-Based Intrusion Detection Systems Using UNSW-NB15 Dataset
Various machine learning classifiers have been employed recently to enhance network intrusion detection. In the literature, researchers have put forth a wide range of intrusion detection solutions. The accuracy of the machine learning classifiers intrusion detection is limited by the fact that they were trained on dated samples. Therefore, the most recent dataset must be used to train the machine learning classifiers. In this study, UNSW-NB15, machine learning classifiers are trained using the most recent dataset. A taxonomy of classifiers based on eager and lazy learners is used to train the chosen classifiers, such as K-Means (KNN), Polynomial Features, Random Forest (RF), and Naive Bayes (NB), Linear Regression. In order to decrease the redundant and unnecessary features in the UNSW-NB15 dataset, chi-Square, a filter-based feature selection technique, is used in this study. When comparing these machine learning classifiers, performance is measured in terms of accuracy, mean squared error (MSE), precision, recall, and F1-score with or without feature selection technique. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024. -
Enhanced Level Brain Tumor Identification Using CNN, VGG16 and ResNet Models
The comprehension of brain growths is significantly improved through the identification and categorization of these disorders. Still, their discovery is relatively grueling due to their variability in terms of position, shape, and size. Fortunately, deep literacy has revolutionized the field and significantly improved recognition, prediction, and opinion in various healthcare areas, including brain excrescences. The main goal of this study is to thoroughly review exploration that utilizes CNN, VGG16, and RESNET infrastructures to classify brain excrescences using MRI images. The performance of these models varied significantly, with CNN, VGG16, and RESNET achieving an emotional delicacy of 99.6. Additionally, ResNet and VGG16 achieved rigor of 92.4 and 89.7 independently. Likewise, the visualization of the decision-making processes of these models has provided valuable insight into the features they prioritize. By incorporating these models into their practice, healthcare professionals have the opportunity to enhance their individual capabilities, eventually leading to improved patient outcomes. 2024 IEEE. -
AI-based online interview bot with an interactive dashboard
In recent years, video interviews have become increasingly popular in the recruitment process due to their convenience and efficiency. However, evaluating a candidates communication skills and perceived personality traits from a video interview can be challenging. The agent utilizes natural language processing and computer vision techniques to analyze the candidates verbal and nonverbal behavior during the interview. Specifically, the agent focuses on linguistic features such as fluency, grammar, and vocabulary, as well as nonverbal cues such as facial expressions and body language. Based on these features, the agent predicts the candidates communication skills and perceived personality traits. To validate the effectiveness of the agent, a Talk was conducted with a group of participants who completed video interviews with and without the agent. The results show that the agents predictions of communication skills and perceived personality traits are highly correlated with the ratings given by human evaluators. Additionally, the agent is able to provide valuable insights into the candidates performance that may not be immediately apparent to human evaluators. Overall, the intelligent video interview agent proposed here has the potential to improve the recruitment process by providing more accurate and objective evaluations of candidates communication skills and perceived personality traits. 2025 selection and editorial matter, A. Vadivel, K. Meena, P. Sumathy, Henry Selvaraj, P. Shanmugavadivu and Shaila S. G.; individual chapters, the contributors. -
Triple diffusive convection in a vertically oscillating oldroyd-b liquid /
International Journal of Mechanical and Mechatronics Engineering, Vol.12, Issue 9, pp.863-869, ISSN No: 1307-6892. -
Experimenting with ONOS scalability on software defined network
In traditional network, a developer cannot change the configuration of a router with software programs to control the behavior of the network switches due to closed vendor specific configuration scripts. In order to make the routers/switches programmable, a new architecture of network has to be developed and this gave rise to Software defined networks. It is the new architecture for Computer Networks in which, the old traditional architecture is slowly depreciated. It is very difficult to adapt new technology especially to decide upon which controller has to be considered and what may be its scalability to compete the dynamic circumstances of networks. Many researches are working on possible solutions and look upon SDN to overcome the traditional network limitations. There are many SDN controllers existing amongst them, some are OpenDaylight, Floodlight, Onos, Ryu, Beacon etc. From the existing multiple controllers serving the SDN services to the network, Onos is one of the Controller. ONOS can be deployed on Docker container and it is accessed using its IP as a host. In this paper, authors are contributing for the evaluation of the Performance to check the Scalability of ONOS controller by taking many scenarios which are experimented on the simulation tool of Mininet, Onos Controller, Docker and iPerf. ONOS Controller?s simulated environments are observed for its throughput evaluated in dynamic conditions of a network over Mesh topology by gradually increasing the number of hosts until its supported by the system with optimum resource utilization. 2018, Institute of Advanced Scientific Research, Inc.. All rights reserved. -
Design of pseudo stator generator
In today's energy generation scenario, the extensive use of conventional sources are causing lot of environmental issues. It is necessary that humankind should come up with a strategy to produce clean energy. Even though we cannot completely stop relying on non-renewable sources of energy, lot of research is happening to find the ideal substitute for conventional sources of energy and also for migrating towards renewable energy sources from conventional sources. Its gaining popularity because of the fact that availability of fossil fuels are reducing at an alarming rate. Thus, these research works will aid in producing clean energy and also make the existing systems more efficient. A better substitution would be to design a machine which would use less conventional sources of energy and gives required output. Thus it is necessary to come up with a new technology which would suffice the above stated requirements. The proposed project is an novel idea aimed at designing an alternator which has higher power output at lower RPM when compared to conventional alternators. This model finds application in automobiles, WECS, Aerospace, hybrid vehicles in the near future. 2016 IEEE. -
Thermal analysis of nanofluid flow containing gyrotactic microorganisms in bioconvection and second-order slip with convective condition
Bioconvection in magneto-nanoliquid embedded with gyrotactic microorganisms across an elongated sheet with velocity slip of second order is addressed. Nonlinear thermal radiation and chemical reaction aspects are retained in energy and concentration equations. Numerical simulations for the modeled problem are proposed via RungeKuttaFehlberg-based shooting technique. Special attention is given to the impact of involved parameters on the profiles of motile microorganisms, nanoparticle volume fraction, temperature and velocity. Our simulations figured out that assisting flow generates more heat transfer than the opposing flow situation. The motile microorganisms boundary layer decayed for higher bioconvection Peclet and bioconvection Lewis numbers. 2018, Akadiai Kiad Budapest, Hungary. -
Radiative nonlinear 3D flow of ferrofluid with Joule heating, convective condition and Coriolis force
Characteristics of heat transport mechanism in three-dimensional ferrofluid flow past a deformed surface subjected to the Coriolis and Lorentz forces are analyzed. The impacts of Joule heating, nonlinear thermal radiation, viscous dissipation and convective condition are also accounted. The carrier fluid (water) is embedded by Fe3O4 nanoparticles. The boundary layer approximations are employed in problem statement. Stretching transformations are utilized to form nonlinear ODE system from governed PDE system. The subsequent system is treated numerically via Runge-Kutta-Fehlberg method. Effects of relevant parameters on different flow fields are discussed comprehensively with help of graphs. It is established that the heat transfer rate is enhanced due to Coriolis and Lorentz forces. Furthermore, Fe3O4 nanoparticles enhance the Nusselt number significantly in comparison with Al2O3 nanoparticles. 2017 -
Quadratic convective flow of radiated nano-Jeffrey liquid subject to multiple convective conditions and Cattaneo-Christov double diffusion
A nonlinear flow of Jeffrey liquid with Cattaneo-Christov heat flux is investigated in the presence of nanoparticles. The features of thermophoretic and Brownian movement are retained. The effects of nonlinear radiation, magnetohydrodynamic (MHD), and convective conditions are accounted. The conversion of governing equations into ordinary differential equations is prepared via stretching transformations. The consequent equations are solved using the Runge-Kutta-Fehlberg (RKF) method. Impacts of physical constraints on the liquid velocity, the temperature, and the nanoparticle volume fraction are analyzed through graphical illustrations. It is established that the velocity of the liquid and its associated boundary layer width increase with the mixed convection parameter and the Deborah number. 2018, Shanghai University and Springer-Verlag GmbH Germany, part of Springer Nature. -
A comparative study of magnetite and MnZn ferrite nanoliquids flow inspired by nonlinear thermal radiation
The characteristics of the magnetohydrodynamic (MHD) stagnation point flow of ferrofluids are investigated. The effects of nonlinear thermal radiation, heat generation and viscous dissipation are considered. Two different nanoparticles (Fe3O4 and MnZnFe2O4) are comprised in the base fluid (water). The ordinary differential equations are formed using suitable similarity transformations from the governing partial differential equations. The subsequent nonlinear ordinary differential equations are solved numerically using RKF-45 method. The influence of governing parameters on the results are analysed. It is found that the thermal boundary layer thickens due to the influence of nonlinear radiation and heat generation for both the fluids. The rate of heat transfer is higher for MnZn ferrite-nanofluid in comparison with magnetite nanofluid. 2017 by American Scientific Publishers All rights reserved.