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Computationally Efficient Machine Learning Methodology for Indian Nobel Laureate Classification
A computationally efficient methodology for Indian Nobel Laureate classification is proposed in this study, emphasizing the optimization of image categorization through supervised learning techniques. Leveraging advancements in Convolutional Neural Networks (CNNs), the research aims to enhance the efficiency and precision of image classification tasks. The study utilizes Logistic Regression for dataset analysis, initially employing browser extensions for mass downloading categorized image data. Haar cascade classifiers are then used for data wrangling, focusing on facial, nose, and mouth recognition. Following this, feature engineering through wavelet transformation reduces image dimensionality, preparing the dataset for the chosen ML model, Logistic Regression. The primary focus is to simplify technology for improved image categorization. Support Vector Machines (SVM), Random Forest, and Logistic Regression are examined, with Logistic Regression emerging as the most effective model, achieving an accuracy rate of 87.5%. A thorough evaluation using Confusion Matrices reveals Logistic Regression's superior performance in classifying images of Indian Nobel laureates. A strategic up-sampling approach is implemented to address dataset inconsistencies, ensuring balanced representation across classes. The Haar wavelet transform is then applied for feature extraction, optimizing the dataset for ML models. The dataset is split into training and testing sets (80-20), and the three models are trained and evaluated for accuracy. Logistic Regression proves to be the best performer, offering insights into prominent leaders' identification. The research offers a detailed pipeline for data preprocessing, feature engineering, and model assessment, culminating in a robust image categorization system. Logistic Regression emerges as a reliable method for biographical picture identification, demonstrating superior accuracy over SVM and Random Forest. This research underscores the importance of efficient and accurate image classification methodologies for practical applications in real-world scenarios, particularly in recognizing influential leaders. 2024 IEEE. -
Computationally efficient wavelet domain solver for florescence diffuse optical tomography
Estrogen induced proliferation of mutant cells is a growth signal hallmark of breast cancer. Fluorescent molecule that can tag Estrogen Receptor (ER) can be effectively used for detecting cancerous tissue at an early stage. A novel targetspecific NIRf dye conjugate aimed at measuring ER status was synthesized by ester formation between 17-? estradiol and a hydrophilic derivative of ICG, cyanine dye, bis-1,1-(4-sulfobutyl) indotricarbocyanine-5-carboxylic acid, sodium salt. In-vitro studies provided specific binding on ER+ve [MCF-7] cells clearly indicating nuclear localization of the dye for ER+ve as compared to plasma level staining for MDAMB-231. Furthermore, cancer prone cells showed 4.5-fold increase in fluorescence signal intensity compared to control.; A model of breast phantom was simulated to study the in-vivo efficiency of dye with the parameters of dye obtained from photo-physical and in-vitro studies. The excitation (754 nm) and emission (787 nm) equation are solved independently using parallel processing strategies. The results were obtained by carrying out wavelet transformation on forward and the inverse data sets. An improvisation of the Information content of system matrix was suggested in wavelet domain. The inverse problem was addressed using LevenbergMarquardt (LM) procedure with the minimization of objective function using Tikhonov approach. The multi resolution property of wavelet transform was explored in reducing error and increasing computational efficiency. Our results were compared with the single resolution approach on various parameters like computational time, error function, and Normalized Root Mean Square (NRMS) error. A model with background absorption coefficient of 0.01 mm-1 with anomalies of 0.02 mm-1 with constant reduced scattering of 2.0 mm for different concentration of dye was compared in the result. The reconstructed optical properties were in concurrence with the tissue property at 787 nm. We intend our future plans on in-vivo study on developing a complete instrumentation for imaging a target specific lipophilic dye. Springer International Publishing Switzerland 2014. -
Computer Assisted Unsupervised Extraction and Validation Technique for Brain Images from MRI
Magnetic Resonance Imaging (MRI) of human is a developing field in medical research because it assists in considering the brain anomalies. To identify and analyze brain anomalies, the research requires brain extraction. Brain extraction is a significant clinical image handling method for quick conclusion with clinical perception for quantitative assessment. Automated methods of extracting brain from MRI are challenging, due to the connected pixel intensity information for various regions such as skull, sub head and neck tissues. This paper presents a fully automated extraction of brain area from MRI. The steps involved in developing the method to extract brain area, includes image contrast limited using histogram, background suppression using average filtering, pixel region growing method by finding pixel intensity similarity and filling discontinuity inside brain region. Twenty volumes of brain slices are utilized in this research method. The outcome is achieved by this method is approved by comparing with manually extracted slices. The test results confirm the performance of this strategy can effectively section brain from MRI. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Computer modelling of trace SO2 and NO2 removal from flue gases by utilizing Zn(ii) MOF catalysts
SO2 and NO2 capture and conversion have been investigated via density functional theory (DFT) and grand canonical Monte Carlo (GCMC) simulations using a novel hydrogen-bonded 3D metal-organic framework (MOF) containing a Zn(ii) centre and a partially fluorinated (polar -CF3) long-chain dicarboxylate ligand with a melamine (basic -NH2) co-ligand. Initially, computational single-component isotherms have been determined for SO2 and NO2 gases. These simulations have shown exothermic adsorption enthalpies of ?36.4 and ?28.6 kJ mol?1 for SO2 and NO2, respectively. They have also indicated that SO2 has a high affinity for polar -CF3 and basic -NH2 binding sites of the ligand in the framework pore walls. The lower adsorption capacity of NO2 compared with SO2 is due to weaker electrostatic interactions with the framework. Furthermore, MOF adsorbent selectivity for removing trace amounts of SO2 and NO2 in flue gases has been estimated through the co-adsorption of ternary gas mixtures (SO2/CO2/N2 and NO2/CO2/N2). Together with DFT, the climbing image nudged elastic band (CI-NEB) method has been used for investigating the plausible mechanisms for HbMOF1 catalyzed cycloadditions of SO2 and NO2 with epoxides leading to the formation of cyclic sulphites and nitrates, respectively. 2023 The Royal Society of Chemistry. -
Computer simulation of diesel fueled engine processes using matlab and experimental investigations on research engine
The depletion of conventional fuel source at a fast rate and increasing environmental pollution have motivated extensive research in combustion modeling and energy efficient engine design. In the present work, a computer simulation incorporating progressive combustion model using thermodynamic equations has been carried out using MATLAB to evaluate the performance of a diesel engine. Simulations at constant speed and variable load have been carried out for the experimental engine available in the laboratory. For simulation, speed and Air/Fuel ratios, which are measured during the experiment, have been used as input apart from other geometrical details. A state-of-the-art experimental facility has been developed in-house. The facility comprises of a hundred horsepower water cooled eddy current dynamometer with appropriate electronic controllers. A normal load test has been carried out and the required parameters were measured. A six gas analyzer was used for the measurement of NOx, HC, CO2, O2, CO and SOx. and a smoke meter was used for smoke opacity. The predicted Pressure-Volume (PV) diagram was compared with measurements and found to match closely. It is concluded that the developed simulation software could be used to get quick results for parametric studies. Copyright 2017 ASME. -
Computer Vision Based Automatic Margin Computation Model for Digital Document Images
Margin, in typography, is described as the space between the text content and the document edges and is often essential information for the consumer of the document, digital or physical. In the present age of digital disruption, it is customary to store and retrieve documents digitally and retrieve information automatically from the documents when necessary. Margin is one such non-textual information that becomes important for some business processes, and the demand for computing margins algorithmically mounts to facilitate RPA. We propose a computer vision-based text localization model, utilizing classical DIP techniques such as smoothing, thresholding, and morphological transformation to programmatically compute the top, left, right, and bottom margins within a digital document image. The proposed model has been experimented with different noise filters and structural elements of various shapes and size to finalize the bilateral filter and lines and structural elements for the removal of noises most commonly occurring due to scans. The proposed model is targeted towards text document images and not the natural scene images. Hence, the existing benchmark models developed for text localization in natural scene images have not performed with the expected accuracy. The model is validated with 485 document images of a real-time business process of a reputed TI company. The results show that 91.34 % of the document images have conferred more than 90 % IoU value which is well beyond the accuracy range determined by the company for that specific process. 2023, Crown. -
Computer-based Intelligence and Security
There is a massive increase in the incidence of cyberattacks day by day in the modern enterprise environment. Although humans are behind this task due to the rapid growth in the incidence human intervention is unable to control this. Therefore, something more than human intervention is required to have a check over it. With cyberattacks evolving at a rapid pace and with the increase in the use of devices in todays world, Artificial Intelligence (AI) and Machine Learning (ML) can help to have a check over cybercrime incidences, automate the process of detection of threat, and handle these in a better way as compared to the conventional methods used for controlling cybercrime and cyberattack. AI and ML has shown good results in data information security as these technologies are capable of analyzing a large and wide variety of data and can track threats related to cyberattacks that may cause phishing attacks. As these technologies are capable of learning and improving from past experiences, they can even predict and tell the new variety of attacks that may occur in the coming days. This chapter describes the use of AI in controlling cyberattacks and cybercrime and the expert views on this matter. 2024 selection and editorial matter, S. Vijayalakshmi, P. Durgadevi, Lija Jacob, Balamurugan Balusamy, and Parma Nand; individual chapters, the contributors. -
Computerized grading of brain tumors supplemented by artificial intelligence
For effective diagnosis of health conditions, there is a need to process medical images to obtain meaningful information. The diagnosis of brain tumors begins with magnetic resonance imaging (or MRI) scan. This is followed by segmentation of the medical images so obtained which can prove cumbersome if it were to be performed manually. Determining the best approach to do segmentation remains challenge among multiple computerized approaches. This paper combines both the identification and classification of tumors from the MRI results and is backed by a cloud-based framework to provision the same. The phase of extraction of features includes the utilization of a Hadoop framework and Gabor filter along with variations in terms of orientation and scale. Artificial bee colony algorithm and support vector machine classifier have been used to designate the degree of optimal features and categorize the same. The grading of brain tumors from MRI images can be fulfilled by the aforementioned approach. The said approach is believed to deliver promising results in terms of accuracy, which has also been verified experimentally. 2019, Springer-Verlag GmbH Germany, part of Springer Nature. -
Concentration-dependent luminescence characterization of terbium-doped strontium aluminate nanophosphors
The present investigation describes the synthesis of luminescent terbium-doped strontium aluminate nanoparticles emitting bright green light, which were synthesized through a solid-state reaction method assisted by microwave radiation. Various samples containing different concentrations of Tb were synthesized, and an analysis of their structural and morphological features was conducted using powder x-ray diffraction, Fourier transform infrared spectroscopy and field emission scanning electron microscopy. The band gaps of the samples were determined utilizing the KubelkaMunk method. The quenching mechanism observed was identified to be due to dipoledipole interaction using the Dexter theory. The optimized sample with a terbium concentration of 4at.% has a luminescence lifetime of 1.05 ms with 20.62% quantum efficiency. The results of this study indicate that the terbium-doped strontium aluminate fluorescent nanoparticles exhibit promising potential for a wide range of applications, including bioimaging, sensing and solid-state lighting. 2024 John Wiley & Sons Ltd. -
Concept Drift Detection for Social Media: A Survey
The research over information retrieval from social media data has progressed for streaming data since the last decade. Recently, academic researchers have witnessed users' changing topics, trends, and intent on social media. This change of information with time takes into account the temporal attribute for real-time data, and thus, advances in this domain are exponentially growing. Although concept drift is still not explored due to a shortage of available datasets, concept drift for social media is minimally explored. This manuscript makes attempts to identify the types of concept drift for social media data, discuss the historical perspective of concept drift on social media, and enlist the possible research directions. 2021 IEEE. -
Concept Mapping of Issues of Students Life in University
The undergraduate student body forms around 85.9% of the total number of students enrolled in India, which is a significant population. It has become imperative to understand the issues that these students face during their undergraduate years as a precursor to developing mechanisms and strategies to enable student progress, both academically and developmentally. This study aimed at developing a concept map to outline the various aspects and issues of the undergraduate students life in India utilizing the concept mapping method. Data from participants (n = 141) at different phases was analysed resulting in 49 unique life issues and aspects and 8 clusters. The emerging issues have relevance and implications for teachers, parents, administrators and other stakeholders in structuring and developing services targeted towards undergraduate students in India. 2015, National Academy of Psychology (NAOP) India. -
Conceptual comprehension analysis of a student using soft cosine measure
Knowledge is the substantial wealth of a man and he possesses an innate thirst to acquire it. Knowledge embodies facts or ideas acquired through study, investigation and observation or experience. In this context, technology with its varied techniques comprising endless algorithms in natural language processing (NLP) plays an imperative role in the pursuit of knowledge. Inferences thus gathered are a clear pointer to the content teaching of students. Soft cosine measure algorithm is used in this analysis process to provide an answer regarding the grasping ability of each student with optimum learner participation and creativity. After each lecture, students have to upload their corresponding notes and this in turn would be compared with the teacher's lecture notes. The soft cosine computation gives individual results, on how much each student has comprehended a concept. This new methodology is a much awaited contribution of the educational field. 2022 Author(s). -
Conclusion
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Conclusion
We all can agree at one point: the COVID-19 pandemic has had a massive and unanticipated impact on all the lives of all tourists. The global tourism and hospitalityindustry has been heavily damaged, but the societal impact cannot be overlooked. Consumer behavior, and ultimately consumer spending, has been and will continue to change, and company planning must adapt to these new realities. The major findings of this edited book in the contexts of tourism, destination recovery and crisis management thus have value for the industry and for researchers seeking to understand these changes. Chapter 1 analyses evolution of tourism and hospitality during times of crisis and how these businesses might rebound. Academics in the field of tourism and hospitality can use this collection to understand the most recent studies on crises and recovery. The impact of the COVID-19 crisis on tourism and hospitality was examined in several published pieces. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022. -
Conclusion
Digital data produced through data-processing algorithms has fundamental advantages of transportability, proficiency, and accuracy; but on the other hand, the data thus produced brings in several redundancies. To solve this challenging problem with data transmission in network surroundings, research on information security and forensics provides efficient solutions that can shield the privacy, reliability, and accessibility of digital information from malicious intentions. Despite two decades of rigorous research, multicarrier communications still suffer from high complexity and low convergence, which have an immense practical impact. It is also more challenging to ensure proper transmission of multimodal data. Novel techniques have been proposed that can effectively abate these problems and provide good symbol error rate performance. The singularity expansion method provides a superior way to identify targets. This type of method may be useful for addressing contemporary problems faced by radar and antenna researchers. 2021 John Wiley & Sons Ltd. -
Conclusion
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Conclusion and future research directions
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Concurrent design, modeling and analysis of Microelectromechanical Systems products - Design for 'X' abilities
In this paper, we present the need for concurrent engineering in Microelectromechanical System (MEMS) device and product development. MEMS system is considered as six subsystems: micromachined element design subsystem, microelectronics circuit design subsystem, fabrication subsystem, packaging subsystem, materials subsystem and environment subsystem. Design for 'X' abilities is addressed by considering six subsystems/abilities. A concurrent model is developed using graph theory to show the interaction between subsystems. This work utilizes the advantages of the graph theoretic approach to consider all design aspects together in a single methodology with the help of a multinomial defined using matrix algebra. The design index developed using the proposed methodology shows the interaction among the subsystems and indicates whether the overall design is acceptable or not, by considering all the aspects related to micromachined element design, microelectronics circuit design, fabrication, packaging, materials, environment etc. A MEMS based RF power sensor is designed and the proposed methodology is explained. Simulated results of the RF MEMS power sensor are presented to validate the proposed methodology. A power sensor with VSWR of 1.08002 is reported. 2012 Bentham Science Publishers. -
Conducting Polymers: A Versatile Material for Biomedical Applications
Conducting polymers (CPs) are organic polymers with metallic conductivity or semiconducting properties which have drawn considerable attention globally. They are versatile materials because of their excellent environmental stability, electrical conductivity, economic importance as well as optical and electronic properties. CPs are interesting because they can be functionalized in several ways and the chemical properties are fine-tuned by incorporating new functionalities, making them more suitable in biomedical and other applications. They act as appropriate mediums of biomolecules and can be employed to improve the speed, stability, and sensitivity of various biomedical devices. They can transit between conducting and semiconducting states and have the ability to change mechanical properties by regulated doping, chemical modifications, etc. In this paper, we review the potential biomedical uses of conducting polymers such as smart textiles, bioactuators, hydrogels, and the use of CPs in neural prosthetic devices. 2022 Wiley-VCH GmbH. -
Conducting polymers: A versatile material for biomedical applications /
ChemistrySelect, Vol.7, Issue 42, ISSN No: 2365-6549.
Conducting polymers (CPs) are organic polymers with metallic conductivity or semiconducting properties which have drawn considerable attention globally. They are versatile materials because of their excellent environmental stability, electrical conductivity, economic importance as well as optical and electronic properties. CPs are interesting because they can be functionalized in several ways and the chemical properties are fine-tuned by incorporating new functionalities, making them more suitable in biomedical and other applications.