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Cinnamon A Competent Drug: A Review on Extraction, Analysis and Anticancer Action
Cinnamon, an Indigenous species, is extensively used as a folk medicine in India, China, and other parts of the world due to its therapeutic potential inherited via the latent chemical composition. The vital component presented is cinnamaldehyde, along with cinnamic acid and cinnamate, which contributes to being an anti-inflammatory, antimicrobial, antidiabetic, and anticancer agent together with the capability to control neurological syndromes like Alzheimer's and even Parkinson's diseases. Given the importance of the anticarcinogenic properties of cinnamon on various cell strains concerning the curable effect, this review focuses on evaluating different extraction methods like steam distillation, Soxhlet extraction, microwave-assisted extraction, and more, in addition to a summary of new technologies like gas chromatography, HPLC, DART-MS, and NMR, etc. which paved the way in characterizing the chemical composition of cinnamon. Cinnamaldehyde showed its apoptosis through various mechanistic pathways on an adequate number of cell lines and antineoplastic potential on specific multifaceted cancerous cells, which advocates for continued research and investment in this vital area of drug discovery and suggestions for future scope. 2024 Wiley-VCH GmbH. -
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. -
Circuit Breaker: A Resilience Mechanism for Cloud Native Architecture
Over the past decade, the utilization of cloud native applications has gained significant prominence, leading many organizations to swiftly transition towards developing software applications that leverage the powerful, accessible, and efficient cloud infrastructure. As these applications are deployed in distributed environments, there arises a need for reliable mechanisms to ensure their availability and dependability. Among these mechanisms, the circuit breaker pattern has emerged as a crucial element in constructing resilient and trustworthy cloud native applications in recent times. This research article presents a comprehensive review and analysis of circuit breaker patterns and their role within cloud-native applications. The study delves into various aspects of circuit breakers, encompassing their design, implementation, and recommended practices for their utilization in cloud native applications. Additionally, the article examines and compares different circuit breaker libraries available for employment in modern software development. The paper also presents a concept for improving the circuit breaker pattern, which will be pursued in our upcoming research. 2023 IEEE. -
Circular supply chains in manufacturingQuo vadis? Accomplishments, challenges and future opportunities
Circular approach in manufacturing supply chain (SC) operations yields multiple benefits through optimal utilisation and consumption of resources. This study maps the scope and structure of circularity in the manufacturing SC discipline and explores the evolution of the domain over time. We review 946 journal articles published between 2013 and September 2023. Our study identifies key drivers and barriers to circular economy (CE) deployment in manufacturing SC operations, bibliometric parameters, emerging research themes, decision support tools, theories and applications. Using the theory extension approach, we propose a strategic framework to fortify the deployment of circularity in SCs. This comprehensive study renders a methodological contribution through combined descriptive content analysis and bibliometric and network analyses to evaluate the circular manufacturing SC operations concepts, theories and applications. We posit that manufacturing firms require to deploy innovation-led approaches to embed the CE strategies in their SC operations. We find that the studies investigating green skill development and circularity-culture adoption can facilitate manufacturers to understand the efficacy of circularity in their SC operations. The findings of this study can facilitate the practitioners to identify the links between the CE approaches and their strategic implications and examine CE implementation at the strategic level. 2024 The Authors. Business Strategy and The Environment published by ERP Environment and John Wiley & Sons Ltd. -
Citizen data in distributed computing environments: Privacy and protection mechanisms
Data security is paramount in the increasingly connected world. Securing data, while in transit and rest, and while under usage, is essentialfor deriving actionable insights out of data heaps. Incorrect or wrong data can lead to incorrect decisions. So, the confidentiality and integrity of data have to be guaranteed through a host of technology-inspired security solutions. Organizational data is kept confidentially by the businesses and governments, often in distant locations (e.g., in cloud environments), though more sensitive data is normally kept in house. As the security mechanisms are getting more sophisticated, cyber security attacks are also becoming more intensive, so there is a constant battle between the organisations and the hackers to be one step ahead of the other. In this chapter, the aim is to discuss various mechanisms of accomplishing citizens ' data confidentiality and privacy and to present solution approaches for ensuring impenetrable security for personal data. 2021 by IGI Global. All rights reserved. -
Citizens Perception on Livability in Indian Metropolitan Cities
Background: Livability is a complex and multifaceted concept, but is important in creating places where people can thrive. Investing in the factors contributing to livability can create more attractive, sustainable, and equitable communities. Livability is important for several reasons. It can affect peoples health, well-being, and productivity. It can also influence whether people live in a particular place and whether businesses are willing to invest there. Methods: A survey was conducted to review the top livable cities of India as per the Global Livability Index parameters such as sociocultural, environmental, healthcare, education, and infrastructure aspects. A mixed method approach, having both qualitative and quantitative analyses, was adopted for the survey. The targeted sample size was 100 people, covering stakeholders from around the campus. Findings: As an outcome of the survey analysis, the most livable cities were categorized. Conclusion: In conclusion, policy narratives and frameworks are outlined emphasizing the need and the importance of citizens participation in assessing the quality of life across Indian metropolitan cities. 2024 Springer Publishing Company. -
Citric-Acid-Catalyzed Green and Sustainable Synthesis of Novel Functionalized Pyrano[2, 3-e]pyrimidin- and Pyrano[2, 3-d]pyrazol-amines in Water via One-Pot Multicomponent Approaches
An efficient entry into the preparation of elusive, novel pyrano[2, 3-e]pyrimidin-amines and pyrano[2, 3-d]pyrazol-amines has been accomplished using citric acid as a green catalyst in aqueous medium at 25 C. The strategy successively tolerates a variety of functional groups and interestingly, it is eco-compatible, environment-friendly, propitious and the products are obtained in excellent yields without chromatographic purification. The current methodology unfolds the benefits of citric acid as an effective, expeditious, economical, green catalyst and thus adheres to the principles of green chemistry. Ecstatically, the reaction was scaled to the gram level ascertaining the wide applicability of the protocol in academia and industry. The green metrics (E-factor: 0.0497, Mass intensity: 1.1022, PMI: 1.0497 and Emw: 0.0497) for the reaction was also envisaged and the pathway was found to acquaint excellent green chemistry metrics. 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim -
Citrus for wellness: Exploring the bioactive properties of Citrus medica fruit peel with emphasis on its anticancer, antioxidant, antimicrobial and anthelmintic properties
Citrus medica (Citron) is an underutilised plant consisting of various bioactive elements with numerous medicinal benefits. The present study aimed to evaluate the bioactive properties, including anthelmintic, antimicrobial, antioxidant and anticancer activities, of chloroform extract obtained from the of fruit peel of C. medica. The different types of phytochemicals present in the chloroform extract were analysed using GC-MS. The major components detected included n-hexadecanoic acid, octadecanoic acid, t-tetradecenal, 1-nonadecene etc. Anthelmintic study was conducted using Eisenia fetida as a test organism, revealing a significant anthelmintic effect in the C. medica fruit peel extract compared to the standard drug. Antimicrobial properties were assessed against five test bacterial and fungal strains. Antibacterial tests showed zones of inhibition ranging from 8 to 11 mm, while no prominent zones of inhibition were observed in antifungal tests. The DPPH assay demonstrated significant antioxidant properties of Citron fruit peel extract compared to the standard ascorbic acid. The Chloroform extract of citron fruit peel exhibited significant cytotoxic properties against FaDu (human hypopharyngeal tumour) cell line. The present study indicates the potential of the chloroform extract of C. medica fruit peel to be employed as an anthelmintic, antibacterial, antioxidant and anticancer agent. Hence, it emphasises the prominence that can be given to the dietary consumption of citrus fruit peel in various forms, such as dried peel, powder etc. The Author(s). -
Citrus Medica-derived Fluorescent Carbon Dots for the Imaging of Vigna Radiate Root Cells
Bio-imaging is a crucial tool for researchers in the fields of cell biology and developmental biomedical sector. Among the various available imaging techniques, fluorescence based imaging stands out due to its high sensitivity and specificity. However, traditional fluorescent materials used in biological imaging often suffer from issues such as photostability and biocompatibility. Moreover, plant tissues contain compounds that cause autofluorescence and light scattering, which can hinder fluorescence microscopy effectiveness. This study explores the development of fluorescent carbon dots (Cm-CDs) synthesized from Citrus medica fruit extract for the fluorescence imaging of Vigna radiata root cells. The successful synthesis of CDs with an average size of 6.7nm is confirmed by Transmission Electron Microscopy (TEM). The X-ray diffraction (XRD) analysis and raman spectroscopy indicated that the obtained CDs are amorphous in nature. The presence of various functional groups on the surface of CDs were identified by Fourier transform infrared (FTIR) spectra. The optical characteristics of Cm-CDs were studied by UV-Visible spectroscopy and photoluminescence spectroscopy. Cm-CDs demonstrated strong excitation-dependent fluorescence, good solubility, and effective penetration in to the Vigna radiata root cells with multicolor luminescence, and addressed autofluorescence issues. Additionally, a comparative analysis determined the optimal concentration for high-resolution, multi-color root cell imaging, with Cm-CD2 (2.5mg/ml) exhibiting the highest photoluminescence (PL) intensity. These findings highlight the potential of Cm-CDs in enhancing direct endocytosis and overcoming autofluorescence in plant cell imaging, offering promising advancements for cell biology research. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Clan Culture in Organizational Leadership and Strategic Emphases: Expectations Among School Teachers in India
Understanding the current and preferred organizational culture among school teachers in India is a primary requirement, particularly when the National Educational Policy (NEP) is being implemented. Measuring the competing values using the Organizational Culture Assessment Instrument (OCAI) provides information about the dominant characteristics of the organizational culture and the school teachers' preferences. We surveyed school teachers and received 273 responses. Research revealed that clan culture is the overall current and preferred organizational culture type. Many of the results are not a surprise. However, we found that organizational leadership is currently in the hierarchy culture and strategic emphasis is on adhocracy, whereas teachers prefer a clan culture on these dimensions. Teachers expect school leaders to be the ones who facilitate the path to achieve, provide mentoring, and are instrumental in team building. They prefer a culture that provides for the development of human capital, promotes high trust and transparency among teachers, and offers an opportunity to participate in decision-making. This study is unique as it measures schools' organizational culture that has not been done earlier in the Indian context. The results suggest implications on the leadership practices and the strategic emphasis that need to change, in order to facilitate the implementation of the National Education Policy (NEP). 2022. All Rights Reserved. -
Classification Algorithms Used in the Study of EEG-Based Epileptic Seizure Detection
Epilepsy is a neurological illness that has become more frequent around the world. Nearly 80% of epileptic seizure sufferers live in low- and middle-income nations. In persons with encephalopathy, the risk of dying prematurely is three times higher than in the general population. Three-quarters of people with brain illnesses in low-income countries do not receive the treatment they require. Recurrent seizures are a symptom of epilepsy, characterized by strange bursts of excess energy in mind. Experts agree that most people diagnosed with epilepsy may be managed successfully, provided the episodes are discovered early on. As a result, machine learning plays an essential role in seizure detection and diagnosis. Support Vector Machine(SVM), Extreme Gradient Boosting(Xgboost), Decision Tree Classifier, Linear Discriminant Analysis(LDA), Perceptron, Naive Bayes Classifier, k-Nearest Neighbor(k-NN), and Logistic Regression are eight of the most widely used machine learning classification algorithms used to classify EEG based mostly Epileptic Seizures. Almost all classifiers, according to the study, give an efficient process. Despite this, the results show that SVM is the most effective method for detecting epileptic seizures, with a 96.84% accuracy rate. For diagnosing Epileptic Seizures using EEG signals, the perceptron model has a lower accuracy of 76.21% percent. 2021 IEEE. -
Classification and analysis of Alzheimer's Disease using Deep Learning methods on MRI and PET
Alzheimer's disease (AD) falls in the category of neurodegenerative illness in which an individual loses his or her power to remember things and behaviors. It affects memory in younger patients and as it progresses causes diffuse cortical functions. However, a major issue with the diagnosis and treatment of AD symptoms is that it has complex pathogenesis because of which there is no clinical intervention for its treatment. There is no disease-modifying treatment to cure AD symptoms that increases co-morbidities among the patients. The present research identified this gap and focuses on using Deep Learning methods on MRI and PET data so that there is early diagnosis of AD by healthcare experts and they could propose a better treatment process for reducing AD symptoms. The present research identified that by using deep learning-based approaches particularly ResNet50 architecture, there is the execution of quantitative assessment of brain MRI and PET to acquire insights about the internal abnormalities through self-learning features. It will help in initiating proper treatment and avoiding damage to the brain further. 2022 IEEE. -
Classification and characterization using HCT/HFOSC spectra of carbon stars selected from the HES survey
We present results from the analysis of 88 carbon stars selected from Hamburg/ESO (HES) survey using low-resolution spectra (R ?1330 & 2190). The spectra were obtained with the Himalayan Faint Object Spectrograph Camera (HFOSC) attached to the 2-m Himalayan Chandra Telescope (HCT). Using well-defined spectral criteria based on the strength of carbon molecular bands, the stars are classified into different groups. In our sample, we have identified 53 CH stars, four C-R stars, and two C-N type stars. Twenty-nine stars could not be classified due to the absence of prominent C2 molecular bands in their spectra. We could derive the atmospheric parameters for 36 stars. The surface temperature was determined using photometric calibrations and synthesis of the H-alpha line profile. The surface gravity log g estimates are obtained using parallax estimates from the Gaia DR3 database whenever possible. Microturbulent velocity (?) was derived using calibration equation of log g & ? . We could determine metallicity for 48 objects from near-infrared Ca II triplet features using calibration equations. The derived metallicity ranges from ?0.43 ? [Fe/H] ? ?3.49. Nineteen objects were found to be metal-poor ([Fe/H] ? ?1), 14 very metal-poor ([Fe/H] ? ?2), and five extremely metal-poor ([Fe/H] ? ?3.0) stars. Eleven objects were found to have a metallicity in the range ?0.43 ? [Fe/H] ? ?0.97. We could derive the carbon abundance for 25 objects using the spectrum synthesis calculation of the C2 band around 5165 The most metal-poor objects found will make important targets for follow-up detailed chemical composition studies based on high-resolution spectroscopy, and are likely to provide insight into the Galactic chemical evolution. 2024, The Author(s), under exclusive licence to Springer Nature B.V. -
Classification and correlational analysis on lower spine parameters using data mining techniques
The application of data mining in the field of medical science is slowly gaining popularity. This is due to the fact that enormous statistical inferences from data related to the human body and medicine was a possible with high accuracy rates which was a tedious task in the past. This had led to discoveries and breakthroughs which has saved thousands of lives. Lower back pain is one of the most common issues faced by majority of the population throughout the world. The early detection and treatment of LBP can avoid life threatening issues in the body. Objective: This study aims to create a classification model which can be used to detect an unhealthy spine using the lumbar and sacral parameters. Correlational analysis was performed between different attributes to find distinguishing factors between healthy and unhealthy spine. Method: Classification methods were used such as decision tree and SVM. Correlational analysis was performed using pearson method between each attribute. Results: After creating the model using the different classification methods it was found that Ctree produced the highest accuracy with 92.80% on average. It was also found that there were 6 attribute pairs that had high correlation coefficient to distinguish unhealthy and healthy spine observations. BEIESP. -
Classification and Retrieval of Research Classification and Retrieval of Research Papers: A Semantic Hierarchical Approach
"Classification and Retrieval of Research papers: A Semantic Hierarchical Approach" demonstrates an effective and efficient technique for classification of Research documents pertaining to Computer Science. The explosion in the number of documents and research publications in electronic form and the need to perform a semantic search for their retrieval has been the incentive for this research. The popularity and the widespread use of electronic documents and publications, has necessitated the development of an efficient document archival and retrieval mechanism. Categorizing journal papers by assigning them relevant and meaningful classes, predicting the latent concept or the topic of research, based on the relevant terms and assigning the appropriate Classification labels is the objective of this thesis. This thesis takes a semantic approach and applies the text mining techniques in a hierarchical manner in order to classify the documents. The use of a lexicon containing domain specific terms (DSL) adds a semantic dimension to classification and document retrieval. The Concept Prediction based on Term Relevance (CPTR) technique demonstrates a semantic model for assigning concepts or topics to papers. This Thesis proposes a conceptual framework for organizing and classifying the research papers pertaining to Computer Science. The efficacy of the proposed concepts is demonstrated with the help of Classification experiments. Classification experiments reveal that the DSL technique of training works efficiently when categorization is based on keywords. The CPTR technique, on the other hand, shows very high accuracy; when the classification is based on the contents of the document. Both these techniques lend a semantic dimension to classification. Narrowing down the scope of search at each level of hierarchy enables time efficient retrieval and access of the goal documents. The hierarchical interface for Document Retrieval enables retrieval of the target documents by gradually restricting the scope of search at each level of hierarchy This work comprises of two main components. 1. The Framework for Hierarchical Classification. 2. The Hierarchical Interface for Document Retrieval. Two distinct techniques for classification are proposed in this thesis. These include 1. The use of Domain Specific Lexicon (DSL) which is comparable to a Domain Specific Ontology. 2. The Concept Prediction Based on Term Relevance (CPTR) technique These techniques lend a semantic dimension to classification. Keywords: Text Mining, Classification, Document Retrieval, Hierarchical, Domain specific lexicon (DSL), Probabilistic Latent Semantic Analysis (PLSA) , Concept Prediction based on Term frequency (CPTR) -
Classification Framework for Fraud Detection Using Hidden Markov Model
Machine learning is described as a computer program that learns from experience E with regard to some task T and some performance measure P, if its performance on T improves with E as measured by P. Suppose we have a credit card fraud detection which watches which transactions we mark as fraud or not, and on the basis, it knows how to filter better fraudulent transactions then, E is watching your transactions is fraud or not, T is classifying your transactions as fraud or not, P is number of transactions correctly differentiated as spam or not spam. Machine learning has two types: supervised learning and unsupervised learning. Supervised learning is the type of machine learning where machine is provided with input mapped with its output, and these inputs and outputs are used to make a machine learn a particular function from the trained dataset. There are two branches of supervised learning, i.e., classification and regression. In unsupervised learning, we do not supervise model instead we allow machine to work on its own to discover information. Clustering is type of unsupervised learning. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Classification of a New-Born Infant's Jaundice Symptoms Using a Binary Spring Search Algorithm with Machine Learning
A yellowing of the skin and eyes, called jaundice, is the consequence of an abnormally high bilirubin concentration in the blood. All across the world, both newborns and adults are afflicted by this illness. Jaundice is common in new-borns because their undeveloped livers have an imbalanced metabolic rate. Kernicterus is caused by a delay in detecting jaundice in a newborn, which can lead to other complications. The degree to which a newborn is affected by jaundice depends in large part on the mitotic count. Nonetheless, a promising tool is early diagnosis using AI-based applications. It is straightforward to implement, does not require any special skills, and comes at a minimal cost. The demand for AI in healthcare has led to the realisation that it may have practical applications in the medical industry. Using a deep learning algorithm, we created a method to categorise jaundice cases. In this study, we suggest using the binary spring search procedure (BSSA) to identify features and the XGBoost classifier to grade histopathology images automatically for mitotic activity. This investigation employs real-time and benchmark datasets, in addition to targeted methods, for identifying jaundice in infants. Evidence suggests that feature quality can have a negative effect on classification accuracy. Furthermore, a bottleneck in classification performance may emerge from compressing the classification approach for unique key attributes. Therefore, it is necessary to discover relevant features to use in classifier training. This can be achieved by integrating a feature selection strategy with a classification classical. Important findings from this study included the use of image processing methods in predicting neonatal hyperbilirubinemia. Image processing involves converting photos from analogue to digital form in order to edit them. Medical image processing aims to acquire data that can be used in the detection, diagnosis, monitoring, and treatment of disease. Newburn jaundice detection accuracy can be verified using image datasets. As opposed to more traditional methods, it produces more precise, timely, and cost-effective outcomes. Common performance metrics such as accuracy, sensitivity, and specificity were also predictive. 2023 Lavoisier. All rights reserved. -
Classification of adaptive back propagation neural network along with fuzzy logic in chronic kidney disease
A steady deterioration in kidney function over months or years is known as chronic kidney disease (CKD). Through a range of techniques, such as pharmacological intervention in moderate cases and hemodialysis and renal transport in severe cases, early identification of CKD is crucial and has a substantial influence on reducing the patient's health development. The outcomes show the patient's kidneys' present state. It is suggested to develop a system for detecting chronic renal disease using machine learning. Finding the best feature sets typically involves using metaheuristic algorithms since feature selection is an NP-hard issue with amorphous polynomials. Semi-crystalline tabu search (TS) is frequently used for both local and global searches. In this study, we employ a brand-new hybrid TS with stochastic diffusion search (SDX)-based feature selection. The adaptive backpropagation neural network (ABPNN-ANFIS) is then classified using fuzzy logic. Fuzzy logic may be used to combine the ABPNN findings. Consequently, these techniques can aid experts in determining the stage of chronic renal disease. The Adaptive Neuron Clearing Inference System (ABPNN-ANFIS) was utilised to develop adaptive inverse neural networks using the MATLAB programme. The outcomes demonstrate that the suggested ABPNN-ANFIS is 98 % accurate in terms of efficiency. 2024 -
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Classification of Alzheimer's Disease Stages Using Machine Learning Techniques
Alzheimer s disease (AD) is a type of mental disorder which deteriorates the normal functioning of human brain by reducing the memory capacity of an individual. Age is the most common factor for AD and this disease cannot be reversed or stopped. Doctors can only treat the symptoms of AD which include personality changes and brain structural changes. Analyzing neuro-degenerative disorders, neuroimaging plays an important role in diagnosing subjects with AD and other stages of AD. The proposed research identified this gap and using MRI and PET newlineimages for recognizing AD in its early occurrences by the professionals. This helps in tailoring an appropriate treatment procedure for treating AD. As per literature survey, many researchers have worked with convolutional methods like inbuilt skull stripping with two or more conversions and classified with different CNN architectures. The proposed research experimented advanced skull stripping method and classified using deep learning architectures. This research emphasizes the implementation of ResNet50 architecture with T1 weighted MRI and Amyloid PET images for detecting the abnormalities in the brain patterns based on the image attributes. For the proposed experiment, a total of 5000 T1 weighted MRI data and 3000 newlineAmyloid PET data were used. The collected images were pre-processed with noise removal newlinetechniques and skull stripping method. The ResNet50 is used to classify AD from the data newlineobtained from the ADNI dataset. Pre-processed images /data were fed to the tuned for three class classification on ADNI image data at 200 Epochs shows the accuracy of 97.3% for T1 weighted MRI data and 98% for Amyloid PET data. The experimental results of the proposed model prove that it classifies the images according to various stages with better accuracy than the other existing models by achieving excellent results.