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Data Classification and Incremental Clustering Using Unsupervised Learning
Data modelling, which is based on mathematics, statistics, and numerical analysis, is used to look at clustering. Clusters in machine learning allude to hidden patterns; unsupervised learning is used to find clusters, and the resulting system is a data concept. As a result, clustering is the unsupervised discovery of a hidden data concept. The computing needs of clustering analysis are increased becausedata mining deals with massive databases. As a result of these challenges, data mining clustering algorithms that are both powerful and widely applicable have emerged. Clustering is also known as data segmentation in some applications because it splits large datasets into categories based on their similarities. Outliers (values that are far away from any cluster) can be more interesting than typical examples; hence outlier detection can be done using clustering. Outlier detection applications include the identification of credit card fraud and monitoring unlawful activities in Internet commerce.As a result, multiple runs with alternative initial cluster center placements must be scheduled to identify near-optimal solutions using the K-means method. A global K-means algorithm is used to solve this problem, which is a deterministic global optimization approach that uses the K-means algorithm as a local search strategy and does not require any initial parameter values. Insteadof selecting initial values for all cluster centers at random, as most global clustering algorithms do, the proposed technique operates in stages, preferably adding one new cluster center at a time. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Data Augmentation for Handwritten Character Recognition of MODI Script Using Deep Learning Method
Deep learning-based methods such as convolutional neural networks are extensively used for various pattern recognition tasks. To successfully carry out these tasks, a large amount of training data is required. The scarcity of a large number of handwritten images is a major problem in handwritten character recognition; this problem can be tackled using data augmentation techniques. In this paper, we have proposed a convolutional neural network-based character recognition method for MODI script in which the data set is subjected to augmentation. The MODI script was an official script used to write Marathi, until 1950, the script is no more used as an official script. The preparation of a large number of handwritten characters is a tedious and time-consuming task. Data augmentation is very useful in such situations. Our study uses different types of augmentation techniques, such as on-the-fly (real-time) augmentation and off-line method (data set expansion method or traditional method). A performance comparison between these methods is also performed. 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Data and Its Dimensions
In current times Data is the biggest economic opportunity. As per the studies, it is observed that the world is becoming 2.5 quintillions data-rich every day, with an average of every human contributing 1.7MB of data per second. Every individual has a good appetite for data, as it gives immense insight to explore and expand the business. With the invention of smart devices and innovation in the field of connectivity such as 4G-5G Mobile Networks and Wi-Fi, the generation and consumption of the data are steadily increasing. These smart devices continuously generate data, leading to a bigger pool for better decision-making. This chapter presents data, the various forms and sources, and the concept of Data Science; it discusses how the ownership and value of data are decided; and also highlights the use, abuse, and overuse of the data along with data theft, and a case study to represent data breach. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Data Analytics for Social Microblogging Platforms
Data Analysis for Social Microblogging Platforms explores the nature of microblog datasets, also covering the larger field which focuses on information, data and knowledge in the context of natural language processing. The book investigates a range of significant computational techniques which enable data and computer scientists to recognize patterns in these vast datasets, including machine learning, data mining algorithms, rough set and fuzzy set theory, evolutionary computations, combinatorial pattern matching, clustering, summarization and classification. Chapters focus on basic online micro blogging data analysis research methodologies, community detection, summarization application development, performance evaluation and their applications in big data. 2023 Elsevier Inc. All rights reserved. -
Data Analytics and ML for Optimized Performance in Industry 4.0
Industry 4.0, the fourth industrial revolution, has revolutionized manufacturing and production systems by integrating Data Analytics (DA) and Machine Learning (ML) techniques. Predictive maintenance, which predicts equipment malfunctions and schedules maintenance in advance, is a crucial application of DA and ML within Industry 4.0. It reduces downtime, improves productivity, and lowers costs. Demand forecasting, which uses historical data and ML algorithms to predict future product demand, and anomaly detection, which identifies abnormal patterns or events within large datasets, are also critical applications of DA and ML in Industry 4.0. They enhance operational efficiency and reduce costs. However, the adoption of DA and ML presents several challenges for organizations, including infrastructure, personnel, ethical, and privacy concerns. To realize the benefits of DA and ML, companies must invest in appropriate hardware and software and develop the necessary expertise. They must also handle data responsibly and transparently to ensure privacy and ethical standards. Despite these challenges, the integration of DA and ML in Industry 4.0 is critical for optimized performance, improved productivity, and cost savings. 2024 selection and editorial matter, Nidhi Sindhwani, Rohit Anand, A. Shaji George and Digvijay Pandey; individual chapters, the contributors. -
Data Analysis on Hypothyroid Profiles using Machine Learning Algorithms
Machine learning algorithms enable computers to learn from data and continuously enhance performance without explicit programming. Machine learning algorithms have significantly improved the accuracy and efficacy of thyroid diagnosis. This study identified and analysed the usefulness of several machine-learning algorithms in predicting hypothyroid profiles. The main goal of this study was to see the extent to which the algorithms adequately assessed whether a patient had hypothyroidism. Age, sex, health, pregnancy, and other factors are among the many factors considered. Extreme Gradient Boosting Classifier, Logistic Regression, Random Forest, Long-Term Memory, and K-Nearest Neighbors are some of the machine learning methods used. For this work, two datasets were used and analysed. Data on hypothyroidism was gathered via DataHub and Kaggle. These algorithms were applied to the collected data based on metrics such as Precision, Accuracy, F1 score and Recall. The findings showed that the Extreme Gradient Boosting classification method outperformed the others regarding F1 score, accuracy, precision, and recall. The research demonstrated how machine learning algorithms might predict thyroid profiles and identify thyroid-related illnesses. 2023 IEEE. -
Data analysis in road accidents using ann and decision tree
Road accidents have become some of the main causes for fatal death globally. A report tells that road accident is the major cause for high death rate other than wars and diseases. A study by World Health Organization (WHO), Global status report on road safety 2015 says over 1.24 million people die every year due to road accidents worldwide and it even predicts by 2020 this number can even increase by 20-50%. This can affect the GDP of the Country, for developing countries this can affect adversely. This paper shows the use of data analytics techniques to build a prediction model for road accidents, so that these models can be used in real time scenario to make some policies and avoid accidents. This paper has identified the attributes which has high impact on accident severity class label. IAEME Publication. -
Data Analysis and Machine Learning Observation on Production Losses in the Food Processing Industry
Food wastage and capturing lineage from production to consumption is a bigger concern. Yielding, storage and transportation areas have evolved to a great extent associated to manufacturing and automation which lead to technical advancements in food processing industry. In such situation, losses are generally observed in the crop production which are sometimes minimal and ignored. However, in some cases these losses are huge and are becoming a threat to the both producers and consumers. Here we considered data related to dairy products and analysed the production losses especially while processing them in the treating unit. Literature on parameters and associated data analysis in the form of graphical representation are provided in the appropriate sections of the paper. Linear regression and correlation were envisaged in view of incorporating machine learning techniques understanding production losses. Karl Pearson's correlation provides an observation related to association of parameters which are desired to be less coupled in terms of employing proposed newer methodology. 2023 IEEE. -
Data acquisition using NI LabVIEW for test automation
In a fighter aircraft, the pilot's safety is of utmost importance, and the pressure sensing in the pilot's mask is essential for ensuring the pilot's safety. This innovative solution ensures the swift and accurate measurement of pressure, minimizing the risk of potential hazards and enhancing military aviation safety. Additionally, it provides a robust and reliable solution that can withstand the harsh and challenging conditions often encountered in the field. This chapter explores the advanced capabilities and benefits of utilizing the National Instrument USB-6363, programmed with LabVIEW, in military aviation, highlighting its potential for revolutionizing pressure measurement processes in this critical field. It describes a research study on developing a pressure-sensing system for pilot masks using NI USB 6363 and LabVIEW. 2023, IGI Global. -
Dark matter, dark energy, and alternate models: A review
The nature of dark matter (DM) and dark energy (DE) which is supposed to constitute about 95% of the energy density of the universe is still a mystery. There is no shortage of ideas regarding the nature of both. While some candidates for DM are clearly ruled out, there is still a plethora of viable particles that fit the bill. In the context of DE, while current observations favour a cosmological constant picture, there are other competing models that are equally likely. This paper reviews the different possible candidates for DM including exotic candidates and their possible detection. This review also covers the different models for DE and the possibility of unified models for DM and DE. Keeping in mind the negative results in some of the ongoing DM detection experiments, here we also review the possible alternatives to both DM and DE (such as MOND and modifications of general relativity) and possible means of observationally distinguishing between the alternatives. 2017 COSPAR -
DarcyForchheimer Nanoliquid Flow and Radiative Heat Transport over Convectively Heated Surface with Chemical Reaction
Abstract: Improving the heat transport of energy transmission fluids is a vital challenge in numerous engineering applications such as photovoltaic thermal management, heat exchangers, transport and energy-saving processes, solar collectors, automotive refrigeration, electronic equipment refrigeration, and engine applications. Nanofluids address the challenges of thermal management in engineering applications. The DarcyForchheimer flow of magneto-nanofluid initiated by a stretched plate is investigated with application of the Buongiorno model. The features of the nth order chemical reaction, Rosseland thermal energy radiation, and non-uniform heat sink/source are also scrutinized. The Buongiorno nanoliquid model is implemented, which includes the frenzied motion of the nanoparticles and the thermal diffusion of the nanoparticles (NPs). Thermal and solutal convection heating boundary conditions are also incorporated. Boundary layer approximations are used in the mathematical derivation. The non-linear control problem is deciphered with application of the RungeKutta shooting method (RKSM). The results for the relevant parameters are analyzed in dimensionless profiles. In addition, the friction factor on the plate, the heat transport rate, and the mass transport rate of the nanoparticles are calculated and analyzed. 2022, Pleiades Publishing, Ltd. -
Dandelion Algorithm for Optimal Location and Sizing of Battery Energy Storage Systemsin Electrical Distribution Networks
This paper describes a new way to improve the performance of an EDN by integrating distributed battery energy storage systems (BESs) in the best way possible. This method is based on the Dandelion Algorithm (DA). The search space for BES locations is first predetermined using loss sensitivity factors (LSFs), and then DA is used to determine the optimal locations and sizes. The reduction of real power distribution loss is regarded as the primary objective function, and the impact of BESs is extended to examine the network voltage profile, voltage stability, and GHG emissions. IEEE 33-busEDN is used to calculate the computational efficiency of LSF-DA. Results show that DA is more efficient than Archimedes optimization (AOA), future search algorithm(FSA), pathfinder algorithm(PFA), and butterfly optimization algorithm(BOA) algorithms. Furthermore, the results show that the proposed DA enhances all technological and environmental factors and RDN performance. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Dampers to Suppress Vibrations in Hydro Turbine-Generator Shaft Due to Subsynchronous Resonance
There are numerous applications to evaluate the damage caused by subsynchronous resonance (SSR) to a turbine-generator shaft. Despite multiple applications, there are relatively few studies on shaft misalignment in the literature. In this paper, stresses in the existing turbine-generator shaft due to subsynchronous resonance were studied using finite element analysis (FEA). The 3D finite element model reveals that the most stressed part of the shaft is near the generator terminal. A new nonlinear damping scheme is modeled to reflect the torsional interaction and to suppress the mechanical vibration caused by subsynchronous resonance (SSR). Stresses developed due to the addition of capacitors in the system at high rotational speeds and deformation of the shaft during various modes of oscillations were evaluated. Experimental investigations are carried out in reaction turbine connected to a 3kVA generator. Simulation is carried out for the experimental setup using ANSYS. According to the simulation results, the damper installed near the generator terminal provides satisfactory damping performance and the subsynchronous oscillations are suppressed. 2021, Springer Nature Singapore Pte Ltd. -
Damaged Relay Station: EEG Neurofeedback Training in Isolated Bilateral Paramedian Thalamic Infarct
Stroke is a major public health concern and leads to significant disability. Bilateral thalamic infarcts are rare and can result in severe and chronic cognitive and behavioral disturbances - apathy, personality change, executive dysfunctions, and anterograde amnesia. There is a paucity of literature on neuropsychological rehabilitation in patients with bilateral thalamic infarcts. Mr. M., a 51 years old, married male, a mechanical engineer, working as a supervisor was referred for neuropsychological assessment and rehabilitation with the diagnosis of bilateral paramedian thalamic infarct after seven months of stroke. A pre-post comprehensive neuropsychological assessment of his cognition, mood, and behavior was carried out. The patient received 40 sessions of EEG-Neurofeedback Training. The results showed significant improvement in sleep, motivation, and executive functions, however, there was no significant improvement in memory. The case represents the challenges in the memory rehabilitation of patients with bilateral thalamic lesions. 2024 Neurology India, Neurological Society of India. -
Cytokine see-saw across pregnancy, its related complexities and consequences
During pregnancy, a woman's immune system adapts to the changing hormonal concentrations, causing immunologic transition. These immunologic changes are required for a full-term pregnancy, preserving the fetus' innate and adaptive immunity. Preterm labor, miscarriage, gestational diabetes mellitus, and pre-eclampsia are all caused by abnormal cytokine expression during pregnancy and childbirth. A disruption in the cytokine balance can lead to autoimmune diseases or microbiologic infections, or to autoimmune illness remission during pregnancy with postpartum recurrence. The cytokine treatments are essential and damaging to the developing fetus. The current review summarizes the known research on cytokine changes during pregnancy and their possible consequences for pregnant women. Studies suggest that customizing medication for each woman and her progesterone levels should be based on the cytokine profile of each pregnant woman. Immune cells and chemicals play an important function in development of the placenta and embryo. During pregnancy, T cells divide and move, and a careful balance between proinflammatory and anti-inflammatory cytokines is necessary. The present review focuses on the mother's endurance in generating fetal cells and the immunologic mechanism involved. 2022 International Federation of Gynecology and Obstetrics. -
Cytogenetic Consequences Of Food Industry Workers Occupationally Exposed To Cooking Oil Fumes (Cofs)
Background: Cooking oil fumes (COFs) with smoking habits is a substantial risk that aggravates genetic modifications. The current study was to estimate the biological markers of genetic toxicity counting Micronucleus changes (MN), Chromosome Aberrations (CA) and DNA modifications among COFs exposures and control subjects inherent from South India. Materials and Methods: Present analysis comprised 212 COFs with tobacco users and equivalent number of control subjects. Results: High frequency of CA (Chromatid type: and chromosome type) were identified in group II experimental subjects also high amount of MN and DNA damage frequency were significantly (p < 0.05) in both subjects (experimental smokers and non-smokers). Present analysis was observed absence of consciousnessamong the COFs exposures about the destructive level of health effects of tobacco habits in working environment. Conclusion: COFs exposed workers with tobacco induce the significant alteration in chromosomal level. Furthermore, a high level of rate of genetic diseases (spontaneous abortion) were identified in the experimental subjects. This finding will be helpful for preventive measures of COFs exposed workers and supportive for further molecular analysis 2021,Asian Pacific Journal of Cancer Prevention. All Rights Reserved. -
Cyclic property of iterative eccentrication of a graph
The eccentric graph of a graph G, denoted by Ge, is a derived graph with the vertex set same as that of G and two vertices in Ge are adjacent if one of them is an eccentric vertex of the other. The process of constructing iterative eccentric graphs, denoted by Gek is called eccentrication. A graph G is said to be ?-cyclic(t,l) if G,Ge,Ge2,...,Gek,Gek+1,...,Gek+l are the only non-isomorphic graphs, and the graph Gek+l+1 is isomorphic to Gek. In this paper, we prove the existence of an ?-cycle for any simple graph. The importance of this result lies in the fact that the enumeration of eccentrication of a graph reduces to a finite problem. Furthermore, the enumeration of a corresponding sequence of graph parameters such as chromatic number, domination number, independence number, minimum and maximum degree, etc., reduces to a finite problem. 2023 World Scientific Publishing Company. -
Cybersecurity Threats Detection in Intelligent Networks using Predictive Analytics Approaches
The modern scenario of network vulnerabilities necessitates the adoption of sophisticated detection and mitigation strategies. Predictive analytics is surfaced to be a powerful tool in the fight against cybercrime, offering unparalleled capabilities for automating tasks, analyzing vast amounts of data, and identifying complex patterns that might elude human analysts. This paper presents a comprehensive overview of how AI is transforming the field of cybersecurity. Machine intelligence can bring revolution to cybersecurity by providing advanced defense capabilities. Addressing ethical concerns, ensuring model explainability, and fostering collaboration between researchers and developers are crucial for maximizing the positive impact of AI in this critical domain. 2024 IEEE. -
Cybersecurity Disclosure and Corporate Reputation: Rising Popularity of Cybersecurity in the Business World
This chapter emphasizes the importance of cybersecurity for a corporation as todays organizations are more vulnerable than ever and their enemies are in the form of viruses and malware. The work provides evidence that cybersecurity can have an impact on brand value, market value, and overall corporate reputation. It focuses on depicting the global scenario with reference to cybersecurity disclosures by corporations and how it is important in todays digitized era where data is the most valuable and vulnerable asset. With rapid digitalization, cybersecurity has become a major concern for all businesses, especially when there is financial and reputational damage to cybersecurity breaches and incidents. Even in the absence of clear cybersecurity laws and regulations, corporations are opting for voluntary disclosure. Existing literature explains this as an attempt to mitigate any potential risk or occurred risk through increased transparency which will build the trust of all stakeholders. 2023 by IGI Global. All rights reserved. -
Cybercrimes in the Associated World
Phrases that scarcely existed a decade ago are now a part of our day-to-day lifestyle, as criminals use malicious new technologies to commit cyber attacks against businesses, individuals, and governments. These crimes cause serious harm and impose real threats to victims worldwide either physically or virtually. There are no borders in cyberspace. Attacks can come from any place and at any time. Cybercrime can take many forms, but they all have a digital platform/environment in common. It can be done with both good and bad intentions. But, nowadays, the most common types of cybercrime activities such as phishing scams, identity theft, Internet frauds, online intellectual property or patent infringements, online harassment, and cyber stalking are sadly very widespread in todays associated world. Cyber bullying and online harassment activities spread casually in social media posts and comments or through direct messages and also via emails. The main motive of these messages is to threaten either an individual or a group. Such kinds of cybercrime activities are extremely damaging to the victims mental health. Government agencies working to investigate cybercrimes have reported multiple records of victims developing mental illnesses and even ending up committing suicide. On the other hand we have phishing scams, one of the widespread crime activities. Organizations have detected an increase in the ratio of phishing emails to professional emails from unknown or anonymous service providers appending fake attachments and invoices. These files and attachments may contain malicious payloads to scam people and to create a backdoor in that system, so the attacker can gain access to the system anytime and from anywhere without the victims knowledge. This has been considered as one of the major advantages for the attacker. Cybercrimes have not restricted to only these forms of criminal activities. A wide variety of new attacks have been created and have spread all over the world through commonly used platforms such as social media sites, blogs, and news portals. We are living in a digital world where all our activities are being monitored by someone, somewhere - even keystrokes are being monitored using keyloggers. Nothing seems to be secret and protected unless you are tech savvy. National agencies are keeping a close watch on all individual online activities to prevent illegal activities from happening. No longer the delete option is possible in this digital world; rather, only migration of data from one location to another or from a local server to a cloud server is possible. In our day-to-day lives, several new viruses and attack mechanisms are triggered by attackers by following very new tactics with the help of more complex algorithms. So, its time to advance our knowledge on protecting our valuable assets by spending time in learning and following proper online practices. 2024 selection and editorial matter, S. Vijayalakshmi, P. Durgadevi, Lija Jacob, Balamurugan Balusamy, and Parma Nand; individual chapters, the contributors.