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Data Encryption Algorithm for Local Area Network (LAN)
The volume of traffic moving over the Internet is expanding exponentially every day due to increase in communication through Emails, branch offices remotely connect to their corporate network and commercial transactions. Hence protection of networks and their services from unauthorized modification and destruction is very much needed. TCPIP is the most commonly used communication protocol in the Internet domain. IP packets are exchanged between the end hosts as plain text (without any encryption). As Internet uses PSDN (Packet Switching Data Network) anybody who has access to PSDN can access/modify the data. Hence securing data over the network is difficult. The goal of network security is to provide, authenticity, confidentiality and integrity. Confidentiality is making sure that no body other than the receiver will be able to read the data. Integrity is making sure that the data didnt get modified by intruder or by some other means while it is getting transmitted. Authenticity is making sure that the data is coming from the right sender. In this paper we propose a new data encryption algorithm based on private key (symmetric key) cryptography method. Keys are shared between two end hosts using simple algorithm. Cyber block chaining method is used while encrypting/decrypting the data. Large prime numbers are generated well in advance and kept for further key refreshments. The keys were refreshed periodically, so it gives very minimal time for the hackers to attack the system. As simple operations are used, we will be able to achieve fast and secure data encryption/decryption using this method. The behavior of the proposed approach is verified through various tests. -
Data Economy: Data and Money
The article explores the concept of data economy, which is based on the sharing of data across platforms and ecosystems. Data has evolved from factual information to a new asset for companies worldwide, and the article discusses its evolution from brittle paper records to complex databases and algorithms like blockchain. With a prediction of a data explosion of about 175 zettabytes by 2025, data is used extensively in various domains, from agriculture to healthcare. The article also discusses how the data economy is not domain-specific but is a universal shift as all companies transition to become technology-driven companies. The data network effect is a cycle that uses data to acquire service users and generate more data. This has become a B2B service model that has added profits to various tech giants balance sheets. The article concludes by exploring the current need for data sharing across organizations and the future scope of the data economy. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Data Driven Emergency Response Management for UAV-Based Future Transportation A Case Study
The transportation sector plays a vital role and is heavily involved in emergency response and disaster relief because of the necessity for quick deployment, evacuation and rescue operations, supply chain support, infrastructure restoration, aerial support and surveillance, traffic management, and route optimization, among other things. All of these are areas that can greatly benefit from the use of drones. The capabilities of drones provide an efficient and speedy option for surveying impacted regions, spotting possible dangers, finding survivors, accessing dangerous or inaccessible locations, delivering supplies, and generally facilitating relief efforts with speed and minimal human risk. UAV (Unmanned aerial vehicle)-based transportation can become a precise substitute for time-sensitive items, emergency relief, medical supplies, and more by overcoming obstacles like traffic and geographical accessibility. The success of emergency response management for UAV-based transportation is dependent upon the quality of the data collected and the effectiveness of its analysis and interpretation. Hence, in this chapter, we propose to analyze and explore the challenges and issues involved in the design and implementation of a data-driven emergency response management system for UAV-based future transportation and its applications. 2026 Scrivener Publishing LLC. -
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 in Diabetes Treatment: Approaches and Applications
These days, managing diabetes has become particularly more beneficial with advancements in data analytics, using machine learning, predictive analytics, and patient-generated health data to optimize the outcome for patients. This paper explores the latest techniques and innovations in this field, including predictive modelling, wearable technology integration, and artificial intelligence for better personalized care. The study covers various analytical frameworks, compares the performance of multiple machine learning models, and discusses future directions in the integration of data analytics with telemedicine for diabetes care. The following words refer to diabetes management, data analytics, predictive simulation, AI, and algorithms for learning, wearable technologies, patient-generated health data, predictive analytics, continuous glucose monitoring, health informatics, personalized care, health data privacy, predictive algorithms, electronic health records, diabetes complications, telemedicine, feature selection, and model evaluation along with having patient-centric systems and chronic disease management. 2026 American Institute of Physics Inc.. All rights reserved. -
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 Analytics and Automation for a Broadband Franchise
Its challenging to envision a world without the internet. From acquiring knowledge to ordering food, our lives have become incredibly convenient thanks to it. As technology advances, internet access is becoming easier and more affordable, with India being renowned for having the lowest internet costs. Internet service providers (ISPs) aim to offer better speeds, fewer disruptions, and professional service. They charge fees for allowing customers to shop online, browse the web, stay connected with loved ones, and conduct business. Due to a lack of data comprehension, the company struggles to leverage reports in daily operations. Consequently, BSNL is finding it hard to outshine competitors and become profitable. Key highlights from the project include understanding customer mentality and addressing issues faced by franchise owners. This research aimed to enhance organizational operations by reducing manual interventions and automating customer communication. User sentiments toward the brand and its competitors were analyzed, and exploratory data analysis was conducted to assess the organizations position. Data visualization with Tableau and Python programming were utilized to derive insights from the data. 2025 selection and editorial matter, Shruti Sharma, Ashutosh Sharma, and Trinh Van Chien. -
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 -
DarcyForchheimerBrinkman flow of a Newtonian fluid through an enclosure with two straight boundaries and one curved boundary
The study examines the flow and heat transfer of a Newtonian fluid in a porous medium inside an enclosure with two straight boundaries and one curved boundary. This setup is important for heat storage and energy systems. The aim of this study is to solve the Brinkman-Forchheimer (BF) equation in an enclosure with two straight and one curved boundary. The research also looks to perform a thorough heat transfer analysis to improve the understanding of thermal behaviour in porous medium BF flow. Additionally, the study calculates the Nusselt number using a compatibility condition to ensure the results are physically consistent. Finally, it fits the Nusselt number as a function of the shape factor (s) and the Forchheimer number (F). This helps in capturing the trends in convective heat transfer behaviour within the medium.The main assumptions include a steady, fully developed flow in the z-direction with a constant axial pressure gradient -, and zero axial velocity (w = 0) on all boundaries. The domain in three-dimensions is defined in cartesian coordinates (x,y,z), with on the curved boundary,ensuring the spatial constraint of the geometry. The quasi-linearisation method is used to linearise the governing equations, resulting in a system of linear algebraic equations that is subsequently solved using the alternate direction implicit (ADI) method with an accuracy of. The findings show that an increase in the shape factor (s) results in a plug flow behaviour and better heat retention, as in higher temperature profiles and centreline velocities. In contrast, higher Forchheimer numbers causes a drop in both velocity and temperature due to increased flow resistance. But as F goes up, the Nusselt number always increases, meaning heat is better transferred through convection. The study also shows that hot spots and heat islands form inside the enclosure, especially when the shape factor is higher, because the heat builds up more quickly when there is less resistance, which is an essential thing to think about for things like heat storage systems, where it is crucial to have better thermal efficiency. The Author(s), under exclusive licence to Springer Nature India Private Limited 2025. -
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.