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An Iot Application to Monitor the Variation in Pressure to Prevent the Risk of Pressure Ulcers in Elderly
Pressure sores are a common form of skin problem which occurs with patients who are bedridden or immobile. It is believed that the occurrence of ulcers due to pressure can be prevented. Making best use of resources available and providing comfort to the patient, it is very much important to identify people at risk and provide preventive measures. This work is associated with a method to analyze pressure from pressure points on bedridden patients. A system is presented in this work that continuously monitors the pressure from pressure points using force sensors and sends an alarm to the nurses or caretakers if there is a variation in the pressure exerted on a specific area. 2018 IEEE. -
An iot based wearable device for healthcare monitoring
Nowadays IoT (Internet of Things) devices are popularly used to monitor humans remotely in the healthcare sector. There are many IoT devices that are being introduced to collect data from human beings in a different scenario. These devices are embedded with sensors and controllers in them to collect data. These devices help to support many applications like a simple counting step to an advanced rehabilitation for athletes. In this research work, a mini wearable device is designed with multiple sensors and a controller. The sensors sense the environment and the controller collects data from all the sensors and sends them to the cloud in order to do the analysis related to the application. The implemented wearable device is a pair of footwear, that consists of five force sensors, one gyroscope, and one accelerometer in each leg. This prototype is built using a Wi-Fi enabled controller to send the data remotely to the cloud. The collected data can be downloaded as xlsx file from the cloud and can be used for different analyses related to the applications. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
An IoT-Based Model for Pothole Detection
Maintenance of the good roads plays a very important role in the growth of the country. Poorly maintained roads can lead to potholes which causes severe accidents. To overcome the damage caused by poor roads, the pothole detection model has been proposed in this paper. In recent days, the Internet of Things (IoT)-embedded models are developed in different applications. The main objective of the proposed work is to design the IoT prototype to collect data which can be used to detect potholes and humps. This prototype is embedded with three sensors, namely accelerometer, ultrasonic sensor, and GPS. The data from these sensors is collected by the controller and transmitted by Wi-Fi module to store in the cloud. The collected data can be downloaded as a spreadsheet from the cloud and can be used for different data analysis applications like pothole notifier application. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
An IoT-Based System for Fault Detection and Diagnosis in Solar PV Panels
This abstract describes an IoT-based system for fault detection and diagnosis in solar PV panels. The proposed Fuzzy logic-based fault detection algorithms aims to improve the performance and reliability of solar PV panels, which can be affected by various faults such as shading, soiling, degradation, and electrical faults. The system includes wireless sensor nodes that are deployed on the panels to collect data on their electrical parameters and environmental conditions, such as temperature, irradiance, and humidity. The collected data is then transmitted to a central server for processing and analysis using machine learning algorithms. The system can detect and diagnose faults in real-time, and provide alerts and recommendations to maintenance personnel to take appropriate actions to prevent further damage or downtime. The system has several advantages over traditional manual inspection and maintenance methods, including reduced downtime, lower maintenance costs, and improved energy efficiency. The proposed system has been validated through experimental tests, and the results show that it can accurately detect and diagnose faults in solar PV panels with high reliability and efficiency. 2023 EDP Sciences. All rights reserved. -
An Novel Cutting Edge ANN Machine Learning Algorithm for Sepsis Early Prediction and Diagnosis
Early detection and diagnosis of sepsis can significantly improve patient outcomes, but current diagnostic methods are limited. The problem addressed in this paper is the early detection and diagnosis of sepsis using machine learning algorithms. Sepsis is a life-threatening condition that can rapidly progress and cause organ failure, leading to increased mortality rates. Early detection and treatment of sepsis are critical for improving patient outcomes and reducing healthcare costs. However, sepsis can be challenging to diagnose, and existing methods have limitations in terms of accuracy and timeliness This research proposes a new cutting-edge Optimized Artificial Neural Network machine learning algorithm for sepsis early prediction and diagnosis. The proposed algorithm combines different data sources, including patient vital signs, laboratory results, and clinical notes, to predict the likelihood of sepsis development. The algorithm was evaluated on a large dataset of patient records and achieved promising results in terms of accuracy, Precision and Recall. The proposed algorithm can potentially serve as a valuable tool for clinicians in the early detection and diagnosis of sepsis, leading to better patient outcomes. 2023 American Institute of Physics Inc.. All rights reserved. -
An online signature method using DNA based bio-hash for positive identification and non-repudiation
This work focuses on using biological data as a unique feature to generate e-Signature. DNA, the blue print of life is of unique nature. The signature created using biological data will be difficult to repudiate in the scenario of a legal dispute. Applications of human DNA are not limited to molecular biology, with the advents of fast growing technologies it is possible to inject DNA into e-Signature for positive identification. The proposed methodology uses Signature DNA as a unique biological feature for the registrant. This work has various phases, the first phase includes creating the Signature DNA using hybridized unique DNA segments of the individual (Registrant) which is the unique identification of the user and difficult to duplicate and repudiate. It generates a Bio-Hash of the Signature DNA. The DNA-Hash generated serves for positive identification of the user which computed with the hash of the e-Document and a random value serve as a Bio-Sign (e-Signature) for the e-Document in the second phase. Bio-Sign converted into QR code with a link to the e-Sign service providers website will ensure usability for verification. In the verification phase the verifier scans the QR code which connects to the e-Service provider's web link. The service provider computes and verifies the document and ensures the e-Signature is valid or not to the verifier. If the signer repudiates the signature, positive identification using DNA helps to achieve Non-Repudiation, the last phase. In the scenario of a legal dispute, the registrant cannot repudiate as the authorities can provide positive identification using the DNA Signature for greater assurance. The proposed technique ensures authentication, integrity and Non-Repudiation with Zero knowledge scenario to the verifier. 2017 IEEE. -
An Optimal Load Balancing Framework for Fog-Assisted Smart Grid Applications
The growth of the Internet of Things (IoT) causes a significant amount of data to come in from physical devices and sensors, which adds to the latency and processing delays in smart grid applications. The pay-per-model method of transmitting gathered data that cloud computing offers improves scalability and functionality for end devices, which increases smart grid efficiency. Milliseconds matter in the crucial realms of load balancing, resource usage, and distribution systems, where any latency or jitter is unacceptable. By strategically positioning processing, networking, storage, and communication capabilities at the network edge, fog computing, an outgrowth of cloud technology, successfully addresses current issues in service groups. This paper introduces a unique hybrid framework on a highly virtualized platform and proposes three potential load balancing algorithms: throttled, Round Robin, and a novel Equilibrium Optimizer with Simulated Annealing (EO-SA). The article provides a comprehensive investigation on several load balancing techniques for obtaining optimized services in a smart grid environment thereby focusing on better utilization of network resources and reduction of costs. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
An Optimized Algorithm for Selecting Stable Multipath Routing in MANET Using Proficient Multipath Routing and Glowworm Detection Techniques
Mobile Ad Hoc Networks (MANETs) depend on the selected and constant path with an extended period and the flexibility of the battery power condensed in searching end nodes, leading to numerous link failures. This kind of link damages occurs, and it also affects the packet success rate. We presented a Proficient Multipath Routing and Glowworm detection (PMGWD) technique to overcome such a Manets failure. Initially, a proposed Proficient Multipath Routing (PMR) technique identifies the damaged or failure routes and continues communication inefficiently. Secondly, the Glowworm detection node technique is implemented for both fault node identification and for extending the nodes network lifetime. Another reason to select the glowworm optimization is to update the node based on the glow to improve its neighbor its search space. Lastly, the PMGWD technique is utilized for identifying an optimal route and fault nodes in the manet. It is achieved to correct the identification of fault nodes using the glowworm detection node technique, and it helps to explore more paths for the optimal route by using proficient multipath routing. Hence, this proposed PMGWD technique is used to perform a problem-free communication process in a network system. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
An Outlook of Gender Differential Happiness in India
Studies on happiness and subjective wellbeing, in general, are aplenty, but applying a gender lens to it is comparatively rare, especially in the Indian context. The social construction of gender roles will influence happiness being a subjective matter. This paper explores this idea of gender differential happiness in light of India's peculiar social and cultural context. Using the World Value Survey (WVS) for India (Wave 6) in 2012 and Ordinary Least Square (OLS) regression analysis, the study finds that self-reported happiness is gender differential in India. Factors such as marital status, educational attainments, managerial roles and thrust on women empowerment were found to be vital for happiness for all. However, there are visible patriarchal gender stereotype notions with factors such as individual autonomy and homemaking. 2024 IEEE. -
An Outlook on Sustainable Business Practices through Virtual Reality Marketing
Technologies and businesses blend progressively and work towards creating a sustainable future through the company's marketing strategies. The purpose of the study is to find out the various sustainable outcomes of Virtual Reality Marketing (VRM). The exploratory research identified immersive experience, experiential economy, positive image creation, positive travel decisions, and repeat purchase as the constructs of VRM, and a total of 418 people were surveyed to analyze those constructs. The data were analyzed through statistical tests such as t-test, One-way ANOVA, and Chi-square with the help of SPSS software. The study shows a positive relationship between customers and virtual reality marketing. The results predict that businesses that have incorporated VRM tend to likely have a high-profit margin and more sustainable returns compared to their peer competitors. 2024 IEEE. -
An overview of nanomaterial technologies in the management of wastewater treatment
Nanomaterials are the foundation stone of nanotechnology. It is a broad and trans disciplinary section of exploration. Its developmental commotion has been rising intensively across the world in the last few years. Applications of nanotechnology are abundant in various fields like medicines, electronics, machines, and so on. This paper describes one such application that is nanotechnology in wastewater treatment viz nanosorption, nanophotocatalysis and Nano membrane technology. The Nano compounds engaged in such treatments have been discussed here. As of now the world is in distress from the non-Availability of drinking water and even the very existence of drinking water in nature are becoming toxic due to the addition of chemicals and heavy metals by man-made and by natural happenings as well. The distinctive properties of nanomaterial such as surface area, competent to toil at required and even at low concentration and their potential have prodigious prospects to reform wastewater treatment. Carbon based nanomaterial, metal oxide nanomaterials and nanomebranes have been discussed at this juncture. There are several disputes in treating wastewater with nanomaterials such as insufficient information about the nanomaterial. Still researchers have a lack of knowledge about how these materials are travelling and the effect of nanomaterial on human health. Though the nanostructured catalytic membranes, Nano sorbents and nanophotocatalyst are the established methods to eliminate water pollutants from wastewater, they need more energy and additional investment. 2021 Elsevier Ltd. All rights reserved. -
An Predictive Deep Learning Model is used to Identify Human Tissue-Specific Regulatory Variations For Diabetes
A predictive deep learning model is designed to predict a target variable based on a set of input variables to diagnose the tissue base regulatory variants in the human islets. In this article, the identification on human tissue-specific regulatory variations for Diabetes using the Pima dataset converting data into images, and then the input variables may include genetic data, gene expression data, and the proposed model uses Pima Indian dataset with the attributes such as age, sex, and BMI to predict whether a person has Diabetes or not. And this dataset is incorporated a combination two layered ResNet18 + ResNet50 and SVM classifier. The results obtained are compared with KNN, Naive bayes, SVM Random Forest, Gradient descent and the accuracy achieved is 98%. 2023 IEEE. -
Analyses of the Power Flow through Distributed Generator based on Unsynchronized Measurements
Based on measurements taken from the main substation and the connections between distributed generators and micro-grids that are not in sync, this study suggests a new way to look at the load flow of distributed generation. The conclusions are based on data from a distribution generatora's Load Flow Analysis that was not in sync. Distributed generation is what this approach is based on. Creating a strong communication system and using measurement data from the past are two ways to make this happen. This objective may be achieved with the use of previously gathered measurements. The time-tested backward-forward sweep method is the method of choice for analyzing power flow using unsynchronized data. This is the preferred approach. The angles of synchronization are likely to be unknowns that must be estimated. On a smart grid system with a large number of distributed generation and microgrids, a range of mathematical computations are conducted to verify the correctness of performance predictions produced by the suggested theory. The classic backward-forward sweep was shown to be the most effective method for analyzing power flow based on data that was not synchronized in many instances. This is the strategy that is presently being recommended. Because the angles of synchronization are presumed to be unknown, a mathematical equation must be devised to determine them. The Authors, published by EDP Sciences, 2024. -
Analysing Collaborative Contributions and Sentiments in the Quantum Computing Ecosystem
Quantum computing, a revolutionary paradigm leveraging the principles of quantum mechanics, has emerged as a transformative technology with the potential to solve complex problems at unparalleled speeds. Within the quantum computing ecosystem, companies and research institutes play pivotal roles in advancing hardware, algorithms, and applications. This research explores the transformative landscape of quantum computing, focusing on key contributors such as Google, IBM, D-Wave, Azure, Amazon, Intel, EeroQ, and IonQ. Through sentiment analysis, topic modelling, and thematic analysis, the study aims to comprehensively understand the current state and trends within the quantum computing ecosystem. The findings unveil an overall positive sentiment and identified topics ranging from cloud computing services to quantum computing advancements. Thematic analysis provides actionable insights, emphasizing collaboration within the ecosystem. Rooted in the analysis of secondary data from key companies' articles, the methodology establishes a robust framework for discerning contributions, collaborations, and strategic orientations in quantum computing. 2024 IEEE. -
Analysing Crypto Trends: Unveiling Ethereum and Bitcoin Price Forecasts Through Analytics-Driven Weighted Moving Averages
This research meticulously analyses the performance dynamics of two paramount cryptocurrencies, Bitcoin and Ethereum, over 2,682 observations. Preliminary findings indicate a near alignment in the mean returns of both assets, with Ethereum marginally outperforming Bitcoin. Interestingly, Ethereums superior returns are accompanied by heightened volatility, underlined by its more significant standard deviation. Both cryptocurrencies manifest negative skewness, hinting at a proclivity for negative returns, with Bitcoin showing a sharper skew. Their pronounced kurtosis values attest to the potential for extreme price swings. Regarding forecasting efficacy, the Weighted Moving Average (WMA) method emerges as superior for both assets, yielding the most accurate predictions. At the same time, the Exponential Moving Average (EMA) demonstrates the highest forecast errors. Further, the Relative Strength Index (RSI) evaluation suggests Ethereum may be oversold, alluding to potential investment opportunities. In contrast, Bitcoin, with its mid-range RSI, resides in a neutral zone devoid of clear market signals. The findings shed light on the nuanced performance and forecasting landscape of these leading cryptocurrencies, offering pivotal insights for potential investors. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Analysing Employee Management Using Machine Learning Techniques and Solutions in Human Resource Management
In the contemporary landscape of Human Resource Management (HRM), organizations are increasingly turning to advanced technologies to streamline employee management processes. This study explores the integration of machine learning (ML) techniques as a transformative solution for optimizing HRM practices, with a specific focus on employee management. By leveraging the power of ML algorithms, this research aims to enhance decision-making, efficiency, and overall effectiveness in HRM. The study encompasses a comprehensive analysis of existing HRM challenges, such as talent acquisition, performance evaluation, and employee retention, and proposes ML-based solutions to address these issues. By applying natural language processing, pattern identification, and predictive analytics, businesses may learn a great deal about employee behavior, performance patterns, and possible areas for development. HR professionals are more equipped to make well-informed choices, customize employee experiences, and put proactive talent development initiatives into action thanks to this data-driven approach. Additionally, the study examines the moral issues and difficulties surrounding the use of ML in HRM, stressing the significance of openness, justice, and privacy. By understanding and mitigating these concerns, organizations can successfully harness the transformative potential of ML in employee management, fostering a more dynamic and adaptive HRM framework. The study's conclusions add to the growing body of knowledge on the relationship between technology and HRM and offer useful advice to businesses looking to use cutting-edge approaches to improve labor management procedures. 2024 IEEE. -
Analysing the Impact of CSR Spending by Big 4 Firms on their Financial Profitability
This study delves into this ongoing debate whether socially responsible companies perform better which leads to financial profit or instead have no impact. This study focuses on leading accounting companies i.e., PricewaterhouseCoopers (PwC), Deloitte, Ernst & Young (EY), and KPMG and whether CSR Spending impacts their financial profitability or goes unnoticed. Grenze Scientific Society, 2024. -
Analysing the Influence of Activation Functions in CNN models for Effective Malware Classification
With the advancement of information technology, malware has become a persistent cyber security concern that targets computer systems, smart devices, and wide networks. Due to flaws in performance accuracy, analysis type, and malware classification methodologies that miss unsuspected malware attacks, malware classification has thus always been a significant concern and a challenging subject. Using the Malimg dataset, which has 9349 samples from 25 different families, this study classifies malware using a deep learning algorithm called a convolution neural network and evaluating the accuracy using a number of activation functions in this study. The proposed CNN model for malware classification achieves a high accuracy rate without the need for complex feature engineering. The model achieved the highest accuracy of 96.93% when using the Rectified Linear Unit (ReLU) activation functions whereas Leaky Relu gives accuracy of 96.76%, Pre relu gives 96.36%, ELU gives 95.72% and tanh gives accuracy of 95.58%. 2024 IEEE. -
ANALYSING THE SAFETY OF A CAMPUS USING SPATIAL SYNTAX
Everybody has been in campus environments and academic buildings at some point in their lives. The layout of these structures is crucial because it influences how a person behaves and presents themselves. The use of space syntax enables us to examine how individuals behave in relation to their surroundings and how places are used. The nature of the space and the way people move through it have improved because of the application of space syntax in campus planning.A primary concern is safety, this paper is devoted to comprehending how various user groups navigate across a university. Here, we'll be looking at how students move around and behave in relation to how safe they feel on campus. Each user group's paths, nodes and gathering places will be recorded and we'll confirm both the original puiposes and the current uses of the spaces. Additionally, several maps will be created to support the study that the campus is a safe place to be, including axial mapping and analysis mapping, convex mapping and grid analysis mapping. This with a combination of survey shall be used to understand safety with respect to space syntax. ZEMCH Network. -
Analysis and Actions Planned for Programme Outcomes in Outcome Based Education for a Particular Course
In India many of the technical institutions are NBA (National Board of Accreditation) accredited and the accreditation is a way to maintain quality of education. The outcome-based education (OBE) plays an important role in technical education across the world. So, in this research we will show how we can implement the attainment process related to OBE for a particular course. In this paper we will discuss how the course outcome and mapping of course outcome with program outcome can be defined. Then we will discuss the process to calculate the attainment. Finally, the program gaps were identified for that course and actions were suggested. 2024 IEEE.