bluuebunny/crossref_metadata_2025_split huggingface dataset:
Novel Micro flow Sensor for Air Purge Method to Monitor the State of Charge of Lead Acid Battery
Authors: Adhav Yashwant , Sonawane Dayaram , Patil Chetankumar 
Published: September 2022

Abstract
The foremost aim of the present research study is to measure specific gravity of lead-acid batteries and further know batterys state-of-charge (SoC). In this article, air purge method is used to monitor the status of a battery; back differential pressure is measured using MPX4006 DP MEMS pressure sensor. In air purge method, it is essential to adjust the rate of bubbles in acid. This can be achieved by controlling the compressors speed or controlling the air flow by controllers. When air purge tubes are inserted through a batterys cap, it is difficult to observe bubbles produced in the acid as the battery is nontransparent. If the bubble rate is too slow, the system lowers the output signal and hence stops working. During the calibration of the instrument, air flow measurement through the pipe is of prime importance. In this method, FS1012 MEMS gas flow sensor is used to monitor the discharged air flow. FS1012 is based on the thermo-transfer principle. Compared to the resistive technique, this flow sensor consists of a thermopile for air flow sensing whose signal-noise ratio is exceptional. The output of the sensor is exponential; signal conditioning circuit has been designed to get linear output 0 to 3.3 V standard range. The amplified output of signal conditioning circuit is monitored using TM4C123GXL arm controller. FS1020 is first time proposed to monitor the air flow of a small air purge probe used to monitor the state of charge of a lead-acid battery. FS1020 ensures an ideal flow of the air bubbler system, which improves the performance of the battery charge monitoring system. Conventional flow sensors for example, rotameter cannot be used for very low flow measurement; they are costlier as well as bulky. The minimum flow required to produce sufficient bubbles in acid is 0.50 liter per minute (LPM) which is measured by FS 1012.
Investigation of increasing non-linear load impact on power quality in the distribution system
Authors: Maury Navin , Singh Amit Kumar 
Published: September 2022

Abstract
Now a day’s power quality problem daily increases due to the use of non-linear load in all sectors. So in this paper, we work on power quality improvement help of DVR (dynamic voltage restorer). Power quality issues are present time most thinkful because all sectors use power electronics devices and automation (PLC). In any sector without the use of power electronics devices and non-linear load create more problems because now a day all sectors use embedded and automation systems. The power quality main issue are sag, swells, unbalance, fault, and harmonic in this paper we control and reduce the sag in a fixed distribution system with the help of DVR with a PID controller. PID controller connected in series with DVR which is the controller the voltage sag and its frequency. This combination improves the power quality in the distribution system we can see the improvement by a simulation result shown in the paper we use 11kv subsystem and three-phase step-down transformer which converts 11kv to 440v. This project improves the power quality issue on the distribution side.
Detection of Security Attacks on Edge Computing of IoT Devices through NS2 Simulation
Authors: Kumar Anit , Singh Dhanpratap 
Published: September 2022

Abstract
The limited resource and computation capacity of an IoT device always being a target vector for intruders to use to steal sensitive data from the device. Computational problems later solved by the engineers by introducing an edge server near to IoT device network boundary, but still it is not secure from the hands of intruders. IoT device vulnerabilities still can allow Edge server to give the control of the device to the intruders. Hence both the Edge server and the connected IoT device may fall under the control of an intruder for a misuse. In this paper we study how IoT devices in case of Edge computing falls under security threats. We have used NS2 simulator to identify a malicious node that behaves inconsistent in terms of Packet Delivery Ratio, End-to-End Delay, and Throughput.
Empirical analysis of machine learning-based moisture sensing platforms for agricultural applications: A statistical perspective
Authors: Ukani Neema Amish , Chakole Saurabh S. 
Published: September 2022

Abstract
Modelling of accurate detection & estimation soil moisture sensors requires integration of various signal processing, filtering, segmentation, and pattern analysis methods. Sensing of moisture is generally performed via use of resistive, or capacitive materials, which change their parametric characteristics w.r.t. changes in moisture levels. These sensors are further classified depending upon capabilities of measurements, which include, volumetric sensors, soil water tensor sensors, electromagnetic sensors, time domain reflectometry (TDR) sensors, Neutron probe sensors, tensiometer-based sensors, etc. Each of these sensors are connected to a series of processing blocks, which assist in improving their measurement performance. This performance includes parameters like, accuracy of measurement, cost of deployment, measurement delay, average measurement error, etc. This wide variation in measurement performance increases ambiguity of sensor selection for a particular soil type. Due to this, researchers & soil engineers are required to test & validate performance of different moisture sensors for their application scenario, which increases time & cost needed for model deployment. To overcome this limitation, and reduce ambiguity in selection of optimum moisture sensing interfaces, this text reviews various state-of-the-art models proposed by researchers for performing this task. This review discusses various nuances, advantages, limitations & future research scopes for existing moisture sensing interfaces and evaluates them in terms of statistical parameters like accuracy of detection, sensing & measurement delay, cost of deployment, deployment complexity, scalability, & type of usage applications. This text also compares the reviewed models in terms of these parameters, which will assist researchers & soil engineers to identify most optimum models for their deployments. Based on this research, it was observed that machine learning models are highly recommended for error reduction during moisture analysis. Machine learning prediction models that utilize Neural Networks (NNs) outperform other models in terms of error performance, and must be deployed for high-accuracy & low-cost moisture sensing applications. Based on similar observations, this text also recommends fusion of different sensing interfaces for improving accuracy, while optimizing cost & complexity of deployment. These recommendations are also based on context of the application for which the sensing interface is being deployed. These recommendations must be used to further improve overall sensing performance under multiple deployment scenarios.
Improve Content-based Image Retrieval using Deep learning model
Authors: Kumar Suneel , Singh Manoj Kumar , Mishra Manoj Kumar 
Published: September 2022

Abstract
The complexity of multimedia has expanded dramatically as a result of recent technology breakthroughs, and retrieval of similar multimedia material remains an ongoing research topic. Content-based image retrieval (CBIR) systems search huge databases for pictures that are related to the query image (QI). Existing CBIR algorithms extract just a subset of feature sets, limiting retrieval efficacy. The sorting of photos with a high degree of visual similarity is a necessary step in any image retrieval technique. Because a single feature is not resilient to image datasets modifications, feature combining, also known as feature fusion, is employed in CBIR to increase performance. This work describes a CBIR system in which combining DarkNet-19 and DarkNet-53 information to retrieve images. Experiments on the Wang (Corel 1K) database reveal a considerable improvement in precision over state-of-the-art classic techniques as well as Deep Convolutional Neural Network(DCNN).
Efficient Cluster Head Selection and Trust Based Routing in MANET
Authors: Bharti Meena , Rani Shaveta , Singh Paramjeet 
Published: September 2022

Abstract
Mobile Adhoc Network (MANET) faces different security challenges due to its collaborative and open communication behavior and restricted availability of resources. Routing is hard and vulnerabilities issues are regularly exposed to highly dynamic nature and numerous communication breakdowns induced by mobile nodes. Thus, security measures that can minimize the effects of various attacks must be included in the MANET’s routing protocols. Secure communication with optimal energy is a big challenge in such networks. This paper presents a secure optimal routing scheme. The objective of the scheme is to find the most optimal secure path with low energy consumption using the proposed function. This scheme is divided into two algorithms; the first algorithm finds cluster head effectively and the second computes the trust value of each path for secure communication. The trust value is computed using the trust-based scheme using packet forwarding information. On the basis of the trust value, this algorithm detects the malicious nodes and removes them from the network and then the other proposed routing scheme computes the optimal path. Then path with less hop count and minimum energy usage is selected optimally.
Classification of Sleep using Polysomnography
Authors: Farooq Hemu , Jain Anuj , Shukla M.K. 
Published: September 2022

Abstract
Sleep is often recognized as a necessary component of a person’s well-being and is an extremely vital component of a healthy person’s well-being. Sleep is a state in which a person is both physiologically and psychically at ease. The sleep conception is appraised exceedingly unusual, and it has piqued the interest of researchers all around the world. The stages of sleep are examined in order give benefits for studying sleep utilized for the purpose of research. The ability to diagnose sleep disorders has been demonstrated by carefully examining the sleep score system and its various stages. As can be seen, manual sleep stage classification is a time-consuming method that requires adequate measure for sleep professionals to undertake statistical and quantitative analysis. Furthermore, errors and abnormalities in the categoization of the similar facts can occur frequently. As a result, the adoption of an autonomous scoring system to enable trustworthy classification is becoming increasingly popular. The scheduled task teaches you how to apply an automatic system based on EEG (Electroencephalogram), EMG (Electromyogram) and EOG (Electrooculogram), which is known as a polysomnography test or PSG test. For a total of 30 seconds, the recording was measured in length segments. The standard collection of parameters, which gives the distinct stages of sleep, was obtained from several hospitals in sleep laboratories. Sleep waking, Non-Rapid Eye Movement (Stage 1, Stage2, Stage3), and Rapid Eye Movement are the stages. In clinics, the procedure can be highly effective, especially for neurologist detecting sleep problems.
Machine learning based design of reinforced concrete shear walls subjected to earthquakes
Authors: Rao Praveena , Pollayi Hemaraju , Rao Madhuri 
Published: September 2022

Abstract
Civil engineering structural components are classified according to their projected structural performance in the present building code regulations and design standards. These building design codes are largely based upon previous experimental results of thousands of samples tested to failure and validated with analytical solutions. Machine Learning techniques (ML) is a subset of Artificial Intelligence (AI) that facilitates classification and prediction of structural performances for a broad spectrum of complex structures with greater accuracy. Machine learning models have the potential to make reliable predictions with the help of algorithms. Thereby, saving a tremendous amount of time and resources invested in experimental investigations of large structural components such as shear walls and columns. The ML algorithms can learn from the available data, deduce underlying inter-relationships, make inferences and detect patterns based on previous experience. In the present work, various ML algorithms were implemented to identify the influence of geometrical as well as mechanical characteristics. Database of 393 specimens of reinforced concrete shear walls with rectangular (R), flanged (F) and barbell (B) cross-sections are adopted for the analysis. Shear walls are fundamentally classified into four failure categories which include flexure or due to bending, shear, intermediate flexure-shear and sliding due to shear. The objective of this paper is to classify and predict the shear strength, flexural strength as per the Indian standard code provisions and failure modes of shear walls with the help of ML techniques. Algorithms such as KNearest Neighbors, Naive Bayes, Decision Tree, Random Forest, AdaBoost, LightGBM, XGBoost and Cat-Boost is implemented using Python. Highest accuracy of 85% is achieved on the test set by Random Forest, 83% by CatBoost and 81% by LightGBM boosting algorithms. It is observed that input variables such as aspect ratio (lw/tw), characteristic strength of concrete in compression (f

ck
), characteristic yield strength of steel (f

y
), percentage of steel (ρ), web vertical reinforcement, horizontal reinforcement, boundary element reinforcements play a vital role in governing the shear strength (V

u
) and flexural strength (M

u
) of shear walls.
Privacy Preservation of Digital Health Records in Cloud
Authors: Munjal Kundan , Bhatia Rekha 
Published: September 2022

Abstract
The variety and amount of patient healthcare digital data is rapidly expanding. The field of artificial intelligence has fast proven revolutionary for healthcare, allowing for unprecedented speed and precision in data analysis. Many hospitals have already transitioned to electronic health records (EHRs), a digital version of paper medical information. Information Technology has already aided in simplifying operations in this area, making the process far more efficient and patient-centered than in the past. A patient’s trust may disintegrate in the face of recurrent incidents if there is no precise control and safety in place. As a result, it limits the potential for digital health to promote an age of more accessible, connected, and individualized treatment. When it comes to picking a cloud solution, healthcare providers are most concerned about security and privacy. In this research, we seek to demonstrate various ways that aid patient data privacy while simultaneously allowing cloud services to be used.
Fatigue Detection Post Physical Activity Using Machine Learning Algorithms
Authors: Sharma Ashish , Sethi Gaurav 
Published: September 2022

Abstract
Construction work is purely effortful and the prevention of injuries at construction job sites is essential for encouraging worker’s well being and health which is generally overlooked at the construction sites. World’s construction industry is one amongst those having unsatisfactory work health issues. A large number of laborers and construction workers have to undergo fatigue risk at their job place. This paper describes the current state of the art of the research carried out in case of fatigue assessment after performing some physical activity providing an insight into fatigue, its detection and an overview of the causes of risk fatigue and its countermeasures. A number of subjective and objective fatigue assessment approaches have been used that have further stimulated the inclusion of latest and advanced approaches for fatigue detection. Although individual’s knowledge regarding the fatigue detection approaches has been enhanced, there is as yet minimal research in the field of fatigue detection post physical activity. In this study the stress would be on objective fatigue detection techniques where the acquisition of physiological data of construction workers is required. The study provides a ground for detection of physiological fatigue based on motion capture data in the form of videos.
A Review on the Detection of the Post COVID-19 Symptoms for Long Term Diseased Patients using Machine Learning Algorithms
Authors: Patibandla Anitha 
Published: September 2022

Abstract
Long term diseases require continuous monitoring, sometimes periodic monitoring to verify if any serious concern requires an attention. In recent years, it is noticed that the COVID-19 pandemic has triggered serious concern towards the long-term diseased individuals. As the mortality rate of the COVID-19 clearly indicates that the highest percentage of deaths reflect in the individuals suffering from long term diseases such as diabetes, pneumonia, cardiovascular and acute renal failure. Though they are tested for COVID negative through conventional apparatus, it doesn’t confer that they are completely out of post consequences. Hence a periodic, if necessary continuous monitoring needs to be aided, which in current scenario is a challenging task. Hence, our current article reviews the use of machine learning algorithms to detect and diagnose pre and post COVID-19 effects on long term diseased patients.
A Critical Review on Hand Gesture Recognition using sEMG: Challenges, Application, Process and Techniques
Authors: Kumar Davinder , Ganesh Aman 
Published: September 2022

Abstract
Hand gesture recognition systems are gaining popularity these days due to the ease with which humans and machines can communicate. The goal of hand gesture development is to improve interactions between humans and computers for the purpose of transmitting ideas. In a typical HGR systems, the main steps followed are, data collection, pre-processing, feature extraction and classification. For every stage, a significant number of techniques are available with various other sub steps. This study gives an overview of modern hand gesture recognition techniques, its Physiological and Anatomical Background, working and challenges faced by these systems. Moreover, the role of artificial intelligence in optimizing the performance of HGR systems is also delineated in this paper. Also, the precision and accuracy of the HGR approaches gets affected by the complexity and diversity of various hand movements, therefore, the need for implementing AI based ML and DL methods keeps on rising. Keeping this in mind, the performance of various ML algorithms in recognizing the visual and sensor-based hand gestures is investigated. Moreover, the commonly utilized framework in detecting hand gestures has been explored in numerous standard datasets.
Past, Present and Future of Automated Mammographic Density Measurement for Breast Cancer Risk Prediction
Authors: Pawar Shivaji D. , Joshi Pratibha T. , Savalkar Vishwayogita A. , Sharma Kamal Kr. , Sapate Suhas G. 
Published: September 2022

Abstract
Mammography is one of the essential screening technologies which is helpful to save the lives of women against breast cancer. Prediction of breast cancer from mammograms is not reached on its optimal level; hence there is a constant enhancement in clinical applications for mammographic breast density measurement. Optimal results in breast density measurement can be helpful to provide better care for women who have dense breasts. The sensitivity of digital mammograms reduces significantly in case dense breast, which may lead further to hide the cancerous lesions and may be converted into high stage breast cancer. Many research innovations and clinical applications are developed to support radiologists for the second opinion and predict breast cancer risk in advance. But still, there is an unsolved research question: which one is “dense breast” and which screening modularity is suitable for the dense breast to avoid the risk of breast cancer. Hence, currently, radiologists measure mammographic breast density with the help of BI-RADS classification, which is subjective. The basic inclination of this paper is to provide an overview of different clinical applications used for breast density measurement in the past and present with their potential technologies, advantages, disadvantages and current status of all various clinical applications in the market and provides thoughts on the future of automated mammographic breast density measurement.
Multi-stakeholder analysis of fire risk reduction in a densely populated area in the Netherlands: a case-study in the Veluwe area
Authors: de Hoop Evelien , Brouwers Hilde J H , Buijs Sophie L , Egberts Linde , van Gerrevink Max J , de Ruiter Marleen C , Veraverbeke Sander 
Published: September 2022

Abstract
Reducing the climate change-induced risk of uncontrollable fires in landscapes under nature management, with severe impacts on landscape and society, is particularly urgent in densely-populated and fragmented areas. Reducing fire risk in such areas requires active involvement of a wide diversity of stakeholders. This research letter investigates stakeholders’ needs with regard to fire risk reduction in the Veluwe area in the Netherlands. This densely populated landscape is a popular tourist attraction, and it is one of the most fire-prone landscapes of the Netherlands, with abundant fuels and human ignition sources. We draw upon seven in-depth qualitative interviews with key stakeholders in the Veluwe area, which we situate in a wider review of existing literature. Our analysis demonstrates that the rising incidence of uncontrollable fires poses four types of new challenges to these stakeholders in the Veluwe area. First, stakeholders express the need to reshape existing policy tools and develop novel ones that create synergies between existing policy-priorities (e.g. biodiversity conservation) and fire risk reduction. Second, stakeholders argue for a critical rethinking of the value of landscapes in society, and the diverse roles that fire may play in landscape management research and practice. Third, developing such policy tools requires new modalities and platforms for multi-stakeholder and multi-level collaboration, which are currently lacking because the current and expected future risk of uncontrollable fire is unprecedented. And fourth, the development of effective policy tools requires new knowledge that is interdisciplinary, sensitive towards the local social and ecological characteristics of the area, and which approaches current fire risk challenges and their possible solutions dynamically. While our stakeholder analysis is specific to the Veluwe area in the Netherlands, our findings are also likely to be relevant to other fire-prone nature areas in fragmented landscapes, particularly in Northwestern Europe.