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Pad Vending Machine with Cashless Payment
A vending machine aims to provide required product or the service to the customer with certain ease, wherein not much effort is required. This research work aims to design a pad vending machine with an option of payment using QR code which is implemented using blockchain to make the system much more efficient and reliable than the existing systems present in the Indian market. The system is divided into two parts, first being the working of the machine and second being the mode of payment which is implemented using a blockchain. It is noticed many times that due to unpredictable menstrual flow women tend to face a lot problems. To overcome this problem, a pad vending machine is proposed with certain advancements through which women can help themselves in the stated circumstances. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Pandemic Pulse: Unveiling Insights with the Global Health Tracker Through AI and ML
The current study highlights the importance of data analysis by applying data visualization tools to help you understand the pandemic disease informational component, and how it can be converted into knowledge that might enhance decision-making processes. In Tableau, a software for displaying data, researchers have incorporated a pandemic disease informational component from Coursera to improve assessment and selection. After becoming familiar with the data and the data visualization technological advances, some of it will be expected to conduct an initial investigation to identify significant changes in the data that is under consideration, compile and present this pandemic disease informational component, and enhance the corporate decision-making process. This issue for inquiry highlights the significance of knowledge examination via the use of communication visualization applications to aid in your comprehension of the pandemic disease informational component as well as how it may be changed into knowledge that may enhance the process of arriving at decisions. The creators of the knowledge representation computation application scenario used data from Coursera to improve their studies and make decisions. One will need to conduct an exploratory inquiry to find notable trends within the data after familiarizing oneself with it by utilizing visualization programs to compile and distribute this data to improve the company's decision-making procedures. This specific software is designed to be utilized in an early administrative duties course, an undergraduate accounting data structure course, or a data analytics-only educational program as a basic introduction to an informative visualization computer application. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Paradigm of Green Technologies in Hospitality Industry and its Sustainability Analytics
The function of the study is to investigate the customer attitude towards the sustainable or Green Technologies adopted by the hospitality industry and how this has changed the purchase intentions of the customer. This also explores the disposition of people to pay for and repeat these services and how the new green practice and techniques have changed the brand image. The data was collected through a monitored survey from 448 people across India. The conceptual framework that was formulated is tested during structural equational modelling. As a result of the study, it was found that, green purchase intentions are significantly influenced by the attitude they have towards Green Technologies/services. All stakeholders in the hospitality industry in India will find this paper useful. 2022 IEEE. -
Parallel Algorithm to find Integer k where a given Well-Distributed Graph is k-Metric Dimensional
Networks are very important in the world. In signal processing, the towers are modeled as nodes (vertices) and if two towers communicate, then they have an arc (edge) between them or precisely, they are adjacent. The least number of nodes in a network that can uniquely locate every node in the network is known in the network theory as the resolving set of a network. One of the properties that is used in determining the resolving set is the distance between the nodes. Two nodes are at a distance one if there is a single arc can link them whereas the distance between any two random nodes in the network is the least number of distinct arcs that can link them. We propose two algorithms in this paper with the proofs of correctness. The first one is in lines with the BFS that find distance between a designated node to every other node in the network. This algorithm runs in O(log n). The second algorithm is to identify the integer k, such that the given graph is k-metric dimensional. This can be implemented in O(log n) time with O(n2) processors in a CRCW PRAM. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Parallelizing keyframe extraction for video summarization
In current era, most of the information is captured using multimedia techniques. Most used methods for information capturing is through images and videos. In processing a video, large information needs to be processed and a number of frames could contain similar information which could cause unnecessary delay in gathering the required information. Video summarization can speed up video processing. There are different techniques for video summarization. In this paper key frames are used for summarization. Key frames are extracted using discrete wavelet transforms. Two HD videos having 356 frames and 7293 frames were used as test videos and the runtime was 17 seconds and 98 seconds respectively in CPU and 11 seconds and 53 seconds respectively in GPU. 2015 IEEE. -
Parametric analysis of control parameters for investigating the machinability of inconel 718 using ceramic inserts of round type
Inconel 718 is a nickel-chromium based super alloy and has high corrosion and thermal resistance, high hardness, and high thermal strength at elevated temperatures which makes it difficult to cut. Due to these mechanical properties, it is being used in toughest conditions and hence the tool life is extremely short. This hard to cut metallic alloy has a wide scope in the field of bio medical industry, aerospace industry, bearing industry, steam turbine and nuclear applications and the demand has rapidly been increased in the recent years. Ceramic insert is one such cutting tool being used in the machining of this metal and study is still being conducted to increase the machinability. This paper investigates the machinability characteristics for determining the machinability of Inconel 718 using ceramic insert based on Grey Relation Analysis (GRA) and signal to noise (S/N) ratio. The input parameters such as feed rate, cutting speed and depth of cut are taken into consideration to obtain the suitable response parameters such as minimal surface roughness and low tool wear rate to improvise the machining characteristics of this superalloy. Ceramic inserts had even been cryogenic treated to provide better machining conditions on the Inconel 718. 2023 Author(s). -
Parametric effect of minimum quantity lubrication unit using RSM technique to improve the machinability of Inconel 718
In recent years, a rapid demand of superalloys has been seen in all industrial sectors. Few growing industries such as aerospace and biomedical industries are in need of this superalloy for fabrication of variety of products. Inconel 718 is one such superalloy which is being used for the manufacture of these productions due to high tensile strength, corrosion resistance, hardness and toughness. Due to these superior quality feature of this material friction is being seen at the tool-work interface region. This friction can be reduced by minimum quantity lubrication (MQL) unit which provides coolant at the right time. This paper discusses the minimum quantity lubrication unit used in computer numerical control (CNC) milling machine to improve the machinability of Inconel 718 by reduction of temperature at tool-work interface region and also the parametric effect using response surface methodology (RSM). Minimum quantity lubrication unit allows the cutting fluid to flow out of the nozzle at minimum speed to the cutting region which provides maximum volume of heat removal at very minimized usage of fluid. RSM technique is being implemented to improvise the experimental runs with proper way of extracting the readings and providing the observations. Instead of generating huge datas, RSM shows the accurate path of providing the data in a specified generative table. [copyright information to be updated in production process] 2022 -
Parametric Study on Compaction Characteristics of Clay Sand Mixtures
The behaviour of fine-grained soils can be attributed to their mineral composition and the amount of fines present in them. The present study aims to determine the effect of mineral composition and quantity of fines on the Atterberg limits and compaction characteristics and to determine the correlation between them. Two types of fine-grained artificial soil mixtures were prepared in the laboratory representing kaolinitic and montmorillonitic mineral compositions.The amount of fines was varied at 10% intervals, from 50 to 100%. The Atterberg limits like liquid limit, plastic limit, shrinkage limit, and compaction characteristics like maximum dry density (MDD) and optimum moisture content (OMC) for two compaction energy levels, i.e. standard proctor (SP) and modified proctor (MP) tests, were determined. The correlations were developed between percentage fines and Atterberg limits and similarly between percentage fines, Atterberg limits, and compaction characteristics for artificial mix proportions. The developed correlations were used to predict the properties of natural soil samples, and the predicted and actual values are compared. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Parametrical variation and its effects on characteristics of microstrip rectangular patch antenna
This paper represents a brief description about design of rectangular microstrip patch antenna and its parameter effects in size, efficiency and compactness and parametric analysis in terms of return loss, bandwidth, directivity and gain by using same and different dielectric substrate materials with same and different thickness of rectangular microstrip patch antenna. The important parameters of patch such as L, W, r and h has its own impact in antenna characteristics. This parametrical impact is studied and verified. As thickness of dielectric substrate increases, the gain & directivity of rectangular microstrip patch antenna decreases and bandwidth increases. As r increases, the size of the antenna decreases but when height of dielectric substrate increase antenna size also increases. There will be always a compromise between miniaturization and other antenna characteristics. This antenna is designed for microstrip feed line technique and with center frequency (f0) at 4GHz. The parametric analysis is obtained by comparing the simulated results of rectangular microstrip patch antenna for different cases. The proposed antenna is simulated using HFSS tool at resonance frequency of 4 GHz. 2017 IEEE. -
Parkinsons Disease Progression Prediction using Advanced Machine Learning Techniques
Parkinson's disease (PD) is a neurodegenerative condition that affects people over time and significantly lowers their quality of life. Patients with PD experience both motor and non-motor symptoms. Through clinical evaluation, the Unified Parkinson's Disease Rating Scale (UPDRS) is used to quantify the severity of Parkinson's disease. No definitive diagnostic tests for PD currently exist. Emerging machine learning techniques show potential to forecast future UPDRS scores for making informed medical decisions and enable better disease management. This paper studies research leveraging proteomic data to forecast PD prognosis, focusing on advanced machine learning techniques like CatBoost Regressor, ElasticNet, XGBoost Regressor, RandomForest Regressor, ExtraTrees Regressor and DecisionTree Regressor. 2024 IEEE. -
Partial load shedding using ant colony algorithm in smart grid environment
Effective power distribution methodology is one of the basic necessities to meet the increasing power demand in any power system. Load shedding is done when power demand is more than power generation, to sustain the power system stability. Load shedding methods followed today shed a particular load completely, neglecting the critical consumers within the system. Controlling the loads at individual utility level in smart grid system enables us to put a maximum power limit on utility. Hence by partially shedding the load, demand can be reduced. The technique used here utilizes ant colony algorithm to choose a maximum power limit for each load dynamically based on the importance and priority of load. This method forces the consumer to manage his load internally based on criticality. It also effectively makes use of availability based tariff schemes. 2015 IEEE. -
Patient Monitoring System for Elderly Care using AI Robot
The use of robots in numerous industries has expanded in recent decades. Self-guiding robots have started to arise in human life, particularly in sectors pertaining to the lives of old people. Age-related population growth is accelerating globally. As a result, there is a rising need for personal care robots. The purpose of this requirement is to increase opportunities for mobility and support independence. To meet this demand, a robot with specific functionalities to help older people has been designed. The standard values of healthcare parameters are stored in the database by recording and comparing the current values the system will give an alarm and also sends a message to the doctor or caretaker so that a proper care would be given to the patients. We are including a preset distance value to monitor the elder people. Here we are using some sensors to detect the health parameters from the person. Robot have designed to intimate the family members if any changes occur in the health parameters. It helps the people to stay alone in home with safe manner. 2022 IEEE. -
Pattern Recognition: An Outline of Literature Review that Taps into Machine Learning to Achieve Sustainable Development Goals
The sustainable development goals (SDGs) as specified by the United Nations are a blueprint to make the Earth to be more sustainable by the year 2030. It envisions member nations fighting climate change, achieving gender equality, quality education for all, and access to quality healthcare among the 17 goals laid out. To achieve these goals by the year 2030, member nations have put special schemes in place for citizens while experimenting with newer ways in which a measurable difference can be made. Countries are tapping into ancient wisdom and harnessing newer technologies that use artificial intelligence and machine learning to make the world more liveable. These newer methods would also lower the cost of implementation and hence would be very useful to governments across the world. Of much interest are the applications of machine learning in getting useful information and deploying solutions gained from such information to achieve the goals set by the United Nations for an imperishable future. One such machine learning technique that can be employed is pattern recognition which has applications in various areas that will help in making the environment sustainable, making technology sustainable, and thus, making the Earth a better place to live in. This paper conducts a review of various literature from journals, news articles, and books and examines the way pattern recognition can help in developing sustainably. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
PCRS: Personalized Course Recommender System Based on Hybrid Approach
The traditional system of selecting courses to carry out research work is time consuming, risky and a tedious task, that not only badly affect the performance but the learning experience of a researcher as well. Therefore, choosing appropriate courses in seminal years could help to do research in a better way. This Study presents a recommender system that will suggest and guide a learner in selecting the courses as per their requirement. The Hybrid methodology has been used along with ontology to retrieve useful information and make accurate recommendations. Such an approach may be helpful to learners to increase their performance and improve their satisfaction level as well. The proposed recommender systems would perform better by mitigating the weakness of basic individual recommender systems. 2018 The Authors. Published by Elsevier B.V. -
PE-v-SVR based Architecture to Predict and Prevent Low and Slow-Rate DDoS Attacks using Machine Learning
Distributed Denial of Service (DDoS) attacks continue to emerge; low and slow attacks pose a serious threat. These small-scale attacks often evade traditional security protections and increase the risk of long-term outages and loss of service. Our research aims to develop effective predictive models and strategic defences to detect and mitigate slow DDoS attacks. The proposed model combines Power Spectral entropy and V-Support Vector Regression. More importantly, the version achieves the first-class error price in the variety of zero to at least one, demonstrating its effectiveness in detecting and predicting DDoS attacks. Research results show the effectiveness of the proposed design using PSD (power spectral density) entropy and V-SVR. The best mean square error obtained further confirms the ability of the model in this context. V-SVR in low and sluggish DDoS assaults. 2024 Bharati Vidyapeeth, New Delhi. -
Pedestrian crossing behaviour between transport terminals
Pedestrians possess special requirements for protecting their privacy while interacting with other users of a transport network. There exists a need in order to obtain a deeper knowledge of pedestrian traffic behaviour in between transport terminals. When different transportation terminals come closer, there will be an increased pedestrian flow caused due to change in modes used. The main aim was to analyses the general pedestrian behaviour while crossing a road and to find out different human factors which affect this behaviour. The crossing patterns were observed and also the chances of conflict with vehicles. The paper brought out the fact that the pedestrians always preferred different types of crossings. These varied with the gender and age of the pedestrians and also with luggage carrying or not. There seemed to be a greater flow of pedestrians during the peak hours and then they faced difficulty in crossing due to heavy traffic. Crosswalks are locations in which pedestrians are exposed to fewer rights of accident prevention even though they may approach the roadway and be alert of approaching traffic. Pedestrian unlawful crossing attitude is a crucial factor inside area issue of safety on the road. Thus, there is a requirement to take more steps towards bringing safety. 2023 Author(s). -
Perceived Reality of Self and Others with Two Childhood Trauma Survivors - An Idiographic Case Study
Impacts of childhood trauma can be crucial in understanding personality traits and psychological distress. However, it could be hard to predict if these individuals develop posttraumatic stress or growth. Several quantitative research studies have concluded the connections between childhood trauma and psychopathology or maladaptive personality traits. Various researchers have discovered the negative consequences of early childhood trauma and its long-term effects which may be rudimentary in understanding the causation of life-long psychological and medical deficiencies. This has been very elementary in understanding trait patterns and psychopathology for outcome generalizability and implementing prevention and intervention models. However, these studies still fail to spotlight the importance of the lived experiences of trauma survivors. Nevertheless, the present study is an idiographic single-case study research design used in the exploration of the lived experiences and perceived reality of self and others with two childhood trauma survivors. The Electrochemical Society -
Perception and Practices of EdTech Platform: A Sentiment Analysis
Virtual and digital learning being the new normal, pandemic outburst and unexpected disruption in the functioning of educational services have paved way for online learning services. Considering the fast-Track growth of the education technology (EdTech) industry, in order to sustain, it is imperative for the industry to understand the underlying issues by capturing the end users' perception. The primary purpose of this research is to examine the perception of users towards EdTech platforms A sample of 600 reviews regarding three major EdTech platforms were scraped from MouthShut.com as textual data and analysed using lexicon-based method. The polarity of the sentiments pertaining to the reviews of different platforms was analysed using sentiment analysis. Furthermore, the topic modelling on the reviews was performed using natural language programming. The results revealed a positive sentiment of users towards the EdTech services and platforms. The most influential factors are faculty expertise, interface user-friendliness, syllabus, and pricing model. Our findings help EdTech service providers to understand which factors are driving this dramatic shift in student behaviour so they may develop better strategies to attract and retain consumers. Despite the rise in EdTech platform popularity, this is the first study to investigate perception of EdTech users comprehensively. 2022 IEEE. -
Perception to Control: End-to-End Autonomous Driving Systems
End-to-end autonomous driving systems have garnered a lot of attention in recent years, and researchers have been exploring different ways to make them work. In this paper, we provide an overview of the field with a focus on the two main types of systems: those that use only RGB images and those that use a combination of multiple modalities. We review the literature in each area, highlighting the strengths and limitations of each approach. We also discuss the challenges of integrating these systems into a complete end-to-end autonomous driving pipeline, including issues related to perception, decision-making, and control. Lastly, we identify areas where more research is needed to make autonomous driving systems work better and be safer. Overall, this paper provides a comprehensive look at the current state-of-the-art in end-to-end autonomous driving, with a focus on the technical challenges and opportunities for future research. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Perceptive VM Allocation in Cloud Data Centers for Effective Resource Management
Virtual Machine allocation in cloud computing centers has become an important research area. Efficient VM allocation can reduce power consumption and average response time which can benefit both the end users as well as the cloud vendors. This work presents a perceptive priority aware VM allocation policy named P-PAVA algorithm, which takes into account the priority of an application along with its compute, memory and bandwidth requirement. The algorithm performs allocation of the applications based on the priority it gets using a machine learning based prediction model. Furthermore, to reduce the overhead of the allocation algorithm, parallelization is employed before assigning various workloads. To achieve this, the algorithm employs the First fit technique as a baseline for the requests allocation with a criteria as low priority. When compared to the state of the art algorithm for VM allocation for priority aware applications, P-PAVA performs better on several criteria such as average response time, execution time and power consumption. 2021 IEEE.