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Restrained geodetic domination in the power of a graph
For a graph G = (V,E), S ? V(G) is a restrained geodetic dominating set, if S is a geodetic dominating (gd) set and never consists an isolated vertex. The least cardinality of such a set is known as the restrained geodetic domination (rgd) number. The power of a graph G is denoted as Gk and is obtained from G by making adjacency between the vertices provided the distance between those vertices must be at most k. In this study, we discussed geodetic number and rgd number of Gk. 2024 Author(s). -
Restructuring of Layout Designs and Operational Processes in Production Lines of Manufacturing Companies Globally to Compete Post Pandemic Conditions
Manufacturers globally faced various challenges in terms of sustainability and continuous production in the past COVID conditions and it showed the importance of redesigning the existing processes. All new manufacturing processes should be designed by considering the pandemic situation in the future. The present study is focused on restructuring the layout of an existing production unit in order to cope with any such eventualities. Various models that are suitable for adoption in post-pandemic, have been proposed and their efficiencies are compared in this paper. The authors also investigate how such changes will impact the efficiency of the existing line. Key parameters considered in this paper are the total production hours, line efficiency, balancing delay, and production rate. 2023 ACM. -
Resume Ranking and Shortlisting with DistilBERT and XLM
The research presented in this paper offers a solution to the time-consuming task of manual recruitment process in the field of human resources (HR). Screening resumes is a challenging and crucial responsibility for HR personnel. A single job opening can attract hundreds of applications. HR employees invest additional time in the candidate selection process to identify the most suitable candidate for the position. Shortlisting the best candidates and selecting the appropriate individual for the job can be difficult and time-consuming. The proposed study aims to streamline the process by identifying candidates who closely match the job requirements based on the skills listed in their resumes. Since it is an automated process, the candidate's individual preferences and soft skills remain unaffected by the hiring process. We leverage advanced Natural Language Processing (NLP) models to improve the recruitment process. Specifically, our emphasis lies in the utilization of the distilBERT model and the XLM (Crosslingual Language Model). This paper explores the application of these two models in taking hundreds of resumes for the job as input and providing the ranked resumes fit for the job as output. To refine our approach further, two types of metrics for resume ranking, such as Cosine similarity score and Spatial Euclidean distance, are used, and the results are compared. Intriguingly, distilBERT and XLM result in different sets of top ten ranked resumes, highlighting the nuanced variations in their ranking approaches. 2024 IEEE. -
Retention of a Community Healthcare Worker for Three Decades in a Rural and Remote CHC of Bolba in Jharkhand: A Case Study
The dearth of healthcare personnel in rural areas is a global problem. Even developed countries are struggling to meet demand. In such circumstances, identifying a health worker who worked for a single CHC for three decades necessitates deeper exploration. Individual case studies were employed to investigate the phenomenon, then thematically evaluated using QAD Miner Lite following a lengthy telephonic interview. The study's findings revealed that a rural upbringing, social class, economic factors, and behaviourism influenced the altruism of Community Healthcare Workers (CHWs). As a result, external and internal factors influenced CHW to service rural areas. But extrinsic factors worked in tandem with intrinsic factors to influence CHW's willingness to serve the rural areas. Rural healthcare shortages exist despite the National Rural Health Mission (NRHM) execution. A substantial amount of the population's health is entrusted to 20 percent of health workers, who account for disproportionately 75.05 percent of rural health outcomes. The Electrochemical Society -
Review and Design of Integrated Dashboard Model for Performance Measurements
This article presents a new approach for performance measurement in organizations, integrating the analytic hierarchy process (AHP) and objective matrix (OM) with the balanced scorecard (BSC) dashboard model. This comprehensive framework prioritizes strategic objectives, establishes performance measures, and provides visual representations of progress over time. A case study illustrates the methods effectiveness, offering a holistic view of organizational performance. The article contributes significantly to performance measurement and management, providing a practical and comprehensive assessment framework. Additionally, the project focuses on creating an intuitive dashboard for Fursa Foods Ltd. Using IoT technology, it delivers real-time insights into environmental variables affecting rice processing. The dashboard allows data storage, graphical representations, and other visualizations using Python, enhancing production oversight for the company. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Review of open space rules and regulations and identification of specificities for plot-level open spaces to facilitate sustainable development: An Indian case
Rapid urbanization and an increase in the alteration of natural resources have led to climate crises, driving the need to promote sustainable development. Urban open space management plays a vital role in such scenarios. Research on urban open spaces has been mainly conducted at regional, municipal, and neighborhood scales. Rarely has the focus been on the plot-level potentials and management of open spaces. Therefore, the study looks into the Indian development control rules and regulations and identifies that although these stipulate the percentage of open space for development on each plot, specificities for open spaces are unclear. Further, the study analyses quantitative and qualitative aspects of open spaces for selected group housing schemes in Pune city. The inquiry shows that per capita open space in Pune is comparatively lower than national standards. The quantitative aspects include FSI, building ground coverage, built-up area, number of floors, and number of dwelling units, and each relates to open spaces in one way or another. The qualitative interpretations disclose that a plot-level open space can significantly impact the regional-level open space network. Hence, the research advocates a bottom-up approach wherein plot-level open space can become the focus in formulating new norms and policies for sustainable development. Published under licence by IOP Publishing Ltd. -
Review on Emerging Internet of Things Technologies to Fight the COVID-19
The Internet of Things (IoT) has been gaining attention in various disciplines ranging from agriculture, health, industries and home automation. When a pandemic first breaks out early detection, isolating the infected, and tracing the contacts are the most important challenges. IoT protocols like Radio-frequency identification (RFID), Wireless Fidelity (WiFi), Global Positioning System (GPS) are gaining popularity for providing solutions to these challenges. IoT based applications in the health sector are benefitting COVID-19 (coronavirus disease of 2019) patients during this pandemic situation. This article explores and reviews the various Internet of Things enabled technologies and applications used in screening, contact tracing, and surveillance. IoT based telemedicine processes are very useful during the pandemic COVID-19. The purpose of this paper is to deliver an overall understanding of the existing and proposed technologies of IoT based solutions to make the situations better during COVID-19. 2020 IEEE. -
Review On Image based Coffee Bean Quality Classification: Machine Learning Approach
Specialty coffee's demand is growing worldwide as coffee drinkers continue to look for the freshest and highest-quality flavors. Depending upon the quality, there are two categories in the coffee industry, that is specialty coffee and commodity/commercial coffee. Coffee beans are graded via visual inspection and cupping. A 300g sample of green coffee beans is used for visual assessment, and faulty beans are counted. As per the 'Specialty Coffee Association of America' (SCAA), defect can be either primary or secondary. For a coffee to be a specialty, it should have less than 5 secondary defects and zero primary defects. In this survey we have presented the coffee bean quality-related research which includes various machine learning approaches in classifying the coffee beans. The study has achieved quite promising prediction accuracies and was evaluated with test data. We have done a study on coffee bean quality classification and are willing to contribute an arabica coffee bean dataset and detection of coffee bean quality using transfer learning with higher accuracy. 2022 IEEE. -
Review on Image Processing-Based Building Damage Assessment Techniques
Quick damage assessment is essential for starting efficient emergency response operations following natural calamities or any other kind of disasters. After a disaster, it is crucial for rescue departments to produce judgments and distribute the resources based on a fast retrieval of precise building damage status. A ground survey is used to implement traditional building assessment, and this is labor-intensive, dangerous, and time-consuming. Studies on building damage extraction over the past few decades have generally concentrated on localizing and evaluating the destructed structures, analyzing the ratio of damaged constructions, and determining the sort of destruction each construction has sustained. Recent research trends are mainly concentrated on the utilization of data collected from multiple sensors for the damage assessments of buildings. Each stage of digital image processing can be carried out in multiple ways and several novel ideas are emerging every single day. This paper reviews the various damage assessment techniques in the different steps of digital image processing. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023. -
Revisiting psychotherapeutic practices in Karnataka, India: Lessons from indigenous healing methods
Psychotherapeutic practices in India observes a paradigm shift with the current focus on the indigenous movement which has hit the discipline of Psychology like any other stream in Social Sciences and Humanities. The professional challenges and issues faced by the mental health professionals in this country has always revolved around on the 'uncanny' realm of myths, beliefs and religions as far as mental illness is concerned (Prasadarao & Sudhir, 2001). Efforts have been initiated in exploring the cultural and social roots of the health-illness constructs as well as debating on the possibility of 'integration' of these different philosophies. This paper is designed to understand the various therapeutic forms and processes in indigenous healing practices and to analyse the negotiation between indigenous healing practices and psychotherapy with special reference to Karnataka, one of the States situated in the Southern part of India. The study approaches the cultural landscape of Karnataka state based on a qualitative research design wherein in-depth unstructured interview of healers and mental health practitioners and systematic observation of some indigenous healing forms are adopted as methods of data collection. The paper concludes by looking at the challenges of constructing ethnospecific interventions in psychotherapy and the need to develop more cultural-specific theories taking into account the cultural history of the community. -
Revolution of the Indian Agricultural Landscape using Machine Learning and Big Data Techniques: A Systematic Review
The world of Big Data has been rapidly expanding into the domains of Engineering and Machine Learning. The biggest challenge in the Big Data landscape is the incompetence of processing vast amounts of data in a time-efficient manner. The agriculture domain has so long only relied on traditional method for yield prediction. This can be bettered by using novel Machine Learning techniques and innovative thinking. The study provides the review of most of the techniques already implemented in the ML, Big Data and Agriculture domain- traditional and modern- while focusing on highlighting the difference in accuracy between the traditional methods and the more advanced methods. 2022 IEEE. -
Revolutionising Tumour Diagnosis: How Clinical Application of Artificial Intelligence and Machine Learning Enhances Accuracy and Efficiency
This research paper examines the transformative influence of Artificial Intelligence (AI) and Machine Learning (ML) on tumour diagnosis within clinical settings. The advent of AI and ML technologies has revolutionised the field of oncology, offering the unprecedented potential for more accurate, timely, and personalised cancer detection. By leveraging vast datasets of medical images, genomic information, and patient records, these intelligent systems enable the early identification of tumours, classification of cancer types, and prediction of patient outcomes with remarkable precision. This paper delves into the mechanisms through which AI and ML algorithms analyse complex data, highlighting their ability to detect subtle patterns and anomalies that may escape human perception. Moreover, we examine the successful integration of these technologies into clinical workflows, their potential to reduce diagnostic errors, and the implications for patient care and outcomes. As AI and ML continue to emerge, the synergy between technology and clinical expertise promises to enhance tumour diagnosis, ultimately contributing to more effective and personalised cancer treatments. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Revolutionizing Arrhythmia Classification: Unleashing the Power of Machine Learning and Data Amplification for Precision Healthcare
This paper presents a comprehensive exploration of arrhythmia classification using machine learning techniques applied to electrocardiogram (ECG) signals. The study delves into the development and evaluation of diverse models, including K-Nearest Neighbors, Logistic Regression, Decision Tree Classifier, Linear and Kernelized Support Vector Machines, and Random Forest. The models undergo rigorous analysis, emphasizing precision and recall due to the categorical nature of the dependent variable. To enhance model robustness and address class imbalances, Principal Component Analysis (PCA) and Random Oversampling are employed. The results highlight the effectiveness of the Kernelized SVM with PCA, achieving a remarkable accuracy of 99.52%. Additionally, the paper discusses the positive impact of feature reduction and oversampling on model performance. The study concludes with insights into the significance of PCA and Random Oversampling in refining arrhythmia classification models, offering potential avenues for future research in healthcare analytics. 2024 IEEE. -
Revolutionizing Road Traffic Management and Enforcement: Harnessing AI, ML, and Geospatial Techniques
This study investigates the synergistic application of Artificial Intelligence (AI), Machine Learning (ML), and Geospatial Technologies in optimizing traffic management systems. Through a mixed-methods research design, it evaluates the potential of these technologies to enhance urban traffic flow and reduce congestion. The research emphasizes the critical importance of data quality, ethical considerations, and the selection of appropriate technological solutions based on specific urban traffic scenarios. Findings highlight the significant role of integrated AI and geospatial analyses in improving traffic predictions and operational efficiency. Future work will focus on developing more sophisticated models that ensure privacy, equity, and adaptability to new transportation trends. 2024 IEEE. -
Rice Yield Forecasting in West Bengal Using Hybrid Model
Agriculture in India is the primary source of revenue, yet farmers still face challenges. The primary goal of agricultural development is to produce a high crop yield. The Datasets collected for the study of real-world time series include a blend of linear and nonlinear patterns. A mixture of linear and non - linear models, rather than a single linear or non - linear model, gives a more precise forecasting models for time series data. The ARIMA and ANN prediction models are combined in this paper to create a Hybrid model. This model is used to predict rice yield for all 18 West Bengal districts during the Kharif season, based on 20years of information(20002019) collected from various sources such as India Meteorological Department, Area, and production Statistics, DAV from NASA, etc. The hybrid model aims to enhance efficiency indicators such as MSE, MAE, and MAPE, demonstrating excellent performance for rice yield prediction in all the districts of West Bengal. In the future, it can be applied to other crops that can support farmers in their farming. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Risk Assessment Model for Quality Management System
The ecological and economic risk assessment system and its cost were also factored into the document. The distribution of workplace challenges and hazards, represented by quantitative or subjective occupational risk metrics, was typical in the areas of building safety and environmentally responsible workers. Environmental risk assessment refers to the identification & evaluation of risks, the formulation & application of managerial decisions to lessen the chance of unfortunate conditions, and also the substantial decrease of materials or other damages. Risk assessment facilitates the transition from an area of uncertainty to one where outcomes are more or less expected. The Deming-Shewhart cycle, which would be fully linked to the policy process and performance measurement system, appears to be the implementation technique of the ecological and economic structure under consideration. It would be a cyclical sequence of the associated effective measures. A high degree of adaptability to any internally or externally stressful conditions would be ensured by the synthesis of the fundamentals of the management system & mechanisms for controlling environmental potential costs. This also guarantees the rapid identification of expert hazards, optimization and efficiency gains. 2022 IEEE. -
RNA-seq DE genes on Glioblastoma using non linear SVM and pathway analysis of NOG and ASCL5
Differentially Expressed genes related to Glioblastoma Multiforme as an output of RNASeq studies were further studied to conclude new research insights. Glioma is a type of intracranial tumor (within the skull), which can grow rapidly in its malignant stages. Gene expression in Grade II, III and IV Gliomas is analysed using non linear SVM models. The enriched GO terms were identified GOrilla. Pathways related to NOG and ASCL5 gene were studied using Reactome. 2020, Springer Nature Switzerland AG. -
Road Accident Prediction using Machine Learning Approaches
Road accidents create a significant number of serious injuries reported per year and are a chief concern of the world, mostly in underdeveloped countries. Many people have lost their near and dear ones due to these road accidents. Hence a system that can potentially save lives is required. The system detects essential contributing elements for an accident or creates a link among accidents and various factors for the occurrence of accidents. This research proposes an Accident Prediction system that can help to analyze the potential safety issues and predict whether an accident will occur or not. A comparative study of various Machine Learning Algorithms was conducted to check which model can help predict accidents more accurately. The dataset used for this paper is the government record accidents that occurred in a district in India. Logistic Regression, Random Forest, Decision Tree, K-Nearest Neighbor, XGBoost, and Support Vector Machine are among the Machine Learning models used in this paper to predict accidents. The Random Forest algorithm gave the highest accuracy of 80.78% when the accuracies of the Machine Learning models were compared. 2022 IEEE. -
Role of AI in Enhancing Customer Experience in Online Shopping
AI-powered tools and applications may provide customers with a positive, effective, and customized purchasing experience. By studying client preferences and behaviours, AI systems can anticipate future customer needs, improving and personalizing the shopping experience. The main aim of this study is to examine the role of artificial intelligence (AI) on enhancing customer experience. The results of this study revealed that there is a positive significant relationship between AI features like perceived convenience, personalization and AI-enabled service quality and Customer experience. A total of 416 responses were analysed using a structured questionnaire. The findings indicate significant role of trust as factor, mediating the effects of independent variables on customer experience. Data was analysed using T-test, ANOVA and regression. 2024 IEEE. -
Role of Artificial Intelligence and Robotics in Shaping the Students: A Higher Educational Perspective
An unprecedented shift in technology has begun in the modern era. Robotics and artificial intelligence (AI) advancements have created fresh positions while de-skilling or retraining many existing ones. Technical developments at higher education institutions (HEIs) protect students against potential changes in their field of study brought on by A) and prepare them for success in the workplace. This research aims to investigate how, over the past 150 years; globalization has fundamentally changed human civilization. Conventional education confronts enormous challenges as energy, the internet of things, and the cyber-physical systems they oversee diminish. One may argue that energy, the internet of things, and the cyber-physical systems that are under its jurisdiction are the foundations of all future education. The demise of these systems presents a significant threat to traditional schooling. Students' screen time is increased by this action, which has an impact on their mental health. Five-fold cross-validation with 210 students from Delhi NCR and abroad is beneficial for the classification techniques SVM, Naive Bayes, and Random Forest. The study examined the factors that contributed to an increased rate of mental health issues among undergraduate students in Delhi, India, following the introduction of the COVID-19 virus. The results have demonstrated that while technology's practical applications will likely have a positive influence on education in the future, there may be negative effects as well. This is an opportunity for educators and learners to support excellence and remove obstacles that prevent many kids and schools from achieving it. Therefore, in the future, every nation will need to create an education system that is more technologically sophisticated. 2024 IEEE.