International Journal of Multidisciplinary Research and Explorer
type: channel
description: The International Journal of Multidisciplinary Research and Explorer (IJMRE) is an esteemed international peer-reviewed academic journal that publishes 4 issues per year, quarterly. Its primary objective is to deliver authentic and reliable contributions to the global research community. Supported by a strong editorial board of subject experts, IJMRE ensures a rigorous peer-review process while maintaining efficiency and quality. Renowned for its academic prominence, the journal covers a wide spectrum of fundamental and emerging research topics across multiple disciplines. By offering valuable publishing opportunities, IJMRE strengthens research competencies and fosters the growth of academic trends worldwide. With its active presence on academic journal sites and journal article platforms, IJMRE has established itself as a trusted resource for scholars, researchers, and professionals seeking credible and high-quality research publications. International Journal of Multidisciplinary Research and Explorer (IJMRE) Publisher: Academics Achievers Education and Research Foundation Editor-In-Chief: Dr. Jaydip Kumar Address: Umrapur, Ibrahimpur, District Raebareli, Pin-229212 Email: admin@academicsachievers.in No. of Issues Per Year: 4 | Frequency: Quarterly ISSN (Online): 2833-7298 ISSN (Print): 2833-7301 Publication Fee: IJMRE has no publication fee up to 10 pages, and for extra pages, authors need to pay 5 USD per page. For details on article withdrawal, please see our Article Withdrawal Policy Article Template: click here
publisher: Academics Achievers Education And Research Foundation
language: en
publication name: International Journal of Multidisciplinary Research and Explorer
issn: 2833-7301
copyright: This is an Open Access article distributed under the term's of the Creative Common Attribution 4.0 International License permitting all use, distribution, and reproduction in any medium, provided the work is properly cited.
items: Assessment of E-Waste Workers Knowledge Levels Regarding Potential Health Risks Associated With Electronic Waste In Ladipo Market, Lagos State, Nigeria
items: Synthesis and Characterization of Copper (II) Complex Bearing 1-Phenyliminomethyl-Naphthalen-2-ol
items: Creative Accounting and Accountability Failures in the Philippine Health Sector: A Case Study of PhilHealth during COVID-19
items: Evaluating the NEP 2020 Vision For Higher Education In India: A Critical Perspective
items: Bibliometric Review of Financial Risk Tolerance: A Scientometric Analysis using Biblioshiny
items: Empowering the Grid: Applications and Challenges of Machine Learning in Renewable Energy Resources
items: Understanding North-East Woman’s Participation in State Governance- A Power Dynamics
items: Strategic Inventory Control for Deteriorating Products under Time-Sensitive Demand
items: Cloud-Enabled Intelligent Personalization and Secure Transaction Framework for Scalable E-Commerce Platforms
items: Enhanced Cloud Resource Optimization Using Secretary Bird Optimization Algorithm and Dynamic Workload Balancing
items: SENSOR FUSION TECHNIQUES FOR DRONES IN IOT-BASED SURVEILLANCE SYSTEMS
items: Efficient Test Case Generation in Software Testing Using DistilGPT-2 and EfficientNet-Lite
items: Efficient Test Case Prioritization in Software Testing Using DistilRoBERTa for Fault Detection Optimization
items: Cloud-Based E-Commerce Sales Exploratory Data Analysis and Cloud Storage Integration
items: Cloud-Based AI solutions for credit card fraud detection with feedforward neural networks in banking sector
items: Real-Time Path Planning for IoT-Enabled Autonomous Vehicle Robotics Using RRT and A * Algorithms
items: High- and Low-Frequency TENS with Paravertebral Ozone Therapy for Chronic Low Back Pain and Radiculopathy: A Pilot Study
items: SALES FORECASTING EFFECT ON PHARMACIES
items: A Fuzzy Logic-Based Decision Support System for Early Detection of COVID-19: A Review and Comparative Analysis
items: Optimizing Inventory Management: Strategies for Deteriorating Items with Time-Based Demand
items: Assessing Stakeholder Awareness and Perception of NEP-2020: A Study on Policy Implementation and challenges
items: India-UK Relations Roadmap 2030 And Beyond: A Comprehensive Strategic Vision
items: Exploring Teachers' Perceptions of Parent-Teacher Partnership to Support Learners with Intellectual Disabilities in Primary Schools
items: Relationship between Purpose in Life and Psychological Distress among Older Adults
items: Comparative Analysis of Machine Learning Algorithms to Predict Type II Diabetes
items: Creating A Python Algorithm for A Robot to Identify RGB Colours of A 2D Captured Image
items: Technology Transfer in Renewable Energy: A Study on Wind and Solar Energy in India
items: Framework for Technology Transfer in India's Wind Energy Sector: Supporting Electric Vehicle Charging Infrastructure and Sustainable Growth
items: Dynamics of Financial Inclusion in India
items: Automatic Identification of Epilepsy Seizures from EEG Signals Using a Hybrid CNN and LSTM Model

Assessment of E-Waste Workers Knowledge Levels Regarding Potential Health Risks Associated With Electronic Waste In Ladipo Market, Lagos State, Nigeria
type: item
description: The growing challenge of electronic waste (E-waste) management, particularly in developing countries like Nigeria, has become a significant environmental and public health concern. This study assessed the knowledge levels and practices of e-waste workers regarding health risks associated with e-waste in Ladipo Market, Lagos State, Nigeria. Using a descriptive cross-sectional design, 96 e-waste workers, including repairers, recyclers, and waste scavengers, were surveyed through structured questionnaires. Socio-economic characteristics, awareness levels, and work practices were analyzed using frequencies, percentages, and correlation techniques. Results indicate a significant knowledge gap, with only 35% of respondents aware of long-term health risks such as respiratory issues and toxic metal exposure. Despite this, 85% of participants reported engaging in informal disposal methods, including open burning and improper handling. which contribute to environmental degradation and public health risks. Correlation analysis revealed a strong positive relationship (r = 0.72) between knowledge levels and the adoption of safety measures, emphasizing the role of education in promoting safer practices. Furthermore, workers with higher education levels demonstrated better adherence to safety measures. This study highlights the urgent need for structured interventions, including targeted educational programs and policy-driven regulatory frameworks, to improve awareness and promote sustainable e-waste management practices. Recommendations include enforcing stricter environmental laws, establishing formal recycling systems, and creating community awareness campaigns to mitigate the health and environmental risks associated with e-waste.
subject: Articles
  topic: section
subject: Electronic Waste (E-waste)
  topic: keywords
subject: E-waste Management
  topic: keywords
subject: Health Risks
  topic: keywords
subject: Ladipo Market
  topic: keywords
subject: Sustainable Practices
  topic: keywords
creator: Festus Adewole Adenekan
rights: Copyright (c) 2025 Festus Adewole Adenekan (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-09-07
publication date: 2025-09-07
starting page: 81
ending page: 103
doi: 10.70454/IJMRE.2025.05036

Synthesis and Characterization of Copper (II) Complex Bearing 1-Phenyliminomethyl-Naphthalen-2-ol
type: item
description: A novel Schiff base ligand, 1-phenyliminomethyl-naphthalen-2-ol was synthesized by condensation of 2-hydroxyl-1-naphtaldehyde and aniline, and subsequently complexed with Cu(II) ions to become a stable metal-ligand complex. The synthesized Schiff base and Cu(II) complex have been characterized using UV–Visible spectroscopy and Fourier-transform infrared spectroscopy. The UV-Vis analysis revealed that metal-ligand charge transfer which depicted low band at 406, 350 nm (for the two molar ratio 1:1, 2:1) respectively. The FTIR absorption spectra confirmed the formation of the imine (C=N) bond and coordination of the phenolic oxygen and azomethine nitrogen. NH bonds revealed coordination at 3340 and 3366 cm-1 while CN bonds showed no coordination and 1161 cm-1, both at 1:1, 2:1 respectively. In conclusion, these findings supports the successful formation of a bidentate Schiff base to Cu(II) complex with a potential application in biological and catalysis studies.
subject: Articles
  topic: section
subject: Cu(II) Complex
  topic: keywords
subject: Schiff Base
  topic: keywords
subject: UV-Vis and FT-IR Spectroscopy
  topic: keywords
subject: 2-hydroxyl-1-naphtaldehyde
  topic: keywords
subject: Aniline
  topic: keywords
creator: Idowu Daniel
creator: Daniel Ayodele Idowu
creator: Chidera Godswill Obideje
creator: Ejikeme Peter Igwe
creator: Okiki Quadri
creator: Emmanuel Ogbonnia Oko
creator: Gabriel Lagbel
rights: Copyright (c) 2025 Idowu Daniel, Daniel Ayodele Idowu, Chidera Godswill Obideje, Ejikeme Peter Igwe, Okiki Quadri, Emmanuel Ogbonnia Oko, Gabriel Lagbel (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-09-07
publication date: 2025-09-07
starting page: 65
ending page: 80
doi: 10.70454/IJMRE.2025.05035

Creative Accounting and Accountability Failures in the Philippine Health Sector: A Case Study of PhilHealth during COVID-19
type: item
description: This study investigates creative accounting practices in the Philippine Health Insurance Corporation (PhilHealth), focusing on financial manipulation and accountability breakdowns during the COVID-19 pandemic. The current paper uses a qualitative case study and document analysis of government audit reports, legislative inquiries, and media investigations (2018–2021) to reveal how procurement irregularities, unliquidated cash advances, and disguised operating losses distorted the agency’s financial position. These practices, facilitated by weak internal controls and governance failures, undermined transparency and jeopardized the implementation of universal health care. Anchored in public sector accountability and creative accounting theories, the study concludes with actionable recommendations, including the institutionalization of forensic audits, digital transparency reforms, and stronger whistleblower protections. The findings underscore systemic weaknesses that enabled financial misreporting and highlight the pressing necessity for structural reforms in public financial management.
subject: Articles
  topic: section
subject: accountability
  topic: keywords
subject: audit
  topic: keywords
subject: creative accounting
  topic: keywords
subject: corruption
  topic: keywords
subject: PhilHealth
  topic: keywords
subject: public sector
  topic: keywords
subject: universal health care
  topic: keywords
creator: TIMOLEON LIANZA
rights: Copyright (c) 2025 TIMOLEON LIANZA (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-09-07
publication date: 2025-09-07
starting page: 54
ending page: 64
doi: 10.70454/IJMRE.2025.05034

Evaluating the NEP 2020 Vision For Higher Education In India: A Critical Perspective
type: item
description: The National Education Policy 2020 has brought a revolutionary change in the education system of India. Emphasis is placed on each stage of education. This study seeks to analysis how the National Education Policy 2020 (NEP 2020) supports student-focused and skill-oriented higher education, while also assessing the inclusive strategies it proposes for diverse social groups within the higher education sector. Education is about access, equity, quality, affordability, and accountability. The emphasis will be on learning, regardless of the student. Along with education, moral development and holistic development have also been said about. The study used only secondary sources such as authoritative government publications and policy documents, research journal articles, and reliable online sources. It explores the key ideas, goals and potential impacts of the National Education Policy (NEP) 2020 through a systematic analytical approach.
subject: Articles
  topic: section
subject: Higher Education Policy in India
  topic: keywords
subject: 21st Century Education Reforms
  topic: keywords
subject: Equity and Access in Higher Education
  topic: keywords
subject: Multidisciplinary and Vocational Education
  topic: keywords
subject: Internationalization of Indian Education
  topic: keywords
creator: PRITI GOSWAMI
creator: SUSMITA SAU
rights: Copyright (c) 2025 PRITI GOSWAMI, SUSMITA SAU (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-09-07
publication date: 2025-09-07
starting page: 34
ending page: 53
doi: 10.70454/IJMRE.2025.05033

Bibliometric Review of Financial Risk Tolerance: A Scientometric Analysis using Biblioshiny
type: item
description: This study provides a bibliometric analysis of financial risk tolerance research papers. This study addressed bibliometric analysis using information from Scopus. The study in this work is exploratory and descriptive, based on bibliometric methodologies and tools used to documents contained in the Scopus bibliographic database, which yielded a total of 1084 documents spanning the years 1979 to 2025. This study report made use of Biblioshiny, an open-source platform based on the R programming language, as well as VOS viewer, in order to carry out a variety of bibliometric studies and assessments. The topic of financial risk tolerance was examined using the bibliographic data, which provided a complete description of the subject matter. According to the findings of the analysis, the most significant areas of focus for research on risk tolerance over the past several years have been on financial literacy, personality factors, and financial decision making. Over the past two decades, there has been an upward trend in the amount of research done on the FRT.
subject: Articles
  topic: section
subject: Financial Risk Tolerance
  topic: keywords
subject: Investment
  topic: keywords
subject: Financial Management
  topic: keywords
subject: Scientometric Analysis
  topic: keywords
creator: VYSHAK P K
creator: JAYARAJAN T K
rights: Copyright (c) 2025 VYSHAK P K, Dr. JAYARAJAN T K (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-09-07
publication date: 2025-09-07
starting page: 12
ending page: 33
doi: 10.70454/IJMRE.2025.05032

Empowering the Grid: Applications and Challenges of Machine Learning in Renewable Energy Resources
type: item
description: Integration of renewable energy systems, into the electrical grid has been investigated in the present research, with a special focus on the use of machine learning (ML) techniques in power system operations. In the framework of renewable energy, it critically investigates the applications of machine learning (ML) in forecasting, efficiency improvement, problem detection, and system optimization. The paper also discusses the primary challenges to implementing AI-driven solutions in contemporary power systems, including the need for quick decisions, cybersecurity risks, limitations on data availability and quality, and the difficulties of integrating with current grid infrastructure. This paper aims to provide an in-depth understanding of how intelligent algorithms are transforming the future of the electrical sector by highlighting both the revolutionary potential and the implementation challenges of AI technology in energy systems.
subject: Articles
  topic: section
subject: Renewable energy sources
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Regression
  topic: keywords
subject: Clustering
  topic: keywords
subject: Classifications
  topic: keywords
creator: Akshay Juneja
creator: Deepak Painuli
creator: Ishant Jagotra
creator: Priyanka Kumari
creator: Hutashan Vishal Bhagat
rights: Copyright (c) 2025 Akshay Juneja, Deepak Painuli, Ishant Jagotra, Priyanka Kumari, Hutashan Vishal Bhagat (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-09-07
publication date: 2025-09-07
starting page: 1
ending page: 11
doi: 10.70454/IJMRE.2025.05031

Understanding North-East Woman’s Participation in State Governance- A Power Dynamics
type: item
description: The northeastern states of India, popularly known as the Seven Sisters of India (Assam, Manipur, Meghalaya, Nagaland, Tripura, Mizoram, and Arunachal Pradesh), along with their brother state Sikkim, stand out with their unique geographical location and are home to several multi-ethnic communities. The region has witnessed several ethnic uprisings following the complexities of autonomy-related movements, the integration process, and the share in the natural resources. Needless to say, with the kind of societal structure prevailing in this region, women’s participation has been a major factor in ethnic movements in the past and even in the present. Indubitably, their participation in the societal and civil sphere of life is impressive. However, their involvement and presence in the larger political scenario, such as state institutions, are hardly felt. The central inquiry of the paper is to analyse the role of women in ethno-political conflicts and parallelly examine the power-sharing dynamics of women in North-Eastern regions. Methodologically, this research involves a comprehensive study of documents from the Election Commission of India, focusing mainly on the percentage of women candidates in the northeast contesting in Lok Sabha elections from the time period of 2014 to 2024. Through the analysis of this data, the research will try to underscore the reason for the invisibility of women's role in larger power sharing where there is paradoxically huge participation of women in ethno-political issues.
subject: Articles
  topic: section
subject: Women
  topic: keywords
subject: North-Eastern Region
  topic: keywords
subject: Ethno-Political Movements
  topic: keywords
subject: Political Participation
  topic: keywords
subject: Power Sharing
  topic: keywords
creator: Srikant Mahali
creator: Reshmi Biswas
rights: Copyright (c) 2025 Srikant Mahali, Reshmi Biswas (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 44
ending page: 60
doi: 10.70454/IJMRE.2025.05026

Strategic Inventory Control for Deteriorating Products under Time-Sensitive Demand
type: item
description: Efficient management of inventory is essential for businesses handling perishable or time-sensitive products. This study delves into advanced inventory models incorporating time-varying demand, pricing dynamics, and product deterioration. Drawing on a comprehensive review of research conducted by A. Sharma and collaborators between 2016 and 2024, the article presents strategic approaches to optimize inventory levels, reduce operational costs, and enhance overall supply chain performance. It emphasizes the role of dynamic pricing mechanisms, partial backordering, and accurate demand forecasting in developing responsive inventory control systems. The findings offer valuable insights for retailers, manufacturers, and logistics professionals while outlining promising avenues for future research in this evolving domain.
subject: Articles
  topic: section
subject: Inventory control,
  topic: keywords
subject: deteriorating items
  topic: keywords
subject: time-dependent demand
  topic: keywords
subject: pricing
  topic: keywords
subject: partial backlogging
  topic: keywords
creator: Trilok Chand Bagh
rights: Copyright (c) 2025 Trilok Chand Bagh (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 39
ending page: 43
doi: 10.70454/IJMRE.2025.05025

Cloud-Enabled Intelligent Personalization and Secure Transaction Framework for Scalable E-Commerce Platforms
type: item
description: This work discusses the evolution of a cloud platform for intelligent personalization and safe transaction management in scalable e-commerce sites. With the growth of e-commerce, sites are compelled to provide individualized user experience with transaction safety and privacy. On-premises solutions cannot process heavy loads of data and traffic and are therefore unsuitable for today's dynamic e-commerce environment. Cloud computing offers a substitute in the form of offering elastic scalability, high-performance data processing capabilities, and seamless integration of AI/ML solutions for intelligent personalization. The central concern of this research is to design and develop a cloud-based system with both personalized user experience and secure transactions.Based on machine learning algorithms, the system will offer real-time one-to-one recommendations and content in accordance with the user's own taste, and which will grow user usage and satisfaction. Behind the scenes, robust security features like encryption and authentication processes are part of the transactional process of securing confidential customer data from web threats and ensuring compliance with privacy regulations. The proposed model integrates scalability, high performance, and security in addressing the requirements of modern-day e-commerce sites. By difficult testing and benchmarking with current systems, the study measures the performance of the cloud solution to improve personalia, security, and transaction effectiveness. This publication goals at communicating perceptibleinfo to e-commerce in the upcoming and location an instance for businesses which wish to make changes and reform their sites as well as market safe and personalia buying on behalf of the client.
subject: Articles
  topic: section
subject: Cloud Computing
  topic: keywords
subject: Intelligent Personalization
  topic: keywords
subject: Secure Transactions
  topic: keywords
subject: E-Commerce Scalability
  topic: keywords
subject: Cybersecurity
  topic: keywords
creator: Nagendra Kumar Musham
creator: Sathiyendran Ganesan
creator: Venkata Sivakumar Musam
creator: Purandhar . N
rights: Copyright (c) 2022 Nagendra Kumar Musham, Sathiyendran Ganesan, Venkata Sivakumar Musam, Purandhar. N (Author) https://creativecommons.org/licenses/by/4.0
date: 2022-06-30
publication date: 2022-06-30
starting page: 29
ending page: 45
doi: 10.70454/IJMRE.2022.21001

Enhanced Cloud Resource Optimization Using Secretary Bird Optimization Algorithm and Dynamic Workload Balancing
type: item
description: In this paper, a framework for optimizing cloud resource allocation using the Secretary Bird Optimization Algorithm and dynamic workload balancing has been presented. The presented method tackles inefficiency in cloud computing systems, such as inefficiency in task scheduling and resource overload, through dynamic task distribution among resources. With the introduction of SBOA, prompted by the innate behavior of secretary birds, and workload balancing, the approach achieves maximum resource utilization, minimum makespan, and lower computational expense. Global attention mechanisms are further incorporated into the framework to direct computational effort at the most salient features, which improves efficiency. Simulation results show improved cloud resource optimization by a significant extent over conventional approaches, rendering the method feasible for dynamic, large-scale cloud environments.
subject: Articles
  topic: section
subject: Cloud Resource Optimization
  topic: keywords
subject: Secretary Bird Optimization Algorithm
  topic: keywords
subject: Dynamic Workload Balancing
  topic: keywords
subject: Task Scheduling, Metaheuristic Algorithms
  topic: keywords
creator: Guman Singh Chauhan
creator: Rahul Jadon
creator: Kannan Srinivasan
creator: R. Hemnath
rights: Copyright (c) 2022 Guman Singh Chauhan, Rahul Jadon, Kannan Srinivasan, R. Hemnath (Author) https://creativecommons.org/licenses/by/4.0
date: 2022-06-30
publication date: 2022-06-30
starting page: 46
ending page: 58
doi: 10.70454/IJMRE.2022.20901

SENSOR FUSION TECHNIQUES FOR DRONES IN IOT-BASED SURVEILLANCE SYSTEMS
type: item
description: This work introduces a novel system incorporating Internet of Things (IoT) empowered drones, robotics, and deep learning methods, specifically Convolutional Neural Networks (CNN), to identify areas affected by landslides. The system uses modern sensors including cameras, LiDAR scanners, and environmental sensors, offering detailed information from distant and dangerous environments. The CNN model analyses this information to classify areas as damaged or undamaged with very high precision. Robustness of the system is emphasized using multi-sensor data acquisition, efficient preprocessing, and application of transfer learning to deliver optimal performance. Results reflect a high classification accuracy of over 99%, validating the system's capability in real-time landslide detection and disaster management. The current work seeks to contribute to more scalable and efficient disaster management systems using IoT and AI technologies.
subject: Articles
  topic: section
subject: Internet of Things
  topic: keywords
subject: Deep Learning methods
  topic: keywords
subject: Convolutional Neural Network
  topic: keywords
subject: Surveillance systems
  topic: keywords
subject: Landslide Detection
  topic: keywords
subject: Sensor Fusion
  topic: keywords
creator: Dinesh Kumar Reddy Basani
creator: Basava Ramanjaneyulu Gudivaka
creator: Raj Kumar Gudivaka
creator: Rajya Lakshmi Gudivaka
creator: Sri Harsha Grandhi
creator: Aravindhan Kurunthachalam
rights: Copyright (c) 2022 Dinesh Kumar Reddy Basani, Basava Ramanjaneyulu Gudivaka, Raj Kumar Gudivaka, Rajya Lakshmi Gudivaka, Sri Harsha Grandhi, Aravindhan Kuruntha chalam (Author) https://creativecommons.org/licenses/by/4.0
date: 2022-09-30
publication date: 2022-09-30
starting page: 36
ending page: 49
doi: 10.70454/IJMRE.2022.20501

Efficient Test Case Generation in Software Testing Using DistilGPT-2 and EfficientNet-Lite
type: item
description: Software testing plays a critical role in ensuring software reliability, yet traditional test case generation approaches often suffer from high computational overhead and inefficiency. Traditional methods, including genetic algorithms, struggle with scalability and fail to optimize execution time while maintaining high test coverage. To address these limitations, this paper proposes a lightweight deep learning-based test case generation approach using DistilGPT-2 and EfficientNet-Lite. Unlike conventional deep learning models, our method efficiently generates both text-based and GUI-based test cases while reducing computational cost. The novelty of this approach lies in integrating CodeT5-Small for feature extraction, DistilGPT-2 for textual test case generation, and EfficientNet-Lite with an RNN for GUI-based testing, enabling a more effective, low-resource test generation pipeline. The results demonstrate that our method achieves higher test coverage (95%), improved efficiency (90%), and greater testing reliability (98%) compared to advanced genetic algorithms, while also reducing computational overhead to 60%. Compared to existing approaches, our method outperforms traditional AI-based testing solutions in terms of accuracy, fault detection rate, and efficiency. The proposed method enhances software testing by minimizing redundant test cases, improving execution pass rates, and ensuring broader code coverage, making it a scalable and cost-effective solution for modern software development. This work paves the way for lightweight transformer-based models in test case generation, ensuring robust test automation with minimal resource consumption.
subject: Articles
  topic: section
subject: Lightweight Deep Learning
  topic: keywords
subject: Test Case Generation
  topic: keywords
subject: Software Testing
  topic: keywords
subject: DistilGPT-2
  topic: keywords
subject: EfficientNet-Lite
  topic: keywords
creator: Naga Sushma Allur
creator: Sharadha Kodadi
creator: Durga Praveen Deevi
creator: R. Pushpa kumar
rights: Copyright (c) 2022 Naga Sushma Allur, Sharadha Kodadi, Durga Praveen Deevi, R. Pushpakumar (Author) https://creativecommons.org/licenses/by/4.0
date: 2022-06-30
publication date: 2022-06-30
starting page: 16
ending page: 28
doi: 10.70454/IJMRE.2022.20201

Efficient Test Case Prioritization in Software Testing Using DistilRoBERTa for Fault Detection Optimization
type: item
description: The very critical phase in SDLC is software testing, where application reliability, security, and efficiency are ensured. However, increasing complexity in software has made traditional test case prioritization (TCP) methods difficult, with regards to high execution time and computational overhead. The existing approaches such as Genetic Algorithms (GA) are highly computationally expensive, and the adaptability of test cases with new evolvement cannot really be integrated with the processes. This study proposes an artificial intelligence-based approach with DistilRoBERTa for test case prioritization to improve fault detection and optimized test execution. Unlike traditional methods, DistilRoBERTa uses deep learning to analyze semantic and historical defect data of the test cases to intelligently prioritize. The proposed method achieves 93% test case coverage (against 90% in GA), 90% execution efficiency (against 85%), and 96% reliability (against 95%) while significantly reducing computational overhead to 53% (against 70%). All these aspects, therefore, make the results much more scalable, efficient, and adaptable as compared to software testing. An edge over competitive heuristic-based TCP methods is that the proposed model offers faster execution coupled with minimal resource consumption—the perfect environment for extensive testing. Management of test cases proves to be one of the important tasks since there are a large number of test cases in software. This paper develops an automated Intelligent test case prioritization process. A centralized intellectual resource is established through the complete understanding of test cases, their interdependencies, requirement analysis, defect analysis, and processing of information for prioritization. The application of the resulting development would open new horizons to the evolution of intelligent testing.
subject: Articles
  topic: section
subject: Test Case Prioritization
  topic: keywords
subject: Software Testing
  topic: keywords
subject: DistilRoBERTa
  topic: keywords
subject: Fault Detection
  topic: keywords
subject: Deep Learning
  topic: keywords
creator: Sathiyendran Ganesan
creator: Aravindhan Kurunthachalam
rights: Copyright (c) 2021 Sathiyendran Ganesan, Aravindhan Kurunthachalam (Author) https://creativecommons.org/licenses/by/4.0
date: 2022-06-30
publication date: 2022-06-30
starting page: 58
ending page: 73
doi: 10.70454/IJMRE.2021.10501

Cloud-Based E-Commerce Sales Exploratory Data Analysis and Cloud Storage Integration
type: item
description: E-commerce has highlighted the requirement to use advanced cloud technology and data analysis techniques to power customer experience and business efficiency. This paper is a detailed study of integrating cloud-based solutions and machine learning (ML) techniques in the analysis of e-commerce sales data. The research uses a customer credit card database to contrast and validate the performance of various ML models, i.e., Random Forest, Decision Tree, Logistic Regression, etc., in predicting customer churn. The performance of the models is compared against metrics such as Accuracy 92.5%, Precision 91.0%, Recall 93.0%, F1-Score 92.0%, and AUC-ROC 0.94 which identify Random Forest as the most superior performing model. The study then continues to probe the impact of integration of the cloud into storage and discovers that it is efficient, scalable, and secure when handling vast data. The key contribution of the cloud solutions in the e-commerce environment is in accordance with this research as scalability in business, optimized utilization, and hassle-free customer experience emerge. This book finishes by providing lines of future work on hybrid cloud solutions, real-time prediction systems, and protection of data privacy.
subject: Articles
  topic: section
subject: Cloud Computing
  topic: keywords
subject: E-Commerce
  topic: keywords
subject: Data Analytics
  topic: keywords
subject: Data Preprocessing
  topic: keywords
subject: Performance Metrics
  topic: keywords
subject: Random Forest
  topic: keywords
creator: Karthikeyan Parthasarathy
creator: G. Arulkumaran
rights: Copyright (c) 2021 Karthikeyan Parthasarathy, G. Arulkumaran (Author) https://creativecommons.org/licenses/by/4.0
date: 2021-09-30
publication date: 2021-09-30
starting page: 20
ending page: 33
doi: 10.70454/IJMRE.2021.10301

Cloud-Based AI solutions for credit card fraud detection with feedforward neural networks in banking sector
type: item
description: The increasing complexity of fraud strategies, combined with the rapid increase in transaction volumes, makes credit card fraud detection an important area of consideration in the banking sector. Due to the complexities of fraudulent behavior in the context of large transaction sizes, traditional techniques for fraud detection based on rules have become incapable of meeting the challenge. Cloud-based AI solutions based on Feedforward Neural Networks (FNNs) provide the best option when looking for an advanced adaptive approach. This solution benefits from cloud storage to provide highly efficient management of large volumes of transactional data with maximum scalability and flexibility. FNNs aid in differentiating and recognizing complex fraud patterns, while cloud technology ensures that such a system is proficient in adjusting to transaction volume fluctuations. The integration of these technologies resulted in the model achieving a 99.89% accuracy, 98.67% precision, and 95.76% recall. The F1-score of 96.78% shows a balanced performance in precision versus recall. This convergence of AI and cloud technologies promotes accurate fraud transaction detection with reduced false-positive responses and the possibility of continuous adaptation to new fraud schemes. All said, Cloud-Based AI with FNNs is a system that is most potent to secure financial transactions and customers' interests in the banking sector.
subject: Articles
  topic: section
subject: Feedforward Neural Networks
  topic: keywords
subject: Banking Sector
  topic: keywords
subject: Fraud Detection Systems
  topic: keywords
subject: Transaction Data
  topic: keywords
subject: Cloud-Based AI
  topic: keywords
creator: Charles Ubagaram
creator: Karthick.M
rights: Copyright (c) 2021 Charles Ubagaram, Karthick.M (Author) https://creativecommons.org/licenses/by/4.0
date: 2021-03-30
publication date: 2021-03-30
starting page: 16
ending page: 28
doi: 10.70454/IJMRE.2021.10101

Real-Time Path Planning for IoT-Enabled Autonomous Vehicle Robotics Using RRT and A * Algorithms
type: item
description: One of the main purposes of this work is to provide a path planning framework for IoT-enabled autonomous vehicles through the use of RRTs and A*. These were designed to maximize actual real-time navigation and decision-making in very dynamic and complex situations considering obstacles and uncertainties in the environment. In cases that have unknown or nonregular barriers, the RRT algorithm is employed to visualize the environment rapidly to derive an initial feasible path across the configuration space. Following the developments of RRT paths, the A algorithm* will address topics brought about by their construction in order for the route to be smooth, efficient, and have the shortest length. A synergism between the two techniques makes these systems adapt in real time to changes in the environment and in transportation conditions while preserving computational economy. From the performance evaluation, joining the strategy increases these very important parameters, such as the energy consumption, path length, and the time to reach the destination, by a huge percentage. The model consumes energy that is reduced by about 23% in comparison with conventional approaches, decreases path length by 12-15% and decreases time to objective up to 50%. These results indicate that the RRT + A* model works very well to enhance the effectiveness and efficiency of autonomous vehicle navigation in changing conditions. This framework can be used in applications like robotics and autonomous driving, and it represents a viable answer for real-time energy-efficient optimal path planning.
subject: Articles
  topic: section
subject: Autonomous Vehicle Navigation
  topic: keywords
subject: Dynamic Environments
  topic: keywords
subject: Energy Efficiency
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Obstacle Avoidance
  topic: keywords
subject: Path Optimization
  topic: keywords
subject: Real-Time Navigation
  topic: keywords
creator: Nagendra Kumar Musham
creator: R. Hemnath
rights: Copyright (c) 2021 Nagendra Kumar Musham, R.Hemnath (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-03-31
publication date: 2025-03-31
starting page: 66
ending page: 84
doi: 10.70454/IJMRE.2021.10701

High- and Low-Frequency TENS with Paravertebral Ozone Therapy for Chronic Low Back Pain and Radiculopathy: A Pilot Study
type: item
description: Background: Chronic low back pain (CLBP) with radiculopathy is a leading cause of disability worldwide. Conventional treatments generally have limited efficacy, high costs, and associated adverse effects. Objective: To evaluate the efficacy and safety of high-and low-frequency TENS (HLF-TENS) with paravertebral ozone therapy (POT)in chronic low back pain (CLBP) and radiculopathy. Methods: This non-randomized pilot study was conducted with 88 consecutive patients meeting the inclusion criteria. Participants underwent 12 sessions of combined HLF-TENS and POT, without a control group. Pain intensity, neuropathic symptoms, and disability were assessed using the Numeric Rating Scale (NRS); the modified Michigan Neuropathy Screening Instrument (MNSI), and the modified Oswestry Disability Index (ODI), respectively. Assessments were performed at baseline, midpoint, and post-treatment, with long-term follow-up (1–2 years) via telephone interviews. Results: The study included 35 men (40%) and 53 women (60%), mean age of 51.3 years (range 20–80). Post-treatment, 80% of participants showed statistically significant improvements in pain, neuropathic symptoms, and disability. Pain NRS scores decreased from 8.58 to 1.64, neuropathic symptom scores from 7.34 to 1.14, and ODI scores from 8.05 to 2.12 (p< 0.001). At long-term follow-up (1–2 years), 80% of respondents remained asymptomatic, with no adverse effects reported. Conclusions: The combination of HLF-TENS and POT significantly reduced pain, neuropathic symptoms, and disability in CLBP patients with radiculopathy. These findings suggest a promising, non-invasive treatment option; however, randomized controlled trials are necessary to confirm efficacy.
subject: Articles
  topic: section
subject: High-and low-frequency stimulation
  topic: keywords
subject: Transcutaneous electrical nerve stimulation
  topic: keywords
subject: Paravertebral Ozone Therapy
  topic: keywords
subject: Chronic low back pain
  topic: keywords
subject: Radiculopathy
  topic: keywords
creator: Reinaldo Milla
creator: Quintanar-Escorza Martha Angelica
creator: Vazquez-Raygoza Alejandra Guadalupe
creator: Cisneros MarínMariana
rights: Copyright (c) 2025 Reinaldo Milla (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 24
ending page: 38
doi: 10.70454/IJMRE.2025.05023

SALES FORECASTING EFFECT ON PHARMACIES
type: item
description: Nowadays, as technology is advancing to previously unheard-of levels, every company and organization is finding it difficult to balance inventory and customer expectations. Every organization relies heavily on sales, and being able to predict the future helps in making strategic and intelligent sales decisions. The majority of businesses still struggle with revenue forecasting because it is usually the first step in developing the company's annual budget. Over time, a company's estimation could suffer if its sales projections are consistently inaccurate. Sales forecasting therefore affects the entire company to improve their overall growth strategy. An essential part of any business's sales operations is sale forecasting. For a business to supply the necessary quantity at the appropriate time, an accurate sales forecast is essential. Executives use the predictions to assess future performance and plan for organizational expansion. In this study, we use the machine learning techniques of naive forecasting and linear regression to try and predict a retail company's sales. The difference between the linear regression and naïve forecasting approaches is demonstrated using a computational example, and we have found that the linear regression yields better results than the naïve forecasting approaches. Additionally, we used the ARIMA model for the linear regression approach to forecast the sales for the upcoming five days.
subject: Articles
  topic: section
subject: ARIMA model
  topic: keywords
subject: Linear regression
  topic: keywords
subject: Naive Forecasting
  topic: keywords
subject: Predictions
  topic: keywords
subject: Sales Forecasting
  topic: keywords
creator: Dr. Manish Kumar
rights: Copyright (c) 2025 Dr. Manish Kumar (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 10
ending page: 23
doi: 10.70454/IJMRE.2025.50202

A Fuzzy Logic-Based Decision Support System for Early Detection of COVID-19: A Review and Comparative Analysis
type: item
description: The global COVID-19 pandemic, resulting from the infection by the SARS-CoV-2 virus, is emphasizing the urgent need for rapid and accurate diagnostic methods for the control of the infection spread. Lab-based testing methods can take time, leading to diagnostic delays and a high risk of transmission. Fuzzy Logic-Based Expert System in Early Detection of COVID-19 Symptoms and Risk Assessment in Real-Time. We provide a systematic review of published fuzzy logic models for the COVID-19 diagnosis along with details of their methodologies, accuracy, and clinical usability. Results support that fuzzy logic systems improve diagnostic efficiency, lessen healthcare pathways, and enable decision-making in a timely manner.
subject: Articles
  topic: section
subject: Fuzzy logic
  topic: keywords
subject: artificial intelligence
  topic: keywords
subject: medical decision-making
  topic: keywords
subject: expert systems
  topic: keywords
subject: COVID-19 diagnosis
  topic: keywords
creator: Nidhi Mishra
rights: Copyright (c) 2025 Nidhi Mishra (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 1
ending page: 9
doi: 10.70454/IJMRE.2025.50201

Optimizing Inventory Management: Strategies for Deteriorating Items with Time-Based Demand
type: item
description: Effective inventory management is crucial for businesses dealing with perishable or deteriorating goods. This research article examines various inventory models that account for time-dependent demand, price fluctuations, and product deterioration. By synthesizing findings from multiple studies by Sharma and colleagues (2015–2024), this paper explores key strategies for optimizing stock levels, minimizing costs, and improving supply chain efficiency. The analysis highlights the importance of dynamic pricing, fractional backlogging, and demand forecasting in inventory control systems. Practical implications for retailers, manufacturers, and logistics managers are discussed, along with future research directions.
subject: Articles
  topic: section
subject: Inventory management
  topic: keywords
subject: deteriorating items
  topic: keywords
subject: time-dependent demand
  topic: keywords
subject: dynamic pricing
  topic: keywords
subject: backlogging
  topic: keywords
creator: B V Padamwar BVP
rights: Copyright (c) 2025 B V Padamwar BVP (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-03-31
publication date: 2025-03-31
starting page: 29
ending page: 35
doi: 10.70454/IJMRE.2025.50103

Assessing Stakeholder Awareness and Perception of NEP-2020: A Study on Policy Implementation and challenges
type: item
description: The National Education Policy (NEP) 2020 aims to reform India's education system by introducing progressive changes. However, its success depends on stakeholder awareness and acceptance. This study investigates parental awareness and perception of NEP-2020, given that parents play a crucial role in their children's education. Using a quantitative approach, data was collected from 300 parents through a structured survey. The findings reveal a low level of awareness and mixed perceptions regarding the policy. The study suggests targeted awareness programs and parental engagement initiatives for effective policy implementation.
subject: Articles
  topic: section
subject: NEP-2020
  topic: keywords
subject: Parental Awareness
  topic: keywords
subject: Stakeholder Perception
  topic: keywords
creator: Jagirdar Lubna Batool Batool
rights: Copyright (c) 2025 Jagirdar Lubna Batool Batool (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-03-31
publication date: 2025-03-31
starting page: 23
ending page: 28
doi: 10.70454/IJMRE.2025.50102

India-UK Relations Roadmap 2030 And Beyond: A Comprehensive Strategic Vision
type: item
description: The India-UK Roadmap 2030 outlines a comprehensive strategic vision for deepening bilateral ties between India and the United Kingdom across key sectors, reinforcing both nations’ commitment to economic growth, sustainability, and global leadership. As the global geopolitical and economic landscape undergoes rapid transformation—shaped by the UK's post-Brexit realignments and India’s emergence as a key global actor—the roadmap serves as a forward-looking framework to deepen collaboration. The paper critically examines the five key pillars of the roadmap: economic cooperation, with a focus on the much-anticipated India-UK Free Trade Agreement (FTA); defense and security, including intelligence-sharing and Indo-Pacific cooperation; health and innovation, emphasizing joint research, pandemic preparedness and digital healthcare; climate action, highlighting commitments under COP26 and joint green energy initiatives; and people-to-people ties, driven by diaspora contributions, education partnerships, and evolving immigration policies. Additionally, the paper explores the challenges that may hinder progress, such as unresolved trade barriers, geopolitical divergences, security trust deficits, post-Brexit uncertainties, and strategic misalignments in the Indo-Pacific. Beyond 2030, India-UK relations will need strategic recalibration to adapt to emerging global trends. The paper examines possible future scenarios—ranging from strengthened economic integration to potential geopolitical divergences—and outlines strategic pathways to institutionalize cooperation in an evolving multipolar world. The significance of this study lies in its comprehensive analysis of how India and the UK can navigate these opportunities and challenges to create a sustainable, forward-thinking and adaptive partnership beyond 2030. It aims to provide strategic insights into how the roadmap can serve as a living document, evolving with emerging global realities, and evolve beyond 2030, ensuring that b…
subject: Articles
  topic: section
subject: India-UK Relations
  topic: keywords
subject: Strategic Partnership
  topic: keywords
subject: Bilateral Ties
  topic: keywords
subject: Free Trade Agreement (FTA)
  topic: keywords
subject: Security and Defense Cooperation
  topic: keywords
subject: Geopolitical Challenges
  topic: keywords
subject: Future Scenarios
  topic: keywords
subject: Strategic Pathways
  topic: keywords
creator: SALONI BAL
creator: Akansha Khaiba
rights: Copyright (c) 2025 SALONI BAL, Akansha Khaiba (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-03-31
publication date: 2025-03-31
starting page: 36
ending page: 65
doi: 10.70454/IJMRE.2025.50104

Exploring Teachers' Perceptions of Parent-Teacher Partnership to Support Learners with Intellectual Disabilities in Primary Schools
type: item
description: The study explored teachers’ perceptions of parent-teacher partnerships for enhanced access to equitable education for learners with intellectual disabilities in mainstream primary schools. A qualitative case study was utilised to collect data from 8 teachers who taught in mainstream primary schools. Data was sourced through face-to-face semi-structured interviews. The findings reveal that while various challenges hamper effective partnerships between parents and teachers in mainstream primary schools, a certain level of teamwork exists between parents and teachers, with more opportunities – yet to be fully explored – to strengthen this partnership further. Hence, a need to harness various strategies to improve parent-teacher relationships to create a more functional environment that meets the needs of learners with intellectual disabilities.
subject: Articles
  topic: section
subject: parent-teacher partnership
  topic: keywords
subject: intellectual disabilities
  topic: keywords
subject: support
  topic: keywords
subject: primary school
  topic: keywords
creator: Mahlape Tseeke
rights: Copyright (c) 2025 Mahlape Tseeke (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-03-31
publication date: 2025-03-31
starting page: 1
ending page: 22
doi: 10.70454/IJMRE.2025.50101

Relationship between Purpose in Life and Psychological Distress among Older Adults
type: item
description: The study investigates the relationship between psychological distress and purpose in life among older adults, along with gender differences in these variables. The sample consists of 80 older adults aged 65 to 79 years, selected from various parts of Kerala, primarily Palakkad, using a purposive sampling method to ensure equal representation of genders and minimize bias. The participants include 40 males and 40 females. Results reveal a significant negative correlation between purpose in life and psychological distress, indicating that higher purpose in life is associated with lower psychological distress. Furthermore, a t-test analysis shows no significant gender difference in purpose in life, suggesting similar perceptions of life purpose among male and female participants. However, a significant gender difference is observed in psychological well-being with females reporting higher levels of distress than males. These findings highlight the protective role of purpose in life against psychological distress and the need for gender-sensitive interventions to support older adults' mental well-being.
subject: Articles
  topic: section
subject: purpose in life
  topic: keywords
subject: psychological distress
  topic: keywords
subject: older adults
  topic: keywords
creator: Shimil
rights: Copyright (c) 2024 SHIMIL (Author) https://creativecommons.org/licenses/by/4.0
date: 2024-12-30
publication date: 2024-12-30
starting page: 10
ending page: 19
doi: 10.70454/z1v1fg74

Comparative Analysis of Machine Learning Algorithms to Predict Type II Diabetes
type: item
description: Machine Learning (ML) models are becoming robust and more accurate nowadays as the rapid increase in the amount and quality of training data. Researchers are proposing complex models for real-life problems to achieve higher accuracy, which requires high computing and other resources. In the context of the healthcare disease diagnosis, detection and prediction is still a challenge. Early diagnosis of a disease or ailment helps in timely recovery. Moreover, health been core to every individual, a lot of work is being done in this field to improve upon by using all available information. Current paper experiments on Pima Indian Diabetes Dataset (PIDDS) in two stages A and B. The main objective of this study is to review the accuracy of the applied machine learning algorithms and analyze their efficiency in predictions. Another essential objective is to show the efficacy of simpler models. Fields like computer vision and NLP have given rise to deep learning with complex and high computational models setting the trend to apply them in almost all the fields While they help where we have an abundance of data and complex relationships, simpler models still can do wonders and on their day can challenge these behemoths. We have also applied preprocessing methods (imputation, feature selection, scaling and discretization) to improve the classification accuracy. The algorithms selected for this problem are Logistic regression (LR), Artificial Neural Networks (ANN), Support Vector Machine(SVM), Naïve Bayes (NB), and Decision Tree(DT). LR provided the best accuracy, and the rest of the models are very close to each other.
subject: Articles
  topic: section
subject: Machine learning
  topic: keywords
subject: Disease prediction
  topic: keywords
subject: classification
  topic: keywords
subject: Logistic Regression
  topic: keywords
creator: Arun Singh Yadav
creator: Puneet Misra
rights: Copyright (c) 2024 Arun Singh Yadav, Puneet Misra (Author) https://creativecommons.org/licenses/by/4.0
date: 2024-01-25
publication date: 2024-01-25
starting page: 50
ending page: 55

Creating A Python Algorithm for A Robot to Identify RGB Colours of A 2D Captured Image
type: item
description: Robots face many problems in identifying RGB colours in captured images, which affects their performance in many applications. These challenges include different lighting, colour matching, constraints, and environment. The same object will appear in assorted colours in different light, making it difficult for robots to identify and distinguish colours. differences, especially in complex scenes. For example, under certain conditions blue and green hues will be remarkably similar to each other, resulting in misclassification. Cheap or poorly calibrated sensors do not detect colour accurately, resulting in inaccurate identification. Additionally, reflections and shadows can alter colour perception, adding another layer of difficulty to accurate colour identification. Calibration, normalization, and machine learning techniques. A simple Python algorithm for colour correction and analysis is here.
subject: Articles
  topic: section
subject: Python Algorithm
  topic: keywords
subject: Robot
  topic: keywords
subject: RGB Colours
  topic: keywords
subject: Image
  topic: keywords
subject: Google Colab
  topic: keywords
creator: Appurva Jain
creator: Rahul Singh
creator: Pawan Kumar
creator: Ruchika Bhakhar
creator: Manish Kumar
rights: Copyright (c) 2024 Appurva Jain, Rahul Kumar Singh, Pawan Kumar, Ruchika Bhakhar, Manish Kumar (Author) https://creativecommons.org/licenses/by/4.0
date: 2024-12-30
publication date: 2024-12-30
starting page: 1
ending page: 9
doi: 10.70454/660bw503

Technology Transfer in Renewable Energy: A Study on Wind and Solar Energy in India
type: item
description: This paper investigates the process of technology transfer within India’s renewable energy sector, focusing specifically on the wind and solar energy industries. Using a mixed-method approach, which includes case studies, site visits, and a Delphi study, the research highlights critical barriers, enablers, and strategic considerations for effective technology transfer. Findings emphasize the need for robust technology transfer models that address India's unique challenges in renewable energy implementation, along with recommendations for policy and practical interventions to enhance adoption rates.
subject: Articles
  topic: section
subject: Technology Transfer
  topic: keywords
subject: Renewable Energy
  topic: keywords
subject: Wind Energy
  topic: keywords
subject: Solar Energy
  topic: keywords
subject: India, Public-Private Partnerships
  topic: keywords
creator: Devendra Sharma
creator: Dr. M. K. Agrawal
rights: Copyright (c) 2024 Devendra Sharma, Dr. M. K. Agrawal (Author) https://creativecommons.org/licenses/by/4.0
date: 2023-03-30
publication date: 2023-03-30
starting page: 1
ending page: 11
doi: 10.70454/dmjqgj31

Framework for Technology Transfer in India's Wind Energy Sector: Supporting Electric Vehicle Charging Infrastructure and Sustainable Growth
type: item
description: This study presents a framework for managing technology transfer in India's wind energy sector, with a focus on supporting electric vehicle (EV) charging infrastructure. Using data from literature, interviews, Delphi studies, case studies, ISM modeling, and MICMAC analysis, the research offers strategies for fostering collaboration among stakeholders, expanding EV charging networks, and enhancing wind energy capacity through new installations and grid integration. It highlights barriers such as limited access to foreign technology, infrastructure gaps, and funding, while emphasizing facilitators like government support and international expertise. The study advocates for policies that incentivize EV adoption and wind energy use, with infrastructure, government backing, and funding identified as key enablers. Strong communication, project management, and adaptability are essential for successful technology transfer, positioning India to leverage global expertise for sustainable growth in the wind energy sector.
subject: Articles
  topic: section
subject: ISM modeling
  topic: keywords
subject: Delphi studies
  topic: keywords
subject: MICMAC analysis
  topic: keywords
subject: EV charging networks
  topic: keywords
subject: charging infrastructure
  topic: keywords
creator: Devendra Sharma
creator: Dr. M. K. Agrawal
rights: Copyright (c) 2024 Devendra Sharma, Dr. M. K. Agrawal (Author) https://creativecommons.org/licenses/by/4.0
date: 2023-03-30
publication date: 2023-03-30
starting page: 17
ending page: 27

Dynamics of Financial Inclusion in India
type: item
description: “Financial inclusion helps revitalize people out of poverty and can help speed economic development. It can draw more women into the mainstream of economic activity, harnessing their contributions to the society. Indhrawathi Sri Mulyani according to the Indonesian Finance Minister, a former World Bank Group head.The National Bureau of Economic Research claims that “12.4% of those in the top quartile of financial knowledge relied on books and newspapers as their primary source of financial knowledge, compared to only 3.9% for those in the lowest quartile of financial competence”. Growth that is inclusive can only occur via appropriate mechanisms that channel all their sources from top to bottom with innumerable sources from the economy. Given that India has the largest rural population in the world, financial inclusion is a novel idea that uses creative methods to encourage rural residents to open bank accounts. The goal of financial inclusion is to offer banking and financial services to the whole population at a reasonable cost in a fair, transparent, and equitable manner. However, low-income households frequently do not have access to bank accounts and must pay for numerous visits and time to obtain banking services, such as opening a savings account or obtaining a loan. As a result, these families find it more challenging to save money and make long-term financial plans. The concept of financial inclusion, its evolution, IFI, needs, benefits, customer engagement strategies, initiatives, expansion and modes of financial inclusion like ATMs branch openings, loan utilization, mobile transactions, etc. are all insisted upon in this comprehensive study. An attempt is made to address the general topic of financial inclusion in India in this paper.
subject: Articles
  topic: section
subject: Financial Inclusion
  topic: keywords
subject: Reserve Bank of India
  topic: keywords
subject: Inclusive Growth
  topic: keywords
subject: Financial services
  topic: keywords
subject: ATM cards, Commercial banks
  topic: keywords
subject: Mobile Banking, loans
  topic: keywords
subject: Bank Accounts
  topic: keywords
subject: IFI
  topic: keywords
creator: Chinnama Naidu Jammu
creator: G.Venkata Chalam
rights: Copyright (c) 2024 Chinnama Naidu Jammu, Prof G.Venkata Chalam (Author) https://creativecommons.org/licenses/by/4.0
date: 2024-09-30
publication date: 2024-09-30
starting page: 52
ending page: 73

Automatic Identification of Epilepsy Seizures from EEG Signals Using a Hybrid CNN and LSTM Model
type: item
description: The neurological illness known as epilepsy is characterized by a disruption in the normal functioning of the brain and can be found in severe cases. It is estimated that more than 10 percent of the whole population across the entire planet is affected by this ailment Every single day. When acquiring information on the brain's electrical activity, electroencephalograms, often known as EEGs, are utilized rather frequently by researchers. In this paper, an end-to-end system is proposed that utilises a combination of two deep learning models, namely Convolutional Neural Networks (CNNs) and Long Short-Term Memory Networks (LSTM), to classify electroencephalogram (EEG) data of epilepsy disordered people into three distinct categories: preictal, normal, and seizure. The findings of the experiment were obtained by making use of a dataset that is well-known and easily available, which Bonn International University provided. Within this CNN-LSTM classification model, the tasks of feature extraction, selection, and classification are all carried out in an automated fashion. Because of this, there is no longer a requirement for a manually devised methodology for feature extraction. In this study, the performance of the CNN-LSTM model is studied and assessed with relation to specificity, sensitivity, and accuracy. This is accomplished through the usage of the 10-fold cross-validation approach. The accuracy is 99.33%, the sensitivity is 99.33%, and the specificity is 99.66% concurrently, as indicated by the findings that were gathered while the trials were being carried out and the data that were collected. The results of our research indicate that deep learning approaches are the most appropriate choices for categorization when compared to other methods that are currently deemed to be state-of-the-art.
subject: Articles
  topic: section
subject: Epilepsy
  topic: keywords
subject: LSTM
  topic: keywords
subject: Machine learning
  topic: keywords
subject: deep learning
  topic: keywords
subject: classification
  topic: keywords
creator: Pankaj Saraswat
creator: Dr. Sandeep Chahal
rights: Copyright (c) 2022 Pankaj Saraswat, Dr. Sandeep Chahal (Author) https://creativecommons.org/licenses/by/4.0
date: 2022-06-30
publication date: 2022-06-30
starting page: 1
ending page: 15
doi: 10.70454/8anjm435