International Journal of Multidisciplinary Research and Explorer
The International Journal of Multidisciplinary Research and Explorer (IJMRE) is a peer-reviewed, open-access journal published quarterly. The journal focuses on the dissemination of high-quality research in science, engineering, management, social sciences, education, humanities, and technology and interdisciplinary studies that address real-world challenges. IJMRE aims to provide a platform for researchers, academicians, and professionals to publish original research articles, review papers, and case studies that demonstrate methodological rigor and practical relevance. The journal emphasizes interdisciplinary approaches that integrate multiple domains to advance knowledge and innovation. All submissions undergo a structured peer-review process to ensure academic quality, originality, and ethical publishing standards. The journal maintains a diverse editorial board and encourages contributions from the international research community to promote global academic exchange. International Journal of Multidisciplinary Research and Explorer (IJMRE) Publisher: Academics Achievers Education and Research Foundation, India 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
Academics Achievers Education And Research Foundation
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Transformer-Based Encoder-Decoder Model For Enhanced Air Quality Prediction
Air quality prediction remains a critical challenge due to the complex spatiotemporal dependencies inherent in pollutant data. We propose a transformer-based encoder-decoder model to address this challenge, focusing on accurate and robust air quality index (AQI) forecasting. The proposed method processes historical pollutant measurements, including PM2.5, PM10, and NO2, through a multi-head self-attention mechanism to capture long-range dependencies and nonlinear interactions. The model employs a sliding window approach to generate sequential input-output pairs, which are then normalized and fed into stacked transformer encoders for feature extraction. A global average pooling layer condenses the temporal information into a fixed-length representation, enabling precise AQI prediction through a dense output layer. The architecture incorporates residual connections and layer normalization to stabilize training, while dropout regularization mitigates overfitting. Experiments on the Delhi air quality dataset demonstrate the model’s effectiveness, achieving competitive performance in terms of mean squared error and mean absolute error. Furthermore, the transformer’s ability to model intricate temporal patterns without recurrent structures offers computational advantages over traditional sequence models. The results highlight the potential of attention-based architectures for environmental monitoring tasks, particularly in scenarios where interpretability and scalability are paramount. This work contributes to the growing body of research on deep learning for air quality prediction, providing a framework that balances accuracy, efficiency, and generalizability
Copyright (c) 2025 Hemant Kumar Pandey, Dr. Kaneez Zainab (Author) https://creativecommons.org/licenses/by/4.0
Comparative Analysis of Morphological Approaches for Low-Resource Indo-Aryan Languages: Case Studies in Angika, Maithili, and Hindi
Morphological analysis is important and challenging sub-task in Natural Language Processing (NLP), particularly for morphologically rich Indo-Aryan languages. Yet, some regional languages as Angika and Maithili are still under-resourced due to the absence of annotated corpora and compu- tational tools. This work is a comparative study of morphological analysis in the context of Angika, Maithili and Hindi, covering low-and little-resource scenarios. The work compares rule-based, finite-state transducer and hybrid methods and concentrates on the treatment of inflectional and derivational morphology. Linguistically motivated rules and small lexical re- sources are used for low resource languages, while Hindi is used as a reference language. Rule-based and hybrid models perform more robustly and have better interpretable results in low-resource settings than the purely data-driven models. The study demonstrates the need for LingA (linguistic analyzers) to seamlessly combine linguistic knowl- edge with computational techniques, in order to develop efficient morpho-analysis tools for Indo-Aryan lan- guages that are still under-represented typologically.
Copyright (c) 2025 Alok Kumar (Author) https://creativecommons.org/licenses/by/4.0
Review Analysis of Growth Dynamics of Indian Automotive Industry: A study focus on trends in Production, Sales and Exports
The Indian car industry is one of the world's largest and a key driver of the country's economic progress. This research paper examines the growth trends in the Indian auto industry from 2020-21 to 2024-25, with a focus on production, domestic sales, and export performance in three major vehicle categories: passenger vehicles, commercial vehicles, and two-wheelers & three-wheelers. The study uses trustworthy data from the Society of Indian Automobile Manufacturers (SIAM), India Brand Equity Foundation (IBEF), and government reports, applying methods like trend analysis and Average Annual Growth Rate (AAGR) calculations to reveal the main trends in the industry. The study revealed that the car industry has experienced constant expansion in production capacity. The overall sales trend is good, with a robust rebound after a decrease in fiscal year 2021-22. Over the past three years, this sector has shown resiliency and sustained development. In the last five years, the export pattern has altered.
Copyright (c) 2026 Barmeda Dipalee Hasmukhbhai, Prof (Dr.) P S Hirani (Author) https://creativecommons.org/licenses/by/4.0
Improving Instrument Landing System (ILS) Precision using Magneto-Gyroscope-Based Sensor Fusion
The Instrument Landing System (ILS), however, is a well-established but expensive and infrastructure dependent aviation support system that must also battle electromagnetic interference (EMI). This paper presents a portable infrastructure free system using embedded systems and sensor fusion which replicates ILS functionality. An MPU-6050 gyroscope and LIS3MDL magnetometer coupled to an STM32F103 microcontroller allow the system to compute its real-time attitude, as well as deviation from a virtual glide slope centerline and runway centerline. This is aided by a complementary filter which helps to combine sensor data that will reduce gyroscope drift and noise from externals like magnetic interference. The MCU-That-Wals PCB was designed in Altium and KiCad and is small enough to use EMI reducing layout practices, supporting UART, I2C and optional Wi-Fi Telemetry. Firmware includes drivers, fusion logic, and RTC synchronized data logging. Obtaining valid responses confirms successful sensor integration and tracking orientation. Some target applications are UAVs, urban air mobility and remote landings. In the future GNSS, advanced filtering, autopilot integration and real-time dashboards will also be added leading to an enabling technology for cost effective, scalable and fully autonomous landing systems.
Copyright (c) 2026 shashank Bachahalli, Sathvik S, Vaishnavi (Author) https://creativecommons.org/licenses/by/4.0
Digital Twin Framework for Real-Time Monitoring and Optimization of Ultrasonic Vibration-Assisted EDM using AI and IoT
Ultrasonic vibration-assisted electrical discharge machining (UV-EDM) requires careful tuning of multiple in teracting parameters to balance Material Removal Rate (MRR) and surface roughness (Ra). This paper presents an integrated Digital Twin framework coupling real-time IoT sensor streams with Gaussian Process Regression (GPR) surrogate modeling, NSGA-II multi-objective optimization, and Analytic Hierarchy Process (AHP) decision support. Validation on 150 experimental UV-EDM trials demonstrates: (1) predictive R2 = 0.92 for MRR and R2 = 0.89 for Ra, (2) a 14.6% improvement in MRR, and (3) a 9.2% reduction in Ra compared to baseline settings. Beyond numerical performance, the proposed system high lights the value of hybrid intelligence by combining data-driven learning with physical domain understanding to achieve con sistent machining quality under varying operational conditions. The digital twin’s continuous feedback loop allows operators to visualize tool wear, spark energy, and machining stability in real time, promoting proactive decision-making rather than reactive control. Furthermore, the framework’s modular design ensures scalability across different EDM configurations, mak ing it adaptable for both research and industrial deployment. Overall, this work establishes a practical pathway toward smart manufacturing by bridging the gap between physical machining and its virtual counterpart through AI and IoT integration.
Copyright (c) 2026 Tanvi Muttin, Sunayana P Singh, Tameem Ulla, Vagarth Pandey (Author) https://creativecommons.org/licenses/by/4.0
AI-Driven Prediction and Optimization of Surface Roughness and Residual Stresses in Turning of 42CrMo4 + QT Steel
Accurately predicting surface roughness and residual stress is important for keeping machined parts the right size and making sure they hold up well over time. This study introduces a hybrid approach that combines Random Forest Regression and Gaussian Process Regression to predict and improve outcomes when turning 42CrMo4 + QT steel. The MaRoReS dataset, which is publicly available, includes machining parameters along with vibration and cutting force data. The RFR model is used to find the main process parameters and measure how they affect surface integrity. At the same time, the GPR model offers predictions that take uncertainty into account for optimizing multiple objectives. We trained and validated the model by using grid search along with 10-fold cross-validation. The hybrid approach they tried managed to get an R² value above zero. The proposed hybrid approach achieved R² > 0.98 and RMSE < 0.12 μm for surface roughness prediction, outperforming traditional regression and neural models reported in literature. A Pareto-based optimization strategy identified the optimal parameter window (Vc = 160 m/min, f = 0.15 mm/rev, ap = 0.6 mm), resulting in a 28% improvement in surface finish and a 35% reduction in tensile residual stresses. The results highlight that the proposed RF–GPR framework offers a practical and transparent approach to data-driven process optimization. It also shows strong potential for integration into digital twin–based machining environments.
Copyright (c) 2026 Shreyas Chakravarthy, Shyam Kaushik B M, Soma Partha Sai, Prashant Doranahalli, Sujan R, Gajanan M Naik (Author) https://creativecommons.org/licenses/by/4.0
Prediction of Cutting Temperature in Plasma Arc Machining Using Deep Learning: A Comprehensive Hybrid Framework
Predicting the cutting temperature accurately is essential for maximizing the quality of Plasma Arc Machining (PAM) and reducing heat-affected areas. In order to achieve temperature prediction with RMSE 99%, this paper suggests a novel hybrid framework that combines pretraining with the Finite Element Method (FEM), Physics-Informed Neural Networks (PINN), and meta-learning. We summarize the results of 20 cutting-edge studies and offer a workable 10-step implementation guide that takes into account the needs for real-time control, synthetic data generation, and sim-to-real transfer. Comparative analysis shows verified physical consistency, with improvements of 29% over CNN-only approaches and 59% over conventional ANN methods. This work lays out a workable plan for integrating intelligent manufacturing into non-traditional machining operations.
Copyright (c) 2026 Sagar Ganiga , Safwan Sayeed, Samridhi Srivastava , Shikha Prasad, Monisha S R, Dr. Gajanan M Naik (Author) https://creativecommons.org/licenses/by/4.0
Impact of Digital Financial Inclusion on Job Creation, Purchasing Power, and Standard of Living in North-Western Nigeria
This study examines the impact of financial inclusion and digital financial inclusion on jo, purchasing power, and standard of living in North-Western Nigeria. This research employed a quantitative design to examine the effect of digital financial inclusion on poverty alleviation in Nigeria. A survey method was utilized to capture respondents’ perspectives, experiences, and opinions on the subject, relying on primary data collection from structured questionnaires. The data was collected from 415 households in the region; the research employs Partial Least Squares Structural Equation Modelling (PLS-SEM) to investigate the relationships between financial inclusion, digital financial inclusion and poverty reduction via (job creation, purchasing power, and standard of living). Findings of the research indicate that both financial inclusion and digital financial inclusion positively and significantly drives job creation by enabling entrepreneurship and reducing barriers to credit; the results also further revealed that digital financial inclusion are positive and significantly affect the purchasing power and household living standards. Thus, indicating that digital financial inclusion facilitates welfare improvements through better access to remittances, education, and healthcare spending. Policy implication for this study include recommendations foe regulatory agencies and service providers to expand digital financial infrastructure: Increase mobile money agents in rural areas to improve accessibility, enhance mobile banking and USSD network, It’s also recommend that future studies should expand to other regions of Nigeria to enhance generalizability.
Copyright (c) 2026 Umaru Hussaini, Muhammed Umar Dikko (Author) https://creativecommons.org/licenses/by/4.0
Pottery Industry and Value Chain Model: A Conceptual Study
Decrease market demand, low profitability, labour turnover and unsound financial situation is what characterise pottery industry in today`s scenario. Value chain model primary activities of inbound logistic, operations, outbound logistic, marketing & sales and service activities are needed systematically to be executed with support activities of procurement; human resource management, technological development and infrastructure, in case of pottery industry, these activities to be performed systematically require organising of artisans, government assistance and social as well as psychological motivation. Thus, a new framework is framed keeping these points in view. The study using literatures of various authors attempts to study pottery industry using value chain model and suggest a viable solution in the form of new framework to improve the situation of pottery industry.
Copyright (c) 2025 Vaishali Silakari, Dr Priti Goswami (Author) https://creativecommons.org/licenses/by/4.0
Comparative Insights into State-of-the-Art Cryptographic Techniques and Design Approaches
The modern communication technology has experienced huge changes with the rapid growth of the Internet. This expansion has made human beings more dependent on it, but has also raised security issues during information exchange. While being transferred over a public network, attackers may monitor and capture the data packets if they are unencrypted. Consequently, the communication process may be interrupted and intercepted. Attackers can combine multiple methods to grab the transmitted data. Therefore, security of information is imperative prior transmission. Cryptography is a security measure to make data unintelligible to intruders. Numerous research studies have been conducted over the decades to introduce a variety of cryptography methods. Additionally, a number of analyses have been carried out to determine the optimal encryption technique based on specific characteristics, each with its own shortcomings. This prompts individuals to implement robust security measures. This paper provides a comprehensive analysis of contemporary cryptography methods, while also reviewing several existing related research efforts.
Copyright (c) 2025 Most. Mazriha Akter Mohua (Author) https://creativecommons.org/licenses/by/4.0
A Comparative Study of ECG Sampling Rates in Predicting Cardiac Arrhythmias Using a Deep Learning Approach
Cardiac arrhythmias are a group of conditions that have caused great concern among healthcare professionals because they have a high incidence and prevalence worldwide, and their early diagnosis and effective treatment are essential to alleviate the negative impacts they can have on people’s quality of life. In recent years, many studies have suggested Deep Learning models for diagnosing cardiac arrhythmias by analysing data from electrocardiograms (ECG). The heart’s electrical activity can be measured using different ECG sampling rates, and the chosen rate can impact the model’s prediction accuracy and associated computational costs. This study presents a comparative analysis of various sampling rates for predicting cardiac arrhythmias using a Deep Learning model and a large public dataset. The findings can serve as a guideline for doctors and researchers to select the most appropriate sampling rates to achieve greater efficiency when using the ECG and better outcomes in predicting cardiac arrhythmias.
Copyright (c) 2025 Rodrigo Alexandre Dos Santos (Author) https://creativecommons.org/licenses/by/4.0
Enhancing Melanoma Skin Cancer Detection and Classification: U-Net segmentation and feature fusion of Deep Learning Algorithms
Human skin cancer is the most prevalent type of cancer and poses a significant threat to life, particularly, melanoma skin cancer, which exhibits a high mortality rate. Accurate detection and classification of melanoma skin cancer continue to be essential for timely treatment and improving patient diagnosis. Traditionally, painful, invasive and time-consuming biopsies are used to detect melanoma skin cancer. However, recently, computer-aided diagnosis of melanoma has become crucial. Thus, in this study, the researcher proposes a novel approach combining the feature extraction capability of the VGG16 and ResNet-50 deep learning algorithms with the precise segmentation power of the U-Net architecture. By combining these methodologies, the study aimed to achieve enhanced accuracy in distinguishing melanoma skin cancer as benign or malignant. The proposed method leverages the U-net segmentation algorithm to accurately delineate lesion boundaries and highlight crucial diagnostic areas, and the VGG16 and ResNet-50 models to extract high-level features from melanoma images, capturing textures and intricate patterns indicative of melanoma skin cancer. With a precise segmentation algorithm and the fusion of rich features from deep learning models enable a comprehensive analysis of melanoma characteristics, facilitating more reliable classification. The study used a total of 10606 images of melanoma skin cancer, and the experimental result demonstrates promising results, achieving a classification accuracy of 93% in distinguishing benign and malignant cases. As the experimental result shows, it validates the effectiveness of the proposed method in accurate classification of melanoma skin cancer. This study contributes to advancing melanoma detection capability and paving the way for more accurate dermatological diagnostic tools
Copyright (c) 2025 Getnet Tigabie Askale, Belayneh Matebie Taye, Achenef Behulu Yibel, Misganaw Abeje Debasu (Author) https://creativecommons.org/licenses/by/4.0
OVERVIEW OF INTESTINAL HELMINTHIASIS IN NIGERIA: REGIONAL PATTERNS, CONTRIBUTING FACTORS, AND THE PARADOX OF PROLONGED WASH INTERVENTIONS
This study reviewed the current report helminthiasis in Nigeria. This study revealed that prevalence of intestinal helminth in the country has not declined since the1970s. Ascarislumbricoides was the most prevalent helminth in the Southwestern and South-southern parts of Nigeria. Hookworm was the most prevalent helminth infection in the Southeastern while multiple infections were highly prevalent in Northern Nigeria. Nigeria has benefited from the Water, Sanitation and Hygiene (WASH) programme for over thirty years in the aspects of enhanced capacity building for applying personal hygiene and sanitation, improved water sources and provision of sanitation facilities and the National Emergency Group for coordination of emergency preparedness and response for WASH-related diseases. Despite the prevalence of helminth infections in Nigeria is still very high and alarming. The factors identified for the high prevalence include socio-economic status, culture and ignorance; attitudes and behaviours toward hygiene and sanitation are also critical.
Copyright (c) 2025 Chukwuma Ogechi D, andUmoren, Otoh D. (Author) https://creativecommons.org/licenses/by/4.0
Philosophical Reflection on the Changing Face of Economic Development in the Context of Sustainability
This article examines the changing nature of economic development through the lens of sustainability. It explores agriculture’s evolving role within the economy—not only as a productive sector but also as a provider of public goods such as biodiversity, landscape preservation, and food security. The analysis draws on classical and modern theories, including Friedrich List’s stages of national development and A. Fisher’s three-sector model, which outlines the structural shift from agriculture to industry and services. Keynesian interventionism is revisited to highlight the state’s role in stabilizing economies and supporting vulnerable sectors, with the New Deal serving as a practical example. A central focus is the concept of sustainable development, traced from its origins in forestry to its adoption by global institutions. The article emphasizes the need to balance economic growth with environmental protection and social equity. Ecological agriculture is presented as a model aligned with sustainability principles, despite its lower input efficiency. The article synthesizes diverse theoretical perspectives and empirical insights, advocating for a multidimensional approach to development that harmonizes economic, social, and ecological goals. The analysis is based on Polish academic publications, translated by the author for the purpose of this study.
Copyright (c) 2025 Lukasz Chryniewicz (Author) https://creativecommons.org/licenses/by/4.0
Therapeutic Plants Used for Maintaining/Enhancing Health Conditionsin Owode-Yewa South Local Government, Ogun State, Nigeria: An Ethno-Pharmacological Survey
Herbal therapy involves the use of plant-based products for therapeutic purposes and remains a popular and effective treatment option for various illnesses. This study aimed to conduct an ethnobotanical survey of therapeutic plants used for maintaining/enhancing health conditions in Owode-Yewa South Local Government, Ogun State, Nigeria. A total of 50 questionnaires were distributed to gather information about the use of medicinal plants in the treatment of diseases. The collected data were analyzed using Excel 2016 and SPSS 20.Results revealed that sixty (60) therapeutic plants were identified for treating or maintainingthirty (30) different illnesses, with trees (49%) and shrubs (31%) being the most common types. The leaves (42%) and bark (19%) were the most frequently utilized parts of the plants, while decoction (49%) was the predominant method of preparation, with water (67%) serving as the primary solvent. Large number of the plants are mono-therapeutic while few are known as poly-therapeutic while majority of treated of maintained illnesses are Infectious (47%). This study contributes valuable insights into the medicinal plants used in the local area and highlights the urgent need for conservation efforts and the integration of these practices into formal healthcare systems.
Copyright (c) 2025 Umoren Otoh Dayo (Author) https://creativecommons.org/licenses/by/4.0
Rethinking the Northeast : Spatial Politics, Market Imperatives, and Cultural Contestation’s
The North east region of India historically, has been viewed through a strategic lens, during the colonial era the region acted as a strategic buffer against China and France. Post-independence, the Indian state has continued to hold the same perception of the northeast as a region of strategic importance. These imaginaries of the northeast throughout the years have put the region in a state of what many scholars refer to as “ territorial trap”. It was only after the advent of globalization that India adopted a new spatial imagery, under the Look East policy. The northeast was then imagined as a transit point to connect the mainland with Southeast Asian countries, thereby boosting trade and economic activity. However, this evolving perception towards the northeast has been driven by several factors, including geopolitical aspects, the region's internal quest for identity, and the chronic underdevelopment that has persisted throughout the decades. To overcome the challenges faced by the region, the Indian state has invested a considerable amount of capital in building infrastructure and boosting development. However, the region has not witnessed the realized growth that was assumed by policymakers at the center and has only sustained the prevailing state of underdevelopment and marginalization in the region, which, according to scholars like Sanjib Barua, has described as ‘durable disorder’. The paper aims to examine the historical spatial imaginations of the northeastern region and their ramifications. Also, look into the new issues emerging from the overlap of these spatial imaginaries, in doing so the research takes up qualitative method for enquiry, using primary sources like governments reports along with secondary data using works of relevant scholar’s in relevant field.
Copyright (c) 2025 Kunsang Bhutia (Author) https://creativecommons.org/licenses/by/4.0
Green Synthesis of Mediated Ag/MgO Nanohydroxyapatite from Crayfish Using Orange Peel Extract (Citrus aurantium delcis)for BiomaterialsApplications
Conversion of waste such as crayfish shell to resourceful biomaterial like Hydroxylapatite is a led-light to the realization of United Nation vision 2030.Waste crayfish shell are being generated in tons of million across the globe annually thus constituting environmental pollution, while the demand for the synthesis of pure and biologically active materials is on the increase.This present work is aimed at investigating the facile synthesis of crayfish waste derived hydroxyapatite (CFSHAP) and substituent synthesis of its orange peel extract mediated Ag/MgOnHAP nanocomposite, as a potential biomaterial in bone tissue engineering application. The phase purity, composition, size, functional groups and surface morphology of the apatite were elucidated using spectroscopic (X-ray Diffraction (XRD); FourierTransform Infrared Spectroscopy (FR-IR) and Scanning Electron Microscopy). The results SEM showed that Ag/MgO- CFSHAP nanoparticles have round morphologies and the SEM-EDS revealed the characteristic Ag, Mg, Ca/P and O composition, FT-IR analysis confirmed relevant hydroxylapatite functional groups like carbonate, phosphate and hydroxyl groups while XRD analysis revealed a well crystalline monophasic HAp powder comparabie to the MAP reference model (JCPDF no. 00-064-0738). The 1000°c calcined Ag/MgO-CFSHAP also displayed stable phase stability which could lead to increased densification and porosity.The use of Ag will introduce antibacterial properties, while MgO will introduce osseointergration into the HAp
Copyright (c) 2025 Adeyinka Ademilua (Author) https://creativecommons.org/licenses/by/4.0
Assessment of E-Waste Workers Knowledge Levels Regarding Potential Health Risks Associated With Electronic Waste In Ladipo Market, Lagos State, Nigeria
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.
Copyright (c) 2025 Festus Adewole Adenekan (Author) https://creativecommons.org/licenses/by/4.0
Spiritual Growth Analysis using Fuzzy Logic in Hinduism: A Scientific Consecration Model
Spiritual growth is a fundamental aspect of Hindu philosophy, emphasizing self-awareness, devotion, meditation, ethical conduct, and detachment as key elements of self-realization and enlightenment. However, measuring an individual's spiritual progress remains subjective and lacks a structured quantitative approach. This research presents a fuzzy logic-based framework to model and evaluate spiritual growth by incorporating key Hindu spiritual principles. The study defines five key input variables—Self-Awareness, Devotion (Bhakti), Meditation (Dhyana), Ethical Conduct (Dharma), and Detachment (Vairagya)—and a single output variable, Spiritual Level. A Mamdani fuzzy inference system is developed using MATLAB, employing trapezoidal and triangular membership functions to handle uncertainties in spiritual assessment. The system integrates rule-based inference to categorize an individual’s spiritual state into four levels: Beginner, Intermediate, Advanced, and Enlightened. The proposed model offers a structured methodology to analyze spiritual progress, aiding spiritual practitioners and researchers in assessing personal growth through computational intelligence. Simulation results validate the model's effectiveness, demonstrating its potential application in personalized spiritual guidance.
Copyright (c) 2025 Prithwiraj Jana, Parthajit Bisal (Author) https://creativecommons.org/licenses/by/4.0
Synthesis and Characterization of Copper (II) Complex Bearing 1-Phenyliminomethyl-Naphthalen-2-ol
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.
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
Creative Accounting and Accountability Failures in the Philippine Health Sector: A Case Study of PhilHealth during COVID-19
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.
Copyright (c) 2025 TIMOLEON LIANZA (Author) https://creativecommons.org/licenses/by/4.0
Evaluating the NEP 2020 Vision For Higher Education In India: A Critical Perspective
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.
Multidisciplinary and Vocational Education
Internationalization of Indian Education
Copyright (c) 2025 PRITI GOSWAMI, SUSMITA SAU (Author) https://creativecommons.org/licenses/by/4.0
Bibliometric Review of Financial Risk Tolerance: A Scientometric Analysis using Biblioshiny
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.
Copyright (c) 2025 VYSHAK P K, Dr. JAYARAJAN T K (Author) https://creativecommons.org/licenses/by/4.0
Empowering the Grid: Applications and Challenges of Machine Learning in Renewable Energy Resources
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.
Copyright (c) 2025 Akshay Juneja, Deepak Painuli, Ishant Jagotra, Priyanka Kumari, Hutashan Vishal Bhagat (Author) https://creativecommons.org/licenses/by/4.0
Understanding North-East Woman’s Participation in State Governance- A Power Dynamics
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.
Copyright (c) 2025 Srikant Mahali, Reshmi Biswas (Author) https://creativecommons.org/licenses/by/4.0
Strategic Inventory Control for Deteriorating Products under Time-Sensitive Demand
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.
Copyright (c) 2025 Trilok Chand Bagh (Author) https://creativecommons.org/licenses/by/4.0
Cloud-Enabled Intelligent Personalization and Secure Transaction Framework for Scalable E-Commerce Platforms
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.
Copyright (c) 2022 Nagendra Kumar Musham, Sathiyendran Ganesan, Venkata Sivakumar Musam, Purandhar. N (Author) https://creativecommons.org/licenses/by/4.0
Enhanced Cloud Resource Optimization Using Secretary Bird Optimization Algorithm and Dynamic Workload Balancing
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.
Task Scheduling, Metaheuristic Algorithms
Copyright (c) 2022 Guman Singh Chauhan, Rahul Jadon, Kannan Srinivasan, R. Hemnath (Author) https://creativecommons.org/licenses/by/4.0
SENSOR FUSION TECHNIQUES FOR DRONES IN IOT-BASED SURVEILLANCE SYSTEMS
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.
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
Efficient Test Case Generation in Software Testing Using DistilGPT-2 and EfficientNet-Lite
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.
Copyright (c) 2022 Naga Sushma Allur, Sharadha Kodadi, Durga Praveen Deevi, R. Pushpakumar (Author) https://creativecommons.org/licenses/by/4.0