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 Publisher Email: admin@academicsachievers.in Journal Email: editors@ijmre.com 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
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.
Enhancing mechanical properties of dissimilar pure Cu/Mg AZ31B friction stir welds via ultrasonic assistance
-Joining dissimilar pure copper (Cu) and magnesium (Mg) alloy AZ31B through friction stir welding (FSW) presents considerable metallurgical challenges due to contrasting thermo physical properties and brittle intermetallic compound formation at the weld interface. This study examines FSW of Cu/Mg AZ31B butt joints using a tool rotational speed of 1070 rpm, welding speed of 31.5 mm/min, tilt angle of 2°, and a 1.5 mm pin offset toward the Mg AZ31B side to optimize heat generation and material flow. Conventional FSW produced an ultimate tensile strength (UTS) of 110 Map, limited by inadequate inter-material mixing and interfacial defects. To overcome these constraints, ultrasonic-assisted friction stir welding (UAFSW) was implemented, superimposing high-frequency acoustic energy to enhance material plasticization, reduce flow stress, and promote dynamic recrystallization in the weld nugget. The UAFSW joints achieved UTS of 125 Map, representing a ~13.6% improvement over conventional FSW. This enhancement is attributed to acoustic softening and cavitations driven grain refinement, improved inters material mixing, and reduced interfacial defect density. These findings establish ultrasonic assistance as a viable strategy for welding dissimilar material systems, with significant implications for electrical, automotive, and lightweight structural applications requiring reliable Cu–Mg joints.
Copyright (c) 2026 Waman Kharat (Author) https://creativecommons.org/licenses/by/4.0
An Analytical Review of IoT-Based Health Monitoring Systems Integrating CNN–LSTM for Early Disease Detection
The purpose of the review paper is to review the use of the IoT-based real-time health monitoring systems and CNN -LSTM deep learning models to detect diseases in their early stages. We have the intention to explore how the IoT technologies and hybrid deep-learning structures can be useful in improving the continuous patient monitoring and predictive healthcare outcomes. The study design will comprise a critical review of the literature and comparative analysis of the recent sources dedicated to the IoT-based healthcare systems, sensors and deep learning models such as CNN, LSTM, and Bi-LSTM. The literature review of the system architectures, data processing techniques, and performance measurements are analyzed. The findings demonstrate that the IoT devices can be used to obtain physiological measurements in an extremely efficient way in real time, and CNNLSTM models can extract features and recognize temporal patterns and can, therefore, be utilized to predict illnesses more efficiently as compared to traditional machine learning techniques. A number of studies document high accuracy rates with a few models recording more than 98 percent disease prediction rates. Nonetheless, other issues, including data privacy, interoperability, computational complexity, and lack of labeled datasets, are still of great concern. The review finds that incorporating IoT and hybrid deep learning models provide an exciting way to create intelligent, scalable, and patient-centered healthcare systems and that future developments will rely on edge computing, explainable AI, and secure data-sharing systems.
Copyright (c) 2026 Komal Kulshreshtha, Shashank Swami (Author) https://creativecommons.org/licenses/by/4.0
Solar powered wireless EV charging system with Auto boost power and Foreign object detection
this study proposes an innovative solution for an automated system that will allow the contactless charging of electric vehicles with enhanced safety and control. The system integrates solar energy with battery backup (hybrid energy) that will supply continuous charging to the vehicle. The system is controlled by an Adriano that ensures the seamless switching of the energy sources, depending on the environmental conditions. TheArduinoUnomicrocontrollerwillcontinuouslymonitor and control the voltage levels by regulating the switching relay. The energy is transferred through coils wirelessly between the transmitter and receiver. The receiver captures the energy and rectifies it to DC and subsequently boosts it to around 12.6V to optimally charge a battery. Safety is enhanced by an integrated metal detection system, which will prevent system operation and contactless charging in the presence of foreign metallic objects between the coils. Other features include a USB socket for vehicle to grid applications, and an LCD for the display of the system’s parameters as it is being charged. There are substantial improvements to existing systems in the areas of safety and dependency on traditional energy sources. The system is also economical and sustainable, fulfilling the requirements for an advanced system of eco friendly, automated integrated transport EV charging systems.
Copyright (c) 2026 Shalini MS, Keerthana B, Chaithra S, Thanushree S, Vinushree S5 (Author) https://creativecommons.org/licenses/by/4.0
An Explainable Artificial Intelligence Framework for Medical Diagnosis
Artificial intelligence (AI) has recently gained prominence in healthcare. It is increasingly applied in disease detection and clinical decision-making. Although the results generated by such applications are highly accurate, many of them exhibit limited transparency. Most often, they operate as a "black box," i.e., while their output invisible, no clear explanation exists for how a particular result was reached. Such opacity is counterproductive in healthcare since trust and accountability are key considerations. To tackle the problem, the concept of Explainable Artificial Intelligence (XAI) has emerged. XAI refers to research direction dedicated to increasing the transparency of models to enable users to trace decisions. In this paper, we suggest a framework in which a traditional machine learning algorithm is combined with explain ability techniques to achieve an effective balance between accuracy and interpretability. We compare conventional and explainable artificial intelligence algorithms using different evaluation criteria such as accuracy, the level of interpretability, clinician trust, and time spent validating the output. Based on the analysis of our experiments, it becomes evident that the explainable AI model remains sufficiently accurate (approximately87%) but increases the visibility of the decision-making process. Furthermore, it saves time needed for decision validation. In general, the research reveals that the integration of explain ability helps increase the practical application of AI-based solutions to healthcare practice. It becomes possible for AI-powered tools not only to present the output but also assist doctors in comprehending and relying on it.
Copyright (c) 2026 Abhishek Bansal (Author) https://creativecommons.org/licenses/by/4.0
Detection and Classification of Parkinson disease using various features extraction model and deep Learning Techniques for early stages
Parkinson's disease (PD) is a neurological movement disorder characterized by a slow, progressive worsening of symptoms, such as a tremor in one hand and a generalized sense of stiffness. More than 6 million individuals throughout the globe are afflicted. In the early stages of the illness, when symptoms are hard to identify, there is currently no convincing finding for this condition by non-specialist practitioners. To better understand patients, an RNN-based predictive analytics system is developed. The issue can be resolved with a small margin of error utilizing deep learning methods. If input data sets are to be used for analysis, they should be retrieved from the UCI Machine Learning repository. The goal of this research is to create an auditory feature-based method for detecting Parkinson's disease. Several machine learning methods are used to model the retrieved characteristics. In this study, we apply an RNN-based classification technique to identify PD patients' samples from those of healthy individuals. The RNN network is taught acoustic characteristics and a spectrogram of the speaker's voice. Only auditory characteristics are used in the training of RNN models. Using optimization strategies, Deep Learning (RNN) algorithms, and health care data, this research establishes whether or not people have Parkinson's disease. To test the efficiency and performance of the proposed method, a comparative research is conducted.
Copyright (c) 2026 Ms. Tejashree Ladhe (Author) https://creativecommons.org/licenses/by/4.0
Smart-Adaptive: Intelligent Recommendation Systems for Personalized Multi-Platform Learning
Conventional education systems have a standardized teaching methodology which fails to serve the needs of the different students when it comes to their unique learning requirements. The disparities in the level of understanding, previous knowledge, and the speed at which the learner perceives are usually ignored, and they may lead to diminished interest and disproportionate learning results. In order to meet this challenge, the present paper presents a Personalized Learning Platform that could be used in both web and mobile platforms where the material would be adjusted based on performance and learning behavior of the students. The proposed platform is an adaptive learning method based on AI to analyze interaction with students, assessment performance, and progress trends. According to this analysis, the system suggests appropriate learning materials and topics and especially on areas where the learner has been struggling. The platform helps with learning in the form of performance based content delivery, tracking of progress and personalized feedback
Copyright (c) 2026 Mr. Manish Pandey, Mayank Shukla, Alok Kumar (Author) https://creativecommons.org/licenses/by/4.0
Green Taxes and Their Role in Shaping Sustainable Consumption
This paper looks into the awareness & attitude of the public for green tax as an instrument to popularize the concepts of circular economy and sustainable development in Bangalore. Environmental tax is a double edged sword, it discourages environmentally harmful activities and it encourages environmentally friendly consumption and production habits. 101 respondents were selected and data were collected through a questionnaire that collected the demographic profile, green tax awareness and green tax effectiveness attitude of the selected respondents. The findings indicate that there was a lot of variation in awareness and acceptance, and that respondents generally believed that there was a potential to reduce pollution with these taxes, but there were significant concerns about the long-term economic impact of these taxes. Chi-square tests and one-way ANOVA (Welch's) were used to investigate the relationship between green tax awareness and consumer behavior, customer purchases and long-term environment. The study reveals a significant gap in public awareness and in the communication structure available for this, highlighting the need for policy makers to be able to use the green taxes as a tool to promote sustainable consumption in the context of a circular economy.
Copyright (c) 2026 Dr.Renu Rathi (Author) https://creativecommons.org/licenses/by/4.0
Use of Multimedia-Assisted Instruction and its Effectiveness in Promoting Learner Engagement among Elementary Teachers in Castillejos District
The integration of technology in education has enhanced teaching practices and learner participation in classrooms. This study examined the use of multimedia-assisted instruction and its effectiveness in promoting learner engagement among public elementary school teachers in Castillejos District, Zambales. Specifically, it determined the extent of multimedia-assisted instruction use and its effectiveness in behavioral, emotional, cognitive, and social engagement among learners. The study is significant in providing insights into the role of multimedia tools in improving teaching and learning processes. A descriptive–quantitative research design was utilized using a structured survey questionnaire administered to 165 randomly selected public elementary school teachers. Data were analyzed using frequency, weighted mean, standard deviation, Analysis of Variance (ANOVA), and Pearson-r correlation. Findings revealed that teachers highly utilized multimedia-assisted instruction and perceived it as highly effective in promoting learner engagement. No significant differences were found when respondents were grouped according to profile variables. However, a significant positive relationship existed between multimedia-assisted instruction and learner engagement, indicating that effective multimedia integration enhances student participation and involvement in learning. The study concluded that multimedia-assisted instruction is an effective tool in improving learner engagement in elementary education. It is recommended that schools strengthen teacher training on multimedia integration and provide adequate technological resources to support effective instruction.
Copyright (c) 2026 Michael G Albino, Jastine Anne Q. Mendoza1 (Author) https://creativecommons.org/licenses/by/4.0
The Cost of Over-sharing: How Daily Facebook Posting Compromises Personal and Digital Security
This study explores the risks associated with posting often on Facebook and exposes the security threat posed by the use of smart phones in making one’s life vulnerable. By applying architectural and criminological theories such as Routine Activity Theory and Context Collapse, this paper explores how nine specific threat vectors have been created. It is observed that the creation of large sets of daily activity information, check ins, and high-definition media allows determined perpetrators to develop predictive behavior algorithms that facilitate generative AI deep fake extortion, home burglary, and economic fraud. Additionally, using context collapse through platforms means that the overshares by individuals reduce their social boundaries and end up negotiating away the security space for the entirety of the social network. The response to these structural threats requires an urgent change in behavior by learning to disengage from intimate sharing and implementing stringent security measures.
Copyright (c) 2026 Sharif Ratul Hassan (Author) https://creativecommons.org/licenses/by/4.0
Artificial Intelligence Academic Usage and Its Impact on Students: A Survey-Based Study
Artificial Intelligence (AI) has rapidly emerged as a transformative change in higher education, reshaping how students learn, solve problems, and engage with academic tasks. From generating content and assisting with coding to providing personalized learning support, AI-powered tools are increasingly becoming integral part to the academic experience. AI tools such as ChatGPT, DeepSeek, Grammarly, Claude are significantly influencing the academic environment in engineering education. The present study investigates the usage patterns, perceived benefits, and educational impact of AI tools among engineering students. A survey was done in engineering colleges and data were collected through purposive sampling from 96 engineering students through a structured questionnaire titled AI Academic Usage and Impact Scale (AAUIS). The collected data was analysed using excel, the findings reveal that the majority of students use AI tools daily or several times a week for learning technical concepts, project documentation, coding support, brainstorming, and assignment completion. Most students felt that AI tools help them learn more efficiently by saving time, simplifying complex concepts, and supporting their academic work. Many reported that these tools make studying, coding, and completing assignments easier and more productive. At the same time, some students expressed concerns that excessive reliance on AI may reduce independent thinking, creativity, and problem-solving skills. The study suggests that AI has become an important part of engineering students’ learning experiences; however, its use should be balanced with critical thinking, responsible practices, and clear ethical guidelines to ensure that technology supports, rather than replaces, meaningful learning.
Copyright (c) 2026 Jagirdar Lubna Batool Batool (Author) https://creativecommons.org/licenses/by/4.0
Chronic Low Back Pain and Radiculopathy: An Overview, Key Points, and Treatment Approaches
Low back pain (LBP) is a major health, social, and economic concern worldwide. It was once believed to be mainly caused by nerve compression from a degenerated intervertebral disc (DID), but it’s now seen as a complex condition involving mechanical, inflammatory, compressive, immune, and degenerative factors. Even with medical progress, treatment remains tough, as many options offer limited relief or cause unwanted side effects. When standard methods don’t work, surgery is often considered, though it has its own risks and limits. This review examines the pathophysiology and the role of inflammation in the DID and nerve root damage, and presents a minimally invasive, outpatient treatment aimed at targeting both the disc and affected nerves to reverse the problem rather than just easing compression. In a study of 88 patients followed over one and two years, results showed significant improvements in pain, neuropathic symptoms, and disability, with no reported side effects.
Copyright (c) 2026 Milla-Villeda Reinaldo Humberto (Author) https://creativecommons.org/licenses/by/4.0
ESG Governance as a Value Driver: Evidence Across Industries and Investor Perception
While economic growth and globalization have heightened ESG concerns, lack of clarity remains regarding whether ESG governance drives economic value across different industries. This study clarifies this ambiguity, by showing ESG oriented companies do drive economic value, but the values differ depending on the industries the companies belong to. The “one-size-fits-all" ESG metrics fail to account for industry specific materiality because the governance risks in tech industries differ from those energy or services sector. This gap has a significant impact on how we understand the ESG principles and ESG scores. This study addresses this gap by examining ESG performance across the agriculture, manufacturing, education, energy and services sectors using financial and secondary data. This research evaluates the impact of each ESG pillar on different industry sectors and aggregates ESG scores on key performance metrics by including ROA, ROE, and Tobin's Q. The findings are expected to demonstrate that materiality-weighted factors have strong, sector-specific economic consequences. This research aims to improve valuation accuracy and strengthen investor confidence in sustainable markets.
Copyright (c) 2026 Dr. Zaker Ul Oman, APRAJITA GOSWAMI, KOLLURU SRI NAGA SNIGDHA (Author) https://creativecommons.org/licenses/by/4.0
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