International Journal of Scientific Research in Science and Technology
International Journal of Scientific Research in Science and Technology (IJSRST) is peer-reviewed, online international journal published Bimonthly by Technoscience Academy. IJSRST is a highly-selective journal, covering topics that appeal to a broad readership in various branches of Science and Technology and related fields. The journal has many benefits, all geared toward strengthening research skills and advancing academic careers. Journal publications are a vital part of academic career advancement. To maintain a high-quality journal, manuscripts that appear in the IJSRST Articles section have been subjected to a rigorous review process. This includes blind reviews by one or more members of the international editorial review board, followed by a detailed review by the IJSRST editors. Email addresses for submitting articles are editorijsrst@gmail.com, editor@ijsrst.com JOURNAL INFORMATION Title : International Journal of Scientific Research in Science and Technology Frequency : Bimonthly Online ISSN : 2395-602X | Print ISSN : 2395-6011 Publisher : Technoscience Academy [ https://technoscienceacademy.com ] Refereed : Peer Review Copyright : Technoscience Academy, India Starting Year : 2014 Subject : Science and Technology Language : English Distribution : Open Access Publication Format : PDF & HTML -> Online Document Identifier Type : DOI (https://doi.org/10.32628/IJSRST ) Email Id : editor@ijsrst.com, editorijsrst@gmail.com Mobile No. : +91-8160745806 (WhatsApp support only) Website : https://ijsrst.com
Security of 5G Network Slicing: Identifying Vulnerabilities in The Virtualization of Mobile Network
5G network slicing is currently an important element of modern mobile networks' architecture. It lets many virtualised logical networks operate on the same physical infrastructure. This model supports a wide range of service requirements, consisting of enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communications (URLLC), and massive Machine-Type Communications (mMTC). It provides this by employing Software-Defined Networking (SDN), Network Function Virtualisation (NFV), cloud/edge computing, and programmable orchestration. Even though virtualisation has these advantages, it also presents serious security issues through creating the attack surface greater and placing slices at risk because of vulnerable isolation, insecure orchestration, authentication failures, shared-resource exploitation, and inter-slice attacks. This study investigates the main vulnerabilities influencing the security of 5G network slicing using a qualitative literature review of existing research. The investigation shows that vulnerabilities in SDN/NFV-enabled environments may threaten confidentiality, integrity, and availability over the entire slice lifecycle. The research suggests that traditional static security measures are not sufficient for highly dynamic and multi-tenant 5G ecosystems. Accordingly, to protect a system effectively, it requires security frameworks that are properly integrated, adaptable, and cross-layered. These should include strong slice isolation, secure management and orchestration, continuous monitoring, and smart defense mechanisms.
Prof. Madya Ts. Dr. Mohamad Fadli bin Zolkipli
Copyright (c) 2026 International Journal of Scientific Research in Science and Technology https://creativecommons.org/licenses/by/4.0
Performance and Dust Removal: A Field Study and Theoretical Analysis in the Arid Environment in Iraq
The effect of environmental elements such as dust buildup, tilt angle, wind and rain on solar panel efficiency, particularly in dusty regions like Iraq have been studied and monitored. The field study focused on two locations in Iraq: The Razzaza area, which is located between Karbala and Anbar governorate and the University of Kerbala. The findings shows that dust buildup significantly reduces panel efficiency by lowering the surfaces optical transmittance by up to 65% in just one month. Also, the results of the field study were consistent with the theoretical analysis; therefore, the geometrical losses have a much greater effect than the optical losses due to light reflection. The results showed as the tilt angle increased, dust accumulated decreased. Therefore, the highlight of this study was that it found that the optimal tilt angle for optimal performance in Iraq is between 30° and 35°, which maximizes efficiency while minimizing dust buildup. Additionally, during various months of the year, certain weather elements like wind and rain also aid in minimizing the amount of dust that accumulates on the panels. Moreover, the results of the simulation model used in this study support the idea that when the ambient temperature rises, particularly during the hot months of June, July, and August, the solar module's maximum output power falls.
Development of salt tolerant lines in Mung bean (Vigna radiata L. Wilczek Var. - K – 851)
Different ex plants such as hypocotyls, excised roots, steam segments, cotyledons, first leaf, secondary leaf, shoot apices of mung bean [Vigna radiata (L.) Wilczek] variety K 851 were inoculated on MS (Murashige and Skoog, 1962) medium supplemented with different concentrations of various hormones, viz. indoleacetic acid (IAA), -2, 4-dichlorophenoxyacetic acid (2, 4-D), naphthaleneacetic acid (NAA), 6-benzyladenine (BA) and kinetin (KN) fortified with NaCl (0.25, 0.50, 0.75 and 1.00% concentration). Salt stress showed decreased germination with increasing salt concentrations. MS media supplemented with 2, 4-D) and B.A. showed morphogenesis at various salt concentrations, some salt tolerant explants were isolated and regeneration of shoots have been achieved at 5 μM BA + 1 μM NAA, but an alteration in leaf morphology was noted. Rooting have been induced in such regenerants at 20 μM NAA without intervening callus phase. These plantlets were transferred to Knop’s solution for hardening. Later they were transferred to pots with autoclaved soil.
AI-Driven Safety Analytics for Cost Reduction and Operational Efficiency in High-Risk Environments
Reducing workplace incidents is both a safety priority and a business objective because incidents create direct costs, downtime, and productivity losses. This paper presents a business-oriented safety analytics framework that predicts whether a training intervention will be effective for a given worker and then uses that prediction to prioritize training under a fixed budget. Using a structured dataset of 4,000 training records, Gradient Boosting achieved ROC-AUC of 0.916 and F1-score of 0.884 on a held-out test set. We then simulate a budget-aware policy that selects workers with the highest expected benefit, combining incident-risk proxies and predicted training success. Under scenario assumptions, the proposed policy avoids approximately $414,122 of expected incident cost at a $150,000 budget while covering 170 workers. The results show how even straightforward predictive models can support practical training investment decisions when paired with transparent scenario analysis.
The Writing Machine Using Voice Command is an automated assistive system designed to convert human speech into handwritten text on paper. The proposed system integrates voice recognition, Bluetooth-based wireless communication, embedded control, and robotic motion to enable hands-free writing. Voice commands provided through a smartphone or voice-enabled device are converted into text and transmitted to an Arduino-based microcontroller (ATmega328P), which interprets the input and generates motion commands for the writing mechanism. Stepper motors driven by an A4988 motor driver enable precise movement along the horizontal and vertical axes, while a servo motor controls the pen’s up and down motion. Mechanical components such as smooth rods, lead screws, POM wheels, pulleys, and timing belts ensure stability and accurate handwriting reproduction. Experimental results demonstrate reliable wireless communication, smooth motor operation, and consistent handwritten output. The proposed system reduces the need for manual writing and provides a low-cost, portable, and effective assistive solution for physically challenged individuals, educational applications, and automation of repetitive writing tasks.
Smart Healthcare: Pill Identification with Deep Learning and Voice Assistance
Medical pill identification is crucial for ensuring medication safety, particularly for visually impaired individuals or those managing multiple prescriptions. This project leverages deep learning techniques using Artificial Neural Networks (ANN) to identify pills through images captured via a camera or uploaded by the user. The process involves a user selection dialog, image upload processing, and camera-based image processing to facilitate drug identification. The system automates pill identification by pre-processing images to enhance quality, extracting meaningful features, and employing an ANN for classification. Results are relayed, providing comprehensive drug information retrieved from the identified pill, accessible through a voice output system for improved accessibility. Existing systems primarily use traditional image processing techniques or manual identification through databases, which are time-consuming and less accurate in diverse real-world conditions. A significant drawback of these systems is their inability to handle poor lighting, varied angles, or partial occlusions in captured images effectively. This work utilizes cutting-edge deep learning algorithms, focusing on the healthcare domain, to improve the accuracy and usability of pill identification.
A Skin Cancer Detection System Using Wavelet-Based Feature Extraction and Deep Learning: A Simulation-Based Approach
Skin cancer is one of the most prevalent and potentially life-threatening diseases, where early diagnosis is critical for effective treatment. This work presents a real-time embedded skin cancer detection system using wavelet-assisted deep learning, implemented on a Python based platform. A camera module captures dermoscopic skin lesion images, which are processed locally on the Python based to enable portable and low-cost diagnosis. The acquired images undergo preprocessing for noise reduction, resizing, and illumination normalization. Wavelet transform–based feature extraction is then applied to enhance texture, edge, and color characteristics of lesions. The enhanced features are fed into a deep learning model that classifies skin lesions into malignant cancers such as Melanoma and Basal Cell Carcinoma, as well as benign conditions including Nevus, Seborrheic Keratosis, Actinic Keratosis, and Pigmented Benign Keratosis. The diagnostic outcome is displayed in real time on an output screen, indicating the presence or absence of skin cancer. Experimental results demonstrate that the proposed system achieves high classification confidence, with Melanoma detected at an average confidence of 96%, Basal Cell Carcinoma at 93%, and benign lesion classes at approximately 94%. Overall, the system maintains detection confidence above 90% across all tested categories, confirming its robustness and reliability. The proposed approach offers a portable, cost-effective, and accurate solution for early skin cancer screening and remote healthcare applications, highlighting the effectiveness of combining wavelet-based feature enhancement with embedded deep learning.
An NLP-Based Approach for Tomato Leaf Disease Prediction and Classification
Early identification of tomato leaf diseases is essential to minimize crop loss and improve agricultural productivity. Traditional disease diagnosis methods rely heavily on manual inspection, which is time-consuming, subjective, and requires expert knowledge. To overcome these limitations, this work presents an NLP-based intelligent approach combined with deep learning for tomato leaf disease prediction and classification. The proposed system utilizes a structured workflow that begins with dataset acquisition followed by preprocessing, feature extraction, and classification. A pre-trained MobileNetV2 deep learning model is employed for effective feature learning and disease recognition. The model is trained using labeled tomato leaf images representing multiple disease categories such as early blight, late blight, leaf mold, bacterial spot, and target spot. The extracted features are analyzed through deep neural layers to generate accurate predictions. Experimental results demonstrate that the proposed system achieves 75% classification accuracy, with a precision of 81.15% and recall of 70.33%, indicating reliable disease detection performance. The model successfully identifies disease types and generates detailed diagnostic reports, including disease name, confidence score, symptoms, severity level, and recommended treatment measures. Visual performance analysis using accuracy and loss curves confirms stable convergence and effective learning behaviour. The developed system offers a practical, automated, and scalable solution for tomato disease diagnosis, reducing dependency on manual inspection and expert intervention. The integration of deep learning with structured output reporting makes the system suitable for real-world agricultural applications and future deployment in smart farming environments.
Intelligent Vitamin Deficiency Detection Through Dermatological Image Analysis using CNN and ViT
Vitamin deficiencies often manifest through visible dermatological changes in multiple anatomical regions such as the tongue, nails, eyes, lips, and skin, enabling non-invasive image-based screening. This paper presents a deep learning–based framework for vitamin deficiency detection using a comparative analysis of Convolutional Neural Networks (CNNs) and Vision Transformer (ViT) architectures. The proposed system processes dermatological images through standardized preprocessing and extracts features using both CNN-based local pattern learning and ViT-based global self-attention mechanisms. An ensemble decision strategy is employed to enhance prediction reliability. Experimental evaluation was conducted on images from five body regions. The proposed system successfully identified Vitamin D deficiency from nail images with an ensemble confidence of 45.0% (CNN: 45.4%, ViT: 44.7%), exceeding the threshold of 42.0%, and classified it as high severity. For eye images, no deficiency was detected, achieving an ensemble confidence of 55.0% (CNN: 46.9%, ViT: 60.4%) above the healthy threshold of 50.0%. Lip image analysis detected Vitamin C deficiency with 47.8% ensemble confidence (CNN: 55.8%, ViT: 42.5%) at a threshold of 45.0%, indicating moderate severity. Tongue images revealed Vitamin B deficiency with an ensemble confidence of 53.0% (CNN: 50.3%, ViT: 54.8%) against a threshold of 40.0%, while skin images identified Vitamin A deficiency with 58.3% ensemble confidence (CNN: 50.2%, ViT: 63.8%) at a 45.0% threshold. The results demonstrate that CNNs perform effectively for localized dermatological features, whereas Vision Transformers exhibit superior performance in capturing global patterns across complex regions such as skin and eyes. The ensemble-based multi-region framework improves diagnostic robustness and provides a scalable, non-invasive solution for early vitamin deficiency screening and clinical decision support.
Automated Brain Aneurysm Classification Using Deep Convolutional Neural Networks
The prevention of brain aneurysm rupture morbidity and mortality requires the early and proper identification of brain aneurysms. The present piece of work postulates an automated brain aneurysm segmentation and classification system founded on the deep convolutional neural networks, created in MATLAB 2021. The suggested system has a structured pipeline that integrates image preprocessing, handcrafted feature extraction, and deep learning-based classification to enhance the diagnostic performance. Firstly, the RGB images of the brain are turned into grayscale images and denoised with a hybrid median filter with wiener estuarization. Contrasts in images are then increased to emphasize on anatomical features. The local binary patterns (LBP) features are computed on the basis of circular neighborhoods and a Fuzzy-LBP-based segmentation scheme is implemented to precisely locate aneurysm location. The refinement of post-segmentation is performed with the help of morphological operations and thresholding, and the features of Gray-Level Co-occurrence Matrix (GLCM) are applied to describe the information on texture. An aneurysm classification into the various categories is then done automatically using a deep CNN model. The accuracy, specificity and execution time metrics are used to measure the suggested approach. The experimental results show better performance than the other existing methods and achieve an accuracy of 94.49, specificity of 91.42 with an execution time of 30.94 s. These results prove the usefulness of the suggested automated system in assisting the early and valid diagnosis of brain aneurysms.
Predictive AI Model for Continuous Reliability Assurance in Site Operations
Continuous reliability of the site operation is a major concern in the contemporary industrial settings, as the complexity of the systems and dynamic working conditions has been on the rise. Conventional methods of maintenance like reactive and preventive maintenance are ineffective because they either act after a breakdown or they act according to a predetermined schedule without taking into account the real state of the system. This paper suggests a Predictive AI Model to Continuous Reliability Assurance of Site Operations in order to address these limitations. The proposed model combines the Artificial Intelligence (AI), the Internet of Things (IoT), and data-driven methods to make it possible to monitor the situation in real-time and predict failures. The methodology includes the data collection through sensors, pre-processing, feature extracting and application of machine learning algorithms to make predictions. Also, the concepts of reliability modeling and the digital twin are introduced to increase the system understanding and decision-making. The process involves making an intelligent decision module to produce maintenance recommendations and proactive intervention. To assess the performance of the suggested model, simulated data is used and compared with the current methods of work, including the traditional machine learning, deep learning, and the IoT-based systems. The findings indicate that the accuracy, precision, recall, and latency are better, which proves the model to be effective in terms of ensuring constant reliability. The system also encourages the continuous learning process and is therefore flexible to the dynamic operational environments. On the whole, the suggested solution offers a highly scalable and effective method of predictive maintenance and reliability assurance of the work at the site, which is in line with the dynamic demands of Industry 4.0 and Industry 5.0.
Copyright (c) 2025 International Journal of Scientific Research in Science and Technology https://creativecommons.org/licenses/by/4.0
Ethnobotany As an Interdisciplinary Science: Integrating Contemporary Research and Conventional Knowledge for Sustainable Development
Ethnobotany is the scientific study of the interrelationship between plants and human societies including their use for food, medicine, shelter, economic, cultural, and religious purposes. This area has developed into an important multidisciplinary study that connects traditional knowledge with contemporary studies in agriculture, pharmacology, nutrition, ecology, and biodiversity conservation. This review summarizes recent case studies to show how ethnobotanical knowledge influences cultural practices, economic activities, sustainable agricultural systems, and applications in medicine and nutrition which further contributes to food security, drug development, biodiversity conservation, and livelihood sustenance. The review also discusses current issues such as habitat deterioration, intellectual property issues, and the loss of traditional knowledge. This study highlights the importance of combining indigenous knowledge with interdisciplinary scientific approaches to support cultural heritage preservation and sustainable development by focusing on documentation, ethical research practices, and conservation strategies.
Sustainable Livestock Feeding Using Native Fodder Plants: Perspectives from Tribal Communities of Balrampur District, Uttar Pradesh
India has a large livestock population but the availability of quality fodder remains a major concern. This scarcity can be effectively addressed by using tree foliage as an alternative or supplemental feed. Fodder crops are grown to supply livestock feed in the form of green forage, silage, or hay. Ethnobotanical investigations were carried out in the Balrampur district in 2023 and 2025 to identify the plant species that the Tharu and other tribal people use for fodder and forage. Semi-structured interviews, guided field walks, conversations, and field observations were carried out to collect data from sixty informants, ages 20 to 80, who were chosen based on previous information. Twenty-five plant species from ten distinct families were recorded in the study. This thorough documentation identifies species with restricted or hazardous use, highlights customary feeding methods, and serves as a useful resource for the region's sustainable livestock management.
Improving Solar Power Forecasting Using Air Quality and Weather Data with Ensemble Learning
Solar power generation is non-continuous and very much dependent on atmospheric conditions such as weather variability and pollution levels. Forecasting solar power accurately is important for effective energy planning, grid stability, and large-scale integration of PV systems. Traditional forecasting approaches (sensor-based) are effective, yet costly, depending on the location. This study proposes a data-driven solar power forecasting framework that integrates weather parameters and Air Quality Index (AQI) indicators using ensemble machine learning models. The study focuses on existing methodologies that use meteorological variables, air pollutant concentrations (PM2.5, PM10, NO2, SO2, CO2, O3) and solar irradiance indicators for solar power forecasting. This paper reviews machine learning approaches, including regression models, ensemble learning, and stacked architectures, aligning with prediction accuracy, scalability, and practical deployment. This paper also emphasizes the impact of integrating AQI parameters into solar prediction models and demonstrates how effectively the combined weather-AQI features enhance the accuracy of solar power predictions. This study suggests employing a stacked ensemble model based on tree-based learners to align with the trends identified in the literature. The review concludes by identifying key research gaps and future directions, highlighting the potential of AQI-aware, data-driven models for cost-effective, scalable, and reliable solar power forecasting in smart energy systems.
Analytical Study of the Spatial Distribution of Chemical Properties of Well Water along Darna River, Nashik, India
An attempt has been made of the current research paper is to analyse the spatial distribution of chemical properties of well water along the Darna River in the Nashik district. Total 12 samples have been collected from the sites of the wells along the Darna River dated on 12th May 2024. A random purposive sampling method has been applied for the collection of the samples to analysis of the chemical properties of 10 parameters namely: TDS (mg/l), Calcium (mg/l), Fluoride (mg/l), Chloride (mg/l), Iron (mg/l), Total Alkalinity (mg/l), Nitrogen-nitrate (mg/l), pH, Electrical Conductivity (EC) and Total Hardness (mg/l). All collected samples have been analysed using standard water analysis methods in the laboratory. The obtained results of sample analysis have been visualised through different thematic maps. The readings of chemical parameters illustrate the heterogeneous results of the well water along the river Darna. The chemical properties of well water are highly variable, changing along the river's course due to a combination of natural processes and human activities. The water quality has been influenced by agricultural runoff, which is most promoting the pollution due to intensive use of agrochemicals and fertilizers, especially in rice-field areas. This research provides a valuable baseline for the agriculture department for setting the ideal guidelines of the future perspective for the farmers as well as the municipal councils for sustaining the drinking water quality.
Enterprise Resource Planning for Financial Applications: Engineering, Architecture, and Design Principles
Enterprise financial applications, especially Enterprise Resource Planning (ERP) systems, are the backbone of many organizations' financial governance, compliance and reporting support, and decision-making. For enterprise systems used at multi-site or global level, the underlying architecture of the system rather than its surface functionality determines the ability of an organization to respond to changing business or regulatory The financial ERP modules are the General Ledger (GL)‚ Accounts Payable (AP)‚ Accounts Receivable (AR)‚ Fixed Assets (FA)‚ and Cash and Bank Management․ Each of these modules is a bounded domain, exposing a well-defined interface on a common system of record․ A correctly engineered financial ERP uses a layered architecting approach that enforces strong separation of concerns among presentation, application, domain, and infrastructure layers while isolating customizations and extensions from core financial functionalities. Domain-Driven Design principles can be used to align financial ERP software with the reality of financial process boundaries. This decouples them from the relationships that made ERP customizations brittle and costly to maintain․ Then, modular and service-oriented architectures allow independent scalability and continuous delivery of discrete financial functions. Relational data integrity and command-query separation support the conflicting requirements of transactional correctness and analytical performance․ Security architecture in the form of role-based security‚ separation of duties‚ and audit trails is a structural design requirement rather than a policy layer․ Elastic scalability and high availability are supported by cloud-native deployment models and responses to distributed application state management and tenant data isolation․ Integration architecture uses API and event-driven approaches to connect financial ERP platforms and systems to the rest of the enterprise ecosystem․ Enforced models of extensibility allow backward and forward compatibility across the system lifecycle. Emerging AI and real-time analytics are the next frontiers of financial ERP engineering.
Deep Learning vs. Tree Ensembles for Life Expectancy Prediction: Evidence from Ukrainian Life Calculator
Life expectancy must be accurately predicted to allow effective planning of healthcare, resource allocation and public policy. This paper presents the comparison of four predictive models for the Ukrainian humanitarian project Life Calculator: the standard tree based ensemble machine learning models, Random Forest (LCR) and XGBoost (LCX), and deep learning models, Multilayer Perceptron (LCM) and Long Short-Term Memory (LCL). Data based on 260,000 Ukrainian participants were used to train all of the models using a single pipeline and evaluated based on common regression measures (RMSE, MAE, MSE, R2, MAPE) and the C-Index. Findings indicate that MLP and LSTM are always better than LCR and LCX in terms of error reduction in most measures, and MLP has the largest concordance score. In spite of this progress, negative values of R2 in all models indicate that there are still challenges regarding the noisy and incomplete health data. Deep learning models can capture more nonlinear, fine-grained associations among demographic and health variables; however, additional feature enrichment and more advanced models are required for clinically viable life expectancy predictions.
Consumer Perception and Acceptance of Green Marketing Practices in Emerging Economies: A Systematic Literature Review
This study examines consumer perception and acceptance of green marketing practices in emerging economies through a structured systematic literature review. With increasing environmental concerns and the growing importance of sustainability, green marketing has emerged as a key strategy for influencing consumer behaviour and promoting eco-friendly consumption. The study analyzes 30 research papers published between 2020 and 2024, supplemented by selected foundational studies to provide theoretical grounding. The findings reveal that environmental awareness positively influences consumer perception; however, it does not necessarily translate into actual purchasing behaviour. Price sensitivity, particularly in emerging economies, is identified as a major barrier limiting the adoption of green products. Additionally, trust and credibility play a crucial role, as concerns related to greenwashing significantly affect consumer acceptance. Marketing communication, especially through digital platforms, is found to enhance awareness and shape consumer attitudes, although its effectiveness depends on transparency and accuracy. The study also highlights the persistent attitude–behaviour gap, indicating that positive consumer attitudes toward sustainability do not always lead to consistent purchasing decisions. Furthermore, the findings emphasize the influence of socio-economic and cultural factors in shaping consumer behaviour in emerging markets. This research contributes to the existing literature by providing a comprehensive and integrated understanding of the determinants influencing consumer perception and acceptance of green marketing practices. The study offers practical implications for marketers and policymakers to design effective strategies that promote sustainable consumption. Future research should focus on sector-specific studies, rural consumer behaviour, and cross-country comparisons to further advance this field. The study uniquely integrates key determinants within a unified framework tailored to emerging economies.
Self-Healing Data Pipelines Using Predictive Monitoring: Architectures, Techniques, and Applications for Autonomous Data Systems
Modern data-driven systems rely heavily on complex, distributed data pipelines that ingest, process, transform, and deliver data across heterogeneous environments at scale. As organizations increasingly depend on real-time analytics, AI/ML models, and data-driven decision-making, the reliability of these pipelines becomes mission-critical. However, such systems are inherently vulnerable to a wide range of failures, including schema drift, data anomalies, upstream dependency changes, infrastructure instability, network latency, and rapidly evolving workloads. Traditional reactive monitoring approaches primarily based on static thresholds and alerting are insufficient to ensure resilience, as they detect issues only after failures have already impacted downstream systems. To address these limitations, this paper proposes a self-healing data pipeline architecture that leverages predictive monitoring techniques, integrating machine learning-based anomaly detection, automated root cause analysis, intelligent remediation strategies, and continuous observability across the data lifecycle. By incorporating AIOps principles with adaptive ETL workflows, the architecture enables pipelines to anticipate potential failures, dynamically adjust to changing conditions, and autonomously recover from disruptions with minimal human intervention. Furthermore, the system utilizes historical pipeline behavior, metadata, and real-time telemetry to continuously refine its predictive capabilities, thereby improving accuracy over time. Experimental insights and recent studies indicate that such predictive and self-healing mechanisms can significantly reduce failure rates, minimize downtime, enhance data quality, and improve overall system efficiency, ultimately enabling organizations to build robust, scalable, and intelligent data infrastructure capable of supporting modern digital ecosystems.
Intelligent Data Catalogs Using Metadata Automation: Architectures, Standards, and Scalable Frameworks for Modern Data Ecosystems
Modern enterprises generate vast volumes of structured and unstructured data across distributed environments, including cloud platforms, on-premises systems, IoT streams, and data lakes, creating significant challenges in organizing, accessing, and governing these assets effectively. Traditional data management approaches, which rely heavily on manual documentation, siloed repositories, and static metadata definitions, struggle to ensure discoverability, governance, data quality, and usability at scale, often leading to data redundancy, inconsistency, and limited trust in analytics outcomes. In response to these challenges, intelligent data catalogs powered by automated metadata ingestion, enrichment, and classification have emerged as a critical solution for enabling efficient data discovery, end-to-end lineage tracking, regulatory compliance, and collaborative data usage across organizations. These systems leverage advanced techniques such as machine learning, semantic modeling, and knowledge graphs to transform metadata into a dynamic, context-aware asset that supports real-time insights and decision-making. This paper explores the evolution of metadata systems from early foundational frameworks in the 2000s, which emphasized standardization and interoperability, to modern intelligent data catalog platforms developed prior to 2024 that integrate automation, scalability, and semantic intelligence. It highlights key architectural models, metadata lifecycle automation techniques, and distributed system considerations, while synthesizing insights from established metadata standards, academic literature on data catalogs, and large-scale metadata management systems to propose a comprehensive, scalable framework for building intelligent, metadata-driven ecosystems that enhance data accessibility, governance, and enterprise innovation.
Copyright (c) 2024 International Journal of Scientific Research in Science and Technology https://creativecommons.org/licenses/by/4.0
Community-Based Isolation Versus Facility-Based Care for Drug-Resistant Tuberculosis: A Cluster-Randomized Hybrid Effectiveness–Implementation Trial
The Drug-resistant tuberculosis presents a major global health challenge, requiring prolonged treatment and complex care delivery models. This study evaluates the comparative effectiveness of community-based isolation and facility-based care using a hybrid effectiveness–implementation trial framework. Evidence suggests that community-based directly observed therapy improves treatment success and reduces adverse events compared to facility-based care . Additionally, decentralized care models reduce patient burden and improve adherence, particularly in low-resource settings . This study integrates clinical outcomes, adherence rates, and implementation feasibility to assess both models. Results indicate that community-based care enhances treatment success, reduces mortality, and improves patient satisfaction, while facility-based care provides better clinical supervision but faces challenges related to accessibility and cost. The findings support a shift toward patient-centered, decentralized care models for DR-TB management. The study highlights the need for integrated health systems that balance clinical oversight with accessibility to optimize treatment outcomes.
Beta Thalassemia Prevalence in Industrial Areas of West Bengal: A Comprehensive Review
Beta thalassemia is one of the most common hereditary hemoglobinopathies. It continues to be a major public health concern in India. Industrial populations, especially in West Bengal, are special populations with unique demographic characteristics. These populations are prone to various diseases, which are influenced by various risk factors. This review aims to discuss the prevalence trends of beta thalassemia among industrial populations in West Bengal, identify the major risk factors that influence this condition, and discuss the existing methods for screening this condition. A comprehensive literature search was conducted using various literature sources, epidemiological studies, research articles, and various publications on this topic. A literature search was done on various databases, existing literature on global prevalence, national trends among Indians, regional trends among West Bengal populations, and existing literature on this topic. According to various literature sources, the total carrier frequency among Indians is approximately 3-4%. The prevalence among industrial populations in West Bengal is moderate to high. This is because these populations are mixed populations due to migration. Moreover, they are prone to various diseases due to consanguineous marriages, lack of awareness, and lack of screening programs. In conclusion, this condition continues to be a major public health concern.
Anemia Among CKD Patients: A Review of Risk Factors
Chronic kidney disease (CKD) has emerged as a critical worldwide health issue which brings substantial health burdens and death risks. Anemia stands out as the predominant medical complication of CKD which leads to detrimental effects on patient safety and their overall well-being. This review examines all possible risk factors which lead to anemia in CKD patients while it explains the current scientific knowledge about the disease's underlying mechanisms and methods for diagnosing and treating it. Anemia in CKD primarily results from erythropoietin deficiency, impaired iron metabolism, chronic inflammation, and reduced red blood cell lifespan. The prevalence of anemia and its severity increase with the progression of CKD while various demographic factors and clinical factors and nutritional factors and socioeconomic factors affect its development. The burden of diabetes and hypertension and iron deficiency together with limited access to medical care create additional healthcare challenges. Anemia in CKD leads to cardiovascular complications and cognitive decline and increased mortality. The process of early diagnosis requires hemoglobin assessment together with iron studies while clinicians need to implement appropriate therapeutic interventions which include both iron supplements and erythropoiesis-stimulating agents and new treatments which feature HIF inhibitors. The implementation of modifiable risk factor solutions combined with educational initiatives about risk factors will lead to better patient results especially in Indian resource-constrained environments.
StartupNest: A Full-Stack Cloud Platform Enabling Student–Startup Workforce Integration
The rapid growth of the startup ecosystem has created a strong demand for flexible, cost-effective workforce solutions, while students pursuing higher education seek opportunities to gain practical experience and improve employability. However, existing job portals and internship platforms primarily focus on full-time employment or structured corporate internships, making them unsuitable for early-stage startups and students interested in short-term or project-based roles. This paper presents StartupNest, a full-stack cloud-based platform designed to enable structured collaboration between students and startups. The system allows startups to post internship, project-based, and early-career opportunities, while students can explore roles, submit applications, and track their application status through dedicated dashboards. The platform incorporates role-based authentication, secure data storage, and cloud deployment to ensure scalability, accessibility, and data integrity. Additionally, intelligent features such as skill-based matching enhance the efficiency of connecting students with suitable opportunities. The proposed system demonstrates an effective, scalable, and user-centric approach to bridging the gap between student talent and startup workforce requirements, thereby supporting experiential learning and improving talent acquisition for startups.
YogSaarthiAI: A Vision-Based AI System for Yoga Posture Analysis and Fitness Guide
The efficacy of yoga practice is fundamentally tied to precise postural alignment; however, the absence of real-time, personalized feedback in modern digital training platforms frequently leads to suboptimal execution and potential musculoskeletal injury. To bridge this critical gap, this paper introduces YogSaarthiAI, an advanced, real-time virtual yoga assistant that leverages computer vision and deep learning to deliver highly accurate, full-body posture analysis and dynamic correction. Unlike conventional fitness applications that rely on opaque, computationally heavy models or provide limited binary evaluations, YogSaarthiAI utilizes a highly optimized, dual-path hybrid architecture. The system processes live webcam video streams through the MediaPipe framework to extract 33 distinct skeletal landmarks, which are mathematically transformed into ten resolution-independent, scale-invariant geometric angle features. A custom-trained Deep Neural Network (DNN) classifies the user's movements across nine fundamental yoga poses, achieving a robust test accuracy exceeding 95% with a minimal inference time of approximately 10 milliseconds. Operating in parallel, a deterministic template-matching engine compares live biomechanical vectors against expert-derived pose blueprints to generate graduated, joint-specific corrective feedback. A significant contribution of this work is the engineering of a novel Symmetry Mirroring Heuristic, designed to resolve unilateral body occlusion during lateral poses (such as the Plank and Bridge), which empirically reduced false corrective alarms from 40% to under 5% during live execution. Deployed on an asynchronous FastAPI and ReactJS web infrastructure, the platform sustains an end-to-end frame processing latency of 200 to 400 milliseconds, ensuring instantaneous instructional delivery. Comprehensive system evaluations and user acceptance testing confirm that YogSaarthiAI provides objective, pedagogically valuable guidance, establishing a scalable, accessible, and computationally efficient paradigm for automated biomechanical assessment.
Characterization of Soil Organic Carbon Fractions in Agricultural Fields of Beed District, Maharashtra, India
Soil organic carbon (SOC) plays a pivotal role in maintaining soil fertility, ecosystem sustainability, and agricultural productivity. The present investigation aims to characterize SOC fractions in selected agricultural fields of Beed district, Maharashtra, India, representing semi-arid agro-climatic conditions. Soil samples were collected from multiple sites under varying cropping systems and analyzed for total organic carbon and its fractions, including particulate organic carbon (POC), humus carbon (HC), and resistant organic carbon (ROC). The Walkley–Black wet oxidation method was employed for SOC estimation, while fractionation was performed using physical and chemical separation techniques. The results indicated significant spatial variability in SOC fractions, with higher labile carbon fractions observed in organically managed fields compared to conventionally cultivated soils. The study highlights the importance of SOC fractionation in understanding soil health dynamics and carbon sequestration potential in drought-prone regions. The findings have implications for sustainable soil management practices in rural agricultural systems.
Biogenic Synthesis and Characterization of TiO₂ Nanoparticles Using Lepidagathis Leaf Extract
For the first time, titanium dioxide (TiO₂) nanoparticles were synthesized through a green route using leaf extracts from three underexplored species of the Acanthaceae family—Lepidagathis sabui, Lepidagathis Shrirangii, and Lepidagathis ushae. The phytochemicals present in the extracts acted as natural reducing and stabilizing agents, enabling an eco-friendly and cost-effective synthesis without hazardous chemicals. The nanoparticles were thoroughly characterized using UV–Vis, FTIR, SEM, TEM, and EDS analyses. UV–Vis DRS spectroscopy showed an absorption edge with a band gap of ~3.2 eV, typical of TiO₂ semiconductors and indicative of photocatalytic potential. FTIR confirmed Ti–O–Ti vibrations, SEM and TEM revealed uniformly distributed semi-spherical nanoparticles, and EDS verified titanium and oxygen as the primary constituents. These results establish Acanthaceae leaf extracts as sustainable bioresources for TiO₂ nanomaterial fabrication, highlighting their promise in photocatalysis, environmental remediation, and broader nanotechnology applications.
Ethical Dilemmas of Artificial Intelligence and Human Employment Rights
Artificial Intelligence (AI) has become one of the most transformative technologies of the twenty-first century. Its rapid integration into industries such as manufacturing, healthcare, finance, transportation, and communication has significantly increased productivity and efficiency. However, the widespread adoption of AI also raises complex ethical challenges, particularly regarding human employment rights. Automation systems increasingly perform tasks that were once carried out by human workers, creating concerns about job displacement, economic inequality, labor rights, and social justice. This paper examines the ethical dilemmas associated with AI-driven automation and its impact on employment. It analyzes key issues such as job displacement, the right to work, technological inequality, and the dignity of labor. The study also evaluates real-world examples of automation and discusses policy recommendations such as reskilling initiatives, ethical AI development, and stronger labor protections. The paper argues that while AI has the potential to enhance economic growth and innovation, governments and organizations must ensure that technological advancement does not undermine human welfare and employment rights. A balanced approach that combines technological progress with ethical responsibility is necessary for sustainable and inclusive economic development.
Neurowall-DNN: Gradient-Guided Defensive Neural Architecture for Multi-Class Network Attack Detection
The rapid proliferation of sophisticated multi-class cyberattacks has exposed critical limitations in conventional Intrusion Detection Systems (IDS), particularly in handling evolving attack patterns and zero-day threats. Most existing deep learning-based IDS models rely on static optimization strategies, leading to reduced adaptability and higher false alarm rates under dynamic network conditions. To bridge this gap, this paper proposes NeuroWall-DNN, a gradient-guided defensive neural architecture designed for adaptive and resilient multi-class network attack detection. The proposed framework integrates a deep neural network with gradient-based reinforcement feedback, enabling dynamic parameter adjustment and enhanced feature discrimination. Adaptive gradient optimization is employed to strengthen decision boundaries, while reinforcement-driven updates improve convergence stability and attack generalization. The model is evaluated using NSL-KDD and CICIDS2017 benchmark datasets under a multi-class classification setting. Experimental results demonstrate an overall accuracy of 98.4% on NSL-KDD and 99.1% on CICIDS2017, outperforming conventional DNN and hybrid IDS baselines in detection rate and false positive reduction. The proposed architecture achieves faster convergence and improved robustness against unseen attack categories, establishing NeuroWall-DNN as a scalable and intelligent defense mechanism for next-generation cybersecurity infrastructures.
IOT-Enabled Low-Cost Hearing Aid with Flutter Control App
Hearing loss has a major impact on communication, situational awareness and personal safety, especially in noisy and urban environments where auditory cues are essential. Most hearing aids are designed as passive amplifiers of sound and generally do not dynamically adapt to variations in acoustic environment or personal taste. In this paper, we will explain the complete design and development of an IoT-based smart hearing aid system based on the ESP32-S3 microcontroller that implements I2S digital audio processing, as well as a mobile application developed in Flutter. Environmental sound is captured using an I2S MEMS microphone and processed in real time through digital signal processing techniques such as gain control, low-pass filtering, and high-pass filtering to enhance speech intelligibility while reducing unwanted noise. The processed audio is delivered via an I2S audio amplifier to earphones or a miniature speaker. Wireless connectivity enables real-time customization of hearing parameters through a mobile interface. A TP4056-based battery charging and protection module ensures portability, safety, and energy efficiency. The proposed system provides a low-cost, scalable, and user-centric assistive solution suitable for wearable hearing aid applications.