International Journal of Scientific Research in Science, Engineering and Technology
International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET) is peer-reviewed, online international journal published Bimonthly by Technoscience Academy. IJSRSET is a highly-selective journal, covering topics that appeal to a broad readership in various branches of Science, Engineering 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 IJSRSET 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 IJSRSET editors. Email addresses for submitting articles are ijsrset@gmail.com, editor@ijsrset.com JOURNAL INFORMATION Title : International Journal of Scientific Research in Science, Engineering and Technology Frequency : Bimonthly Online ISSN : 2394-4099 | Print ISSN : 2395-1990 Publisher : Technoscience Academy [ https://technoscienceacademy.com ] Refereed : Peer Review Copyright : Technoscience Academy, India Starting Year : 2014 Subject : Science, Engineering and Technology Language : English Distribution : Open Access Publication Format : PDF & HTML -> Online Document Identifier Type : DOI (https://doi.org/10.32628/IJSRSET ) Email Id : editor@ijsrset.com, ijsrset@gmail.com Mobile No. : +91-8160745806 (WhatsApp support only) Website : https://ijsrset.com
A Legal Technology and Digital Trust Infrastructure Framework for Bridging Digital Trust Between Artificial Intelligence and Human Intelligence
The accelerating integration of artificial intelligence (AI) into governance, financial services, healthcare, and legal decision-making systems has created significant efficiency gains while simultaneously raising complex concerns regarding transparency, accountability, and institutional trust. As algorithmic systems increasingly participate in high-stakes decision processes traditionally governed by human expertise, the establishment of robust digital trust frameworks becomes essential. This study investigates how legal technology platforms combined with digital trust infrastructures can create verifiable mechanisms for trustworthy interaction between artificial intelligence and human intelligence. The paper proposes a novel Legal Trust Verification Algorithm (LTVA) designed to operationalize digital trust in AI-mediated environments through a multi-layered framework integrating blockchain-based legal audit trails, explainable AI verification modules, and policy-compliant decision validation protocols. The proposed algorithm is evaluated against existing governance-oriented AI verification approaches including Explainable Boosting Machines (EBM), SHAP-assisted Random Forest models, and policy-constrained Bayesian decision networks. Experimental simulations using institutional decision datasets demonstrate that the LTVA framework achieves superior performance in trust validation accuracy, regulatory compliance verification, and interpretability consistency. Graphical analysis illustrates measurable improvements in decision transparency scores and legal audit traceability compared with baseline methods. The results suggest that integrating legal technology infrastructures with algorithmic trust verification models can significantly reduce the trust deficit between automated systems and human stakeholders. The study therefore advances a scalable governance architecture capable of supporting responsible human-AI collaboration across regulatory jurisdictions while strengthening digital institutional trust in AI-enabled decision systems.
Copyright (c) 2026 International Journal of Scientific Research in Science, Engineering and Technology https://creativecommons.org/licenses/by/4.0
Reimagining Myth through Magical Realism: A Critical Exploration of Mythological Symbolism and Narrative Enchantment in the Novels of Ashwin Sanghi
Mythology has long served as a powerful narrative framework through which societies interpret cultural values, philosophical beliefs, and historical memory. In contemporary literature, the reinterpretation of mythological narratives has gained renewed significance as writers attempt to bridge the gap between ancient traditions and modern intellectual inquiry. Among the contemporary Indian authors who have successfully revitalized mythological storytelling, Ashwin Sanghi occupies a prominent position. His novels creatively combine mythology, history, religion, and suspense, presenting ancient mythological symbols within modern narrative structures. One of the most distinctive features of Sanghi’s storytelling is his subtle integration of magical realism, a literary technique that blends supernatural elements with realistic settings to create narratives where the extraordinary appears as a natural part of everyday life. Through this narrative strategy, Sanghi transforms mythological themes into engaging contemporary stories while maintaining their symbolic and philosophical depth. Magical realism, originally associated with Latin American literature, has evolved into a global narrative technique used to reinterpret cultural traditions and collective memory. The technique allows writers to integrate myth, folklore, and spirituality into realistic narratives without disrupting the logical structure of the story. In the context of Sanghi’s novels, magical realism functions as a bridge between ancient mythological imagination and modern rational inquiry. His narratives often revolve around mysterious historical events, hidden codes, religious symbols, and mythological legends that appear to transcend conventional boundaries between myth and reality. These elements create a sense of narrative enchantment in which mythological ideas interact with contemporary settings, thereby encouraging readers to reconsider the relevance of mythological traditions in modern life. This study aims to critically examine the use of magical realism and mythological symbolism in the novels of Ashwin Sanghi, with particular attention to how these narrative strategies contribute to the reinterpretation of mythology in contemporary fiction. Sanghi’s works frequently draw inspiration from Indian mythological texts such as the Mahabharata, the Ramayana, and various Puranic traditions, while simultaneously incorporating historical research, philosophical reflection, and suspense-driven storytelling. Through this fusion of myth and modernity, Sanghi creates narratives that invite readers to explore the enduring cultural significance of mythological traditions. His novels suggest that mythology is not merely a relic of the past but a dynamic narrative system capable of generating new meanings within contemporary contexts. A central focus of this research is the examination of mythological symbolism within Sanghi’s narrative universe. Mythological symbols, archetypes, and motifs frequently appear in his works as narrative clues that guide characters toward deeper philosophical revelations. These symbols often function as connectors between historical events and mythological traditions, creating narrative layers in which past and present intersect. By embedding mythological references within modern plots, Sanghi constructs a narrative structure that encourages readers to decode the symbolic meanings hidden within mythological stories. This symbolic framework contributes to the sense of narrative enchantment that characterizes his novels, where mythological elements appear to subtly influence contemporary events and human experiences. Another important dimension of the study is the exploration of how magical realism enhances the narrative complexity of Sanghi’s works. Unlike traditional fantasy literature, magical realism does not completely separate the supernatural from the real world. Instead, it allows mystical or mythological elements to coexist naturally within realistic environments. In Sanghi’s fiction, historical settings, academic research, archaeological discoveries, and religious traditions often coexist with mysterious coincidences and myth-inspired revelations. This blending of realism and myth creates a narrative atmosphere in which the boundaries between historical fact, mythological imagination, and contemporary experience become fluid. As a result, readers are encouraged to question conventional distinctions between myth and reality. The narrative enchantment present in Sanghi’s novels also reflects a broader cultural phenomenon in which mythological narratives are reinterpreted to address modern intellectual concerns. In contemporary societies shaped by rapid technological advancement and globalization, mythology continues to function as a cultural resource that offers symbolic frameworks for understanding identity, morality, and collective memory. Sanghi’s reinterpretation of mythological narratives demonstrates how ancient myths can be reimagined through modern storytelling techniques while retaining their philosophical relevance. His narratives suggest that mythological symbols continue to influence cultural consciousness even within technologically advanced societies. Furthermore, the study highlights the role of mythological reinterpretation in preserving cultural heritage while simultaneously adapting it to modern literary forms. By embedding mythological ideas within suspense-driven narratives, Sanghi makes traditional myths accessible to a wider readership. His works appeal not only to readers interested in mythology but also to those attracted by historical thrillers and intellectual mysteries. This narrative strategy ensures that mythological traditions remain relevant in contemporary literary culture. In this sense, Sanghi’s novels contribute to the broader revival of mythological storytelling within modern Indian literature. The research adopts a qualitative literary approach to analyze the narrative techniques and symbolic structures present in Sanghi’s works. Through close textual analysis, the study examines how magical realism and mythological symbolism interact within the narrative framework to produce a sense of narrative enchantment. Particular attention is given to the ways in which mythological references shape character motivations, plot development, and thematic exploration. By analyzing these narrative elements, the study seeks to demonstrate how Sanghi’s fiction creates a dialogue between mythological imagination and modern rational inquiry. Ultimately, the study argues that Ashwin Sanghi’s novels represent a significant contribution to contemporary mythological fiction by integrating magical realism with mythological reinterpretation. Through this narrative fusion, Sanghi transforms ancient myths into dynamic storytelling frameworks that resonate with modern readers. His works reveal how mythology continues to function as a powerful cultural narrative capable of inspiring curiosity, intellectual reflection, and imaginative exploration. By reimagining myth through magical realism, Sanghi not only revitalizes traditional mythological narratives but also expands the possibilities of contemporary literary storytelling.
SkillNetAl: Smart Skill Matching and Academic Collaboration Platform
SkillNetAI is an intelligent collaboration and skill‑matching platform designed to improve team formation and cooperative learning among students. Traditional project team formation methods often rely on random allocation or manual instructor selection, which may lead to imbalanced teams and inefficient collaboration. The proposed system addresses this limitation by using data‑driven techniques to analyze student skills, interests, and learning preferences in order to generate balanced and productive teams. SkillNetAI collects structured data from students including technical competencies, domain knowledge, collaboration preferences, and prior project experiences. Using this information, the platform generates skill profiles and applies intelligent matching algorithms to assemble teams that maximize complementary abilities while maintaining fairness and diversity. The system also provides a collaboration dashboard that enables communication, task allocation, and progress monitoring. By continuously analyzing team performance and interaction data, SkillNetAI adapts team recommendations and supports personalized learning development. The proposed system not only improves project outcomes but also promotes peer learning, knowledge sharing, and practical skill development. The architecture is designed to be scalable and can be integrated with modern learning management systems.
SQL Server Database Engineering and T-SQL Optimization
Enterprise-scale relational schemas demand engineering portfolios exceeding 75 stored procedures, triggers, functions, pointers, and views across SQL Garçon performances from 2008 to 2019. Query execution times drop by over 45 on high-volume line-of-business reporting workloads through targeted T-SQL optimization. SQL Garçon Integration Services packages automate data backups and serialization, while SQL Garçon Reporting Services delivers parameterised dashboards, adding up knockouts of thousands of records daily for stakeholder opinions. Proficiency in Language-Integrated Query to SQL, Extensible Markup Language handling, exception operation, and performance tuning tackles trade channels and compliance reporting. This capability extends to DB2 hybrids with cursor control and package design for data integrity. listed views in Azure SQL migrations maintain sub-second responses under peak loads, icing robust data layers for Model-View-Controller architectures.
Copyright (c) 2024 International Journal of Scientific Research in Science, Engineering and Technology https://creativecommons.org/licenses/by/4.0
Intelligent Breast Cancer Detection and Classification from Medical Imaging Using Hybrid CNN-DLF model
Heart disease is one of the worst things that can happen to women all over the world. It's very important to get the right evaluation as soon as possible to increase the chance of survival and cure. This work shows how deep learning can be used to look for and sort breast cancer in mammogram images. People say that EfficientNet-B0 and Channel Attention should be used together with CAHF-Net. This network will learn to focus more on separating different features in medical pictures. The DDSM and other related mammography digital databases for the screening of mammography images were used to obtain better diversity and representation of the clinical context. The images were preprocessed by laboratory cleansing, and noise cleansing from the images in addition to the data augmented techniques mentioned in the previous sentence to include: image rotation, image flipping and image scaling to from the best set for the model to learn from to enhance the overall model intelligence and significantly reduce overfitting. The CAHF-Net module improves features related to tumors using ConvNet with BN layers, and Channel Attention to enhance and focus features on tumors. They are sent to a classification head that has fully-connected (FC) layers, a DropOut layer, and a SoftMax layer so that breast tissues can be put into multiple groups. The test results show that the system worked well, with 99.55% accuracy in the training and 88.52% accuracy in the validation. The AUCs for the training were 99.98% and for the validation they were 95.72%. It also did well on the test data, where it got 88.24% memory and 89.10% accuracy. It works to find both dangerous and healthy breast tissue, so you can trust it. The suggested CAHF-Net design is faster and more accurate at diagnosing problems than other deep learning models that are already out there. Radiologists can find breast cancer earlier with this answer. It can also help cut down on false diagnoses of other diseases and encourage doctors to use smart computer-aided diagnosis (CAD) tools to help them make decisions.
Copyright (c) 2025 International Journal of Scientific Research in Science, Engineering and Technology https://creativecommons.org/licenses/by/4.0
Development of a Solar-Powered Wireless Power Transfer System for Electric Vehicle Charging
The rapid growth of electric vehicles has created an increasing demand for efficient and sustainable charging technologies. Conventional plug-in charging methods require physical connectors, which may lead to safety concerns, cable wear, and limited charging convenience. Wireless power transfer technology has emerged as a promising solution for electric vehicle charging by enabling contactless energy transmission between the charging station and the vehicle. At the same time, integrating renewable energy sources such as solar power with wireless charging systems can further enhance sustainability and reduce dependence on conventional electrical grids. This paper presents the design and performance evaluation of a solar powered wireless charging system for electric vehicles. The proposed system utilizes a photovoltaic panel as the primary energy source, which converts solar radiation into electrical energy. The generated electrical power is regulated and supplied to a wireless power transmission unit consisting of a transmitter coil and a receiver coil. Energy is transferred through inductive coupling between the two coils and is then used to charge the electric vehicle battery through a power conditioning circuit. A prototype system was developed using a photovoltaic module, power regulation circuit, wireless transmitter and receiver coils, and a rechargeable battery unit. Experimental investigations were carried out to evaluate the performance of the system under different operating conditions, including variations in solar power availability and coil separation distance. The results show that the proposed system is capable of delivering stable wireless charging with reasonable efficiency for short transmission distances. The efficiency of power transfer decreases with increasing coil distance due to reduced magnetic coupling, while the solar output power varies depending on solar irradiance. The experimental results demonstrate that the integration of solar energy with wireless power transfer can provide an environmentally friendly and convenient charging solution for electric vehicles. The proposed system offers potential applications in smart parking infrastructure, autonomous charging stations, and a sustainable transportation system
Eventcraft: College Event Management System
The College Event and Management System (CEMS) is a mobile-based application developed using the MERN stack (MongoDB, Express, React Native, Node.js) to provide a comprehensive digital solution for managing academic and event activities at educational institutions. An overwhelming majority of traditional methods of college management are done manually, making them resource-intensive, time-consuming, and prone to errors. By using CEMS, students can easily access all services, participate in activities, and receive notifications from anywhere through a single mobile app, while administrators will have the ability to track attendance, generate certificates, and integrate payment processing using Razorpay and Nodemailer. In addition to centralizing all information into one location, this application will help reduce the administrative burden on faculty members by eliminating the need for them to use multiple systems (e.g., email, paper records) to communicate and manage student activities. Thus, CEMS provides a mobile application solution that improves operational efficiencies while providing a centralized platform for managing events and other academic services.
Hybrid Machine Learning Framework for Early Prediction of Cardiovascular Disease Using Logistic Regression and Artificial Neural Networks
Cardiovascular diseases are one of the major reasons for mortality worldwide, and hence there is a need to develop efficient diagnostic tools. With the advancement in artificial intelligence and machine learning, efficient intelligent healthcare systems can be designed to support the decision-making process in the medical community. This study proposes a machine learning model using a combination of logistic regression and neural networks for the early prediction of cardiovascular disease. The proposed model makes use of the clinical data available, including the attributes of the patients. The data preprocessing steps such as handling missing values, feature normalization, and feature selection are performed on the data to increase the accuracy of the model. The model makes use of logistic regression as a baseline model and neural networks to efficiently handle the complex relationships in the data. The performance metrics used to evaluate the model include accuracy, precision, recall, F1-score, and area under the ROC curve. This is further supported by the experimental results, which show that the performance of the neural network model is better than the traditional models, with an accuracy of about 86% and AUC-ROC of 0.90. The importance of the features also indicates the clinical factors such as age, cholesterol level, blood pressure, and smoking status as the major factors affecting the risk of cardiovascular disease. The proposed framework proves the effectiveness of machine learning methods for the early detection of the disease and the diagnostic decisions of the clinicians. The research also proves the effectiveness of AI-based predictive models for the prevention of cardiovascular diseases.
From Fragmented Data Landscapes to Unified Enterprise Ecosystems: Foundations for Platform-Led Digital Transformation
Digital transformation initiatives are increasingly driven by platform oriented enterprise models that depend on seamless data interoperability, scalable analytics capabilities, and coordinated governance across complex organizational environments. Despite these requirements, many enterprises continue to operate with disconnected data infrastructures created by legacy systems, departmental data silos, and incompatible integration architectures. These fragmented environments restrict the ability of digital platforms to exchange information efficiently, delay analytical decision processes, and introduce governance risks across enterprise operations. Addressing these structural limitations requires the development of unified enterprise data ecosystems capable of supporting large scale platform integration and resilient data management practices. This research examines the architectural transition from fragmented enterprise data landscapes toward unified data ecosystems that enable platform led digital transformation. The study proposes a resilient ecosystem architecture that integrates distributed data infrastructure, platform integration services, governance intelligence mechanisms, and enterprise analytics capabilities within a coordinated operational framework. To evaluate the effectiveness of the proposed architecture, a multi industry dataset representing financial services, healthcare information systems, retail supply chain platforms, and manufacturing operations was analyzed. The research compares the proposed ecosystem model with traditional centralized data warehouse architectures and service oriented integration approaches using evaluation indicators including integration latency, interoperability reliability, governance compliance rate, and data processing throughput. Experimental findings demonstrate measurable improvements in cross platform data accessibility, reduction in integration delays, and enhanced governance monitoring when compared with conventional enterprise data architectures. The results highlight the strategic role of unified enterprise ecosystems in enabling scalable digital platforms and sustainable enterprise transformation. The study contributes an empirically evaluated architectural framework that supports organizations seeking to modernize fragmented data environments while establishing resilient foundations for platform led digital innovation.
Transforming Master Data into Strategic Infrastructure: Governance Maturity, Intelligent Automation, and Enterprise Integration Leadership
Master data management has traditionally been positioned as a governance discipline focused on control, standardization, and regulatory compliance. However, contemporary enterprise environments characterized by distributed cloud platforms, intelligent automation capabilities, and real time digital ecosystems demand a more strategic interpretation. This paper reconceptualizes master data as foundational infrastructure rather than administrative oversight, arguing that governance maturity, intelligent automation, and enterprise integration leadership must converge to enable sustained enterprise intelligence. Drawing upon cross-industry architectural patterns, organizational design principles, and system integration models, the study presents a structured transformation pathway that elevates master data from operational stewardship to strategic enterprise asset. The analysis explores governance capability progression, automation embedded within data lifecycle processes, and leadership frameworks required to orchestrate complex multi system environments. It further proposes an integrated maturity model linking policy enforcement, data quality intelligence, interoperability architecture, and executive accountability into a unified infrastructure perspective. By synthesizing architectural modernization, intelligent monitoring mechanisms, and cross functional stewardship alignment, the paper demonstrates how master data can support resilience, regulatory adaptability, predictive analytics, and scalable enterprise integration. The contribution advances a forward looking paradigm in which master data infrastructure functions as the connective core of intelligent enterprises, enabling value creation beyond compliance toward innovation, agility, and long term strategic advantage.
Enterprise Data Architecture Modernization
AI-Integrated IoT System for Intelligent Monitoring and Automation in Smart Gardening
The rapid expansion of urban agriculture and home-based food production has intensified the need for intelligent, resource-efficient gardening systems capable of adapting to dynamic environmental conditions. Conventional automated irrigation systems predominantly rely on static threshold-based control mechanisms, which often fail to respond optimally to fluctuating soil, climatic, and plant physiological parameters. This study proposes an AI-integrated Internet of Things (IoT) framework designed to enable intelligent monitoring, predictive analytics, and adaptive automation in smart gardening environments. The proposed system incorporates a multi-layer architecture consisting of distributed environmental sensors, an edge-processing unit for real-time data filtering and decision support, and a cloud-based machine learning engine for predictive modeling and optimization. Soil moisture, temperature, humidity, light intensity, and nutrient-level data were continuously collected and processed to train supervised learning models for soil moisture prediction and irrigation demand forecasting. In addition, a reinforcement learning–based scheduler was implemented to dynamically regulate irrigation timing and volume according to environmental variability and plant growth stages. Experimental validation was conducted in a controlled smart garden setup over multiple growth cycles. The AI-driven system demonstrated improved prediction accuracy in soil moisture estimation and achieved significant reductions in water consumption compared to conventional rule-based irrigation approaches, while maintaining optimal plant health indicators. Furthermore, edge-based deployment reduced system latency and enhanced real-time responsiveness without imposing excessive energy overhead. The findings confirm that integrating artificial intelligence with IoT infrastructure can substantially enhance the efficiency, sustainability, and autonomy of smart gardening systems. The proposed framework offers a scalable and adaptable solution for urban agriculture, contributing to water conservation, energy efficiency, and precision resource management in small-scale food production environments.
Climate-Induced Occupational Health Risks: Addressing Heat Stress, Air Quality and Emergency Preparedness
Climate change represents an escalating determinant of occupational health risk through intensifying heat exposure, degraded air quality, and increased extreme weather events. This narrative review synthesizes empirical and policy literature to examine the prevalence, mechanisms, and consequences of climate-induced occupational hazards. Evidence indicates substantial reductions in manual labor capacity under heat stress, increased cardiopulmonary morbidity associated with PM2.5 exposure, and heightened strain on emergency response systems. Vulnerability is disproportionately concentrated among outdoor workers, low-wage laborers, and populations in low- and middle-income countries. A conceptual Climate–Occupational Health Risk Pathway Model is proposed to integrate exposure intensity, worker vulnerability, and adaptive capacity into a unified analytical framework. Economic implications include significant productivity losses and increased healthcare expenditures. Persistent gaps in surveillance, exposure modeling, and regulatory integration constrain effective adaptation. Strengthening climate-responsive occupational policy requires predictive monitoring systems, targeted protections for high-risk groups, and integration of forward-looking climate projections into workplace governance. Building resilient occupational systems is essential to mitigating both health and economic consequences of accelerating climate change.
Ultra-Low Noise Photodetection in Exfoliated GaSe: Realizing Sub-Femtoampere Dark Currents for High-Detectivity Sensing
This study investigates the strategic development and optoelectronic characterization of ultra-low noise photodetectors fabricated from mechanically exfoliated gallium selenide ("GaSe" )nanosheets. While contemporary research in 2D optoelectronics predominantly targets high responsivity through internal gain mechanisms-often incurring significant noise penalties-this work adopts a “noise-centric” design philosophy to optimize the specific detectivity (D^* ). By integrating a ∼20" nm" thick "GaSe" flake within a precision-engineered metal–semiconductor–metal (MSM) architecture utilizing "Cr/Au" electrodes, we demonstrate an unprecedented dark current floor of 1" fA " at zero bias, maintaining sub-100" fA" stability up to a 2" V" operating regime. Spectroscopic analysis reveals a sharp responsivity cutoff at 620" nm" , correlating precisely with the 621" nm " photoluminescence (PL) emission peak, thereby confirming band-to-band transition dominance. Although the device yields a conservative responsivity of 2.7" mA/W" , the radical suppression of both shot noise and thermal noise components enables a specific detectivity that rivals state-of-the-art commercial silicon-based sensors. These findings underscore the viability of "GaSe " as a primary candidate for “green” electronics and battery-less, always-on remote sensing applications, where extreme noise minimization is the critical performance metric.
Chemical Hazard Management in the Workplace: The Relationship between Cancers and Occupational Hazard Exposures in the US Manufacturing Industry
Occupational cancer remains a leading cause of work-related mortality globally, with chemical exposures in the United States manufacturing sector contributing substantially to preventable cancer burden. Manufacturing workers encounter diverse carcinogens including diesel exhaust, organic solvents, polycyclic aromatic hydrocarbons (PAHs), benzene, asbestos, and metallic compounds, many classified as Group 1 carcinogens by the International Agency for Research on Cancer (IARC). This review synthesizes epidemiological evidence linking occupational chemical exposures to lung cancer, mesothelioma, leukemia, bladder cancer, kidney cancer, and non-Hodgkin lymphoma, emphasizing exposure–response relationships and cumulative dose effects. The analysis evaluates regulatory frameworks, particularly Occupational Safety and Health Administration (OSHA) permissible exposure limits (PELs), and identifies critical limitations including outdated standards, enforcement variability, and insufficient integration of health equity considerations. Disproportionate exposure burdens among racialized minorities, foreign-born workers, and lower socioeconomic groups highlight structural inequities within occupational risk distribution. Methodological challenges including exposure misclassification, latency effects, and the healthy worker effect are examined for their implications on risk estimation. A conceptual occupational carcinogen pathway framework is proposed to integrate hazard identification, exposure assessment, regulatory controls and surveillance mechanisms. Synthetic exposure–response modeling further illustrates the quantitative relationship between cumulative exposure and attributable cancer risk. The findings underscore the need for regulatory modernization, enhanced exposure monitoring technologies, upstream substitution strategies, and equity-focused prevention policies. Strengthening chemical hazard management in manufacturing is essential to reducing long-term occupational cancer incidence and advancing sustainable industrial health protection.
Enhancing Motor Imagery EEG Classification Using a Modified Bidirectional LSTM Network
Motor imagery (MI) Electroencephalography (EEG) classification is a key step for supporting brain computer interface (BCI) applications, yet high accuracy remains challenging due to inter subject variability and signal noise. This paper uses the BNCI Horizon 2020 dataset under an all subjects together training setting to build general model that learns from all participants. Signal representation is improved through preprocessing scenarios including Normalization and principal component analysis (PCA), followed by two Bidirectional Long-Short-Term Memory (BiLSTM) architectures, a baseline design and a modified design with higher capacity and added batch normalization and dropout to stabilize training and reduce overfitting. Results show that the modified BiLSTM achieves strong multiclass performance, reaching 92.35% training accuracy and 92.23% testing accuracy for the 8-class task, and approximately 99.98% accuracy for both training and testing in the 7-class task. These findings highlight that combining appropriate preprocessing with an improved BiLSTM design can increase the reliability of motor imagery EEG classification for practical BCI use.
Leveraging NLP and Retrieval-Augmented Generation (RAG) Models for Automated Business Intelligence Query Resolution
The rapid expansion of enterprise data ecosystems has intensified the demand for intelligent Business Intelligence (BI) systems capable of translating natural language queries into accurate analytical insights without requiring technical expertise. Traditional BI query interfaces rely heavily on structured query languages and predefined dashboards, creating bottlenecks in decision-making workflows and limiting accessibility for non-technical stakeholders. This paper proposes a novel framework titled Adaptive Contextual Retrieval-Augmented Intelligence Engine (ACRIE), which integrates advanced Natural Language Processing (NLP) with Retrieval-Augmented Generation (RAG) to automate business intelligence query resolution with improved semantic understanding, contextual grounding, and analytical accuracy. The proposed model combines transformer-based semantic parsing using Domain-Tuned BERT embeddings, hybrid vector–symbolic retrieval, and a dynamically optimized RAG pipeline that retrieves enterprise knowledge artifacts prior to response synthesis. Unlike conventional RAG implementations that rely solely on similarity search, ACRIE introduces a Context-Aware Query Reformulation Algorithm (CAQRA) and a Multi-Stage Evidence Ranking Mechanism (MSERM) to minimize hallucinations and enhance numerical reasoning during analytical query generation. Performance is evaluated against baseline systems including Text-to-SQL transformers, standard RAG architectures, and rule-based BI assistants across benchmark enterprise datasets. Experimental comparisons demonstrate measurable improvements in query resolution accuracy, latency reduction, and analytical consistency. The proposed framework achieves higher semantic alignment scores and improved retrieval precision while maintaining scalable inference performance suitable for real-time analytics environments. Graph-based evaluations and comparative performance visualizations illustrate gains in response correctness, contextual relevance, and decision-support reliability. The findings establish that integrating adaptive retrieval intelligence with domain-aware NLP significantly advances automated BI systems, enabling conversational analytics that bridge the gap between enterprise data warehouses and executive decision-making processes. The study contributes a scalable architecture and algorithmic innovation capable of redefining next-generation intelligent analytics platforms.
Adaptive Contextual Retrieval-Augmented Intelligence Engine
Unveiling the Layer-Tunable Optoelectronic Fingerprints of GaSe Nanoflakes
This study systematically investigates how varying the number of layers in gallium selenide (GaSe) nanoflakes influences their optoelectronic performance, particularly in photodetection applications. GaSe nanoflakes with precisely controlled layer numbers were mechanically exfoliated and thoroughly characterized using AFM, Raman, and PL spectroscopy. Optoelectronic devices were fabricated in metal–semiconductor–metal (MSM) geometry, and their performance parameter-including external quantum efficiency (EQE), photoresponsivity, and detectivity-were evaluated under controlled illumination conditions. Our findings reveal a clear dependence of optoelectronic properties on the number of layers, with specific layer counts exhibiting optimal device performance. Raman and PL spectra indicate significant shifts correlating directly with layer thickness, elucidating underlying changes in electronic band structure and carrier dynamics. These observations provide crucial insights for optimizing GaSe-based devices. This work not only demonstrates the feasibility of tailoring the optoelectronic properties of GaSe nanoflakes through precise control of layer numbers but also paves the way for designing highly efficient, tunable photodetectors suitable for next-generation optoelectronic applications.
A Survey on Financial Fraud Detection in E-commerce Transactions using Deep Learning
The rapid expansion of e-commerce platforms has revolutionized global trade and consumer behavior, offering unprecedented convenience and accessibility. However, this digital transformation has concurrently catalyzed a parallel surge in sophisticated financial fraud attempts, resulting in substantial economic losses amounting to billions of dollars annually. Traditional rule-based fraud detection systems and conventional machine learning approaches increasingly demonstrate inadequacy when confronted with the evolving complexity, scale, and adaptive nature of modern fraudulent activities. This survey paper provides a comprehensive examination of deep learning methodologies applied to financial fraud detection within e-commerce transactions. It systematically reviews the landscape of fraud types, evaluates the efficacy of various deep learning architectures including Convolutional Neural Networks, Recurrent Neural Networks, Long Short-Term Memory networks, Autoencoders, and hybrid ensemble models. The paper identifies critical research gaps pertaining to imbalanced datasets, concept drift, explainability requirements, and real-time processing constraints. Through synthesis of contemporary literature from 2020 onwards, this survey proposes a methodological framework that addresses identified limitations while highlighting promising directions for future investigation. The findings underscore the transformative potential of deep learning in augmenting fraud detection capabilities, whilst acknowledging persistent challenges that necessitate interdisciplinary collaboration between data scientists, financial institutions, and regulatory bodies.
Poly (Vinyl) Chloride Membrane Copper-Selective Electrode Based On a Diamine Donor Schiff Base Ionophore
The newly synthesized Schiff base N,N′-(3,3′-dimethyl-[1,1′-biphenyl]-4,4′-diyl)bis(1-(2-nitrophenyl)methanimine) (S) was employed as an ionophore for the fabrication of Cu²⁺-selective poly(vinyl chloride) membrane electrodes. The influence of different plasticizers, including o-nitrophenyloctyl ether (o-NPOE), tri-n-butyl phosphate (TBP), dibutyl sebacate (DBS), tri(2-ethylhexyl) phosphate (TEHP), chloronaphthalene (CN), and di(2-ethylhexyl) adipate (DEHA), as well as the anion excluder potassium tetrakis(p-chlorophenyl)borate (KTpClPB), was systematically evaluated. The membrane composition containing S/KTpClPB/PVC/o-NPOE in a mass ratio of 5:4:80:125 mg exhibited optimal electroanalytical characteristics. The sensor displayed a linear potentiometric response to Cu²⁺ in the concentration range of 1.0 × 10⁻⁷ to 1.0 × 10⁻¹ M, with a detection limit of 3.0 × 10⁻⁸ M and a near-Nernstian slope of 29.6 mV per decade in the pH range 2.5–7.5. The response time was approximately 15s.The electrode functioned reliably in partially non-aqueous media containing up to 20% (v/v) methanol, ethanol, or acetone and maintained stable performance for nearly three months. Selectivity coefficients obtained by the Fixed Interference Method confirmed excellent selectivity toward Cu²⁺ in the presence of common interfering ions. The proposed sensor was successfully applied for the determination of copper in edible oils, tomato plant samples, and river water.
Sensor Integration Based Power Management System
In the present era, the demand for electrical energy is increasing rapidly due to population growth and continuous technological advancements. To meet this demand in a sustainable manner, intelligent systems for monitoring, analysis, and management of energy consumption are essential. This project focuses on the integration of multiple electrical sensors with a microcontroller to acquire real-time energy data. The sensor data is transmitted to a data processing platform and visualized using interactive dashboards developed in Power BI. These dashboards convert raw sensor readings into clear and understandable real-time insights for end users, enabling effective monitoring of power usage patterns. In addition to real-time visualization, historical energy consumption data is analyzed to perform trend analysis and prediction of future energy usage. The predictive analysis helps in identifying consumption patterns and supports informed decision-making for efficient energy management. The proposed system provides a scalable and cost-effective solution for real-time monitoring, visualization, and prediction of energy consumption, contributing to energy conservation and optimized power utilization.
A Comprehensive Literature Review of Algorithm and Data Structure Visualization Tools: Research Gaps and Future Directions
Algorithm and data structure visualization tools are vital educational resources bridging abstract theory and practical understanding. These foundational yet challenging topics remain difficult to learn due to their dynamic and mathematically intensive nature. This literature review systematically examines ten recent research papers (2008–2025) on interactive visualization systems for algorithmic education, evaluating their visualization techniques, interaction methods, technological implementations, and pedagogical frameworks. Key findings reveal a fragmented field where systems effectively visualize basic algorithms like sorting and searching but offer limited coverage of complex data structures and advanced topics. While contemporary tools use modern web technologies (React, GSAP, D3.js), they emphasize technical features over pedagogical value. Significant gaps remain in adaptive learning support, rigorous educational evaluation, mobile accessibility, and personalized pathways, with most tools operating as standalone demonstrations rather than integrated learning environments. The review highlights the need for holistic, pedagogically informed, and technologically robust visualization systems. Future tools should integrate comprehensive algorithmic coverage with adaptive features, multimodal interaction, and evidence-based instructional design to transcend simple demonstration and foster deeper learner engagement, conceptual application, and personalized educational journeys.
Sacred Groves and Seed Sovereignty the Role of Indigenous Belief Systems in Biodiversity Conservation in India
India faces urgent biodiversity decline because of deforestation and monoculture farming and urban development and climate change, which have caused severe losses to forest ecosystems and agricultural biodiversity during the past several decades. The protection of the environment in this ecological area can benefit from indigenous conservation practices which include sacred groves and seed sovereignty as traditional ecological methods that rely on cultural heritage. Sacred groves serve as community-protected areas which people establish to protect specific forest areas from unauthorized access in order to honor their deities and ancestral spirits. Seed sovereignty enables communities to decide which indigenous seeds they will choose for their agricultural practices while they maintain control over seed conservation and distribution activities, which helps to preserve agricultural biodiversity. Indigenous cosmology connects both systems because it teaches people to see nature as sacred and to follow the moral and ritual duties which control its use. This research investigates the role of belief systems as ecological governance tools which manage resource utilization and help communities develop their resilience capacity. The research utilized qualitative methods through case studies and ethnographic methods which involved observing participants and conducting interviews while documenting biodiversity to discover three main results which included high species diversity and intergenerational knowledge transmission and decentralized governance. The study demonstrates that India can achieve sustainable development through the combination of sacred ecology and seed sovereignty with formal environmental policies which will improve local governance and protect biodiversity through culturally appropriate methods.
Advancing Numerical Solutions for Singular Boundary Value Problems Using Differential Transformation with Nonlinear Adjustments
This comprehensive research explores advanced numerical methodologies for solving singular boundary value problems (SBVPs) employing differential transformation techniques with enhanced nonlinear adjustment strategies. The study integrates sophisticated mathematical frameworks including continuous genetic algorithms, Adomian decomposition methods, and modified differential transformation approaches to address the inherent complexities in singular differential equations. Through rigorous convergence analysis and extensive error estimation, this work demonstrates superior accuracy compared to conventional “reproducing kernel Hilbert space methods (RKHSM). Numerical examples with detailed tabulation and graphical representations validate the effectiveness of the proposed approach across linear and nonlinear singular boundary value problems. The findings establish differential transformation as a robust and efficient computational technique for handling complex singularities in ordinary differential equations.
Enhancing Financial Risk Assessment through Hybrid Simulation Models Using Oracle Crystal Ball and Python Monte Carlo Engines
Financial risk assessment has become increasingly complex due to volatile markets, interconnected financial systems, and the growing volume of uncertain variables influencing investment and operational decisions. Traditional deterministic models often fail to capture uncertainty distributions and nonlinear dependencies inherent in modern financial environments. This study proposes a hybrid simulation framework that integrates Oracle Crystal Ball’s structured probabilistic forecasting environment with Python-based Monte Carlo simulation engines to enhance predictive accuracy, flexibility, and analytical scalability in financial risk modeling. The proposed approach combines enterprise-grade spreadsheet simulation capabilities with programmable stochastic modeling to enable advanced scenario analysis, risk quantification, and probabilistic forecasting. Oracle Crystal Ball provides intuitive assumption modeling, sensitivity analysis, and decision optimization tools, while Python extends computational depth through customizable Monte Carlo algorithms, statistical libraries, and automated simulation workflows. The hybrid architecture allows synchronized simulations, enabling analysts to validate results across platforms while improving reproducibility and computational efficiency. The methodology applies probabilistic modeling to portfolio risk evaluation, cash-flow uncertainty analysis, and value-at-risk estimation under multiple economic scenarios. Simulation outputs are evaluated using distribution fitting, convergence diagnostics, and comparative performance metrics. Results demonstrate improved risk visibility, reduced estimation bias, and enhanced robustness compared with standalone simulation environments. The study contributes a scalable framework suitable for financial institutions, risk analysts, and enterprise decision systems seeking integrated analytics solutions. By bridging commercial simulation software and open-source computational modeling, the research advances hybrid risk analytics capable of supporting data-driven financial governance and strategic planning under uncertainty.
(σ,τ) –Reverse Left Centralizer on prime r-rings
The goal of this article to propose the entrance of (σ,τ) - reverse left centralizer and Jordan (σ,τ) – reverse left centralizer on -rings .
Optimization of Machining Parameters in the Turning Process of A357 Alloy for Improved Performance and Surface Quality
Surface finish is a critical quality characteristic in mechanical parts, influencing their functionality and durability. This study focuses on optimizing the cutting parameters to achieve the lowest surface roughness in the turning operation of A357 aluminum alloy. The effects of spindle speed, feed rate, and depth of cut on surface roughness were investigated experimentally using a tungsten carbide cutting tool. Multiple regression analysis (MRA) and analysis of variance (ANOVA) were employed to evaluate the significance of cutting parameters and to develop predictive models for surface roughness (Ra). The objective was to determine the optimal turning parameters and establish a correlation between surface roughness and the cutting variables. The results provide insights into the relationship between machining parameters and surface finish, offering a predictive framework for optimizing turning processes
Study of Structural, Electrical and Optical Properties of Nanostructured TiO2 thin Films
Thin films of nanostructured Titanium Oxide (TiO2) were deposited by Spray pyrolysis method onto glass substrate at temperature 400 0C using 15 % TiCl3 as precursor. The deposited films were annealed 2 hrs at 500 0C. The structural, electrical and optical properties of TiO2 thin films were studied by X-ray diffraction, two probe resistivity set ups and UV-VIS spectroscopy, respectively. The tetragonal (anatase) crystal structured TiO2 thin films were confirmed by X-ray diffraction (XRD) and preferential orientation along the 101 plane was confirmed from structural analysis. The Crystallite size is found to be 47 nm. The optical properties of TiO2 thin films were estimated using absorption spectrum in the range of 300 nm to 900 nm and direct band gap energy found to be 3.32 eV.
Migrating Traditional Systems to Cloud-Native Microservices at Item Master
It leads the migration of Item Master's monolithic Product Information System to a cloud-native microservices architecture on Amazon Web Services. This shift delivers scalability, enhanced fault tolerance, and accelerated product development cycles for food and beverage operations. Data modelling addresses product scales while ensuring compliance with GS1 norms. Virtualisation occurs through Elastic Container Service, serverless prosecution via Lambda functions, and content delivery via CloudFront. prize, transfigure, cargo processes integrate external and internal data flows with perfection. Nonstop integration and nonstop deployment channels, established with Jenkins and Terraform, automate deployments and reduce errors. Crucial issues include minimised time-outs and alignment with business expansion needs. The practical significance lies in contemporizing heritage platforms to deliver flexible, growth-acquainted pall services.
Distributed AI Infrastructure Orchestration: A Hyperscale Multi-Cloud Framework for Geographic Load Balancing with Renewable Energy Optimization
The exponential growth of artificial intelligence workloads has created unprecedented demand for geographically distributed data center infrastructure capable of delivering petascale computing while minimizing carbon emissions and operational costs. This paper introduces a novel hyperscale multi-cloud orchestration framework that dynamically distributes AI training and inference workloads across geographically dispersed data centers based on real-time optimization of renewable energy availability, grid carbon intensity, computational resource availability, and network latency constraints. The proposed paradigm employs a three-tier hierarchical architecture comprising regional orchestration controllers, edge inference accelerators, and centralized policy optimization engines that collectively minimize a multi-objective cost function balancing energy efficiency, carbon footprint, quality of service, and infrastructure utilization. We formulate the workload distribution problem as a constrained mixed-integer nonlinear program and develop a heuristic decomposition algorithm achieving near-optimal solutions with polynomial time complexity. Simulation results using realistic workload traces from large language model training, computer vision inference, and recommendation system scenarios across five continental data center regions demonstrate forty-two percent reduction in carbon emissions, thirty-one percent decrease in energy costs, and twenty-three percent improvement in GPU utilization compared to conventional single-region deployment. The framework achieves these improvements while maintaining service level agreements with 99.95th percentile latency under 150 milliseconds for interactive inference requests. This research establishes foundational principles for sustainable hyperscale AI infrastructure that aligns computational demand with renewable energy generation patterns while preserving application performance requirements.
A Preliminary Study on the Green Synthesis, Characterization, In Vitro Antioxidant, Anti-Inflammatory, And Cytotoxic Properties of Silver Nanoparticles Using Both Aqueous and Ethanol Leaf Extracts of Annona Muricata (Soursop)
The use of nanoparticles across various fields has garnered significant attention due to their unique properties and diverse applications. This preliminary study explored the green synthesis of silver nanoparticles (AgNPs) using aqueous and ethanol extracts of Annona muricata (soursop) leaves as reducing and stabilizing agents. The AgNPs synthesized using leaf extracts were characterized by FTIR, SEM-EDX, TEM, and DLS analyses. The FTIR spectra indicated the presence of phytochemicals acting primary as reducing and capping agents. SEM analysis revealed predominantly spherical to quasi-spherical nanoparticles with sizes ranging from ~20-70 nm, while EDX confirmed the elemental presence of silver (Ag). TEM showed mostly spherical nanoparticles with sizes ranging from ~3-30 nm (AgNPs synthesized from 5 mL ethanol extract) to ~10-40 nm (AgNPs synthesized from 3 mL aqueous extract). DLS analysis indicated moderate polydispersity and negative zeta potentials (-21.7 mV to -29.4 mV), suggesting good colloidal stability, with ethanol-derived AgNPs exhibiting superior properties. The AgNPs displayed significant, concentration-dependent in vitro bioactivities. Those synthesized with 5 mL ethanol leaf extract exhibited the highest ferric reducing antioxidant power (FRAP: 324 μg AAE/mL), DPPH radical scavenging (64.29%), and ABTS⁺ scavenging activity (73.9%). They also demonstrated potent anti-inflammatory activity (78.82% BSA denaturation inhibition at 50 μg/mL), surpassing the standard drug diclofenac sodium, along with notable cytotoxicity in the brine shrimp lethality assay (LC₅₀: 32.5 μg/mL). These findings highlight the effectiveness of A. muricata leaf extracts, especially ethanol extract, for the eco-friendly synthesis of AgNPs, yielding nanoparticles with promising antioxidant, anti-inflammatory, and cytotoxic potential.