EPH-International Journal of Science And Engineering
EPH – International Journal of Science and Engineering is a peer-reviewed, open access scholarly journal that publishes original research and review articles across all major areas of science and engineering Journal Information Journal Title: EPH – International Journal of Science and Engineering Online ISSN: 2454-2016 Published Since: 2015 Language: English Publication Frequency: Quarterly Publication Format: Online Publisher: E-Pub Solution Private Limited
PERFORMANCE EVALUATION OF DATA HANDLING ALGORITHMS (DT, KNN, RF AND DNN) UNDER THE HANDLING OF TWO DATASET OF DIABETES
Diabetes is a condition that affects people of all ages these days, as everyone is aware. This paper discusses the implementation of algorithms based on two separate diabetes datasets. The algorithms were chosen based on the highest value and accuracy. In this paper analyze the four algorithms such as DT, KNN, RF and DNN. The main objective of this paper is to handle data with different and large datasets because data is the most difficult to handle. In the previous research, diabetes was predicted in different cases and their conclusions were also different. In this paper, the DNN algorithm is used and compared with other algorithms under two different datasets. In this paper, compare the DNN algorithm with the proposed algorithms such as DT, RF and KNN on large and two different datasets of diabetes. The first dataset is taken from Pima Indians Diabetes Database (PIDD) and the second dataset is collected through survey from Manjira Devi Hospital Uttarkashi (MDHU). On the basis of these two datasets, algorithms are compared.
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/
INTEGRATING GRAPH DATABASE MODELING INTO AGILE SYSTEM ANALYSIS AND DESIGN: A CONCEPTUAL FRAMEWORK FOR SCALABLE INFORMATION SYSTEMS
Relational database modeling is still the most popular in system analysis and design (SAD), but has structural constraints in relationship-intensive domains like social networks, fraud detection, recommendation systems, and knowledge graphs (Angles & Gutierrez, 2008; Robinson et al., 2015). These constraints are aggravated in agile systems with iterative delivery and emergent design (Beck et al., 2001; Fowler, 2002; Schwaber & Sutherland, 2017), in which architectural decisions that are susceptible to traversal are often deferred. This study presents the Graph-Agile Framework, a design science artifact that brings the concepts of graph database modeling into agile SAD using a three-tier architecture: Strategic Alignment, Tactical Integration, and Operational Adaptation. Grounded in design science research and theory synthesis (Gregor & Hevner, 2013; Hevner et al., 2004; Jaakkola, 2020a), the framework institutionalizes traversal-aware reasoning, schema optionality, and sprint-level scalability validation into the iterative workflows. An expert-based evaluation (N = 40) assessed structural clarity, theoretical grounding, agile alignment, scalability support, and adoption feasibility. Findings indicate consistently high expert-rated internal coherence and compatibility with agile practices, providing preliminary perceptual support for the framework’s utility. The study advances SAD theory beyond relational assumptions and extends agile methodology by formalizing traversal-aware architectural governance for scalable graph-centric system development.
NYLON-6: POLYMERIZATION CHEMISTRY, PROPERTIES, AND INDUSTRIAL APPLICATIONS –AN INDUSTRIAL REVIEW
Nylon-6, a widely used polyamide, stands out among synthetic polyamides as a versatile, high- performance fibre owing to its unique macromolecular structure, balanced physicochemical properties, and broad processability. It is synthesized by the ring-opening polymerization of ε- caprolactam through hydrolysis, polyaddition, and polycondensation under controlled thermal and pressure conditions. The polymerization process is critical in defining the final polymer’s characteristics, including molecular weight distribution, residual monomer content, and relative viscosity, which directly influence fiber-forming capability. Advanced process control systems regulating temperature, pressure, level, and flow are indispensable for ensuring stability, reproducibility, and energy efficiency at industrial scale. The structural property relationship of Nylon-6 highlights its superior tensile strength, elasticity, abrasion resistance, dyeability, and thermal stability (Tm ≈ 220°C),with crystallinity levels around 50–55%. Post-polymerization treatments, such as hot washing and solid-state aging, further improve viscosity control and spinnability. Modified formulations with additives or resin blends enhance resistance to heat, solvents, and fire, expanding its range of applications. Due to this combination of mechanical robustness and processability, Nylon-6 is extensively employed in technical textiles (tire cords, carpets, fishing nets, parachutes, and sutures), apparel fabrics, packaging films, and engineering plastics processed via injection molding, extrusion, and blow molding. This Industrial Case study consolidates polymerization principles, control strategies, material properties, and applications of Nylon-6, providing a comprehensive perspective for advancing both research and industrial practices.
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
A THEORETICAL FRAMEWORK FOR CLOUD AND DATA ANALYTICS CONVERGENCE IN SCALABLE MANAGEMENT INFORMATION SYSTEMS
With the growing use of data-intensive environments in the operations of an organizations and the higher education institutions, Management Information Systems (MIS) must shift away from the known-data-processing-environment to the higher analytical support and timely decision-making environment. Despite the fact that data analytics and cloud computing are recognized as crucial agents of organizational intelligence and scalability, a large portion of the literature currently in publication views the two technologies as distinct disciplines. These has been the focus on elaborating on how cloud and analytics capabilities interact in systematic relations to serve scalable MIS and this is specifically in complex and multidisciplinary academic settings. The paper proposes a theoretically grounded conceptual framework that clarifies how data analytics and cloud computing can be integrated to provides a platform for scalable MIS. The study incorporates cloud computing, data analytics, distributed system, and information system literature using a theory synthesis approach. It is designed based on three construct domains, including cloud infrastructure elements, data analytics capabilities and MIS scalability requirements and operationalized by five convergence mechanisms: dynamic resource orchestration, analytics-support scalability, bidirectional feedback loops, data-compute co-location, and adaptive optimization. Seven theoretical propositions are developed to inform future empirical research. The research work also adds to MIS theory by explaining structural functional relationship between cloud infrastructure and analytics capabilities in scalable system design. Furthermore, it provides viable guidelines to the institutions that seek to achieve the development of analytics-enabled MIS designs that can withstand the increasing levels of data, changing decision needs, and available resource limitations.
BIOTECHNOLOGICAL APPROACHES TO PLASTIC WASTE DEGRADATION
Plastic pollution is a persistent global environmental problem driven by massive production of recalcitrant polymers such as polyethylene (PE), polypropylene (PP) and polyethylene terephthalate (PET). Biotechnological strategies—microbial degradation, enzyme catalysis, fungal decomposition, engineered microbial consortia, and enzyme engineering—offer promising, potentially sustainable routes to degrade or upcycle plastic waste under controlled conditions. Key advances include discovery of PET-degrading bacteria (Ideonella sakaiensis) and PETases, development of engineered PET hydrolases with enhanced activity and thermostability, and evidence that fungi and mixed microbial consortia can attack diverse polymers. However, the intrinsic chemical inertness of polyolefins (PE/PP), variable additives, and inconsistent experimental standards complicate translation from lab demonstrations to scalable processes. Integrated approaches that combine pretreatment (oxidation, UV, thermal), enzyme cocktails, synthetic biology (pathway engineering, secretion systems), and process engineering (bioreactors, immobilized enzymes, coupled upcycling pathways) may overcome these hurdles and produce value-added products (monomers, oligomers, biopolymers). Life-cycle impacts, CO₂ release, and techno-economic feasibility require careful assessment. Continued discovery of novel enzymes, rational enzyme design, and development of robust consortia optimized for mixed-waste streams are essential next steps toward industrially relevant biotechnology solutions for plastic waste management.
Copyright (c) 2025 EPH-International Journal of Science And Engineering https://creativecommons.org/licenses/by/4.0
IN-VITRO AND IN-VIVO STUDY OF SELF-MICROEMULSIFYING DRUG DELIVERY SYSTEM FOR HYPERTENSION
Aim: The aim of the study was to formulate and evaluate self-micro emulsifying drug delivery system (SMEDDS) of nifedipine for hypertension. Methodology: A series of formulations with different compositions were selected in the microemulsion region for assessment of self-emulsification time and droplet size. The optimized SMEDDS formulation was used for in vitro dissolution and pharmacokinetic studies in rats. The optimized formulation was further used for the preparation of various Solid SMEDDS(S-SMEDDS) formulations. formulations were evaluated for stability, robustness to dilution and self-emulsification efficiency. The in vitro release was almost similar for the S-SMEDDS as well liquid within 5 min. Results: The optimized formulation had a particle size of 11.48nm, a transmittance of 98.2%, a zeta potential of -11.86 mV, and a polydispersibility index of 0.243. The solid SMEDDS tablet exhibited improved drug release (99.5% in 60 minutes) compared to the marketed tablet (67.09.75%) and pure drug (26.17%). This study demonstrates the potential of the SMEDDS approach to enhance the solubility and in-vitro drug release of drugs. Conclusion: studies illustrated that adsorption to solid carrier technique could be a useful method to prepare the solid SMEDDS tablets from liquid SMEDDS, which can improve oral absorption of nifedipine, nearly equivalent to the liquid SMEDDS, but better in the formulation stability, drugs leakage and precipitation, etc.
PLANT DISEASE RECOGNITION SYSTEM USING CONVOLUTIONAL NEURAL NETWORKS (CNNS) AND TRANSFER LEARNING
Global food security and agricultural output are seriously threatened by plant diseases, especially when diagnosis is erroneous or delayed. For large-scale farming, manual crop inspection is laborious, subjective, and unfeasible. This work proposes a Plant Disease Recognition System based on Convolutional Neural Networks (CNNs) and transfer learning to address these issues. The suggested solution makes use of a pretrained MobileNetV2 architecture that has been refined using the publicly accessible PlantVillage dataset, which includes more than 54,000 tagged leaf images from various crop species and disease categories. In order to enhance generalization under various visual situations, the methodology places a strong emphasis on robust image preprocessing, such as scaling, normalization, and data augmentation. Users can contribute leaf photos and get real-time illness predictions by accuracy of about 98%. Confusion matrix analysis shows very little misdiagnosis, mostly in cases of early-stage or visually comparable diseases. The results verify that transfer learning dramatically shortens training times without sacrificing classification performance, which qualifies the system for real-world use. The work offers further extensions toward mobile deployment and real-world field validation while highlighting the promise of CNN-based solutions for early plant disease diagnosis.
Copyright (c) 2022 EPH-International Journal of Science And Engineering https://creativecommons.org/licenses/by/4.0
ADVANCED LANGEVIN DYNAMICS FOR PREDICTING SPIN DISTRIBUTIONS IN HEAVY-ION FUSION
The angular momentum (spin) distribution of compound nuclei formed in heavy-ion fusion reactions is a key quantity governing fission probability, gamma-ray multiplicity, and evaporation-residue survival. Traditional approaches based on sharp cutoff formulas, coupled-channel barrier penetration, or mean-field dynamics often fail to reproduce experimentally observed spin widths, especially near the Coulomb barrier and in reactions forming very heavy or superheavy systems. In this study, I developed and applied an advanced multi-dimensional Langevin framework to predict spin distributions in heavy-ion fusion, treating the reaction as a stochastic transport process in a collective coordinate space, including radial separation, deformation, mass asymmetry, orientation, and rotational degrees of freedom. Dissipation is modelled using one-body wall-and-window friction, whereas random forces obey the fluctuation-dissipation theorem. Deformation-dependent inertia and realistic nucleus-nucleus potentials were consistently incorporated. Simulations for representative reactions such as , , and reproduce the observed broadening of spin distributions with increasing beam energy, enhanced angular momentum damping in mass-asymmetric systems, and separation of fusion and quasi-fission events. The results demonstrate that advanced Langevin dynamics provides a robust and physically transparent tool for predicting spin distributions and for guiding the design of fusion reactions aimed at producing heavy and superheavy nuclei.
FORMULATION AND CHARACTERIZATION OF HARD, GELATIN-LIKE CAPSULES BASED ON CORN STARCH, AGAR-AGAR, SODIUM ALGINATE AND HPMC FOR ORAL DELIVERY OF SMALL MOLECULES
This study develops and evaluates plant-based hard capsules formulated using corn starch, agar-agar, sodium alginate, and hydroxypropyl methylcellulose (HPMC) as an alternative to traditional gelatin capsules. The polymers were optimized by adjusting viscosity, polymer ratios, and plasticizer concentration to achieve dip-coating compatibility. Physicochemical characterizations included viscosity, swelling index, moisture content, capsule wall thickness, tensile strength, and disintegration time. Structural confirmation was performed using SEM, FTIR, and DSC. The optimized formulation exhibited uniform film morphology, improved thermal stability, inter-polymer hydrogen bonding, and mechanical strength comparable to gelatin. Dissolution studies revealed rapid and reproducible drug release in simulated gastric and intestinal fluids. Compared with literature-reported gelatin, HPMC, alginate, and starch capsules, the proposed composite formulation showed balanced mechanical properties, controlled hydration, low moisture sensitivity, and faster disintegration. The study demonstrates that a synergistic blend of starch, agar, alginate, and HPMC can serve as a robust, low-cost, plant-based alternative for oral drug delivery systems.
Copyright (c) 2025 International Journal of Science And Engineering https://creativecommons.org/licenses/by/4.0
CONTINUAL YOLO-BASED DETECTION FOR LONG-TERM MONITORING OF THE GREATER ONE-HORNED RHINO
YOLO-based object detectors support fast wildlife monitoring across diverse habitats. Static training limits these detectors in the field because ecological conditions shift over time. Continual learning provides an incremental update process that preserves earlier knowledge while absorbing new information. This article reviews recent progress in continual learning for YOLO detection, with emphasis on experience replay, self-distillation, and human-in-the-loop supervision. These approaches protect past knowledge, reduce annotation demands, and deliver more stable predictions during long-term deployments. A focused case study on Greater One-Horned Rhino monitoring shows how adaptive learning pipelines strengthen detection reliability during seasonal changes, altered terrain, and new camera placements. The review outlines methods with strong potential for long-term conservation work and highlights future directions for resilient wildlife monitoring systems.
MESTA FIBER (HIBISCUS SABDARIFFA): A REVIEW OF ITS PROPERTIES, APPLICATIONS, AND ROLE IN PROMOTING SUSTAINABILITY
The increasing world demand for eco-friendly natural fibers and products is gaining interest in sustainable fibers as alternatives to man-made fibers. Bast fibers such as linen, banana, jute, bamboo, sisal, kenaf, and Mesta have been used for a long time. Bast fibers are the raw materials of the present and the future, not just for textiles but also for modern eco-friendly composite materials, medicine, cosmetics, food, biopolymers, agro-fine chemicals, and energy. The retting is the biggest problem with extracting the fibers. Dew retting as well as stagnant water and running water retting are the conventional methods to separate the long bast fibers. Due to higher tensile property, mechanical performance, environmental benefits, and economic viability, Mesta has shown significant promise compared to synthetics. They can be grown in numerous climates, and, under the right conditions, they don't hurt the ecology too much or at all. The study looks into the important physical and mechanical properties and benefits of Mesta fibers, as well as their possible uses. It focuses on how they might help promote sustainable consumption and green industrial practices. The review talks on Mesta's environmental, technical, and social-economic importance, showing that they are still crucial for accomplishing ecologically sustainable development goals and coming up with novel bio-based materials.
TRANSFORMING THE TRAVEL INDUSTRY THROUGH AI-DRIVEN HOTEL BOOKING AND TOURISM INNOVATIONS
The integration of artificial intelligence (AI) into hotel booking and tourism is fundamentally transforming the travel industry by enhancing personalization, operational efficiency, and data-driven decision-making. This research paper analyzes the evolution, benefits, and ethical challenges of AI-powered hotel booking systems within the broader context of smart tourism destination management. Drawing upon recent scholarly work, the paper examines the technical architecture, empirical evaluation, and stakeholder implications of these systems. The discussion further addresses the role of transparency, fairness, and data privacy, emphasizing the necessity of responsible AI adoption. Ultimately, the paper argues that while AI-driven hotel booking systems offer substantial advantages for tourists, providers, and policymakers, their widespread and ethical deployment requires ongoing commitment to inclusivity, regulatory compliance, and user trust.
UNSUPERVISED EXPLORATION OF HIERARCHICAL STRUCTURE IN HUMAN ACTIVITY RECOGNITION DATA
Human Activity Recognition (HAR) datasets contain complex patterns that supervised models exploit with labeled training, but it remains unclear what latent structure exists in the data itself. This paper presents an unsupervised exploratory analysis of a smartphone sensor HAR dataset to uncover inherent activity groupings without using activity labels. We apply a range of clustering algorithms (k-means, Gaussian mixture, hierarchical agglomerative, density-based HDBSCAN, spectral clustering) and dimensionality reduction methods (Principal Component Analysis – PCA, t-distributed Stochastic Neighbor Embedding – t-SNE, Uniform Manifold Approximation and Projection – UMAP, and a feed-forward autoencoder) to identify natural clusters of sensor feature vectors. Quantitatively, we evaluate clustering quality using internal metrics (silhouette coefficient) and external metrics against true labels (Adjusted Rand Index – ARI, and Normalized Mutual Information – NMI). The results reveal a dominant two-cluster division separating static postures from dynamic movements, with finer sub-clusters roughly corresponding to the six known activities when clustering is applied hierarchically. UMAP non-linear embedding dramatically improved cluster separability and alignment with classes, outperforming PCA. Analyzing feature importance in each cluster showed that features related to body orientation and acceleration dynamics differentiate activities. These findings demonstrate that unsupervised learning can automatically discover meaningful activity groupings (e.g. distinguishing stationary vs. moving behaviors) and key distinguishing sensor features, without any labels. The study provides insights into intrinsic HAR data structure, which can inform feature design and hierarchical modeling in future activity recognition systems.
EXPLAINABLE MACHINE LEARNING FOR EVALUATING DIABETES PREDICTION MODELS
The present study has investigated the use of Explainable AI (XAI) machine learning models for diabetes prediction, with a particular focus on SHAP (Shapley Additive Explanations) values and XGBoost importances (gain). Using the publicly available Pima Indians Diabetes dataset there were various classification models: Logistic Regression, Random Forest, and XGBoost trained and evaluated based on the parameters: Accuracy, and AUC. Global and local explainability was further assessed through SHAP bar plots and XGboost gain importances plots were used for analyzing structural feature importances. Results gathered suggested that stabilized glucose level and age were the most influential predictors of diabetes across models even though differences emerged in the ranking of the feature HDL (High-Density Lipoprotein) cholesterol. Overall, while SHAP provided a hybrid understanding of local and global patient-specific explanations, XGboost gain importances gave structural importance. The discussion section also includes how the combination of these two tools provided a hybrid, comprehensive and nuanced understanding about how these models bridge the “black-box” gap in clinical decision-making, ensuring trust, clarity and transparency in the prediction of AI models for sensitive domains like healthcare applications.
HALLUCINATIONS OF THE GENERALIST, BRITTLENESS OF THE EXPERT: A STUDY ON FINE-TUNED OPEN SOURCE LLM FOR SOC ANALYSIS
In the evolving landscape of cyber defense, where knowledge is the decisive edge, the practical deployment of Large Language Models (LLMs) is fraught with challenges. General-purpose models from large providers present significant cost and data privacy barriers for most Security Operations Centers (SOCs), while open-source models often hallucinate in structured domains like MITRE ATT&CK. This study investigates these trade-offs by fine-tuning the DeepSeek R1 Distilled LLaMA 8B model on a curated corpus of MITRE ATT&CK semantics, leveraging the Unsloth framework to demonstrate a reproducible pipeline on consumer-grade GPUs. Notably, the entire fine-tuning process was conducted using commercial Kaggle infrastructure, showing that effective LLM specialization can be achieved without access to high-end hardware. Our evaluation contrasts this fine-tuned "Focused Specialist" with the untuned "Creative Generalist" base model. The results reveal a critical trade-off: the base model, while a superior contextual reasoner, suffers from catastrophic factual hallucinations, making it dangerously unreliable. In contrast, our fine-tuned model achieves 84% exact-match accuracy and proves its trustworthiness, but this specialization introduces a "brittleness" where its knowledge is static and less creatively applied. Our LLM-as-a-Judge evaluation confirmed the fine-tuned model was the superior co-pilot in 73% of test cases, achieving a 25% higher average quality score.
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
ADAPTIVE CONTROL ALGORITHM FOR GRID SUPPORTIVE SOLAR PV POWER GENERATING SYSTEM
Grid stability, voltage executives, and power quality are all experiencing fresh challenges as solar photovoltaic, or PV, systems becomes progressively integrated with the power grid. An adaptive control methodology for grid-supportive solar PV power generating systems is presented in this paper to overcome these problems. The suggested algorithm automatically changes control parameters in real time in response to changing grid conditions and fluctuations in solar irradiation, as compared to traditional fixed-parameter control strategies. The system guarantees an effortless transition across grid-connected and islanded modes while applying reactive power support, voltage and frequency management, and maximum power point tracking (MPPT). The final results of the simulation show how successfully adaptive control performs to maintain grid stability, enhanced power quality, and boost system reliability depending on unpredictable demand and generation scenarios. This method supports more renewable energy to be linked to the electrical grid, and these supports the development of a stronger and more environmentally friendly energy infrastructure.
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/deed.en
INNOVATIONS IN COASTAL SHIPPING: PROPULSION, SAFETY, REGULATIONS AND TRAINING FOR THE FUTURE SEAFARERS
Both operational modernization and a focus on the environment are driving big changes in coastal shipping. Recent achievements in technology, engines, employee learning and regulations concerning coastal ships are reviewed in this work. Hybrid and zero-emission forms of power such as batteries, technologies aided by wind and engines using LNG, methanol or ammonia are being pushed because they cut emissions and link to the targets of the International Maritime Organisation for reducing carbon. At the same time, advances in safety, like self-driving systems, smarter onboard navigation tools and resilient protection against digital threats, are altering how safe ships operate along crowded and environmentally sensitive coasts. The development of cybersecurity standards and environmental regulations is having a big impact on this transition. As a result of these new technologies, organisations in Maritime Education and Training are creating engaging courses that target digital systems, alternative fuels and autonomous operations. Key strategies for MASS utilisation are presented, along with examples of regulatory modifications in maritime training and the use down to earth dual-fuel technologies. All these changes suggest that coastal shipping is switching to become more intelligent, secure and environmentally responsible.
Maritime Autonomous Surface Ships (MASS)
MODELING AND ANALYSIS OF HALF-WAVE DIPOLE ANTENNAS IN MATLAB: A COMPREHENSIVE REVIEW
Radio frequency (RF) and electromagnetic (EM) systems still rely heavily on half-wave dipole antennas due to their practical design, reliable operation, and adaptability to a variety of communication applications. The development of contemporary wireless technologies such as 5G, the Internet of Things (IoT), and wearable technology has led to a growing demand for accurate dipole antenna design and analysis to fulfill performance requirements. Because simulation tools may save development cycles and improve the accuracy of design forecasts, RF engineers are increasingly using them to do this. Of them, MATLAB has emerged as a popular platform for antenna modeling because it provides a very flexible environment that accommodates both sophisticated, algorithm-level modifications and streamlined design procedures. This article provides a thorough analysis of MATLAB-based methods for half-wave dipole antenna modeling. Antenna Toolbox, user-developed solutions using the Method of Moments (MoM), and hybrid approaches that combine MATLAB with full-wave solvers like CST Microwave Studio and Ansys HFSS are all covered. Each method is evaluated based on its precision, processing requirements, flexibility, and applicability to certain use cases. The balance between usability, level of control over the modeling process, and the accuracy of the simulated outcomes is among the trade-offs included in the analysis. The study discusses the shortcomings of these approaches as well as their advantages, including the lack of generally recognized benchmarking standards, the abstraction level of solvers, and the low precision of near-field modeling. This review is useful for both academic researchers and professionals in the business since it provides a comprehensive assessment of existing procedures and identifies areas that require improvement. It attempts to guide anyone looking for dependable and effective MATLAB antenna modeling solutions, as well as suggestions for future advancements in simulation-driven antenna design.
Copyright (c) 2024 International Journal of Science And Engineering https://creativecommons.org/licenses/by/4.0
6 DOF 3D PRINTED ROBOT FOR VISION BASED SORTING
This paper presents a low-cost and efficient object sorting system using a fully 3D-printed robotic arm with six degrees of freedom (6DOF). The system uses two types of sorting methods: sensor-based sorting and camera-based (vision-based) sorting. In the sensor-based method, sensors and servo-controlled deflectors on the conveyor belt help in allowing or blocking objects based on their physical properties like shape and color. In the vision-based method, a camera controlled by a servo captures images of the objects, and a Convolutional Neural Network (CNN) processes these images to identify the object’s shape, color, and type. Only the correctly identified objects are sent to the robotic arm for picking and placing. The 3D-printed arm is cost-effective and offers flexibility in design. Tests show that the system works accurately and quickly in different conditions. This sorting system is useful for industrial automation, education, and research. The integration of both vision and sensor-based sorting increases reliability and reduces error rates. The modular nature of the system allows for easy upgrades and maintenance. The robotic arm’s motion is precisely controlled based on real-time object data, improving the overall efficiency of sorting tasks. The use of 3D printing also allows quick customization for specific applications. This system demonstrates a strong potential for smart and automated manufacturing environments.
BRAND LOYALTY: HOW COMPANIES RETAIN CUSTOMERS IN A COMPETITIVE MARKET
In an increasingly competitive and dynamic market landscape, brand loyalty has emerged as a crucial strategy for companies aiming not only to sustain growth and profitability but also to differentiate themselves from competitors. In the face of constantly evolving consumer preferences, digital transformation, and the rise of global and local competitors, fostering brand loyalty provides a strategic advantage that goes beyond immediate sales. This research paper investigates the key drivers of brand loyalty and the various strategies companies employ to retain customers over time. Using a combination of qualitative and quantitative methods, including structured surveys conducted among 500 consumers and detailed case studies of globally successful brands such as Apple, Nike, Starbucks, Amazon, and Coca-Cola, this study highlights the multifaceted nature of customer loyalty. The findings reveal that factors such as consistent product and service quality, emotional engagement, personalized customer experiences, transparent and consistent brand communication, and robust loyalty programs are central to cultivating long-term relationships. Moreover, brands that align themselves with customers' values and social expectations tend to enjoy stronger loyalty and advocacy. The study concludes that companies that strategically invest in building meaningful and enduring customer relationships are more likely to gain a sustained competitive edge, enhance customer lifetime value, increase brand equity, and secure a loyal customer base in a saturated marketplace.
OPTIMIZING BATTERY TOPOLOGY FOR ENHANCED LIFESPAN AND REDUCED STRESS THROUGH INNOVATIVE CURRENT FLOW MANAGEMENT APPROACHES
In an era prioritizing environmental sustainability, this paper addresses a key challenge in solar-powered electric vehicles (EVs): optimizing energy flow within batteries. Focusing on the variable nature of solar energy, which often leads to inconsistent charging and reduced battery life, the study proposes a novel control system. This system employs advanced algorithms and battery management techniques to adapt to solar fluctuations, harmonizing charging and discharging cycles. Significant improvements include enhanced battery lifespan and solar power utilization efficiency, contributing to a 6.5% increase in vehicle range. This research underlines the potential for more efficient, eco-friendly solar EVs, advancing sustainable transportation technology.
PROACTIVE DIAGNOSIS OF MUNG BEAN LEAF DISEASES IN A CONTROLLED ENVIRONMENT USING MACHINE LEARNING TECHNIQUES
This paper puts forth a model that classifies Mung (Vigna mungo L.) leaves to check if they are fit as a fiddle or have caught some disease, using a Machine Learning algorithm. The dataset was made in a proper controlled setup, where each data item (image) shows just one leaf against a white background, collected from the South Gujarat side of India. Support Vector Machine (SVM) models were trained for doing this work, focusing on spotting three types of Mung leaf diseases, as well as when a leaf is perfectly healthy. The model nicely pulls out the detailed features related to different diseases. Experiment results show that the SVM gets an identification accuracy of 86.4% on the Mung leaf image dataset. If diseases are caught early, it can help our kisaans in increasing their yield. The main aim was to automatically identify Mung leaf diseases using advanced machine learning techniques and image data.
Copyright (c) 2025 International Journal of Science And Engineering https://creativecommons.org/licenses/by/4.0/
AUTOMATED LAND COVER MAPPING FROM LISS-III MULTISPECTRAL IMAGES USING SUPERVISED CLASSIFICATION WITH DEEP LEARNING
Remote sensing (RS) is the technique of finding and understanding information from a long distance or remote location using sensors. Land use and land cover mapping are fundamental tasks for planning and management. The deep neural network was used to perform the study. This study shows the semantic segmentation of LISS-III multispectral image using a fully convolutional network (FCN): U-net and Tiramisu. We present an innovative dataset, based on these LISS-III images that contained 4 different spectral bands (Band – 2 (Blue), Band-3 (Green), Band-4(Red), and Band-5 (Nearly Infrared), the false color composite (FCC) images and the ground truth mask images to classify total 4 classes (Surface water , Flora , Idle Land, and Housing zones) which holds 1470 labeled images where 1,255 images are used to train the model while 215 images are reserved for validation and evaluation. An FCN-based U-Net model with skip connections was trained on 256 × 256 × 3 input images to produce 256 × 256 × 4 one-hot encoded segmentation masks, representing four distinct land cover classes. Results confirmed that the U-Net architecture performs exceptionally well in semantic segmentation of LISS-III multispectral imagery for land use/land cover classification. The U-Net model successfully classified four land cover classes with an overall accuracy of 84%, significantly outperforming the Tiramisu architecture, which achieved a comparatively moderate accuracy of 52%.
A STUDY ON FABRICATION OF ECO EVAPORATOR AND PRODUCING DRINKABLE WATER FROM THE WASTE WATER OF STANDING WATER BODIES
Water scarcity remains a critical challenge globally, with millions of people lacking access to clean drinking water. This project explores the development of a simple, solar-powered water purification system designed to address water contamination issues in areas with limited access to electricity. The solar still uses the sun's energy to evaporate water, leaving impurities behind, and then condenses the vapor to produce clean, drinkable water. The study focuses on the effectiveness of this solar still in purifying water from Kanbargi Lake, which exhibited high levels of contamination including elevated pH, TDS, EC, turbidity, and BOD. The results showed significant improvements in water quality, with reduced turbidity, lower TDS and EC, and a notable decrease in BOD, making the water safer for consumption. This simple, sustainable technology has the potential to address water quality issues in remote and underserved regions. Furthermore, the future scope of the technology includes scaling up the system, integrating it with other purification methods, and incorporating renewable energy sources for enhanced efficiency. By empowering local communities to manage their water purification needs, this technology offers a promising, eco-friendly solution to the global water crisis.
INTERNET OF THINGS (IOT) IN SMART CITIES: CHALLENGES AND OPPORTUNITIES
The Internet of Things (IoT) has emerged as a transformative force in the development of smart cities, enabling real-time data collection, automation, and enhanced urban management. This study explores the opportunities and challenges associated with IoT implementation in smart city ecosystems. The research adopts a descriptive and analytical approach, incorporating a theoretical framework validated through case studies to assess the impact of IoT on urban infrastructure. The findings reveal that IoT integration improves energy efficiency, traffic management, and environmental sustainability, with potential reductions in urban carbon emissions by 15% over the next decade. However, security vulnerabilities, interoperability challenges, and scalability constraints remain critical concerns, as 70% of IoT networks are susceptible to cyber threats. Comparative analysis with previous studies underscores the role of AI and blockchain in enhancing IoT security and decision-making processes. The research adds value to current knowledge by examining IoT-based urban changes through an organized assessment framework which demonstrates the necessity of regulatory guidelines and secure systems. Future research needs to conduct experimental testing and predictive simulations which will enhance the capability of smart city implementation optimization.
GRAPH DATABASES FOR FRAUD DETECTION: A FRESH LOOK AT FINANCIAL SECURITY
In the ever-evolving landscape of financial security, fraud detection remains a paramount concern for institutions worldwide. Graph databases have emerged as a powerful tool in this battle against financial crime, offering a fresh perspective on how organizations can uncover hidden patterns and connections within their data. Unlike traditional relational databases, which often struggle with complete and large volumes of interconnected data, graph databases excel in visualizing and analyzing these intricate networks. By leveraging their unique structure, financial institutions can swiftly identify suspicious activities that may go unnoticed. For instance, banks can analyze transaction patterns and relationships between customers, accounts, and transactions to reveal potential fraud rings. Real-world applications have demonstrated the efficacy of graph databases in real-time fraud detection, allowing organizations to respond more swiftly to emerging threats. The strengths of graph databases lie in their ability to handle vast amounts of interconnected data and their intuitive querying capabilities, enabling data analysts to explore relationships in a way that mirrors human reasoning. By connecting seemingly disparate data points, these systems illuminate the pathways that fraudsters may take, thus enhancing the effectiveness of detection algorithms. Moreover, comparing graph databases to traditional approaches underscores their value; where traditional systems may require cumbersome joins and complex queries, graph databases provide a more efficient and flexible means of data exploration. Ultimately, the adoption of graph databases in the financial sector represents a significant shift towards proactive fraud detection strategies, empowering institutions to safeguard their assets and maintain the trust of their customers.
Copyright (c) 2019 International Journal of Science And Engineering https://creativecommons.org/licenses/by/4.0
Modernizing Legacy Financial Systems with Kubernetes
The financial sector has long grappled with legacy systems that hinder agility and innovation, posing significant compliance challenges and limiting the ability to scale operations effectively. This paper explores the compelling business case for modernizing these outdated financial systems using Kubernetes, a powerful orchestration platform for containerized applications. We delve into the substantial return on investment that organizations can achieve by transitioning to cloud-native architectures, emphasizing how Kubernetes facilitates the development and deployment of scalable applications that adapt to changing market demands. Real-world examples from the Fintech industry illustrate how companies have successfully navigated this transformation, highlighting the benefits of increased operational efficiency, enhanced security, and improved compliance with regulatory requirements. By transitioning from traditional software to a modern containerized environment, financial institutions can streamline their operations and unlock new opportunities for innovation and growth. This journey towards modernization is essential for remaining competitive in an increasingly digital landscape, where customer expectations continue to evolve. The insights presented in this paper underscore why embracing Kubernetes is not merely a technical upgrade but a strategic imperative for financial organizations aiming to thrive in the future. Through a detailed examination of the challenges and opportunities associated with this transition, we provide a roadmap for organizations seeking to modernize their financial systems, ultimately demonstrating that moving to containerization and cloud-native applications is beneficial and essential in today’s rapidly changing financial ecosystem.
Copyright (c) 2019 International Journal of Science And Engineering
Driving Innovation in Financial Services with DevOps
DevOps is redefining financial services by driving innovation and operational efficiency in an industry traditionally burdened by strict regulatory requirements. Early adopters in fintech and banking are overcoming these challenges by implementing DevOps practices that enable rapid yet compliant development cycles. By integrating development and operations teams, financial institutions can reduce costs, streamline processes, and quickly bring new features to market. Key success metrics—such as deployment frequency, lead time for changes, and reduced incident rates—demonstrate the impact of DevOps in accelerating time-to-market and improving reliability without compromising compliance. Leading examples from fintech show how continuous integration and continuous delivery (CI/CD) pipelines minimize delays and ensure high standards across development cycles. Moreover, automated compliance checks, efficient monitoring, and incident management are helping financial services companies maintain regulatory standards while adapting swiftly to market demands. DevOps enables collaborative, transparent work environments where teams can pivot and adjust real-time strategies to meet changing business needs. Financial institutions embracing DevOps are witnessing a paradigm shift, transforming a costly, slow-moving process into a dynamic and agile workflow that aligns with customer expectations and regulatory mandates. These advancements are enhancing customer satisfaction and positioning financial institutions to remain competitive in a fast- evolving digital landscape. As financial organizations look to the future, DevOps offers a robust framework for bridging the gap between innovation and compliance, making it a critical component of success in modern financial services.
OPTIMIZING DATA PIPELINES FOR HIGH-PERFORMANCE MACHINE LEARNING IN AWS
Strong and efficient data pipelines enable rapid data collecting, preparation, and transformation thereby defining the applications of high-performance machine learning. Improving data pipelines in the present computing environment will enhance the opportunities of machine learning models. This work demonstrates how precisely leveraging AWS resources in machine learning applications could significantly increase the data pipeline building and deployment efficiency. AWS offers several tools, including S3 for scalable storage, Lambda for serverless computing, Glue for data integration, and SageMaker for machine learning model building and deployment. Engineers and data analysts might design tailored pipelines with these technologies allowing real-time analysis and data management automation. Including unstructured as well as organized input into machine learning models improves model efficiency and accuracy. Notwithstanding the benefits, improving data pipelines in AWS brings difficulties including higher data latency, guaranteed seamless service compatibility, and low cost related to major data throughput. Tight monitoring, continuous performance enhancement, and effective error control are needed for an increasingly more challenging pipeline management approach. Dealing with these challenges calls on a strong awareness of AWS architectural concepts and machine learning requirements. Among other benefits, a competent ML pipeline built on AWS provides shorter processing times, improved scalability, and higher data security. It enables rapid experimentation, iterative development, and effective resource use. The article examines important elements of pipeline optimization, provides suggestions on best practices, creative concepts, and practical solutions helping businesses to make good use of AWS machine learning. In a data-centric society, the focus is on the main influence of simplified data pipelines in enabling business innovation and increasing machine learning activities. Through continuous pipeline development, organizations could maintain a competitive edge in rapid developing digital industries.
INTELLIGENT COST OPTIMIZATION FOR EMR WORKLOADS USING CUSTOM APIS
Given the rising demand for big data processing and the current dynamic economic environment, which presents challenges for businesses, especially in terms of managing cloud computing costs has become a large issue. Although cloud infrastructure provides scalability and adaptability, poor management of it could cause significant expenses. Large data set processing inside distributed computing systems largely relies on Amazon Elastic MapReduce (EMR). Although Electronic Medical Records in the healthcare sector and other businesses managing large amounts of data might find EMS suitable, its dynamic and scalable capabilities could potentially lead to cost inefficiencies. Inappropriate scaling or too generous resource allocation can lead to resource waste and higher running expenses. This white paper examines cutting-edge cost-optimal methods designed especially for E MR workload management. Electronic Medical Records (EMRs) are confidential patient records requiring efficient, safe, reliable processing—qualities lacking in which case significant charges could arise. The study stresses on a simple approach the usage of tailored application programming interfaces (APIs). These APIs let one automate important chores such dynamic job scheduling, real-time instance selection, autonomous scaling, and ongoing cost monitoring. By means of automation, businesses may guarantee that computing resources are distributed precisely where and when needed, therefore avoiding the inefficiencies connected with set configurations. Dynamic work scheduling distributes tasks depending on real-time data, therefore optimizing resource use all day. Companies can identify the most reasonably priced and task-appropriate computer instances by means of selective instance selection, therefore avoiding a homogeneous approach that might result in inefficiencies or insufficient performance. By allowing systems to dynamically change resources in response to various demands, autonomous scaling guarantees performance while eliminating needless resource allocation and so maximizes efficiency.