EPH-International Journal of Science And Engineering
type: channel
language: en
publication name: EPH-International Journal of Science And Engineering
issn: 2454-2016
items: ADVANCES IN BUSINESS PROCESS AUTOMATION: LOW-CODE PLATFORMS AND ENTERPRISE APPLICATIONS
items: A COMPARATIVE STUDY ON INTERPRETABLE MACHINE LEARNING BASED SURVIVAL MODELS FOR CVD WITH CLINICAL DATA
items: A SYSTEMATIC REVIEW OF DATA VISUALIZATION TECHNIQUES AND STORYTELLING WITH DATA
items: EXPERIMENTAL STUDY ON HEAT TRANSFER ENHANCEMENT USING NANOFLUIDS
items: PERFORMANCE EVALUATION OF DATA HANDLING ALGORITHMS (DT, KNN, RF AND DNN) UNDER THE HANDLING OF TWO DATASET OF DIABETES
items: INTEGRATING GRAPH DATABASE MODELING INTO AGILE SYSTEM ANALYSIS AND DESIGN: A CONCEPTUAL FRAMEWORK FOR SCALABLE INFORMATION SYSTEMS
items: NYLON-6: POLYMERIZATION CHEMISTRY, PROPERTIES, AND INDUSTRIAL APPLICATIONS –AN INDUSTRIAL REVIEW
items: A THEORETICAL FRAMEWORK FOR CLOUD AND DATA ANALYTICS CONVERGENCE IN SCALABLE MANAGEMENT INFORMATION SYSTEMS
items: BIOTECHNOLOGICAL APPROACHES TO PLASTIC WASTE DEGRADATION
items: IN-VITRO AND IN-VIVO STUDY OF SELF-MICROEMULSIFYING DRUG DELIVERY SYSTEM FOR HYPERTENSION
items: PLANT DISEASE RECOGNITION SYSTEM USING CONVOLUTIONAL NEURAL NETWORKS (CNNS) AND TRANSFER LEARNING
items: ADVANCED LANGEVIN DYNAMICS FOR PREDICTING SPIN DISTRIBUTIONS IN HEAVY-ION FUSION
items: FORMULATION AND CHARACTERIZATION OF HARD, GELATIN-LIKE CAPSULES BASED ON CORN STARCH, AGAR-AGAR, SODIUM ALGINATE AND HPMC FOR ORAL DELIVERY OF SMALL MOLECULES
items: CONTINUAL YOLO-BASED DETECTION FOR LONG-TERM MONITORING OF THE GREATER ONE-HORNED RHINO
items: MESTA FIBER (HIBISCUS SABDARIFFA): A REVIEW OF ITS PROPERTIES, APPLICATIONS, AND ROLE IN PROMOTING SUSTAINABILITY
items: TRANSFORMING THE TRAVEL INDUSTRY THROUGH AI-DRIVEN HOTEL BOOKING AND TOURISM INNOVATIONS
items: UNSUPERVISED EXPLORATION OF HIERARCHICAL STRUCTURE IN HUMAN ACTIVITY RECOGNITION DATA
items: EXPLAINABLE MACHINE LEARNING FOR EVALUATING DIABETES PREDICTION MODELS
items: HALLUCINATIONS OF THE GENERALIST, BRITTLENESS OF THE EXPERT: A STUDY ON FINE-TUNED OPEN SOURCE LLM FOR SOC ANALYSIS
items: ADAPTIVE CONTROL ALGORITHM FOR GRID SUPPORTIVE SOLAR PV POWER GENERATING SYSTEM
items: INNOVATIONS IN COASTAL SHIPPING: PROPULSION, SAFETY, REGULATIONS AND TRAINING FOR THE FUTURE SEAFARERS
items: MODELING AND ANALYSIS OF HALF-WAVE DIPOLE ANTENNAS IN MATLAB: A COMPREHENSIVE REVIEW
items: 6 DOF 3D PRINTED ROBOT FOR VISION BASED SORTING
items: BRAND LOYALTY: HOW COMPANIES RETAIN CUSTOMERS IN A COMPETITIVE MARKET
items: OPTIMIZING BATTERY TOPOLOGY FOR ENHANCED LIFESPAN AND REDUCED STRESS THROUGH INNOVATIVE CURRENT FLOW MANAGEMENT APPROACHES
items: PROACTIVE DIAGNOSIS OF MUNG BEAN LEAF DISEASES IN A CONTROLLED ENVIRONMENT USING MACHINE LEARNING TECHNIQUES
items: AUTOMATED LAND COVER MAPPING FROM LISS-III MULTISPECTRAL IMAGES USING SUPERVISED CLASSIFICATION WITH DEEP LEARNING
items: A STUDY ON FABRICATION OF ECO EVAPORATOR AND PRODUCING DRINKABLE WATER FROM THE WASTE WATER OF STANDING WATER BODIES
items: INTERNET OF THINGS (IOT) IN SMART CITIES: CHALLENGES AND OPPORTUNITIES
items: GRAPH DATABASES FOR FRAUD DETECTION: A FRESH LOOK AT FINANCIAL SECURITY

ADVANCES IN BUSINESS PROCESS AUTOMATION: LOW-CODE PLATFORMS AND ENTERPRISE APPLICATIONS
type: item
description: The automation of business processes (BPA) is among the key contributing factors of digital transformation that will assist in making an organization more efficient, cost-effective and maximize the value of decision-making. With the appearance of low-code solutions, the world of automation systems has shifted once again, since these systems can now be developed quickly and with little knowledge of programming. This research paper tries to explain in details how BPA has evolved especially focusing on how low-code development platforms are becoming relevant and their utilisation in enterprise systems. Reviewing will be done systematically using existing literature on automation systems, workflows models and intelligent systems. The current research will touch upon the background theories and automation plans, along with discussing real-life uses of platforms in industries. Moreover, intelligent automation and artificial intelligence will be discussed in order to shed light on the latest technological trends. As a result, the findings show that low-code development can significantly improve the flexibility and accessibility of automation systems despite the challenges associated with integration, governance and complexity of the system. Moreover, research gaps identified throughout the investigation are crucial for further research in this field.
subject: Articles
  topic: section
subject: Business Process Automation
  topic: keywords
subject: Low-Code Platforms
  topic: keywords
subject: Digital Transformation
  topic: keywords
subject: Intelligent Automation
  topic: keywords
subject: Enterprise Applications
  topic: keywords
creator: Varun Anish Reddy Yeruva
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by/4.0/
date: 2023-12-11
publication date: 2023-12-11
starting page: 48
ending page: 58
doi: 10.69980/ecmckf46

A COMPARATIVE STUDY ON INTERPRETABLE MACHINE LEARNING BASED SURVIVAL MODELS FOR CVD WITH CLINICAL DATA
type: item
description: Cardiovascular disease stands as a major cause of death around the globe, thus stressing the need for proper prediction of prognosis and survival in the clinical setting. In classic diagnostic methods the disease presence is treated as a main factor but survival analysis provides the possibility of predicting the time-to-event by including the model information about the censored observation and disease progression. This study presents an interpretable survival modeling framework to understand cardiovascular disease outcomes using clinical data. Classical survival models, ensemble machine learning–based survival models, and deep learning–based survival methods are developed and tested systematically. Performance on the part of the model is examined using concordance index (C-index); interpretability through hazard ratios, coefficient analysis, and feature relevance is stressed. The experimental results show better predictive performance in ensemble survival models, and the requirement for clinical interpretability in classical survival models. These findings provide support for incorporating transparent survival models into clinical decision-support systems for cardiovascular disease prognosis.
subject: Articles
  topic: section
subject: Cardiovascular disease
  topic: keywords
subject: survival analysis
  topic: keywords
subject: interpretable machine learning
  topic: keywords
subject: Cox proportional hazards
  topic: keywords
subject: ensemble survival models
  topic: keywords
subject: clinical data
  topic: keywords
creator: E. Mekala
creator: N. Paranjothi
creator: G Manimannan
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/
date: 2026-04-17
publication date: 2026-04-17
starting page: 75
ending page: 87
doi: 10.69980/aa2j8s13

A SYSTEMATIC REVIEW OF DATA VISUALIZATION TECHNIQUES AND STORYTELLING WITH DATA
type: item
description: In today’s data-driven environment, organizations produce vast amounts of data every day. However, without effective analysis and clear communication, this information does not translate into meaningful insights. Data visualization and data storytelling play a crucial role in converting complex datasets into clear, actionable knowledge. This paper examines the core principles, methods, tools, and practical applications of data visualization and storytelling. It highlights how combining strong analytical insights with well-structured narratives enhances decision-making across sectors such as business, healthcare, education, and public policy.
subject: Articles
  topic: section
subject: Data Visualization
  topic: keywords
subject: Data Storytelling
  topic: keywords
subject: Information Design
  topic: keywords
subject: Data Analytics
  topic: keywords
subject: Visual Communication
  topic: keywords
subject: Business Intelligence
  topic: keywords
creator: Binu Mol T. V
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/
date: 2026-04-10
publication date: 2026-04-10
starting page: 65
ending page: 74
doi: 10.69980/bass5w18

EXPERIMENTAL STUDY ON HEAT TRANSFER ENHANCEMENT USING NANOFLUIDS
type: item
description: Heat transfer enhancement is a critical requirement in modern engineering systems such as power plants, automotive cooling, and electronic devices. Conventional heat transfer fluids, including water and ethylene glycol, have limited thermal conductivity, which restricts their performance in high-efficiency applications. This study experimentally investigates the potential of nanofluids to improve heat transfer characteristics. Aluminum oxide (Al₂O₃) nanoparticles were dispersed in distilled water at different volume concentrations (0.1%, 0.3%, and 0.5%) to prepare nanofluids using ultrasonic agitation for uniform stability. Experiments were conducted in a heated tube under controlled laminar and turbulent flow conditions. The convective heat transfer coefficient and Nusselt number were evaluated and compared with those of the base fluid. The results showed a significant enhancement in heat transfer performance, with up to 30% improvement at higher nanoparticle concentrations. This enhancement is attributed to increased thermal conductivity, Brownian motion, and micro-convection effects. However, an increase in viscosity and pressure drop was also observed, indicating a trade-off between heat transfer improvement and flow resistance. Overall, nanofluids demonstrate strong potential as advanced heat transfer fluids for efficient thermal systems.
subject: Articles
  topic: section
subject: Nanofluids
  topic: keywords
subject: Heat transfer enhancement
  topic: keywords
subject: Thermal conductivity
  topic: keywords
subject: Convective heat transfer
  topic: keywords
subject: Nanoparticles
  topic: keywords
creator: Heet Masada
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/
date: 2026-03-30
publication date: 2026-03-30
starting page: 56
ending page: 64
doi: 10.69980/ny7jzx97

PERFORMANCE EVALUATION OF DATA HANDLING ALGORITHMS (DT, KNN, RF AND DNN) UNDER THE HANDLING OF TWO DATASET OF DIABETES
type: item
description: 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.
subject: Articles
  topic: section
subject: Diabetes Prediction
  topic: keywords
subject: PIDD
  topic: keywords
subject: MDHU
  topic: keywords
subject: KNN
  topic: keywords
subject: DT
  topic: keywords
creator: Dr. Vinay Bhatt
creator: Prof. (Dr.) Ajay Kumar Mishra
creator: Dr. Vivek Bhatt
creator: Prof (Dr.) Praveen Kumar
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/
date: 2026-02-25
publication date: 2026-02-25
starting page: 32
ending page: 39
doi: 10.53555/7nmskx22

INTEGRATING GRAPH DATABASE MODELING INTO AGILE SYSTEM ANALYSIS AND DESIGN: A CONCEPTUAL FRAMEWORK FOR SCALABLE INFORMATION SYSTEMS
type: item
description: 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.
subject: Articles
  topic: section
subject: graph database modeling
  topic: keywords
subject: agile system analysis and design
  topic: keywords
subject: property graph model
  topic: keywords
subject: schema evolution
  topic: keywords
subject: scalable information systems
  topic: keywords
subject: iterative development
  topic: keywords
creator: Brenda Balala
creator: Reagan Ricafort
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/
date: 2026-02-25
publication date: 2026-02-25
starting page: 40
ending page: 55
doi: 10.53555/wfehqv74

NYLON-6: POLYMERIZATION CHEMISTRY, PROPERTIES, AND INDUSTRIAL APPLICATIONS –AN INDUSTRIAL REVIEW
type: item
description: 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.
subject: Articles
  topic: section
subject: Fibre Formation
  topic: keywords
subject: Melt Spinning Nylon-6
  topic: keywords
subject: Polymerization and Process Control
  topic: keywords
subject: Technical Textile
  topic: keywords
subject: Fibre Properties
  topic: keywords
creator: Mr. Sunil S. Kajave
creator: Dr. Rohit P. Naik
creator: Ms. Pratiksha S. Singh
creator: Mr. Rohit K. Dholugade
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-02-14
publication date: 2026-02-14
starting page: 11
ending page: 31
doi: 10.53555/m2hyw924

A THEORETICAL FRAMEWORK FOR CLOUD AND DATA ANALYTICS CONVERGENCE IN SCALABLE MANAGEMENT INFORMATION SYSTEMS
type: item
description: 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.
subject: Articles
  topic: section
subject: cloud computing
  topic: keywords
subject: data analytics
  topic: keywords
subject: management information systems
  topic: keywords
subject: scalability
  topic: keywords
subject: higher education
  topic: keywords
subject: theoretical framework
  topic: keywords
subject: conceptual model
  topic: keywords
creator: Brenda M. Balala
creator: Dr. Reagan Ricafort
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-02-12
publication date: 2026-02-12
starting page: 1
ending page: 10
doi: 10.53555/xt7g0888

BIOTECHNOLOGICAL APPROACHES TO PLASTIC WASTE DEGRADATION
type: item
description: 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.
subject: Articles
  topic: section
subject: Plastic biodegradation
  topic: keywords
subject: PETase
  topic: keywords
subject: microbial consortia
  topic: keywords
subject: enzyme engineering
  topic: keywords
subject: plastic upcycling
  topic: keywords
creator: Jalshree Jainul Desai
rights: Copyright (c) 2025 EPH-International Journal of Science And Engineering https://creativecommons.org/licenses/by/4.0
date: 2025-06-27
publication date: 2025-06-27
starting page: 87
ending page: 94
doi: 10.53555/89hyv822

IN-VITRO AND IN-VIVO STUDY OF SELF-MICROEMULSIFYING DRUG DELIVERY SYSTEM FOR HYPERTENSION
type: item
description: 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.
subject: Articles
  topic: section
subject: Solid SMEDDS
  topic: keywords
subject: in-vitro drug
  topic: keywords
subject: pharmacokinetic studies
  topic: keywords
subject: self-emulsification
  topic: keywords
subject: microemulsion
  topic: keywords
creator: Hitesh Chaturvedi
creator: Dr. Aniruddh Singh Deora
rights: Copyright (c) 2025 EPH-International Journal of Science And Engineering https://creativecommons.org/licenses/by/4.0
date: 2025-06-27
publication date: 2025-06-27
starting page: 70
ending page: 86
doi: 10.53555/bysn1g47

PLANT DISEASE RECOGNITION SYSTEM USING CONVOLUTIONAL NEURAL NETWORKS (CNNS) AND TRANSFER LEARNING
type: item
description: 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.
subject: Articles
  topic: section
subject: Plant Disease Detection
  topic: keywords
subject: CNNs
  topic: keywords
subject: Transfer Learning
  topic: keywords
subject: MobileNetV2
  topic: keywords
subject: PlantVillage dataset
  topic: keywords
creator: Payal Gulati
rights: Copyright (c) 2022 EPH-International Journal of Science And Engineering https://creativecommons.org/licenses/by/4.0
date: 2022-09-27
publication date: 2022-09-27
starting page: 9
ending page: 17
doi: 10.53555/nkqe2507

ADVANCED LANGEVIN DYNAMICS FOR PREDICTING SPIN DISTRIBUTIONS IN HEAVY-ION FUSION
type: item
description: 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.
subject: Articles
  topic: section
subject: Heavy-ion fusion
  topic: keywords
subject: spin distribution
  topic: keywords
subject: Langevin dynamics
  topic: keywords
subject: nuclear dissipation
  topic: keywords
subject: quasi-fission
  topic: keywords
subject: compound nucleus
  topic: keywords
subject: angular momentum
  topic: keywords
subject: nuclear viscosity
  topic: keywords
creator: DR. RAHUL KUMAR
rights: Copyright (c) 2022 EPH-International Journal of Science And Engineering https://creativecommons.org/licenses/by/4.0
date: 2022-03-27
publication date: 2022-03-27
starting page: 12
ending page: 18
doi: 10.53555/cdbes518

FORMULATION AND CHARACTERIZATION OF HARD, GELATIN-LIKE CAPSULES BASED ON CORN STARCH, AGAR-AGAR, SODIUM ALGINATE AND HPMC FOR ORAL DELIVERY OF SMALL MOLECULES
type: item
description: 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.
subject: Articles
  topic: section
subject: Plant-based capsules
  topic: keywords
subject: Starch-agar composite
  topic: keywords
subject: HPMC
  topic: keywords
subject: Sodium alginate
  topic: keywords
subject: Capsule characterization
  topic: keywords
subject: Oral drug delivery
  topic: keywords
creator: Ajay Pal Singh
creator: Dr. Chander Shekar
rights: Copyright (c) 2025 International Journal of Science And Engineering https://creativecommons.org/licenses/by/4.0
date: 2024-12-27
publication date: 2024-12-27
starting page: 10
ending page: 21
doi: 10.53555/bnkd6h19

CONTINUAL YOLO-BASED DETECTION FOR LONG-TERM MONITORING OF THE GREATER ONE-HORNED RHINO
type: item
description: 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.
subject: Articles
  topic: section
subject: Continual Object Detection
  topic: keywords
subject: Wildlife monitoring
  topic: keywords
subject: YOLO
  topic: keywords
subject: replay memory
  topic: keywords
subject: self-distillation
  topic: keywords
subject: human-in-loop
  topic: keywords
subject: catastrophic forgetting
  topic: keywords
creator: Nelson R Varte
creator: Kaustubh Bhattacharyya
creator: Navajit Saikia
rights: Copyright (c) 2025 International Journal of Science And Engineering https://creativecommons.org/licenses/by/4.0
date: 2025-06-27
publication date: 2025-06-27
starting page: 65
ending page: 69
doi: 10.53555/qn83mp49

MESTA FIBER (HIBISCUS SABDARIFFA): A REVIEW OF ITS PROPERTIES, APPLICATIONS, AND ROLE IN PROMOTING SUSTAINABILITY
type: item
description: 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.
subject: Articles
  topic: section
subject: Mesta fiber
  topic: keywords
subject: Hibiscus sabdariffa
  topic: keywords
subject: Natural fibers
  topic: keywords
subject: Eco-friendly textiles
  topic: keywords
subject: Fiber characterization
  topic: keywords
subject: Sustainable consumption
  topic: keywords
subject: Biodegradable materials
  topic: keywords
creator: Gourawwa Kumbar
creator: Chandrasekhara S.M
creator: Dinesh Y.N
creator: Mamatha G.P.K
creator: Pulikeshi
rights: Copyright (c) 2025 International Journal of Science And Engineering https://creativecommons.org/licenses/by/4.0
date: 2025-06-27
publication date: 2025-06-27
starting page: 56
ending page: 64
doi: 10.53555/0gthey33

TRANSFORMING THE TRAVEL INDUSTRY THROUGH AI-DRIVEN HOTEL BOOKING AND TOURISM INNOVATIONS
type: item
description: 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.
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Smart Tourism
  topic: keywords
subject: TCMS
  topic: keywords
subject: Bayesian Logic regression
  topic: keywords
subject: AI adoption
  topic: keywords
creator: Prof. Ravi Khatri
creator: Sumedh Teltumbade
creator: Dr. Ranjit Kumar
creator: Dr. Vishal Sirsat
creator: Dr Sandeep Kulkarni
rights: Copyright (c) 2025 International Journal of Science And Engineering https://creativecommons.org/licenses/by/4.0
date: 2025-03-27
publication date: 2025-03-27
starting page: 50
ending page: 55
doi: 10.53555/mkdkzp84

UNSUPERVISED EXPLORATION OF HIERARCHICAL STRUCTURE IN HUMAN ACTIVITY RECOGNITION DATA
type: item
description: 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.
subject: Articles
  topic: section
subject: Human Activity Recognition (HAR)
  topic: keywords
subject: Unsupervised Clustering
  topic: keywords
subject: Dimensionality Reduction
  topic: keywords
subject: Wearable Sensors
  topic: keywords
subject: Feature Analysis
  topic: keywords
creator: Chayse Monteen
creator: Aakash Dhananjay Shanbhag
rights: Copyright (c) 2025 International Journal of Science And Engineering https://creativecommons.org/licenses/by/4.0
date: 2025-03-28
publication date: 2025-03-28
starting page: 36
ending page: 49
doi: 10.53555/ym736w23

EXPLAINABLE MACHINE LEARNING FOR EVALUATING DIABETES PREDICTION MODELS
type: item
description: 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.
subject: Articles
  topic: section
subject: Diabetes prediction
  topic: keywords
subject: Explainable AI (XAI)
  topic: keywords
subject: SHAP
  topic: keywords
subject: XGBoost importances
  topic: keywords
subject: machine learning
  topic: keywords
creator: Saumyaa Dhakan
rights: Copyright (c) 2025 International Journal of Science And Engineering https://creativecommons.org/licenses/by/4.0
date: 2025-06-27
publication date: 2025-06-27
starting page: 27
ending page: 35
doi: 10.53555/azgy5g23

HALLUCINATIONS OF THE GENERALIST, BRITTLENESS OF THE EXPERT: A STUDY ON FINE-TUNED OPEN SOURCE LLM FOR SOC ANALYSIS
type: item
description: 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.
subject: Articles
  topic: section
subject: Large Language Models
  topic: keywords
subject: Cyber Threat Intelligence
  topic: keywords
subject: MITRE ATT&CK, Fine-Tuning
  topic: keywords
subject: DeepSeek
  topic: keywords
subject: Security Operations Center
  topic: keywords
creator: Abhinav Sharma
creator: Jawahar Thakur
creator: T.P. Sharma
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
date: 2025-06-27
publication date: 2025-06-27
starting page: 13
ending page: 26
doi: 10.53555/1vhsgg43

ADAPTIVE CONTROL ALGORITHM FOR GRID SUPPORTIVE SOLAR PV POWER GENERATING SYSTEM
type: item
description: 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.
subject: Articles
  topic: section
subject: Solar Photovoltalic
  topic: keywords
subject: Adaptive control algorithum
  topic: keywords
subject: Power Quality
  topic: keywords
creator: Prabhakar Prasad Pandey
creator: Swatantra Verma
creator: Manoj Kumar Dewangan
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/deed.en
date: 2025-06-27
publication date: 2025-06-27
starting page: 1
ending page: 12
doi: 10.53555/k20zhv37

INNOVATIONS IN COASTAL SHIPPING: PROPULSION, SAFETY, REGULATIONS AND TRAINING FOR THE FUTURE SEAFARERS
type: item
description: 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.
subject: Articles
  topic: section
subject: Decarbonization
  topic: keywords
subject: Dual-fuel propulsion
  topic: keywords
subject: Maritime Autonomous Surface Ships (MASS)
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: IMO regulations
  topic: keywords
creator: Mahesh Patil
creator: Sanjeet Kanungo
rights: Copyright (c) 2025 International Journal of Science And Engineering https://creativecommons.org/licenses/by/4.0/
date: 2025-12-27
publication date: 2025-12-27
starting page: 32
ending page: 41
doi: 10.53555/ephijse.v11i1.315

MODELING AND ANALYSIS OF HALF-WAVE DIPOLE ANTENNAS IN MATLAB: A COMPREHENSIVE REVIEW
type: item
description: 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 advancement…
subject: Articles
  topic: section
subject: Half-wave dipole antenna
  topic: keywords
subject: MATLAB simulation
  topic: keywords
subject: Method of Moments
  topic: keywords
creator: Sonu Rani
rights: Copyright (c) 2024 International Journal of Science And Engineering https://creativecommons.org/licenses/by/4.0
date: 2024-12-27
publication date: 2024-12-27
starting page: 1
ending page: 9
doi: 10.53555/ephijse.v10i3.314

6 DOF 3D PRINTED ROBOT FOR VISION BASED SORTING
type: item
description: 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.
subject: Articles
  topic: section
subject: 6DOF Robotic Arm
  topic: keywords
subject: Vision-Based Object Sorting
  topic: keywords
subject: , Intelligent Sorting System
  topic: keywords
creator: Puja Thorat
creator: Rajendra Kapghate
creator: Mayur Divate
creator: Gaurav Garad
creator: Tejas Sonawane
rights: Copyright (c) 2025 International Journal of Science And Engineering https://creativecommons.org/licenses/by/4.0/
date: 2025-12-27
publication date: 2025-12-27
starting page: 26
ending page: 31
doi: 10.53555/ephijse.v11i1.313

BRAND LOYALTY: HOW COMPANIES RETAIN CUSTOMERS IN A COMPETITIVE MARKET
type: item
description: 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.
subject: Articles
  topic: section
subject: Brand Loyalty
  topic: keywords
subject: Customer Retention
  topic: keywords
subject: Competitive Market
  topic: keywords
subject: Loyalty Programs
  topic: keywords
creator: Nishit Nirmal Kachhala
rights: Copyright (c) 2025 International Journal of Science And Engineering https://creativecommons.org/licenses/by/4.0/
date: 2025-12-27
publication date: 2025-12-27
starting page: 20
ending page: 25
doi: 10.53555/ephijse.v11i1.312

OPTIMIZING BATTERY TOPOLOGY FOR ENHANCED LIFESPAN AND REDUCED STRESS THROUGH INNOVATIVE CURRENT FLOW MANAGEMENT APPROACHES
type: item
description: 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.
subject: Articles
  topic: section
subject: environmental
  topic: keywords
subject: management
  topic: keywords
subject: TOPOLOGY
  topic: keywords
creator: Aniket K. Tandel
creator: Dr. Piyush S. Jain
creator: Dr. Darshini Shukla
rights: Copyright (c) 2025 International Journal of Science And Engineering https://creativecommons.org/licenses/by/4.0/
date: 2025-12-27
publication date: 2025-12-27
starting page: 8
ending page: 19
doi: 10.53555/ephijse.v11i1.311

PROACTIVE DIAGNOSIS OF MUNG BEAN LEAF DISEASES IN A CONTROLLED ENVIRONMENT USING MACHINE LEARNING TECHNIQUES
type: item
description: 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.
subject: Articles
  topic: section
subject: Mung leaf
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: SVM
  topic: keywords
creator: Akruti Naik
creator: Nirav Desai
rights: Copyright (c) 2025 International Journal of Science And Engineering https://creativecommons.org/licenses/by/4.0/
date: 2025-12-27
publication date: 2025-12-27
starting page: 1
ending page: 7
doi: 10.53555/ephijse.v11i1.310

AUTOMATED LAND COVER MAPPING FROM LISS-III MULTISPECTRAL IMAGES USING SUPERVISED CLASSIFICATION WITH DEEP LEARNING
type: item
description: 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%.
subject: Articles
  topic: section
subject: remote sensing
  topic: keywords
subject: LISS- III multispectral image
  topic: keywords
subject: land use land cover classification
  topic: keywords
subject: deep learning
  topic: keywords
subject: U-Net
  topic: keywords
subject: Tiramisu
  topic: keywords
subject: Fully Convolutional Network (FCN)
  topic: keywords
creator: Nirav Desai
creator: Akruti Naik
rights: Copyright (c) 2025 International Journal of Science And Engineering https://creativecommons.org/licenses/by/4.0
date: 2025-09-27
publication date: 2025-09-27
starting page: 31
ending page: 36
doi: 10.53555/ephijse.v11i1.309

A STUDY ON FABRICATION OF ECO EVAPORATOR AND PRODUCING DRINKABLE WATER FROM THE WASTE WATER OF STANDING WATER BODIES
type: item
description: 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.
subject: Articles
  topic: section
subject: Eco evaporator
  topic: keywords
subject: Fabrication
  topic: keywords
subject: pH
  topic: keywords
subject: BOD
  topic: keywords
creator: Manjunath Sharanappanavar
creator: Sahilkumar Naik
creator: Mahantesh Biradar
creator: Sachin Wali
creator: Vinayak Majukar
rights: Copyright (c) 2025 International Journal of Science And Engineering https://creativecommons.org/licenses/by/4.0
date: 2025-09-27
publication date: 2025-09-27
starting page: 25
ending page: 30
doi: 10.53555/ephijse.v11i1.308

INTERNET OF THINGS (IOT) IN SMART CITIES: CHALLENGES AND OPPORTUNITIES
type: item
description: 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.
subject: Articles
  topic: section
subject: Internet of Things (IoT)
  topic: keywords
subject: Smart Cities
  topic: keywords
subject: Urban Infrastructure
  topic: keywords
creator: Shamim Ahmad Khan
creator: H. Arivazhagan
creator: Sudipto Kumar Sahu
creator: B Ravinder
creator: Advocate Varun Goel
rights: Copyright (c) 2025 International Journal of Science And Engineering https://creativecommons.org/licenses/by/4.0
date: 2025-09-27
publication date: 2025-09-27
starting page: 18
ending page: 24
doi: 10.53555/ephijse.v11i1.305

GRAPH DATABASES FOR FRAUD DETECTION: A FRESH LOOK AT FINANCIAL SECURITY
type: item
description: 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.
subject: Articles
  topic: section
subject: Graph Databases
  topic: keywords
subject: Fraud Detection
  topic: keywords
subject: Financial Security
  topic: keywords
creator: Jayaram Immaneni
creator: Muneer Salamkar
rights: Copyright (c) 2019 International Journal of Science And Engineering https://creativecommons.org/licenses/by/4.0
date: 2024-12-27
publication date: 2024-12-27
starting page: 22
ending page: 31
doi: 10.53555/ephijse.v5i4.304