International Journal of Engineering and Science Research
Nutribaby AI: Smart Baby Nutrition Management System And Feeding Guidance With Chatbot
NutriBaby AI Hub is an intelligent infant nutrition management platform designed to assist parents and caregivers in delivering timely, balanced, and developmentally appropriate nutrition for infants and toddlers. Adequate nutrition during early childhood is essential for physical growth, cognitive development, and immune system strengthening. Despite its importance, many caregivers experience difficulties in planning suitable meals, maintaining feeding schedules, and meeting nutritional requirements because of limited expert guidance and demanding daily routines.This research presents an Artificial Intelligence–driven solution that integrates personalized meal planning, a conversational chatbot, and automated feeding reminders into a unified digital platform. The proposed system evaluates key parameters such as the child’s age, weight, feeding stage, and dietary preferences to generate customized nutritional recommendations and meal schedules. The embedded chatbot provides real-time responses to frequently asked questions related to baby feeding, safe food practices, portion sizes, and nutrient-rich food choices. In addition, the reminder mechanism notifies caregivers about feeding times, hydration intervals, and meal consistency, thereby supporting regular healthy habits.The platform is intended to minimize caregiver stress, improve decision-making related to child nutrition, and encourage healthier early-life development. NutriBaby AI Hub demonstrates the practical application of Artificial Intelligence in pediatric nutritional care by enhancing awareness, accessibility, and day-to-day nutrition monitoring. The system can serve as a valuable digital assistant for modern families seeking evidence-based infant feeding support.
Artificial Intelligence, Infant Nutrition, Baby Feeding Management, Personalized Meal Planning, Chatbot Assistance, Feeding Reminder System, Child Health Monitoring, Smart Healthcare Application.
Namani Bhavini, Patan Sameer, Dudi Bhargav Kumar
Copyright (c) 2026 Authors https://creativecommons.org/licenses/by-nc-nd/4.0
Smart Price Comparison and Best Deal Prediction
The rapid growth of online retail platforms has created a need for efficient tools that assist consumers in identifying the most economical purchasing options. This paper presents a Smart Price Comparison Assistant, an intelligent web-based system developed to compare product prices across multiple e-commerce platforms and support informed buying decisions. The proposed solution integrates a Python-based backend for data processing, React and Node.js for interactive frontend and server-side functionalities, and MongoDB for scalable data management.The system enables users to search for products and receive consolidated results from different online marketplaces through an intuitive interface. To improve comparison accuracy, machine learning techniques are employed to recognize equivalent or closely related products even when naming conventions, specifications, or listing formats differ across platforms. In addition, the system examines historical and real-time pricing patterns to estimate favorable purchasing periods, allowing users to optimize spending.Beyond price analysis, the platform incorporates product ratings, customer reviews, discount availability, and seller credibility indicators to promote transparency and trust. The architecture is designed to ensure responsiveness, scalability, and efficient retrieval of updated information. By minimizing the time and effort required for manual price comparison, the proposed assistant offers a practical and data-driven approach to modern online shopping.The Smart Price Comparison Assistant demonstrates how intelligent analytics, predictive modeling, and user-centered design can enhance digital commerce experiences by delivering cost-effective recommendations and simplifying purchasing decisions.
Price Comparison, Machine Learning, E-commerce, Product Matching, Price Prediction, Consumer Analytics, Smart Shopping.
Telluri Sushma, Lonavath Swapna, Tala Swetha
House Price Prediction Using Machine Learning Algorithms:
This research article presents a comprehensive, end-to-end machine learning system for residential house price prediction, trained and evaluated on a synthetic California housing dataset comprising 10,000 records with nine input features. Five supervised regression algorithms are systematically compared: Linear Regression, Ridge Regression, Decision Tree, Random Forest, and Gradient Boosting Regressor. Following a rigorous preprocessing pipeline—missing value imputation, label encoding of categorical variables, and outlier removal—an 80/20 stratified train-test split is applied. Evaluation using four standard metrics (R², Adjusted R², Mean Absolute Error, Root Mean Squared Error) demonstrates that the Gradient Boosting Regressor achieves superior performance with R² = 0.8924, MAE = $20,267, and RMSE = $25,273. The best-performing model is deployed as a full-stack Flask web application with SQLite-backed user authentication, real-time prediction, seven interactive EDA visualizations, and a model comparison dashboard. Mathematical formulations for all five algorithms, system architecture, data flow diagrams, algorithmic pseudocode, and performance analyses through bar and distribution charts are provided. Results confirm that ensemble tree-based methods reduce prediction error by up to 16% over linear baselines and can achieve practical real-time deployment without GPU infrastructure.
House Price Prediction, Gradient Boosting, Random Forest, Linear Regression, Ridge Regression, Decision Tree, scikit-learn, Flask, California Housing, Supervised Regression, Machine Learning Deployment
Mukannaf Ahmed, Syed Muhtishim Najeed, Md. Faizan, Syed Muzammil Hussaini
Resume Forge: A Dynamic Web-Based Resume Builder with Integrated ATS Compatibility Scoring and Real-Time Preview
In today's fiercely competitive employment landscape, the quality and ATS-compatibility of a resume decisively influence a candidate's prospects of reaching the interview stage. Traditional resume creation methods — desktop word processors, cloud-based builders requiring registration, and LaTeX-based typesetting systems — impose significant friction: manual formatting overhead, lack of real-time visual feedback, privacy concerns from server-side data storage, and an almost universal absence of integrated Applicant Tracking System (ATS) evaluation. Over 90% of Fortune 500 companies deploy ATS software that automatically screens and ranks submitted resumes before any human review occurs, yet no freely available, privacy-respecting tool provides job seekers with actionable ATS feedback during the drafting process. This paper presents ResumeForge, a fully client-side single-page application (SPA) engineered using HTML5, CSS3, JavaScript ES6, Bootstrap 5.3.0, and html2pdf.js. The system features a split-panel interface — a dark-themed form editor paired with a live A4-sized resume preview — enabling instantaneous visual feedback. Seven comprehensively structured resume sections (Personal Information, Work Experience, Education, Skills, Projects, Certifications, and Awards) are supported with full CRUD operations. Three professionally designed templates (Professional, Modern, Minimal/Creative) permit one-click switching without data loss. A checklist-based ATS scoring engine evaluates resumes on a weighted 100-point scale across five categories, providing color-coded feedback and percentile classification. A keyword matching algorithm performs real-time comparison of resume content against any pasted job description. All data processing occurs locally within the browser — zero server communication, zero registration, zero cost — ensuring absolute data privacy. Experimental evaluation demonstrates sub-100ms live preview update latency, 100% test pass rate across 20 structured test cases, and cross-browser compatibility on Chrome, Firefox, Edge, and Safari.
Resume Builder, Applicant Tracking System (ATS), Client-Side Web Application, HTML5, CSS3, JavaScript ES6, Bootstrap 5, PDF Generation, Responsive Design, XSS Prevention, localStorage, Single-Page Application (SPA).
Abdullah Khan, Gaziuddin Aijaz, Syed Khaja Moin Uddin
Health Insurance Price Prediction Using Machine Learning Regression Models
This paper presents a comprehensive Health Insurance Price Prediction system that employs six supervised machine learning regression algorithms to estimate individual insurance premiums with high accuracy. The system processes a dataset of 5,000 records characterised by six demographic and lifestyle features — age, sex, BMI, number of dependents, smoking status, and geographic region. Ridge Regression achieves the best generalisation performance with an R² score of 92.6%, a Mean Absolute Error (MAE) of $2,264.59, and a Root Mean Squared Error (RMSE) of $3,306.26 on a held-out test set of 1,500 records. The trained model is deployed as a Flask web application featuring secure user authentication, real-time prediction delivery (mean latency < 90 ms), personalised prediction history, and an interactive EDA dashboard. Statistical analysis confirms that smoking status is the dominant cost driver (Pearson r ≈ 0.79), followed by age (r=0.30) and BMI (r=0.20). Five-fold cross-validation (mean R²=92.5%, σ=0.8%) validates model robustness. Result analysis is supported by twelve graphs covering model performance, EDA patterns, risk distribution, and system benchmarks.
Health Insurance; Price Prediction; Machine Learning; Ridge Regression; Flask Web Application; Scikit-learn; Exploratory Data Analysis; Bootstrap 5; SQLite; Regression Analysis.
Mohammed Razaan Siddiqui, Mohammed Qutubuddin Fasi, Mohammed Riyaz
Enhancing Diagnostic Accuracy Through Generative AI And Synthetic Data Generation For Robust Medical Imaging
Medical imaging diagnosis is critically constrained by the scarcity of annotated datasets, class imbalance, and limited generalization of deep learning models. This paper presents a Generative Adversarial Network (GAN)-based framework that synthesizes high-fidelity medical images to augment training corpora for Convolutional Neural Network (CNN) classifiers. The proposed architecture combines a conditional GAN generator G(z|c) with a discriminator D(x|c), whose adversarial minimax objective drives convergence toward realistic synthetic distributions. Augmented datasets reduce overfitting and improve sensitivity, specificity, and F1-score across disease categories. Experimental validation on benchmark datasets demonstrates classification accuracy improvements of 4.3–7.8 percentage points over baseline non-augmented models. Comparison with state-of-the-art augmentation strategies confirms the superiority of GAN-based synthesis. Results establish the proposed framework as a scalable, data-efficient solution for clinical decision-support systems.
Generative Adversarial Networks; Medical Image Synthesis; Convolutional Neural Networks; Data Augmentation; Deep Learning; Diagnostic Accuracy
Abdul Rahman, Mohd Abdul Khaliq, Abdul Baseer Khan Sajid
Medical Chatbot using Gamma LLMV2 and Comparison Using BERT Models
The study introduces a sophisticated medical chatbot that uses vector-based retrieval and Meta’s LLaMA 2 model to provide accurate, context-aware recommendations on symptoms, drugs, and diets. The system incorporates Pinecone for vector embeddings, LangChain for interactions, and Python for logic. ”The GALE Encyclopedia of Medicine,” a 637-page medical dataset, is broken up into text segments for effective semantic search.Flask is used for web infrastructure and Streamlit for interaction with the chatbot’s front end. User queries use LLaMA 2 to deliver answers, create embeddings, and do vector searches. While handling edge circumstances and data quality provide issues, evaluation emphasizes accuracy, timeliness, and engagement. Real-time updates and sophisticated fine-tuning are examples of upcoming enhancements.In terms of language interpretation, generation, and reasoning, Gamma LLM v2 performs better than proprietary models when compared to other models. This enables fine-tuning on bespoke datasets and lessens the need for APIs. It outperforms RoBERTa (0.77), MedBERT (0.95), and BERT (0.86) in medical question-answering tasks and is available in 7B, 13B, and 70B parameters. Index Terms— Gamma LLM-V2,Chat bot, BioBERT, MedBERT, Langchain.
Mr. Mohammed Sami , Mr. Abdul Shafiq, Mr. Shah Alam Hussaini
A field deployable imaging system for detecting microplastics in the aquatic environment
Microplastic (MP) pollutants, which are widely distributed and resistant to decomposition, pose serious threats to both ecosystems and human health. Developing efficient, field deployable, and low-cost MP detection systems is of paramount importance for promoting sustainable development. In this work, we design an integrated imaging system for detecting MPs in various aquatic environments. To address the challenge of image degradation caused by scattering effects, a de-scattering algorithm based on synthetic polarization holography is proposed. Calibration results demonstrate a substantial enhancement in image contrast and spatial resolution. Furthermore, our imaging system enables simultaneous capture of amplitude, phase, and polarization information, which is advantageous for the quantification and identification of MPs. Consequently, this field deployable imaging system offers an efficient solution for realtime monitoring of MPs in turbid aquatic environments.
Microplastic detection, imaging system, polarization, holography, scattering media.
Mohd. Fazal, Shaik Abdul Arif, Mohd. Najmee
Crowd Monitoring System With Iot And Cloud Integration
The rapid growth in urbanization and increasing population in public spaces such as shopping malls, educational institutions, offices, and transportation hubs, effective crowd management has become essential. Smart cities require intelligent monitoring systems to ensure safety, avoid overcrowding, and maintain smooth movement of people within premises. A Smart Crowd Management System using IoT enables real-time monitoring of occupancy levels and provides timely alerts when crowd density exceeds safe limits. By integrating sensors, microcontrollers, and cloud-based applications, such systems help authorities track the number of people inside a facility and manage space utilization efficiently. The system can generate alerts when the occupancy reaches a predefined threshold and provide live updates through IoT platforms. This improves safety, enhances operational efficiency, and supports better decision-making in public and private infrastructures. IoT-based crowd monitoring solutions are cost-effective, scalable, and suitable for modern smart building environments.
IoT, Crowd Management, Occupancy Monitoring, Smart Buildings, IR Sensors, NodeMCU, Blynk App.
Ms. K. Madhavi, Ms. H. Mamtha, Mr. B. Nikhil
3d Modeling And Design Of A G+2 Commercial Building
The rapid pace of urbanization in India, particularly in metropolitan hubs such as Hyderabad, has transformed the dynamics of land use and intensified the demand for compact, multifunctional commercial developments. With increasing population density, rising real estate costs, and limited availability of large plots, small-scale mixed-use buildings have emerged as a practical and sustainable solution to address both economic and spatial challenges in dense urban environments. These developments not only maximize land utilization but also contribute to vibrant urban streetscapes, supporting retail activity at the pedestrian level while accommodating offices or small enterprises above. This project presents a comprehensive architectural modelling and documentation exercise for a Ground + 2 (G+2) commercial building, designed to balance retail visibility with flexible upper-level usage. The ground floor is strategically conceptualized as a high-visibility retail zone, incorporating large glazed storefronts to enhance transparency and customer engagement, wide entrances to encourage footfall, and a welcoming lobby that improves accessibility and circulation. The upper floors are designed as adaptable modules capable of accommodating offices, small enterprises, or limited residential functions depending on zoning provisions, thereby ensuring long-term versatility to meet evolving urban needs. Such flexibility is critical in rapidly developing cities like Hyderabad, where market demands and land-use policies are constantly shifting. Beyond its immediate architectural value, the study demonstrates how BIM can streamline workflows, reduce costs, and promote sustainability in small-scale commercial projects. By integrating design, documentation, and visualization within a single digital environment, BIM enables architects and developers to achieve efficiency in project delivery, minimize rework, and optimize material usage. Sustainability is further promoted through energy-conscious design strategies, such as maximizing natural light via glazed storefronts and ensuring adequate ventilation through setbacks and balconies. Ultimately, the project underscores the importance of adaptability, compliance, and efficiency in contemporary urban design. As cities like Hyderabad continue to expand, small-scale mixed-use commercial buildings will play a critical role in shaping sustainable and resilient urban environments. Through the integration of BIM technologies, architects and planners can not only meet current demands but also anticipate future challenges, ensuring that the built environment remains responsive to the needs of both businesses and communities.
Autodesk Revit, Building Information Modelling (BIM), G+2 Commercial Building, Mixed-Use Development, Ground Floor Shops, Architectural Modelling, Hyderabad Urban Design.
Dr. Gugulothu Vikas , Mr. Mohammed Jalaluddin
Mr. Syed Burhan Uddin, Mr. Mohammed Faraz Uddin, Mr. Saif Ali
Sustainable Project Monitoring And Control Using Primavera P6 For Smart Infrastructure Development
Project monitoring and control is the process of collecting, recording, and reporting information concerning project performance. Project controlling uses the data from monitor activity to bring actual performance to planned performance. The present study deals with the project monitoring process of “Standard Design Factory”, a four storeyed (G+3) factory building whose construction is in progress at Hyderabad, Telangana. A comparison between the planned progress of construction work and actual progress is performed in this study using project management software Primavera P6. Despite well-established principles and policies of project monitoring the process itself may not be efficiently accomplished in a project, because of those practical problems existing or arising in the project Such an attempt in realizing the practical problems in implementation of project monitoring and control will contribute to proper recognition of the problem areas and putting in place the control process to rectify the deviations.
Project Monitoring, Project Control, Construction Management, Primavera P6, Planned vs Actual Progress, Schedule Performance, Construction Delay Analysis.
Mr. Mohammed Jalaluddin *1, Dr. Khaja Fareeduddin *2
Mr. Syed Gulam Yaseen*3, Md Ikhwan Uddin*3, Mr. Mohd Yawar *4, Mr. Singamayum Arshad *5
Analysis Of Cost Overrun In G+3 Residential Building Using Primavera P6
Construction cost overrun is one of the most persistent challenges in the residential building industry in India, with studies consistently showing that 60–70% of projects exceed their original budgets. The present study investigates and analyses cost overrun in a G+3 (Ground Plus Three Floors) residential building project using Oracle Primavera P6 Professional project management software. A detailed project schedule comprising 87 activities across six Work Breakdown Structure (WBS) levels — Foundation Construction, Ground Floor, First Floor, Second Floor, Third Floor, and Finishing Works — was developed in Primavera P6 for a building project with a planned duration of 550 working days, commencing 27th March 2026 and targeted for completion on 4th May 2028. A primary baseline was formally created and assigned as the approved plan of record. Construction progress was updated to the data date of 14th April 2026, at which point two foundation activities were completed on schedule and one was in progress. The study demonstrates that Primavera P6's activity-level cost tracking, baseline comparison reporting, Critical Path Method (CPM) scheduling, and progress updating capabilities are effective tools for identifying and quantifying cost overrun in residential construction projects. Corrective and preventive recommendations are formulated, including baseline revision, material procurement planning, and adoption of formal progress monitoring cycles.
Cost Overrun, Primavera P6, G+3 Residential Building, Baseline Schedule, Work Breakdown Structure, Construction Project Management, Critical Path Method.
Mr. Rg. Nauman Khan , Mr. Mohammed Jalaluddin
Mr. Mohd Kaif , Mr. Mohammed Rakhshan Danish, Mr. Abdul Majid , Mr. Md. Zameer Khan
3d Modelling Of G+6 Resedential Building Using Revit Software
The rapid evolution of the construction industry has shifted the focus from traditional 2D drafting to the multi-dimensional environment of Building Information Modelling (BIM). This project, titled "3D Modelling of a G+6 Residential Building using Revit Software," aims to demonstrate the practical application of BIM in the architectural design and visualization of a high-rise residential structure. The primary objective is to develop a high-fidelity 3D digital representation that integrates architectural aesthetics with functional spatial planning. Unlike manual drafting, the use of Revit allows for a parametric design approach, where any modification in the floor plans is automatically reflected in elevations, sections, and 3D views, ensuring design consistency and reducing errors. The methodology begins with the creation of a comprehensive architectural layout for a Ground plus Six (G+6) floors residential building, adhering to standard building by-laws and functional requirements. Each floor is meticulously modelled to include essential structural and architectural elements such as walls, windows, doors, staircases, and floor finishes. The project further explores the advanced visualization capabilities of Revit, including the application of realistic materials, lighting analysis, and high-quality rendering to provide a lifelike walkthrough of the residential units. This visual clarity is crucial for stakeholders to comprehend the spatial arrangement and aesthetic appeal of the building before the commencement of physical construction. Furthermore, the project highlights the advantages of BIM in terms of documentation and coordination. By utilizing Revit's scheduling features, the model facilitates the automatic generation of architectural schedules, enhancing the efficiency of the design process. The outcome of this work is a complete architectural BIM model that serves as a single source of truth for design data, laying a strong foundation for future integration with structural analysis and construction management. This project concludes that Revit is an indispensable tool for modern civil engineers, offering superior accuracy, time efficiency, and a robust platform for complex architectural modelling in urban residential development.
Revit Architectural, Building Information Modelling, G+6 Residential Building, 3D modelling.
Mr. Guguloth Sai Kumar *1, Dr. Khaja Fareeduddin *2
Mr. Abdul Sajeed*3, Mr. Shaik Irfan*4, Mr. Mohammed Tasin Arsalan*5, Mr. Abdul Khader Khan*6
Planning And Scheduling Of A G+6 Residential Building Using Primavera P6
Application of primavera P6 in planning and scheduling of a G+6 Residential building Project. Effective planning and scheduling are critical to the successful execution of construction projects, ensuring timely completion, cost control, and optimal resource utilization. This current study focuses on the application of Primavera P6 software for planning and scheduling of G+6 Residential building project. A detailed Work Breakdown Structure (WBS) was developed to organize project activities logically. Activity durations are estimated based on standard productivity rates, construction practices, and available resources. Dependencies between activities are defined to establish the project sequence, and network diagrams were generated to identify the Critical Path. Resource allocation, including labor, materials, and equipment, was carried out to analyze resource utilization and avoid conflicts also in case of delayed activities the overall impact on the project is also assessed and appropriately rescheduled to compensate and optimize the project planning efficiently. Furthermore, the study highlights the importance of integrating planning with estimation and costing. By linking activity durations with resource requirements, engineers can forecast project expenses and cash flow requirements more accurately. This integration is particularly valuable for civil engineers working on consultancy projects, estimation reports, and residential building designs, as it supports better financial planning and project control. The results demonstrate that Primavera P6 significantly improves coordination among stakeholders, and helps in minimizing overall delays and cost overruns. The study concludes that adopting advanced project management tools like Primavera P6 in residential construction leads to better decision-making, efficient resource management, and successful project completion within planned time and budget.
Primavera P6, Residential Building, Construction Project Planning, Project Scheduling Work Breakdown Structure (WBS), Critical Path Method (CPM), Resource Allocation.
Dr. T. Dhilip *1, Dr. Khaja Fareeduddin *2
Mr. Mohammad Mudasser*3, Azharuddin*4, Mr. Mohammed Talib Faisal*5, Mr. Mohammed Affan Sofi*6
Planning, Scheduling And Resource Allocation Of A Small Commercial Building In Primavera P6
Construction project management is one of the most critical disciplines in the civil engineering domain. The successful execution of a construction project depends largely on how effectively it is planned, scheduled, and monitored against defined objectives of time, cost, and quality. In developing economies like India, the construction sector contributes substantially to Gross Domestic Product (GDP) and is one of the largest employers of skilled and unskilled labour. Despite its scale, the industry continues to suffer from chronic delays, cost overruns, and poor resource utilisation problems that are largely attributable to inadequate planning tools and methods. This project presents a comprehensive study of planning, scheduling, and resource allocation for a small commercial building (Project ID: CM-4) using Oracle Primavera P6, one of the most powerful and widely used project management software solutions available in the construction industry today. The project spans from 02 April 2026 to 27 July 2027 and encompasses eighteen distinct Work Breakdown Structure (WBS) elements, covering the full construction lifecycle from General Conditions through Final Inspections. The study involves the development of a detailed project schedule within Primavera P6, including the creation of the Enterprise Project Structure (EPS), Organisational Breakdown Structure (OBS), Work Breakdown Structure (WBS), and a complete activity network with 176 logical predecessor-successor relationships across 126 activities. Resources including labour crews, specialist sub-contractors, and equipment have been assigned to activities based on project requirements. The critical path has been identified (36 activities with -7 days total float), a baseline schedule established, and all standard Primavera P6 reports generated.Results demonstrate that the use of Primavera P6 significantly improves project visibility, enables accurate forecasting of project duration and cost, and supports proactive identification of scheduling conflicts and resource overloads. The study concludes that the adoption of structured scheduling tools such as Primavera P6 is essential for the timely and cost-effective delivery of commercial building projects.
Primavera P6, Project Scheduling, Resource Allocation, Critical Path Method, Work Breakdown Structure, Commercial Building, Construction Management.
Mr. Shyam Kumar , Mr. Mohammed Jalaluddin
Mr. Mifzaal Mudassar , Mr. Mohammed Abdul Kabeer , Mr. Mohammed Abdullah Ather, Mr. Syed Faizaan Ahmed
Project Planning, Scheduling, Resource Allocation, And Control Using Primavera P6.
Effective project management is essential in construction and engineering projects to ensure completion within time and budget. Proper planning, scheduling, and resource management reduce uncertainties and improve productivity. Primavera P6 is a powerful project management software widely used for handling complex projects through structured planning, tracking, and control mechanisms. The implementation of project management using Primavera P6 starts with defining the Enterprise Project Structure (EPS) and Organizational Breakdown Structure (OBS) to establish proper project hierarchy and responsibility. A detailed Work Breakdown Structure (WBS) is then created to divide the project into manageable components. All project activities are identified, sequenced logically, and assigned realistic durations based on scope and site conditions. Relationships such as Finish-to-Start (FS), Start-to-Start (SS), and others are defined to create an accurate project network. After schedule development, resources including labor, materials, and equipment are assigned to each activity along with their respective costs. Primavera P6 then performs schedule calculations using the Critical Path Method (CPM) to determine project duration and identify critical activities. Resource leveling techniques are applied to resolve over-allocation issues. A baseline schedule is established for performance comparison. During project execution, regular updates are entered into the system to monitor progress, analyze variances, generate S-curves, and prepare performance reports for effective control and timely corrective actions. In many construction projects, poor planning, improper resource allocation, and lack of monitoring lead to delays, cost overruns, and resource conflicts. Absence of a structured scheduling system causes miscommunication and inefficient coordination among teams. Manual tracking methods often result in inaccurate data and delayed corrective actions. Without proper software tools like Primavera P6, identifying critical activities and managing risks becomes difficult, affecting overall project performance.
Primavera P6, Project Planning, Project Scheduling, Resource Allocation, Critical Path Method (CPM), Project Monitoring, Construction Management.
Dr. Mohammed Moiz *1, Dr. Khaja Fareeduddin *2
Mr. Mohd Imtiyaz Siddiqui, Mr. Md Mannan, Mr. Md Yashir Hassan, Mr. Shaik Shoebuddin
A Study On 3d Bim Modelling And Structural Analysis Of A G+4 Residential Building Using Revit And Etabs.
This project presents the Analysis, Design, and BIM-based Modelling of a multi-storey G+4 reinforced cement concrete (RCC) residential building using advanced structural engineering and modelling software tools, including CSI ETABS, CSI SAFE, CSI Detail, and Autodesk Revit. The primary objective of this study is to develop an efficient and accurate structural system by integrating conventional design practices with modern Building Information Modeling (BIM) techniques. The structural analysis of the building is carried out using ETABS, where the complete 3D model is developed by defining grid systems, material properties, and structural components such as beams, columns, and slabs. The structure is analysed under various loading conditions including dead load, live load, wind load, and seismic load in accordance with relevant Indian Standard codes such as IS 456:2000, IS 875, and IS 1893:2016. The software provides critical outputs such as bending moments, shear forces, axial forces, storey drift, and base shear, which are used for the safe and economical design of structural elements. The design of beams, columns, and slabs is performed using limit state design principles to ensure structural safety, serviceability, and durability. Foundation design is carried out using SAFE, where soil-structure interaction, bearing capacity, and settlement considerations are incorporated to design safe and stable footing systems. Reinforcement detailing is further developed using CSI Detail, which generates accurate bar bending schedules (BBS) and detailed reinforcement drawings, reducing manual errors and improving construction efficiency. The integration of ETABS, SAFE, CSI Detail, and Revit ensures a streamlined workflow from analysis and design to detailing and modelling. This approach significantly improves accuracy, reduces design conflicts, and enhances project efficiency. The study highlights the importance of BIM in modern construction practices by demonstrating its role in improving visualization, coordination, cost-effectiveness, and decision-making throughout the project lifecycle. Overall, this project emphasizes the adoption of integrated software tools and BIM technology for the development of safe, economical, and sustainable multi-storey residential b
Reinforced Cement Concrete (RCC), G+4 Residential Building, Structural Analysis, Structural Design, Building Information Modelling(BIM), CSI ETABS, CSI SAFE, CSI Detail, Autodesk Revit, Seismic Analysis, Load Calculation.
Dr. Khaja Fareeduddin *1, Ms. Qudsiya Fathima*2
Mr. Mohammed Abdulla Ahmed, Abdus Sami , Mr. Mohammed Muied Ahmed , Mr. Mohammed Abdul Hannan Zabi
Architectural Planning And 3d Bim Modelling Of A G+5 Building Using Autodesk Revit & E-Tabs Design
This project, titled “Architectural Planning and 3D BIM Modelling of a G+5 Building Using Autodesk Revit,” focuses on the comprehensive planning, design, and digital modelling of a multi-story building using advanced Building Information Modelling (BIM) technology. The main objective of this project is to demonstrate the effectiveness of BIM in improving accuracy, coordination, and efficiency in modern construction practices compared to traditional 2D drafting methods. The project begins with the selection of a suitable plot and identification of functional requirements for a G+4 building. Architectural planning is carried out by considering essential factors such as space utilization, ventilation, natural lighting, circulation, and user comfort. Proper zoning of spaces including living areas, bedrooms, kitchens, and service areas is ensured to achieve an efficient and practical design. The planning is also carried out in accordance with standard building bylaws and safety regulations. After the planning phase, a detailed 3D model of the building is developed using Autodesk Revit. BIM enables the creation of an intelligent digital model where each building component such as walls, doors, windows, floors, and staircases contains both geometric and functional information. One of the key advantages of BIM is that any changes made in the model are automatically updated throughout all views, including plans, elevations, and sections, thereby reducing errors and saving time. The project also involves the generation of detailed architectural drawings directly from the BIM model, including floor plans, elevations, and sectional views. In addition, schedules for doors, windows, and materials are prepared using Revit’s automated features, which help in quantity take-off and cost estimation. Visualization tools such as 3D views and renderings are used to present a realistic representation of the building, enhancing understanding and communication among stakeholders. In conclusion, this project highlights the importance of BIM technology in the construction industry and demonstrates how Autodesk Revit can be effectively used for architectural planning and modelling. The use of BIM not only improves design accuracy and efficiency but also supports better decision-making, coordination, and overall project management, making it an essential tool for modern civil engineering projects.
Building Information Modelling (BIM), Autodesk Revit, Architectural Planning, 3D Modelling, G+4 Building, Multi story Building Design, Digital Construction, Space Planning and Zoning, Building Components (Walls, Doors, Windows), Construction Efficiency , E-tab.
Mr. Shaik Nawaz Ramzane , Mr. Mohammed Jalaluddin
Mr. Mohammed Fouzan Ahmed, Mr. Mohammed Dilawar Hussain , Mr. Mohammed Abdul Naser , Mr. Shoukath Aziz
Seismic Analysis And Design Of Multi-Storey Rc Building With Viscous Dampers Using Etabs
Earthquakes are among the most destructive natural hazards, causing significant damage to structures, especially multi-storey reinforced concrete (RC) buildings. In seismic regions, buildings are subjected to lateral dynamic forces that induce vibrations, leading to structural distress or failure if not properly designed. Conventional seismic design, as per IS 1893 (Part 1): 2016, mainly rely on increasing the strength and stiffness of structural elements. However, this approach can result in uneconomical designs and may not effectively control structural response such as excessive displacement and drift. To address these challenges, modern techniques involve the use of energy dissipation devices like viscous dampers, which helps in reducing seismic effects by absorbing a portion of the input energy. This study aims to evaluate the effectiveness of viscous dampers in improving the seismic performance of multi-storey RC buildings. A multi-storey RC building is modeled and analyzed in ETABS as per IS 1893 (Part 1): 2016 provisions. The analysis is carried out using response spectrum method. Initially, the building is analyzed without dampers to determine parameters such as storey displacement, storey drift, base shear, and member forces. Later, viscous dampers are introduced at suitable locations in the structure. The building is re-analyzed, and the results are compared with the conventional model. The effectiveness of dampers are evaluated based on reduction in displacement, drift, and forces in structural members. The study concludes that the incorporation of viscous dampers significantly enhances the seismic performance of the building. There is a notable reduction in storey displacement and inter-storey drift, indicating improved structural stability and safety. Additionally, the forces in structural members are reduced, leading to more economical and efficient design. Hence, viscous dampers prove to be an effective solution for controlling seismic effects in multi-storey RC buildings and can be widely adopted in earthquake-resistant design practices.
Seismic Analysis, Viscous Dampers, Response Spectrum Method, Storey Drift Control.
Mr. Alluri Anilkumar , Mr. Mohammed Jalaluddin
Mr. Mohammed Qasim Pasha, Mr. Syed Abdul Sami, Mr. Md Ilhaaj Shah Makakmayum, Mr. Mahammad Sameer
Implementation Of Building Information Modeling Using Autodesk Revit And Its Applications In Modern Civil Engineering
The construction industry is undergoing a significant transformation with the adoption of digital technologies, among which Building Information Modeling (BIM) has emerged as a revolutionary approach. This project focuses on the implementation of BIM using Autodesk Revit and explores its applications in modern civil engineering practices. BIM is a comprehensive process that involves the generation and management of digital representations of physical and functional characteristics of a structure, enabling improved planning, design, construction, and management throughout the project lifecycle. The primary objective of this study is to demonstrate how BIM can be effectively utilized to enhance efficiency, accuracy, and collaboration in civil engineering projects. Autodesk Revit, a leading BIM software, is used to create a detailed 3D model of a building, integrating architectural, structural, and service components into a single coordinated model. This integrated approach helps in minimizing design conflicts, reducing errors, and improving visualization, which is not achievable through traditional 2D drafting methods. The project highlights various features of Revit such as parametric modeling, clash detection, quantity takeoff, and real-time collaboration. By implementing BIM, engineers and stakeholders can identify potential issues during the design phase itself, thereby reducing costly modifications during construction. Additionally, BIM facilitates better resource management, cost estimation, and scheduling, leading to optimized project execution. Furthermore, this study examines the applications of BIM in modern civil engineering, including infrastructure development, sustainable construction, and facility management. BIM supports green building practices by enabling energy analysis and efficient material usage, contributing to environmental sustainability. It also plays a crucial role in lifecycle management by providing a digital database that can be used for maintenance and future renovations. The results of this project demonstrate that BIM implementation using Autodesk Revit significantly enhances productivity, coordination, and decision-making in construction projects. Despite initial challenges such as software learning curve and implementation cost, the long-term benefits of BIM outweigh these limitations. In conclusion, the adoption of BIM represents a paradigm shift in civil engineering, promoting innovation and efficiency. This project emphasizes the importance of integrating BIM into modern construction practices and showcases its potential to improve the overall quality and performance of engineering projects.
Building Information Modeling (BIM), Autodesk Revit, Civil Engineering, Digital Modeling 3D Visualization, Parametric Modeling, Clash Detection, Quantity Takeoff, Project Lifecycle, Design Integration, Cost Estimation, Resource Management.
Mr. Mohammed Jalaluddin , Ms. Qudsiya Fathima
Mr.Mohammed Asim Khan, Mir Mansoor Ali, Mr. Mohammed Kashif Uddin , Mr. Mohammed Hamza Alam
Structural Design & Construction Planning Of A Residential Building Using Etabs & Primavera
This project presents the structural design and construction planning of a residential building using ETABS and Primavera P6. The primary objective of the study is to integrate structural analysis with effective project scheduling to achieve a safe, economical, and time-efficient construction process for a multi-storey residential building. The structural design phase was carried out using ETABS, where the building was modelled as a G+3 reinforced concrete framed structure. All relevant loads including dead loads, live loads, and wind loads were applied in accordance with standard design codes. The analysis focused on determining critical parameters such as storey displacement, base shear, bending moments, and axial forces to ensure structural stability and serviceability. In the construction planning phase, Primavera P6 was used to develop a detailed project schedule. Work Breakdown Structure (WBS), Organizational Breakdown Structure (OBS), and Enterprise Project Structure (EPS) were defined to organize project activities systematically. Logical sequencing of construction activities, along with durations and dependencies, was established to generate a realistic baseline schedule. The study also highlights the importance of monitoring project progress through scheduling tools to minimize delays and improve resource coordination. The integration of ETABS and Primavera provides a comprehensive approach that combines structural safety with efficient project management. The results demonstrate that proper planning and accurate structural design significantly enhance project execution efficiency, reduce time overruns, and ensure compliance with design standards.
Structural Analysis, Reinforced Concrete, Multi-storey Building, ETABS, Primavera P6, Load Analysis, Wind Load, Storey Displacement, Project Planning, Scheduling, Work Breakdown Structure (WBS), Construction Management, RCC Design, Project Management Tool.
Mr. G. Srikanth, Mr. Mohammed Jalaluddin
Mr. Sabeel Ahmed Khan , Mr. Abraar Meraj Quraishi , Mr. Mohammed Hamza , Mr. Mohammed Hamza Shareef
Facial Emotion Recognition Using Deep Learning: A CNN-Based Web-Deployed Affective Computing System
Facial Emotion Recognition (FER) is a transformative application of affective computing that automatically identifies human emotional states from facial images. This paper presents a comprehensive web-based FER system built on a custom Convolutional Neural Network (CNN) — EmotionCNN — featuring four progressively scaled convolutional blocks (32, 64, 128, 256 filters) with Batch Normalization, ReLU activations, and Max Pooling, culminating in three fully connected layers for classification into seven Ekman emotion categories: Happy, Sad, Angry, Surprise, Fear, Disgust, and Neutral. The network is trained on a controlled synthetic dataset of 4,200 grayscale 48×48 pixel images generated via OpenCV drawing primitives, achieving 100% accuracy on a held-out 700-image test set. Three classical baseline classifiers — Logistic Regression (83.29%), Random Forest (77.43%), and SVM with RBF kernel (65.00%) — are systematically evaluated for comparative analysis. Face localization employs the Viola–Jones Haar Cascade detector for efficient CPU-bound frontal-face detection. The complete system is deployed as a full-stack Flask application with PyTorch inference, SQLite persistence, and a Bootstrap 5 dark-violet responsive interface, incorporating drag-and-drop upload, real-time confidence-scored predictions, per-user history tracking, and a fivechart analytics dashboard. Mathematical formulations of the convolutional operation, Batch Normalization, ReLU, Softmax, and Adam optimizer are presented alongside the system architecture, algorithmic pipeline, UML diagrams, and a comprehensive results analysis.
Facial Emotion Recognition, Deep Learning, Convolutional Neural Networks, PyTorch, OpenCV, Haar Cascade, Flask, Affective Computing, Batch Normalization, Adam Optimizer
Mohammed Adnan Hussain, Syed Salman Jalal
Spam Detection for Smart Home IoT Devices Using Machine Learning: A Comprehensive Framework
This paper presents an intelligent spam detection framework for securing smart home Internet of Things (IoT) devices using machine learning. With the number of connected smart home devices exceeding 25 billion globally, network security against spam injection, unauthorized access, and anomalous device communications has become critically important. The proposed system analyzes IoT network traffic from the REFIT Smart Home dataset—1,664 records with 13 features encompassing source/destination addresses, device types, locations, operations, and timestamps—to classify communications as Normal (valid) or Spam. A four-stage preprocessing pipeline applies Label Encoding for 10 categorical features, Simple Imputation for missing values, Standard Scaling for normalization, and Principal Component Analysis (PCA) to reduce 11 features to 10 principal components capturing ≈98% of total variance. Five machine learning classifiers are systematically evaluated: Bagging Classifier with SVC base estimator (99.4% accuracy), Decision Tree with Gini criterion (99.4%), AdaBoost with 100 estimators (95.5%), Voting Classifier combining Logistic Regression, Random Forest, and Gaussian Naive Bayes (89.2%), and Gaussian Naive Bayes (89.2%). A Keras Sequential deep learning model (Input(10)→Dense(4,ReLU)→Dense(4,ReLU)→Dense(1,Sigmoid)) trained for 200 epochs with Adam optimizer provides complementary validation. Each model produces a spamicity score representing device trustworthiness. The Flask web application offers role-based access (admin for model management, user for predictions) with Docker containerization and an optional Next.js frontend. This article presents the mathematical foundations of all algorithms, the complete system architecture, algorithmic pseudocode, and thorough results analysis including comparative tables, PCA variance analysis, learning curve data, and cross-validation findings.
IoT Security, Spam Detection, Smart Home, Machine Learning, Bagging Classifier, Decision Tree, PCA, AdaBoost, Voting Classifier, Gaussian Naive Bayes, Flask, Deep Learning, REFIT Dataset, Spamicity Score
Mohammed Faizan Uddin, Mohammed Farhan, Mohammed Faris Uddin Ansari, Mohammed Ikram Uddin
Car Insurance Claim Prediction Using Machine Learning: A Comparative Study of Ensemble and Classical Classifiers
This paper presents a comprehensive web-based machine learning platform for predicting car insurance claim outcomes using four classification algorithms: Gradient Boosting, Random Forest, Support Vector Machine (SVM), and Logistic Regression. Insurance companies face the dual challenge of class imbalance (≈74% no-claim, ≈26% claim) and non-linear interactions between policyholder features that defeat traditional actuarial GLM models. The system processes a synthetic dataset of 10,000 policy records across 17 features—including driving experience, credit score, annual mileage, speeding violations, DUIs, and past accidents—after applying a two-stage preprocessing pipeline: LabelEncoding for 9 categorical variables and StandardScaler normalization for 8 numeric features, followed by a stratified 80/20 train-test split. Gradient Boosting achieved the highest performance (Accuracy = 91.95%, Precision = 89.45%, Recall = 78.27%, F1 = 83.49%), surpassing SVM (91.10%), Logistic Regression (90.25%), and Random Forest (90.10%). The full-stack Flask application integrates scikit-learn inference, SQLitebacked authentication (Werkzeug PBKDF2-SHA256 hashing), prediction history, Chart.js analytics dashboards, and Docker containerization—all within a Bootstrap 5 dark-themed UI. This article details the mathematical foundations of all four algorithms, the end-to-end system architecture, the ML pipeline, algorithmic pseudocode, and rigorous results analysis with comparative tables and performance graphs.
Car Insurance Claim Prediction, Gradient Boosting, Random Forest, SVM, Logistic Regression, Flask, scikit-learn, SQLite, Chart.js, Bootstrap 5, Docker, Class Imbalance, Feature Engineering
Shadman Ahmad, Shaik Sufyaan Ahmed, Syed Affan Hussain Syed Barey, Syed Jawad
Enhancing Network Security Using Machine Learning-Based Anomaly Detection: A Random Forest Approach
This research article presents a comprehensive investigation into ML-based network security anomaly detection using a Random Forest ensemble classifier. Modern enterprise networks face escalating cyber threats—DDoS attacks, intrusions, malware, phishing, and ransomware—that overwhelm traditional signature-based defenses. The proposed system analyzes 18 network traffic features, including protocol, packet type, anomaly score, severity level, malware indicators, IDS/IPS alerts, and firewall logs, to classify network packets into six distinct attack categories in real time. A Random Forest Classifier with 100 estimators is trained on a synthetically generated dataset of 5,000 packets with realistic, attack-specific feature distributions. Ten LabelEncoders transform categorical traffic attributes into numerical representations suitable for tree-based classification. The full-stack web platform—built with Flask, scikitlearn, SQLite, Bootstrap 5, and Chart.js—achieves 99.80% classification accuracy, outperforming Decision Tree (97.40%), SVM (91.20%), Logistic Regression (89.60%), and Naive Bayes (82.40%) baselines. This paper details the mathematical foundations of the Random Forest algorithm, the system architecture, the feature engineering pipeline, algorithmic pseudocode, and a thorough result analysis with comparative tables and performance graphs.
Network Security, Anomaly Detection, Random Forest, Machine Learning, Flask, scikit-learn, Intrusion Detection, Cybersecurity, Ensemble Learning, Feature Engineering
Mohd Mir Jaffer Ali, Yaqoob Ahmed, Mohd Ismail, Zamil Akhtar
Efficient And Scalable Learning Management System Using MERN Stack
The rapid digital transformation of education has necessitated scalable and efficient platforms for managing learning processes. This research presents the design and implementation of a Learning Management System (LMS) based on the MERN stack (MongoDB, Express.js, React.js, Node.js). The proposed system integrates advanced features such as role-based authentication, GPS-based attendance verification using the Haversine distance formula, auto-grading assessment modules, and real-time progress tracking. The architecture follows a modular and scalable design, ensuring high performance and adaptability across diverse educational environments. Experimental results demonstrate improved efficiency, reduced response latency, and enhanced system reliability compared to conventional LMS solutions. The system provides a cost-effective and extensible solution for community-based education systems. Keywords: LMS, MERN Stack, GPS Attendance, Haversine Algorithm, JWT, Web Applications, E-Learning
Mohammaed Wasi Uddin, Mohd Abrar Siddiqui, Mohd Abdul Aziz Maaz
Social Media Forensics: Cyberbullying & Hate Speech Analysis Using Machine Learning And NLP
The exponential proliferation of user-generated content on social media platforms has created an urgent need for automated systems capable of identifying cyberbullying, hate speech, and offensive language at scale. This paper presents a comprehensive machine learning-based web application — Social Media Forensics (SMF) — that classifies social media text into three categories: Hate Speech, Offensive Language, and Clean Content. The system employs Natural Language Processing (NLP) preprocessing pipelines (tokenization, stop-word removal, lemmatization) combined with TF-IDF (Term Frequency–Inverse Document Frequency) vectorization for feature extraction. Six supervised machine learning classifiers — Logistic Regression, Naïve Bayes, Support Vector Machine (SVM), KNearest Neighbors (KNN), Random Forest, and Gradient Boosting — are systematically trained, evaluated, and compared. The best-performing model achieves approximately 94% classification accuracy. The full-stack web application is developed using Python Flask, SQLite, Bootstrap 5, and Docker containerization, incorporating user authentication, real-time prediction with confidence scoring, analysis history tracking, and a multi-chart analytics dashboard. Mathematical formulations of TF-IDF, Bayes theorem, SVM hyperplane optimization, and informationgain- based ensemble methods are derived. System architecture, algorithmic pseudocode, UML diagrams, and a comprehensive performance comparison across all six classifiers are presented.
Cyberbullying Detection, Hate Speech Classification, Natural Language Processing, TF-IDF, Support Vector Machine, Social Media Forensics, Text Classification, Flask, Affective Computing
Syed S. Quadri · Syed Muzammil Hussaini · Omer Farooq · Mustafa Wasif Hussain
CodeForge: Design and Implementation of an AI-Enhanced Browser-Based Online Integrated Development Environment with Sandboxed Code Execution and Dual-Mode AI Assistance
This paper presents CodeForge, a comprehensive browser-based Online Integrated Development Environment (IDE) that eliminates the installation and configuration barriers associated with traditional desktop development tools. CodeForge integrates the Monaco Editor — the engine powering Visual Studio Code — with syntax highlighting and IntelliSense for six programming languages (Python, JavaScript, Java, C++, HTML, and CSS), a sandboxed subprocess-based Python execution engine with configurable import blocking and a 10-second timeout, and a dualmode AI assistant supporting code analysis, explanation, refactoring, and generation through both an offline patternbased engine (15+ templates) and online Google Gemini API integration. The system is built as a full-stack Flask web application with SQLite persistence, Bootstrap 5 dark-themed responsive UI with emerald-green accents, and Docker containerization for portable deployment. Formally, the system enforces security as is_safe(C) = ∀ b ∈ B : ('import ' + b) ∉ tokens(C), preventing execution of code referencing any of 11 blocked modules. The AI selection function R(action, C, l) provides Gemini-powered responses when available and falls back to deterministic pattern matching otherwise, ensuring consistent availability. Comprehensive testing across 21 test cases achieves a 100% pass rate. Comparative evaluation confirms CodeForge's superiority over Replit, CodePen, JSFiddle, Jupyter Notebooks, and VS Code Web in the combination of AI-assisted coding, self-hostability, zero cost, and Monaco-grade editing.
Online IDE, Monaco Editor, Code Execution Sandbox, AI Code Assistant, Dual-Mode AI, Flask, Bootstrap 5, Docker, Gemini API, SQLite, Browser-Based Development, Code Snippet Management.
Mohammed Abdul Waqeed ,Mohd Yaseen , Fardeen Ahmed , Mohd Wahaj Ather
URL Shield: Detecting Malicious URLs Using Machine Learning Techniques
The proliferation of cyber threats through malicious URLs has become one of the most significant challenges in cybersecurity, with phishing attacks alone causing over $10.3 billion in losses globally in 2022. This paper presents URLShield, a comprehensive machine learning-based web application that classifies URLs as Legitimate or Malicious using 28 engineered features extracted directly from URL strings — without loading the target page, querying DNS, or using WHOIS data — achieving sub-100ms classification latency.The system implements a comparative analysis of eight diverse machine learning algorithms: Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Naive Bayes, Gradient Boosting, and Multi-Layer Perceptron Neural Network, trained on a balanced synthetic dataset of 10,000 URLs (5,000 legitimate + 5,000 malicious) with intentional 8% label noise to simulate real-world classification ambiguity. Gradient Boosting achieves the highest accuracy of 92.35% with 93.10% recall — the production model for URLShield.The 28 features span four categories: character count features (13), binary flag features (7), structural features (5), and ratio features (3), capturing URL length, special characters, HTTPS usage, IP-based addresses, URL shorteners, suspicious TLDs, and phishing keyword counts. The Flask web application provides PBKDF2-SHA256 authenticated user sessions, real-time URL prediction with confidence scores, prediction history in SQLite, 12-chart EDA gallery, Chart.js interactive model comparison dashboard, role-based admin access, and Docker deployment. The system demonstrates that feature-only URL analysis achieves over 92% accuracy — competitive with content-based approaches at 100× lower latency and zero security risk.
Malicious URL Detection · Machine Learning · Gradient Boosting · Feature Engineering · Phishing Detection · Cybersecurity · Flask · URL Classification · Ensemble Learning · Random Forest · SVM · Neural Network · SQLite
Mohammed Masood Ullah Fouzan Mohammed Khan · Muaaz Hamad · Shoaib Mohammed Basheer
Smart Property Hub: Revolutionizing Real Estate Management Online
The Smart Property Hub is a comprehensive full-stack web-based real estate management platform that digitizes and streamlines property listing, discovery, and booking workflows. Built using Python Flask as the backend framework, SQLite as the relational database, and Bootstrap 5 for the dark-themed responsive UI, the system implements a three-role architecture (Admin, Seller, Buyer) enforced through Python decorator-based RBAC. Sellers manage property listings across four types (Apartment, Villa, Plot, Commercial) with 12 comprehensive attributes; buyers browse listings using a five-criteria dynamic SQL search engine and submit booking requests; administrators exercise full platform oversight through real-time statistics and cascade-safe user management. The platform comprises 17 distinct routes, 15 Jinja2 HTML templates, and a 3-table SQLite schema (users, properties, bookings) with PBKDF2-SHA256 password hashing, parameterized SQL injection prevention, and PRAGMA foreign key enforcement — all containerizable via Docker for zero-cost cloud deployment.
Real Estate Management, Flask, SQLite, Role-Based Access Control, Property Listing, Dynamic SQL Search, Booking Workflow, Bootstrap 5, PBKDF2-SHA256, Full-Stack Web Development, Docker
Shaikh Suleman, Abdul Hadi Zaid, Mohd Amanullah Khan, Abdul Rahman