International Journal of Pharmacy with Medical Sciences
Editor- In-Chief: Dr. Charles M Title proper: International Journal of Pharmacy with Medical Sciences (IJPAMS) Subject: Pharmacy, Medical, AI Computer Science, IOT, Science and Technology Publisher Name: Zestera Publications ISSN: 3068-2738 Dates of publication: 2021 Frequency: Quarterly Language: English Country: USA Medium: Online Indexed by: CROSSREF, MDPI scilit, Google Scholar Ext Title DOI of the Journal: http://doi.org/10.64751/issn.3068-2738 E-mail: editor@ijpams.com Impact Factor : 6.0
First-Trimester Uterine Artery Doppler for Early Risk Prediction of Adverse Maternal and Perinatal Outcomes
Because of the frequent occurrence of placental dysfunction in early pregnancy and the lack of recognition until complications occur, adverse maternal and perinatal outcomes continue to be significant challenges in obstetric care. Early detection of high-risk pregnancy is thus gaining importance for better maternal and neonatal health. The proposed study is to present an ACFO, HDRI and XGBoost classifier with the FMF First-Trimester Screening Dataset. Combination of maternal clinical parameters, uterine artery Doppler indices and biochemical markers are being used for predicting adverse outcomes. The results of the experiments are showing an accuracy of 95% for prediction, which shows better identification of HYDR, FGR, preterm birth, and neonatal complications. The proposed framework is helping to enable early decision making in clinical care and personalised antenatal care.
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An Early Pregnancy Uterine Artery Doppler Framework for Predicting Maternal and Perinatal Adverse Outcomes Using Risk-Stratified Clinical Analytics
There are many developments, but still significant problems in pregnancy that cause morbidity in mother and fetus like preeclampsia, FGR and premature birth. Early identification of high-risk pregnancy is of great importance for good clinical outcome. The aim of this study is to develop a RSCAF based on the measurement of the uterine artery Doppler performed in the first trimester of pregnancy, which will allow to predict the adverse maternal and perinatal outcomes. The framework is based upon the use of uterine artery PI to identify vulnerable pregnancies early in pregnancy, risk stratification, maternal outcomes monitoring and assessment of perinatal outcomes. Experimental analysis showed high risk pregnancy groups had a higher incidence of preeclampsia, gestational hypertension, fetal growth restriction, SFA and preterm delivery. Moreover, the proposed framework was able to obtain an accuracy of 92.6%, sensitivity of 90.8%, specificity of 93.9%, precision of 89.7%, and F1- score of 90.2%. The results indicate the capability of the proposed system to assist in early risk detection and give timely medical support which will enhance the health of mothers and newborns.
THE ROLE OF DATA-DRIVEN DECISION MAKING IN ENHANCING RETURN ON INVESTMENT (ROI) AND SALES PERFORMANCE: AN EMPIRICAL STUDY.
As digital technologies become more widespread, organisations are using data in new and exciting ways, for strategic and operational decision making. Data-Driven Decision Making (DDDM) is one of the most important business competencies in the use of data analytics, business intelligence and predictive models to optimize the performance of the organization (Davenport & Harris, 2017; Wamba et al., 2020). This study is designed to explore how DDDM can improve Return on Investment (ROI) and Sales Performance in organizations. The research is designed to explore how DDDM relates to ROI and how sales performance relates to the research, as well as how the analytics capability relates to organizational effectiveness. The study employed a quantitative approach and primary data was gathered from 340 respondents consisting of marketing professionals, sales managers, business analysts, data analysts, and employees of SMEs and corporate organizations. A structured questionnaire using Google Forms was used to collect data with a 5-point Likert Scale to measure it. The data were processed by using SPSS and SEM techniques and then analyzed statistically by using correlation analysis, multiple regression analysis and Structural Equation Modeling (SEM). The results showed that there was a very high positive correlation of DDDM with ROI (r = 0.712) and DDDM with Sales Performance (r = 0.685). The results of regression analysis showed that DDDM was able to significantly predict the sales performance (β = 0.512, p < 0.001), and the result of SEM showed the positive effect of DDDM on ROI and Sales Performance. Moreover, it was found that analytics capability positively impacts organizational performance by enhancing the quality of decision making and optimizing the use of organizational resources (Mikalef et al., 2020). The authors' findings indicate that companies with data-driven approaches are well equipped to benefit from the financial results, sales growth and sustainable competitive advantage. The results offer important insights for managers aiming to leverage analytics to create value for their business
Assessment of Insulin Resistance in Patients with Chronic Obstructive Pulmonary Disease Attending a Tertiary Care Hospital
Introduction: Chronic Obstructive Pulmonary Disease (COPD) is increasingly recognized as a systemic inflammatory disorder with multiple extrapulmonary manifestations. Insulin resistance (IR) is one such metabolic abnormality that contributes to cardiovascular morbidity and poor outcomes in COPD patients. Aim: To assess insulin resistance in patients with COPD and to evaluate its association with disease severity. Materials and Methods: This hospital-based crosssectional study was conducted in a tertiary care hospital and included 100 participants: 50 clinically stable COPD patients diagnosed according to ATS/ERS guidelines and 50 age- and sex-matched healthy controls. Fasting blood glucose, fasting serum insulin, lipid profile parameters, and anthropometric measurements were recorded. Insulin resistance was assessed using the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR). Surrogate markers such as triglyceride-to-HDL ratio (TG:HDL) and triglyceride-glucose (TyG) index were also calculated. Pulmonary function tests were performed using spirometry. Statistical analysis was carried out using SPSS version 25, and a p-value <0.05 was considered statistically significant. Results: COPD patients had significantly higher fasting blood glucose, fasting insulin levels, HOMA-IR, TG:HDL ratio, and TyG index compared to controls (p<0.001). Insulin resistance showed a significant progressive increase with advancing stages of COPD. HOMA-IR demonstrated a strong association with disease severity. Conclusion: Insulin resistance is significantly increased in patients with COPD and correlates positively with disease severity. Early identification of metabolic abnormalities may help reduce long-term cardiovascular and metabolic complications in COPD patients.
CARALLUMA-MEDIATED GREEN SYNTHESIS OF AG–FE “BIMETALLIC NANOPARTICLES” FOR OPTICAL, STRUCTURAL APPLICATIONS
The use of plant extract in the green production of “bimetallic nanoparticles” is gaining traction in the scientific community. ‘nanoparticles’ of Ag-Fe were created by employing the extract of the Caralluma lasaintha plant as a bio-reducing and stabilising agent. In order to examine the nanoparticles' structural integrity, we compared the as-prepared (IAF-P) and thermally treated (IAF-T) ‘Ag–Fe’ nanoparticles. ‘nanoparticles’ were stabilised and formed in part by plant biomolecules, as shown by FTIR data that verified the presence of phytochemical functional groups such as hydroxyl, carbonyl, and ‘C-O’ groups. The XRD results showed that both the untreated and thermally treated samples included crystalline nanoparticles, with an average crystallite size of 22 nm and 28 nm, respectively. The wide absorption band in the 300-350 nm range of the UV-visible spectra was observed in the optical response ‘Ag–Fe’ nanoparticle system, which is worth mentioning.SEM micrographs showed porous and agglomerated structures of ‘nanoparticles’ in the raw sample, and the relatively compact granular and rod-like structures of the thermally treated sample because of the enhanced particle growth. The analysis of Dielectric shows a reduction in frequency with regards to dielectric loss and dielectric constant which was similar to the behaviour of Maxwell-Wagner interfacial polarization. Thermal treatment led to better dielectric stability and moderately increased AC conductivity of the thermally treated ‘nanoparticles’ through increased crystallinity and fewer organic residues. Therefore, a green synthesis to catalysis, functional nanomaterials, and nanoelectronics synthesis is the green synthesis of ‘Ag–Fe’ “bimetallic nanoparticles” using Caralluma
Konathala Sirisha Rani 1 , K Suresh Babu2*, Boyapati Veeranjaneyulu 3
IMPACT OF URBANIZATION ON GROUND WATER PHYSICOCHEMICAL PROPERTIES IN THE ALWAL AREA, HYDERABAD, TELANGANA.
Water is an indispensable natural resource that plays a vital role in sustaining life, maintaining public health, supporting economic development, and ensuring environmental sustainability. Rapid urbanization and associated anthropogenic activities have significantly altered natural landscapes, thereby affecting groundwater quality in many urban and peri-urban regions. The present study evaluates the impact of urbanization on the physico-chemical characteristics of groundwater in Alwal, a rapidly developing suburban area of Hyderabad, Telangana. The region is characterized by semi-arid climatic conditions and rocky terrain, which strongly influence groundwater occurrence and quality. Groundwater samples were collected from selected borewells located in residential, commercial, and peri-urban areas during different seasons to assess spatial and temporal variations in water quality. Physico-chemical parameters including pH, temperature, total dissolved solids (TDS), alkalinity, hardness, chlorides, nitrates, sulphates, phosphates, and fluoride were analyzed using standard analytical procedures. The results revealed considerable variations among sampling locations and seasons, with higher concentrations of several parameters recorded during the dry season, likely due to reduced dilution and increased groundwater extraction. Elevated levels of TDS, hardness, alkalinity, chlorides, nitrates, phosphates, and fluoride were observed in some samples, indicating the influence of urban activities and local geological conditions on groundwater quality
PHYSICAL ACTIVITY PATTERNS AND ANTHROPOMETRIC PROFILE AMONG WOMEN WITH POLYCYSTIC OVARY SYNDROME
Introduction: Polycystic Ovary Syndrome (PCOS) is a common endocrine disorder associated with metabolic and reproductive abnormalities. Physical inactivity is a key modifiable risk factor. Aim: To assess physical activity patterns and their association with Body Mass Index (BMI) among women with PCOS. Materials and Methods: A cross-sectional descriptive study using convenience sampling was conducted among 100 women aged 20–40 years diagnosed with PCOS. Data were collected using a structured questionnaire including anthropometric measurements and physical activity patterns. Statistical analysis was performed using the chi-square test. Results: The mean age was 26 years. Mean BMI was 29.08 kg/m² and WHR was 0.90. About 75% were physically inactive. Walking was the most common activity. No significant association between BMI and physical activity was found (χ² = 0.81, p = 0.847). Conclusion: Physical inactivity is highly prevalent among women with PCOS. Lifestyle interventions are essential.
Evaluation of the Allelopathic Potential of Aqueous Leaf Extracts of Selected Rashi Vanam Species on the Germination and Seedling Growth of Vigna radiata (Green gram).
Allelopathy plays a significant role in regulating plant–plant interactions through the release of bioactive secondary metabolites that influence germination and early seedling development. The present study investigated the allelopathic potential of aqueous leaf extracts of five selected Rashi Vanam species—Azadirachta indica, Elaeocarpus sphaericus, Ficus religiosa, Pterocarpus santalinus, and Tectona grandis—on the germination and seedling growth of Vigna radiata (L.) R. Wilczek under controlled laboratory conditions. Aqueous extracts were prepared at three concentrations (25%, 50%, and 100%), and their effects were evaluated in terms of germination percentage, root length, shoot length, and seedling vigor index (SVI). The results revealed a clear concentration-dependent response. Among the tested species, Elaeocarpus sphaericus and Azadirachta indica exhibited strong inhibitory effects, significantly reducing germination and seedling growth parameters, particularly at 100% concentration. Root growth was more severely affected than shoot growth, indicating higher sensitivity of radicle development to allelochemicals. In contrast, Ficus religiosa, Pterocarpus santalinus, and Tectona grandis showed negligible to mild stimulatory effects at lower concentrations, suggesting a hormetic response. The response index (RI) values further confirmed the inhibitory potential of Elaeocarpus sphaericus and Azadirachta indica, while other species exhibited neutral or slightly positive effects. The study demonstrates that allelopathic effects are species-specific and concentration-dependent. The strong inhibitory potential of certain Rashi Vanam species highlights their possible application in natural weed management, while also indicating the need for careful species selection and spatial planning in agroforestry systems. Further studies focusing on the isolation and characterization of allelochemicals are recommended to better understand their mechanisms of action.
AN OBSERVATIONAL STUDY ON THE EFFECTS OF BETEL NUT CHEWING ON MASTICATION MUSCLES AND THE DEVELOPMENT OF TRISMUS
Background: Betel nut (areca nut) chewing is a common habit in many Asian countries and is associated with several oral health disorders, including oral submucous fibrosis (OSMF), muscle stiffness, and trismus. Prolonged chewing may adversely affect the muscles of mastication, leading to restricted mouth opening and impaired oral function. Objective: To investigate the effects of betel nut chewing on mastication muscles and its role in the development of trismus among adult individuals. Methods: A cross-sectional observational study was conducted on 120 participants, comprising 60 chronic betel nut chewers and 60 non-chewers. Participants aged between 21 and 80 years were assessed using the Maximum Mouth Opening (MMO) test and the Vertical Three-Finger Test (Ruler Test). Data were analyzed using descriptive statistics and the Mann–Whitney U test to compare oral function between the two groups. Results: The mean age of participants was 41.49 ± 21.71 years. Betel nut chewers demonstrated significantly reduced oral function compared to non-chewers. The mean MMO was lower among chewers (2.18 cm) than nonchewers (2.93 cm). Similarly, the mean Ruler Test score was lower in chewers (30.94 mm) compared to nonchewers (45.65 mm). Mann–Whitney U analysis revealed statistically significant differences in both MMO (U = 674.000, p < 0.001) and Ruler Test scores (U = 170.000, p < 0.001) between the groups. Male participants were more likely to be habitual betel nut chewers than females. Conclusion: Chronic betel nut chewing is significantly associated with reduced mouth opening, impaired mastication muscle function, and an increased risk of trismus. Early identification of oral functional limitations and awareness programs aimed at reducing betel nut consumption may help prevent long-term oral health complications.
ZAINAB KHATOON1 , VALIYA SHEIKH 2 , ZUBIA SALMAN3 , DR. RANJIT TIWARI4
Prescription Pattern of Antibiotics in Febrile Patients Attending the General Medicine Department of a Tertiary Care Teaching Hospital in North Karnataka
Fever is one of the most common presenting complaints in outpatient and inpatient departments and frequently leads to antibiotic prescribing. Although antibiotics play a crucial role in the management of bacterial infections, irrational prescribing practices contribute significantly to antimicrobial resistance, adverse drug reactions, increased healthcare expenditure, and poor therapeutic outcomes. Prescription audit is an effective tool for evaluating prescribing behaviour and promoting rational antibiotic use. To identify commonly prescribed antibiotics among febrile patients, assess monotherapy versus combination therapy, evaluate antibiotic utilisation across different fever diagnoses, and examine adherence to standard treatment guidelines. A prospective observational prescription audit study was conducted in the General Medicine Department of a tertiary care teaching hospital in North Karnataka. A total of 500 prescriptions of febrile patients receiving antibiotics were analysed using a structured prescription audit proforma. Data regarding patient demographics, diagnosis, antibiotic prescribing pattern, route of administration, duration of therapy, monotherapy versus combination therapy, and guideline adherence were collected and analysed using descriptive and inferential statistics. Antibiotic prescribing was highly prevalent among febrile patients. Cephalosporins, penicillins, and macrolides constituted the most frequently prescribed antibiotic classes. Monotherapy was more common than combination therapy. Significant associations were observed between fever diagnosis and antibiotic selection. While a majority of prescriptions adhered to standard treatment guidelines, variations in prescribing practices were identified.
Antibiotic Prescription Pattern, Fever, Drug Utilisation Study, Prescription Audit, Antimicrobial Resistance, Rational Drug Use, General Medicine Department
The Asset Management System is a full-stack web application designed to efficiently manage and track organizational assets such as laptops, mobiles, and other equipment. The system provides a centralized platform for asset creation, updating, deletion, and monitoring, along with maintaining a detailed history of all asset-related activities. This project is developed using the MERN stack (MongoDB, Express.js, React/Next.js, Node.js) and implements secure authentication using JSON Web Tokens (JWT). It also includes Role-Based Access Control (RBAC), where different users such as Admin, Manager, and Employee have specific permissions and access levels. Admins can manage all assets and view history, managers can handle asset operations, and employees have limited access. The system supports advanced features such as searching, filtering, sorting, and pagination for efficient data handling. Additionally, an asset history tracking module records all operations performed on assets, ensuring transparency and accountability. Overall, this project enhances asset utilization, reduces manual effort, improves data accuracy, and provides a scalable solution for modern asset management needs
Healthcare Chatbot: AI-Powered Medical Information Assistant Using Retrieval-Augmented Generation
The rapid advancement of Artificial Intelligence and Natural Language Processing has transformed modern healthcare information systems. This paper presents a Healthcare Chatbot developed using Retrieval-Augmented Generation (RAG) architecture to provide accurate, reliable, and context-aware healthcare responses. The proposed system retrieves medical information from trusted healthcare documents using semantic vector search before generating responses through a Large Language Model. Hugging Face embedding models are used to generate semantic vector embeddings, while Pinecone vector database stores and retrieves contextual healthcare information efficiently. LangChain orchestrates retrieval and prompt generation workflows, and the Groq API provides access to the Llama 3.3 large language model for high-speed response generation. The chatbot is implemented using Flask and deployed using Docker and AWS EC2 cloud infrastructure. Experimental results confirm improved factual accuracy, reduced hallucination, semantic understanding, and fast response generation compared to traditional generative AI systems. The proposed Healthcare Chatbot demonstrates how modern AI technologies can improve healthcare information accessibility for ordinary users through conversational interfaces.
Ensuring women's safety in public and private spaces remains a pressing social and technological challenge. Although numerous mobile applications offer emergency calling and location sharing, most of them operate only after the user manually activates an alert and do not provide any intelligence to assess the surrounding risk. This paper presents the Women Safety Tracker App, an AI-enabled safety platform that combines real-time GPS tracking, machine learning-based risk prediction, and automated emergency communication. The proposed system evaluates multiple contextual parameters, including road condition, traffic density, crowd level, street-light availability, time of day, and historical crime statistics, to estimate the likelihood of danger. Based on this analysis, the system classifies the environment into Safe, Moderate Risk, or High Risk. When a high-risk situation is detected, the application automatically sends a WhatsAppbased SOS alert containing the user's live location to pre-registered emergency contacts. The application is developed using HTML, CSS, and JavaScript for the user interface, Python and Flask for backend services, SQLite for data management, and Twilio API for alert delivery. Experimental testing confirms that the system provides timely alerts, accurate risk classification, and reliable real-time location sharing. The Women Safety Tracker App transforms conventional emergency tools into an intelligent and proactive personal safety system capable of delivering rapid assistance and improving situational awareness
Electric Vehicle Adoption Analysis And Market Trend in India
India stands at a pivotal moment in its transportation history. The rapid proliferation of Electric Vehicles (EVs) across the country signals a fundamental shift from fossil fuel dependency toward a cleaner and more sustainable mobility ecosystem. This project presents a comprehensive data-driven analysis of Electric Vehicle adoption trends across India, leveraging authentic registration data sourced from the Government of India's Vahan Seva Portal. The study employs SQL for structured data querying, transformation, and aggregation, and Microsoft Power BI for interactive dashboard development and visual analytics. The analysis covers the period from 2021 to 2025, encompassing over 70 lakh registered EVs, 28,089 public charging stations, and an annual energy demand of 1.47 crore MWH. Key findings reveal that two-wheelers dominate the Indian EV market with a 57.63% share, Maharashtra leads in total EV volume, and the country witnessed an extraordinary Year-over-Year growth of 210.5% in 2022. The report also identifies critical infrastructure gaps, particularly the 'Range Anxiety' problem, where states like Assam report 1,609 EVs per charging station. The analysis is presented across four Power BI dashboard pages: Overview Dashboard, Deep Analysis, Fuel vs EV Analysis, and a region-specific RTO-level analysis for Odisha. This project demonstrates the power of modern data analytics tools in deriving actionable policy insights from large governmental datasets and contributes to the academic discourse on India's EV transition strategy.
EduConnect: A Scalable E-Learning Platform for Online Education Management
The E-Learning Platform is a secure, scalable, and intelligent web-based learning application designed to empower students and educators by providing seamless access to educational resources while maintaining complete control over user data and platform security. In an era where digital education is rapidly expanding, the platform ensures a reliable and user-friendly environment for online learning, communication, and academic progress tracking. The application provides essential features such as user registration, login, profile management, course enrollment, course creation, assignment submission, quiz participation, and progress monitoring, along with an interactive and responsive user interface for an enhanced learning experience. What differentiates this platform from traditional learning systems is its real-time and efficient academic management capabilities powered by a modern MERN stack architecture. Students can access course materials, track their learning progress, and interact with instructors, while educators can manage courses, upload content, and evaluate student performance through a centralized dashboard. Security and reliability form the core pillars of the system, implemented through JWT-based authentication, BCrypt password hashing, HTTPS encryption, secure RESTful APIs, and database-level protection mechanisms. The architecture follows a modular full-stack web development approach, combining React.js for frontend development, Node.js and Express.js for backend services, and MongoDB for data storage. Tailwind CSS is used for creating a responsive and modern user interface, ensuring accessibility across multiple devices. The system is implemented as a full-stack web application enabling efficient deployment, scalability, and smooth educational content management. Keywords— E-Learning Platform;
Now a days Digital transactions are rapidly increasing as it results in increasing online payment frauds too. In fact, according to the Reserve Bank of India, comparing March 2022 to March 2019, digital payments have risen in volume and value by 216% and 10%, respectively. People are starting to go all-in with digital transactions, but one can’t deny the security issues that loom, and know-how when it comes to online payments. Few years ago, we could have barely seen the online payment, but today UPI payment QR code installed at doorstep. This invited the hoaxers and attackers to develop fraudulent transactions and fool people for some amount of money. Fortunately, the online transactions are monitored and hence could be analysed using the latest tools. In this system, an attempt is made to develop a machine learning model to identify fraudulent transactions in a transaction’s dataset. This study considers the task of applying artificial intelligence to recognize bank fraud. In recent years, due to the COVID-19 pandemic, bank fraud has become even more common due to the massive transition of many operations to online platforms and the creation of many charitable funds that criminals can use to deceive users. The study’s scientific novelty is the development of machine learning models for identifying fraudulent banking transactions and techniques for preprocessing bank data for further comparison and selection of the best results. This paper also details various methods for improving detection accuracy, i.e., handling highly imbalanced datasets, feature transformation, and feature engineering. The recognition of banking fraud using artificial intelligence algorithms is a topical issue in our digital society.
Bhubaneswar Market Plex: A Hyperlocal Digital Marketplace Platform
Bhubaneswar Market Plex is a full-stack, hyperlocal digital marketplace platform specifically designed to serve the commerce needs of the residents of Bhubaneswar, Odisha. The platform addresses a significant gap in the digital commerce ecosystem of Tier-2 Indian cities: the absence of a single unified application that integrates three distinct commerce modes — a second-hand goods marketplace, a quick commerce grocery delivery service, and a fullfeatured e-commerce catalog — in a locally relevant context. The rapid growth of India's digital economy has created strong demand for hyperlocal commerce platforms, yet large national platforms like Amazon, Flipkart, OLX, and Blinkit fail to reflect the specific geography, trade culture, and neighbourhood trust networks of individual cities like Bhubaneswar. This project is conceived to digitise and streamline all three commerce modes in one application, rooted in the geography and economy of Bhubaneswar with real locality names, local store profiles, and a trust-first marketplace design. The platform supports three user roles: Buyer, Seller, and Administrator, each with role-appropriate access enforced via JSON Web Tokens (JWT) and bcrypt-hashed passwords. The Marketplace module provides OLX-style classified listings with condition grading (New, Like New, Good, Fair, Poor), price negotiation flags, and an administrator moderation queue that ensures all listings are reviewed before going live. The Quick Commerce module connects buyers with local Bhubaneswar grocery and essentials stores for fast delivery. The E-Commerce module provides a full product catalog with cart, wishlist, order management, star-rated reviews, and a flash sale system with countdown timers. The platform is built using a modern, production-grade technology stack: React 18 with TypeScript for the frontend, Express 5 on Node.js 24 for the backend, PostgreSQL as the relational database accessed through Drizzle ORM, and TanStack React Query for server state management. The project is structured as a pnpm monorepo with seven shared workspace packages. An OpenAPI 3.0 specification serves as the authoritative API contract from which both React Query hooks and Zod validation schemas are auto-generated using Orval, ensuring end-to-end type safety across the entire application. The database comprises ten relational tables supporting all commerce modules, user roles, order management, cart, wishlist, reviews, and promotional banners. The backend exposes fourteen API route groups with over sixty endpoints, and the frontend implements twenty-one screens including a comprehensive Admin Panel. The application demonstrates sound software engineering practices including monorepo architecture, contract-first API design, end-to-end TypeScript type safety, role-based access control, rate-limited authentication, and responsive UI design with Framer Motion animations. Testing of the platform confirms that all security scenarios, functional requirements, and responsive design breakpoints are met. The system successfully processes user registration, authentication, product browsing, cart management, order placement and lifecycle tracking, marketplace listing with admin moderation, and full administrator CRUD operations. The platform represents a complete, locally relevant digital commerce solution for Bhubaneswar and a strong demonstration of modern full-stack web development practices at the undergraduate level.
AI-Powered Regulatory Compliance Checker for Contracts
The rapid growth of digital contracts across industries such as healthcare, finance, information technology, and government sectors has increased the complexity of regulatory compliance management. Traditional manual compliance verification methods are time-consuming, expensive, and prone to human errors. This research paper presents an AIpowered Regulatory Compliance Checker for Contracts that leverages Natural Language Processing (NLP), Machine Learning (ML), and Large Language Models (LLMs) to automate the analysis of legal agreements and identify compliance risks. The proposed system extracts contractual clauses, maps them with regulatory frameworks such as GDPR, HIPAA, PCI-DSS, and organizational policies, and detects missing or non-compliant clauses. The architecture integrates document ingestion, clause classification, semantic analysis, retrieval-augmented generation (RAG), and risk scoring mechanisms. Experimental evaluation demonstrates improved efficiency, accuracy, scalability, and consistency in contract analysis compared to traditional manual review systems. The research highlights the potential of AI-driven legal technology to transform regulatory compliance management while reducing operational costs and legal risks.
AI Resume Analyzer: A Survey and Implementation Report on Intelligent Resume Evaluation Using Large Language Models
AI Resume Analyzer is an intelligent web-based platform designed to automate and enhance the process of resume evaluation for both job seekers and recruiters. The platform leverages modern artificial intelligence technologies, particularly Large Language Models (LLMs), to parse, analyze, and score resumes against job descriptions in real time. Built using a scalable full-stack architecture, the system integrates powerful AI components including natural language processing, semantic similarity matching, and structured information extraction into a unified resume analysis pipeline. This architecture allows the system to accurately interpret resume content, identify skill gaps, match candidate profiles to job requirements, and deliver actionable feedback to users. By combining these technologies with a modular and responsive interface, AI Resume Analyzer enables flexible deployment, high accuracy, and efficient performance for individuals and organizations seeking automated talent screening and career guidance solutions. A key capability of the platform is its intelligent scoring and feedback system, which evaluates resumes across multiple dimensions including skills match, experience relevance, educational qualifications, and keyword optimization. The system is optimized to deliver near-instant analysis results, ensuring a smooth and responsive user experience. The platform integrates seamlessly with modern web technologies and supports PDF and DOCX resume uploads, enabling users to analyze their documents directly through a browser-based interface without requiring any installation. The system also supports job description input, allowing recruiters to screen multiple candidates efficiently. To simplify the resume improvement process, the platform provides detailed section-wise feedback that guides users on how to strengthen their resumes. Furthermore, AI Resume Analyzer incorporates a Retrieval-Augmented Generation (RAG) approach that enables context-aware suggestions by drawing from curated career guidance knowledge bases. The project report presents the overall system architecture, component design, data flow mechanisms, database schema, infrastructure technologies, and performance benchmarks, demonstrating that production-grade AI resume analysis capabilities can be achieved within an open, extensible, and scalable platform suitable for modern career and recruitment applications.
AI-Based Fashion Recommendation & Virtual Try-On System
The AI-Based Fashion Recommendation and Virtual Try-On System is an intelligent web application designed to revolutionize the online fashion shopping experience. The system integrates artificial intelligence, image processing, and machine learning techniques to provide personalized outfit recommendations and a virtual try-on facility. Users can upload their photographs and virtually try on clothing items without physically wearing them. The system incorporates background removal using the rembg library, outfit generation, wardrobe management, and a recommendation engine powered by AI algorithms. The backend is developed using Flask (Python), while the frontend utilizes HTML, CSS, and JavaScript. Image processing is handled through PIL and rembg libraries. The system eliminates the limitations of traditional online shopping by providing a personalized, interactive, and intelligent fashion experience. Overall, the proposed system enhances user engagement, reduces return rates, and promotes confident purchase decisions
AI-Based Interview and Resume Analyzer System
The AI-Based Smart Interview and Resume Analyzer is an intelligent hiring platform developed to improve and automate the recruitment process using Artificial Intelligence technologies. Traditional recruitment methods require manual resume screening and interview evaluation, which consume significant time and effort for recruiters. The proposed system integrates AI-powered ATS resume analysis, mock interview assessment, job discovery, and social profile evaluation into a centralized web-based platform. The system allows candidates to upload resumes for automated analysis, participate in AI-driven mock interviews using speech and camera features, and receive performance feedback instantly. Recruiters can manage applications and monitor candidate performance through a centralized dashboard. The platform is developed using React.js, TypeScript, Tailwind CSS, Firebase, Firestore, and Google Gemini AI. Overall, the system improves hiring efficiency, reduces manual workload, enhances candidate evaluation accuracy, and provides a smart solution for modern recruitment management.
An Advanced AI Chatbot with Tiered Memory, Retrieval-Augmented Generation, and Model Context Protocol Integration
Modern conversational AI systems face three fundamental limitations: inability to recall prior interactions, no access to private domain knowledge, and no deterministic connection to external services. This paper presents the design and implementation of an advanced AI chatbot that overcomes all three limitations simultaneously. The system introduces: (i) a six-tier hierarchical memory model with exponential decay scoring and dual-write persistence to PostgreSQL and a pgvector semantic index; (ii) a four-stage Retrieval-Augmented Generation (RAG) pipeline combining query rewriting, parent-child chunking, BM25-pgvector hybrid search with Reciprocal Rank Fusion, and crossencoder re-ranking; and (iii) a Model Context Protocol (MCP) integration layer enabling dynamic tool binding via stdio, SSE, and streamable HTTP transports. The system is orchestrated by a LangGraph directed state-graph agent supporting task complexity classification, LLMgenerated multi-step planning, Human-in-the-Loop (HITL) safety interruption, and automatic conversation summarisation. Deployed on a FastAPI, PostgreSQL, and React stack with Docker Compose, the system achieves sub-400 ms median response latency for simple queries and demonstrates coherent, context-aware, multi-step task execution in production conditions. Comparative evaluation shows the tiered memory recall accuracy reaches 91.4% on a held-out biographical quiz benchmark, and the four-stage RAG pipeline outperforms naive single-query vector search by 23 percentage points of mean reciprocal rank. The HITL safety layer blocks 100% of destructive tool invocations in adversarial testing without a single false negative.
Devmeup: unified AI platform for productivity, content creation and career acceleration
In today’s fast-evolving digital era, developers, students, and content creators face challenges in managing multiple tools for productivity, content creation, and career growth. To address these challenges, this project introduces Devmeup, an allin- one AI-powered platform that integrates diverse functionalities into a single, user-friendly system. Devmeup provides a comprehensive suite of tools including AI-based resume analysis with ATS (Applicant Tracking System) score evaluation, enabling users to assess and improve their resumes based on industry standards. The platform also offers content generation features such as YouTube description and tag generation, article rewriting, product description creation, and grammar correction, helping users produce high-quality, optimized content efficiently. In addition, the system includes advanced AI-driven features like mock interview simulation, which prepares users for real-world interview scenarios, chat with PDF, allowing intelligent interaction with uploaded documents, and YouTube-to-notes conversion, which generates structured notes, quizzes, and summaries from video content. These features collectively enhance both learning and productivity.
AMLA: Adaptive Meta-Learning Architecture for Automated Dataset Characterization, Predictive Algorithm Selection, and Feature Augmentation Advising
Algorithm selection remains a critical and persistent bottleneck in applied machine learning: practitioners routinely resort to exhaustive trial-and-error experimentation or subjective domain intuition—approaches that are computationally expensive, methodologically inconsistent, and inaccessible to nonspecialist users. This paper presents the Adaptive Meta-Learning Architecture (AMLA), a unified, domain-agnostic framework that automates algorithm recommendation for structured tabular datasets. AMLA integrates three tightly coupled modules: (i) a Dataset Characterization Engine that extracts a multi-layered, 60-dimensional numerical fingerprint—termed Dataset DNA— encoding statistical, structural, information-theoretic, landmarking, and complexity features; (ii) a Predictive Algorithm Selector, a trained meta-learner that maps Dataset DNA vectors to ranked algorithm recommendations supported by SHAP-based explanations; and (iii) a Feature Augmentation Advisor that diagnoses structural weaknesses within a dataset and prescribes targeted transformations. Unlike existing AutoML systems that operate as opaque black boxes relying on brute-force pipeline search, AMLA delivers interpretable, evidence-backed recommendations through a self-improving Meta-Knowledge Base seeded from OpenML community experiments, augmented by a local validation pipeline. Evaluated across 50 benchmark classification datasets, AMLA achieves a meta-learner Precision@1 of 72%, a 48-percentage-point improvement over random baseline selection and a 21-percentage-point improvement over the mostfrequent- algorithm heuristic (both significant at p < 0.001, Wilcoxon signed-rank test). The system is deployed as a fullstack interactive web application built with Python, scikit-learn, XGBoost, FastAPI, and React. AMLA makes six original contributions to the meta-learning literature, including the Dataset DNA fingerprinting scheme, predictive feature gap analysis, and a closed-loop self-improvement mechanism—capabilities absent from existing open-source tooling.
Design and Implementation of an Uber Clone: Real-Time Ride Hailing Platform for Smart Transportation Operations
Modern urban transportation systems increasingly rely on on-demand, real-time ride-hailing services. Developing a scalable, low-latency infrastructure capable of handling simultaneous multi-entity state synchronization— specifically matching dynamic passengers with moving drivers— presents a complex distributed computing challenge. This paper presents the design and implementation of a high-concurrency, real-time ride-hailing platform utilizing a decoupled architectural framework. The system implements a dynamic cross-platform client architecture using React Native, a stateless asynchronous event-driven backend leveraging Node.js and Express.js, and persistent bi-directional communications facilitated via Socket.io. Data persistence and fast object retrieval are optimized through a hybrid storage layer consisting of MongoDB and Firebase. The system introduces an optimized geographical state-machine logic to seamlessly handle driver dispatching, dynamic fare calculation, and concurrent request mediation. Experimental evaluations indicate that the system sustains low packet latency and reliable transactional integrity under simulated high-load scenarios
DeepPulmoAI: Hybrid Neural Network Selection for Automated Pulmonary Disease Classification
Pulmonary diseases such as pneumonia, tuberculosis, lung cancer, and chronic obstructive pulmonary disease (COPD) represent major global health challenges and require accurate early diagnosis for effective treatment. Medical imaging techniques including chest X-rays and computed tomography (CT) scans are widely used for pulmonary disease detection. However, manual interpretation of pulmonary images is time-consuming and highly dependent on radiological expertise. To address these limitations, this research proposes PulmoNet-X, an intelligent ensemble deep learning framework for pulmonary image classification using appropriate neural network selection and ensemble learning techniques. The proposed system integrates multiple deep learning architectures, including Convolutional Neural Networks (CNN), DenseNet, ResNet, and EfficientNet models, to automatically extract discriminative features from pulmonary images. An adaptive neural network selection mechanism identifies the most suitable model based on image characteristics and classification performance. Ensemble learning strategies are further employed to combine the outputs of multiple neural networks, thereby improving classification accuracy, robustness, and generalization capability. The framework incorporates preprocessing techniques such as image normalization, noise reduction, augmentation, and segmentation to enhance image quality and improve feature extraction efficiency. Explainable Artificial Intelligence (XAI) methods such as Grad-CAM and SHAP are integrated to visualize affected pulmonary regions and provide interpretable diagnostic insights for healthcare professionals. Experimental evaluation is performed using benchmark pulmonary imaging datasets, and the proposed model is compared with existing machine learning and deep learning approaches. Results demonstrate superior classification accuracy, precision, recall, F1-score, and reduced false detection rates. The proposed PulmoNet-X system provides a reliable, scalable, and explainable solution for automated pulmonary disease diagnosis and supports clinicians in making accurate and timely medical decisions.
Pulmonary Image Classification, Ensemble Learning, Deep Learning, Convolutional Neural Networks, ResNet, DenseNet, EfficientNet, Medical Image Analysis, Explainable AI, Grad-CAM, Lung Disease Detection, Chest X-ray Classification, PulmoNet-X, Healthcare Analytics.
1S.Nusrat Yasmeen, 2Dr. D. William Albert
PredictHeart-X: A Novel Explainable Deep Learning Framework for Cardiovascular Disease Prediction
Cardiovascular disease (CVD) remains one of the leading causes of mortality worldwide, necessitating the development of accurate and intelligent predictive systems for early diagnosis and prevention. This research proposes PredictHeart-X, a novel explainable deep learning framework designed for effective cardiovascular disease prediction using clinical and patient health parameters. The proposed model integrates advanced deep learning techniques with explainable artificial intelligence (XAI) mechanisms to enhance prediction accuracy while maintaining interpretability for healthcare professionals. The framework employs a hybrid architecture combining Deep Neural Networks (DNN) and Attention-based learning mechanisms to analyze complex relationships among medical attributes such as age, blood pressure, cholesterol levels, heart rate, electrocardiogram results, and other cardiovascular indicators. Data preprocessing techniques including normalization, missing value handling, and feature optimization are applied to improve model performance and reliability. To address the black-box nature of deep learning, explainability methods such as SHAP and attention visualization are incorporated to identify the most influential clinical features contributing to disease prediction. Experimental evaluation is conducted using benchmark cardiovascular datasets, and the proposed PredictHeart-X model is compared with traditional machine learning algorithms and existing deep learning approaches. The results demonstrate superior prediction accuracy, precision, recall, F1-score, and reduced false prediction rates. The explainable component further assists clinicians in understanding the decision-making process of the model, thereby increasing trust and usability in real-world healthcare applications. The proposed system offers an efficient, scalable, and interpretable solution for early cardiovascular disease detection, supporting healthcare practitioners in making timely and informed medical decisions while improving patient outcomes.
Cardiovascular Disease Prediction, Deep Learning, Explainable Artificial Intelligence (XAI), PredictHeart-X, Hybrid Neural Networks, Attention Mechanism, SHAP Analysis, Healthcare Analytics, Disease Diagnosis, Clinical Decision Support System, Artificial Intelligence in Healthcare, Heart Disease Detection.
EFFICIENT ONLINE HIRING SCAM DETECTION USING AIPOWERED DEEP LEARNING MODELS
The rapid growth of online recruitment platforms and digital hiring services has significantly increased the risk of recruitment fraud and job scams. Fraudulent job postings, fake employer profiles, and phishing-based recruitment activities have caused financial loss, identity theft, and security threats for job seekers worldwide. Traditional fraud detection techniques often fail to identify sophisticated and dynamically changing scam patterns, creating the need for intelligent and automated detection systems. This paper presents an efficient online hiring scam detection framework using AI-powered deep learning models to identify fraudulent recruitment activities with high accuracy and reliability. The proposed system utilizes advanced natural language processing and deep learning techniques to analyze job descriptions, employer information, communication patterns, and recruitment behavior. Multiple deep learning architectures, including convolutional neural networks and recurrent neural networks, are integrated to extract semantic and contextual features from recruitment data. Data preprocessing and feature engineering techniques are employed to improve classification performance and reduce false positives. The framework is trained using labeled datasets containing legitimate and fraudulent job postings to enhance model generalization and robustness. Experimental results demonstrate that the proposed AI-powered framework achieves superior detection accuracy, faster processing speed, and improved scalability compared to traditional machine learning approaches. The system effectively identifies fake job advertisements, phishing attempts, and suspicious recruitment activities in real time. Overall, the proposed model provides a reliable, scalable, and intelligent solution for enhancing trust and security in online recruitment platforms.
Online Recruitment Fraud, Hiring Scam Detection, Deep Learning, Artificial Intelligence, Fake Job Detection, Cybersecurity, Natural Language Processing, Fraud Classification, Recruitment Security, Machine Learning
RECENT ADVANCES IN THE BIOLOGICAL ACTIVITIES OF BENZOTHIAZOLE DERIVATIVES: A REVIEW OF ANTIMICROBIAL AND ANTI-DIABETIC ACTIVITIES
The present paper provides a comprehensive overview of the therapeutic potential of benzothiazole-based compounds, with particular emphasis on their antimicrobial and antidiabetic activities. Various studies have demonstrated that structural modifications in the benzothiazole nucleus significantly influence their biological performance. The presence of different substituents and functional groups plays a key role in enhancing activity against a variety of microbial pathogens as well as in regulating glucose metabolism.
Benzothiazole derivatives; Biological activity; Antimicrobial activity; Antidiabetic activity
COMPARISON OF THE SURFACE ROUGHNESS OF REFRACTORYCAST INVESTMENT MATERIAL BEFORE AND AFTER TREATMENTWITH WAX, HARDENING AGENTS- AN IN-VITRO STUDY
Background: Achieving a smooth surface on refractory casts is essential for the accuracy and longevity of dental prostheses. Phosphate-bonded investment materials, although thermally stable, often exhibit surface roughness during wax pattern fabrication and processing. Aim: To evaluate and compare the surface roughness of refractory cast investment material before and after treatment with hardening agents and subsequent heat treatment. Materials and Methods: Twenty rectangular specimens were fabricated using commercially available phosphatebonded investment material. Initial surface roughness was measured using a contact profilometer. Specimens were randomly divided into two groups (n=10): Group I treated with model hardener and Group II treated with beeswax. Surface roughness was re-evaluated after treatment. All specimens were then subjected to heat treatment at 875°C for 60 minutes, followed by gradual cooling, and final measurements were recorded. Statistical analysis was performed using Student’s t-test. Results: A statistically significant reduction in surface roughness was observed in both groups after hardening and heat treatment (p < 0.05).
CHINDAM HARITHA1 , MAHENDRANADH REDDY2 , MAHADEV SHASTRY3 , VENKAT ADITYA4
Copyright (c) 2026 CHINDAM HARITHA1 , MAHENDRANADH REDDY2 , MAHADEV SHASTRY3 , VENKAT ADITYA4 (Author) https://creativecommons.org/licenses/by-nc-nd/4.0