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
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
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
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
CAUSES, CONSEQUENCES AND CONTROLLING MEASURES OF DOWRY DEATHS IN INDIA
The institution of marriage is an essential building block of societal structure, acting as a catalyst for joyous celebrations and fresh beginnings. Nonetheless, a persistent problem related to marriage, especially from the viewpoint of women in Indian society, is the dowry system. Despite extensive criticism and opposition, the custom remains prevalent manifesting in subtle as well as in overt ways. We still see a heightened and intensified manifestation of the evils of this system due to the problematic approach of the general public. The majority of the population still considers the practice of demanding and expecting bride prices to be acceptable and even justified. This study aimed to grasp the interconnected socio-demographic factors and suggest potential measures for intervention.
Dowry Deaths, Status, Causes, Consequences, Controlling Measures in India
Copyright (c) 2025 Dr. B. Mukunda Naidu (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
DYNAMIC ANALYSIS OF MALWARE USING ARTIFICIAL NEURAL NETWORKS WITH MACHINE LEARNING
Sophisticated malware strains—including ransomware, polymorphic viruses, and advanced persistent threats— continue to outpace conventional signature-driven defenses, demanding a fundamentally more adaptive detection paradigm. This paper introduces a multi-layered malware classification framework that couples dynamic behavioral analysis with an Artificial Neural Network (ANN) classifier and two corroborating detection channels: YARA rulebased pattern matching and crowd-sourced threat intelligence obtained through the VirusTotal API. During controlled sandbox execution, a 28-dimensional feature vector is assembled from file entropy, Win32 API call distributions, portable executable (PE) header attributes, and network-activity indicators. The ANN—a three-layer feed-forward network trained with binary cross-entropy loss and Adam optimization—produces a probabilistic malice score that is subsequently fused with normalized YARA and VirusTotal signals under a weighted risk-scoring formula (ANN 40%, YARA 30%, VirusTotal 30%). Evaluated on a balanced corpus of 12,000 PE executables, the unified system achieves 96.4% detection accuracy, 96.9% precision, and 95.8% recall, surpassing standalone ANN, Random Forest, SVM, and signature baselines by margins of 2.6–18.1 percentage points. End-to-end sample latency averages 4.2 seconds, confirming near-real-time viability. The system is deployed as a Flask web application exposing file-upload, featureentry, and hash-lookup analysis modes, providing analysts with interpretable, actionable verdicts across diverse operational contexts.
D. Abhisheik, G. Hema Sri Latha, L. Samuel
ENHANCING PRECISION AGRICULTURE THROUGH MACHINE LEARNINGBASED GPR SOIL MOISTURE ANALYSIS
Precision agriculture plays a crucial role in improving crop productivity and resource management in large-scale farming. Accurate assessment of soil moisture, particularly in the root zone, is essential for efficient irrigation planning and sustainable agricultural practices. Traditional soil moisture monitoring methods often rely on manual sampling or basic sensor-based techniques, which may provide limited spatial coverage and require significant labor and time. This study proposes an advanced approach that integrates Ground Penetrating Radar (GPR) with machine learning techniques to enhance soil moisture analysis in precision agriculture. GPR technology enables nondestructive and large-scale subsurface soil analysis by capturing electromagnetic signal reflections from different soil layers. However, interpreting GPR signals can be challenging due to noise, soil heterogeneity, and environmental factors. To address these challenges, machine learning algorithms are employed to process and analyze GPR signal data, enabling accurate estimation of root zone soil moisture levels. The proposed system includes data preprocessing, feature extraction from GPR signals, and predictive modeling using machine learning methods such as Random Forest, Support Vector Machine, or Neural Networks. These models learn complex relationships between GPR signal characteristics and soil moisture conditions.The integration of machine learning with GPR technology improves the accuracy, efficiency, and scalability of soil moisture assessment in large agricultural fields. The proposed approach supports data-driven irrigation management, optimized water usage, and improved crop health monitoring, contributing to sustainable and intelligent precision agriculture systems.
N. RADHAMMA, GANDLA NIKHITHA, BENDRAM SURENDHAR, DORAPALLY CHARANTEJA, GANJI MANOJ
ADVANCED SCHIZOPHRENIA DIAGNOSIS USING EEG SIGNAL PROCESSING AND DEEP LEARNING TECHNIQUES
Schizophrenia is a chronic neuropsychiatric disorder affecting perception, cognition, and behavior. Globally it impacts about 1% of the population. In India, nearly 4–5 million people are estimated to live with schizophrenia, with many cases undiagnosed due to stigma and limited specialists. Traditionally, diagnosis relies on clinical interviews and behavioral assessment. The objective is to develop an automated system that diagnoses schizophrenia using EEG signals transformed into Markov Transition Field images and classified through deep learning models for accurate, objective decision support. In the manual system, psychiatrists diagnose schizophrenia through patient interviews, behavioral observation, medical history review, and standardized questionnaires such as DSM-based clinical assessments, often supported by basic EEG visual inspection. Manual diagnosis is subjective, time-consuming, and highly dependent on clinician experience. EEG interpretation lacks quantitative analysis, leading to inter-observer variability, delayed diagnosis, misclassification, and difficulty in identifying subtle neurological patterns. The motivation of this research is to overcome subjectivity and inconsistency in manual diagnosis by introducing automated EEG feature extraction and learning-based classification. By capturing hidden temporal patterns using Markov Transition Fields and deep models, the system improves reliability, scalability, and early detection accuracy. The proposed system converts EEG signals into Markov Transition Field images that preserve temporal dynamics of brain activity. Deep learning models such as VGG16 and Vision Transformer (ViT) extract discriminative spatial–temporal features from these images. Multiple classifiers VGG16-MTF, VGG16-NC, VGG16-KNN-RNC, and ViT-MTF Perceptron are employed to evaluate and enhance diagnostic performance. Ensemble and transformer-based learning reduce noise sensitivity and improve generalization. This machine learning–driven framework enables accurate, objective, and scalable schizophrenia diagnosis, supporting clinicians with reliable EEG-based decision assistance
POTHINENI VENKATESWARA RAO, ERRIC ADAMS POSANPALLY, DIDDI VISHAL, GADDAM SOUJANYA, BEDDALA NAVEEN
A HYBRID APPROACH FOR EV CHARGING LOAD FORECASTING USING SIGNAL NOISE REDUCTION AND TIME SERIES ANALYSIS
Vehicular Ad-Hoc Networks (VANETs) are a key component of intelligent transportation systems, enabling vehicles to communicate for safer and efficient traffic management. In India, rapid urbanization and increasing vehicle density have led to congestion and suboptimal route selection. According to recent reports, Indian cities witness over 30% higher average travel times due to traffic congestion. VANETs can optimize vehicle routing, improve network communication, and enhance road safety by analyzing traffic density, vehicle speed, and network conditions. The objective of this research is to predict optimal vehicle routes and traffic load patterns using machine learning. It aims to automate decision-making in vehicular networks for enhanced routing efficiency and network reliability. Traditionally, traffic and route management rely on manual monitoring using traffic signals, CCTV cameras, and periodic reports from traffic personnel. Vehicles follow pre-defined maps or human instructions to select routes, and charging stations depend on manual scheduling for load management. Manual systems are time-consuming, error-prone, and unable to adapt to real-time traffic fluctuations. They cannot predict congestion, optimize vehicle spacing, or balance charging loads dynamically. Human intervention limits scalability and often results in inefficient route selection and increased travel time. This research aims to overcome the limitations of manual systems by leveraging machine learning for real-time route and load optimization. Improvements include predictive accuracy, automated decisionmaking, adaptive traffic management, and efficient load distribution in vehicular networks, enabling scalable and reliable traffic solutions. The proposed system uses machine learning models for classification and regression. GP Classifier predicts whether a route or charging condition is optimal using Gaussian Process-based probabilistic modeling. KNN Classifier classifies load or route patterns based on similarities to historical data. FusionMind Classifier, a hybrid MLP+Random Forest model, captures complex patterns for accurate classification. For regression, SGD, KNN, and FusionMind Regressors forecast numerical values like vehicle spacing or charging load. These models enable predictive, adaptive, and automated traffic and charging management, reducing congestion and improving system reliability.
D. SUNITHA, ABHIRAM BALNE, CHINTHA MADHU, ERAI DEEPA, GADDIGOPULA MEGHANA
A STUDY ONRECRUITMENT AND SELECTION PROCESS AT BHARATHI CEMENTS PVT.LTD., KADAPA, A.P
Recruitment and selection is a significant role played by Human Resource Management towards assisting organizations to obtain qualified employees. This paper will focus on the recruitment and selection procedure that is used in Bharathi Cements Pvt. Ltd, Kadapa. The aims of the research are to examine the recruitment sources, learn the selection process, research the HR department, and the difficulties in the hiring process. The research will be founded on primary data gathered on 100 employees by means of a structured questionnaire. Data obtained was analyzed by the use of percentage analysis. According to the findings, the greatest source of recruitment is the employee referrals, the selection process is transparent and the organization employs various selection methods. The research recommends the enhancement of campus recruitment, shortening of approval process and use of digital recruiting tools in order to enhance efficiency of hiring.
Recruitment, Selection, Human Resource Management, Hiring Process, Employee Recruitment
A PROJECT REPORT ON EMPLOYEE ENGAGEMENT IN SRINIDHI MILK DIARY
Employee engagement plays a critical role in enhancing productivity, sustainability, and organizational performance within the plantation sector. Plantation operations, characterized by labor-intensive activities such as ploughing, planting, harvesting, and maintenance, depend heavily on workforce motivation and commitment. Engaged employees demonstrate higher levels of job satisfaction, reduced absenteeism, improved safety compliance, and increased output per acre. Factors influencing engagement in plantation settings include fair compensation, safe working conditions, access to skill development programs, recognition systems, and community welfare initiatives. Additionally, participatory management practices and effective communication strengthen trust between workers and management, leading to improved retention rates and operational efficiency. This study highlights the importance of strategic human resource practices tailored to agricultural environments to foster long-term sustainability and competitive advantage in plantation enterprises.
Employee Engagement, Plantation Sector, Agricultural Workforce, Labor Productivity, Job Satisfaction, Workforce Retention, Human Resource Management, Organizational Performance
SOME TRADITIONAL MEDICINAL PLANTS USED BY TRIBES IN TELANGANA STATE.
Traditional Medicinal plants Used by Tribes In Telangana state, which is mainly occupied by tribes revealed that some medicinally important plants were used by herbal healers (ages between 54 - 80 years). In the present study, scientific name, family, vernacular name, habit and medicinal uses have been identified. Traditional Medicine studies were conducted particularly and dense forest where the tribal community inhabits. The present study of the area belonging to the tribes and we were visited, identified and gathered information on different plants used as medicine was recorded by direct observation, casual interview and taken photographs. In the present study, a number of plants which are used by tribes of Telangana State, India for treatment of skin diseases have been documented. The present study is based on the concept of “a plant for cure of a common disease”. For example a single plant was selected for common skin diseases like wounds, ring worm, itching, warts, eczema, vitilago, chicken pocks, measles and etc.. Among these plants, some species like Azadirachta indica, Curcuma langa, Annona squamosa, Plumbago zeylanica, Citrus lemon, Terminalia chebula, Phyllanthus emblica, Hybiscus rosasinenesis, Tridax procumbens, Lucas aspera, Ricinus communis and Mangefera indica were collected from the forest for treatment. From the present analysis plant parts are used roots, stem, bark, leaves, seeds and flowers. The present study concludes that, the used the pharmacognostic values of these plants. Such proven plant species may be used in the formulation of new drugs against different ailments. Hence, there is great need of cultivation and conservation of such plants. Keywords: Folklore Medicine, plants, trees.
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
A STUDY ON JOB SATISFACTION DONE AT ZUARI CEMENT, YERRAGUNTLA
Job satisfaction plays an important role in improving employee performance, motivation, and overall organizational success, especially in labor-intensive industries like cement manufacturing. This study examines the level of job satisfaction among employees at Zuari Cement Pvt Ltd, focusing on its operational units. It analyzes key factors such as working conditions, pay and benefits, workload, promotion opportunities, supervision, job security, and welfare facilities. Using a descriptive research design, primary data were collected through questionnaires, along with secondary data from company records and journals. The findings show moderate satisfaction overall, with improvements needed in career growth and workload management
Job Satisfaction, Employee Performance, Working Conditions, Welfare facilities, cement industry.