International Journal of Food Safety and Public Health
type: channel
description: International Journal of Food Safety and Public Health (AJB), ISSN 2756-3693 is a Open Access, Fully Refereed and Peer Reviewed International Journal. Notably, it is a Referred, Highly Indexed, Online International Journal . IJFSPH is published as a Monthly Journal with 12 issues per year. We also host International and National Conferences to publish their research work. IJFSPH covers all disciplines including Arts, Science, Commerce, Social-Sciences, Management and Engineering. IJFSPH always strives to be a platform for Academicians, new Researchers, Authors, Engineers, Technocrats and Engineering Scholars. Since inception, IJFSPH is continuously publishing original and best quality research articles. The Journal was under International Scholars Journal (ISJ PUBLISHING COMPANY) Till December 2024.
language: en
publication name: International Journal of Food Safety and Public Health
items: Operational And Behavioural Gaps In Mosquito Vector Control In A Forest-Rural Block Of Central India: A Household And Breeding- Habitat Assessment From Devbhog, Chhattisgarh
items: Enhancing Diagnostic Accuracy Through Generative Ai And Synthetic Data Generation For Robust Medical Imaging
items: Healthcare Chatbot System
items: Formulation Of Novel 1,3,4-Oxadiazole Entities And Examination Of Their Antioxidant Efficiency
items: Breast Cancer Detection And Classification Using Machine Learning
items: Elderly Fall Detection Using Deep Learning Techniques
items: Leveraging Deep Learning For Early Detection And Intervention In Diabetic Retinopathy Through Fundus Photography
items: Predictive Modeling Of Drug Safety Using Adverse Drug Reaction Data
items: Flood Hazard Assessment and Spatio-Temporal Analysis in India: Patterns, Causes, Impacts, and Management Perspectives
items: A Controlled Observational Study on the Effectiveness of Bombay Hospital's Rehabilitation Protocol for Cervical and Lumbar Nerve Root Compression
items: Molecular Design Strategies and QSAR-Based Assessment of Synthesized Heterocycles for Diabetes Management
items: Indigenous Knowledge of Medicinal Plants for Livestock Treatment in Bastar District, Chhattisgarh
items: AI-Driven Health Support Chatbot Using Retrieval-Augmented Generation (RAG)
items: Predictive Modelling For Lung Cancer Detection Using Machine Learning Techniques
items: Comparative Analysis of Liver Disease Prediction Using Machine Learning Models
items: AI Powered Food & Nurtrition Analyzer
items: AI Chatbot for Mental Health Using Natural Language Processing and Safety-Aware Dialogue Management
items: https://ijpp.org/journal/index.php/ajb/article/view/481
items: Radiomics Image Processing and Machine Learning: A Comprehensive Review
items: Assessing Digital Health Literacy As A Determinant Of Self-Management In Chronic Disease Populations
items: Impact Of Evidence-Based Nursing On Clinical Outcomes And Safety In Diverse Healthcare Settings: A Multicentric Analysis
items: Nurse-Led and Physician-Led Interventions in Geriatric Care: A Comparative Health Outcomes Analysis
items: Status of Adolescent Health In India
items: Establishing Performance Norms for Skill-Related Physical Fitness among Table Tennis Athletes
items: Ethnobotanical Evaluation Of Wild Plants Used For Food, Medicine, And Cultural Practices
items: Green Analytical Chemistry In Pharmaceutical Analysis
items: Reproductive Health Practices Among Adolescent Married Girls Of Birhor And Kolha Communities In Oupada Block, Balasore
items: Cloud-Enabled Autoencoder with RNN Framework for Early Detection of Chronic Kidney Disease
items: Artificial Intelligence–Based Food Shelf-Life Prediction
items: Grassroots Innovations in Low-Cost Sanitation Management

Operational And Behavioural Gaps In Mosquito Vector Control In A Forest-Rural Block Of Central India: A Household And Breeding- Habitat Assessment From Devbhog, Chhattisgarh
type: item
description: Background: Mosquito-borne diseases remain a persistent public-health concern in forest-rural landscapes where ecological suitability for vectors overlaps with operational constraints and household-level behavioural gaps. Devbhog Block of Gariyaband district, Chhattisgarh, represents such a setting, with scattered rural settlements, agricultural fields, domestic water-storage practices and forest-fringe habitats. This study assessed vector-control coverage, environmental risk, breeding-habitat distribution, simple larval observations and community knowledge, attitude and practices (KAP) in selected villages of Devbhog Block. Methods: A descriptive cross-sectional field assessment was conducted in 10 selected villages. Data were collected from 100 households using a structured survey, transect walks, environmental observations, simple larval assessment and keyinformant interactions with local health personnel. The analysis used frequencies and percentages to describe household risk factors, intervention coverage, breeding habitats, seasonal variation and KAP indicators. Results: Household-level environmental risk was prominent: poor drainage was recorded around 78% of households, uncovered water storage around 69% and stagnant water around 64%. Indoor residual spraying (IRS) coverage was 46%, while complete spraying acceptance was 32%. Mosquito-net ownership was 66%, but regular use was only 37%, suggesting a large ownership-use gap. Larval source management was weak; only 22% of households reported removing stagnant water and 16% participated in cleanliness drives. Eighty-seven potential breeding sites were recorded, dominated by stagnant ponds/ditches, forest pools, domestic containers and paddy fields. Simple larval observations identified Anopheles spp. (41.9%), Aedes spp. (33.8%) and Culex spp. (24.3%). KAP indicators were low, particularly for dengue transmission, breeding-site awareness and community source reduction. Conclusion: Devbhog Block requires locally…
subject: Articles
  topic: section
subject: Devbhog Block; vector control; malaria; dengue; mosquito breeding; KAP; larval source management; Integrated Vector Management; Chhattisgarh.
  topic: keywords
creator: Dilaish Som
creator: Laxmikant
rights: Copyright (c) 2026 Authors https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-04-28
publication date: 2026-04-28
starting page: 97
ending page: 110

Enhancing Diagnostic Accuracy Through Generative Ai And Synthetic Data Generation For Robust Medical Imaging
type: item
description: The scarcity, heterogeneity, and limited availability of high-quality annotated medical imaging data continue to pose a significant challenge in developing robust and generalizable deep learning models for clinical applications. In real-world healthcare scenarios, acquiring large-scale labeled datasets is difficult due to privacy regulations, high annotation costs, and the requirement of expert radiologist involvement. These limitations often result in models that lack generalization and perform poorly on unseen or rare disease cases. To address this issue, this project proposes a Multi-Domain Generative Adversarial Network (MD-GAN) framework designed specifically for cross-organ synthetic medical image synthesis. The proposed system is trained on multiple imaging modalities, including Brain MRI and Lung CT/X-ray datasets, enabling it to learn both shared anatomical structures and modality-specific pathological variations. The architecture incorporates a shared feature extraction backbone that captures common medical patterns across domains, while organspecific conditioning modules ensure that unique disease characteristics are preserved and accurately represented. Through adversarial training between the generator and discriminator, the model produces highly realistic synthetic medical images that can effectively augment existing datasets. Experimental results demonstrate that incorporating MD-GAN-generated synthetic data significantly enhances downstream medical image segmentation performance, particularly improving the Dice Similarity Coefficient (DSC) when compared to models trained exclusively on real-world data. This confirms the effectiveness of the proposed approach in improving diagnostic accuracy and model robustness in medical imaging applications.
subject: Articles
  topic: section
subject: Generative Adversarial Networks (GAN), Medical Imaging, Synthetic Data, MD-GAN, Data Augmentation, Deep Learning.
  topic: keywords
creator: Mr. Syed Naseeruddin
creator: Mr. Mohd Ismail Iqbal, Mr. Syed Muqeet Ahmed, Mr. Syed Nouman Ali
rights: Copyright (c) 2026 Authors
date: 2026-04-20
publication date: 2026-04-20
starting page: 91
ending page: 96

Healthcare Chatbot System
type: item
description: With the rapid advancement of Artificial Intelligence and digital healthcare technologies, automated systems are increasingly being used to provide medical assistance and information. However, many people still face challenges in accessing immediate healthcare guidance, especially for basic medical queries and preliminary diagnosis. A Healthcare Chatbot System offers an efficient solution by providing instant responses, basic medical advice, and health-related information through an interactive conversational interface. This research paper presents the development of a Healthcare Chatbot System that uses Natural Language Processing (NLP) and machine learning techniques to understand user queries and provide relevant healthcare responses. The system is designed to assist users in identifying possible symptoms, suggesting precautionary measures, and guiding them toward appropriate medical consultation when necessary. It integrates a structured medical knowledge base to ensure reliable and accurate information delivery.
subject: Articles
  topic: section
subject:
  topic: keywords
  value: Healthcare Chatbot, Artificial Intelligence, Natural Language Processing (NLP), Machine Learning, Digital Healthcare, Symptom Analysis, Medical Assistance, Conversational AI, Clinical Decision Support, Health Informatics, Automated Diagnosis Support, Patient Engagement
creator: Mr. Mohammed Tajuddin
creator: Mr. Mohammed Omar Khan, Mr. MD Azeem, Mr. Mirza Mohammed Baig
rights: Copyright (c) 2026 Authors
date: 2026-04-20
publication date: 2026-04-20
starting page: 85
ending page: 90

Formulation Of Novel 1,3,4-Oxadiazole Entities And Examination Of Their Antioxidant Efficiency
type: item
description: The 1,3,4-oxadiazole nucleus is a privileged five-membered heterocyclic scaffold with sustained relevance in medicinal chemistry because of its bioisosteric character and capacity to stabilise radical intermediates. The present work was framed to formulate a novel series of 2,5-disubstituted 1,3,4-oxadiazole entities (OX-1 to OX-8) bearing electron-donating and electron-withdrawing substituents, and to assess their antioxidant competence. Aromatic acid hydrazides were synthesised through Fischer esterification, hydrazinolysis, and subsequent cyclodehydration with phosphorus oxychloride (POCl₃) at reflux. Structures were verified through FT-IR, ¹H NMR, ¹³C NMR, and LC-MS. Antioxidant potential was examined through DPPH, ABTS, hydrogen peroxide (H₂O₂), nitric oxide (NO), and ferric reducing antioxidant power (FRAP) assays, with ascorbic acid as reference. Results indicated that OX-6, carrying a 4-hydroxy-3-methoxyphenyl group, exhibited the strongest efficiency (DPPH IC₅₀ = 14.82 μg/mL; ABTS IC₅₀ = 11.46 μg/mL; FRAP = 1187 μM Fe²⁺/g). Hypothesis-testing by one-way ANOVA confirmed substituent-dependent differences (p < 0.05). Electron-donating hydroxyl and methoxy groups markedly enhanced radical scavenging, while nitro and halogen groups reduced activity. The study concludes that properly substituted 1,3,4-oxadiazoles are valuable candidates for antioxidant drug development.
subject: Articles
  topic: section
subject: 1,3,4-Oxadiazole; Cyclodehydration; DPPH scavenging; ABTS assay; Structure–activity relationship
  topic: keywords
creator: Subhash Chandra Verma
creator: Mr. Rameshwar Singh
rights: Copyright (c) 2026 Authors
date: 2026-04-02
publication date: 2026-04-02
starting page: 79
ending page: 84

Breast Cancer Detection And Classification Using Machine Learning
type: item
description: Breast cancer is one of the most prevalent and life-threatening diseases affecting women globally. Early detection and accurate classification are crucial for improving survival rates and reducing mortality. This research paper presents a machine learning-based approach for the detection and classification of breast cancer tumors into benign and malignant categories. The proposed system utilizes supervised learning algorithms such as Logistic Regression, Support Vector Machine (SVM), Decision Trees, and Random Forest to analyze medical data. The system is trained and evaluated using the Wisconsin Breast Cancer Dataset, which contains features extracted from digitized images of fine needle aspirates of breast masses. Data preprocessing techniques such as normalization, feature scaling, and handling missing values are applied to improve model performance. The results indicate that ensemble models like Random Forest achieve higher accuracy compared to traditional classifiers. The proposed system aims to assist healthcare professionals by providing a reliable and efficient diagnostic tool, thereby reducing human error and enabling early-stage detection of breast cancer.
subject: Articles
  topic: section
subject: Breast Cancer, Machine Learning, Random Forest, Classification, Medical Diagnosis, Supervised Learning, Early Detection, Healthcare AI.
  topic: keywords
creator: Ms. Zeba Raqshanh
creator: Mr. Mohd Najeeb Murtuza, Mr. Mohammad Anwar Uddin, Mr. Mohd Ayan Sadiq
rights: Copyright (c) 2026 Authors
date: 2026-04-06
publication date: 2026-04-06
starting page: 74
ending page: 78

Elderly Fall Detection Using Deep Learning Techniques
type: item
description: Elderly Fall Detection Using Deep Learning Techniques is an intelligent healthcare monitoring system designed to automatically detect fall incidents in real time. Falls are one of the leading causes of serious injuries among older adults, often resulting in fractures, hospitalization, or life-threatening complications. The proposed system utilizes deep learning models to analyze video input and identify abnormal human postures and sudden movements associated with falls. Convolutional Neural Networks (CNN) and temporal learning models are employed to extract spatial and motion-based features from video frames. The system distinguishes between normal daily activities such as walking, sitting, or bending and actual fall events with high accuracy. When a fall is detected, the system immediately generates an alert notification to caregivers or family members, ensuring quick assistance. This approach enhances elderly safety, reduces response time in emergencies, and supports independent living through continuous and automated monitoring.
subject: Articles
  topic: section
subject:
  topic: keywords
  value: Elderly Fall Detection, Deep Learning, Convolutional Neural Networks (CNN), Human Activity Recognition, Video Surveillance, Healthcare Monitoring, Real-Time Alert System, Motion Analysis, Smart Healthcare, Assistive Technology.
creator: Mr.Mazhar Ali
creator: Mr.Mohammed Shabbir Ali, Mr. Mohammed Waleed Mohiuddin, Mr.Mohammed Abdul Arfath Sayeed, Mr.Mohammad Haroon Baig
rights: Copyright (c) 2026 Authors
date: 2026-04-06
publication date: 2026-04-06
starting page: 69
ending page: 73

Leveraging Deep Learning For Early Detection And Intervention In Diabetic Retinopathy Through Fundus Photography
type: item
description: Diabetic Retinopathy (DR) remains one of the leading causes of preventable blindness among diabetic patients worldwide. Early detection through expert examination of fundus photographs is critical but time-consuming, expensive, and inaccessible in rural areas. This research proposes an automated deep learning-based system leveraging Convolutional Neural Networks (CNNs) for early detection and multi-stage classification of Diabetic Retinopathy using retinal fundus photography. The system employs advanced image preprocessing techniques including noise reduction, contrast enhancement, and normalization to improve diagnostic accuracy. Feature extraction and hierarchical analysis enable the identification of critical markers including microaneurysms, hemorrhages, and exudates. The proposed CNN architecture demonstrates exceptional performance in classifying DR severity levels with high precision and recall. Evaluation metrics including accuracy, sensitivity, specificity, and F1-score validate the clinical reliability of the system. Results demonstrate that automated screening can significantly reduce healthcare burden, improve diagnostic efficiency, and enable large-scale deployment in resource-limited settings, ultimately contributing to the prevention of diabetes-related blindness worldwide.
subject: Articles
  topic: section
subject:
  topic: keywords
  value: Diabetic Retinopathy, Deep Learning, Convolutional Neural Network (CNN), Fundus Images, Medical Image Processing, Automated Diagnosis, Early Detection, Image Preprocessing, Multi-Class Classification, Computer-Aided Diagnosis, Healthcare AI, Retinal Disease Detection, Feature Extraction, Classification Accuracy, Telemedicine Screening
creator: Mr. Mohd Khaja Moizuddin
creator: Syed Inzemam Hussain, Farhaan Jameel, Mohammed Faraaz
rights: Copyright (c) 2026 Authors
date: 2026-04-06
publication date: 2026-04-06
starting page: 64
ending page: 68

Predictive Modeling Of Drug Safety Using Adverse Drug Reaction Data
type: item
description: Monitoring drug safety is important to protect patients, but traditional systems are slow because they rely on manual reporting and looking at data after problems occur. This study uses machine learning to quickly analyze large datasets like FDA FAERS and WHO VigiBase to detect harmful drug reactions earlier. By processing data such as patient details, drug information, and past reactions, models like decision trees, SVMs, and neural networks can predict serious side effects more accurately. These models improve early warnings, reduce missed risks, and help regulators act faster. Overall, the research shows how AI can make drug safety monitoring more efficient and reliable, despite challenges like complex and imbalanced data.
subject: Articles
  topic: section
subject: FDA
  topic: keywords
subject: WHO
  topic: keywords
creator: Dr. Abdul Rasool Md
creator: Mr. Mohd Azeem,Muhammed Usman Khan,Sufiyan Mehmood Nizami
rights: Copyright (c) 2026 Authors
date: 2026-04-02
publication date: 2026-04-02
starting page: 58
ending page: 63

Flood Hazard Assessment and Spatio-Temporal Analysis in India: Patterns, Causes, Impacts, and Management Perspectives
type: item
description: India is one of the most flood-prone countries in the world, with approximately one-eighth of its total geographical area exposed to recurring flood hazards. Floods in India cause immense loss of human lives, destruction of agricultural land, property, and infrastructure every year. This paper presents a comprehensive spatio-temporal analysis of flood occurrences across India, focusing on the period 2000 to 2019. The study examines the major causes of floods including heavy monsoon rainfall, riverine overflow, glacial lake outburst floods (GLOFs), embankment failures, and rapid urbanisation. Statistical data on flood-affected area, human casualties, crop damage, and economic losses have been compiled and analysed using MS Excel. Three major visualisations — a bar chart of annual flood-affected area, a line graph of human mortality trends, and a pie chart of region-wise flood frequency — have been incorporated to support a clear analytical picture. The study reveals that Bihar, Assam, and Uttar Pradesh are the most consistently flood-affected states. The findings suggest that an integrated flood risk management approach, combining structural measures with non-structural strategies such as early warning systems, land-use planning, and community preparedness, is urgently needed. The paper concludes with policy recommendations and emphasises the role of geospatial technology in flood hazard mapping and mitigation.
subject: Articles
  topic: section
subject: Flood hazard, India, Spatio-temporal analysis, Flood-affected area, Flood risk management, Natural disaster, Monsoon, Riverine flooding, Bihar, Assam
  topic: keywords
creator: Surender Kumar
rights: Copyright (c) 2020 Authors
date: 2020-12-30
publication date: 2020-12-30
starting page: 1
ending page: 12

A Controlled Observational Study on the Effectiveness of Bombay Hospital's Rehabilitation Protocol for Cervical and Lumbar Nerve Root Compression
type: item
description: The causes of musculoskeletal disability in terms of functional independence and quality of life are largely due to cervical and lumbar nerve root compression that are the major causes of musculoskeletal disability globally. The current controlled observational study was designed to assess the efficacy of Bombay Hospital structured multimodal rehabilitation protocol of pain reduction, functional disability and range of motion in patients with cervical and lumbar nerve root compression. Eighty (80) patients (40 cervical, 40 lumbar) were recruited in the period between January 2021 and December 2022 and divided into the rehabilitation group (n=40) and the control group (n=40), respectively. The outcome measures were Visual Analogue Scale (VAS), Neck Disability Index (NDI), Oswestry Disability Index (ODI), and goniometric range of motion measures at baseline, 6 weeks and 12 weeks. The hypothesis was that the structured rehabilitation plan would have significantly more improvements than a conventional treatment. Findings showed statistically significant decreases in VAS (p<0.001), NDI (p<0.01), and ODI (p<0.001) scores of rehabilitation group over controls. The protocol was found to be clinically better in the restoration of the functional capacity. The research concluded that multimodal rehabilitation protocol at Bombay Hospital is an effective and evidence-based intervention in the management of cervical and lumbar nerve root compression on a conservative basis.
subject: Articles
  topic: section
subject: Nerve Root Compression, Rehabilitation Protocol, Cervical Radiculopathy, Lumbar Radiculopathy, Functional Disability
  topic: keywords
creator: Seema Agarwal
creator: Dr. Apoorv Narain
rights: Copyright (c) 2024 Authors
date: 2024-07-30
publication date: 2024-07-30
starting page: 1
ending page: 9

Molecular Design Strategies and QSAR-Based Assessment of Synthesized Heterocycles for Diabetes Management
type: item
description: Diabetes mellitus (DM) represents a critical global metabolic disorder, with an estimated 537 million adults affected worldwide as of 2021. The present study evaluates the molecular design strategies and quantitative structure-activity relationship (QSAR)-based assessment of synthesized heterocyclic scaffolds, including thiazolidinediones, triazoles, oxadiazoles, coumarins, and pyrimidines, as potential antidiabetic agents targeting key enzymes such as α-glucosidase, α-amylase, DPP-IV, and PTP-1B. The objective was to correlate structural descriptors of heterocyclic compounds with their biological activity (IC₅₀) through validated QSAR models and molecular docking studies. A computational methodology integrating 2D/3D-QSAR modeling, pharmacophore mapping, and molecular docking was employed on datasets of synthesized heterocycles reported between 2015 and 2021. The hypothesis posited that electron-withdrawing substituents and hydrogen bond donor/acceptor features on heterocyclic scaffolds significantly enhance antidiabetic potency. Results demonstrated that QSAR models achieved strong statistical validity (R² > 0.85, Q² > 0.60), and docking scores confirmed favorable binding interactions with target proteins. The thiazolidinedione-triazole and coumarin-based hybrids exhibited the most potent IC₅₀ values. In conclusion, QSAR-guided molecular design provides a robust framework for optimizing heterocyclic leads, and future synthesis efforts should prioritize halogen and methoxy substitutions on these scaffolds for enhanced antidiabetic efficacy.
subject: Articles
  topic: section
subject: QSAR, Heterocyclic compounds, Molecular docking, Antidiabetic, α-Glucosidase inhibitors
  topic: keywords
creator: Ashwani Kumar Lahare
creator: Dr. Yogendra Rai
rights: Copyright (c) 2023 Authors
date: 2023-08-30
publication date: 2023-08-30
starting page: 1
ending page: 8

Indigenous Knowledge of Medicinal Plants for Livestock Treatment in Bastar District, Chhattisgarh
type: item
description: The present study investigates the indigenous knowledge of medicinal plants used by tribal communities for livestock treatment in Bastar district of Chhattisgarh, India. The primary objectives were to document ethnoveterinary plant species utilized by tribal healers, identify the plant parts and modes of administration, and assess the prevalence of traditional veterinary knowledge across dominant tribal groups including Gond, Maria, Muria, Bhatra, and Halba. The study adopted a descriptive ethnobotanical research design, employing semi-structured interviews, participatory field observations, and guided field walks among 120 purposively selected informants comprising traditional healers (locally called Guniyas), elderly livestock keepers, and experienced herbal practitioners across seven development blocks of Bastar district. It was hypothesized that the tribal communities of Bastar possess a rich and systematic ethnoveterinary knowledge base linked to the region's biodiversity. Results revealed that 45 plant species belonging to 38 genera and 26 families were used for treating 28 livestock ailments. Leaves (42.2%) were the most frequently used plant part, followed by roots (20.0%) and bark (15.6%). Discussion indicates that this indigenous knowledge system is eroding rapidly due to modernization, deforestation, and generational disconnect. The study concludes that urgent documentation and scientific validation of ethnoveterinary practices in Bastar are essential for biodiversity conservation and sustainable livestock healthcare management.
subject: Articles
  topic: section
subject: Ethnoveterinary medicine, Indigenous knowledge, Medicinal plants, Livestock healthcare, Bastar district
  topic: keywords
creator: Madhusudan Bhagwandas Kurve
creator: Dr. M.K. Chourasia
rights: Copyright (c) 2023 Authors
date: 2023-03-26
publication date: 2023-03-26
starting page: 1
ending page: 7

AI-Driven Health Support Chatbot Using Retrieval-Augmented Generation (RAG)
type: item
description: The increasing adoption of digital healthcare services has created a strong need for intelligent systems that can deliver dependable and interpretable preliminary medical support. This paper proposes an AI-based healthcare assistance chatbot that integrates machine learning–driven disease prediction with a Retrieval-Augmented Generation (RAG) framework to provide accurate, context-aware, and explainable health guidance. The proposed system allows users to describe their symptoms in natural language, which are analyzed using advanced text processing techniques to extract clinically relevant indicators. A supervised learning–based classification model is employed to predict the most likely disease conditions from the extracted symptoms. To enhance the reliability and safety of the generated responses, a RAG architecture retrieves validated medical knowledge from a structured repository, including disease profiles, common symptoms, severity levels, and recommended precautionary measures. This retrieved information is subsequently combined with a generative language model to produce coherent, evidence-supported, and user-friendly health explanations. By grounding the generative output in verified medical data, the proposed approach significantly reduces hallucination risks and improves transparency in automated health assistance. The system not only supports preliminary symptom assessment but also delivers educational insights and promotes timely consultation with healthcare professionals. The experimental results demonstrate that combining machine learning–based disease prediction with retrieval-driven knowledge grounding substantially improves the accuracy, trustworthiness, and overall quality of digital healthcare support systems.
subject: Articles
  topic: section
subject: AI
  topic: keywords
subject: Retrieval-Augmented Generation (RAG)
  topic: keywords
creator: Kilaru Sai Meghana
creator: Mrs. K. Pooja
rights: Copyright (c) 2026 Authors
date: 2026-01-30
publication date: 2026-01-30
starting page: 50
ending page: 57

Predictive Modelling For Lung Cancer Detection Using Machine Learning Techniques
type: item
description: Early and reliable identification of lung cancer plays a critical role in improving patient survival. This paper presents a computer‑aided diagnosis framework for the detection of lung cancer from computed tomography (CT) images using digital image processing and classical machine learning techniques. The proposed pipeline consists of image preprocessing, lesion segmentation, feature extraction and supervised classification. Noise reduction and contrast enhancement are applied during preprocessing, followed by edge‑based and watershed segmentation to isolate candidate regions. Both region‑based and texture‑based features are extracted from the segmented images and are used to train Support Vector Machine (SVM), Random Forest (RF) and Artificial Neural Network (ANN) classifiers. Performance is evaluated using accuracy, precision, recall and F1‑score. Experimental results indicate that the ANN classifier achieves the best overall performance for both feature categories. The proposed approach demonstrates that a structured feature‑driven machine learning framework can effectively support automated lung cancer detection from CT images.
subject: Articles
  topic: section
subject: Lung cancer detection, CT images, image segmentation, feature extraction, machine learning, ANN, SVM, Random Forest.
  topic: keywords
creator: Sangapallavi
creator: Mrs. K. Pooja
rights: Copyright (c) 2026 Authors
date: 2026-01-30
publication date: 2026-01-30
starting page: 46
ending page: 49

Comparative Analysis of Liver Disease Prediction Using Machine Learning Models
type: item
description: Liver disorders represent a major global health challenge and are responsible for a significant number of hospitalizations and long-term complications. Early identification of liver abnormalities is essential for improving treatment outcomes and reducing mortality. However, conventional diagnosis methods rely heavily on laboratory tests and expert interpretation, which are time-consuming and costly. This paper presents a comparative study of machine learning algorithms for predicting liver disease using routinely collected clinical and biochemical parameters. Several supervised learning models, including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbour, Gradient Boosting and Neural Network based models, are implemented and evaluated. The Indian Liver Patient Dataset is used as the benchmark dataset. A complete data processing pipeline is developed, consisting of data cleaning, feature transformation, attribute ranking and model evaluation. The performance of the models is analysed using accuracy, precision, recall and F1-score. Experimental results demonstrate that ensemble and neural-based models provide superior predictive performance compared to traditional classifiers. The study confirms that machine learning can serve as a reliable decision-support tool for early liver disease screening.
subject: Articles
  topic: section
subject: Liver disease prediction, machine learning, clinical data mining, classification, healthcare analytics.
  topic: keywords
creator: Kona Kusumanjali
creator: Dr. Md. Asif
rights: Copyright (c) 2026 Authors
date: 2026-01-30
publication date: 2026-01-30
starting page: 40
ending page: 45

AI Powered Food & Nurtrition Analyzer
type: item
description: Accurate dietary monitoring is an essential requirement for preventive healthcare and lifestyle management. However, most existing diet-tracking applications rely heavily on manual food logging, which is time-consuming and often inaccurate. This paper presents an AI-powered food and nutrition analyzer that automatically identifies food items from captured images and generates nutritional information using cloud-based intelligence. The proposed system integrates real-time image acquisition, vision-based food recognition, and nutrition estimation services to provide calorie and macronutrient details to the user. A modular and scalable architecture is adopted to support real-time processing, easy deployment, and future extensibility. Experimental evaluation on common food items demonstrates reliable recognition and practical nutrition estimation suitable for daily usage scenarios.
subject: Articles
  topic: section
subject: Food recognition, nutrition estimation, computer vision, cloud AI, diet monitoring.
  topic: keywords
creator: Gangula Ganesh
creator: Mrs. K. Pooja
rights: Copyright (c) 2026 Authors
date: 2026-01-30
publication date: 2026-01-30
starting page: 32
ending page: 39

AI Chatbot for Mental Health Using Natural Language Processing and Safety-Aware Dialogue Management
type: item
description: Mental health disorders such as anxiety, stress, depression and emotional distress are increasing rapidly among students and working professionals. Limited availability of mental health professionals, social stigma and high consultation costs prevent many individuals from seeking timely help. Conversational artificial intelligence offers a scalable and privacy-preserving approach for providing preliminary emotional support and guidance. This paper presents the design and implementation of an AI-based mental health chatbot that uses natural language processing, sentiment and emotion analysis, and safety-aware dialogue management to provide empathetic and context-aware responses. The system detects emotional states such as stress, sadness, anxiety and crisis indicators, and provides supportive responses, self-help strategies and helpline guidance when required. The chatbot is implemented using Python, machine-learning-based intent and risk classifiers, and a lightweight web interface for real-time interaction. Experimental evaluation shows that the proposed system achieves high intent classification accuracy and reliable crisis detection while maintaining low response latency. The proposed solution demonstrates the feasibility of deploying conversational AI as an assistive and scalable mental health support tool.
subject: Articles
  topic: section
subject: Mental health chatbot, Natural Language Processing, sentiment analysis, emotion detection, conversational AI, healthcare technology.
  topic: keywords
creator: Mr. M. Syam Babu
creator: M. Bhanu Prakash Reddy
rights: Copyright (c) 2026 Authors
date: 2026-02-10
publication date: 2026-02-10
starting page: 24
ending page: 31

https://ijpp.org/journal/index.php/ajb/article/view/481
type: item
title: Educational Approaches for Strengthening Nurses' Awareness and Acceptance of Artificial Intelligence in Healthcare Delivery: A Hospital-Based Quasi-Experimental Study: Implications for Long-Term Value Creation
description: The adoption of the use of artificial intelligence (AI) in healthcare requires proper readiness among the nursing leaders. This quasi-experimental study was a hospital-based study that identified the effectiveness of structured educational interventions in improving the level of awareness and acceptance of AI technologies by the nurses in healthcare delivery. India Tertiary care hospital Nurses A sample (n=120) was split into experimental and control groups. The experimental participants were provided with a four-week educational intervention on the basics of AI, its medical uses, and ethics. Validated instruments were used to measure levels of knowledge, attitudes, and acceptance, based on the pre-test and post-test assessments. The findings showed a significant difference in the scores of AI awareness (p<0.001) and acceptance (p<0.001) of the experimental group when compared to control. The intervention led to the improvement of knowledge on the AI use in monitoring patients, aiding diagnosis, and optimizing the workflow. Results indicate that pertinent educational strategies can be used to resolve knowledge gaps and minimize AI adoption resistance. This paper has justifications about introducing systematic training to equip nursing labor with AI-based medical settings and eventually adding value to the long-term with the provision of better quality care and operational efficiency.
subject: Articles
  topic: section
subject: Artificial intelligence, nursing education, healthcare technology acceptance, quasi-experimental study, digital health literacy
  topic: keywords
creator: Punam Patel
creator: Dr. Nelson Jewas
rights: Copyright (c) 2025 Authors
date: 2025-11-28
publication date: 2025-11-28
starting page: 1
ending page: 11

Radiomics Image Processing and Machine Learning: A Comprehensive Review
type: item
description: Radiomics, which is the high-throughput extraction of quantitative features of medical images, is a paradigm shift in diagnostic and prognostic medicine. Radiomics can be used to find new biomarkers that could be used to define disease phenotypes in a way that human eyes cannot process to locate them by transforming the standard-of-care imaging into data which can be mined. This is a cumulative review that attempts to compile existing literature to analyze the role of integrating sophisticated image processing pipeline and machine learning (ML) or deep learning (DL) algorithms in radiomics. We investigate the general workflow, including image acquisition and segmentation, feature extraction, feature selection, and model development, and both the possibilities provided by it and the technical difficulties. The review uses various applications such as the assessment of pulmonary diseases, oncology, and cardiac risk prediction as an illustration of the state-of-the-art. Threatening issues like feature reproducibility, model interpretability, heterogeneity of data and the need to have a robust validation are addressed in detail. And, last but not the least, we present future directions, which include the necessity of universal protocols, explainable AI (XAI), multimodal data fusion and ethical application of these potent technologies in clinical practices. DOI: https://doi-ds.org/doilink/01.2026-27294922
subject: Articles
  topic: section
subject: Radiomics, Machine Learning, Deep Learning, Image Processing, Feature Extraction, Predictive Modeling, Quantitative Imaging, Medical Imaging Analytics.
  topic: keywords
creator: Dr. Mukesh Yadav
rights: Copyright (c) 2026 Author
date: 2026-01-17
publication date: 2026-01-17
starting page: 19
ending page: 23

Assessing Digital Health Literacy As A Determinant Of Self-Management In Chronic Disease Populations
type: item
description: Digital health literacy has emerged as a critical determinant of effective self-management in chronic disease populations. This study examines the relationship between digital health literacy levels and self-management outcomes among individuals with chronic conditions, utilizing the eHealth Literacy Scale as the primary assessment tool. A comprehensive literature review was conducted to synthesize evidence from multiple studies examining digital health literacy across various chronic conditions including diabetes mellitus, hypertension, and cardiovascular diseases. The hypothesis posited that higher digital health literacy scores correlate positively with improved self-management behaviors and health outcomes. Findings revealed that patients with elevated digital health literacy demonstrated significantly enhanced self-management capabilities, better treatment adherence, and improved quality of life. The pooled mean eHealth literacy score across chronic disease populations was notably high, with diabetes and hypertension patients showing particularly elevated scores. However, significant disparities exist based on age, education, socioeconomic status, and digital access. The study concludes that digital health literacy serves as a fundamental prerequisite for effective chronic disease self-management in the digital health era, necessitating targeted interventions to address literacy gaps and health inequalities.
subject: Articles
  topic: section
subject: Digital health literacy, chronic disease, self-management, eHealth literacy scale, health outcomes
  topic: keywords
creator: Pravin Prakash P
creator: Dr. Surendra Goswami
rights: Copyright (c) 2024 Authors
date: 2024-01-30
publication date: 2024-01-30
starting page: 1
ending page: 9

Impact Of Evidence-Based Nursing On Clinical Outcomes And Safety In Diverse Healthcare Settings: A Multicentric Analysis
type: item
description: Evidence-based practice has emerged as the cornerstone of contemporary nursing, fundamentally transforming healthcare delivery across diverse settings. This multicentric analysis examined the impact of evidence-based nursing interventions on clinical outcomes and patient safety parameters across 288,581 healthcare facilities spanning 32 countries. The study employed a comprehensive methodology integrating systematic reviews, meta-analyses, and multicentric observational data from 2020-2023. Results demonstrated significant improvements in patient safety indicators, including reduction in mortality rates from 7.75% to 6.27%, decrease in healthcare-associated infections by 34.2-49%, reduction in hospital readmissions from 21.5% to 17.8%, and improvement in medication error rates by 30-70%. Evidence-based nursing implementation correlated with shortened hospital stays from 8.5 to 6 days and enhanced patient satisfaction scores by 5-61%. The analysis reveals that evidence-based nursing practices substantially improve clinical outcomes, reduce adverse events, and optimize resource utilization. These findings underscore the critical importance of systematic integration of research evidence into clinical nursing practice for achieving superior patient safety and healthcare quality outcomes across varied institutional settings.
subject: Articles
  topic: section
subject: Evidence-based nursing, patient safety, clinical outcomes, healthcare quality, multicentric analysis
  topic: keywords
creator: Neeraj Kumar Bansal
creator: Sumant Kumar Vyas
rights: Copyright (c) 2024 Authors
date: 2024-05-29
publication date: 2024-05-29
starting page: 1
ending page: 8

Nurse-Led and Physician-Led Interventions in Geriatric Care: A Comparative Health Outcomes Analysis
type: item
description: Geriatric care delivery faces mounting challenges with aging populations requiring specialized interventions to maintain quality of life and functional independence. This comparative analysis examines health outcomes between nurse-led and physician-led interventions in geriatric populations. The study synthesized evidence from randomized controlled trials and observational studies comparing clinical outcomes, cost-effectiveness, and patient satisfaction between these two care delivery models. Data from multiple healthcare settings including primary care, long-term care facilities, and transitional care programs were analyzed. Results demonstrated that nurse practitioners achieved enhanced outcomes in 58% of measured parameters compared to physician-only care, with particular strengths in chronic disease management, medication adherence, and care coordination. Nurse-led interventions reduced 30-day readmission rates by 0.55% and achieved lower mortality rates. Cost analysis revealed significant savings ranging from $924 to $2,626 per patient depending on condition complexity. Both models demonstrated effectiveness, with nurse-led care excelling in accessibility and preventive services while physician-led care showed advantages in acute clinical decision-making. The findings support integrated care models leveraging strengths of both professionals to optimize geriatric health outcomes.
subject: Articles
  topic: section
subject: Geriatric care, Nurse-led interventions, Physician-led care, Health outcomes, Cost-effectiveness.
  topic: keywords
creator: Preeti Mishra
creator: Sumant Kumar Vyas
rights: Copyright (c) 2023 Authors
date: 2023-07-28
publication date: 2023-07-28
starting page: 1
ending page: 9

Status of Adolescent Health In India
type: item
description: Adolescent health represents a critical public health concern in India, with approximately 253 million adolescents aged 10-19 years constituting the world's largest adolescent population. This study examines the comprehensive status of adolescent health in India, focusing on nutritional deficiencies, mental health challenges, substance abuse, and reproductive health issues. The hypothesis posits that multifactorial determinants including socioeconomic status, educational attainment, and geographical location significantly influence adolescent health outcomes. Utilizing a cross-sectional analytical approach, secondary data from National Family Health Survey-5 (2019-2021) and published literature were systematically reviewed. Results reveal alarming prevalence rates: 59.1% anemia among adolescent girls, 45% undernutrition in girls and 20% in boys, 7.2% adolescent pregnancy, 25-39% depression, 4.5% drug abuse, and 50% of HIV-positive new infections occurring in 10-25 years age group. Statistical analysis demonstrates significant associations between adverse health outcomes and rural residence, low socioeconomic status, and limited education. Discussion emphasizes urgent need for strengthened policy interventions including enhanced implementation of Rashtriya Kishor Swasthya Karyakram and adolescent-friendly health services. The study concludes that comprehensive, multi-sectoral approaches addressing nutritional, mental, reproductive, and behavioral health are essential for improving adolescent well-being and ensuring India's demographic dividend. DOI: https://doi-ds.org/doilink/01.2026-62318779
subject: Articles
  topic: section
subject: Adolescent health, India, Anemia, Mental health, Substance abuse
  topic: keywords
creator: Subhasmita Dhal
rights: Copyright (c) 2026 Authors
date: 2026-01-07
publication date: 2026-01-07
starting page: 10
ending page: 18

Establishing Performance Norms for Skill-Related Physical Fitness among Table Tennis Athletes
type: item
description: Table tennis is a dynamic racket sport requiring exceptional skill-related physical fitness components including speed, agility, power, coordination, reaction time, and balance. This study aimed to establish performance norms for skill-related physical fitness parameters among competitive table tennis athletes and examine age and gender-related differences. A cross-sectional research design was employed with 96 table tennis athletes categorized into junior, senior, and elite groups. Physical fitness assessments included handgrip strength, vertical jump, horizontal jump, sprint tests, modified agility test, flexibility, and reaction time measurements. Results revealed significant age-related improvements in all physical parameters except flexibility, with males demonstrating superior performance in strength, power, and speed components while females showed higher flexibility scores. Performance norms were established across different age categories providing benchmark standards for talent identification and training program development. The study concludes that comprehensive physical fitness assessment protocols are essential for optimizing table tennis performance, and the established norms provide valuable reference standards for coaches and sports scientists working with Indian table tennis athletes.
subject: Articles
  topic: section
subject: Table tennis, Physical fitness, Performance norms, Skill-related fitness, Athletic assessment
  topic: keywords
creator: Mr. Siddhartha Srinet
creator: Dr. Om Prakash Mishra
rights: Copyright (c) 2026 Authors
date: 2026-01-05
publication date: 2026-01-05
starting page: 1
ending page: 9

Ethnobotanical Evaluation Of Wild Plants Used For Food, Medicine, And Cultural Practices
type: item
description: Ethnobotanical knowledge is ancient knowledge that is collected through generations about the use of plants to ensure sustenance, healthcare and other cultural aspects. This paper is a systematic review of wild flora used by indigenous people in various Indian states in terms of food, medicine, and culture. The main goals were to record the species diversity, evaluate the patterns of utilization and estimate the ethnobotanical importance with the help of standardized indices. The research hypothesis was that indigenous people have a rich traditional knowledge on the use of wild plants as resources to their food security and primary healthcare. Through the use of semi-structured questionnaires and participatory rural appraisal methods data was gathered among 150 informants in the sampled villages. Findings identified 102 species of wild plants belonging to 58 families which were used in different ways, Fabaceae, Asteraceae, and Rosaceae were the major families. The statistical analysis indicated that there was a high degree of agreement on the use of the plants with the use values of the reported species being 0.48 to 0.89 as highly significant species and use values of the other species being moderate to low. There were high levels of cultural acceptance of certain species represented by quantitative indices. The paper has come up with the conclusion that the wild plants still play a significant role in indigenous livelihood systems and there is a need to protect and preserve the traditional knowledge of the important ethnobotanical heritage so as not to erode it. DOI: https://doi-ds.org/doilink/12.2025-34916186
subject: Articles
  topic: section
subject: Ethnobotany, Wild edible plants, Indigenous knowledge, Medicinal plants, Traditional food systems.
  topic: keywords
creator: Dr. Jyoti Verma
rights: Copyright (c) 2025 Author
date: 2025-11-28
publication date: 2025-11-28
starting page: 1
ending page: 9

Green Analytical Chemistry In Pharmaceutical Analysis
type: item
description: Green analytical chemistry has emerged as a transformative paradigm in pharmaceutical analysis, addressing critical environmental and health concerns associated with conventional analytical methods. The primary objective of this study was to evaluate the implementation of green analytical chemistry principles in pharmaceutical analysis by examining green solvents, assessment metrics, and chromatographic techniques. Employing a comprehensive review methodology, this research analyzed various green analytical approaches including supercritical fluid chromatography, ultra-high-performance liquid chromatography, and green solvent systems. The hypothesis posited that green analytical methods would demonstrate comparable analytical performance while significantly reducing environmental impact. Results revealed substantial improvements in environmental sustainability metrics, with analytical eco-scale scores ranging from 85-91, AGREE scores between 0.75-0.91, and significant reductions in organic solvent consumption. Green solvents such as ethanol, water, and supercritical carbon dioxide demonstrated excellent analytical performance with reduced toxicity profiles. Discussion highlights the successful integration of green chemistry principles through miniaturization, solvent replacement, and energy-efficient techniques. This research concludes that green analytical chemistry represents a viable, environmentally responsible approach for pharmaceutical analysis without compromising analytical quality, supporting sustainable development goals in the pharmaceutical industry.
subject: Articles
  topic: section
subject: Green analytical chemistry, Pharmaceutical analysis, Sustainable solvents, HPLC, Environmental metrics
  topic: keywords
creator: Twinkal Deepak
rights: Copyright (c) 2025 Author
date: 2025-06-27
publication date: 2025-06-27
starting page: 1
ending page: 7

Reproductive Health Practices Among Adolescent Married Girls Of Birhor And Kolha Communities In Oupada Block, Balasore
type: item
description: Reproductive health practices among tribal adolescent married girls are shaped by cultural traditions, resource availability, and limited health infrastructure access. This descriptive survey investigated practices related to menstrual hygiene, sexual behavior, and nutrition among 100 adolescent married girls (50 Birhor, 50 Kolha) in Oupada Block, Balasore District, Odisha. Using purposive sampling and structured interview questionnaires, the study hypothesized significant differences in reproductive health practices between the two tribes. Results demonstrated that 100% Birhor girls used only undergarments during menstruation versus 88% Kolha girls using old cloth. Menstrual pad washing occurred exclusively in ponds for both groups, with traditional cleaning agents (termite soil, Eukyl potash fruits) preferred over soap. Sexual practices revealed 100% both tribes considered vaginal sex as primary mode, with 64% Birhor and 68% Kolha practicing sex during menstruation. Contraceptive use was absent among Birhors while 70% Kolha girls used temporary methods. Nutritional practices showed universal non-vegetarian preference with 100% consuming Handia and alcohol during menstruation/pregnancy for energy. The study concluded that unhygienic menstrual practices, risky sexual behaviors, and inadequate nutritional practices necessitate urgent culturally-sensitive interventions addressing tribal-specific constraints. DOI: https://doi-ds.org/doilink/12.2025-43719147
subject: Articles
  topic: section
subject: Reproductive health practices, menstrual hygiene, sexual behavior, tribal adolescents, nutritional practices.
  topic: keywords
creator: Subhasmita Dhal
rights: Copyright (c) 2025 Author
date: 2025-10-30
publication date: 2025-10-30
starting page: 1
ending page: 17

Cloud-Enabled Autoencoder with RNN Framework for Early Detection of Chronic Kidney Disease
type: item
description: Prevention of any progression in CKD to an advanced stage necessitates the early diagnosis of the disease. This work presents the Cloud-Enabled Autoencoder with RNN Framework for Early Detection of Chronic Kidney Disease, which exploits the combined strengths of Autoencoders for feature extraction and Recurrent Neural Networks for temporal dependencies from clinical study data. This model uses historical medical data for the prediction of CKD onset, thereby instilling the notion of proactive healthcare. The framework is hosted on a cloud platform so that the scalability and accessibility of advanced functionalities are secured for healthcare professionals. Consequently, this model enhances CKD detection and timely intervention through the integration of Autoencoders for dimensionality reduction and RNNs for sequential pattern recognition, thus improving the patient's quality of care.
subject: Articles
  topic: section
subject: Chronic Kidney Disease (CKD), Autoencoder, Recurrent Neural Networks (RNN), Cloud Computing, Machine Learning in Healthcare, Early Detection Systems
  topic: keywords
creator: Andri Ulus Rahayu
rights: Copyright (c) 2025 Author
date: 2025-07-30
publication date: 2025-07-30
starting page: 1
ending page: 12

Artificial Intelligence–Based Food Shelf-Life Prediction
type: item
description: This work introduces an innovative method for assessing the remaining shelf life of fresh produce during transit, fulfilling a vital requirement in the food supply chain. The suggested system utilizes modern data analytics and machine learning to integrate environmental parameters, including temperature, humidity, and transportation conditions, in order to forecast the deterioration of freshness and nutritional quality of perishable commodities over time. The system evaluates the deterioration rate of fresh produce and determines its remaining shelf life by evaluating real-time sensor data from IoT devices deployed in transportation vehicles. The system employs historical data and predictive modeling to consider fluctuations in transportation conditions and product attributes, hence improving the precision and dependability of shelf life assessments. This project seeks to validate the efficacy and practicality of the suggested system in enhancing supply chain management, minimizing food waste, and guaranteeing the delivery of high-quality fresh produce to consumers through extensive experimentation. DOI: https://doi-ds.org/doilink/12.2025-76795961
subject: Articles
  topic: section
subject:
  topic: keywords
  value: Remaining shelf life, Fresh produce, Perishable food monitoring, Machine learning, Predictive modeling, Internet of Things (IoT), Real-time sensor data, Cold chain logistics, Food quality assessment, Supply chain management, Food waste reduction, Transportation conditions
creator: H.fathima
creator: Dr.K.K.Savitha
creator: Mrs.C.Maheshsathya
creator: T.Vadivel
creator: Ms. M. Leelavathi
creator: Veldandi Srikanth
rights: Copyright (c) 2025 Authors
date: 2025-12-20
publication date: 2025-12-20
starting page: 1
ending page: 4

Grassroots Innovations in Low-Cost Sanitation Management
type: item
description: Grassroots sanitation innovations—locally invented, adapted, or community-governed solutions—have become a practical route to safer sanitation in resource-constrained settings. They range from community mobilization models that end open defecation to frugal toilet designs, informal-settlement service models, and community-run fecal sludge treatment. This paper reviews major categories of grassroots innovations in low-cost sanitation management and explains how they work across the sanitation “service chain” (containment, emptying/transport, treatment, and safe reuse/disposal). It situates these innovations against persistent global gaps: in 2022, 3.5 billion people still lacked safely managed sanitation, including 419 million practicing open defecation. Using illustrative evidence from community-led total sanitation (CLTS), urine-diverting/ecological sanitation, container-based sanitation (CBS), and district-level fecal sludge treatment plants (FSTPs), the paper proposes an actionable framework for designing, financing, and scaling grassroots sanitation while safeguarding inclusion, dignity, and public health.
subject: Articles
  topic: section
subject: grassroots innovation, low-cost sanitation, fecal sludge management, CLTS, ecosan, container-based sanitation, inclusive WASH
  topic: keywords
creator: Shah Swati Champaklal
creator: Dr. Dilip Kumar Sharma
rights: Copyright (c) 2025 Authors
date: 2025-09-29
publication date: 2025-09-29
starting page: 1
ending page: 4