Daily Archives: June 21, 2026

Authors: Mrs. Bhumika Alte, Satyam Mali, Yashraj Mhase, Kishor Hirgal

Abstract: Aspect-Based Sentiment Analysis (ABSA) provides a fine-grained approach to understanding opin-ions by extracting aspect–opinion–sentiment relation-ships from text. It is particularly valuable in domains such as product reviews, customer services, banking, and social media, where identifying specific strengths and weaknesses is essential. The subtask of Aspect-based Sentiment Triplet Extraction (ASTE) extends ABSA by simultaneously identifying aspect terms, corresponding opinion expressions, and their sentiment polarities. This work proposes an improved ABSA framework that integrates pre-trained language models (PLMs) with a pruned syntactic encoding mechanism to efficiently capture both local and global contextual dependencies. Additionally, a dynamic encoding strategy is introduced to overcome the limitations of traditional local encod-ing, which often fails to capture long-range relation-ships between aspects and opinions. The combination of syntactic pruning and dynamic encoding enhances the association between aspect and opinion terms, leading to more accurate sentiment classification. Experimental evaluations on benchmark ABSA datasets are expected to demonstrate that the pro-posed model achieves higher accuracy and robustness compared to existing methods. This approach effec-tively combines syntactic structure and contextual un-derstanding, improving interpretability and performance in aspect-level sentiment prediction tasks.

DOI: http://doi.org/10.5281/zenodo.20787176

Published by: vikaspatanker

Authors: Hitesh Jitendra Jadhav, Santosh Shriram Karvar, Atharv Arun Patil, Gaurav Anil Waje, Gaurav Vijay Barde, Bajirao Subhash Shirole

Abstract: A significant percentage of traffic accidents in the world result from sleepy drivers. Although a number of detection methods have been established, their utility is often problematic. Physiological signals (EEG, ECG) and vision- based behavioral cues (eye closure, yawning) have been studied in the past, and deep learning models such as Convolutional Neural Networks (CNNs) have shown excellent accuracy in controlled settings. Significant gaps still exist, though, especially in the areas of robustness against various lighting conditions and occlusions, validation in on-road scenarios, and non-intrusive, computationally efficient systems appropriate for real-time deployment on mobile platforms. This review highlights the shortcomings of current vision-based approaches while synthesizing and critiquing them. It then suggests a future- focused approach based on a lightweight CNN architecture (like MobileNetV2) optimized for on-device inference with TensorFlow Lite. This work attempts to close the gap between academic research and useful, scalable solutions that can improve road safety by concentrating on a camera-based, non – intrusive system deployable on common Android devices.

DOI: http://doi.org/10.5281/zenodo.20786979

Published by: vikaspatanker

Authors: Rasika Kokate, Saloni Gohad, Vaishnavi Gulave, Tanuja Karpe, Sunita Borse

Abstract: AYUSH (Ayurveda Yoga Naturopathy Unani Siddha and Homeopathy) system is a repository of the wisdom obtained from 8000 plants. But most of this knowledge is available in printed and handwritten Sanskrit and Hindi manuscripts which are computing unfriendly. This study introduces an end-to-end AYUSH knowledge recommendation pipeline based on AI to digitize, interpret and recommend insights from the AYUSH body of knowledge for modern computational intelligence. The framework combines Optical Character Recognition (Tesseract OCR), NLP for Indic languages, Knowledge Graph modelling (Neo4j) and AI-based reasoning (BERT, Random Forest) to convert unstructured manuscripts into searchable knowledge that can be analyzed by human . The system captures herbal, disease and treatment entities, relates the entities semantically, and then provides query-driven recommendations through an intelligent interface. Using a simple interface, researchers would be able to ask for insights such as “What are the herbs that have been associated with anti-inflammatory activity?” This strategy lowers the expense of early stage drug discovery, validates traditional remedies, and forges new roads in integrated health care investigation. This study provides the infrastructure for AI- based analysis of literature on traditional medicine and adds to digital conservation, availability and edification as well as evidence-informed integrated healthcare.

DOI: http://doi.org/10.5281/zenodo.20786139

Published by: vikaspatanker

Authors: Bhuvanesh G, Arun Das, Shivanand Chinnappanavar, Ravikumar M

Abstract: This study aimed to assess suitable lands for agricultural purposes in the Gundlupet taluk of Chamarajanagar district. Leveraging the widely used Analytic Hierarchy Process (AHP) integrated with Geographic Information System (GIS), this research conducted a thorough land use suitability analysis. Key parameters including geomorphological and geological features, relief, slope, drainage density, rainfall, soil texture, and land use and land cover were considered in the analysis. Weights were assigned to these parameters based on their significance and importance, resulting in the generation of an agricultural land suitability map divided into three categories. Upon excluding forested and reservoir areas from the reclassified suitability map, the study estimated that 19.59% of the study area (266 sq. km) is highly suitable for agricultural production, 67.6% (918 sq. km) is moderately suitable, and 12.81% (174 sq. km) is unsuitable for agricultural production in this region. This framework facilitates the early zoning of agricultural land for protection, ensuring sustainable land use development in the future.

DOI: http://doi.org/10.5281/zenodo.20783644

Published by: vikaspatanker

Authors: Lohitha Lakshmi K, Hema Sri S, Shaik Reshma, Hima Sai Nandhan P, Manoj Kumar Reddy S D V

Abstract: Data plays a key role in analysis and machine learning, but working with real-world datasets is often challenging because they usually contain missing values, duplicate entries, inconsistencies, and noise that can affect the accuracy of results. Data cleaning is therefore an essential step, yet it can be time-consuming and often requires programming knowledge, making it less convenient for many users. In this work, we present DataForge, a data preprocessing system designed to make the cleaning process simpler and more accessible. The platform allows users to upload datasets and perform cleaning operations without writing code, using a mix of statistical methods and simple intelligent techniques to handle issues such as missing data, outliers, and duplicate records. Overall, DataForge focuses on reducing the effort required for data preparation while still helping users work with more reliable datasets. This approach also helps users get a clearer idea of their data without going into too much technical detail.

Published by: vikaspatanker

Authors: Ms. Snehal Kadbhane, Mr. Ritesh Khandagale, Dr. Vijaykumar Kale, Dr. Mahesh Thakare, Vaibhav Narwade

Abstract: Background: The near-universal reliance on high-sucrose vehicles in paracetamol oral syrups creates an increasingly untenable clinical tension for vulnerable patient populations—diabetic individuals experiencing glycemic excursions, children at heightened risk of dental caries, and obese or metabolically compromised patients. With global diabetes prevalence now exceeding 537 million adults and dental caries ranking as the world's most prevalent non-communicable condition, the pharmacoeconomic and public health argument for sugar-free alternatives has become irrefutable. Methods: Five trial formulations (F1–F5) of a sugar-free paracetamol oral syrup at 120 mg/5 mL were developed using a Quality by Design (QbD) framework. Sorbitol (20–30% w/v), hydroxypropyl methylcellulose K4M (0.25–0.75% w/v), and sucralose (30–70 mg/100 mL) were systematically varied while all other excipients were held constant. Formulations were evaluated for organoleptic acceptability, pH, viscosity, drug content, density, surface tension, sedimentation ratio, and antimicrobial preservative effectiveness per USP <51> Category 2. The optimized formulation (F3) underwent 90-day accelerated stability testing per ICH Q1A(R2) at 40°C ± 2°C/75% ± 5% RH and was benchmarked against a commercially marketed sugar-free reference product. Results: F3, containing sorbitol 25% w/v, HPMC K4M 0.50% w/v, and sucralose 50 mg/100 mL, emerged as the optimized formulation. It exhibited a pH of 5.82 ± 0.02, viscosity of 92 ± 2.5 cps, drug content of 99.4 ± 0.5% of label claim, and a palatability score of 4.5/5.0—superior to both lower-concentration variants and the marketed comparator (4.2/5.0). Accelerated stability studies confirmed drug content above 98.6% and p-aminophenol below 0.08% at day 90, well within pharmacopoeial limits. All five challenge organisms met USP <51> Category 2 acceptance criteria. Conclusion: The optimized sugar-free paracetamol syrup demonstrates pharmacopoeial compliance, chemical and microbiological stability supportive of a 24-month shelf life, and patient acceptability equivalent or superior to a marketed reference. The formulation strategy—combining a polyol bulk sweetener with a high-intensity non-caloric sweetener and a cellulose-ether viscosity modifier—provides a scientifically validated, clinically advantageous platform for analgesic-antipyretic therapy in patient populations for whom conventional sucrose-based preparations are contraindicated or undesirable.

Published by: vikaspatanker