MedLingua: A conceptual framework for a multilingual medical conversational agent

This study introduces a hybrid model for an advanced medical chatbot addressing crucial healthcare communication challenges. Leveraging a hybrid ML model, the chatbot aims to provide accurate and prompt responses to users' health-related queries. The proposed model will overcome limitations observed in previous medical chatbots by integrating a dual-stemming approach, P-Stemmer and NLTK-Stemmer, accommodating both semitic and non-semitic languages. The system prioritizes the analysis of cognates, identification of symptoms, doctor recommendations, and prescription generation. It integrates an automatic translation module to facilitate a smooth multilingual diagnostic experience. Following the Scrum methodology for agile development, the framework ensures adaptability to evolving research needs and stays current with recent medical discoveries. This groundbreaking idea aims to improve the effectiveness and availability of healthcare services by introducing an intelligent, multilingual chatbot. This technology enables patients to communicate with doctors from diverse linguistic backgrounds through an automated language translation model, eliminating language barriers and extending healthcare access to rural regions worldwide.•A simple but efficient hybrid conceptual model for advancement in smart medical assistance.•This conceptual model can be applied to implement a medical chatbot that can understand multiple languages.•This method can be utilized to address medical chatbot limitations and enhance accuracy in response generation.

A visual dataset for recognition of rice varieties

This article presents a comprehensive dataset sourced from various markets across Bangladesh, highlighting 20 distinct rice varieties predominantly consumed locally. The dataset encompasses a diverse range of rice strains, including Subol Lota, Bashmoti (Deshi), Ganjiya, Shampakatari, Sugandhi Katarivog, BR-28, BR-29, Paijam, Bashful, Lal Aush, BR-Jirashail, Gutisharna, Birui, Najirshail, Pahari Birui, Polao (Katari), Polao (Chinigura), Amon, Shorna-5, and Lal Binni. Using a smartphone camera, low-resolution images capturing the essence of each rice variety were meticulously obtained, resulting in a total of 4,730 images with a non-uniform distribution. The dataset also includes augmented data, totaling 23,650 images. This precisely curated dataset holds significant promise and utility, showcasing diverse attributes, including the unique representation of 20 rice varieties, each characterized by distinct colors, sizes, and potential applications within the agricultural sector.

LuffaFolio: A Multidimensional Image Dataset of Smooth Luffa

This article introduces a comprehensive dataset designed for researchers to classify diseases in Luffa leaves, determine the grade of Luffa from Luffa images, and identify different growth stages throughout the year. The dataset is meticulously organized into three sections, each concentrating on specific facets of Luffa Aegyptiaca, commonly known as Smooth Luffa (Dhundol/). These images were captured in various village fields in Faridpur, Bangladesh. The sections include the assessment of Smooth Luffa quality, the identification of plant diseases, and the documentation of Luffa flowers. The dataset is divided into three sections, totaling 1933 original JPG images. The "Luffa Diseases" section features images of smooth Luffa leaves, depicting various diseases and unaffected leaves. Categories in this section encompass Alternaria Disease, Angular Spot Disease, Holed Leaves, Mosaic Virus, and Fresh Leaves, totaling 1228 JPG raw images. The "Flowers" category comprises 362 JPG raw images, showcasing different maturity stages in smooth Luffa flowers. Finally, the "Luffa Grade" section focuses on categorizing smooth Luffa into fresh and defective categories, presenting 343 JPG raw images for this purpose.

GalliformeSpectra: A hen breed dataset

This article presents a comprehensive dataset featuring ten distinct hen breeds, sourced from various regions, capturing the unique characteristics and traits of each breed. The dataset encompasses Bielefeld, Blackorpington, Brahma, Buckeye, Fayoumi, Leghorn, Newhampshire, Plymouthrock, Sussex, and Turken breeds, offering a diverse representation of poultry commonly bred worldwide. A total of 1010 original JPG images were meticulously collected, showcasing the physical attributes, feather patterns, and distinctive features of each hen breed. These images were subsequently standardized, resized, and converted to PNG format for consistency within the dataset. The compilation, although unevenly distributed across the breeds, provides a rich resource, serving as a foundation for research and applications in poultry science, genetics, and agricultural studies. This dataset holds significant potential to contribute to various fields by enabling the exploration and analysis of unique characteristics and genetic traits across different hen breeds, thereby supporting advancements in poultry breeding, farming, and genetic research.