Volume 14 | Issue 2 |

 Abstract

The "Smart Dustbin for Smart Cities" project aims to revolutionize urban waste management by introducing an intelligent system for segregating and notifying authorities about waste collection. Using soil moisture sensors to detect wet, dry, and metallic waste, this system sorts garbage into designated bins mounted on a servo motor for automated rotation. Additionally, a GSM module is used to notify waste collection authorities when bins are full, ensuring efficient operations. The system, powered by a Raspberry Pi, provides a sustainable solution to urban waste challenges, integrating automation and IoT technologies to promote smart city initiatives..

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 Keywords

Smart Mirror, Raspberry Pi, Face Recognition, IoT, Home Automation

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 Abstract

With the rapid change in the financial world, particularly today, the urgent demand for intelligent solutions to optimize the budget was therefore in great demand, individually as well as corporately. The paper proposes an advisory system for finance based on a cross-platform mobile application of Android and Flutter implementation of ml algorithm, which is designed to analyze users' financial data and come up with a personalized recommendation for budget optimization. The system can identify spending patterns, predict financial directions, and even propose cost-cutting initiatives by using ml algorithms that incorporate clustering and predictive analytics. It uses such core programming languages as Python in the design of the ml models and Dart through Flutter to give users a smooth, high-performance interface for Android and iOS. This makes it accessible and very easy to use to allow users to make real-time, data-driven financial decisions. The solution allows the users to maximize budgets in effective ways by reducing wastefulness and eventually leading towards financial stability. Demonstrated here is how ml-enhanced financial advisory applications have the potential to redesign personal and business finance management.

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 Keywords

ANN, Machine Learning, Flutter, Prediction.

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 Abstract

This project presents the development of a multipurpose stationary vending machine designed to address the growing need for accessible and on-demand of stationary supplies, document printing, and device charging. The proposed vending machine offers an all-in-one solution for students, professionals, and on-the-go users by integrating three key services as a range of essential stationary items (such as pens, notebooks, paper, and other office supplies), a wireless printer for easy and quick document printing, and 3-4 charging points compatible with various devices such as smartphones, tablets, and laptops.This project presents the development of a multipurpose stationary vending machine designed to address the growing need for accessible and on-demand of stationary supplies, document printing, and device charging. The proposed vending machine offers an all-in-one solution for students, professionals, and on-the-go users by integrating three key services as a range of essential stationary items (such as pens, notebooks, paper, and other office supplies), a wireless printer for easy and quick document printing, and 3-4 charging points compatible with various devices such as smartphones, tablets, and laptops.

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Smart Kiosk: An automated multi-purpose vending machine

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 Abstract

Diabetes Mellitus is a metabolic disease chronic in nature involving millions across the globe and may lead to complications that are critical in nature like Diabetic Retinopathy (DR), an illness which causes permanent vision impairment. Prompt detection and precise DR classification are indispensable for effective therapy and prevention of blindness. Manual techniques employed till now for DR are tedious, with potential for variability, so motivating the implementation of computer-assisted techniques in medical imaging. This work provides an extensive review of the recent developments in the era of deep learning, including Convolutional Neural Networks (CNNs), for detecting and classifying DR from retinal fundus images. We review publicly accessible datasets and discuss a range of deep learning models applied in this area. The primary contributions of this study are: (1) providing state-of-the-art deep learning methods for DR diagnosis, (2) comparing their performance on standard datasets, and (3) outlining current shortcomings and possible directions for future work in automated retinal image analysis.

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 Keywords

Deep Learning , Image Classification, Diabetic Retinopathy , Convolution Neural Network , Deep Neural Network

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 Abstract

Early detection of plant diseases is crucial for efficient crop management and maintaining global food security. This report investigates the application of image processing and thermal imaging technologies to identify plant diseases in their initial stages. Traditional methods of disease detection, which depends on visual assessments, can be slow and prone to bias. In contrast, advancements in computervision and remote sensing provide more rapid and objective assessments.

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 Keywords

plant diseases, disease detection, thermal imaging,Image processing, machine learning, convolutional neural networks (CNNs), diseases detection, visual inspection, computer vision.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Study of the Halophilic and Halotolerant Microbial the Halophilic and Halotolerant Microbial Callyspongia fallax

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Study of the Halophilic and Halotolerant Microbial the Halophilic and Halotolerant Microbial Callyspongia fallax

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Chitosan, a biopolymer derived from chitin, has gained prominence due to its biodegradability, biocompatibility, and antimicrobial properties. Traditionally extracted from crustacean shells, chitosan production faces limitations such as allergenicity, seasonal availability, and environmental concerns. This study investigates alternative sources--Labeo rohita fish scales, white button mushroom stalks, and banana peels--for chitosan extraction using acid-alkali chemical treatment. The extracted biopolymers were characterized using Fourier Transform Infrared Spectroscopy (FTIR), solubility testing, and pH analysis. Among the sources, fish scales yielded the highest purity and quantity of chitosan, while mushrooms and banana peels produced chitosan-like compounds with moderate functionality. These materials were further utilized to develop herbal biodegradable bandages and hydrogels, demonstrating promising antimicrobial and wound-healing properties. The findings support the valorization of agro-waste and seafood by-products for sustainable biomedical innovations.

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Sustainable Extraction and Biomedical Applications of Chitosan from Fish Scales, Mushroom Stalks, and Banana Peels

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 Abstract

A common medicinal plant with antibacterial and antifungal qualities is Lantana camara L. (Verbenaceae). In this study, various solvent extracts (methanol, ethanol, chloroform, and aqueous) of L. camara leaves were subjected to phytochemical screening and antifungal assays. The presence of alkaloids, flavonoids, glycosides, carbohydrates, and amino acids was determined by qualitative phytochemical testing. Fluconazole was employed as a positive control in antifungal tests against Aspergillus niger utilizing the agar well diffusion method. The extracts had detectable antifungal action, confirming L. camara's ethnomedical value. According to these results, the leaves of L. camara may contain bioactive substances that could be used to build antifungal medications.This research validates the traditional use of L. camara and highlights its potential as a source of natural antifungal agents, particularly from methanolic leaf extracts.

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Lantana camara, phytochemical screening, antifungal activity, Aspergillus niger, fluconazole

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 Abstract

This study conducted a molecular phylogenetic investigation of bacterial evolution using the gyrB gene, an orthologous marker encoding the B-subunit of DNA gyrase, which is functionally essential for prokaryotic DNA topology control. High-quality gyrB sequences, curated from the NCBI database, underwent Multiple Sequence Alignment (MSA) using Clustal Omega to characterize nucleotide substitution patterns. Phylogenetic trees were computationally derived via the NGPhylogeny.fr platform, revealing evolutionary relationships; concurrently, bioinformatic analysis identified conserved domains critical for gyrase activity. The phylogenetic informativeness of the gyrB methodology was rigorously validated through in silico translational analysis, sequence identity comparisons, and consistent species demarcation, affirming its utility as a reliable molecular chronometer for reconstructing bacterial evolutionary history.

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gyrB gene, molecular phylogeny, DNA gyrase, conserved ortholog, multiple sequence alignment, nucleotide substitution, evolutionary inference, taxonomic stratification.

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 Abstract

Staphylococcus aureus is a formidable opportunistic pathogen, with the emergence of Methicillin-Resistant Staphylococcus aureus (MRSA) posing a critical global health challenge. This study focuses on the Bengal Bay Clone, MRSA ST772, a virulent and multidrug-resistant Community-Acquired MRSA (CA-MRSA) strain increasingly linked to severe infections. Methicillin resistance in S. aureus is primarily mediated by the mecA gene, which encodes the penicillin-binding protein PBP2a. Despite the clinical significance of ST772, the genomic and evolutionary mechanisms underpinning its resistance phenotype remain poorly characterized.

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THE GENETIC AND EVOLUTIONARY BASIS OF METHICILLIN RESISTANCE IN STAPHYLOCOCCUS AUREUS MRSA ST772: A COMPARATIVE AND EVOLUTIONARY BIOINFORMATICS STUDY

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