Volume 14 | Issue 4 |

 Abstract

The Web Based Voting Platform for Institute Election is a secure and centralized web application designed to automate and manage institutional election processes with accuracy, transparency, and controlled access. The system replaces traditional manual voting methods by providing a structured digital environment that ensures voter authentication, vote uniqueness, and administrative control. The platform consists of two independent interfaces: an administrator module and a student voting module. The administrator module enables authorized users to securely log in, register and manage candidate information, maintain election records, and monitor final voting results. It also supports effective management of both current and historical election data. The student voting module ensures secure participation by validating a unique student identification number against the institutional database. Upon Successful verification, an OTP-based authentication mechanism is used to authorize the voting process through the registered mobile number. The system strictly enforces a one-vote-per-student policy, preventing duplicate voting attempts and maintaining election integrity. Students are restricted from accessing vote counts or intermediate results to avoid bias and influence. This web-based platform enhances election reliability, minimizes human intervention, prevents unauthorized access, and provides a scalable and efficient solution suitable for institutional election management.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Web-Based Voting System, Secure Voting, OTP Authentication, Voter Verification, Election Management, One Vote Policy, Access Control, Data Integrity, Digital Elections, Authentication System.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Student dropout is a significant challenge faced by educational institutions, affecting both academic outcomes and institutional reputation. This study proposes a machine learning-based approach to predict students who are at risk of dropping out at an early stage. The model utilizes features such as attendance, academic performance, assignment completion, travel distance, and family support. Multiple algorithms including Random Forest, Logistic Regression, and Decision Tree are implemented and compared. The results demonstrate that the Random Forest model provides better prediction accuracy. The proposed system enables early identification of at-risk students, allowing institutions to take timely intervention measures and improve retention rates.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Student Dropout Prediction, Machine Learning, Random Forest, Early Intervention, Academic Performance, Predictive Analytics, Classification Models, Educational Data Mining

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Student Performance Prediction, Academic Achievement, Machine Learning, Random Forest, Regression Models, Data Visualization, Early Academic Intervention, Predictive Analytics

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Online hotel reviews provide valuable insights into customer experiences; however, such reviews are often short and informal, making automated sentiment analysis challenging. Single classification models frequently struggle to capture sentiment accurately due to limited contextual information and lexical variability. To address this issue, this paper presents a comparative study of voting-based and stacking-based ensemble learning methods for sentiment analysis of hotel reviews. Reviews are preprocessed and decomposed into bigram phrases to analyze the sentimentsin hotel reviews. Multiple feature representations including TF-IDF, Word2Vec, and BERT embeddings are employed to capture lexical, semantic, and contextual information. In the voting ensemble, independent classifiers aggregate predictions using soft voting, while the stacking ensemble integrates base learner outputs through a meta-classifier. Experimental results demonstrate that stacking ensembles achieve more consistent performance on ambiguous hotel review phrases, whereas voting ensembles provide a computationally efficient and reliable baseline. The findings highlight the effectiveness of ensemble strategies for short-text sentiment analysis in hospitality review systems.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Sentiment Analysis, Hotel Reviews, Ensemble Learning, Voting Ensemble, Stacking Ensemble ,Short Text Classification, Bigrams, Deep Language Models

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Managing sports achievement records in educational institutions often involves significant manual effort, leading to data redundancy and lack of immediate accessibility. This paper presents the "NCW Sports Achievement Tracker," a robust web-based application developed using the Flask framework and MongoDB NoSQL database. The system is designed to digitalize student-athlete records, providing features such as dynamic search, batch-wise filtering, and an automated points-based leaderboard. By leveraging a non-relational database schema, the system ensures high scalability and flexibility for diverse sporting disciplines. Experimental results demonstrate that the system effectively manages 191 records with high retrieval accuracy, enhancing the recognition and motivation of student athletes.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

NCW Sports Achievement Tracker: A Scalable Web-Based System for Athletic Recognition and Data Management

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Green synthesis of metal oxide nanoparticles has gained considerable attention due to its eco-friendly nature and reduced environmental impact. In the present review, Taguchi optimization methodology is discussed as an effective statistical tool for optimizing microwave-assisted green synthesis parameters of cobalt oxide (Co?O?) nanoparticles using plant leaf extract. Key variables such as precursor concentration, extract volume, microwave irradiation time, and annealing temperature were optimized using an L9 orthogonal array design. Structural characterization was performed using X-ray diffraction (XRD), confirming the formation of phase-pure cubic spinel Co?O? nanoparticles. Crystallite size was calculated using the Debye-Scherrer equation. The Taguchi approach significantly minimized experimental runs while maximizing crystallinity and structural uniformity. The optimized green microwave-assisted synthesis method provides an energy-efficient, scalable route for producing cobalt oxide nanoparticles for catalytic, electrochemical, and environmental applications.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Green synthesis; Cobalt oxide nanoparticles; Taguchi method; Microwave irradiation; XRD characterization

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Nanomaterials have emerged as a revolutionary class of materials with unique physicochemical properties such as high surface area, quantum effects, and enhanced reactivity. These properties have enabled wide applications in environmental remediation, medicine, agriculture, electronics, and energy sectors. Despite their significant advantages, nanomaterials also pose potential risks to ecosystems and human health due to their toxicity, persistence, and bioaccumulation tendencies. This review critically examines both the positive and negative impacts of nanomaterials on the environment. Beneficial applications include water purification, air pollution control, soil remediation, and green energy solutions. However, unintended environmental exposure can lead to toxicity in aquatic systems, soil microorganisms, and human tissues. The study compiles data on commonly used toxic nanoparticles, their environmental pathways, and mechanisms of toxicity. It also discusses safer alternatives and sustainable approaches such as green synthesis and biodegradable nanomaterials. The balance between technological advancement and environmental safety is essential for sustainable development. The findings emphasize the need for stringent regulations, risk assessment frameworks, and eco-friendly nanotechnology practices to minimize adverse effects while maximizing benefits.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Nanomaterials; Environmental Impact; Toxicity; Nanoparticles; Sustainable Nanotechnology

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Climate change has emerged as a critical global concern affecting various sectors, including tourism. The Thane district of Maharashtra, characterized by its diverse geography, coastal ecosystems, forested regions, and cultural heritage, is particularly vulnerable to climatic variations. This review examines the impact of climate change on tourism in Thane district by integrating geographical, socio-cultural, and environmental perspectives. Thane's proximity to the Arabian Sea, its position within the Konkan region, and the presence of rivers such as Ulhas and Vaitarna contribute to its tourism potential but also increase its vulnerability to flooding, rising temperatures, and extreme weather events [1][2]. Over the past decade, observable changes in rainfall patterns, increasing temperatures, and deteriorating air quality have influenced tourist inflow and destination sustainability. Tribal communities and local cultural traditions, including Tarpa dance and Warli art, form an integral part of tourism, yet face disruption due to ecological stress. This study employs qualitative and quantitative methods, including literature survey, field observations, and statistical analysis, to assess climate trends and their implications for tourism and employment. The findings indicate that climate change affects natural attractions, infrastructure, seasonal tourism, and livelihoods. Sustainable tourism planning, climate-resilient infrastructure, and community participation are essential to mitigate adverse impacts and promote eco-tourism. The study concludes that integrating environmental conservation with tourism development is crucial for the long-term sustainability of Thane district.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Climate Change; Tourism; Thane District; Tribal Culture; Sustainable Development

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

A novel series of halogen-substituted heterocyclic derivatives based on the 1,2,4-triazole scaffold was synthesized and evaluated for their biological potential. In the present investigation, chloro-, bromo-, and fluoro-substituted derivatives of 5-(4,7-dihydrothiazolo[5,4-c]pyridin-2-yl)-4-phenyl-4H-1,2,4-triazole-3-thiol were successfully prepared through an efficient synthetic protocol. The structures of the synthesized compounds were confirmed using various spectroscopic techniques, including FT-IR, 1H NMR, 13C NMR, and mass spectrometry. The spectral data were found to be in good agreement with the proposed molecular structures. The newly synthesized derivatives were further screened for their biological activities against selected microbial strains to evaluate their antimicrobial potential. The results revealed that several halogen-substituted compounds exhibited significant inhibitory activity compared to the standard drugs. The presence of electron-withdrawing halogen substituents such as chloro, bromo, and fluoro on the aromatic ring was found to influence the biological activity of the synthesized molecules. These findings suggest that the incorporation of halogen atoms into the triazole-thiazolopyridine framework may enhance biological efficacy and could provide promising lead structures for the development of new bioactive heterocyclic compounds.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

1,2,4-Triazole; Thiazolopyridine; Halogenated derivatives; Chloro, bromo and fluoro substituents; Spectral characterization; Antimicrobial activity; Heterocyclic compounds; Bioactive molecules.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Multi-walled carbon nanotubes; Spray pyrolysis; Cobalt oxide catalyst; Castor oil precursor; XRD characterization

  License

Creative Commons Attribution 4.0 and The Open Definition