Daily Archives: October 26, 2026

Authors: Peace Onya Ali, Ikechukwu Sampson Chikwe, Olubunmi Abiola Adewusi, Tasie Chimezie Michael, Boniface Tersoo Iorhuna, Edjere Akpoobaro Obadejire, Erienu Obruche Kennedy

Abstract: Environmental pollution is a major global challenge that threatens environmental quality, human health, biodiversity, and ecological sustainability. This review examines the sources of environmental pollutants, pollutant removal techniques, their effectiveness, and their impacts on environmental quality and ecological sustainability. Major sources of pollution include industrial activities, agricultural practices, domestic waste, petroleum operations, transportation, and improper waste disposal. Common pollutants include heavy metals, petroleum hydrocarbons, pesticides, plastics, particulate matter, and emerging contaminants. Conventional removal techniques, such as filtration, adsorption, chemical precipitation, and biological treatment, are discussed alongside emerging approaches, including phytoremediation, nanotechnology, membrane filtration, electrochemical treatment, and advanced oxidation processes. The review highlights that the effectiveness of these techniques depends on pollutant characteristics, treatment conditions, operational costs, and environmental requirements. Although pollutant removal technologies can improve air, water, and soil quality, some generate secondary waste, consume substantial energy, or produce potentially harmful transformation products. These limitations emphasize the need for integrated treatment strategies, effective waste management, pollution prevention, and continuous environmental monitoring. Sustainable pollution control should prioritize affordable, energy-efficient, and environmentally safe technologies that support ecosystem restoration, biodiversity conservation, and resource recovery. The review concludes that combining conventional and emerging remediation techniques with stronger environmental regulations and sustainable production practices is essential for reducing pollution and promoting long-term ecological sustainability.

DOI: https://doi.org/10.5281/zenodo.23659503

Published by: vikaspatanker

Authors: Bhuvaneswaran R, Dharshan M, Nandhakumar S, Selvaragavan J, Indhuja N

Abstract: Email remains a common channel for personal, educational, and business communication, but unsolicited messages and deceptive email campaigns create security and productivity concerns. This paper presents an AI- and natural-language-processing-based workflow for classifying email text as spam or legitimate (ham). The proposed pipeline validates labelled email data, normalizes text, converts messages into numerical features using Term Frequency–Inverse Document Frequency (TF-IDF), and applies a supervised classifier. Multinomial Naive Bayes is used as the baseline model, while Logistic Regression and a linear Support Vector Machine can be considered for comparison. The workflow separates dataset preparation, feature extraction, training, prediction, and evaluation so that the same fitted pipeline is used for new messages. Model quality is assessed using accuracy, precision, recall, F1-score, and a confusion matrix. The project report does not provide verified numerical evaluation results; therefore, performance values should be obtained by running the implementation on the selected labelled dataset and should not be assumed. The design provides a reproducible educational prototype that can be extended with URL analysis, multilingual processing, and email-client integration.

DOI: https://doi.org/10.5281/zenodo.23657931

Published by: vikaspatanker

Authors: Ajay Saagar P, Kavihai Arasu S, Ramachandran K, Samuel S

Abstract: Artificial intelligence tools for conversation, programming and speech are usually built and used in isolation, which forces users to carry context manually from one tool to another. This paper presents the COLLABRO AI Ecosystem, a multi-agent platform that unifies conversational assistance, autonomous software engineering and audio intelligence within a single coordinated architecture. Natural Language Processing (NLP), Machine Learning (ML) and Deep Learning (DL) techniques are used to interpret user input, infer intent and decompose goals into tasks. An orchestrator agent routes these tasks to specialised agents for dialogue, code understanding, generation and debugging, and speech and audio analysis. A shared context store lets the agents maintain a unified understanding of the user’s goal, exchange intermediate artefacts and improve through feedback. A governance layer embeds data privacy, transparency and responsible automation into every agent interaction. We describe the architecture, the coordination and routing workflow, candidate implementation techniques and the ethical safeguards, and we propose an evaluation protocol with per-agent and cross-agent metrics. The modular design supports software development, enterprise automation, content creation and accessibility applications. This paper reports design and methodology; no quantitative performance results are claimed, and measured evaluation is identified as future work.

DOI: https://doi.org/10.5281/zenodo.23657542

Published by: vikaspatanker