VOLUME 14, ISSUE 10, OCTOBER 2026

Abstract: This study developed a fortified bio‑liquid fertilizer from poultry manure and lignocellulosic biomass through anaerobic co‑digestion under mesophilic conditions (35–37 °C) for 14 days, followed by mineral fortification with urea, single superphosphate (SSP), and muriate of potash (MOP) to achieve an NPK 12:6:8 formulation. Physicochemical analyses confirmed improved nutrient balance, stability, and sustained release compared with unfortified bio‑liquid. Nitrogen release reached ~90% by day 20, phosphorus ~70%, and potassium ~50%, supporting gradual nutrient availability. The fortified product demonstrated potential as a cost‑effective and eco‑friendly alternative to synthetic NPK 15:15:15, while retaining organic carbon for soil health. These findings highlight agro‑waste valorization as a viable pathway for sustainable fertilizer production, aligning with circular economy principles and agricultural intensification.

Keywords: Agro-waste valorization, Fortified Bio-liquid Fertilizer, Anaerobic digestion, Circular Economy

Abstract: Indian Sign Language (ISL) is an important visual language used by deaf and hard-of-hearing people in India for communication. However, many people who do not understand sign language face difficulties in communicating with ISL users. In many situations, communication depends on another person who understands sign language, which may not always be available. Therefore, there is a need for a system that can automatically recognize Indian Sign Language gestures and convert them into understandable text and speech.

This research proposes an Indian Sign Language Text/Speech Translation System to make communication between ISL users and non-sign-language users easier and more accessible. The system provides a digital method to capture ISL gestures through a camera, process the input, identify the signs, and convert the recognized gestures into text. The generated text can then be converted into speech using a text-to-speech component. The proposed system can use computer vision, image processing, machine learning or deep learning techniques, and natural language processing for recognizing and translating ISL gestures. The system can support real-time communication and reduce the need for manual interpretation in basic communication situations. It can also provide a foundation for future development of multilingual translation and improved recognition of continuous signs.

Keywords: Indian Sign Language, ISL Translation, Sign Language Recognition, Text Translation, Speech Generation, Computer Vision, Deep Learning, Natural Language Processing.

Abstract: The rapid growth of smart phones, GPS technology, and location-based service as created new opportunities. or automating attendance management. Traditional attendance systems commonly depend on manual registers or conventional digital methods, which can require considerable time and may allow inaccurate or proxy attendance. This research presents a Geo-Location Based Attendance Tracking Mobile Application designed to record attendance by verifying the user's current geographical location.

The proposed system uses GPS-based location detection and a predefined geographical boundary, or geo-fence, to determine whether auseris present within an authorized attendance area. When the user's location satisfies the defined criteria, the application records attendance along with date and time. The system is intended to provide a simple, automated, anduser- friendly approach to attendance tracking while reducing manual work.

The study focuses on system design, location verification, attendance recording, and application evaluation. The proposed application can be useful in educational institutions and organizations where attendance needs to be associated with physical presence at a specified location. Future extensions may include face verification, cloud synchronization, analytics, and stronger location-spoofing protection.

Keywords: Geo-Location, GPS, Attendance Tracking, Mobile Application, Geo-Fencing, Location-Based Services, Automated Attendance.

Abstract: Artificial Intelligence (AI) has become an important part of modern higher education. Postgraduate students increasingly encounter AI-based tools for searching information, understanding difficult concepts, writing and improving text, coding, summarising content, preparing presentations, generating ideas, and supporting research. This research studies the awareness and adoption of AI tools among postgraduate students, with emphasis on knowledge of AI tools, frequency of use, academic purposes, perceived benefits, challenges, trust, privacy, accuracy, academic integrity, and responsible use. The study follows a descriptive quantitative survey design using a structured questionnaire. The target population is postgraduate students, and the proposed data collection method is a questionnaire-based survey using convenience or purposive sampling. The literature review includes studies on student use of Generative AI, technology acceptance, trust, and learning outcomes. The paper presents a research framework, questionnaire, proposed analysis plan, and responsible-use recommendations. Numerical findings are intentionally not fabricated; final percentages and statistical results should be inserted after actual questionnaire data are collected.

Keywords: Artificial Intelligence, Generative AI, AI Awareness, AI Adoption, Postgraduate Students, Higher Education, AI Literacy, Academic Integrity.

Abstract: The enterprise software landscape has experienced a monumental shift towards natural language interfaces, driven largely by the advent of Large Language Models (LLMs). However, generic LLMs frequently suffer from domain hallucination, data privacy vulnerabilities, and an inability to interface with proprietary corporate knowledge. This research presents the architecture and implementation of a Conversational Chatbot Builder designed specifically to construct smarter, domain-specified virtual assistants. The proposed system employs a Retrieval-Augmented Generation (RAG) framework, seamlessly integrating pre-trained foundation models with specialized vector databases. By transforming institutional datasets into high-dimensional semantic embeddings, the system grounds the virtual assistant's responses entirely in verified, contextually relevant data. The platform abstracts the complexities of NLP orchestration, offering a dynamic pipeline for query ingestion, intent classification, semantic search, and prompt injection. Furthermore, the architecture implements rigorous security guardrails, including real-time Personally Identifiable Information (PII) redaction and strict Role-Based Access Control (RBAC). Extensive empirical evaluations demonstrate that this domain-specified approach significantly reduces response latency compared to heavy generalized inference, while entirely mitigating factual hallucinations. The resulting conversational interface achieves human-like fluency combined with deterministic accuracy, representing a paradigm shift in how organizations deploy secure, intelligent virtual agents.

Keywords: Conversational AI, Chatbots, Retrieval-Augmented Generation, RAG, Natural Language Processing, LLMs, Vector Databases, Domain-Specific AI.