Volume 14 | Issue 6 |

 Abstract

Automated personal finance management intersects two distinct but deeply interrelated challenges: precise transaction-level semantic categorization and jurisdiction-specific statutory tax compliance. In India, these challenges are compounded by a dual-regime income tax structure introduced under the Finance Act 2020 and substantially reformed in 2023, requiring taxpayers to compare liabilities under both regimes before filing. This paper presents FinanceAI, a full-stack intelligent system that unifies personal finance management with an end-to-end Indian income tax engine. The core technical contribution is a confidence-gated hybrid classification pipeline that applies deterministic keyword-driven rules for high-confidence transactions while delegating ambiguous items to an LLM (LLaMA-3.1-8B-Instant via Groq) at a configurable confidence threshold (? = 0.70). The system implements complete FY 2024-25 tax slab logic for both the Old and New regimes, including Chapter VI-A deductions, rebate under Section 87A, and standard deduction differentials. Classified transaction streams are automatically converted into double-entry accounting ledger and journal entries. Downstream, a document generation engine produces portal-compatible ITR-1 Sahaj JSON, Form 16 Tax Computation Certificates, and Form 26AS TDS Reconciliation PDFs using ReportLab. A context-aware AI Chartered Accountant (AI-CA) chatbot grounded in the user's live financial data provides personalized advisory backed by an offline rule fallback. Experimental analysis demonstrates that the hybrid pipeline achieves substantially higher classification accuracy on domain-specific Indian financial transactions compared to a purely rule-based baseline, while maintaining deterministic fallback integrity. The system operates fully locally with no mandatory external API dependency, positioning it as a viable open-source alternative to proprietary tax-filing platforms. Results confirm the feasibility of LLM-augmented statutory compliance in resource-constrained personal finance contexts.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Personal Finance Management, Hybrid Transaction Classification, Large Language Models, Indian Income Tax, Dual-Regime Tax Computation, ITR-1 Sahaj, Double-Entry Accounting, AI Advisory Systems, FinTech.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

There are many varieties of English. The most common of which are dialectic and Register. Dialect is a regional form of language. It may be of specific region or group. The group can be regional, social, ethnic, or it can be based on gender or particular class. Register is measured on a formality scale. The determination of language in register is done on the basis of situation it is used. There are five types of register i.e. Frozen/Static (laws or bibliography style), Formal/regulated (announcements by judges), consultative/Professional (Client-Lawyer, Doctor-Patient), Casual/Group (between peers and friends), Intimate/Personal (between Siblings, Parents, Best Friends). The present paper is concerned with the use of language in the "Consultative and Professional" type. These languages are created by professionals with some abbreviations or short form. It depends upon the profession or the group can be informal or formal. It is different from standard language. The aim of the present paper is to study the legal English as English for Specific Purposes.

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Creative Commons Attribution 4.0 and The Open Definition

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Legal English as English for Specific Purposes

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

THE CONTRIBUTIONS OF ANNIE MASCARENE IN KERALA LEGISLATURE AND CHRISTIAN COMMUNITY

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

BURNING FIRE BALL, MEMBER OF PARLIAMENT

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This research investigates the complex tension between the adoption of English-medium education and the sustained vitality of the Nyishi language, the largest indigenous community in Arunachal Pradesh, India. As English-dominant schooling becomes the primary vehicle for socio-economic mobility, it simultaneously functions as a powerful agent of linguistic shift, marginalizing Nyishi orality the traditional repository of the community's cosmology, customary laws, and ecological knowledge. Through a qualitative analysis, this study examines how institutionalized English pedagogy inadvertently devalues vernacular oral traditions, creating a generational "proficiency gap" where the mastery of academic English correlates with the erosion of indigenous linguistic competence. The findings reveal that while English proficiency is essential for modern integration, the current educational framework lacks structural mechanisms to integrate, preserve, or validate the oral-centric heritage of the Nyishi people. This paper argues for a decolonial pedagogical approach that moves beyond monolithic language policies, proposing a "bilingual bridge" model that fosters English proficiency without necessitating the abandonment of ancestral orality. By situating the Nyishi experience within the broader discourse of global linguistic homogenization, this research underscores the urgent need to redefine educational success in a way that reconciles professional advancement with the preservation of indigenous cultural identity.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Keywords: Nyishi language, orality, English-medium instruction, mother tongue education, Arunachal Pradesh, indigenous knowledge, language erosion, multilingual education

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Extensible Markup Language (XML) is widely used for structured data representation and data exchange across web applications, enterprise systems, and distributed platforms [source: 1]. Efficient XML parsing plays a crucial role in improving application performance; however, selecting the most suitable parser for varying file sizes and hardware configurations remains a significant challenge [source: 1]. Existing approaches typically rely on predefined parsing strategies that may not adapt effectively to dynamic processing environments [source: 1]. To address this issue, machine learning-based frameworks have been explored to predict efficient XML parsing algorithms based on input characteristics and system parameters [source: 1]. This study analyzes an existing hybrid machine learning framework that utilizes Artificial Neural Networks (ANN) and Support Vector Machines (SVM) to optimize XML parsing performance [source: 1]. The framework profiles multiple parsing algorithms such as SAX, StAX, DOM, JDOM, and PXTG across different file sizes and processing cores to generate a dataset [source: 1]. The dataset is then used to train classification models that predict the most efficient parser for a given configuration [source: 1]. Although the framework improves parsing efficiency, it has several limitations including restricted algorithm diversity, limited evaluation metrics, and constrained dataset configurations [source: 1]. To overcome these challenges, an enhanced intelligent XML parsing optimization system is proposed [source: 1]. The proposed system integrates advanced machine learning models, expanded parser profiling, and comprehensive evaluation metrics to improve prediction accuracy and scalability [source: 1]. Additionally, adaptive learning mechanisms and distributed processing environments are incorporated to support large-scale XML data processing [source: 1]. The improved framework aims to provide more accurate parser selection, better resource utilization, and faster XML parsing performance in modern data-intensive applications [source: 1]

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Creative Commons Attribution 4.0 and The Open Definition

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XML Parsing, Machine Learning, Parser Optimization, Artificial Neural Networks (ANN), Support Vector Machines (SVM), Performance Prediction, Distributed Processing, Parser Profiling, Data-Intensive Applications [source: 1]

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Creative Commons Attribution 4.0 and The Open Definition

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Non strabismic; binocular vision; convergence insufficiency; headache; accommodative dysfunction; vergence; orthoptic evaluation

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Creative Commons Attribution 4.0 and The Open Definition

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occupational radiation dose; cardiac catheterisation; interventional cardiology; double dosimetry; effective dose; Ghana

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Creative Commons Attribution 4.0 and The Open Definition

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A significant advancement in the functionality of medical devices with in biomedical engineering is represented by the integration of cloud computing with machine learning (ML). To enhance the medical research and patient care, biomedical big data is abruptly increasing due to it, combine the large-scale and different healthcare data with modern computing and intelligent technologies. It has the inclusion of advancements in genomics, medical imaging, wearable sensors and electronic health record systems. Traditional computational systems are not proficient to process such large-scale and heterogeneous datasets. Cloud computing provides the infrastructure for storing and managing huge volumes of healthcare data generated by medical devices, facilitating easy access and processing from anywhere, anytime. Meanwhile, medical devices analyze this data, identify patterns and make predictions, enhancing diagnostic accuracy and patient outcomes with machine learning algorithms. Cloud computing integrate with high-performance machine learning (HPML) provides scalable storage, parallel processing and intelligent analytics for biomedical applications. This paper presents an introspective study of cloud architectures, machine learning models, healthcare applications, benefits, limitations and future tendencies in biomedical big data analytics.

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Creative Commons Attribution 4.0 and The Open Definition

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Cloud Computing, Machine Learning, Medical Devices, Biomedical Engineering, Data Processing, Real-Time Monitoring, Predictive Analytics, Patient Outcomes, Healthcare, AI Integration.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Digital banking has changed the way banking services are delivered and accessed by customers. It allows people to use banking facilities anytime and anywhere through digital platforms without visiting a physical branch. Traditional methods such as cheques and demand drafts are gradually being replaced by online transactions and digital payment systems. To support this change, commercial banks are investing in digital transformation initiatives. These initiatives help banks improve their efficiency, service quality, and overall performance. Studies show that digital transformation can increase revenue and improve operational effectiveness. Although banks may face higher costs during the initial stages of implementation, these costs generally decrease over time. The success of digital transformation also depends on factors such as bank size, age, customer awareness, and digital infrastructure. Research has found that the adoption of digital payments is influenced by more than just the features of the technology. Business environments and organizational factors also play an important role in customer acceptance. Understanding these factors helps banks develop better digital services. Digital transformation has become a key strategy for maintaining competitiveness in the banking sector. It enables banks to meet changing customer expectations and improve service delivery. Therefore, digital banking continues to play an important role in the growth and development of modern commercial banks.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Digital banking, Digital transformation, Bank performance, Digital payments, and Banking efficiency.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Abstract: Concrete is one of the most widely used construction materials in the world, and its performance largely depends on the quality of water used during mixing and curing. Recent studies have indicated that magnetized water can improve the properties of concrete by altering the molecular structure of water, leading to better hydration of cement particles. This experimental study investigates the effect of magnetized water on the fresh and hardened properties of concrete. In this research, water was passed through a magnetic field to produce magnetized water and was subsequently used for concrete mixing. Concrete specimens were prepared using both normal water and magnetized water for comparison. Tests were conducted to evaluate workability, compressive strength, split tensile strength, and flexural strength at different curing periods. The results obtained from the experimental investigation were analyzed to determine the influence of magnetized water on concrete performance. The findings indicate that concrete prepared with magnetized water exhibits improved workability and enhanced mechanical properties compared to conventional concrete. An increase in compressive strength, tensile strength, and durability characteristics was observed due to improved cement hydration and reduced water cluster size. The use of magnetized water also has the potential to reduce water consumption and improve the overall efficiency of concrete production. Therefore, the study concludes that magnetized water can be considered a promising alternative to ordinary mixing water in concrete technology, contributing to the development of stronger, more durable, and sustainable concrete structures.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Magnetized Water, Concrete, Compressive Strength, Workability, Cement Hydration, Sustainable Construction, Durability.

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Creative Commons Attribution 4.0 and The Open Definition