Volume 14 | Issue 4 |

 Abstract

The rapid growth of financial markets and the increasing availability of financial data have created a need for intelligent systems capable of analyzing and interpreting financial information efficiently. Traditional financial platforms often provide raw financial data but lack intelligent interpretation and decision-support capabilities. This paper presents FinanceMCP, an AI-driven financial intelligence platform designed to assist users in analyzing stocks, mutual funds, IPOs, and macroeconomic indicators within the Indian financial ecosystem. The proposed system integrates real-time financial data sources with analytical tools and artificial intelligence techniques to provide actionable insights for investors. The platform combines financial technical analysis algorithms such as Relative Strength Index (RSI) and Moving Average Convergence Divergence (MACD) with a machine learning model based on Random Forest Regression to estimate portfolio resilience during financial stress conditions. In addition, the platform integrates a Large Language Model (LLM), Claude by Anthropic, which generates natural-language explanations for financial indicators and analytical results. The architecture of FinanceMCP follows a modular design consisting of a React-based user interface, FastAPI backend services, integrated financial APIs, and a machine learning module for predictive analytics. Experimental evaluation shows that combining machine learning models with LLM-based interpretation significantly improves user understanding of financial data and supports more informed investment decisions. The proposed framework demonstrates how artificial intelligence and financial analytics can be integrated to build intelligent financial advisory platforms. Keywords: Financial Intelligence, Machine Learning, Large Language Models, Claude LLM, FinTech Analytics, Portfolio Risk Prediction, Technical Analysis

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Financial Intelligence, Machine Learning, Large Language Models, Claude LLM, FinTech Analytics, Portfolio Risk Prediction, Technical Analysis

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

To guarantee safe and effective operation, thermal power plants need constant monitoring of vital parameters including temperature, vibration, voltage, and pressure. In big industrial settings, traditional cable monitoring systems are expensive and challenging to maintain. This article describes the design and implementation of an Arduino Uno and LoRaWAN-based smart industrial monitoring system. Sensors like the MAX6675 K-type thermocouple sensor, vibration sensor, voltage sensor, and pressure sensor provide real-time data to the system. LoRaWAN is used to send the gathered data wirelessly across great distances. The suggested system offers dependable data transfer, minimal power consumption, and long-range communication (up to 2 km). The technology is appropriate for thermal power plant applications since experimental findings show precise sensing and consistent communication.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Smart Monitoring, LoRaWAN, Arduino Uno, MAX6675, Vibration Sensor.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Workforce Task Automation is an automated system which uses technological tools to automate mundane activities such as task allocation, reporting, monitoring progress, etc., for the purpose of enhancing business operations. In its findings, this study points out several flaws in the existing manual systems for managing workforces which include the delay in tasks, human errors, misunderstanding, and lack of visibility of tasks at all times. These are addressed through the proposed system which uses smart scheduling, automated alerts, role based permissions, and central data processing for increasing efficiency and collaboration. As can be seen from the above description, the use of automation enhances the accuracy of tasks, minimizes costs of doing business, and speeds up process performance

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 Keywords

task automation

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

OncoEvolveAI is a self-evolving artificial intelligence framework designed to enhance tumor detection and optimize personalized therapy strategies in oncology. The system integrates advanced deep learning models with adaptive learning mechanisms to continuously improve its performance using real-time clinical data. By leveraging medical imaging, patient history, and genomic information, OncoEvolveAI provides highly accurate tumor identification and classification, reducing diagnostic errors and enabling early detection. A key feature of the framework is its ability to evolve through feedback loops, where model parameters are dynamically updated based on treatment outcomes and new data inputs. This ensures that the system remains relevant and effective across diverse patient populations and cancer types. Additionally, OncoEvolveAI incorporates predictive analytics to recommend optimized treatment plans tailored to individual patients, considering factors such as tumor characteristics, genetic markers, and response to previous therapies. The proposed framework aims to bridge the gap between diagnosis and treatment by providing an end-to-end intelligent solution for clinicians. Experimental evaluations demonstrate improved accuracy, efficiency, and adaptability compared to traditional AI models. Overall, OncoEvolveAI represents a significant step toward precision medicine, offering a scalable and robust approach to cancer care through continuous learning and intelligent decision support.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial Intelligence, Breast Cancer,Deep Learning,Active Learning

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The integrity of professional and legal transactions relies heavily on authentic certificates. However, the proliferation of sophisticated digital manipulation tools has made it easy to create fraudulent documents that are indistinguishable to the human eye. Current manual verification processes are labor-intensive, slow, and prone to human error, creating significant bottlenecks. Standard automated tools like QR code verification often fail to detect visual anomalies or sub-pixel discrepancies in the document's actual content. This project proposes an automated web-based forensic tool designed to identify document tampering at a sub-pixel level. By integrating Error Level Analysis (ELA) via OpenCV to highlight JPEG compression inconsistencies and a specialized Convolutional Neural Network (CNN) for high-precision classification, the system targets a detection accuracy of over 95%. Furthermore, Explainable AI (XAI) heatmaps are generated to provide transparent visual evidence of the specific regions contributing to the forgery decision.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial Intelligence, Certificate Authentication, Convolutional Neural Networks, Document Forensics, Error Level Analysis, Explainable AI.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

IoT, Smart Waste Management, Arduino UNO, Ultrasonic Sensor, ESP8266, GSM Alert, Adafruit IO, Real-Time Monitoring, Smart City, Embedded Systems.

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Creative Commons Attribution 4.0 and The Open Definition

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Rapid digitalization has led to an alarming increase in electronic waste (e-waste), which poses serious environmental and health hazards. Manual segregation and disposal remain inefficient and unsafe. This paper presents WasteWise, a comprehensive software system equipped with advanced Machine Learning and Computer Vision technologies to automate e-waste detection, classification, and management. The system features multi-label waste detection using Convolutional Neural Networks (CNNs), environmental lifecycle analysis with Eco-Score computation, intelligent recycling/upcycling suggestions, a multilingual AI chatbot assistant, and a reward-based eco-wallet. Additionally, it includes a community waste heatmap for location- based waste tracking and non-waste image filtering. The model integrates a Python-based backend with a React.js frontend, delivering a scalable, interactive, and sustainable digital waste management solution.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Waste Management, Smart Waste Segregation ,Artificial Intelligence (AI), Machine Learning (ML), Deep Learning Image Classification

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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

 Keywords

Industrial effluents, Biosorbent, Antioxidant, Chromium reduction, GC-MS

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The rapid growth of educational video repositories has made it even more important to find ways to condense content that keep its meaning and structure intact. Conventional summarization methods, predominantly reliant on temporal sampling or motion estimation, inadequately capture the perceptual redundancy characteristic of instructional videos, where information is frequently conveyed through gradual structural evolution rather than dynamic motion. This paper suggests a flexible multi scale structural similarity optimization framework for selecting frames that are aware of redundancy. The method uses hierarchical perceptual similarity modeling to find important transitions and get rid of unnecessary visual information. A principled threshold optimization formulation is presented to achieve a balance between compression efficiency and structural preservation, independent of supervised training. The framework is tested on a big dataset of more than 500 educational videos, more than 100 of which are long recordings that last more than five hours. Results from real world tests show that the system works well in a variety of teaching formats, cutting down on redundancy while keeping the structure intact. The suggested method is a computationally efficient and easy to understand alternative to data heavy learning based models. This makes it good for use in real world educational systems that need to be able to grow.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Structural Similarity Index, Multi scale Analysis, Video Summarization, Frame Selection, Educational Video Processing

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This paper examines whether climate change governance and the accelerating energy transition justify a broader approach to parent company liability for environmental harm. The central argument is that separate legal personality remains an important organizing principle of company law, but it cannot be treated as an absolute shield where the parent company designs, directs, monitors, and publicly represents the environmental and transition strategy of the corporate group. Contemporary climate governance is reshaping corporate behavior through disclosure rules, due diligence duties, litigation pressure, and investor scrutiny. At the same time, energy transition pathways require large-scale capital reallocation, operational restructuring, and group-wide risk management. These developments expose the limits of a purely formal model that allocates environmental harm only to the operating subsidiary while allowing strategic control, financing, and sustainability representation to remain concentrated at the parent level. Using recent case law, regulatory developments, and current global energy-transition data, the paper argues for a functional control approach. Under this approach, liability should turn not on ownership alone, but on the parent's real role in setting environmental standards, supervising compliance, controlling transition risk, and shaping harmful outcomes. Such an approach does not abolish separate legal personality. It instead aligns liability with governance reality. The paper concludes that effective climate-era accountability requires company law, due diligence regulation, and climate disclosure regimes to operate together so that environmental harm is attributed to the entity or entities that actually control the relevant risk.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

This paper examines whether climate change governance and the accelerating energy transition justify a broader approach to parent company liability for environmental harm. The central argument is that separate legal personality remains an important organizing principle of company law, but it cannot be treated as an absolute shield where the parent company designs, directs, monitors, and publicly represents the environmental and transition strategy of the corporate group. Contemporary climate gov

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Creative Commons Attribution 4.0 and The Open Definition