Modern Retrieval Augmented Generation often lacks an inherent understanding of document-specific relationships and structured knowledge. By combining large language models and graph-based retrieval, the PDF Query Chatbot presented in this research fills this gap and provides more precise and contextually aware responses. In order to explicitly record entity relationships and structural dependencies, the system uses Neo4j to create a knowledge graph after extracting textual content from the uploaded documents. To facilitate a semantic similarity search, the text is simultaneously shredded and embedded in a vector store. A hybrid retrieval system that combines vector-based search for contextual relevance and graph traversal for relational comprehension is activated when a user submits a query. To produce grounded, document-specific responses, the results of the two retrieval pipelines were combined and sent to the LLM. By synergizing graph databases, semantic search, and LLMs, this architecture provides a context-aware solution for intelligent document interaction, addressing key limitations in traditional LLM-based question resolution systems.

In today's digital world, processing multilingual documents is critical for business, legal tasks, and information retrieval. This study describes a Multilingual Document Processing System that uses Optical Character Recognition (OCR) and Retrieval-Augmented Generation (RAG) to extract, query and summarize text in multiple languages. The system employs advanced OCR models to correctly recognize text from scanned documents, images, and handwriting in various scripts. By incorporating RAG, it improves comprehension and response generation, allowing users to retrieve and summarize information in English even when the original language is different. This approach takes advantage of recent advances in natural language processing, large language models (LLM), and multimodal AI to address challenges in multilingual data accessibility, knowledge synthesis, and real-time communication. The system provides a scalable AI-driven solution to improve document processing, eliminate language barriers, and increase global user engagement. AWS services support scalable document processing but cold starts in AWS Lambda hinder real time tasks.

Accurate product classification in e-Commerce and supply chain management is essential to smooth operations and enhance the customer experience. While Large Language Models (LLMs) perform exceptionally in natural language processing, they encounter issues like model hallucination and dependence on outdated information. Furthermore, LLMs often rely on outdated data. This paper introduces an open source cloud-based RAG model, using Amazon Web Services (AWS) and vector databases to address these issues. The RAG architecture combines retrieval-based and generation-based methods, allowing them to supplement responses with up-to-date information from external sources, thus reducing the risk of model hallucination. The project employs a Vector DB deployed in EC2 to improve contextual understanding and retrieval capabilities of these large language models. Through comprehensive experimentation and AWS deployment, the RAG system improved contextual comprehension and increased the accuracy of the generated output. Semantic similarity search results significantly improve retrieval performance.

Power consumption forecasting plays a critical role in effective energy management and resource allocation. However, accurate prediction of power consumption presents unique challenges due to the diverse nature of energy use patterns. This intricate interplay requires the application of advanced forecasting methodologies capable of effectively capturing the temporal dynamics and inherent non-linear relationships embedded within power consumption data across diverse time horizons. In accordance with these findings, this research delves into the complexities of the prediction of energy consumption by proposing a novel approach. We leverage long-short-term memory (LSTM) networks, a type of recurrent neural network (RNN), for long-term energy usage predictions due to their ability to model temporal dependencies. We utilized a polyregression model for short-term forecasting tasks, capitalizing on its effectiveness with smaller, non-cyclical datasets. The results demonstrate the superiority of LSTMs in handling complex relationships within power usage data. In contrast, the polyregression model, while achieving an acceptable R2 of 0.763 for smaller datasets, struggled with limited data points. These findings contribute significantly to the field of power usage forecasting by highlighting the effectiveness of long-Short-Term Memory networks in handling large, featurerich datasets and providing a robust and reliable approach for utilities and energy providers seeking to improve their load forecasting capabilities, leading to more efficient resource management and grid operation.

Assessment of a person's mental health is a complex phenomenon that affects many people around the world. This study aims to develop an accurate and efficient method to identify possible mental health concerns through text analysis, such as written messages and social media posts. This paper uses the capabilities of Amazon SageMaker Autopilot and KNIME to deliver improved model performance. This approach yields an improvement in accuracy compared to baseline models, demonstrating the potential to take advantage of cloud-based machine learning platforms and data science tools to streamline workflows. The results of the experiment demonstrate the effectiveness of the proposed approach. Specifically, XGBoost achieved the highest accuracy of 93.6%, outperforminag Linear Learner and Multilayer Perceptron.

This work explores the critical role of autoverification in laboratory medicine, where timely and accurate test results are imperative for effective patient care. Auto-verification systems can significantly reduce the time to report and enhance the reliability of test outcomes, which is particularly crucial in time-sensitive diagnostic environments. This study delves into meticulous preprocessing of clinical data to prepare them for analysis, addressing challenges such as data inconsistency and missing values. By integrating advanced machine learning (ML) and deep learning (DL) models, we develop robust algorithms aimed at automating the verification of laboratory test results. Furthermore, we demonstrate the feasibility of these algorithms by replicating the processes in KNIME, a data analytics platform. This not only substantiates the scalability of our approach but also underscores its potential for real-world application in improving diagnostic workflows and patient outcomes.

The Internet of Things has spurred urban growth and increased environmental pollution. This paper introduces EcoCoin, a mobile app that rewards college campus students for sustainable actions. In addition, it explores how facial recognition can deliver personalized ads based on expressions, improving the retail experience with real-time recommendations. The paper concludes with an overview of Amazon cloud services' advanced features, underscoring their utility for developers to incorporate sophisticated image and video analysis, and highlighting technology's role in achieving social goals.

This work proposes a cloud-based attendance system that uses face recognition technology to authorize identity. The system uses the Amazon Web Services (AWS) Rekognition service and a serverless architecture. The proposed system provides a reliable and tamperproof solution to track attendance, eliminating the need for manual record keeping and minimizing human involvement. It also offers potential benefits, such as improved security and transparency in attendance management.

The digital revolution has made access to information very easy. The onset of the COVID-19 pandemic also called for further digitization. Every organization; be it an office, an educational institute or a government entity, was forced to shift to an all virtual mode of operation. This led to the conduct of online examinations with very little time for formulating an anti-cheating examination pattern. Audio and video proctoring tools are considered helpful but are very expensive and do not provide a method to detect plagiarism in the handwritten text. This is a serious problem for academic enterprises and institutes where there is a need for plagiarism detection in the submitted assignments, answer-scripts against the information available on the Internet as well as against other submissions. This paper presents a plagiarism detection system for handwritten text in English. The proposed system uses authentication tools/services, cloud storage, and optical character recognition (OCR) services to automate the process of checking plagiarism between two handwritten documents, as well as plagiarism with respect to all information available online.

Air pollution is one of the vital issues to ponder in current environmental situation as it has major impact on human health and environment. Real time monitoring of air pollution will help in calculating air quality index to issue health advisories as well as for taking necessary actions to meet standards. Air pollutants in the form of ground-level ozone and particulate matter have been the major pollutants in recent times. The proposed prototype in this paper deals with a smart Mongoose OS based monitoring system to monitor the harmful pollutants' concentration continuously. Mongoose OS is a cross-platform IoT operating system, which provides generic infrastructure layer for smart products and minimal foot print on edge device. As Monitoring is carried out uninterrupted, concerned officials get notified whenever a certain pollutant surpasses the threshold. Prototype is designed using cost effective low power ESP32 development board and appropriate sensors to monitor CO, CO2, NH3, Smoke particulates ( PM2.5) and Ozone. The values of pollutants (unit of measurement is PPM) captured by this prototype can be sent through message brokers to the cloud server set up using Losant IoT platform, by configuring it as Wi-Fi access point. PPM values of pollutants are then displayed on to losant device logs through MQTT protocol. Losant device ID, API access tokens play a key role in providing access to the users on Losant.

The concentration of pollutants in the environment is increasing at a rapid rate and one of the major sources of air pollution is vehicular emission. Vehicle emissions mainly constitute of Carbon monoxide, Nitrogen oxide and other toxic gases. These gases when present in the lower atmosphere adversely affect the health of human beings and causes respiratory problems like asthma, stroke, emphysema, lung cancer etc. Since this issue needs an immediate attention, environmentalist and other government bodies are coming up with new techniques. Health of the people can be improved by monitoring the air pollution level and notifying them whenever the quality of air degrades beyond a certain level. This paper proposes a system which incorporates the Radio Frequency Identification (RFID) technology for detecting the vehicles emission level and notifying the vehicle owner and the concerned authorities if the measured values exceed the standard limits for taking appropriate action. The proposed system also employs Maximum Spanning Tree(MAXST) algorithm to optimize the number of readers to be installed and hence reduces the installation cost.

Having one or the other form of security system is a must as it acts as the first line of defence in case of any break-ins. Houses with no advanced security systems usually have a higher chance of being targeted than those which are installed with sophisticated security systems. The essential part of security is intruder detection system. Till date several solutions have been proposed which make use of PIR sensor and in most of them, the owner is notified every time the sensor detects a motion. This leads to several false alarms as it might not always be an intruder. The solution proposed in this paper aims to reduce this false alarm rate. The proposed solution leverage's the human tendency to carry their mobile phones with them wherever ever they go and their habit to use the Internet services while being connected to the home access point rather than mobile data while at home. The security system uses a PIR sensor and is imparted with contextual/environmental awareness, which will let it take better decision as to when to notify the owner and hence reducing the false alarm rate. The contextual awareness of the system is possible due to the promiscuous mode of operation in ESP8266. The system scans for Wi-Fi packets and identifies the origin and destination MAC addresses of the devices communicating. The system then checks for the MAC address of the owner's phone to know his/her presence. Then it decides whether or not to notify the owner in case of any motion. The Instance of intrusion will also be logged in a SQL database.

The standard and ongoing communication technology is an unalloyed certainty for the advancement of Internet of Things (IoT) applications. Selection of required protocol for the present application plays an important role. Different protocols are used based on our requirements and application. This paper showcases the implementation of home automation that includes security and measures ambient temperature using CoAP protocol. Within a home network, different IoT devices are connected and communication is established among themselves using CoAP protocol. A CoAP server helps in updating data to cloud and accessing over local network. The data is updated on to the cloud for further analysis and status of devices at home is retrieved.

In 21stcentury one of the revolutionary technologies is Blockchain. It can bring regularity ideas to public and private sectors which improve the present management failures. To permit peer-to-peer transactions, Blockchiain maintains continuously growing list of records as a distributive database. In future, the existing conventional energy sources cannot meet the electricity demand. The renewable electricity generation is growing to balance supply and demand and accounts to share in the overall power supply. In the concept of smart cities development, The energy distribution without any intermediaries has a major concern. This emerging blockchain technology provides distributive and decentralized solutions for energy transactions. In this paper, a permissioned blockchain that uses hyperledger fabric to provide a peer-to-peer energy transacting network in order to accommodate the growing volume of renewable energy supply.

The emerging technology Internet of Things is giving rise to connected workers which makes the worker more safety aware to prevent occupational risks. In this work, a smart wearable device for workers in industrial scenarios with ESP-32 based setup using IoT is proposed. This device monitors the ambient conditions like temperature, humidity using DHT-11, measures hazardous gases like CO (in PPM) presence using MQ-7 sensor and also monitors worker's pulse rate (in beats/min) using pulse-rate sensor, body temperature (in°C) using LM-35 sensor and acceleration of the worker using accelerometer sensor in the working environment. The worker can request for emergency help in critical situations like heart stroke by pressing the emergency push button and the worker can be alerted based on sensor readings about hazards like gas, heat exposures. The overall system uses MQTT publish/subscribe services built by NODE-RED flows to communicate with MOSQUITTO broker, installed on RASPBERRY PI.