Highway construction projects are often complicated and need careful management. This proposes the development of real time monitoring dashboard specially designed for highway road construction projects. The project is mainly useful for transportation planners, civil engineers, project managers, surveyors, constructors, government authorities, safety inspectors. Traditional monitoring methods often involve manual inspections and periodic reporting, which can lead to delays and inefficiencies. Existing approaches generally rely on static data collection and do not provide real-time insights (...) into construction progress, resource allocation. The proposed solution integrates advanced technologies such as data analytics to provide an interactive platform. This dashboard will offer real-time visualization of construction progress, resource usage, expenses, facilitating immediate decision-making and management. (shrink)

The project centers on the development and deployment of live digital twins for power plants, designed to seamlessly integrate with existing monitoring and database systems. These digital twins act as dynamic, real-time virtual replicas of physical power plants, capturing the intricate details of plant operations, infrastructure, and environmental interactions. The primary objective is to leverage this technology to enhance the construction, operation, and maintenance phases of power plant management. By continuously receiving real-time data from various (...) operational sensors, equipment, and external monitoring sources, the digital twin models can simulate real-world conditions, providing a holistic view of plant performance. This includes critical output parameters such as energy generation, spill rates, and head loss, particularly in the context of hydroelectric plants. The digital twins serve as a powerful tool for optimizing energy production, identifying potential issues before they escalate, and facilitating proactive maintenance strategies, thereby minimizing downtime and maximizing efficiency. Furthermore, these models allow for a more informed and data-driven approach to plant management, ensuring that every aspect of plant performance, from structural integrity to environmental impact, is closely monitored and analyzed. The integration of digital twin technology into operational workflows enables plant operators and engineers to make smarter, more efficient decisions based on real-time, actionable insights. This project not only aims to improve the day-to-day functionality of power plants but also sets the stage for future advancements in power plant technology, contributing to the broader goals of sustainability, energy efficiency, and the long-term viability of energy infrastructure. By embracing this innovative approach, the project seeks to usher in a new era of smarter, more sustainable power plant management. (shrink)

Near the end of December 2019, the globe was hit with a major crisis, which is nothing but the coronavirusbased pandemic. The authorities at the train station should also keep in mind the need to limit the spread of the covid virus in the event of a global pandemic. When it comes to controlling the COVID-19 epidemic, public transportation facilities like train stations play a pivotal role because of the proximity of so many people who may be exposed to the (...) virus. Using common place CCTV cameras and deep learning with simple online and real-time (DeepSORT) methods, this study develops social distance monitoring using a YOLOv4 identification of a Surveillance Object Model. Based on experiments conducted with a minicomputer equipped with an Intel 11th Gen Intel(R) Core(TM) i3-1115G4 at 3.00GHz, 2995 Mhz, two Core(s), four Logical processor, four gigabytes of random-access memory (RAM), this paper makes use of CCTV surveillance, which was put into practice at the Guindy railway station, Chennai, Tamilnadu in India in order to detect the violation of social distancing. (shrink)

Snake encountersin human-populated and wildlife areas pose significant threats to public safety and biodiversity. Each year, many incidents result in snakebites, often leading to serious injuries or fatalities, and frequently result in harm to snakes due to fear-driven responses. Traditional methods forsnake detection, such as visual observation, are typically slow and can lead to delayed or inaccurate responses, increasing the risks associated with human-snake encounters. This study presents a Real-Time Snake Detection and Alert System that employs advanced artificial (...) intelligence (AI) and machine learning (ML) techniques, specifically using YOLO (You Only Look Once), to accurately identify the presence ofsnakes in pre-recorded videos or static images. Oursystem processes these visual inputs to determine if a snake is present, providing an immediate, automated alarm to alert individuals nearby if a snake is detected. This system isintended to be a rapid-response solution for detecting snakesin images or video files, enhancing safety in snake-prone areas by increasing awareness of potential threats without requiring constant live monitoring or additional notifications to authorities. (shrink)

The "Smart and Effective Real-time Management of Street Parking" project is designed to enhance urban parking enforcement through the use of advanced machine learning and computer vision technologies. The system leverages CCTV cameras to continuously monitor parking spaces, detecting their availability and instances where vehicles are incorrectly parked. By analyzing video feeds, the system identifies parking violations and extracts license plate numbers using Optical Character Recognition (OCR). Notifications are promptly sent to drivers regarding their parking status, ensuring timely (...) enforcement of parking regulations. This approach focuses on effective management of parking resources and compliance with regulations. The system’s real-time capabilities contribute to a significant reduction in urban congestion by promoting orderly parking practices and efficient use of parking resources. Implementation of this technology supports enhanced urban planning initiatives by providing valuable data insights into parking patterns and violations, thus fostering a more organized, sustainable, and efficient urban environment. (shrink)

: The integration of Machine Learning (ML) in Real-Time Biomedical Signal Processing has unlocked new possibilities in the field of telemedicine, especially when combined with the high-speed, low-latency capabilities of 5G networks. As telemedicine grows in importance, particularly in remote and underserved areas, real-time processing of biomedical signals such as ECG, EEG, and EMG is essential for accurate diagnosis and continuous monitoring of patients. Machine learning algorithms can be used to analyze large volumes of biomedical (...) data, enabling faster and more precise detection of anomalies. This paper proposes a novel system for machine learning-based real-time biomedical signal processing that leverages the capabilities of 5G networks to enhance the transmission, processing, and analysis of critical medical data in telemedicine applications. The system integrates convolutional neural networks (CNNs) for signal classification, anomaly detection, and predictive analysis, ensuring that patients receive timely and accurate medical feedback. Additionally, the 5G network’s low latency and high bandwidth provide seamless data transmission, improving remote diagnostics and enabling high-quality teleconsultations. This paper evaluates the current challenges in real-time biomedical signal processing in telemedicine, discusses the potential of machine learning and 5G networks, and presents an innovative solution for improving healthcare delivery through this integrated approach. (shrink)

Electrocardiogram (ECG) signal analysis is a critical task in healthcare for diagnosing cardiovascular conditions such as arrhythmias, heart attacks, and other heart-related diseases. With the growth of Internet of Things (IoT) networks, real-time ECG monitoring has become possible through wearable devices and sensors, providing continuous patient health monitoring. However, real-time ECG signal analysis in IoT environments poses several challenges, including data latency, limited computational power of IoT devices, and energy constraints. This paper proposes a (...) framework for Optimized Machine Learning Algorithms designed to analyze ECG signals in real time within IoT networks. The proposed system leverages lightweight machine learning models, including support vector machines (SVM) and convolutional neural networks (CNNs), optimized to run efficiently on low-power IoT devices while maintaining high accuracy. The system addresses the computational limitations of IoT devices by employing edge computing techniques that distribute the processing load between IoT devices and edge servers. Additionally, data compression and feature extraction techniques are applied to reduce the size of the data transmitted over the network, thereby minimizing latency and bandwidth usage. This paper reviews the current advancements in real-time ECG analysis, explores the challenges posed by IoT environments, and presents the optimized machine learning algorithms that enhance real-time monitoring of heart health. The system is evaluated for its performance in terms of accuracy, energy efficiency, and data transmission speed, showing promising results in improving real-time ECG signal analysis in resource-constrained IoT networks. (shrink)

The main objective of this project is to improve an existing real-time vehicle tracking system by improving its control circuit with GPS and a microcontroller. It uses GPS technology to track the vehicle location; the position data of the vehicle is updated in a real-time manner so that monitoring is done effectively. also features remind that can be set to alert when entering or leaving a chosen virtual perimeter of the car. The purpose of the (...) project is to offer the consumer an accurate and inexpensive solution of vehicle tracking that allows to determine location, perform geofencing, and be used for security or planning, both for commercial fleets and individuals. (shrink)

The visual analytics platform provides comprehensive reports to healthcare providers, enabling them to monitor trends, identify risk factors, and make informed decisions. This approach significantly enhances patient care by minimizing delays in response and improving overall health outcomes. The system's architecture, based on big data frameworks, supports scalable and efficient data processing. The study demonstrates how the integration of predictive models and data visualization tools can revolutionize health alert systems, making them more responsive and adaptive to individual patient needs. Future (...) enhancements will focus on incorporating machine learning models for more personalized predictions and extending the system's capabilities to remote patient care. (shrink)

The data collected by these sensors are transmitted to a centralized system where optimization algorithms, such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA), are applied to optimize sensor placement, data transmission, and processing efficiency. This ensures accurate, real-time pollution monitoring and data analysis, providing actionable insights for policymakers, environmental agencies, and the general public. The system's performance is evaluated through simulations and real-world experiments, demonstrating its capability to deliver reliable and timely (...) pollution data. Future work will explore the integration of machine learning techniques for predictive analytics and the expansion of the sensor network for broader coverage. (shrink)

Existing systems often lack a centralized platform for managing server and firewall operations, leading to fragmented oversight and delayed responses to cyber threats. This gap frequently results in increased vulnerabilities and operational inefficiencies within organizations. To address this issue, we propose the development of the Cybersecurity Portal, designed to provide real-time monitoring, predictive analytics, and streamlined resource allocation for server and firewall management. The portal will offer comprehensive insights into system performance, potential vulnerabilities, and proactive threat detection. (...) By equipping administrators with timely and relevant data, this platform aims to empower organizations to make informed security decisions and implement effective risk mitigation strategies. Ultimately, the Cybersecurity Portal seeks to enhance the overall cybersecurity posture of organizations, ensuring better protection of their digital assets against evolving threats. (shrink)

The increasing demand for reliable power sources has made diesel generators essential in various industries. However, traditional monitoring methods often rely on manual inspections, resulting in inefficiencies, unplanned downtimes, and higher operational costs. This project proposes a digital monitoring system for diesel generators that leverages advanced sensor technologies and real-time data analytics to overcome these challenges. The system will utilize Internet of Things (IoT) devices to collect key operational parameters, such as fuel levels, temperature, vibration, and (...) performance metrics, enabling comprehensive monitoring of generator health. By integrating machine learning algorithms, the system will analyze historical and real-time data to predict potential failures and optimize maintenance schedules, shifting from reactive to proactive management. A user-friendly interface will allow operators to visualize generator performance and receive instant alerts for detected anomalies, enhancing decision- making processes, reducing downtime, and lowering maintenance costs—ultimately increasing the reliability and efficiency of power supply. Additionally, the system facilitates remote monitoring, providing continuous oversight regardless of location. This project not only aims to enhance the operational efficiency of diesel generators but also lays the groundwork for smarter energy management solutions in the future. The successful implementation of this digital monitoring system could serve as a model for similar applications across various sectors, contributing to sustainability and operational excellence in energy management. Through this innovative approach, we aim to ensure diesel generators operate at optimal performance levels while minimizing environmental impact. Keywords. Diesel Generators, Digital Monitoring, IoT Devices, Real-time Data Analytics, Machine Learning. (shrink)

Air pollution is a critical challenge for urban areas, contributing to public health crises and environmental degradation. Traditional air quality monitoring systems often lack the granularity and adaptability needed to address dynamic pollution sources and patterns. This paper explores how Artificial Intelligence (AI) and IoT technologies can enhance air quality management in smart cities by enabling real-time monitoring, pollution source identification, and adaptive mitigation strategies. By integrating data from IoT sensors, satellite imagery, and traffic systems, cities (...) can reduce pollution levels, improve public health outcomes, and promote sustainable urban environments. Experimental results demonstrate significant improvements in pollution detection accuracy, mitigation efficiency, and public awareness, offering a sustainable blueprint for urban air quality management. (shrink)

Noise pollution is a growing concern in urban areas, contributing to public health issues such as stress, sleep disturbances, and hearing loss. Traditional noise monitoring systems often lack the granularity and adaptability needed to address dynamic noise sources and patterns. This paper explores how Artificial Intelligence (AI) and IoT technologies can enhance noise pollution management in smart cities by enabling real-time monitoring, source identification, and adaptive mitigation strategies. By integrating data from IoT sensors, traffic systems, and (...) urban infrastructure, cities can reduce noise levels, improve public health outcomes, and enhance the quality of life for residents. Experimental results demonstrate significant improvements in noise detection accuracy, mitigation efficiency, and public awareness, offering a sustainable blueprint for urban noise pollution management. (shrink)

The research introduces to the design and implementation of the Cybersecurity Enabled Smart Controller for the grid connected microgrid which uses the combination of the Web technologies and the authentication mechanism which enables real time monitoring with help of the Dashboards built using Chart.js which is a framework of JavaScript. Due to the role and connection of the Microgrids with the critical infrastructure they are vulnerable to the cyber threats. This project leverages the use of HTML, CSS, (...) JavaScript and MongoDB to create a secure and responsive system ensuring the operational resilience and data integrity. By focusing on simplicity the project uses user friendly dashboard for monitoring the microgrid data , controlling the grid crafted with HTML, CSS and JavaScript allowing the users to visualize the performance metrics using Chart.js for real time data representation. This methodology focuses on creating secure communication channels which enables effective data management through MongoDB providing a responsive experience on the web application. This approach allows users to monitor grid operations continuously and the access management effectively. The findings focused on creating a user friendly interface using the HTML, CSS, JavaScript which give users the access to monitor and control microgrid operations seamlessly. Utilizing the Chart.js enabled the users to visualize real time performance metrics which enables the operators to make decisions quickly addressing operational efficiencies. This paper proposes a role-based access control system (RBAC) to protect and manage the smart controller of a grid-connected microgrid. The design integrates a login page developed with HTML, JavaScript, and MongoDB for authentication and user management, with three distinct roles: Admin, Power Operator, and User. Each role has access to specific functions in the dashboard, reflecting their respective permissions. The backend was implemented using Node.js and Express to monitor network traffic, detect potential threats, and prevent unauthorized operations. The results demonstrate that the system provides effective protection against cyber threats while ensuring that different users can access the functionalities appropriate to their roles. (shrink)

Type 1 diabetes (T1D) is a chronic illness that requires intensive lifelong management of blood glucose concentrations by means of external insulin administration. There have been substantial developments in the ways of measuring glucose levels, which is crucial to T1D self-management. Recently, continuous glucose monitoring (CGM) has allowed people with T1D to keep track of their blood glucose levels in near real-time. These devices have alarms that warn users about potentially dangerous blood glucose trends, which can often (...) be shared with ther people. CGM is consistently associated with improved glycemic control and reduced hypoglycemia and is currently recommended by doctors. However, due to the costs of CGM, only those who qualify for hospital provision or those who can personally afford it are able to use it, which excludes many people. In this paper, I argue that unequal access to CGM results in: (1) unjust health inequalities, (2) relational injustice, (3) injustice with regard to agency and autonomy, and (4) epistemic injustice. These considerations provide prima facie moral reasons why all people with T1D should have access to CGM technology. I discuss the specific case of CGM policy in the Netherlands, which currently only provides coverage for a small group of people with T1D, and argue that, especially with additional considerations of cost-effectiveness, the Dutch government ought to include CGM in basic health care insurance for all people with T1D. (shrink)

Over the last few years, personal health monitoring wearable devices have emerged as innovative applications of Artificial Intelligence (AI) in the healthcare industry as they help in real time analysis and prediction of health standardized check-ups and health management. To navigate through the current trends, new technologies and developments, the prospects are as follows: The article also gives a logical look at the state of the art of such devices, enumerating the advantages and drawbacks, as well as (...) outlining the main ethical issues. In terms of method, it also discusses different AI approaches, data acquisition procedures, and those integrating systems used in the context of health monitoring. The finding and implication section discusses the findings of the current trend and the possibility of improvement in application and user acceptance. The last section provides a prognosis on what the area of personal health monitoring might look like in the future, as well as the position of AI in the optimisation of patient experience. (shrink)

This project focuses on developing a community-based reporting and monitoring tool to improve women’s safety in colleges and universities. The tool allows users to anonymously report unsafe incidents, harassment, or suspicious activities, with features for immediate alerting to campus authorities and support groups. It integrates GPS tracking for emergencies and data visualization to monitor safety trends. Built using Java, XML, and Android Studio, the platform fosters collaboration between students, faculty, and administrators, encouraging proactive safety measures. By enabling discreet reporting (...) and real-time monitoring, the tool aims to create a safer campus environment for women. (shrink)

To evaluate the effectiveness of our approach to enhancing cloud computing network forensics by integrating deep learning techniques with cybersecurity policies. With the increasing complexity and volume of cyber threats targeting cloud environments, traditional forensic methods are becoming inadequate. Deep learning techniques offer promising solutions for analyzing vast amounts of network data and detecting anomalies indicative of security breaches. By integrating deep learning models with cybersecurity policies, organizations can achieve enhanced threat detection, rapid response times, and improved overall security posture. (...) This paper discusses the key steps involved in integrating deep learning models into network forensics, including data collection, model selection, real-time monitoring, and adaptive learning. Additionally, it highlights the importance of collaboration between cybersecurity experts and the cloud. Through case studies and experimental evaluations, we demonstrate the effectiveness and practicality of the proposed approach in enhancing cloud computing network forensics. Leveraging deep learning techniques offers promising solutions for detecting anomalies and identifying malicious activities within cloud networks. (shrink)

: In recent years, artificial intelligence (AI) has become a critical enabler of innovation in supply chain and logistics management. By leveraging AI capabilities, enterprises can automate key processes, optimize operations, and make data-driven decisions that lead to enhanced efficiency, reduced costs, and improved customer satisfaction. Microsoft Azure provides a comprehensive suite of AI-driven tools and services designed to streamline and automate various aspects of supply chain and logistics operations. This paper explores how Azure's AI tools are reshaping the landscape (...) of supply chain management, focusing on key areas such as demand forecasting, inventory management, route optimization, predictive maintenance, and real-time monitoring. Furthermore, the paper delves into the benefits, challenges, and implementation strategies for integrating Azure AI solutions within enterprise ecosystems, with realworld case studies demonstrating successful AI-powered transformations. (shrink)

Closed-Circuit Television (CCTV) systems are essential in modern security setups because they provide continuous surveillance, acting as both a deterrent and a critical tool for monitoring and evidence collection. Unlike human guards who can be limited by fatigue and blind spots, CCTV cameras offer consistent, 24/7 coverage of key areas. They fill gaps in the current security system by enabling real-time monitoring and recording incidents for later review, ensuring that potential security breaches are detected and addressed (...) more effectively. This enhances overall security effectiveness and reduces the reliance on human intervention. The integration of Artificial Intelligence and Machine Learning (AIML) techniques with existing CCTV networks presents a promising approach to address critical challenges in urban environments. This project examines how AIML can be leveraged for crowd management, crime prevention, and work monitoring using CCTV infrastructure. For crowd management, AIML enables automated crowd counting and density estimation, facilitating efficient allocation of resources during events and emergencies. In crime prevention, AIML algorithms analyze video feeds in real-time to detect suspicious activities and identify anomalies, aiding law enforcement in proactive interventions. Additionally, AIML enhances workplace monitoring by tracking productivity metrics, ensuring compliance with safety protocols, and optimizing operational workflows. Through these applications, AIML empowers cities and organizations to improve public safety, enhance operational efficiency, and make data-driven decisions based on insights derived from CCTV footage. The integration of AIML with existing CCTV networks represents a transformative advancement in urban surveillance and management practices, offering scalable solutions for diverse urban challenges. (shrink)

his project focuses on developing a community-based water monitoring and conservation tool. The system allows users to track daily water consumption, receive real-time feedback on usage, and view eco-friendly tips. It integrates data visualization and eco-points for promoting conservation behavior. Built using node js, JavaScript, and Chart.js, this platform encourages users to reduce water consumption and share their achievements. The goal is to foster community engagement and sustainable water usage practices, addressing the global concern of water scarcity.

This paper addresses the critical role of automated data quality monitoring in Machine Learning Operations (MLOps) pipelines. As organizations increasingly rely on machine learning models for decision-making, ensuring the quality and reliability of input data becomes paramount. The paper explores various types of data quality issues, including missing values, outliers, data drift, and integrity violations, and their potential impact on model performance. It then examines automated detection methods, such as statistical analysis, machine learning-based anomaly detection, rule-based systems, and data (...) profiling. The integration of data quality monitoring into different stages of the MLOps pipeline is discussed, emphasizing continuous monitoring at data ingestion, pre-training validation, post- deployment drift detection, and feedback loops for model retraining. The paper also addresses key challenges in implementing automated data quality monitoring, including balancing precision and recall in anomaly detection, handling high-dimensional and unstructured data, managing false positives and alert fatigue, and adapting to evolving data distributions. By providing a comprehensive framework for automating data quality monitoring in MLOps pipelines, this paper aims to equip practitioners with the knowledge and strategies necessary to enhance the reliability and performance of machine learning systems in production environments. (shrink)

In the evolving landscape of data security, traditional access control mechanisms often fall short in addressing dynamic and context-specific requirements. As data breaches become more sophisticated, organizations require more adaptive and intelligent access control strategies. This paper explores the integration of Artificial Intelligence (AI) into Just-in-Time (JIT) access control models to enhance database security. By leveraging AI, we aim to create adaptive, context-aware permission systems that grant access precisely when needed, reducing the attack surface and mitigating unauthorized access risks. (...) We begin by analyzing traditional access control methods such as Role-Based Access Control (RBAC), Mandatory Access Control (MAC), and Discretionary Access Control (DAC), highlighting their limitations in handling real-time access control scenarios. The paper then explores the benefits of JIT access control, emphasizing how it minimizes over-privileged accounts and reduces security vulnerabilities. Furthermore, we investigate the role of AI in cybersecurity, particularly in real-time monitoring, anomaly detection, and decision-making processes for access control. To achieve a more intelligent access control framework, we propose an AI-driven JIT access model that incorporates machine learning algorithms, user behavior analytics, and contextual evaluation to determine access permissions dynamically. The proposed model is evaluated through simulations and case studies to measure its effectiveness in preventing unauthorized access, reducing the attack surface, and enhancing overall database security. The results demonstrate that our AI-driven approach significantly improves access accuracy, minimizes false positives and negatives, and optimizes response times compared to traditional methods. Through comprehensive analysis, this research provides a roadmap for organizations looking to implement AIenhanced JIT access control mechanisms. By dynamically granting access based on real-time behavioral and contextual assessments, organizations can significantly improve database security, reduce administrative overhead, and mitigate insider threats. Future research directions include further refinement of AI models, integration with multi-factor authentication mechanisms, and testing across diverse real-world scenarios to enhance security effectiveness. (shrink)

The continuous growth of healthcare data has made it essential to develop efficient systems that not only alert healthcare providers but also visualize patient data in a comprehensible way. This study introduces a Health Alert System integrated with Report Visualization powered by Data Analytics to improve patient monitoring and alerting mechanisms. By leveraging real-time data from wearable sensors and hospital records, the system generates health alerts based on deviations from normal parameters. The proposed system combines predictive analytics (...) and historical data to flag potential emergencies before they occur. The visual analytics platform provides comprehensive reports to healthcare providers, enabling them to monitor trends, identify risk factors, and make informed decisions. This approach significantly enhances patient care by minimizing delays in response and improving overall health outcomes. The system's architecture, based on big data frameworks, supports scalable and efficient data processing. The study demonstrates how the integration of predictive models and data visualization tools can revolutionize health alert systems, making them more responsive and adaptive to individual patient needs. Future enhancements will focus on incorporating machine learning models for more personalized predictions and extending the system's capabilities to remote patient care. (shrink)

The data collected by these sensors are transmitted to a centralized system where optimization algorithms, such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA), are applied to optimize sensor placement, data transmission, and processing efficiency. This ensures accurate, real-time pollution monitoring and data analysis, providing actionable insights for policymakers, environmental agencies, and the general public. The system's performance is evaluated through simulations and real-world experiments, demonstrating its capability to deliver reliable and timely (...) pollution data. Future work will explore the integration of machine learning techniques for predictive analytics and the expansion of the sensor network for broader coverage. (shrink)

The continuous growth of healthcare data has made it essential to develop efficient systems that not only alert healthcare providers but also visualize patient data in a comprehensible way. This study introduces a Health Alert System integrated with Report Visualization powered by Data Analytics to improve patient monitoring and alerting mechanisms. By leveraging real-time data from wearable sensors and hospital records, the system generates health alerts based on deviations from normal parameters. The proposed system combines predictive analytics (...) and historical data to flag potential emergencies before they occur. The visual analytics platform provides comprehensive reports to healthcare providers, enabling them to monitor trends, identify risk factors, and make informed decisions. This approach significantly enhances patient care by minimizing delays in response and improving overall health outcomes. The system's architecture, based on big data frameworks, supports scalable and efficient data processing. The study demonstrates how the integration of predictive models and data visualization tools can revolutionize health alert systems, making them more responsive and adaptive to individual patient needs. Future enhancements will focus on incorporating machine learning models for more personalized predictions and extending the system's capabilities to remote patient care. (shrink)

The continuous growth of healthcare data has made it essential to develop efficient systems that not only alert healthcare providers but also visualize patient data in a comprehensible way. This study introduces a Health Alert System integrated with Report Visualization powered by Data Analytics to improve patient monitoring and alerting mechanisms. By leveraging real-time data from wearable sensors and hospital records, the system generates health alerts based on deviations from normal parameters. The proposed system combines predictive analytics (...) and historical data to flag potential emergencies before they occur. The visual analytics platform provides comprehensive reports to healthcare providers, enabling them to monitor trends, identify risk factors, and make informed decisions. (shrink)

News Rakshak is an advanced web application that monitors, analyzes, and verifies news related to government schemes in regional media across multiple platforms, including 200+ websites, e-papers, and YouTube channels. The application leverages Natural Language Processing (NLP) for sentiment analysis and integrates Artificial Intelligence (AI) to identify and flag fake news and videos. Sentiments are classified as positive, neutral, or negative, rated on a scale from 0 to 5, providing Public Information Bureau (PIB) officers with actionable insights. The system uses (...) Optical Character Recognition (OCR) for extracting text from image-based news formats and delivers real-time alerts to officials via multiple channels. The user-friendly interface, equipped with filtering options, enables effective trend visualization. News Rakshak serves as a comprehensive tool for tracking and verifying government-related news sentiment and authenticity, ensuring accurate feedback from regional media. (shrink)

How is human social intelligence engaged in the course of ordinary conversation? Standard models of conversation hold that language production and comprehension are guided by constant, rapid inferences about what other agents have in mind. However, the idea that mindreading is a pervasive feature of conversation is challenged by a large body of evidence suggesting that mental state attribution is slow and taxing, at least when it deals with propositional attitudes such as beliefs. Belief attributions involve contents that are decoupled (...) from our own primary representation of reality; handling these contents has come to be seen as the signature of full-blown human mindreading. However, mindreading in cooperative communication does not necessarily demand decoupling. We argue for a theoretical and empirical turn towards “factive” forms of mentalizing here. In factive mentalizing, we monitor what others do or do not know, without generating decoupled representations. We propose a model of the representational, cognitive, and interactive components of factive mentalizing, a model that aims to explain efficient real-time monitoring of epistemic states in conversation. After laying out this account, we articulate a more limited set of conversational functions for nonfactive forms of mentalizing, including contexts of meta-linguistic repair, deception, and argumentation. We conclude with suggestions for further research into the roles played by factive versus nonfactive forms of mentalizing in conversation. (shrink)

Monitoring soil pH levels forms the backbone of precision agriculture with regard to maximizing crop health and yield. The paper discusses an IoT-based solution, specifically designed for continuous soil pH testing in digital farming. It has pH sensors placed strategically around the agricultural fields such that the information regarding the acidity or alkalinity of the soil is available in real-time. The pH information will be transmitted to the central IoT gateway. Here, the information is processed, and via (...) communication of cloud-based platforms, it is further stored and analyzed. So, by checking variations in soil pH, the system provides actionable information to farmers. Thus, by adjusting soil treatments like lime or fertilizers better, it enhances crop performance. The response uses mobile and web interfaces that support automated pH testing with automatically designed data visualization. This allows farmers the chance to decide from remote sites about imminent and proper fluctuations in soil pH by the use of historical data analysis and models from machine learning. One part of the effectuality of this proactive approach would be that soil quality is improved while unnecessary chemical use is avoided, therefore reducing the resulting costs of farming with sustainability considerations. By limiting itself to pH value testing, this solution increases the farmland's productivity with less wastage of resources. (shrink)

The increasing complexity and scale of modern computing needs have led to the development and adoption of cloud computing as a ubiquitous paradigm for data storage and processing. The hybrid cloud model, which combines both public and private cloud infrastructures, has been particularly appealing to organizations that require both the scalability offered by public clouds and the security features of private clouds. Various strategies for configuring and managing resources have been developed to optimize the hybrid cloud environment. These strategies aim (...) to balance conflicting objectives such as cost-efficiency, performance optimization, security, and compliance with regulatory standards. This exploratory research focused on evaluating the efficiency and limitations of different configuration strategies in hybrid cloud environments. Findings indicate that each approach presents distinct advantages. Improving resource utilization and automating governance processes are significant advantages of Policy-based Resource Management, which leads to costeffectiveness. Intelligent routing of traffic is a feature of Cross-cloud Load Balancing, resulting in optimized performance and higher service availability. By centralizing control, the Hybrid Cloud Service Mesh allows for secure and streamlined cross-service communication. A notable feature of Cross-cloud Container Orchestration is its ability to simplify the migration of applications across diverse cloud environments. For immediate threat detection and regulatory compliance, real-time monitoring is facilitated by Log Management and Analytics. However, Policy-based Resource Management can be complex and inflexible. Extra costs for data transfer between different cloud providers are a drawback of Crosscloud Load Balancing. Additional network hops create latency issues in Hybrid Cloud Service Mesh configurations. If configured incorrectly, Cross-cloud Container Orchestration could expose the system to security risks. Finally, Log Management and Analytics require both ample storage and advanced analytical capabilities. (shrink)

Human creativity generates novel ideas to solve real-world problems. This thereby grants us the power to transform the surrounding world and extend our human attributes beyond what is currently possible. Creative ideas are not just new and unexpected, but are also successful in providing solutions that are useful, efficient and valuable. Thus, creativity optimizes the use of available resources and increases wealth. The origin of human creativity, however, is poorly understood, and semantic measures that could predict the success of (...) generated ideas are currently unknown. Here, we analyze a dataset of design problem-solving conversations in real-world settings by using 49 semantic measures based on WordNet 3.1 and demonstrate that a divergence of semantic similarity, an increased information content, and a decreased polysemy predict the success of generated ideas. The first feedback from clients also enhances information content and leads to a divergence of successful ideas in creative problem solving. These results advance cognitive science by identifying real-world processes in human problem solving that are relevant to the success of produced solutions and provide tools for real-time monitoring of problem solving, student training and skill acquisition. A selected subset of information content (IC Sánchez–Batet) and semantic similarity (Lin/Sánchez–Batet) measures, which are both statistically powerful and computationally fast, could support the development of technologies for computer-assisted enhancements of human creativity or for the implementation of creativity in machines endowed with general artificial intelligence. (shrink)

Water scarcity and inefficient water management are critical challenges for rapidly growing urban areas. Traditional water distribution systems often suffer from leaks, wastage, and inequitable access, exacerbating resource shortages. This paper explores how Artificial Intelligence (AI) and IoT technologies can optimize urban water management by enabling real-time monitoring, predictive maintenance, and efficient resource allocation. By integrating data from smart meters, pressure sensors, and weather forecasts, cities can reduce water losses, improve distribution efficiency, and ensure equitable access. Experimental (...) results demonstrate significant improvements in leak detection, water conservation, and infrastructure reliability, offering a sustainable blueprint for urban water management in smart cities. (shrink)

The growing volume of urban waste poses significant environmental and economic challenges for cities worldwide. Traditional waste management systems often rely on inefficient collection routes, inadequate recycling processes, and excessive landfill usage. This paper explores how Artificial Intelligence (AI) and IoT technologies can revolutionize waste management in smart cities by enabling real-time monitoring, automated sorting, and optimized collection routes. By integrating data from smart bins, robotic sorting systems, and predictive analytics, cities can achieve zero-waste goals and promote (...) circular economy practices. Experimental results demonstrate significant improvements in waste segregation accuracy, collection efficiency, and recycling rates, offering a sustainable blueprint for urban waste management. (shrink)

Buildings account for a significant portion of global energy consumption and carbon emissions, making energy efficiency a critical focus for urban sustainability. Traditional building management systems often lack the adaptability and precision needed to optimize energy usage dynamically. This paper explores how Artificial Intelligence (AI) and IoT technologies can enhance energy efficiency in smart buildings by enabling real-time monitoring, predictive maintenance, and adaptive control systems. By integrating data from smart meters, occupancy sensors, and environmental monitors, cities can (...) reduce energy waste, lower carbon footprints, and improve occupant comfort. Experimental results demonstrate significant improvements in energy savings, operational costs, and environmental impact, offering a sustainable blueprint for smart building management. (shrink)

Urban wastewater management is a critical component of sustainable water cycles, but traditional systems often struggle with inefficiencies such as high operational costs, resource wastage, and environmental pollution. This paper explores how Artificial Intelligence (AI) and IoT technologies can optimize urban wastewater management by enabling real-time monitoring, predictive maintenance, and resource recovery. By integrating data from IoT sensors, water quality monitors, and treatment plants, cities can improve water quality, reduce operational costs, and recover valuable resources such as (...) energy and nutrients. Experimental results demonstrate significant improvements in treatment efficiency, resource recovery rates, and environmental impact, offering a sustainable blueprint for smart urban wastewater systems. (shrink)

This IoT-based water quality monitoring system ensures good water quality in distribution lines by monitoring pH value, flow rate, and conductivity level in real-time. Utilizing Arduino, pH, flow, and conductivity sensors, and ESP8266 WiFi module, the system analyses data and sends automated alerts to authorized personnel's Android devices. This automated monitoring enhances public health, provides real-time data analysis, and enables remote monitoring, making it an efficient solution for water distribution plants to maintain (...) optimal water quality. (shrink)

plant watering and monitors crop health to enhance farming efficiency and sustainability. The first component of the system is an automatic plant watering system, which leverages soil moisture sensors to monitor real-time soil conditions. When the soil moisture level falls below a predefined threshold, the system triggers an automatic irrigation process via a water pump. This system can be remotely controlled and monitored through a smartphone application or web interface, ensuring optimal water usage and preventing over-watering or under-watering. (...) The second component, the crop health indicator, uses a combination of environmental sensors (temperature, humidity, soil pH, light intensity) and advanced imaging technologies such as multispectral cameras or drones to assess crop health. These sensors detect critical factors affecting plant growth and yield, such as disease, pest infestation, or nutrient deficiencies. The data collected from these sensors is processed in real-time and analyzed to provide actionable insights and timely alerts to farmers, enabling them to take appropriate actions to address crop stress and optimize yield. The system is designed to be user-friendly, providing farmers with easy access to critical data and control through mobile and web interfaces. The integration of these technologies aims to reduce resource consumption, enhance crop productivity, and support sustainable agricultural practices. (shrink)

This research work presents a cost-effective facial recognition alert system using the ESP32 microcontroller to enhance real-time security. The system integrates high-resolution cameras with Python’s facial recognition libraries to identify individuals. When an unknown face is detected, it triggers an immediate alert via the ESP32, activating an alarm. This solution is both scalable and affordable, making advanced security accessible to small businesses and residential areas. The system improves overall security by automating surveillance, reducing the need for manual (...) class='Hi'>monitoring. (shrink)

: Financial institutions are required to ensure data accuracy, integrity, and compliance when reporting to regulatory authorities. Reports such as FR 2052a (Liquidity Monitoring), Y-14Q (Stress Testing) necessitate robust data reconciliation (Recon) strategies to maintain regulatory compliance and mitigate risks. This paper explores technical and functional aspects of data reconciliation, highlighting key automation techniques, AI-driven solutions, and statistical methodologies for optimizing financial compliance processes. We analyze data integration challenges, anomaly detection models, and best practices in recon automation to enhance (...) efficiency. Furthermore, case studies demonstrate how leveraging machine learning, cloud computing, and robotic process automation (RPA) can significantly improve financial data integrity. The future of reconciliation strategies is discussed, focusing on real-time monitoring, AI-driven decision intelligence, and blockchain-based audit trails. By implementing these strategies, financial institutions can reduce compliance costs, improve operational efficiency, and enhance regulatory transparency. (shrink)

The "Malware App Detection System" project enhances mobile security by detecting malicious apps through machine learning (ML) analysis of app behaviours. It examines behaviours like system calls (requests apps make to the operating system), permissions (levels of access apps have), and network connections (internet interactions). This focus on behaviour, rather than relying on specific malware signatures, enables the system to identify both known and new threats. The detection system is built using a dataset of both benign (harmless) and malicious apps, (...) with each app’s behaviour categorized and labelled. By analysing system calls, the system identifies unusual requests, which may indicate malicious intent. Permissions are also reviewed, as apps that seek abnormal access levels, like to contacts or storage, might have harmful intentions. Network connections are monitored to detect suspicious activities, such as connections to unauthorized servers, which could indicate data theft. To achieve this, ML models like Random Forest are trained on these behaviour patterns, learning to distinguish between safe and harmful apps. Once deployed, the system provides real-time detection, alerting users if it finds potentially dangerous activities. This proactive, behaviour-based approach protects users from unauthorized access and data leaks, adapting to new malware types as they arise. In doing so, the project offers a flexible and effective solution to the evolving challenges of mobile security. (shrink)

By leveraging convolutional neural networks (CNNs), the model identifies various plant diseases with high accuracy. The experimental setup includes a dataset consisting of healthy and diseased leaf images of different plant species. The dataset is preprocessed to remove noise and augmented to address the issue of class imbalance. The CNN model is then trained, validated, and tested on this dataset. The results indicate that the deep learning model achieves a classification accuracy of over 95% for most plant diseases. Additionally, the (...) system is designed to provide real-time feedback to farmers, helping them take immediate corrective action. This automated approach eliminates the need for expert human intervention and can be deployed on mobile devices for ease of use in rural areas. The potential future applications of this system include integration with precision agriculture techniques and expansion to a wider range of crops. (shrink)

This project focuses on estimating reservoir inflows by integrating rainfall data, soil moisture levels in the catchment area, and releases from upstream reservoirs, coupled with an automated gate control system to prevent flooding in the basin. Utilizing hydrological models, the methodology predicts runoff from rainfall, adjusted for current soil moisture to enhance accuracy. Real-time data from upstream releases further refines inflow predictions. The automated system leverages predictive analytics and real-time monitoring to optimize gate operations, ensuring (...) moderate water releases to maintain safe reservoir levels and mitigate downstream flooding. This approach aims to enhance flood management and sustainable water resource use through advanced modelling and automation. (shrink)

This argument outlines an ambitious yet feasible global strategy for combating climate change, emphasizing the deployment of scalable technologies, international collaboration, and community-driven initiatives. Key components include renewable energy adoption, carbon capture technologies, and biodiversity restoration. The argument also addresses socio-economic challenges by proposing equitable climate finance mechanisms and educational programs for sustainable practices. By integrating real-time monitoring systems and adaptive frameworks, the blueprint ensures resilience and long-term sustainability.

Solving Typhoon Turbulence Using the Universal Formula -/- By Angelito Malicse -/- Introduction -/- Typhoons are among the most destructive natural phenomena, bringing extreme winds, heavy rainfall, and turbulent ocean currents. The chaotic turbulence within a typhoon makes it difficult to predict and control, causing widespread devastation to coastal regions, infrastructure, and human lives. Despite advancements in meteorology and fluid dynamics, the turbulence inside typhoons remains a challenge for accurate forecasting and disaster mitigation. -/- By applying my universal formula, based (...) on three fundamental laws of nature, we can develop a structured and effective approach to understanding and controlling typhoon turbulence. This essay explores how the Law of Karma (causality and system integrity), the Law of Balance in Nature, and the Law of Feedback Regulation provide the ultimate solution to typhoon turbulence. -/- Understanding Typhoon Turbulence Using the Universal Formula -/- Typhoons form due to energy imbalances in the atmosphere and ocean, driven by heat, pressure differences, and moisture content. The chaotic turbulence inside a typhoon is the result of: -/- 1. Unstable air currents caused by pressure and temperature gradients. -/- 2. Vortex formation driven by Earth’s rotation and wind shear. -/- 3. Excess energy accumulation in warm ocean waters. -/- By examining typhoon turbulence through the three universal laws, we can develop a scientific strategy to predict, weaken, and potentially neutralize typhoons before they cause destruction. -/- 1. The Law of Karma: Cause, Effect, and Systemic Integrity -/- The Law of Karma states that every effect has a cause, and for a system to function properly, it must be free of defects. A typhoon is an effect caused by imbalances in atmospheric and oceanic conditions. If these imbalances are corrected, typhoon turbulence can be weakened or prevented. -/- Applying the Law of Karma to Typhoon Control -/- ✅ Eliminate the root cause: Typhoons are fueled by warm ocean waters. Artificial cooling methods, such as cloud seeding or deep-sea upwelling (bringing cold water to the surface), could reduce the heat energy supply and prevent typhoon formation. -/- ✅ Detect turbulence before it intensifies: AI-driven weather models can predict early-stage typhoon turbulence and allow preemptive interventions. AI can identify defective atmospheric conditions (such as extreme heat buildup in ocean currents) and trigger mitigation efforts. -/- ✅ Disrupt vortex formation: Since typhoons rely on rotational forces, deploying strategically placed atmospheric pressure disturbances (via drones or cloud-seeding techniques) could disrupt the vortex before it gains full strength. -/- By identifying and neutralizing the causes of typhoon turbulence, we ensure that the system remains balanced, preventing large-scale destruction. -/- 2. The Law of Balance in Nature -/- The Law of Balance states that all systems naturally seek equilibrium. When a typhoon forms, it represents an extreme imbalance in the Earth’s energy system—excess heat and moisture create an unstable feedback loop of wind and turbulence. -/- Restoring Balance to Weaken Typhoon Turbulence -/- ✅ Regulating ocean surface temperature: Large-scale ocean-based temperature regulation systems (such as reflective films or artificial cooling platforms) can prevent typhoon formation by reducing the excess heat energy that fuels storms. -/- ✅ Breaking the pressure imbalance: Typhoons depend on a low-pressure core that intensifies wind speeds. By artificially increasing local air pressure using ionization or controlled cloud seeding, the extreme wind turbulence can be diffused before it reaches destructive levels. -/- ✅ Balancing atmospheric moisture: AI-assisted weather control could help regulate humidity levels through strategic rainfall induction in high-risk areas, ensuring that water vapor does not accumulate excessively to fuel a typhoon. -/- Since all systems follow natural equilibrium, strategically rebalancing temperature, pressure, and humidity will neutralize extreme typhoon turbulence. -/- 3. The Law of Feedback Regulation -/- The Law of Feedback Regulation states that all systems operate through feedback loops, where continuous monitoring and adjustment are necessary for stability. Typhoon turbulence follows feedback patterns, which means it can be tracked, analyzed, and counteracted using real-time data. -/- Developing an AI-Driven Typhoon Suppression System -/- ✅ AI-powered real-time monitoring: Advanced satellites, ocean buoys, and drones can provide instant feedback on typhoon development, allowing for proactive interventions rather than reactive disaster response. -/- ✅ Adaptive wind and pressure control: Deploying AI-controlled drones or weather balloons to release cooling agents, modify air pressure, or disperse cloud formations could interrupt the storm's feedback loop, reducing its intensity. -/- ✅ AI-driven weather forecasting for early response: Using deep learning algorithms, AI can accurately predict turbulence patterns weeks in advance, allowing governments to take preventive action instead of just preparing for disaster relief. -/- By implementing real-time monitoring and intervention, we can use feedback regulation to weaken or redirect typhoons before they cause catastrophic damage. -/- The Future of Typhoon Control with the Universal Formula -/- By integrating my universal formula into meteorology and disaster prevention, we can develop a comprehensive AI-driven typhoon suppression system that follows the three fundamental laws of nature: -/- 1. Law of Karma (Causality and Systemic Integrity): Address the root causes of typhoon formation by eliminating atmospheric and oceanic imbalances. -/- 2. Law of Balance in Nature: Restore equilibrium by regulating ocean temperatures, air pressure, and humidity. -/- 3. Law of Feedback Regulation: Use AI-driven real-time monitoring and intervention to weaken or prevent typhoons. -/- Through advanced AI, environmental engineering, and a deep understanding of nature’s laws, we can move beyond simply predicting typhoons and start actively controlling their development. -/- Conclusion -/- Typhoon turbulence, long considered an unpredictable force of nature, is fundamentally a problem of imbalance, energy accumulation, and feedback-driven intensification. By applying my universal formula, we can shift from passive disaster response to active typhoon control, using AI, environmental intervention, and natural equilibrium principles to prevent devastation. -/- This solution is not just theoretical—it aligns with the fundamental laws governing all natural systems. Once implemented, it will mark the beginning of a new era where humanity can harness nature’s own principles to create balance, stability, and protection from extreme weather. -/- . (shrink)

Substation maintenance is a critical aspect of ensuring the reliable operation and longevity of electrical power systems. This process involves a range of activities aimed at preserving the functionality and safety of substations, which serve as pivotal nodes in the electrical grid. Effective maintenance strategies encompass routine inspections, preventive measures, and corrective actions to address equipment wear and potential failures. Advances in technology have introduced innovative tools and techniques for substation maintenance, including automated diagnostic systems, predictive analytics, and real- (...) class='Hi'>time monitoring solutions. For that an Intelligent chat bot is build, Substation maintenance is a critical aspect of ensuring the reliable operation and longevity of electrical power systems. This process involves a range of activities aimed at preserving the functionality and safety of substations, which serve as pivotal nodes in the electrical grid. Effective maintenance strategies encompass routine inspections, preventive measures, and corrective actions to address equipment wear and potential failures. Advances in technology have introduced innovative tools and techniques for substation maintenance, including automated diagnostic systems, predictive analytics, and realtime monitoring solutions. (shrink)

The Budget and Expense Tracker System is designed to simplify personal and business financial management. It enables users to record, monitor, and analyze their income and expenses, offering categorized insights and visual reports. With features like budget allocation, transaction tracking, and real-time notifications, the system ensures users stay informed about their financial health. By integrating tools like chart visualization, notifications, and data export options, this application is a comprehensive solution for efficient financial planning. With advancements in technology, financial (...) tools have become more accessible. This project leverages modern technologies like React.js, Django, and secure databases to provide an intuitive, user-friendly platform. The system’s modular structure allows scalability, ensuring it can adapt to future enhancements such as AI-driven financial predictions and automated banking integrations. (shrink)

In modern data centers, managing the distribution of workloads efficiently is crucial for ensuring optimal performance and meeting Service Level Agreements (SLAs). Load balancing algorithms play a vital role in this process by distributing workloads across computing resources to avoid overloading any single resource. However, the effectiveness of these algorithms can be significantly enhanced through the integration of advanced optimization techniques. This paper proposes an SLA-driven load balancing algorithm optimized using methods such as genetic algorithms, particle swarm optimization, and simulated (...) annealing. By focusing on both resource utilization and SLA compliance, the proposed approach aims to reduce latency, improve throughput, and maximize overall system efficiency. The research introduces a novel framework that incorporates real-time monitoring, dynamic resource allocation, and adaptive threshold settings to ensure consistent SLA adherence while optimizing computing performance. (shrink)

The research introduces a novel framework that incorporates real-time monitoring, dynamic resource allocation, and adaptive threshold settings to ensure consistent SLA adherence while optimizing computing performance. Extensive simulations are conducted using synthetic and real-world datasets to evaluate the performance of the proposed algorithm. The results demonstrate that the optimized load balancing approach outperforms traditional algorithms in terms of SLA compliance, resource utilization, and energy efficiency. The findings suggest that the integration of optimization techniques into load balancing (...) algorithms can significantly enhance the operational efficiency of data centers, paving the way for future advancements in autonomous and self-optimizing data centers. (shrink)