Intelligent Load Balancing Framework for Optimal Resource Utilization in Fog-enabled IoMT Environment

The rapid adoption of Internet of Things (IoT) technologies in healthcare has given rise to the Internet of Medical Things (IoMT), which has transformed patient care and medical services. The IoMT, when combined with Fog Computing, provides a powerful paradigm for processing and analyzing healthcare data at the network edge. This paper proposes an innovative intelligent load balancing framework designed specifically for fog-enabled IoMT environments for optimizing resource utilization, improving system performance, and ensuring timely and efficient healthcare service delivery. The framework dynamically distributes computing tasks among fog nodes based on real-time parameters such as node capacity, latency, and workload. By combining machine learning (ML) models and data analytics, the system adapts to changing patterns in medical data, ensuring adaptive load distribution and faster response times. The proposed framework addresses the unique challenges facing healthcare applications, such as low latency and energy consumption in data transmission

An Intelligent Particle Filter with Neural Network for Fault Location and Classification in Microgrid

Microgrid concept is initiated due to increasing involvement of distributed generation resources with the utility grid. Microgrid provide reliable and sustainable power but the protection of microgrid become challenging due to bidirectional power flow, dual mode of operation (grid connected and islanded mode). Faults in the microgrid reduce its stability and efficiency. Identification, classification, and location of faults are crit-ical for rapid restoration and microgrid protection. This research proposes a neural network-based intelligent particle filter for microgrid fault detection and classifica-tion. Even with low fault current, which is typical of inverter-based DGs, the suggest-ed method seeks to precisely identify fault kinds, locations, and directions. The fea-tures are extracted from data using S-Transform, then extracted features are esti-mated using particle filter. A neural network is then used for classification and finali-zation of location. The proposed scheme provides extremely precise fault detection, ensuring that the classification and location of the fault are promptly identified for effective protection and service restoration

Smart Home, It's Vulnerability Assessment Through Penetration Testing

Smart home systems, driven by IoT technologies, offer automation and remote control of household devices but also introduce significant security risks. This project develops a smart home prototype using Arduino Uno, ESP32, and relays to simulate common automation features. The goal is to assess system vulnerabilities through penetration testing techniques. Various security weaknesses were identified using Kali Linux tools like Nmap, Wireshark, and Metasploit, including insecure communication and poor authentication. The study highlights the importance of proactive testing and proposes mitigation strategies to enhance smart home security. This research emphasizes the need for integrating cybersecurity practices in smart home development to prevent potential threats and ensure a secure IoT environment

Automatic Solar Battery Charging System with Grid Backup

The increasing demand for sustainable and uninterrupted power supply has driven advancements in hybrid energy systems. This paper presents the design and implementation of an Arduino-based intelligent power switching and monitoring system for solar and grid hybrid energy sources. The system utilizes a solar panel to charge a battery through a charge controller, while also integrating grid power as a backup. A voltage sensor is used to monitor the battery level, and an Arduino microcontroller is employed to control a relay module, which intelligently switches the load between solar and grid power based on predefined thresholds. The LCD module displays real-time system status, enhancing user interaction. Additionally, a battery charge adaptor is included to ensure backup charging when solar input is insufficient. This system optimizes the use of renewable energy while maintaining uninterrupted power supply to the load, making it ideal for smart homes, rural electrification, and energy-efficient applications. Experimental results demonstrate reliable switching behavior and effective power utilization, validating the system’s practical viability

Refining Color Scheme Generation: Iterative K-Means Clustering and ARI Evaluation

Color goes beyond mere visual sensation, holding profound sway over emotions, thoughts, and perceptions. It communicates, evokes moods, and significantly influences judgments. Research underscores its importance, with up to 90% of product assessments being based solely on color, highlighting its pivotal role in crafting memorable experiences and defining brand identities. The fusion of art and technology presents a captivating synergy within the realm of image-derived color schemes. Color palette generation from images is pivotal in graphic design, interior decoration, and digital media. This study delves into methodologies for extracting dominant colors from images and generating cohesive color schemes. Leveraging K-Means clustering with the Within-Cluster Sum of Squares (WCSS) method, we showcase superior performance compared to traditional approaches. The evaluation of palette coherence using the Adjusted Rand Index (ARI) facilitates consistency within the generated color schemes. Integrating methodologies with design tools and advanced color harmonies opens avenues for further innovation and customization. This study underscores the transformative potential of image-based color scheme generation, bridging the gap between computational analysis and creative expression. Through the convergence of artistry and technological prowess, we aim to enhance the design landscape and enrich user experiences across various applications and industries

Multiclass Brain Tumor Classification Using Transfer Learning

Tumors are a collection of abnormal cells that multiply enormously than required which leads to cancer and divergent and also can be fatal, if not identified at an early stage. Usually, brain scan described as Magnetic resonance imaging (MRI) is deployed for high transparency and representation in different angles but causes huge delay in declaring the result of the test. In this project, images obtained from these tests are carefully observed and classified by implementing Deep Residual Network (RESNET) to classify the type of tumor. There are four types of tumors such as glioma, meningioma, pituitary, and no tumor. Brain tumor classification (Multi Label) – CNN dataset has been imported to train and test the model. This deep learning model is a sophisticated approach which is developed to classify the tumor based on the image, so that appropriate treatment can be given on time. The output determines the type of tumor if present, otherwise no tumor with accuracy of 87% using epochs

Enhancing Location Accuracy by Minimizing RMS Using RSS-AMLE in WSN

Over the past decade, there has been significant growth in wireless sensor networks, particularly in the context of industrial applications. Mobile sensor networks have garnered research interest due to their ability to facilitate communication between various devices. Still, the mobility of these nodes gives rise to challenges such as network coverage and connectivity issues. Addressing these challenges necessitates accurate estimation of sensor node locations, a critical factor in network performance. Numerous methods, such as Angle of Arrival (AOA) and Time of Arrival (TOA), have been proposed for node localization. Still, these methods are plagued by localization errors and high implementation costs. To overcome these localization errors in wireless sensor networks, we present an adaptive approach based on the Received Signal Strength (RSS) model. This model views localization as a non-convex problem and employs an adaptive maximum likelihood estimation to minimize localization errors. An extensive simulation study is carried out to measure the performance of the intended approach in minimizing the localization error. The results unequivocally demonstrate that our localization scheme achieves higher accuracy in locating sensor nodes while reducing deployment costs. Comparative analysis against existing methods further underscores the significance of our approach

Optimizing Cloud Resource Management Using PSO

This research explores how cloud resource management is changing in businesses, with a focus on Amazon Web Services (AWS) as the leader in cloud computing. It highlights how crucial excellent resource management is to attaining scalability, cost-effectiveness, and peak performance. The study explores on using Particle Swarm Optimization (PSO) as a cutting-edge optimization method in cloud computing settings. It talks about the difficulties brought on by fluctuating workloads and the requirement for clever resource allocation strategies. Additionally, the study assesses several optimization techniques using performance parameters including computing overhead, convergence time, and solution quality. These techniques include PSO, Genetic Algorithm (GA), and Firefly Algorithm (FA). In-depth simulations and case studies with organizations such as Siemens and Deloitte are used in the study to demonstrate how these algorithms work best in cloud environments to maximize resource usage, cut costs, and improve overall service quality. In the end, it emphasizes the continuous requirement for optimizing techniques to successfully handle the complexity of cloud computing ecosystems

Centralized Database and Automation: Key to Overcome the Challenge of Missing or Inaccurate Standard Settlement Instructions

To achieve effective and automated payment processes, straight-through processing (STP) has been implemented in the financial sector. The implementation of STP is, however, still hampered by the existence of incorrect or absent standing settlement instructions (SSIs). This research study explores the reasons of missing or incorrect SSIs in the banking sector, their effects, and potential fixes. The complexity of the situation is further increased by the examination of the variety of channels used by parties to transmit their SSIs. According to the report, incomplete or inaccurate SSIs are a significant cause of payment failures and unneeded expenses, hence a workable solution is required. The research recommends the implementation of a centralized SSI database that can be accessed by all parties involved in the payment process, as well as the automation of SSI updates to ensure accuracy and efficiency

Sorting Visualizer: A Visual Journey Through Sorting Algorithms

This paper, which is based on the importance of sorting algorithms, will carefully compare the features of various algorithms, beginning with their work effectiveness, algorithm execution, introductory concepts, sorting styles, and other aspects, and make conclusions in order to create more effective sorting algorithms. Searching techniques and sorting algorithms are not the same. Sorting is placing the provided list in a predetermined order, which can be either ascending or descending, whereas searching is predicated on the possibility of finding a specific item in the list. Only a section of the data is sorted, and the piece of data that's actually used to establish the sorted order is the key. The maturity of this data is being compared. Depending on the kind of data structure, there are several algorithms for doing the same set of duties and other conditioning, and each has pros and cons of its own. Numerous sorting algorithms have been analysed grounded on space and time complexity. The aim of this relative study is to identify the most effective sorting algorithms or styles. This relative study grounded on the same analysis allows the user to select the applicable sorting algorithm for the given situation