A Comprehensive study on Satellite Image Super-resolution using Diffusion and GAN based model

Object detection and feature extraction from satellite images is a crucial step while using satellite images for purposes like navigation, urban planning, weather monitoring, etc. While deep learning approaches are too common for object detection task, but when the satellite images are of low quality, the small objects are missed by detection model due to their size and visibility issue.  In this paper we propose a study of two broad areas of Generative AI models namely GANs and Diffusion model and their ability to handle the low-resolution images to improve overall detection problem. We train SRGAN and Diffusion based super-resolution model on custom real-time datasets and present a comprehensive performance evaluation and analysis.  We found that Diffusion model increased the object detection rate by almost 130% when compared with Raw image object detection

CONNECTING THE DOTS: LINKING CORONARY DISEASES WITH COVID-19 PATIENTS THROUGH SUPPORT VECTOR MACHINE ALGORITHM

The COVID-19 pandemic has left a lasting impact on global health, with a significant portion of survivors experiencing persistent health effects termed as ‘LONG COVID’ or ‘POST COVID-19 SYNDROME’. In this research, we propose a novel approach utilizing Support Vector Machine (SVM) algorithm to analyse patient data and predict the multifaceted nature of post-COVID-19 Syndrome, particularly focusing on the interlinkage of coronary diseases with COVID-19 patients. Our methodology involves collecting and analysing extensive patient data, including pre-conditions and post-conditions of COVID-19, to identify patterns and associations between various health issues. By leveraging the high-dimensional capabilities of SVM, we aim to provide accurate predictions and insights into the long-term health complications of COVID-19 survivors, thereby contributing to a better understanding of this critical area of healthcare. This approach stands out due to its ability to handle nonlinear relationships, noise in data, and large datasets effectively, offering valuable insights for healthcare professionals in managing post-COVID-19 complications

A Novel Technique To Access Sensitive Medical Data With Access Policies

Ensuring the efficient and secure access of sensitive health records is one of the main issues challenging healthcare systems. This work offers a novel method that combines the Harmony Search Algorithm (HSA) with Attribute-Based Encryption (ABE) to provide strict data security, patient privacy, and robust access controls. Inspired by the evolution of musical harmony, the Harmony Search Algorithm successfully integrates ABE fundamentals to create and enhance controls on access that manage the retrieval of personal medical records. A dynamic framework is managed through this association, whereby HSA optimizes the development and growth of access controls and ABE presents a fine-grained, attribute-based method to encrypting and decrypting sensitive data. This creative approach makes use of the HSA's ability to adapt access rules continuously to meet changing legal requirements and healthcare needs. The ABE algorithm offers local management of data access through making sure that only allowed entities with the necessary features can decode specific medical information, which enhances data security. With a primary focus on ensuring legal compliance, the framework's development was influenced by tight healthcare data laws, patient confidentiality, and ethical values. The recommended methodology offers an optimal combination of data security concepts and efficiency methods, representing an important progress in the domain of medical data management. This method integrates HSA and ABE to provide a framework that is safe, flexible and responsible for obtaining private medical information. This will maintain the security of patients and safety while expanding data useful for specified organizations

DEVELOPMENT AND EXPERIMENTAL AND CFD ANALYSIS OF INCLINED PLATE AND TUBE HEAT EXCHANGER

The Heat exchanger is the most important and widely applicable device in this sector so it is also area where these innovations are carried out regularly. Plate & Tube type Heat Exchangers have a number of applications in the pharmaceutical, petrochemical, chemical, power, and dairy, food & beverage industry. Recently, plate heat exchangers are commonly used when compared to other types of heat exchangers such as shell and tube type in heat transfer processes because of their compactness, ease of production, sensitivity, easy care after set-up and efficiency. The temperature approach in plate heat exchangers may be as low as 1 °C whereas shell and tube heat exchangers require an approach of 5 °C or more. However, plate and tube heat exchangers have inherent shortcomings such as the contact resistance between fins and tubes, the existence of a low performance region behind tubes, etc. The plate and tube heat exchangers which are in use are flat type if we incline the plates at some angle to the pipes we can get various data by experimenting the device at various angles. This data can be analyzed and we can have conclusion about its efficiency and effectiveness can be calculated

Proactive Fault Localization and Alarm Correlation in DWDM Networks

“Proactive Fault Localization and Alarm Correlation in DWDM Networks” approach proposes a new fault localization algorithm for DWDM networks where every entity of DWDM network participates in correlation of alarms and thus reduces the list of suspected components shown to the network operators

PHISHEILD WEBSITE DETECTION SYSTEM FOR SAFEGUARDING ONLINE SECURITY

The increasing prevalence of cyber threats increases the need to identify effective phishing websites. Phishing attacks, often disguised inside legitimate-looking websites, compromise necessary details that needs to be kept private can cause big problems for people and groups, giving urgent importance as a potential economic loss , identity theft, and data breaches associated with successful phishing attempts are high. Machine learning algorithms have an important role to play in this context, with Light GBM emerging as a particularly useful approach. Its intrinsic capacity to effectively handle huge quantities data and arrive at swift and accurate conclusions distinguishes it from other models Light GBM is effective in handling imbalanced data, and its powerful object analytics capabilities make it a powerful tool for understanding subtle patterns of indicators phishing activities enlightens Instead, it sets a benchmark for efficiency and reliability in cyber-security In in this research, we solve the important challenge of phishing website detection by exploiting the features of URL domains. A detailed analysis of the datasets resulted in a robust model, in which Light GBM was identified as the final model of choice The metrics for each class include precision, recall, and F1-score,benign, defacement, phishing and malware were meticulously assessed, demonstrating The algorithm's capabilities to effectively identify phishing websites. The research results do additionally assist to the advancements in hacking systems for detection, underscore the significance of employing Light GBM for enhanced classification accuracy. This research establishes a foundation for future endeavors in web security and holds practical implications for improving cybersecurity measures to protect users from potential threats

From Paper to Digital: Transitioning Employee Processes with an Employee Management System

Teachers and students form the core of any educational institution, be it a college or school. However, traditional methods of managing their tasks often result in cumbersome paperwork and complex processes. This paper delves into the creation of a cost-effective system aimed at alleviating these challenges. Unlike many existing computerized systems that primarily focus on attendance, leave, and salary management, this proposed system seeks to address a broader scope. Typically, crucial employee data, including personal information, task assignments, leave records, and work allocations, are manually handled. To streamline these operations, a web-based Employee Management System is suggested herein. This system not only saves considerable time but also ensures accurate pay calculations, thereby enhancing efficiency. In contrast to the technologies discussed in existing literature, this solution offers a more user-friendly approach. The primary objective of this endeavor is to evaluate and enhance employee performance within the institution

ECHO: Empowering Children’s Healthcare with Humanoid Empathy

Individuals with Autism Spectrum Disorder (ASD) face multifaceted challenges in social interaction and communication, necessitating innovative approaches to support their unique needs. Current statistics highlight the pressing need for effective interventions, with ASD prevalence rates continuing to rise globally. However, existing solutions often encounter limitations, such as subjective assessments and lack of personalized approaches. To address these challenges, this paper presents a comprehensive review of studies to assess and support individuals with ASD. By synthesizing findings from diverse studies utilizing various robotic platforms, including Robotis Mini, Romo, CommU, RobotParrot, Zeno, and ONO robots, this review elucidates the potential of robotics in facilitating accurate assessment, personalized intervention, and enhanced engagement for individuals with ASD [1]. Furthermore, the incorporation of advanced technologies, including multimodal data analysis and real-time gesture recognition algorithms, underscores the interdisciplinary nature of this research domain. While promising, the implementation of robotics in ASD intervention is not without drawbacks, including technical limitations and ethical considerations. Through ongoing exploration and innovation, robotics holds the potential to revolutionize the landscape of ASD support, fostering greater inclusivity, empowerment, and quality of life for individuals across the autism spectrum [2]. We have been designed a robot as a diagnostic toy for mentally challenged children, integrating both hardware and software components to facilitate interactive and engaging experiences aimed at assessing behavioral, and social-emotional skills while offering support and companionship in therapeutic settings. Advanced features such as face recognition, emotion detection, object and colour recognition, teaching modules, and comprehensive reporting systems drive foster learning and development, and empower children to reach their full potential in a supportive and engaging environment. This innovative approach aims to enrich the lives of mentally challenged children, fostering positive outcomes through tailored interventions and interactive experiences

Novel YOLOv5 Model for Automatic Detection of Cowpea Leaves: Smart Agriculture

Implementing artificial intelligence, specifically deep learning algorithms, to enhance agricultural productivity is a great initiative, especially in a country like India where agriculture is a crucial sector. Using TensorFlow and Keras for this purpose provides a solid foundation, given their popularity and extensive documentation. Using deep learning to identify and classify cowpea leaves can indeed streamline various agricultural processes, such as monitoring plant health, pest detection, and yield estimation. The utilization of YOLOv5, a CNN-based architecture, for the binary classification of cowpea leaves against other leaves like mangoes is a smart choice. Transfer learning can further optimize this model by leveraging pre-trained weights from similar tasks, which can significantly reduce the computational resources and time required for training. As you proceed with this experiments and model development, ensure robust data collection and preprocessing, as the quality of input data greatly influences the performance of deep learning models. Additionally, consider integrating techniques for data augmentation to further enhance the model's generalization capabilities. Continued research and development in this area can lead to significant advancements in agricultural practices, ultimately benefiting farmers and contributing to food security

Urban Traffic Detection for Autonomous Vehicles

Autonomous vehicles leverage advanced sensors, artificial intelligence, and automation, enabling self-navigation without human intervention. These vehicles hold the potential to significantly improve road safety, enhance efficiency, and revolutionize transportation systems by reshaping how vehicles perceive, interpret, and respond to their environment. The demand for such vehicles arises from the desire for improved urban planning, decreased parking needs, and flexible public transportation. Automation reduces errors, optimizes traffic flow, and produces favorable economic results. This study underscores the crucial importance of advanced traffic and lane detection in reinforcing the reliability and safety of autonomous vehicles, playing a pivotal role in their ongoing evolution. The proposed system operates in real-time, employing dynamic traffic data to inform decision-making. It integrates inputs from cameras, processing parameters such as lane positions, obstacles, and traffic symbols. A centralized control system, comprising Raspberry Pi and Arduino as master-slave components, employs specialized models for lane, object, and traffic symbol detection. This architecture guarantees continuous real-time decision-making and optimizes resource allocation, promoting a resilient and adaptive autonomous driving paradigm. The comprehensive nature of this approach not only aligns with contemporary transportation requirements but also proactively tackles the challenges anticipated in the future urban mobility landscape. &nbsp