<p class="wow fadeInDown">This is the component of project implementation that helps determine goals, constraints, workflow management strategies, tasks, and deliverables.</p>
<p>Microorganism identification in water bodies is critical for assessing water quality and potential threats to human and environmental health. Several studies have focused on exploiting microscopic pictures and image processing techniques to identify microorganisms.
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<p>[1]Used automated image analysis
techniques to classify and quantify microorganisms in
water samples, for example. They proved the power of
image processing in recognizing and distinguishing
different types of microorganisms, providing vital
insights on microbial ecology and water quality.
The classification of tuberculosis bacteria will examine
the degree of classification accuracy using two different
classification approaches. Using digital preparation
photographs, tuberculosis bacteria are classified using
k- nearest neighbor and backpropagation methods.
Single tuberculosis bacterium and multiple tuberculosis
bacteria are the two categories of bacteria used in this
method [3]. </p>
<p>Using computer vision and machine learning
techniques, the classification of bacteria is being
computerized. In this classification, researchers have
looked into a method for reprogramming "Inception,"
one of the best open-source neural network models, to
classify microscopic pictures of bacteria. To
comprehend the distinct structure of each bacterium
and how it forms colonies, the network gathered
hundreds of features from the images for edge-
detection and pattern recognition. Using a dataset of
500 microscopic photos of bacteria, researchers were
able to retrain the neural network on 400 of the images
and test its prediction accuracy on the remaining 250.
Researchers ultimately discovered the ideal values of
the parameters to obtain the prediction accuracy of
almost 95% after executing numerous processes of
preprocessing image data, tweaking the neural
network, and testing its several fine-tuned versions [4].</p>
<h5>Application of Advanced Analytical Techniques </h5>
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<p style="text-align: justify ; font-family: 'Raleway', sans-serif; font-size: 12px;"> The research largely relies on traditional methods, such as culture techniques and manual counting, missing opportunities to leverage advanced analytical techniques, like modern technology and machine learning, for more accurate and cost-effective water quality assessment</p>
<h5>Microorganisms Identification and Classification</h5>
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<p style="text-align: justify;font-family: 'Raleway', sans-serif; font-size: 12px;">While some research has automated microorganism counting, it has not addressed the crucial aspects of identifying and categorizing these microorganisms, creating a research gap in these areas for a more comprehensive understanding of water quality
<p style="text-align: justify;font-family: 'Raleway', sans-serif; font-size: 12px;">Certain research projects have proposed innovative methods but raised concerns about their potentially high costs, necessitating further research into developing economically viable solutions for water quality assessment to ensure practical application in diverse settings
<p style="text-align: justify; font-family: 'Raleway', sans-serif; font-style: none; font-size: 14px">The research problem at hand originates from the limitations of conventional techniques for identifying microorganisms in water, which are often confined to specific species and rely on labor-intensive manual or outdated automated methods. This deficiency hinders a comprehensive understanding of the diverse array of microorganisms inhabiting water ecosystems, which is essential for assessing water quality and ecological health. To address this shortfall, the research aims to pioneer the utilization of advanced microscopic imaging technology, enabling the examination of a wide range of microorganisms with efficiency and precision. However, a novel challenge arises – the need to make this advanced technology accessible and user-friendly. The proposed web-based application is envisioned as a solution that simplifies the identification process, broadening access to researchers, educators, and enthusiasts, thus facilitating their active participation in the study of waterborne microorganisms. This dual objective seeks to advance microorganism identification while creating an inclusive tool for community involvement, ultimately promoting collaborative research and a deeper understanding of water quality, health implications, and ecological dynamics in aquatic ecosystems.</p>
<p style="text-align: justify;font-family: 'Raleway', sans-serif; font-style: none; font-size: 14px">The research solution entails the development of an integrated platform known as the "Blue Watch" application. This application is designed to address multiple aspects of water quality assessment and management. Firstly, it provides an intuitive interface to easily identify water quality by utilizing advanced microscopic imaging technology. Users can efficiently detect and classify microorganisms and fungi types present in water samples, enhancing our understanding of waterborne life. Additionally, the application incorporates satellite imagery analysis to determine the pollution status of water bodies, thereby serving as a vital tool for monitoring environmental health. Furthermore, it offers a predictive model for the Water Quality Index (WQI) by leveraging physical parameters of water. This model not only assesses current water quality but also forecasts the WQI for the next 12 months, facilitating proactive water resource management and environmental sustainability. The "Blue Watch" application represents a comprehensive and user-friendly solution that integrates cutting-edge technology for holistic water quality assessment and long-term monitoring. </P>
<h5 style="color: #6b615a"><strong>Identifying microorganisms using water samples
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<p style="color: #877e78; text-align: justify; font-family: 'Raleway', sans-serif; font-style: none; font-size: 14px">In this component, the first step involves classifying microscopic images to identify microorganisms as either bacteria or protozoa. Once identified, the focus shifts to differentiating between bacteria and protozoa before proceeding with detailed classification. Subsequently, the research progresses to categorizing specific types of bacteria and protozoa, enabling differentiation and cataloging of diverse species and strains within these microorganism groups.
<h5 style="color: #6b615a"><strong>Identifying fungus types using microscopic images.
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<p style="color: #877e78; text-align: justify; font-family: 'Raleway', sans-serif; font-style: none; font-size: 14px">This component involves building a binary classification model to identify whether a microscopic image depicts a fungus or not. If the image is identified as a fungus, further analysis is conducted to specify the exact type among Aspergillus spp., Candida spp., and Mycorrhizal Fungi. Following the identification of the exact fungus type, the system produces an output indicating the fungus type and assesses its impact on the environment, classifying it as harmful or useful.
<h5 style="color: #6b615a"><strong>Identifying color of the water body using image processing techniques and detecting water quality based on satellite images</strong></h5>
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<p style="color: #877e78; text-align: justify; font-family: 'Raleway', sans-serif; font-style: none; font-size: 14px">The primary goal is to utilize satellite imagery in a web application to identify water body colors and assess reservoir water quality. This user-friendly platform aims to provide accessible water quality monitoring. Users can analyze satellite images to identify water bodies and evaluate their quality based on color.
<h5 style="color: #6b615a"><strong>Using the WQI to evaluate groundwater quality. </strong></h5>
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<p style="color: #877e78; text-align: justify; font-family: 'Raleway', sans-serif; font-style: none; font-size: 14px">The research involves a detailed analysis of Water Quality Index (WQI) and its correlation with various physical parameters like turbidity, total dissolved solids, temperature, Secchi depth, and conductivity. The study explores the complex relationships and interactions among these variables, examining their impact on overall water quality assessment. Additionally, a comprehensive predictive model for WQI is developed, incorporating a wide range of influencing variables and advanced machine learning methods to enhance accuracy. Long-term analysis spanning several decades examines historical data, identifying nuances in water quality. The research investigates the effects of factors such as climate change, urbanization, land use, pollution sources, and regulatory actions on long-term water quality trends.</p>
<p> Secure X, a sophisticated set of network infrastructure management tools designed to handle the intricacies of modern network environments. system consists of 4 main components.
Network devices bulk configuration tool
Network Self-Healing Tool
DDoS Attack Detection and Mitigation
Brute force attack detection using machine learning
Develop python configuration templates for each DHCP,ACL,NAT,VPN,SNMP,HSRP,IP SLA,NTP,AAA configuration. templates should contain placeholders for variables such as IP addresses, interface names, subnet masks, authentication credentials. Develop a Python script that reads the input data and configuration templates, then generates device-specific configuration files based on the provided parameters. Deploy the generated configuration files to the respective devices using appropriate network protocols (SSH). Continuously monitor network performance and user feedback to identify areas for improvement in the bulk configuration process.
Develop scripts to gather relevant network health metrics from network devices, such as CPU and memory usage, interface status, routing table information, and device logs. Implement algorithms to automatically detect common failure scenarios (e.g., interface flapping, high CPU utilization, BGP neighbor down). Implement automated remediation actions, such as configuration changes, service restarts, failover mechanisms, or dynamic routing adjustments, to mitigate the impact of failures.
Capture the network traffic using python script & create a pcap file. obtain the proposed CSE-CIC-IDS 2018 dataset benchmark dataset.prepare the data & train & evaluate the model. Develop the user interface application to upload your network packet captured file and select whatever the algorithm among three algorithms (Decision Tree , Random Forest , Support vector machine ) you prefer to detect brute force attack on ssh & ftp protocol through a drop-down list.
Comparative Analysis of Machine Learning Algorithms for DDoS Attack Detection using IDS 2018 Dataset and Development of a User-friendly Interface for Real-time Detection. The algorithms that were selected are based on earlier research. focused on detecting specific attack types, namely DoS attacks (GoldenEye, Slowloris, Hulk, SlowHTTPTest) and DDoS attacks (LOIC- HTTP). The performance of each algorithm was evaluated against these attack types to provide insights into their effectiveness in real-world scenarios.
Multiple network devices can be configured simultaneously with the help of the Bulk Configuration Tool, which simplifies deployment procedures and lowers mistake rates. By using clever algorithms, the Network Self-Healing Tool proactively monitors and fixes network problems, reducing downtime and improving reliability. The DDoS Attack Detection Tool ensures service availability by quickly identifying and mitigating DDoS attacks through the use of sophisticated anomaly detection techniques. By capturing the network traffic & identifying and preventing brute-force login attempts on network devices, the Machine Learning Driven Brute Force Attack Detection System improves security.
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<!-- <p>Once the system identifies that the leaves are infected, then the response will be captured by the crowdsourcing platform. The Google Map will be updated with the real time locations (latitude and longitude) of the infected palms. In Addition, the system will automatically send notifications to the farmers and other stakeholders who are at the risk of infection.</p> -->
<p>Progress Presentation I reviews the 50% completetion status of the project. This reveals any gaps or inconsistencies in the design/requirements.</p>
<p>Progress Presentation II reviews the 90% completetion status demonstration of the project. Along with a Poster presesntation which describes the project as a whole.</p>
<p>Final Report evalutes the completed project done throughout the year. Marks mentioned below includes marks for Individual & group reports and also Final report.</p>
<p style="text-align: justify;"> We are excited to announce that we have submitted our research paper for consideration at the prestigious 'WorldS4 UK 2024' (World Conference on Smart Trends in Systems, Security and Sustainability). Our paper explores cutting-edge developments and insights in the field of computing and aligns with the conference's mission to advance the frontiers of knowledge. We eagerly anticipate the opportunity to present our research at WorldS4 UK 2024, where we can engage with leading experts and contribute to the vibrant exchange of ideas in the world of computing.</P>
