This project is developed on Python programming with Machine Learning algorithm, K-Nearest Neighbor (KNN) algorithm
The project used Audio pre-processing with MFCC technique to extract feature.
ML is used to train and detect the infant cry type. There are five types used in this project are
Belly Pain
Burping
Discomfort
Hungry
Tired
The project gets accuracy of 88% with KNN algorithm
Python Demo video
Landslide is one of the repeated geological hazards during rainy season, which causes fatalities, damage to property and economic losses in all parts of the world. Landslides are responsible for at least 17% of all fatalities from natural hazards worldwide. Due to global climate change, the frequency of landslide occurrence has been increased and subsequently, the losses and damages associated with landslides also have been increased.
Python Project demo video
Explore how artificial intelligence can be used for Face Mask Detection and Social Distance Detection. Learn about cutting-edge technologies that enhance safety and compliance in public spaces and workplaces
Python Demo
Stock price prediction has long been a challenging problem in the financial industry, with researchers and practitioners continuously seeking more accurate and robust methods. This study explores the application of hyperparameter-tuned machine learning (ML) models for stock price prediction. Leveraging the power of advanced ML algorithms and hyperparameter optimization techniques, this research aims to improve the accuracy of stock price forecasts and provide insights into the effectiveness of different models.
Algorithms Used
Decision Tree algorithm
KNN algorithm
Random forest algorithm
The research begins by collecting historical stock price data, along with relevant financial indicators and market sentiment features. Hyperparameter optimization techniques such as grid search optimization are then applied to fine-tune the models and enhance their predictive performance.
Python Demo
The stock price prediction project also implemented using
Clustering Analysis
www.finalsemprojects.com/stock-prediction-using-clustering-and-classification-model/
Stock price prediction using deep learning project demo can be seen here
www.finalsemprojects.com/stock-price-prediction-in-machine-learning-and-deep-learning/
Discover how deep learning is transforming sign language detection. Learn about innovative models that accurately interpret gestures, enhancing communication for the hearing-impaired. Explore the future of AI-powered sign language recognition
Python Project Demo video
Image enhancement using deep learning
Video demo
In today’s interconnected world, wireless networks play a pivotal role in facilitating seamless communication and data exchange. However, with the benefits of these networks come security risks, including the notorious “Blackhole Attack.” In this post, we will delve into the world of Blackhole Attack detection, exploring the threats it poses and the cutting-edge techniques to safeguard your network.
A Blackhole Attack occurs when a malicious node within a wireless network deceives other nodes by falsely claiming to have the shortest path to the destination. Instead of forwarding data packets as expected, the attacker drops them, creating a virtual “black hole” that swallows data. This can lead to severe consequences, such as data loss, network congestion, and compromised communication integrity.
Detecting Blackhole Attacks is a formidable task due to their deceptive nature. Attackers disguise themselves as legitimate nodes, making it difficult to distinguish their malicious behavior from normal network behavior. Moreover, these attacks can be launched in various wireless network types, such as MANETs and WSNs, adding complexity to the detection process.
Insider Attack protection with Secure Authentication using ECC algorithm is developed in NS2 simulation tool.
The algorithm used in the project are
Elliptic curve cryptography
Advanced standard encryption algorithm
Breadth first search and depth first search
Demo of the project
Malaria Classification using CNN algorithm-Deep Learning techniques with multiple class classification is achieved with good accuracy.
This research holds immense significance in the realms of healthcare, epidemiology, and medical research. Malaria, a mosquito-borne infectious disease caused by Plasmodium parasites, poses a significant global health burden, particularly in tropical and subtropical regions. Accurate and timely classification of malaria cases has numerous advantages implications, thus establishing a critical need for precision in this area.
One of the most immediate implications of accurate malaria classification lies in enabling early detection and treatment. By accurately identifying the specific Plasmodium species causing the infection, healthcare providers can tailor treatment strategies accordingly. Different species of Plasmodium respond differently to various antimalarial drugs, which underscores the importance of timely and accurate classification to ensure that patients receive the most effective treatment regimen. This reduces the risk of disease progression and potential complications, thereby enhancing patient outcomes.
Malaria’s overlapping symptoms with other febrile illnesses make misdiagnosis a common occurrence, leading to potential adverse consequences, especially in areas with limited access to diagnostic facilities. Accurate classification plays a pivotal role. This ensures that patients receive appropriate care and treatment, effectively mitigating the risk of incorrect therapies. By minimizing the likelihood of misdiagnosis, accurate classification also reduces the unnecessary use of antimalarial drugs, thereby contributing to the broader effort of preventing drug resistance.
Accurate classification of malaria cases serves as a cornerstone for effective disease surveillance and control strategies. Public health authorities can design and implement targeted interventions that are tailored to the specific malaria dynamics of a region.
The significance of accurate malaria classification is magnified in the context of epidemiological studies. By precisely identifying the Plasmodium species involved in infections, researchers gain valuable insights into disease patterns and transmission dynamics. Epidemiological studies that aid in understanding the broader context of malaria transmission, contributing to the formulation of evidence-based public health policies.
The varied interactions between different Plasmodium species and mosquito vectors underscore the importance of accurate malaria classification. By accurately determining the parasite species responsible for infections, researchers and health authorities can better understand the vector–parasite relationships that play a pivotal role in disease transmission. This understanding, in turn, guides targeted strategies for vector control, thereby interrupting the chain of transmission and reducing the prevalence of the disease.
Another crucial aspect where accurate classification comes into play is in monitoring drug resistance. Different Plasmodium species exhibit varying levels of susceptibility to antimalarial drugs. By precisely classifying cases, researchers and healthcare professionals can closely monitor the efficacy of treatments and swiftly detect emerging drug-resistant strains.
Accurate classification of malaria cases plays a pivotal role in informing public health planning efforts. Precise data on disease burden, distribution, and trends are essential for allocating resources for diagnosis, treatment, and prevention programs. By offering a clear picture of the disease landscape, accurate classification ensures that public health initiatives are effectively targeted, thereby maximizing their impact and efficiency.
The significance of accurate malaria classification extends to the realm of global health initiatives. With accurate data as a foundation, international efforts to combat malaria, including initiatives by the World Health Organization’s (WHO) Global Malaria Program and collaborative partnerships, gain the necessary insights to formulate effective strategies and allocate resources where they are most needed.
Accurate classification of malaria cases not only supports immediate healthcare needs but also propels advancements in medical research. Insights gained from precise parasite identification contribute to ongoing research into the biology, genetics, and epidemiology of Plasmodium parasites. These findings drive the development of new diagnostic tools, treatment strategies, and interventions, thereby pushing the boundaries of medical science.
In the broader context, the ultimate beneficiaries of accurate malaria classification are the patients themselves. By facilitating timely and tailored treatment, accurate classification reduces morbidity and mortality associated with malaria. Beyond individual patients, accurate classification also contributes to reducing the economic and social burden of malaria on affected communities and societies as a whole.
Malaria classification using CNN algorithmis done using two algorithms, one is machine learning and other is deep learning
The objective of our study is to implement malaria cell type detection system using Convolutional Neural Networks (CNN) from images. There are four classes of detection can be considered, Falciparum, Malariae, Ovale and Vivax detection. Our objective is to implement a machine learning algorithm and a deep learning algorithm and compare their performance. The main aim to classify the given input image to one of four classes by the trained model from input dataset.
Falciparum, Malariae, Ovale and Vivax are the types of Malaria disease identified using this project. This is a multi-class classification model. The experimental result shows that our CNN model achieved around 70% of accuracy.
Please visit under here
In conclusion, the significance of accurate malaria classification reverberates across various sectors, from individual patient care to global health initiatives and medical research. Its impact extends to improved treatment, enhanced disease surveillance, and the prevention of misdiagnosis. With its multifaceted implications, accurate malaria classification stands as a pivotal pillar in the global effort to control, prevent, and eventually eradicate malaria.
Similar type of project can checked in our following links
Breast Cancer Detection using CNN algorithm
Liver Disease detection using CNN algorithm
Helmet, mask and social distancing is detected using artificial intelligence
You can check the demo under here
