In the modern environment, it is quite difficult to detect Heart Disease (HD) through early stage symptoms. Heart disease is the cause of mortality if the diagnosis is delayed including death occurs to prevent these issues. This paper proposes an innovative approach for the detection of heart disease from HD dataset, incorporating advanced techniques. Initially, data cleaning and data normalization is applied to the HD to eliminate or to find the duplicate values. Next, the images are segmented using K means clustering, allowing for the separation of relevant regions associated with HD. Features are then optimized from the segmented HD using the Dung Beetle Optimization (DBO) algorithm, capturing optimized values for further analysis. Finally, a Bidirectional Long Short Term Memory (BiLSTM) framework is proposed as a hybrid model to enhance the classification of HD images, leveraging both forward and backward temporal information to overcome challenges in disease analysis. The assessment of proposed work using python software reveals that the proposed framework with BiLSTM classifier ranks with improved accuracy of 91.56% when compared to the other techniques.
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An Ai-Driven Tsunami Prediction Framework Using Chicken Swarm Optimized Artificial Neural Network
Tsunami detection in the coastal area is a major problem and a big impact on the environment, therefore early detection and training for tsunami need to be carried out to reduce the impact of casualties and losses incurred. Machine learning techniques, with their ability to identify patterns and anomalies, offer an approach for tsunami detection. This paper introduces a novel tsunami detection model combining the Chicken Swarm Optimization (CSO) Algorithm and Artificial Neural Network (ANN) classifier. Using the data pre processing technique such as data cleaning and data visualization the input dataset is processed and featured using speedup data transformation the raw data is converted in to certain data using feature engineering. This hybrid approach effectively analyses the detection of tsunami by leveraging the strengths of both methods. The model is trained and evaluated using the earthquake dataset, achieving a commendable accuracy of 92%. The software used here is python. The proposed model demonstrates superior results compared to existing techniques, offering an efficient solution for environment.
A Multi-Scale Attention Efficient Network (Mae-Net) For Gastrointestinal Disease Detection and Classification
Gastrointestinal diseases (GIDs) are increasingly prevalent worldwide, necessitating robust methods for their early detection and diagnosis. This study proposes a novel Multi scale Attention Efficient Network (MAE Net) for identifying abnormalities in the gastric region using time–frequency analysis. The Kvasir dataset is employed as the primary source of input images. The methodology begins with a preprocessing phase where the input images are resized, converted to grayscale, and enhanced for texture. This is followed by segmentation using approximate spatial fuzzy c means clustering. Features are extracted using wavelet transform coefficients to analyze frequency and time series data. The decomposed image classes are then input into the MAE Net model for training and testing, enabling accurate classification and prediction. The performance of the proposed model is evaluated using standard metrics such as accuracy, precision, recall, and F1 score. Comparative analysis with existing methods on similar datasets demonstrates the superiority of the proposed approach in terms of classification performance. This work highlights the potential of MAE Net in advancing the detection and diagnosis of gastrointestinal abnormalities.
A Data-Driven Approach to Air Pollution Management with Self-Tuning Deep Regression Neural Network
Air Qualities (AQ) are developed to be a serious environmental and health concern as a result of worldwide population growth. A predictive model for air quality is essential for the timely prevention and management of air pollution. This study proposes a novel Self Tuning Deep Regression Neural Network (STDRNN) for identifying air pollution in the environment using deep neural network. The Air Quality Index (AQI) dataset is employed as the primary source of input data. The methodology begins with a data pre processing phase where the input data finds the missing data and shows the duplicate AQ values using data imputation and remove outlier method. This is followed by feature construction where the each AQ label is converted into an integer AQ value and scaling the features to a similar range using feature encoding and feature scaling method. The AQI data are then given to the STDRNN model for training and testing, enabling accurate prediction. The performance of the proposed model is evaluated using standard metrics such as RMSE, MSE and R2 coefficient. Using the python software AQI dataset shows the comparative analysis with existing methods on similar datasets demonstrates the superiority of the proposed approach in terms of selection performance. This work highlights the potential of STDRNN in advancing the accuracy of the model as well as its promising potential applications.
A Streamlined Vgg-19 Classification Model for Multi-Crop Leaf Disease Prediction
Every country economy is depending heavily on agriculture because it produces crops. Identifying leaf diseases is one of the most crucial parts of keeping a country that is agriculturally advanced. Artificial intelligence (AI) in agriculture has emerged as the most significant application in recent years. An early prediction of the type of disease affecting the plant leaves is an essential objective. To classify the current diseases type and distinguish between healthy and infected leaves, the proposed study mainly employs the Visual Geometry Group (VGG 19) classification model. The features extraction of images using a wavelet transform for extracting the features of the leaves. The model is trained and evaluated using the Apple Leaf Disease Symptoms dataset, and the adaptive wiener filter is used to enhance the images and remove noise from the leaf images, while the k means clustering algorithm divides the regions in the leaf images. Accuracy, sensitivity, specificity, recall, and F1 score were among the performance measure parameters that were computed and tracked. This classifier is implemented using the python software. The findings show that it offers greater accuracy and the results are then contrasted with those of earlier classification methods. By identifying plant illnesses early and implementing the right treatment, this approach aims to assist farmers in protecting resources and avoiding financial loss.
Enhancing Intrusion Detection with Cuckoo Search Optimized XGBoost Classifier for Network Traffic Analysis
An Intrusion Detection System (IDS) is crucial for ensuring network security by identifying and mitigating malicious activities. With the rise in interconnected systems, vulnerabilities have become a significant concern, requiring advanced detection mechanisms. Machine learning techniques, with their ability to identify patterns and anomalies, offer a promising approach for anomaly based IDS. This paper introduces a novel intrusion detection model combining the Cuckoo Search Algorithm (CSA) and the XGBoost classifier. This hybrid approach effectively analyzes network traffic and detects intrusions by leveraging the strengths of both methods. The model is trained and evaluated using the UNSW NB15 dataset, achieving a commendable accuracy of 92.06%. Key contributions include handling missing values, outlier analysis, one hot encoding, and feature scaling to enhance detection performance. The proposed model demonstrates superior results compared to existing techniques, offering an efficient solution for securing network infrastructures.
Novel Butterfly Optimization Algorithm-Based Secure Node Selection Strategy for Wireless Sensor Network
Wireless Sensor Networks (WSNs) allow new applications and necessitate non traditional routing design paradigms due to several constraints. A long network lifetime requires minimal device complexity and low energy consumption, which requires a proper balance between communication and signal/data processing capabilities. The objective of this paper is to increase the network lifetime while minimizing overall energy. Currently, clustering and routing algorithms are widely used in WSNs to increase the network lifetime. The Butterfly Optimization Algorithm (BOA) is used in this work to select the best cluster head from a set of nodes. The cluster head is chosen based on the nodes residual energy, node degree, node centrality and space to the base station and neighbours. Comparing the existing methodology to current algorithms BOA, BOA exposures that it outperformed LEACH PRO and EECHIGWO in terms of network lifetime, throughput, delay and energy consumption.
Modified Convolutional Neural Networks for Efficient and Precise Brain Tumor Diagnosis in Clinical Application Use
Brain tumor is the growth of abnormal brain cells, some of which have the potential to develop into cancer. For medical professionals, it is quite difficult to diagnose brain tumors early. They assess the Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) scans for finding the brain tumor. Searching through the vast number of MRI images by hand to find a brain tumor is now extremely time consuming and inaccurate. Using basic imaging techniques to identify aberrant brain areas is challenging. This work uses image processing techniques to automatically detect and classify brain tumors. There are four primary steps in the methodology: The first step is image preprocessing, in which the Gaussian filter is used to enhance and filter the images. Second, the Global Thresholding approach for image segmentation. Third, the features of the image are extracted using the Local Binary Pattern (LBP). Fourth, the classification of brain cancers is done using the Modified Convolutional Neural Network (CNN).This classifier is implemented using the python software. The findings show that it offers greater accuracy and the results are then contrasted with those of earlier classification methods. This classification algorithm is executed with greater performances and detect the brain tumor in MRI images with more favorable results.
Energy-Efficient Photovoltaic-Based Motor Drive With Interleaved Boost Converter For Electric Vehicle
The Electric Vehicles (EVs) are revolutionary in the transport sector and leads to the phasing out of conventional fuel vehicles. The Renewable Energy Sources (Res) is used for EV. The EV vehicles are cost effective, environmental friendly. For the proportion of EV, Permanent Magnet Brush Less Direct Current (PMBLDC) motor is used in the Photovoltaic (PV) system. The energy from the PV system is always low due to the environmental conditions, inorder to boost the voltage, this paper proposes Interleaved Boost Converter (IBC) to hybridize energy alternatives in EVs. The energy obtained from the PV system is DC. To step up the DC voltage from the PV array it is required level an interleaved boost converter is integrated. It ensure continous power flow, reduces ripple current and improves the overall power quality of the system.. The drive system’s performance for various operating modes, such as stable and varying load conditions are examined from the simulations. The PMBLDC motor helps in effectively managing speed with accordance with the PI controller. The developed system’s is evaluated by MATLAB simulations to verify its effectiveness. The presented converter achieves the highest efficiency of (93%) compared to other conventional methods.
An Efficient Framework for Lung Cancer Prediction Using LBP Extraction and Cascaded CNN
Lung cancer, which affects both men and women, is currently the leading cause of cancer related fatalities globally. Lung cancer is mostly caused by smoking. It is thought that 90% to 95% of cases of lung cancer begin in the epithelial cells lining the bigger and smaller airways (bronchi and bronchioles), while the illness can grow elsewhere in the lung. Medical responsibilities make a better treatment decision for patient diagnosis and treatment to find a lung cancer at early stage using the classification models. This study’s main goal is to identify lung cancer by employing Cascaded Convolutional Neural Networks (CNN) with Local Binary Pattern and Wiener Filter. The lung image dataset is taken as the input. The lung images have been pre processed by Wiener filter. After, the processed images are segmented using the Otsu Binary Threshold method. The features of the cancer affected images are to be extracted using the Local Binary Pattern approach. This work implements in a python platform for developing a classification of lung images using the Cascaded CNN classifier. The proposed Cascaded CNN technique provides more accuracy than other classification models for predicting lung cancer. The proposed classifier shows the highest specificity 95% of and sensitivity of 94%.