A Hybrid Features Based Detection Method for Inshore Ship Targets in SAR Imagery

Convolutional Neural Networks(CNNs)have recently attracted much attention in the ship detection from Synthetic Aperture Radar(SAR)images.However,compared with optical images,SAR ones are hard to understand.Moreover,due to the high similarity between the man-made targets near shore and inshore ships,the classical methods are unable to achieve effective detection of inshore ships.To mitigate the influence of onshore ship-like objects,this paper proposes an inshore ship detection method in SAR images by using hybrid features.Firstly,the sea-land segmentation is applied in the pre-processing to exclude obvious land regions from SAR images.Then,a CNN model is designed to extract deep features for identifying potential ship targets in both inshore and offshore water.On this basis,the high-energy point number of amplitude spectrum is further introduced as an important and delicate feature to suppress false alarms left.Finally,to verify the effectiveness of the proposed method,numerical and comparative studies are carried out in experiments on Sentinel-1 SAR images.

Efficient Explanation and Evaluation Methodology Based on Hybrid Feature Dropout

AI-related research is conducted in various ways,but the reliability of AI prediction results is currently insufficient,so expert decisions are indispensable for tasks that require essential decision-making.XAI(eXplainable AI)is studied to improve the reliability of AI.However,each XAI methodology shows different results in the same data set and exact model.This means that XAI results must be given meaning,and a lot of noise value emerges.This paper proposes the HFD(Hybrid Feature Dropout)-based XAI and evaluation methodology.The proposed XAI methodology can mitigate shortcomings,such as incorrect feature weights and impractical feature selection.There are few XAI evaluation methods.This paper proposed four evaluation criteria that can give practical meaning.As a result of verifying with the malware data set(Data Challenge 2019),we confirmed better results than other XAI methodologies in 4 evaluation criteria.Since the efficiency of interpretation is verified with a reasonable XAI evaluation standard,The practicality of the XAI methodology will be improved.In addition,The usefulness of the XAI methodology will be demonstrated to enhance the reliability of AI,and it helps apply AI results to essential tasks that require expert decision-making.

A Hybrid Feature Selection Framework for Predicting Students Performance

Student performance prediction helps the educational stakeholders to take proactive decisions and make interventions,for the improvement of quality of education and to meet the dynamic needs of society.The selection of features for student’s performance prediction not only plays significant role in increasing prediction accuracy,but also helps in building the strategic plans for the improvement of students’academic performance.There are different feature selection algorithms for predicting the performance of students,however the studies reported in the literature claim that there are different pros and cons of existing feature selection algorithms in selection of optimal features.In this paper,a hybrid feature selection framework(using feature-fusion)is designed to identify the significant features and associated features with target class,to predict the performance of students.The main goal of the proposed hybrid feature selection is not only to improve the prediction accuracy,but also to identify optimal features for building productive strategies for the improvement in students’academic performance.The key difference between proposed hybrid feature selection framework and existing hybrid feature selection framework,is two level feature fusion technique,with the utilization of cosine-based fusion.Whereas,according to the results reported in existing literature,cosine similarity is considered as the best similarity measure among existing similarity measures.The proposed hybrid feature selection is validated on four benchmark datasets with variations in number of features and number of instances.The validated results confirm that the proposed hybrid feature selection framework performs better than the existing hybrid feature selection framework,existing feature selection algorithms in terms of accuracy,f-measure,recall,and precision.Results reported in presented paper show that the proposed approach gives more than 90%accuracy on benchmark dataset that is better than the results of existing approach.

Automatic Classification of Cardiac Arrhythmias Based on Hybrid Features and Decision Tree Algorithm

Accurate classification of cardiac arrhythmias is a crucial task because of the non-stationary nature of electrocardiogram(ECG)signals.In a life-threatening situation,an automated system is necessary for early detection of beat abnormalities in order to reduce the mortality rate.In this paper,we propose an automatic classification system of ECG beats based on the multi-domain features derived from the ECG signals.The experimental study was evaluated on ECG signals obtained from the MIT-BIH Arrhythmia Database.The feature set comprises eight empirical mode decomposition(EMD)based features,three features from variational mode decomposition(VMD)and four features from RR intervals.In total,15 features are ranked according to a ranker search approach and then used as input to the support vector machine(SVM)and C4.5 decision tree classifiers for classifying six types of arrhythmia beats.The proposed method achieved best result in C4.5 decision tree classifier with an accuracy of 98.89%compared to cubic-SVM classifier which achieved an accuracy of 95.35%only.Besides accuracy measures,all other parameters such as sensitivity(Se),specificity(Sp)and precision rates of 95.68%,99.28%and 95.8%was achieved better in C4.5 classifier.Also the computational time of 0.65 s with an error rate of 0.11 was achieved which is very less compared to SVM.The multi-domain based features with decision tree classifier obtained the best results in classifying cardiac arrhythmias hence the system could be used efficiently in clinical practices.

Posture Detection of Heart Disease Using Multi-Head Attention Vision Hybrid(MHAVH)Model

Cardiovascular disease is the leading cause of death globally.This disease causes loss of heart muscles and is also responsible for the death of heart cells,sometimes damaging their functionality.A person’s life may depend on receiving timely assistance as soon as possible.Thus,minimizing the death ratio can be achieved by early detection of heart attack(HA)symptoms.In the United States alone,an estimated 610,000 people die fromheart attacks each year,accounting for one in every four fatalities.However,by identifying and reporting heart attack symptoms early on,it is possible to reduce damage and save many lives significantly.Our objective is to devise an algorithm aimed at helping individuals,particularly elderly individuals living independently,to safeguard their lives.To address these challenges,we employ deep learning techniques.We have utilized a vision transformer(ViT)to address this problem.However,it has a significant overhead cost due to its memory consumption and computational complexity because of scaling dot-product attention.Also,since transformer performance typically relies on large-scale or adequate data,adapting ViT for smaller datasets is more challenging.In response,we propose a three-in-one steam model,theMulti-Head Attention Vision Hybrid(MHAVH).Thismodel integrates a real-time posture recognition framework to identify chest pain postures indicative of heart attacks using transfer learning techniques,such as ResNet-50 and VGG-16,renowned for their robust feature extraction capabilities.By incorporatingmultiple heads into the vision transformer to generate additional metrics and enhance heart-detection capabilities,we leverage a 2019 posture-based dataset comprising RGB images,a novel creation by the author that marks the first dataset tailored for posture-based heart attack detection.Given the limited online data availability,we segmented this dataset into gender categories(male and female)and conducted testing on both segmented and original datasets.The training accuracy of our model reached an impressive 99.77%.Upon testing,the accuracy for male and female datasets was recorded at 92.87%and 75.47%,respectively.The combined dataset accuracy is 93.96%,showcasing a commendable performance overall.Our proposed approach demonstrates versatility in accommodating small and large datasets,offering promising prospects for real-world applications.