Abstract-The heart is important organ of human body part. Life is completely dependent on efficient working of the heart. What if a heart undergoes a disorder, cardiovascular diseases are the most challenging disease for reducing patient count. According to survey conducted by WHO, about 17 million people die around the globe due to cardiovascular diseases i.e 29.20% among all caused death, mostly in developing countries. Thus there is a need of getting rid of the this complicated task CVD using advanced data mining techniques, in order to discover knowledge of Heart disease prediction. In this paper, we propose an efficient hybrid algorithmic approach for heart disease prediction. This paper serves efficient prediction technique to determine and extract the unknown knowledge of heart disease using hybrid combination of K-means clustering algorithm and artificial neural network. In our proposed model we considered 14 attribute out of 74 attributes of UCI Heart Disease Data Set [19]. This technique uses medical terms such as age, weight, gender, blood pressure and cholesterol rate etc for prediction. To perform grouping of various attributes it uses k-means algorithm and for predicting it uses Back propagation technique in neural networks. The main objective of this paper is to develop a prototype for predicting heart diseases with higher accuracy rate.
With the technological advancements of the modern era, the easy availability of image editing tools has dramatically minimized the costs, expense, and expertise needed to exploit and perpetuate persuasive visual tampering. With the aid of reputable online platforms such as Facebook, Twitter, and Instagram, manipulated images are distributed worldwide. Users of online platforms may be unaware of the existence and spread of forged images. Such images have a significant impact on society and have the potential to mislead decision-making processes in areas like health care, sports, crime investigation, and so on. In addition, altered images can be used to propagate misleading information which interferes with democratic processes (e.g., elections and government legislation) and crisis situations (e.g., pandemics and natural disasters). Therefore, there is a pressing need for effective methods for the detection and identification of forgeries. Various techniques are currently employed for the identification and detection of these forgeries. Traditional techniques depend on handcrafted or shallow-learning features. In traditional techniques, selecting features from images can be a challenging task, as the researcher has to decide which features are important and which are not. Also, if the number of features to be extracted is quite large, feature extraction using these techniques can become time-consuming and tedious. Deep learning networks have recently shown remarkable performance in extracting complicated statistical characteristics from large input size data, and these techniques efficiently learn underlying hierarchical representations. However, the deep learning networks for handling these forgeries are expensive in terms of the high number of parameters, storage, and computational cost. This research work presents Mask R-CNN with MobileNet, a lightweight model, to detect and identify copy move and image splicing forgeries. We have performed a comparative analysis of the proposed work with ResNet-101 on seven different standard datasets. Our lightweight model outperforms on COVERAGE and MICCF2000 datasets for copy move and on COLUMBIA dataset for image splicing. This research work also provides a forged percentage score for a region in an image.
Nowadays, a lot of significance is given to what we read today: newspapers, magazines, news channels, and internet media, such as leading social networking sites like Facebook, Instagram, and Twitter. These are the primary wellsprings of phony news and are frequently utilized in malignant manners, for example, for horde incitement. In the recent decade, a tremendous increase in image information generation is happening due to the massive use of social networking services. Various image editing software like Skylum Luminar, Corel PaintShop Pro, Adobe Photoshop, and many others are used to create, modify the images and videos, are significant concerns. A lot of earlier work of forgery detection was focused on traditional methods to solve the forgery detection. Recently, Deep learning algorithms have accomplished high-performance accuracies in the image processing domain, such as image classification and face recognition. Experts have applied deep learning techniques to detect a forgery in the image too. However, there is a real need to explain why the image is categorized under forged to understand the algorithm’s validity; this explanation helps in mission-critical applications like forensic. Explainable AI (XAI) algorithms have been used to interpret a black box’s decision in various cases. This paper contributes a survey on image forgery detection with deep learning approaches. It also focuses on the survey of explainable AI for images.
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