Data Mining - Concepts and Techniques.

We propose a theoretically and practically improved density-based, hierarchical clustering method, providing a clustering hierarchy from which a simplified tree of significant clusters can be constructed. For obtaining a “flat” partition consisting of only the most significant clusters (possibly corresponding to different density thresholds), we propose a novel cluster stability measure, formalize the problem of maximizing the overall stability of selected clusters, and formulate an algorithm that computes an optimal solution to this problem. We demonstrate that our approach outperforms the current, state-of-the-art, density-based clustering methods on a wide variety of real world data.

Density-based clustering based on hierarchical density estimates

Towards secure intrusion detection systems using deep learning techniques: Comprehensive analysis and review

We address the problem of restoring a high-quality image from an observed image sequence strongly distorted by atmospheric turbulence. A novel algorithm is proposed in this paper to reduce geometric distortion as well as space-and-time-varying blur due to strong turbulence. By considering a suitable energy functional, our algorithm first obtains a sharp reference image and a subsampled image sequence containing sharp and mildly distorted image frames with respect to the reference image. The subsampled image sequence is then stabilized by applying the Robust Principal Component Analysis (RPCA) on the deformation fields between image frames and warping the image frames by a quasiconformal map associated with the low-rank part of the deformation matrix. After image frames are registered to the reference image, the low-rank part of them are deblurred via a blind deconvolution, and the deblurred frames are then fused with the enhanced sparse part. Experiments have been carried out on both synthetic and real turbulence-distorted video. Results demonstrate that our method is effective in alleviating distortions and blur, restoring image details and enhancing visual quality.

Restoration of atmospheric turbulence-distorted images via RPCA and quasiconformal maps

At present, a multifaceted clinical disease known as heart failure disease can affect a greater number of people in the world. In the early stages, to evaluate and diagnose the disease of heart failure, cardiac centers and hospitals are heavily based on ECG. The ECG can be considered as a regular tool. Heart disease early detection is a critical concern in healthcare services (HCS). This paper presents the different machine learning technologies based on heart disease detection brief analysis. Firstly, Naïve Bayes with a weighted approach is used for predicting heart disease. The second one, according to the features of frequency domain, time domain, and information theory, is automatic and analyze ischemic heart disease localization/detection. Two classifiers such as support vector machine (SVM) with XGBoost with the best performance are selected for the classification in this method. The third one is the heart failure automatic identification method by using an improved SVM based on the duality optimization scheme also analyzed. Finally, for a clinical decision support system (CDSS), an effective heart disease prediction model (HDPM) is used, which includes density-based spatial clustering of applications with noise (DBSCAN) for outlier detection and elimination, a hybrid synthetic minority over-sampling technique-edited nearest neighbor (SMOTE-ENN) for balancing the training data distribution, and XGBoost for heart disease prediction. Machine learning can be applied in the medical industry for disease diagnosis, detection, and prediction. The major purpose of this paper is to give clinicians a tool to help them diagnose heart problems early on. As a result, it will be easier to treat patients effectively and avoid serious repercussions. This study uses XGBoost to test alternative decision tree classification algorithms in the hopes of improving the accuracy of heart disease diagnosis. In terms of precision, accuracy, f1-measure, and recall as performance parameters above mentioned, four types of machine learning (ML) models are compared.

/pdf/machine-learning-technology-based-heart-disease-detection-283shs0y.pdf

Machine Learning Technology-Based Heart Disease Detection Models

Heart disease, alternatively known as cardiovascular disease, is the primary basis of death worldwide over the past few decades. To make an early diagnosis, a data-driven prediction model considering the associate risk factors in heart disease can play a significant role in healthcare domain. However, to build such an effective model based on machine learning techniques, the quality of the data, e.g., data without “anomalies” or outliers, is important. This research investigates anomaly detection in the healthcare domain to effectively predict heart disease using unsupervised K-means clustering algorithm. Our proposed model first determines an optimal value of K using the Silhouette method to form the clusters for finding the anomalies. After that, we eliminate the identified anomalies from the data and employ the five most popular machine learning classification techniques, such as K-nearest neighbor, random forest, support vector machine, naive Bayes, and logistic regression to build the resultant prediction model. The efficacy of the proposed methodology is justified using a standard heart disease dataset. We also take into account the data plotting to test the exactness of the detection of anomalies in our experimental analysis.

https://link.springer.com/content/pdf/10.1007/s42979-021-00518-7.pdf

A Data-Driven Heart Disease Prediction Model Through K-Means Clustering-Based Anomaly Detection

Image data experiences geometric distortions and spatial-temporal varying blur due to the strong effects of random spatial and temporal variations in the optical refractive index of the communication path. Simultaneously removing these effects from an image is a challenging task. An efficient approach is proposed in this paper to address this problem. The approach consists of four steps. First, a frame selection strategy is employed by proposing an unsupervised k-means clustering technique. Second, a B-spline-based nonrigid image registration is carried out to suppress geometric distortions. Third, a spatiotemporal kernel regression is proposed by introducing the local sharp patch concept to fuse the registered frame sequences into an image. Finally, a blind deconvolution technique is employed to deblur the fused image. Experiments are carried out with synthetic and real-world turbulence-degraded data by implementing the proposed method and two recently reported methods. The proposed method demonstrates significant improvement over the two reported methods in terms of alleviating blur and distortions, as well as improving visual quality.

Restoring atmospheric-turbulence-degraded images.

Network-on-chip-based communication schemes represent a promising solution to the increasing complexity of system-on-chip problems. In this paper, we propose a new mesh-like topology called the shortly connected mesh technology (ScMesh), which is based on the traditional mesh topology, to exploit the graph symmetry properties of interconnection networks. This proposed topology not only enhances network performance by reducing the network diameter, but also provides a lower area/energy solution for interconnection network scenarios. This study analyzes and compares the performance of ScMesh to some newly improved topologies, including the WK-recursive, extended-butterfly fat tree, and diametrical mesh topologies. The experiment results indicate that ScMesh outperforms the other topologies, with throughput increases of 47.71, 33.45, and 18.64 % as well as latency decreases of 45.71, 35.84, and 14.58 % compared to the extended-butterfly fat tree, WK-recursive and diametrical mesh topologies, respectively. In addition, ScMesh achieves 41.22, 32.23, and 15.01 % lower energy consumption and 38.96, 27.43, and 18.21 % lower area overhead than the extended-butterfly fat tree, WK-recursive, and diametrical mesh topologies, respectively.

A shortly connected mesh topology for high performance and energy efficient network-on-chip architectures

Faking smart industry: exploring cyber-threat landscape deploying cloud-based honeypot

This paper presents an effective heart disease prediction model through detecting the anomalies, also known as outliers, in healthcare data using the unsupervised K-means clustering algorithm. Most existing approaches for detecting anomalies are based on constructing profiles of normal instances. However, such techniques require an adequate number of normal profiles to justify those models. Our proposed model first evaluates an optimal value of K using Silhouette method. Next, it intends to locate anomalies that are far from a certain threshold distance with respect to their clusters. Finally, the five most popular classification techniques such as K-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machines (SVM), Naive Bayes (NB), and Logistic Regression (LR) are applied to build the resultant prediction model. The effectiveness of the proposed methodology is justified using a benchmark dataset of heart disease.

/pdf/an-effective-heart-disease-prediction-model-based-on-machine-1gg0dfgv5k.pdf

Md. Hasan Furhad

Papers

A Data-Driven Heart Disease Prediction Model Through K-Means Clustering-Based Anomaly Detection

Restoring atmospheric-turbulence-degraded images.

A shortly connected mesh topology for high performance and energy efficient network-on-chip architectures

Faking smart industry: exploring cyber-threat landscape deploying cloud-based honeypot

An Effective Heart Disease Prediction Model based on Machine Learning Techniques