Skin Lesion Segmentation Using an Ensemble of Different Image Processing Methods.

In recent times, there has been a huge increase in the average number of cases of skin cancer per year, which sometimes become life threatening for humans. Early detection of various skin diseases through automated detection techniques plays a crucial role. However, the presence of numerous artefacts makes this task challenging. Dermoscopic images exhibit various variations, including hair artefacts, markers, and ill-defined boundaries. These artefacts make automatic analysis of skin lesion quite a difficult task. To address these issues, it is essential to have an accurate and efficient automated method which will delineate a skin lesion from the rest of the image. Unfortunately, due to the presence of several types of skin artefacts, there is no such thresholding method that can provide a sufficient segmentation result for every type of skin lesion. To overcome this limitation, an ensemble-based method is proposed that selects the optimal thresholding based on an objective function. A group of state-of-the-art different thresholding methods such as Otsu, Kapur, Harris hawk, and grey level are used. The proposed method obtained superior results (dice score = 0.89 with p-value ≤ 0.05) as compared to other state-of-the-art methods (Otsu = 0.79, Kapur = 0.80, Harris hawk = 0.60, grey level = 0.69, active contour model = 0.72). The experiments conducted in this study utilize the ISIC 2016 dataset, which is publicly available and specifically designed for skin-related research. Accurate segmentation will help in the early detection of many skin diseases.

Alzheimer Disease Classification through Transfer Learning Approach.

Alzheimer's disease (AD) is a slow neurological disorder that destroys the thought process, and consciousness, of a human. It directly affects the development of mental ability and neurocognitive functionality. The number of patients with Alzheimer's disease is increasing day by day, especially in old aged people, who are above 60 years of age, and, gradually, it becomes cause of their death. In this research, we discuss the segmentation and classification of the Magnetic resonance imaging (MRI) of Alzheimer's disease, through the concept of transfer learning and customizing of the convolutional neural network (CNN) by specifically using images that are segmented by the Gray Matter (GM) of the brain. Instead of training and computing the proposed model accuracy from the start, we used a pre-trained deep learning model as our base model, and, after that, transfer learning was applied. The accuracy of the proposed model was tested over a different number of epochs, 10, 25, and 50. The overall accuracy of the proposed model was 97.84%.

Botnet Attack Detection in IoT Using Machine Learning.

There are an increasing number of Internet of Things (IoT) devices connected to the network these days, and due to the advancement in technology, the security threads and cyberattacks, such as botnets, are emerging and evolving rapidly with high-risk attacks. These attacks disrupt IoT transition by disrupting networks and services for IoT devices. Many recent studies have proposed ML and DL techniques for detecting and classifying botnet attacks in the IoT environment. This study proposes machine learning methods for classifying binary classes. This purpose is served by using the publicly available dataset UNSW-NB15. This dataset resolved a class imbalance problem using the SMOTE-OverSampling technique. A complete machine learning pipeline was proposed, including exploratory data analysis, which provides detailed insights into the data, followed by preprocessing. During this process, the data passes through six fundamental steps. A decision tree, an XgBoost model, and a logistic regression model are proposed, trained, tested, and evaluated on the dataset. In addition to model accuracy, F1-score, recall, and precision are also considered. Based on all experiments, it is concluded that the decision tree outperformed with 94% test accuracy.

Biocompatibility and Hemolytic Activity Studies of Synthesized Alginate-Based Polyurethanes.

Many investigators have focused on the development of biocompatible polyurethanes by chemical reaction of functional groups contained in a spacer and introduced in the PU backbone or by a grafting method on graft polymerization of functional groups. In this study, alginate-based polyurethane (PU) composites were synthesized via step-growth polymerization by the reaction of hydroxyl-terminated polybutadiene (HTPB) and hexamethylene diisocyanate (HMDI). The polymer chains were further extended with blends of 1,4-butanediol (1,4-BDO) and alginate (ALG) with different mole ratios. The structures of the prepared PU samples were elucidated with FTIR and 1H NMR spectroscopy. The crystallinity of the prepared samples was evaluated with the help of X-ray diffraction (XRD). The XRD results reveal that the crystallinity of the PU samples increases when the concentration of alginate increases. Thermogravimetric (TGA) results show that samples containing a higher amount of alginate possess higher thermal stability. ALG-based PU composite samples show more biocompatibility and less hemolytic activity. Mechanical properties, contact angle, and water absorption (%) were also greatly affected.

Sensor-Based Gym Physical Exercise Recognition: Data Acquisition and Experiments.

Automatic tracking and quantification of exercises not only helps in motivating people but also contributes towards improving health conditions. Weight training, in addition to aerobic exercises, is an important component of a balanced exercise program. Excellent trackers are available for aerobic exercises but, in contrast, tracking free weight exercises is still performed manually. This study presents the details of our data acquisition effort using a single chest-mounted tri-axial accelerometer, followed by a novel method for the recognition of a wide range of gym-based free weight exercises. Exercises are recognized using LSTM neural networks and the reported results confirm the feasibility of the proposed approach. We train and test several LSTM-based gym exercise recognition models. More specifically, in one set of experiments, we experiment with separate models, one for each muscle group. In another experiment, we develop a universal model for all exercises. We believe that the promising results will potentially contribute to the vision of an automated system for comprehensive monitoring and analysis of gym-based exercises and create a new experience for exercising by freeing the exerciser from manual record-keeping.

Computer-Aided Brain Tumor Diagnosis: Performance Evaluation of Deep Learner CNN Using Augmented Brain MRI.

Brain tumor is a deadly neurological disease caused by an abnormal and uncontrollable growth of cells inside the brain or skull. The mortality ratio of patients suffering from this disease is growing gradually. Analysing Magnetic Resonance Images (MRIs) manually is inadequate for efficient and accurate brain tumor diagnosis. An early diagnosis of the disease can activate a timely treatment consequently elevating the survival ratio of the patients. Modern brain imaging methodologies have augmented the detection ratio of brain tumor. In the past few years, a lot of research has been carried out for computer-aided diagnosis of human brain tumor to achieve 100% diagnosis accuracy. The focus of this research is on early diagnosis of brain tumor via Convolution Neural Network (CNN) to enhance state-of-the-art diagnosis accuracy. The proposed CNN is trained on a benchmark dataset, BR35H, containing brain tumor MRIs. The performance and sustainability of the model is evaluated on six different datasets, i.e., BMI-I, BTI, BMI-II, BTS, BMI-III, and BD-BT. To improve the performance of the model and to make it sustainable for totally unseen data, different geometric data augmentation techniques, along with statistical standardization, are employed. The proposed CNN-based CAD system for brain tumor diagnosis performs better than other systems by achieving an average accuracy of around 98.8% and a specificity of around 0.99. It also reveals 100% correct diagnosis for two brain MRI datasets, i.e., BTS and BD-BT. The performance of the proposed system is also compared with the other existing systems, and the analysis reveals that the proposed system outperforms all of them.

Predicting Clinical Outcome in Acute Ischemic Stroke Using Parallel Multi-parametric Feature Embedded Siamese Network.

Stroke is the second leading cause of death and disability worldwide, with ischemic stroke as the most common type. The preferred diagnostic procedure at the acute stage is the acquisition of multi-parametric magnetic resonance imaging (MRI). This type of imaging not only detects and locates the stroke lesion, but also provides the blood flow dynamics that helps clinicians in assessing the risks and benefits of reperfusion therapies. However, evaluating the outcome of these risky therapies beforehand is a complicated task due to the variability of lesion location, size, shape, and cerebral hemodynamics involved. Though the fully automated model for predicting treatment outcomes using multi-parametric imaging would be highly valuable in clinical settings, MRI datasets acquired at the acute stage are mostly scarce and suffer high class imbalance. In this paper, parallel multi-parametric feature embedded siamese network (PMFE-SN) is proposed that can learn with few samples and can handle skewness in multi-parametric MRI data. Moreover, five suitable evaluation metrics that are insensitive to imbalance are defined for this problem. The results show that PMFE-SN not only outperforms other state-of-the-art techniques in all these metrics but also can predict the class with a small number of samples, as well as the class with high number of samples. An accuracy of 0.67 on leave one cross out testing has been achieved with only two samples (minority class) for training and accuracy of 0.61 with the highest number of samples (majority class). In comparison, state-of-the-art using hand crafted features has 0 accuracy for minority class and 0.33 accuracy for majority class.

Classification of Microarray Gene Expression Data Using an Infiltration Tactics Optimization (ITO) Algorithm.

A number of different feature selection and classification techniques have been proposed in literature including parameter-free and parameter-based algorithms. The former are quick but may result in local maxima while the latter use dataset-specific parameter-tuning for higher accuracy. However, higher accuracy may not necessarily mean higher reliability of the model. Thus, generalized optimization is still a challenge open for further research. This paper presents a warzone inspired "infiltration tactics" based optimization algorithm (ITO)-not to be confused with the ITO algorithm based on the Itõ Process in the field of Stochastic calculus. The proposed ITO algorithm combines parameter-free and parameter-based classifiers to produce a high-accuracy-high-reliability (HAHR) binary classifier. The algorithm produces results in two phases: (i) Lightweight Infantry Group (LIG) converges quickly to find non-local maxima and produces comparable results (i.e., 70 to 88% accuracy) (ii) Followup Team (FT) uses advanced tuning to enhance the baseline performance (i.e., 75 to 99%). Every soldier of the ITO army is a base model with its own independently chosen Subset selection method, pre-processing, and validation methods and classifier. The successful soldiers are combined through heterogeneous ensembles for optimal results. The proposed approach addresses a data scarcity problem, is flexible to the choice of heterogeneous base classifiers, and is able to produce HAHR models comparable to the established MAQC-II results.

Synthesis and molecular characterization of chitosan/starch blends based polyurethanes.

Starch/chitosan modified polyurethanes (PUs) were synthesized by step growth polymerization reaction between -NCO terminated prepolymer and chain extenders (1,4-Butanediol/starch/chitosan). Isophorone diisocyanate (IPDI) was reacted with hydroxyl-terminated polybutadiene (HTPB) to synthesize prepolymer and was further reacted with different moles ratio of starch/chitosan to produced five samples of polyurethane (PU). These samples were characterized by Fourier transformed infrared (FTIR) and Proton nuclear magnetic resonance (1H NMR) spectroscopy. The surface characterizations of PUs were done by scanning electron microscope (SEM). Thermogravimetric analysis showed that the thermal stability of PUs was higher when the mixture of both natural materials was used at equal amounts. It is concluded that combination of both starch and chitosan are efficient for the synthesis of PUs.