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Prediction of the stage of cancer plays an important role in planning the course of treatment and has been largely reliant on imaging tools which do not capture molecular events that cause cancer progression. Gene-expression data–based analyses are able to identify these events, allowing RNA-sequence and microarray cancer data to be used for cancer analyses. Breast cancer is the most common cancer worldwide, and is classified into four stages — stages 1, 2, 3, and 4 [2]. While machine learning models have previously been explored to perform stage classification with limited success, multi-class stage classification has not had significant progress. There is a need for improved multi-class classification models, such as by investigating deep learning models. Gene-expression-based cancer data is characterised by the small size of available datasets, class imbalance, and high dimensionality. Class balancing methods must be applied to the dataset. Since all the genes are not necessary for stage prediction, retaining only the necessary genes can improve classification accuracy. The breast cancer samples are to be classified into 4 classes of stages 1 to 4. Invasive ductal carcinoma breast cancer samples are obtained from The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) datasets and combined. Two class balancing techniques are explored, synthetic minority oversampling technique (SMOTE) and SMOTE followed by random undersampling. A hybrid feature selection pipeline is proposed, with three pipelines explored involving combinations of filter and embedded feature selection methods: Pipeline 1 — minimum-redundancy maximum-relevancy (mRMR) and correlation feature selection (CFS), Pipeline 2 — mRMR, mutual information (MI) and CFS, and Pipeline 3 — mRMR and support vector machine–recursive feature elimination (SVM-RFE). The classification is done using deep learning models, namely deep neural network, convolutional neural network, recurrent neural network, a modified deep neural network, and an AutoKeras generated model. Classification performance post class-balancing and various feature selection techniques show marked improvement over classification prior to feature selection. The best multiclass classification was found to be by a deep neural network post SMOTE and random undersampling, and feature selection using mRMR and recursive feature elimination, with a Cohen-Kappa score of 0.303 and a classification accuracy of 53.1%. For binary classification into early and late-stage cancer, the best performance is obtained by a modified deep neural network (DNN) post SMOTE and random undersampling, and feature selection using mRMR and recursive feature elimination, with an accuracy of 81.0% and a Cohen-Kappa score (CKS) of 0.280. This pipeline also showed improved multiclass classification performance on neuroblastoma cancer data, with a best area under the receiver operating characteristic (auROC) curve score of 0.872, as compared to 0.71 obtained in previous work, an improvement of 22.81%. The results and analysis reveal that feature selection techniques play a vital role in gene-expression data-based classification, and the proposed hybrid feature selection pipeline improves classification performance. Multi-class classification is possible using deep learning models, though further improvement particularly in late-stage classification is necessary and should be explored further.

Deep learning provides the healthcare industry with the ability to analyse data at exceptional speeds without compromising on accuracy. These techniques are applicable to healthcare domain for accurate and timely prediction. Convolutional neural network is a class of deep learning methods which has become dominant in various computer vision tasks and is attracting interest across a variety of domains, including radiology. Lung diseases such as tuberculosis (TB), bacterial and viral pneumonias, and COVID-19 are not predicted accurately due to availability of very few samples for either of the lung diseases. The disease could be easily diagnosed using X-ray or CT scan images. But the number of images available for each of the disease is not as equally as other resulting in imbalance nature of input data. Conventional supervised machine learning methods do not achieve higher accuracy when trained using a lesser amount of COVID-19 data samples. Image data augmentation is a technique that can be used to artificially expand the size of a training dataset by creating modified versions of images in the dataset. Data augmentation helped reduce overfitting when training a deep neural network. The SMOTE (Synthetic Minority Oversampling Technique) algorithm is used for the purpose of balancing the classes. The novelty in this research work is to apply combined data augmentation and class balance techniques before classification of tuberculosis, pneumonia, and COVID-19. The classification accuracy obtained with the proposed multi-level classification after training the model is recorded as 97.4% for TB and pneumonia and 88% for bacterial, viral, and COVID-19 classifications. The proposed multi-level classification method produced is ~8 to ~10% improvement in classification accuracy when compared with the existing methods in this area of research. The results reveal the fact that the proposed system is scalable to growing medical data and classifies lung diseases and its sub-types in less time with higher accuracy. Graphical Abstract

In this paper, we proposed a novel classification method for face recognition which adopts the functionalities of linear discriminant and regression. Linear discriminant and regression analysis methods have benefits regarding minimising time, memory usage and better feature extraction. Linear regression and discriminant classification (LRDC) makes use of the principle that a sample class lie in a linear subspace, proposed method represents a predicted image as a linear combination of class-specific galleries. LRDC belongs to the category of nearest subspace classification and finds the set of optimal discriminant projection vectors by adopting singular value decomposition (SVD) and null space, and the decision made for a class with the minimum distance. LRDC is extensively evaluated by applying it to different classified datasets and compared with the state-of-the-art algorithms.

In Bioinformatics, sequence alignment algorithm aims to find out whether biological sequences (e.g., DNA, RNA, or Protein sequences) are related or not. A variety of algorithms are developed, Smith–Waterman Algorithm (SW) is a well-known local alignment algorithm to find the similarity of two sequences and provides optimal result using dynamic programming. As the size of sequence database is doubling about every 6 months, the computational time also increases. Sequence alignment algorithms performance can have improved by using the parallel computing technology on the GPU. In this paper, we proposed a method to improve the performance of SW algorithm by using GPU’s shared memory instead of global memory. By using shared memory, the data being transferred between the global memory and processing elements is reduced, which in turn improves the performance. The tabulated result showed positive sign of correctness in proposed method and tested using UniProt sequence database.

Face recognition has received significant attention in the recent past and numerous applications have deployed. Face recognition systems produce pleasing performance under controlled scenarios, and they produce a poor performance with real-world scenarios. High recognition rate and less training time are the two primary requirements in face recognition system. In this survey, various neural network based classification techniques for face recognition application are considered, such as Artificial Neural Network, Adaptive CNN (ACNN), convolutional neural network, Extreme Learning Machine and probabilistic neural network. The accuracy is calculated based on local, global and hybrid feature vector. Finally, paper summaries the algorithms in-terms of some crucial parameters and highlights the suitable algorithm for various applications.

In this paper, a new job aware scheduling algorithm for IaaS cloud is proposed. As we know IaaS cloud provides an increase in computing power, storage capacity and lowering the hardware cost and also it offers cost efficiency, scalability, elasticity and dynamic service according to requested application. Scheduling in cloud is vital as it plays an important role for ripe the benefits in-terms of cost and make-span. In scheduling, the jobs are mapped based on the characteristics and user requirements. Parameters like cost, load and resource are to be considered while scheduling. In IaaS cloud, the users pay for the resources they need for computation and the resources should be utilized efficiently for the benefit of both users and providers. Hence, scheduling should consider the jobs cost and has to fully utilize the resources to reduce the make-span, cost and increase the throughput of the system. Aggrandized job aware scheduling algorithm does load balancing in cloud with respect to the services based on resource and cost. The parameters such as make-span, number of tasks executed and cost for execution are considered to evaluate the performance of proposed algorithm.

A pulmonary nodule is a small round or oval-shaped growth in the lung. It may also be called a spot on the lung or a coin lesion. Pulmonary nodules are smaller than three millimeter in diameter. If the growth is larger than that, it is called a pulmonary mass and is more likely to represent a cancer than a nodule. Complete manual diagnosis of lung cancer puts a burden on the radiologists who need to spend hours reading through CT scanned images to identify Region of Interests (ROIs) to schedule follow-ups. Since,cancer is curable when diagnosed at an early stage, lung cancer screening plays an important role in preventive care. Although both Low Dose Computed Tomography (LDCT) and Computed Tomography (CT) scans provide more medical information than normal chest x-rays,there is very limited access to these technologies in rural areas. Recently,there is a trend in using Computer-Aided Diagnosis (CADx) to assist in screening and diagnosing of cancer from biomedical images. Accurate computer-aided diagnosis of lung cancer can effectively reduce their workload and help training new radiologists.

Brain tumor is one of the most devastating forms of human illness, especially when occurring in the posterior fossa and involving the brainstem. Tumors in the posterior fossa are considered some of the most critical brain lesions. This is primarily due to the limited space within the posterior fossa, as well as the potential involvement of the vital brainstem nuclei. The aim of this study is to analyze the incidence, clinical features, surgical outcome, complications, and prognosis in a series of 37 pediatric patients with posterior fossa tumors who underwent surgery between September 2012 and January 2015 from the Department of Neurosurgery, King George Hospital, Visakhapatnam (both prospective and retrospective study). A series of 37 cases were treated by the Department of Neurosurgery, King George Hospital, between August 2012 and January 2015. Posterior fossa tumors are predominantly seen in children with a peak incidence in the first decade. The most common presenting symptoms are raised intracranial pressure with headache and vomiting. Majority of the tumors are medulloblastomas, ependymomas, and cerebellar astrocytomas. The most common location is the cerebellar vermis, followed by the cerebellar hemispheres, followed by the forth ventricle and then the brainstem.