Opposition-based Learning (OBL) is a new concept in machine learning, inspired from the opposite relationship among entities. In 2005, for the first time the concept of opposition was introduced which has attracted a lot of research efforts in the last decade. Variety of soft computing algorithms such as, optimization methods, reinforcement learning, artificial neural networks, and fuzzy systems have already utilized the concept of OBL to improve their performance. This survey has been conducted on three classes of OBL attempts: a) theoretical, including the mathematical theorems and fundamental definitions, b) developmental, focusing on the design of the special OBL-based schemes, and c) real-world applications of OBL. More than 380 papers in a variety of disciplines are surveyed and also a comprehensive set of promising directions are discussed in detail.

Opposition based learning: A literature review

Over the last decade, it has been demonstrated that many systems in science and engineering can be modeled more accurately by fractional-order than integer-order derivatives, and many methods are developed to solve the problem of fractional systems. Due to the extra free parameter order, fractional-order based methods provide additional degree of freedom in optimization performance. Not surprisingly, many fractional-order based methods have been used in image processing field. Herein recent studies are reviewed in ten sub-fields, which include image enhancement, image denoising, image edge detection, image segmentation, image registration, image recognition, image fusion, image encryption, image compression and image restoration. In sum, it is well proved that as a fundamental mathematic tool, fractional-order derivative shows great success in image processing.

Fractional calculus in image processing: a review

A review of meta-heuristic algorithms for reactive power planning problem

The health sector has quickly become a target for cyberattacks. Hospitals are especially sensitive to these sorts of attacks as any disruption in operations or even disclosure of patient personal information can have far-reaching consequences. The objective of this study was to map the available literature on cyberattacks on hospitals and to identify the different domains of research, while extracting the recommendations and guidelines put forth in the literature. Four databases (PubMed, Web of Science, ProQuest, and Scopus) were searched using standardized and adapted search syntax in order to identify relevant manuscripts published between 1997 and 2017. These were screened by two reviewers and included or excluded based on inclusion and exclusion criteria. Data from articles were then extracted and analyzed. The search identified 818 records of which 97 were included. Of the 97, 32% were published in 2017 while around 40% of the articles were published prior to the last three years. Six domains of research emerged through the analysis, which are included here: context and trends in cybersecurity (27.8%), connected medical devices and equipment (29.9%), hospital information systems (14.4%), raising awareness and lessons learned (6.2%), information security methodology (15.4%), and specific types of attacks (6.2%). There is a generally growing interest in the research field, but the available literature remains limited in number. There are important aspects of cybersecurity (e.g. cloud storage and access management) as well as specific medical fields that rely on various medical devices that have been neglected. Recommendations are available, but comprehensive guidelines and standardized best practice measures are still necessary.

/pdf/the-state-of-research-on-cyberattacks-against-hospitals-and-4cz8q1gmuy.pdf

The state of research on cyberattacks against hospitals and available best practice recommendations: a scoping review

The manual analysis of brain tumor on magnetic resonance (MR) images is time-consuming and subjective. Thus, to avoid human errors in brain tumor diagnosis, this paper presents an automatic and accurate computer-aided diagnosis (CAD) system based on ensemble classifier for the characterization of brain tumors on MR images as benign or malignant. Brain tumor tissue was automatically extracted from MR images by the proposed segmentation technique. A tumor is represented by extracting its texture, shape, and boundary features. The most significant features are selected by using information gain-based feature ranking and independent component analysis techniques. Next, these features are used to train the ensemble classifier consisting of support vector machine, artificial neural network, and $$k$$
 -nearest neighbor classifiers to characterize the tumor. Experiments were carried out on a dataset consisting of T1-weighted post-contrast and T2-weighted MR images of 550 patients. The developed CAD system was tested using the leave-one-out method. The experimental results showed that the proposed segmentation technique achieves good agreement with the gold standard and the ensemble classifier is highly effective in the diagnosis of brain tumor with an accuracy of 99.09 % (sensitivity 100 % and specificity 98.21 %). Thus, the proposed system can assist radiologists in an accurate diagnosis of brain tumors.

Computer-aided diagnosis system for tissue characterization of brain tumor on magnetic resonance images

Investigation of a beach and its wave conditions is highly requisite for understanding the physical processes in a coast. This study composes spatial and temporal correlation between beach and nearshore processes along the extensive sandy beach of Nagapattinam coast, southeast peninsular India. The data collection includes beach profile, wave data, and intertidal sediment samples for 2 years from January 2011 to January 2013. The field data revealed significant variability in beach and wave morphology during the northeast (NE) and southwest (SW) monsoon. However, the beach has been stabilized by the reworking of sediment distribution during the calm period. The changes in grain sorting and longshore sediment transport serve as a clear evidence of the sediment migration that persevered between foreshore and nearshore regions. The Empirical Orthogonal Function (EOF) analysis and Canonical Correlation Analysis (CCA) were utilized to investigate the spatial and temporal linkages between beach and nearshore criterions. The outcome of the multivariate analysis unveiled that the seasonal variations in the wave climate tends to influence the bar–berm sediment transition that is discerned in the coast.

Spatial and temporal correlation between beach and wave processes: implications for bar-berm sediment transition

The arrival of CT and MRI imaging in the last two decades used for analysis of various diseases in medical field. Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) sequences have become essential in healthcare systems and an integral segment of a patient's medical record. Such medical image data needed a huge amount of resources for storage and transmission. Compression of medical images for efficient use of storage space and transmission bit rate has become a necessity. A quasigroup successfully creates an astronomical number of keys which is used to confuse the hackers in determining the original data. However, quasi group encryption is not capable in diffusing the information of the plain text. Hence, in this study the DICOM images are encrypted using a chained Hadamard transforms and Number Theoretic Transforms to introduce diffusion along with the quasigroup transformation. A secure Fast Two Dimensional Discrete Fractional Fourier Transform (DFRFT) and a SPIHT Algorithm with Huffman Encoder is used for compress the image and it provides a secure compression of medical images as compared to the other transform. The experimental results evaluate the performance of the proposed encryption approach based on the PSNR and MSE it shows the proposed approach gives better results.

A secure fast 2D — Discrete fractional fourier transform based medical image compression using hybrid encoding technique

The conventional image embedding technique is watermarking which applies DCT to the host image. The problem appeared in this technique is that the size of the host image should be greater than the signature image , thereby reducing the signal to noise ratio and degrades the system performance. In our proposed system image fusion techniques are being used. At the basic two signature images are being fused. Both significant and insignificant pixels of two images are transmitted. This project can fuse different types of images like RGB images, Gray scale images(medical, satellite),normal photo images. The proposed system discussed is a User Interactive Model. User Interactive in the sense, merely four different compression techniques can be simulated and the performance measure can also be done. The user can choose his/her compression techniques based on the specifications they need. Two levels of security are being embedded with images. The sizes of images are not a constraint in this system as computations are in array editor. Varied dimension images can also be used. Fusing multiple images upto 8 has been proposed in this system. The simulation process is done by MATLAB 7.0. In order to improve the efficiency of the project, Memory allocated for the program, Elapsed time for the compression to run and compression ratio for fused image and compressed image is being formulated.

/pdf/image-fusion-techniques-4kb9qmz9rl.pdf

Image fusion techniques

Particle Swarm Optimization is currently employed in several optimization and search problems due its ease and ability to find solutions successfully. A variant of PSO, called as Improved PSO has been developed in this paper and is hybridized with the simulated annealing approach to achieve better solutions. The hybrid technique has been employed, inorder to improve the performance of improved PSO. This paper shows the application of hybrid improved PSO in Scheduling multiprocessor tasks. A comparative performance study is reported. It is observed that the proposed hybrid approach gives better solution in solving multiprocessor job scheduling.

/pdf/on-performance-analysis-of-hybrid-algorithm-improved-pso-1qoqqnrqzp.pdf

On performance analysis of hybrid algorithm (improved PSO with simulated annealing) with GA, PSO for multiprocessor job scheduling

Transformation model plays a vital role in medical image processing. This paper describes a new Transformation model (NTM) which is hybrid of linear and non linear Transformations techniques for the detection of tumor. In NTM, patient image is compared with reference images, which is block based. An image similarity measure quantifies the degree of similarity between intensity patterns in two images. The choice of an image similarity measure depends on the modality of the images to be registered. In this paper contrast checking, sum of squared intensity differences (SSD), calculation of white cells and point mapping are used.

/pdf/exploration-on-selection-of-medical-images-employing-new-44742zt6pa.pdf

Exploration on Selection of Medical Images employing New Transformation Technique

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