An Improved Jellyfish Algorithm for Multilevel Thresholding of Magnetic Resonance Brain Image Segmentations

Image segmentation is vital when analyzing medical images, especially magnetic resonance (MR) images of the brain. Recently, several image segmentation techniques based on multilevel thresholding have been proposed for medical image segmentation; however, the algorithms become trapped in local minima and have low convergence speeds, particularly as the number of threshold levels increases. Consequently, in this paper, we develop a new multilevel thresholding image segmentation technique based on the jellyfish search algorithm (JSA) (an optimizer). We modify the JSA to prevent descents into local minima, and we accelerate convergence toward optimal solutions. The improvement is achieved by applying two novel strategies: Ranking-based updating and an adaptive method. Ranking-based updating is used to replace undesirable solutions with other solutions generated by a novel updating scheme that improves the qualities of the removed solutions. We develop a new adaptive strategy to exploit the ability of the JSA to find a best-so-far solution; we allow a small amount of exploration to avoid descents into local minima. The two strategies are integrated with the JSA to produce an improved JSA (IJSA) that optimally thresholds brain MR images. To compare the performances of the IJSA and JSA, seven brain MR images were segmented at threshold levels of 3, 4, 5, 6, 7, 8, 10, 15, 20, 25, and 30. IJSA was compared with several other recent image segmentation algorithms, including the improved and standard marine predator algorithms, the modified salp and standard salp swarm algorithms, the equilibrium optimizer, and the standard JSA in terms of fitness, the Structured Similarity Index Metric (SSIM), the peak signal-to-noise ratio (PSNR), the standard deviation (SD), and the Features Similarity Index Metric (FSIM). The experimental outcomes and the Wilcoxon rank-sum test demonstrate the superiority of the proposed algorithm in terms of the FSIM, the PSNR, the objective values, and the SD; in terms of the SSIM, IJSA was competitive with the others.     

DRFP-tree: disk-resident frequent pattern tree

Frequent itemset mining methods basically address time scalability and greatly rely on available physical memory. However, the size of real-world databases to be mined is exponentially increasing, and hence main memory size is a serious bottleneck of the existing methods. So, it is necessary to develop new methods that do not fully rely on physical memory; new methods that utilize the secondary storage in the mining process should be the target. This motivates the work described in this paper; we mainly propose (Disk Resident Frequent Pattern) DRFP-Growth as a disk based approach similar to FP-Growth. DRFP-growth uses DRFP-tree, which is treated exactly as FP-tree when constructed in main memory and gets into a modified structure when it turns into disk resident to overcome the main memory bottleneck. This way, we are able to mine for frequent itemsets from databases of arbitrary sizes without being restricted by the available physical memory. In other words, we initially try to mine the database using the original FP-growth; we expand into the secondary memory only if we run out of physical memory. So, DRFP-growth is very comparable to FP-growth for small databases and high support threshold values. On the other hand, using DRFP-growth, we are still able to mine huge databases for low support threshold values (the only limitation is the available secondary storage rather than physical memory). The reported test results demonstrate how the proposed approach succeeds for cases where main memory based approaches fail.     

STNR: A suffix tree based noise resilient algorithm for periodicity detection in time series databases

Periodicity detection has been used extensively in predicting the behavior and trends of time series databases. In this paper, we present a noise resilient algorithm for periodicity detection using suffix trees as an underlying data structure. The algorithm not only calculates symbol and segment periodicity, but also detects the partial (or sequence) periodicity in time series. Most of the existing algorithms fail to perform efficiently in presence of noise; although noise is an inevitable constituent of real world data. The conducted experiments demonstrate that our algorithm performs more efficiently compared to other algorithms in presence of replacement, insertion, deletion or a mixture of any of these types of noise.     

Enhanced Adaptive Brain-Computer Interface Approach for Intelligent Assistance to Disabled Peoples

Assistive devices for disabled people with the help of Brain-Computer Interaction (BCI) technology are becoming vital bio-medical engineering. People with physical disabilities need some assistive devices to perform their daily tasks. In these devices, higher latency factors need to be addressed appropriately. Therefore, the main goal of this research is to implement a real-time BCI architecture with minimum latency for command actuation. The proposed architecture is capable to communicate between different modules of the system by adopting an automotive, intelligent data processing and classification approach. Neuro-sky mind wave device has been used to transfer the data to our implemented server for command propulsion. Think-Net Convolutional Neural Network (TN-CNN) architecture has been proposed to recognize the brain signals and classify them into six primary mental states for data classification. Data collection and processing are the responsibility of the central integrated server for system load minimization. Testing of implemented architecture and deep learning model shows excellent results. The proposed system integrity level was the minimum data loss and the accurate commands processing mechanism. The training and testing results are 99% and 93% for custom model implementation based on TN-CNN. The proposed real-time architecture is capable of intelligent data processing unit with fewer errors, and it will benefit assistive devices working on the local server and cloud server.     

On the self-similarity of 1/f sequences synthesized by recursive filtering

This paper describes an approximate method for synthesizing sequences of statistically self-similar processes and analyses its performance to generate sample sequences with this statistical property. The method is based upon approximating the infinite dimensional difference equation which describes the FARIMA(0, α, 0) model by a finite dimensional difference equation. The parameters estimation for parameterizing the binomial coefficients is performed by using deterministic signal modeling techniques. The three techniques considered are: Prony, Steiglitz MacBride, and Shaw methods. In addition to allow considerable savings in memory requirements and great reduction in computation time, the performance analysis results show that the generated sequences are statistically self-similar in the sense that the estimated Hurst parameter is very close to that imposed in the sequence generator.     

Efficient content-based image retrieval using Multiple Support Vector Machines Ensemble

With the evolution of digital technology, there has been a significant increase in the number of images stored in electronic format. These range from personal collections to medical and scientific images that are currently collected in large databases. Many users and organizations now can acquire large numbers of images and it has been very important to retrieve relevant multimedia resources and to effectively locate matching images in the large databases. In this context, content-based image retrieval systems (CBIR) have become very popular for browsing, searching and retrieving images from a large database of digital images with minimum human intervention. The research community are competing for more efficient and effective methods as CBIR systems may be heavily employed in serving time critical applications in scientific and medical domains. This paper proposes an extremely fast CBIR system which uses Multiple Support Vector Machines Ensemble. We have used Daubechies wavelet transformation for extracting the feature vectors of images. The reported test results are very promising. Using data mining techniques not only improved the efficiency of the CBIR systems, but they also improved the accuracy of the overall process.     

A measurement framework for object-oriented software testability

Testing is an expensive activity in the development process of any software system. Measuring and assessing the testability of software would help in planning testing activities and allocating required resources. More importantly, measuring software testability early in the development process, during analysis or design stages, can yield the highest payoff as design refactoring can be used to improve testability before the implementation starts.

This paper presents a generic and extensible measurement framework for object-oriented software testability, which is based on a theory expressed as a set of operational hypotheses. We identify design attributes that have an impact on testability directly or indirectly, by having an impact on testing activities and sub-activities. We also describe the cause-effect relationships between these attributes and software testability based on thorough review of the literature and our own testing experience. Following the scientific method, we express them as operational hypotheses to be further tested. For each attribute, we provide a set of possible measures whose applicability largely depends on the level of details of the design documents and the testing techniques to be applied. The goal of this framework is twofold: (1) to provide structured guidance for practitioners trying to measure design testability, (2) to provide a theoretical framework for facilitating empirical research on testability.     

Cardiac and vascular structure and function are related to lipid peroxidation and metabolism

The present study investigated possible relationships between left ventricular mass, intima-media thickness of the carotid artery (IMT), total arterial compliance, and lipid status in a population sample of 58 apparently healthy subjects aged 20 to 69. By stepwise multiple regression analysis, including age, blood pressure, and smoking, left ventricular mass index, measured by M-mode echocardiography, increased by 13.0 g/m² for each 1 standard deviation (SD=0.11 μM, r=0.60, P<0.01) increase in plasma malondialdehyde and 9.50 g/m² per SD increase in plasma 8-iso-prostaglandin F_2α in women only (SD=8.88 ng/L, r=0.44, P=0.01). Each 1-SD (SD=0.27 g/L) increase in apolipoprotein B was associated with a 63 μm increase in IMT (r=0.47, P=0.01) and a 0.27 mL/min/m²/mm Hg (r=−0.60, P<0.01) decrease in stroke index/pulse pressure ratio, reflecting total arterial compliance in women. In men, each 1-SD increase in the proportion of stearic acid (18∶0) in serum cholesterol esters (SD=0.12 percent units) reduced the transmitral E/A ratio, measured by Doppler echocardiography, reflecting left ventricular diastolic function, by 0.10 units (r=−0.29, P<0.05). Thus, important cardiovascular characteristics, such as left ventricular mass, left ventricular diastolic function, carotid IMT, and total arterial compliance, were independently predicted by indices of lipid metabolism and peroxidation in apparently healthy subjects.