ஜர்னல் ஆஃப் கம்ப்யூட்டர் சயின்ஸ் & சிஸ்டம்ஸ் பயாலஜி

In this study one evaluate the efficiency of a new method to characterize the distribution of bone tissue within a human structure that could be wildly explored in research but especially in medical fields due to its simplicity and efficiency. Two different methods were used to characterize the distribution of tissue in the bone structure. The first was open source software (Image J) with a dedicated plug-in and the second was commercial software (Simpleware ScanIP). Using temporomandibular joint DICOM files, one evaluated the largest section of the condyle and the distance between the top of the condyle and the section with the least cancellous bone as a way to verify the accuracy of the method and the way that user’s skills influence the results. Finally, we built two 3D models based on the extreme cases considering the bone distribution. Results from the two methods were compared in order to validate the new proposed method. The comparison showed that the proposed method has enough feasibility to perform the bone distribution analysis, since the results, precision and repeatability were successfully confirmed. Furthermore, one states that the principal advantage of this method is the faster evaluation of the bone structures with a very satisfying precision which augurs good perspectives during its use in the medical fields.

This paper deals with the workers and machine performance measurements in manufacturing system. In simulating the manufacturing system, discrete event simulation involves the modeling of the system as it improves throughput time and is particularly useful for modeling queuing systems. In modeling this system, an ARENA simulation model was developed, verified, and validated to determine the daily production and potential problem areas for the various request levels in the case company (Ethiopia Plastic Factory). The results obtained show that throughput time in the existing system is low because of occurrence of bottlenecks, and waiting time identified. Therefore, some basic proposals have been drawn from the result to raise awareness of the importance of considering human and machine performance variation in such simulation models. It presents some conceptual ideas for developing a worker manager for representing worker and machine performance in manufacturing systems simulation models. The company workers’ and machine performance level is raised by identifying the bottle neck area. They were recommended to amend the main basic bottle necks identified.

Mining association rule is a key issue in information mining. Nevertheless, the customary models overlook the differences among the trades, and the weighted association rule mining does not process on databases with simply binary attributes. Paper propose a novel frequent patterns and execute a tree (FP-tree) structure, which is an intensified prefix-tree structure for securing compacted, critical information about patterns, and make a capable FP-tree-based mining framework, FP improved function algorithm is utilized, for mining the complete arrangement of patterns by example frequent development. Here in this paper tackles the purpose of making extraordinary and weighted itemsets, i.e. infrequent weighted itemset mining problem. The two novel brilliance measures are proposed for figuring the infrequent weighted itemset mining issue. Moreover, the algorithm are tackled which perform IWI which is more negligible IWI mining. Additionally we used the infrequent itemset for decision based structure. The general problem of the beginning of dependable definite rules is difficult for the grounds that theoretically no provoking procedure without any other person can guarantee the rightness of affected theories. In this manner this system expects the sickness with the exceptional signs. Implementation study shows that proposed algorithm enhances the framework which is effective and adaptable for mining both long and short diagnostics rules. Framework enhances results of foreseeing rare diseases of patient.

Purpose: The Central Vision Analyzer (CVA) is an interactive, automated computer device that rapidly thresholds central acuity under conditions mimicking customary photopic and mesopic activities. In sequence, the CVA may test up to 6 environments, and in this series was used to test 3 mesopic environments (98% and 50% MC against 1.6 cd/m2 background, 25% MC against 5 cd/m2), then 3 glare environments (98%, 10% and 8% MC, against 200 cd/m2 background). This report compares the CVA thresholded acuity with that measured utilizing standard letter acuity charts.

Methods: In 481 normal eyes acuity was measured with best spectacle and contact lens refraction using both CVA and 0.1 logMAR ETDRS charts presenting similar contrast and luminance. In addition for 162 emmetropic, eyes, acuity was tested with a 15% MC chart placed outdoors with sun overhead and with sun at 15° off-axis and compared with the CVA thresholded acuity at 10% and 8% MC presented in a darkened room.

Results: All CVA modules demonstrated high Pearson correlation coefficients (r=0.51 to r=0.94, p<0.01), Bland and Altman statistical similarity with the acuity measured from similar contrast charts as well as between the acuity measured with a 15% MC letter chart with the sun overhead and CVA 10% glare module and between acuity with a 15% MC chart viewed with the sun 15° off-axis and that with CVA 8% glare module presented in the darkened room.

Conclusions: The CVA demonstrates the ability to accurately threshold the acuity of normal eyes compared with chart acuity under conditions of contrast, luminance and fixation times simulating normal photopic and mesopic activities and appears to provide the clinician rapidly with a better understanding of visual function under a variety of day and evening tasks.

E-learning enabled education system has grabbed the world higher education market in a skyrocketing manner. Most of the internationally acclaimed universities have already installed e-learning and ICT (Information and Communication Technologies) based education by which students in due course do their studies in their own place and own pace as well. The rapid growth of e-learning based education in parallel boosts the GER of the specific zone too. In this paper a study has been undertaken to canvas the present scenario of Universities and central institutes of West Bengal in terms of information and service.

Background: Running shoe compliance and track surface stiffness can reduce peak vertical foot forces. It is therefore of interest to measure directly the force-deflection curve for running shoes in the heel and forefoot areas. This study compares these measurements with similar work on track and field surfaces, and derives a mathematical stress-strain model useful over the entire force range.

Methods: Six different running shoes from 4 popular brands are measured to determine vertical spring stiffness. The heel and ball areas are tested with 3.8 and 5.1 cm (1.5 and 2.0 in.) diameter heels in the force range of 0 to 0.13 kN and 2.0 to 2.6 kN (0 to 30 lbf. and 450 to 600 lbf.). The results show a factor of 2 difference from one shoe to the next, holding test area, heel diameter and force range constant. Load increments are applied on a time scale of 0.1 seconds, comparable to typical foot contact times during running.

Results: The measured spring constants are essentially independent of plunger area, a useful simplification. For a given shoe, the ball area can be three times less compliant than the heel.

Conclusion: Heel spring constants at the high force levels fall in the range from 290 kN/m to 600 kN/m (20,000 to 42,000 lbf/ft.), and thus approximate optimal track stiffness. In terms of theory, an exponential function derives from the observation that the data fall along a straight line on semi-log co-ordinates. This mathematical model enables calculation of running shoe compressive response and spring constant at physiological force levels.

Currently, due to the availability of massive biomedical data on each individual, both healthcare and life Science is becoming data-driven. The input-attributes are structured/un-structured data with many challenges, including sparse-binary attributes with imbalanced outcomes, non-unique distributed structure and high-dimensional data, which hamper efforts to make a clinical decision in clinical practice. In recent decades, considerable effort has been made toward overcoming most of these challenges, but still there is an essential need for significant improvements in this field, especially after integrating both omics and phenotype data for future personalized medicine. These challenges motivate us to use the state-of-the-art of big data analytics and large-scale machine learning frameworks to confront most of the challenges and provide proper clinical solutions to assess physicians in clinical practice at the bedside and subsequently provide high quality care while reducing its cost.

This research proposes a new recursive screening incremental ranking machine learning paradigm to empower the desired classifiers, especially for imbalanced training data, to create suitable data-driven clusters without prior information and later reduce the dimensionality of large biomedical data sets. The new framework combines many binary-attributes based on two criteria: (i) the minimum power value for each combination and (ii) the classification power of such a combination. Next, these sets of combined attributes are investigated by physicians to select the proper set of rules that make clinical sense and subsequently to use the result to empower the desired healthcare event (binary or multinomial target) at the bedside. After empowering the target class categories, we select the k-significant risk drivers with a suitable volume of data and high correlation to the desire outcome, and next, we establish the proper segmentation using AND-OR associative relationships. Finally, we use the propensity score to handle the imbalanced data, and next, we build break-through machine learning/data mining predictive models based on functional networks’ maximum-likelihood and Newton-Raphson iterative matrix computation mechanism to expedite the implementations within high performance computing platforms, such as scalable MapReduce HDFS, Spark MLlib, and Google Sibyl.

Comparative studies with both simulated and real-life biomedical databases are carried out for identifying specific biomedical and healthcare outcomes, such as asthma, breast cancer, gene mutations selection and genomic association studies for specific complex diseases. Results have shown that the proposed incremental learning scheme empower the new classifier with reliable and stable performance. The new classifier outperforms the current existing predictive models in both high quality outcome and less expensive in execution time, especially, with imbalanced and sparse with high-dimensional big biomedical data. We recommend future work to be conducted using real-life integrated clinic-genomic big data with genome-wide association studies for future personalized medicine.

The nitrogen cycle is one of the most important nutrient cycles in terrestrial ecosystems. Environmental bacteria maintain the global nitrogen cycle by metabolizing organic as well as inorganic nitrogen compounds. It is thought that most of the microbial taxa cannot be cultured outside of their natural environment, thus, microbial diversity remains poorly described before a decade. But the metagenomic techniques developed recently have therefore greatly extended our knowledge of microbial genetic diversity. The objective of this work was to analyze the taxonomic composition of three metagenome communities from the soil sample mainly rain forest, temperate broadleaf and temperate grassland via MG-RAST. Using the M5NR database, the affinities were tested for the sequences of known metabolic function against both SEED subsystems and KEGG metabolic pathways using a maximum e-value of 1e-5. Although there are a number of metabolic functions that can be tested but we probed particularly for enzymes related to the components of nitrogen cycle. The results explain the potential taxonomic diversity of nitrate reducing bacteria with the dominance of Bradyrhizobium japonicum from soil sample.