We exhibit a general family of distributions named “Kumaraswamy odd Burr G family of distributions” with four additional parameters to generalize any existing baseline distribution. Some statistical properties of the family are derived, including rth moments, mth incomplete moments, moment generating function and entropies. The parameters of the family are estimated by the maximum likelihood (ML) method for complete sam- ples as well as censored samples. Some sub-models of the family are considered and it is noted that their density functions can be symmetric, left-skewed, right-skewed, unimodal, bimodal and their hazard rate functions can be increasing, decreasing, bathtub, upside- down bathtub and J-shaped. Simulation is carried out for one of the sub-models to check the asymptotic behavior of the ML estimates. Applications to reliability (complete and censored) data are carried out to check the usefulness of some sub-models of the family.
In this work, we introduce a new Burr XII power series class of distributions, which is obtained by compounding exponentiated Burr XII and power series distributions and has a strong physical motivation. The new distribution contains several important lifetime models. We derive explicit expressions for the ordinary and incomplete moments and generating functions. We discuss the maximum likelihood estimation of the model parameters. The maximum likelihood estimation procedure is presented. We assess the performance of the maximum likelihood estimators in terms of biases, standard deviations, and mean square of errors by means of two simulation studies. The usefulness of the new model is illustrated by means of three real data sets. The new proposed models provide consistently better fits than other competitive models for these data sets.
Cancer is a deadly disease that arises due to the growth of uncontrollable body cells. Every year, a large number of people succumb to cancer and it's been labeled as the most serious public health snag. Cancer can develop in any part of the human anatomy, which may consist of trillions of cellules. One of the most frequent type of cancer is skin cancer which develops in the upper layer of the skin. Previously, machine learning techniques have been used for skin cancer detection using protein sequences and different kinds of imaging modalities. The drawback of the machine learning approaches is that they require humanengineered features, which is a very laborious and time-taking activity. Deep learning addressed this issue to some extent by providing the facility of automatic feature extraction. In this study, convolution-based deep neural networks have been used for skin cancer detection using ISIC public dataset. Cancer detection is a sensitive issue, which is prone to errors if not timely and accurately detected. The performance of the individual machine learning models to detect cancer is limited. The combined decision of individual learners is expected to be more accurate than the individual learners. The ensemble learning technique exploits the diversity of learners to yield a better decision. Thus, the prediction accuracy can be enhanced by combing the decision of individual learners for sensitive issues such as cancer detection. In this paper, an ensemble of deep learners has been developed using learners of VGG, CapsNet, and ResNet for skin cancer detection. The results show that the combined decision of deep learners is superior to the finding of individual learners in terms of sensitivity, accuracy, specificity, F-score, and precision. The experimental results of this study provide a compelling reason to be applied for other disease detection.
Groundnut (Arachis hypogaea) is an important oil seed and cash crop. In Pakistan, it is grown in sandy loam soil of Pothwar region of Punjab Province. The main problem being faced by growers is its postharvest losses depending upon the soil texture, cultivar and climatic conditions. To minimize these postharvest losses, left-over groundnut pods are picked manually by farm workers either in sitting or bending posture which is laborious, costly and time consuming. In this research tractor mounted groundnut pods picking machine was tested in term of collected pods (%), damaged pods (%), left-over pods (%) and un-dug pods (%) at three tractor forward speeds (0.8-1.4 kmh -1 , 1.4-2.0 kmh -1 , 2.0-2.6 kmh -1 ) against three soil moisture contents (7.8%, 8.7%, 10.5%). The collected data were analyzed statistically using CRD design in appropriate software Statistix 8.1. The statistically analysis indicated that at speed (1.4-2.0 kmh -1 ) highest pods collection efficiency, lowest damaged pods, left-over pods and undug pods were observed 91.7, 0.9, 6.1 and 1.2% respectively. At moisture content M1 (7.8%) machine showed highest picking efficiency (91.08%). The economical comparison showed that there was 33.75% decrease in cost of operation and 68.75% decrease in time of operation using groundnut pods picking machine than traditional pods picking method. The machine performance was satisfactory and it is recommended to pick leftover pods in the field after harvesting to save time and cost of operation and sowing of winter crops.
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