Dimensionality reduction of microarray data is a very challenging task due to high computational time and the large amount of memory required to train and test a model. Genetic programming (GP) is a stochastic approach to solving a problem. For high dimensional datasets, GP does not perform as well as other machine learning algorithms. To explore the inherent property of GP to generalize models from low dimensional data, we need to consider dimensionality reduction approaches. Random projections (RPs) have gained attention for reducing the dimensionality of data with reduced computational cost, compared to other dimensionality reduction approaches. We report that the features constructed from RPs perform extremely well when combined with a GP approach. We used eight datasets out of which seven have not been reported as being used in any machine learning research before. We have also compared our results by using the same full and constructed features for decision trees, random forest, naive Bayes, support vector machines and k-nearest neighbor methods.
Slaughterhouse workers (SHW) are at increased risk of hepatitis which can occur due to different organisms and should be investigated for viral, bacterial, and parasitic organisms. Slaughter house personnel including butchers are at a higher risk of infections from cuts and blood-letting, with the possible risk of the transmission of blood-borne pathogens to their colleagues. The objective of this review is to evaluate the common etiologies of hepatitis in SHW which will assist in the assessment of these patients presenting with transaminitis. Types of Microorganisms causing hepatitis with their reservoirs, routes of transmission, laboratory diagnosis, clinical features, treatment options and preventive strategies are included in this review. Proper investigation and awareness is of utmost importance as it causes significant financial constraints derived from workers health cost and from livestock production losses when the disease is confirmed. The work up is essential because infected workers might be a source of infections to other colleagues, family and the consumers.
Forensic medical examination serve two purposes i.e.to preserve mental and physical health of the victim as well as collection of forensic evidence 1,2. Collection and documentation of evidence whether in form of injures or biological material is help to validate the objects and the accoster's past.3 The outline of wounds also has a criminal worth because they are related to the result of lawful proceedings4. The works assessment explores the variables linked to genital harm occurrence and places that are informed in a sequence of surveying examinations of medicinal proceedings 5.The occurrence of perfect indication of erotic harms in the U.S. ranges from 5-27%, in Italy 11.5%, in Thailand 42% and in Denmark 38%. In Israel, as in another place in the countries, few cases of erotic stabbing in children have vibrant indication of a erotic style. 6,7,8,9. Objective: To evaluate incidence and comparison of physical and biological evidence in victims of sexual assault and their relation to time interval between examination and incident. Methodology: The retrospective cross sectional study was placed during June 2019 to December 2020 on cases reported in the DHQ Hospital Rawalpindi with follow up reports. Total 108 cases were reported during this period. Data was collected from DHQ Hospital Rawalpindi with follow up reports. Examination results were included presence and absence of physical injuries located genital region and other parts of the body, and presence and absence of biological evidence .The fallouts of investigation were linked to parameters such as sex, age and length of time since assault. Data was analyzed by using SPSS version 19 Results: Female victim: Out of 108 cases 77 was female.61% was unmarried and 39% was married (Figure 1). 44.2% cases was fall between age range of 16-20years.2.6%cases between age group of 45-50 years.51.9% cases belong to rural area. While 48.1% cases belong to urban. Vaginal swab was positive in 79.2%.genital injuries was present in 13% cases.11.7% married and 1.3% unmarried. Other injuries present in 6.5%married.7.8%unmarried. Fresh hymen injuries present in 13%.old in 41.6%. Male victims: Total 31 in number.67.7%in rural area ,while 32.3% in urban.45.2% (14)between age group 11-15 years .Anal swab was positive in 64.5%.(20 in number).Bleeding was present in 32.3% (10)cases. Bruises in 41.9%.abrasion was present in 48.4%.(15)25% in 11-15 years age group. Genital injuries was present in 45.2%(14).other injuries 28.1%.anal swab with injuries positive in 34.4%.negative in 12.5%.Finding on clothes was present on 12.5%. Conclusion & Recommendations: Rape or sexual assault in the absence of prior sexy knowledge, genital or physique harms are usually found in adolescents. The possibility of rape in nonappearance of any hurt, with or deprived of permission cannot be excluded. A competent forensic examiner must examines and follow up the victims of sexual violence. The forensic examiner must have technical and scientific skills that are medicinal and stabbing history taking, whole body examination, and organic article collects, recording damages, clinical pediatric practice, interpretation of findings and reports and prosecution. Keywords: Genital harms, adolescent, body injuries, Prosecution
<p>There is a huge and rapidly increasing amount of data being generated by social media, mobile applications and sensing devices. Big data is the term usually used to describe such data and is described in terms of the 3Vs - volume, variety and velocity. In order to process and mine such a massive amount of data, several approaches and platforms have been developed such as Hadoop. Hadoop is a popular open source distributed and parallel computing framework. It has a large number of configurable parameters which can be set before the execution of jobs to optimize the resource utilization and execution time of the clusters. These parameters have a significant impact on system resources and execution time. Optimizing the performance of a Hadoop cluster by tuning such a large number of parameters is a tedious task. Most current big data modeling approaches do not include the complex interaction between configuration parameters and the cluster environment changes such as use of different datasets or types of query. This makes it difficult to predict for example the execution time of a job or resource utilization of a cluster. Other attributes include configuration parameters, the structure of query, the dataset, number of nodes and the infrastructure used. Our first main objective was to design reliable experiments to understand the relationship between attributes. Before designing and implementing the actual experiment we applied Hazard and Operability (HAZOP) analysis to identify operational hazards. These hazards can affect normal working of cluster and execution of Hadoop jobs. This brainstorming activity improved the design and implementation of our experiments by improving the internal validity of the experiments. It also helped us to identify the considerations that must be taken into account for reliable results. After implementing our design, we characterized the relationship between different Hadoop configuration parameters, network and system performance measures. Our second main objective was to investigate the use of machine learning to model and predict the resource utilization and execution time of Hadoop jobs. Resource utilization and execution time of Hadoop jobs are affected by different attributes such as configuration parameters and structure of query. In order to estimate or predict either qualitatively or quantitatively the level of resource utilization and execution time, it is important to understand the impact of different combinations of these Hadoop job attributes. You could conduct experiments with many different combinations of parameters to uncover this but it is very difficult to run such a large number of jobs with different combinations of Hadoop job attributes and then interpret the data manually. It is very difficult to extract patterns from the data and give a model that can generalize for an unseen scenario. In order to automate the process of data extraction and modeling the complex behavior of different attributes of Hadoop job machine learning was used. Our decision tree based approach enabled us to systematically discover significant patterns in data. Our results showed that the decision tree models constructed for different resources and execution time were informative and robust. They were able to generalize over a wide range of minor and major environmental changes such as change in dataset, cluster size and infrastructure such as Amazon EC2. Moreover, the use of different correlation and regression techniques, such as M5P, Pearson's correlation and k-means clustering, confirmed our findings and provided further insight into the relationship of different attributes and with each other. M5P is a classification and regression technique that predicted the functional relationships among different job attributes. The use of k-means clustering allowed us to see the experimental runs that shows similar resource utilization and execution time. Statistical significance tests, were used to validate the significance of changes in results of different experimental runs, also showed the effectiveness of our resource and performance modelling and prediction method.</p>
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