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In this paper, we present a method for comparing and evaluating different collections of machine learning algorithms on the basis of a given performance measure (e.g., accuracy, area under the curve (AUC), F-score). Such a method can be used to compare standard machine learning platforms such as SAS, IBM SPSS, and Microsoft Azure ML. A recent trend in automation of machine learning is to exercise a collection of machine learning algorithms on a particular problem and then use the best performing algorithm. Thus, the proposed method can also be used to compare and evaluate different collections of algorithms for automation on a certain problem type and find the best collection. In the study reported here, we applied the method to compare six machine learning platforms – R, Python, SAS, IBM SPSS Modeler, Microsoft Azure ML, and Apache Spark ML. We compared the platforms on the basis of predictive performance on classification problems because a significant majority of the problems in machine learning are of that type. The general question that we addressed is the following: Are there platforms that are superior to others on some particular performance measure? For each platform, we used a collection of six classification algorithms from the following six families of algorithms – support vector machines, multilayer perceptrons, random forest (or variant), decision trees/gradient boosted trees, Naive Bayes/Bayesian networks, and logistic regression. We compared their performance on the basis of classification accuracy, F-score, and AUC. We used F-score and AUC measures to compare platforms on two-class problems only. For testing the platforms, we used a mix of data sets from (1) the University of California, Irvine (UCI) library, (2) the Kaggle competition library, and (3) high-dimensional gene expression problems. We performed some hyperparameter tuning on algorithms wherever possible. The online supplement is available at https://doi.org/10.1287/ijoc.2018.0825 .

There is a need to scrutinise and retrieve information from data in today's world. Clustering is an analytical technique which involves dividing data into groups of similar objects. Every group is called a cluster, and it is formed from objects that have affinities within the cluster but are significantly different to objects in other groups. The aim of this paper is to look at and compare two different types of hierarchical clustering algorithms. Partition and hierarchical clustering are the two main types of clustering techniques. Hierarchical clustering algorithm is one of the algorithms discussed here. The aforementioned algorithms are described and analysed in terms of factors such as dataset size, data set type, number of clusters formed, consistency, accuracy, and efficiency. Hierarchical clustering is a cluster analysis technique that aims to create a hierarchy of clusters. A hierarchical clustering method is a set of simple (flat) clustering methods arranged in a tree structure. These methods create clusters by recursively partitioning the entities in a top-down or bottom-up manner. We examine and compare hierarchical clustering algorithms in this paper. The intent of discussing the various implementations of hierarchical clustering algorithms is to assist new researchers and beginners to understand how they function, so they can come up with new approaches and innovations for improvement.

Aim:To evaluate the efficacy and safety of glycopyrrolate - xylazine - propofol anesthesia in buffalo calves.Materials and Methods:The study was conducted on six clinically healthy male buffalo calves, 6-12 months of age, and weighing between 130 and 170 kg. In all the animals; glycopyrrolate (0.01 mg/kg, IM), xylazine (0.1 mg/kg, IM) and 1% propofol as single bolus (1.5 mg/kg, intravenous), were administered. The parameters observed included behavioral changes, physiological; hematological and blood biochemical parameters.Results:Muzzle and nostrils became dry in all the animals after glycopyrrolate administration. A decrease in spontaneous activity and mild cutaneous analgesia was noticed after xylazine administration. After administration of propofol, loss of swallowing reflex, palpebral reflex, corneal reflexes, periosteal reflex and complete analgesia was observed. There was no significant change in rectal temperature and heart rate. However, heart rate remained elevated during anesthesia. Respiratory rate decreased significantly after propofol administration. There was a significant increase in plasma glucose after the xylazine and propofol administration which remained elevated till recovery. A significant decrease in chloride level was seen after propofol administration.Conclusions:Glycopyrrolate - xylazine - propofol anesthetic combination may safely be used for short duration anesthesia in buffalo calves.

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The recommendation system is very popular nowadays. Recommendation system emerged over the last decade for better findings of things over the internet. Most websites use a recommendation system for tracking and finding items by the user's behavior and preferences. Netflix, Amazon, LinkedIn, Pandora etc. platform gets 60%-70% views results from recommendation. The purpose of this paper is to introduce a recommendation system for local stores where the user gets a nearby relevant recommended item based on the rating of other local users. There are various types of recommendation systems one is User-based collaborative filtering by which the system built upon and uses user's past behavior like ratings and gives similar results made by another user. In collaborative filtering uses Euclidean distance algorithm is used to find the user's rate score to make relations with other users and Euclidean distance similarity score distinguish similarity between users. K-nearest neighbor algorithm is used to implement and find the number of users like new user where K is several similar users. Integrate with map interface to find shortest distances among stores whose product are recommended. The dataset of JSON is used to parse through the algorithm. The result shows a better approach towards the recommendation of products among local stores within a region.

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