The data mining applications such as bioinformatics, risk management, forensics etc., involves very high dimensional dataset. Due to large number of dimensions, a well known problem of "Curse of Dimensionality" occurs. This problem leads to lower accuracy of machine learning classifiers due to involvement of many insignificant and irrelevant dimensions or features in the dataset. There are many methodologies that are being used to find the Critical Dimensions for a dataset that significantly reduces the number of dimensions. These feature reduction and subset selection methods reduce feature set, that eventually results in high classification accuracy and lower computation cost of machine learning algorithms. This paper surveys the schemes that are majorly used for Dimensionality Reduction mainly focusing Bioinformatics, Agricultural, Gene and Protein Expression datasets. A comparative analysis of surveyed methodologies is also done, based on which, best methodology for a certain type of dataset can be chosen.

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8 91. Video recordings of animals are used for many areas of research such as collective movement, animal 10 space-use, animal censuses and behavioural neuroscience. They provide us with behavioural data at 11 scales and resolutions not possible with manual observations. Many automated methods are being 12 developed to extract data from these high-resolution videos. However, the task of animal detection and 13 tracking for videos taken in natural settings remains challenging due to heterogeneous environments. 14 2. We present an open-source end-to-end pipeline called Multi-Object Tracking in Heterogenous environ-15 ments (MOTHe), a python-based application that uses a basic convolutional neural network for object 16 detection. MOTHe allows researchers with minimal coding experience to track multiple animals in their 17 natural habitats. It identifies animals even when individuals are stationary or partially camouflaged.18 3. MOTHe has a command-line-based interface with one command for each action, for example, finding 19 animals in an image and tracking each individual. Parameters used by the algorithm are well described 20 in a configuration file along with example values for different types of tracking scenario. MOTHe 21 doesn't require any sophisticated infrastructure and can be run on basic desktop computing units.22 4. We demonstrate MOTHe on six video clips from two species in their natural habitat -wasp colonies 23 on their nests (up to 12 individuals per colony) and antelope herds in four different types of habitats 24 (up to 156 individuals in a herd). Using MOTHe, we are able to detect and track all individuals in 25 these animal group videos. MOTHe's computing time on a personal computer with 4 GB RAM and i5 26 processor is 5 minutes for a 30-second long ultra-HD (4K resolution) video recorded at 30 frames per 27 second. 28 5. MOTHe is available as an open-source repository with a detailed user guide and demonstrations at 29 Github (https://github.com/tee-lab/MOTHe).30 1 Introduction 31Video-recording of animals is increasingly becoming a norm in behavioural studies of space-use patterns, be-32 havioural neuroscience, animal movement and group dynamics [1, 2]. High-resolution images from aerial pho-33 tographs and videos can also be used for animal census [3, 4, 5]. This mode of observation can help us gather 34 high-resolution spatio-temporal data at unprecedented detail and help answer a novel set of questions that were 35 previously difficult to address. For example, we can obtain movement trajectories of animals to describe space-36 use patterns of animals, to infer fine-scale interactions between individuals within groups and to investigate 37 how these local interactions scale to emergent properties of groups [6, 7, 8, 9, 10, 11, 12, 13]. To address these 38 questions, as a first step, videos need to be converted into data -typically in the form of positions and trajec-39 tories of animals. Manually extracting this information from videos can be time-consuming, tedious and, often 40 not feasible...

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