Andrew Jackson Essays (268 words) - Andrew Jackson, Rachel Jackson

Andrew Jackson Andrew Jackson (1767-1845) Andrew Jackson was born on March 15, 1767, to a family of immigrants in the Waxhaw settlement on the western frontier of South Carolina. Jackson's parents died when he was 14, and was brought up by an uncle who was a slave owner. He became a lawyer at the age of 20 and as a prosecuting attorney in Nashville, Tennessee. He married Rachel Donelson Robards on January 17,1794, whose father was very trusted and well known. This helped Jackson's career and social standing. Jackson and his wife were unaware, however, at the time of their marriage that her divorce from her first husband was not technically over, and his political enemies referred to the couple as adulterers. After helping to draft the Tennessee constitution in 1796, Jackson was elected the state's first congressman, serving one year in the U.S. House of Representatives and then for a year in the U.S. Senate. In 1798 he was appointed judge of the superior court in Tennessee. In 1802 Jackson was elected major general of the Tennessee militia. He became the seventh president of the United States in 1828, and served two successful terms. Jackson left office in 1837. In 1845, Rachel Jackson was insulted by one of Jackson's political enemies. Jackson, being the ?Man of steel? that he was, challenged the man to a duel. Jackson was shot, yet did not fall, he then raised his pistol, and laid out his opponent. His honor was eon, but his life was lost. He died that day, June 8,1848, in Nashville, Tennessee. American History

A Tree Grows in Brooklyn Vocabulary Terms

A Tree Grows in Brooklyn Vocabulary Terms Betty Smiths first novel,  A Tree Grows in Brooklyn, tells the coming-of-age story of Francie Nolan and her second-generation immigrant parents struggling to provide for their family. Its widely believed Smith herself was the basis for the character of Francie. Heres a vocabulary list from A Tree Grows in Brooklyn. Use these terms for reference, study, and discussion. Chapters I-VI: tenement: an apartment building, usually in a low-income area, that is without luxury amenities ragamuffin: a child whose appearance is unkempt and uncivilized cambric: a finely woven white linen interminable: long and dull with little sign of ending (or terminating) premonition: a warning or feeling about something that will happen in the future (usually negative) vestibule: a reception area or foyer, often in a school or church Chapters VII-XIV: fetching: attractive or pretty, beguiling peculiar: unusual or suprising, out of the ordinary bucolic: of or in the countryside, literally a shepherd or cowhand sprig small shoot or twig of a plant, usually decorative or garnish filigree: a delicate ornamentation or detail usually gold or silver, on jewelry banshee: from Irish folklore,  a female spirit whose high-pitched wailing signals an imminent death (on the) dole: unemployed and receiving benefits from the government. Chapters XV-XXIII: prodigious: impressively large, awesome languorous: without energy or liveliness, sluggish gallantly  do something in a brave or heroic way dubious: having doubt or uncertainty, skeptical horde: a large unruly crowd saunter  to walk at a leisurely pace relegate: to demote or assign to a lower category Chapters XXIV-XXIX: gratis: free, without cost contempt:  disrespectful dislike conjecture: opinion based on incomplete information, speculation surreptitious: secretive, sneaky vivacious: animated, lively, happy-go-lucky thwarted: prevented from accomplishing something, disappointed sodden: drenched, thoroughly soaked Chapters XXX-XXXVII:   lulled: calmed, settled down putrid: decaying with a foul odor   debonair: sophisticated, charming lament: to mourn, or feel sad about a loss fastidious: having exacting attention to detail Chapters XXXIII-XLII: contrite: apologetic, feeling sincere regret for a misdeed contorted: twisted or misshapen infinitesimal: so small as to be irrelevant or unmeasurable Chapters XLIII-XLVI:   contemptuously: disrespectfully, disdainfully poignant: creating or evoking a feeling of sadness or empathy genuflect: to kneel and show deference or reverence especially in a house of worship vestment: garment worn by a member of clergy or religious order Chapters XLVII-LIII: vaudeville: variety show with comedic and slapstick performances rhetorically: speaking in a theoretical or speculative manner, not literally mollify: to pacify or appease matriculate: to enroll and pass through a school or course of study munitions: collection of weapons Chapters LV-LVI: prohibition: forbidding, or, period in American history when alcohol was illegal. jauntily: cheerful and arrogant, lively sachet: small perfumed bag This vocabulary list is just one part of our study guide on A Tree Grows in Brooklyn. Please see the links below for other helpful resources: Review: A Tree Grows in BrooklynQuotes from A Tree Grows in Brooklyn

Response to Rothstein and Jacobsens The Goals of Education Essay

Response to Rothstein and Jacobsens The Goals of Education - Essay Example History instruction was thought to teach students good judgment, enabling them to learn from prior generations’ mistakes and successes and inspiring them to develop such character traits as honesty, integrity, and compassion.†(Rothstein, et.al.) Benjamin Franklin also highlighted the importance of teaching history and â€Å"in 1749 [he] proposed that Pennsylvania establish a public school that should, he said, place as much emphasis on physical as on intellectual fitness because â€Å"exercise invigorates the soul as well as the body.† (Rothstein, et. al.)George Washington went a step further. He argued that â€Å"goals for public schools were also political and moral.†(Rothstein, et. al.) The suggestions of Thomas Jefferson were purely political. He â€Å"most often linked with education in the public mind, thought universal public education needed primarily to prepare voters to exercise wise judgment.†(Rothstein, et.al.) So, the perspective about education changed, as time rolled by and change in the political leadership of the country. I am reminded of the parable of four visually challenged (blind) persons in argument about the shape of the elephant. One had the feel of its long tail and argued that the shape of the elephant is long. The second one, who touched one of its legs, said the shape is like the tree. The third one touched its trunk and said the shaped is like a rubber hose. The fourth one had the feel of the ear and said that the shape is like a giant leaf. An eye surgeon, who listened to their conversation, took them to his dispensary, operated upon their eyes and he was able to restore the eyesight. When he showed them the elephant, they realized that their judgment was wrong. Similar is position of the politicians, sociologists, academicians and the bureaucrats, when they tender opinions and try to frame the policy on the system of education that needs to be adopted in America. None has the comprehensive outlo ok about the genuine needs of the students in a multicultural, multi-ethnic society that suffered from the bane of racism for more than two centuries. The goal of education needs to be man making. Within this broad goal, all other subsidiary yet important goals are integrated. According to the authors, reading scores alone will not go to mold an individual into a responsible citizen with abilities in the area of work ethic, physical and mental health, social skills and appreciation of the arts and literature. I am in broad agreement with the observation of the authors. The schools need to accept the challenges in these areas to enable the students to pursue their diverse goals and a new accountability system needs to be formulated. I also agree with the well-researched conclusions of the authors. The social, economic and cultural life is impacted by the materialistic civilization. With women opting out for full time jobs in a big way and with less or no time at their disposal to dev ote to their home and children, the other influencing source to mold the character of students is the school environment. Proper conditions need to be created for the school administration and the teachers, by providing them with incentives to enable them to accept the challenges with a sense of responsibility. After churning the literature on educational initiatives over the past two hundred and fifty years, the authors have listed eight important outcomes that emerge, when the educational system is poised on the brink of change. Those are: â€Å"Basic academic skills and knowledge, Critical thinking and problem solving, Appreciation of the arts and literature, Preparation for skilled employment, Social skills and work ethic,

Reaction paper Assignment Example | Topics and Well Written Essays - 250 words - 3

Reaction paper - Assignment Example It was very important to her, this act of maturity, so much so that when, after visiting her semi-comatose mother at the hospital, she went out for lunch with a cousin and remembered that she had left them at her mother’s bedside, she made her cousin turn the car back and rushed in to retrieve them. Conversation in June About Mothers by Hwang Sun-Won is just that: a group of people gathered together in June and talking about mothers. A man related how his son, who had always been a daddy’s boy, ran back to his mother when she called out to him, rather than flee to South Korea with him. He claims that mothers are absolute beings for their children. A man disagrees, relating how his mother eloped with a lover when he was very small, never to return, and how much he hated her. He recounts that when he was wounded in a war, and was about to die, his mother’s apparition came to him, but he rejected her – his hatred being that strong. However, the group states that in effect it was his mother’s apparition that had saved his life. Another man tells the story of a woman who was escaping to South Korea in a boat with a group of people; for fear of being caught by the North Korean shore guard, she threw her crying infant into the river. The narrator feels a de ep hatred for the woman, however, before the hatred becomes permanent, the man relating the story tells the group that the woman was lactating and when her breasts became swollen, she cut off her nipples herself with

Competition law Essay Example for Free

Competition law Essay A situation in which a single company or group owns all or nearly all of the market for a given type of product or service. By definition, monopoly is characterized by an absence of competition, which often results in high prices and inferior products. According to a strict academic definition, a monopoly is a market containing a single firm. In such instances where a single firm holds monopoly power, the company will typically be forced to divest its assets. Antimonopoly regulation protects free markets from being dominated by a single entity. Explanation Monopoly Monopoly is the extreme case in capitalism. Most believe that, with few exceptions, the system just doesnt work when there is only one provider of a good or service because there is no incentive to improve it to meet the demands of consumers. Governments attempt to prevent monopolies from arising through the use of antitrust laws. Of course, there are gray areas; take for example the granting of patents on new inventions. These give, in effect, a monopoly on a product for a set period of time. The reasoning behind patents is to give innovators some time to recoup what are often large research and development costs. In theory, they are a way of using monopolies to promote innovation. Another example are public monopolies set up by governments to provide essential services. Some believe that utilities should offer public goods and services such as water and electricity at a price that is affordable to everyone.

Mosquito Species Detection using Smart Phone

Mosquito Species Detection using Smart Phone Abstract-According to WHO(World Health Organization) re-ports, among all disease transmitting insects mosquito is the most hazardous insect. In 2015 alone, 214 million cases of malaria were registered worldwide. Zika virus is another deadly disease transmitted from mosquitoes. According to CDC report, in 2016 62,500 suspected case of Zika were reported to the Puerto Rico Department of Health (PRDH) out of which 29,345 cases were found positive. There are 3500 different species of mosquitoes present in the world out of which 175 types is found in United States. But only few of them are responsible for these above mentioned fatal disease. Therefore classification between hazardous and regular mosquitoes are very important. For regular person with no expertise in this field would be almost impossible to identify the difference. Even for the mosquito-expert, identifying different species is a very tedious and time consuming job. Hence in this paper, we have tried to classify 7 different species of dead mosquitoes with total 60 samples collected from Hillsborough County Mosquito and Aquatic Weed Control Unit,Tampa Florida by capturing image from smart phone cameras. With our approach we want to enable non-expert population to early identify the risk and act pro-actively. We pre-processed the image for removing noise and applied random forest classification algorithm to distinguish different species. Achieved good precision,recall,F1 measure and aggregate 83:3% accuracy. We are also planning to develop a smart-phone application which will leverage this learning model and help in empowering population to identify mosquito species without any knowledge in this field. INTRODUCTION Of all animals, mosquitoes are amongst the most deadly in spreading diseases. Mosquito borne diseases like Malaria, Dengue, West Nile Fever, and most recently Zika Fever have extracted devastatic tolls on humanity [1]. Combating the spread of mosquitoes is an important health-care agenda across the globe, and several organizations across the globe serve this purpose. For instance, one such organization is the American Mosquito Control Association (AMCA) is spread over 50 countries and conducts numerous programs to educate citizens of the dangers posed by mosquitoes and how to control them. According to CDC report, there are about 3500 different species of mosquitoes in the world, out of which about 175 different species are found in the USA. Among programs designed to combat mosquitoes spread, identification of the type and number of species in any par-ticular area is very important. Across the world, numerous mosquito control organizations have dedicated personnel that lay traps to catch mosquitoes in specific areas, and dedicated personnel visually look at each captured sample (via a mag-nifying glass) to identify the type of mosquito. It takes upto a minute to identify each sample, and with more samples, the time taken to identify each sample can take hours, and naturally significant manual effort. Contributions of this Paper: In this paper, we aim to de-sign a system that combines images from smart-phone cameras with machine learning algorithms for automatic detection of the type of mosquito species from their images. Towards this extent, our specific contributions are: a). Building a database of mosquito images: We visited the Hillsborough County Mosquito and Aquatic Weed Control in Tampa in Fall 2016 to collect numerous samples of mosquitoes that were captured in traps set up the county personnel. Subsequently, the personnel helped us visually identify the type of each sample. As a result, we collected 60 samples, that belonged to seven different species.Table I presents our database. Subsequently, each sample was imaged via a Sam-sung Galaxy S5 phone via multiple angles (at the same indoor light conditions) for a total of 200 images. This served as our database for subsequent classification. b). Designing Pre-processing Techniques: Generally, images are vulnerable to the different type of noises due to different environment condition and user expertise. Therefore, images need to be pre-processed for any noise removal and also for smoothening. In the process of noise removal, we need to make sure that edges and boundary of images are preserved otherwise images will lose the key information. We used median filter as it works very effectively when edges need to be preserved. This filter is widely used in image processing technique [2]. c). Designing Random Forest Based Classifiers: Random Forest is an ensembled supervised machine learning algorithm. It is a collection of decision trees, where each trees has been grown using subset of training dataset selected randomly. In most of the cases, it has shown significant improvement in accuracy as compare to other classification algorithm. Apart from that, it works very well on outliers and noise. It handles larger dataset efficiently and quickly without over-fitting the model as only a subset of training set is selected for each split.[3] [4] We conducted an extensive performance evaluation for our proposed techniques.We evaluated our experiment on 60 image samples of seven different species. 10-fold cross validation technique has been used and achieved 83:3% accuracy using RGB features. The rest of the paper is organized as follows. In section II, related works are discussed. Followed by section III where experimental set up and data collection process are described. Section IV contains the detail about preprocessing of image data, extracting and selecting features, building the learning model using classification method and different metrics lever-aged for showing the results. We talked about experimental evaluation and validation in detail in section V. Finally, dis-cussion and conclusion sections are VI and VII respectively. RELATED WORK There are many studies which are dedicated to leverage the use of smart phone camera for image recognition. In this section we have emphasized few of the related and important works done. A. Related Work on Image Recognition In [5] system was developed for determining the effec-tiveness of soil treatment on plant stress using smart-phone cameras.In this paper, 34 images of plant leaves are captured using smart phone in two soils that is biosolids and unamended tailings. Then each images was preprocessed using mean, me-dian filter followed by segmentation into pixels.They extracted RGB,R,G,B,HSV and YCbCr features from the segmented pixels. Random Forest which is a supervised classification algorithm was designed to detect the stress of leaves and achieved 91.24% accuracy. A [6] survey has been done on Pixel-Based skin color detection techniques. They have applied various color spaces like RGB, Normalized RGB, HSV and YCrCb for recognizing skin. RGB is the most widely used color spaces for processing and storing digital images. Wen et.al [7] has proposed image-based automated insect iden-tification and classification method. In this paper eight insect species have been selected for experiment. These insects were frozen to retreive a non damaging kill of the insect and then they were placed on a white balance panel under the reflectance light base of a Nikon stereoscopic zoom microscope SMZ1000 (Nikon, Tokyo) with Plan Apochromat 0.5 objective. Images of these were taken by a DS-Fi1 color digital camera which was placed on the microscope. Features which had been taken in these are color, texture, invariants, contour and geometric. In color features, HSV color space features were considered. T Many classification algorithm i.e. minimum least square linear classifier (MLSLC), normal densities based linear classifier (NDLC), K nearest neighbor classifier (KNNC), nearest mean classifier (NMC), and decision tree (DT) were used for testing and training the model. Among these NDLC classification algorithm outperfor ms other classifier. 1) Comparing our Work w.r.t. Related Work: Our work is focused on capturing mosquitoes images from smart phone camera and using the captured image for training and testing the learning model. In [7], authors have identified insect species but it needs lab set up with microscope and high resolution digital camera which is not available in house generally. We have extracted RGB features for classification which is most widely used color spaces [6]. EXPERIMENTAL SETUP AND DATA COLLECTION In this section, we have discussed data collection process our experiment. A. Data Collection We collected dead mosquito species samples from Hillsbor-ough County Mosquito and Aquatic Weed Control Unit,Tampa Table I: Mosquito Species and Number of Samples Specie Name Number of Samples Cx Nigrip 10 An Quadrim 6 Ma Titillans 7 Ps Columpi 10 An Crucians 10 Ps Ferox 7 Cq Perturbans 10 Table II: Camera Specification Camera Specification Value Sensor Resolution 16 MP Focus Adjustment automatic Special Effect HDR Camera Light Source Daylight Florida. We carefully identified seven species, mentioned in Table I for our study. Since, dead mosquito physical properties like color, del-icateness etc changes as time passes. So, images of dead mosquitoes were taken in a single day to make sure envi-ronmental conditional are same while taking these images. A Samsung Galaxy S5 smartphone was used for capturing images in regular day light. Each sample image was taken based on the knowledge aware fusion described on the mosquito and aquatic control weed control unit web site. A total of 60 images were captured for our study, having following camera configuration, mentioned in Table II. OUR APPROACH We have implemented two steps in our approach. First, pre processing of image has been done for noise removal and feature selection using filter like median,mean. Second, building a learning model using a classification algorithm based on random forest. Here our main aim is to build a learning model for identi-fying each mosquitoes species. The challenge here we faced is the image size. Images which were captured from smart phone is of 2988 X 5322 pixels. We reduced their size to 256 X 256 pixels to decrease its data dimensionality. To remove the noise from each sample we applied median filter technique.This has been elaborated in the next subsection. Since, our images were already in dark color.It is mandatory to keep background and foreground in contrast for building the model reasonably well. So, we did not use any segmentation technique as it converts the background into black. Here,we are using Random Forest, a supervised learning algorithm and used 10-fold cross validation technique for learning and testing. The process flow of our algorithm is described in Figure 2. For proceeding further, we need labeled image data for training the model. All images were tagged manually under the guidance of mosquito experts. Noise Removal Generally, digital images are susceptible to different type of noise. It can occur by several ways like capture, transmission etc. Accuracy of the result are affected badly by the same. There are many filters used to remove and reduce noise from image. Sharpening Filter: It refers as a enhancing technique which highlights edges and line details in the image. In this procedure, original image is passed through high pass filter which extracts its high frequency components and then the scaled output of high pass filter is added to original image which results in sharpened image. [8] Mean Filter: This filtering technique refers to replacing each pixel value in an image with the mean of pixel values of its neighbors which falls in the sliding window of n*n size. This technique removes noise more effectively if large window size is considered.This is also called average filter. [8] Median Filter: It is a nonlinear filtering technique. The approach behind this filtering technique is to replace each pixel value in the window of n * n size pixel by the median of all pixel values in that particular window.It is very used in digital image processing and it preserves edges while removing noise. We have used this filtering technique with 3*3 pixels window size for removing the noise from our digital images. The output with median filter and without this is shown in Figure 1. [2] Feature Selection Feature extraction and selection is very critical part of any supervised learning algorithm. Extraction is about reducing the data dimensionality as the size of data grows and its dimension increases and becomes very difficult to handle it manually . And then the need of automation comes into the picture. Feature Selection is a process of selecting those features which are most relevant for our problem and eliminating unnecessary, irrelevant and redundant features of data that do not contribute to the accuracy of learning model. In our proposed model, we are identifying different species of mosquitoes. Each species have contrastive color. As we can see in Figure 3, each mosquitoes have similar shapes but differ-ent body and wings color.So,the correct color channels or the combination of channel is important to take into consideration for the features. Few of the color channels are RGB, HSV etc. RGB has Red, Green and Blue channels. In RGB, each component supports a range of intensity levels from 0 to 255 (integer valued)[9] . Here, we extracted RGB feature from the mosquito image data. Then for feature selection, we applied Information-Gain attribute selection algorithm which is a good measure for deciding the relevance of an attribute. This feature selection technique generally helps in achieving high accuracy and using this we got 1000 features which serve as an input vector x into Random Forest Classification Algorithm for species detection. We calculated its precision, recall and F1-measure which is mentioned in Table III Table III: Combination of color channels accuracy comparison Combination Precision Recall F1-measure RGB 0.845 0.833 0.834 C. Classification Method Random Forest Algorithm: Random Forests(RF) is an ensemble supervised machine learning algorithm. It consists of a set of decision trees; h(x,i) i = 1, 2,, where x is a feature vector extracted from the smartphone image data and i consists of K integers which are independent identically distributed random vectors. Each decision tree predicts a class independently. A voting is performed on the results from each decision tree and finally the class which gets majority vote will be the final predicted class. The same has been explained in Figure 4 . Given a dataset set that contains N feature vectors, each consisting of M features, the RF algorithm builds the trained model using following steps: N samples are selected at random with replacement from the data set, for training the model of a particular tree. K features are randomly selected from the set of available features, where K M. Among the values for each of the K features drawn, choose the best split according to the Information gain IG(T; a) of the attribute. Information gain is measure of decrease in entropy which is caused by splitting the samples on an attribute. T denote a set of training sample for a single tree. ((x),y) = (x1, x2,.., xk,y) where (x) consist is a single sample and y is its class label. The information gain for an attribute a is as follow: The information gain for an attribute a is as follows: IG (T; a) = H (T ) v val(a) j(x T jTa = v) j :H (x T jxa = v) X x j j (1) [10] Here, xa vals(a) is the value of the ath attribute of example x. The randomization is present in two ways: Random selection of data for bootstrap samples as it is done in bagging Random selection of input features for creating individual base decision trees. Each tree will grow to its maximum size until the stopping criterion has not been fulfilled and there will be no tree pruning. Once the forest has been ensembled, testing data sample will be labeled mosquito species class based on a majority vote among all classes from all decision trees in the forest. Once the forest has been ensembled, testing data sample is labeled with one of the classes (species1; species2::::species7) by taking the majority vote: i.e., it is labeled with the class which has been selected by maximum number of trees. In the RF approach, given a feature sample x to be classified, the conditional probabilities for each class are computed by taking the average of the conditional probabilities given by the trees constructing 4 Figure 1: a) Original Image b) Image after applying sharpening median filterFigure 2: Process description of our experiment a). Cruciansb). Columpic). Feroxd). Nigrip e). Peturbansf). Quadrimg). Titillans Figure 3: Mosquito Color Images the ensemble. These conditional probabilities are computed as follows. Given a decision tree T, and an input feature sample x to be classified, let us denote by v(x) the leaf node where x falls when it is classified by T. The probability P (mjx; T ) that the sample x belongs to the class m, where m 2 fspecies1; species2; :::; species7g (for 7 species of interest to this paper), is estimated by the following equation: P (mjx; T ) = nm (2) n where nm is the number of training samples falling into v(x) after learning and n is the total number of training samples assigned to v(x) by the training procedure. Given a forest consisting of L trees and an unknown feature sample x to be classified, the probability estimate P (mjx) that x belongs to the species m is computed as follows: 1 L (3) P (mjx) = P (mjx; Ti) L =1 Xi P (m x; T ) by where th j i is the conditional probability provided the i tree and is computed according to Eq.(1). As a consequence, for the sample x to be classified, the RF algorithm gives as output the vector: = fP (species1jx) ; P (species2jx) : : : : : : P (species7jx)g The class(species) with the highest probability in the set(4)is chosen as classified class for the ith tree. The final class of our RF algorithm is the one which gets the majority vote among all activities from all decision trees in the forest [11]. The work flow of the RF algorithm with pre-processing, training and testing phase is formally shown in Algorithm 1.[3] [12] D. Metrics The results of Mosquito-Species detection are shown in terms of precision, recall, F1-measure and Confusion Matrix. Each metric is a function of the of the true positives (T P ), false positives (F P ) and false negatives (F N). The precision is the ratio of correctly classified classes to the total number of classes predicted as positive: P recision = T P (5) T P + F P Recall is the ratio of total number of classes predicted as positive to the total number of positive classes: Recall = T P (6) T P + F N 5 Figure 4: Work flow of the Random Forest Algorithm The F1-measure is the weighted average of precision and recall: P recisionRecall F1 = 2 P recision + Recall (7) The Confusion Matrix (CM) is a table that allows the visu-alization used to describe the performance of a classification model. Each column of the matrix represents the instances in a predicted class while each row represents the instance in an actual class (or vice-versa) [13]. Precision indicates the number of samples classified as a particular species actually belonged to that species. Recall gives us the number of species which are correctly classified. The F1-measure denotes the classification models accuracy.It is calculated as the harmonic mean of precision and recall. Confusion matrix makes the system easy to see how much predicted model is getting confused between different species. For example if a species is predicted correctly only 80% of the time, then this matrix will show how the algorithm confused its prediction with the other (wrongly classified) species the remaining 20% of the time. RESULTS Overview of Evaluation Methods: In this paper, we evaluated the performance of our system using 10-fold cross validation that are standard for our problem scope. Cross-validation is a model validation technique for assess-ing how the results of a classification model will generalize to an independent dataset 10-fold cross-validation divides the dataset into 10 subsets, and evaluates them 10 times. Each time, one of the 10 subsets is used as the test set and the other 9 subsets are put together to form a training set. Then, the average error across all 10 trials is computed for final result. It limits problems like over-fitting in the classification model. Results and Interpretations: We used RGB feature men-tioned earlier to train our classification model. To evaluate its accuracy we used 10-fold cross validation technique and calculated precision, recall and F1 measure of each species independently. The evaluation measures of RGB feature are shown in IV have also shown it graphically in Figure 5. Confusion Matrix of the same is shown in Figure 6. Algorithm 1: RF-based Algorithm for Mosquito-Species detection Training Image dataset = Id; Testing Image dataset= Ited; RGB Features extracted from Training Image dataset = F tRGB; RGB Features extracted from Testing Image dataset = F teRGB; Classified Species from Images= M S; Probability that feature F belongs to Species M S = P (M SjF ); No. of trees in Random Forest = 121; Step 1 Pre-Processing: Median filters are applied to remove accidental spikes from Id and Ited. Features F tRGB and F teRGB are extracted from processed data Id and Ited obtained from (1). Step 2 Training: Input: Training data set F tRGB Output: Random Forest model to classify different species of mosquitoes. Select a bootstrap sample of size N from the training data. Grow a decision tree T using following steps. Select K features at random from the set of M features. Choose the best feature/split-point among the K. Split the node into two daughter nodes. Grow the tree to its maximum size that is 6 and let the tree unpruned Step 3 Prediction: Input: Testing data set Ited Output: Final Mosquito Species prediction M Ss. Select the same attributes used for training the model from testing feature set F teRGB. Predict the species from the model using features selected in the above step. 6 Table IV: RGB Features accuracy of each species indepen-dently Species Precision Recall F1-measure An Crucians 0.889 0.8 0.842 An Quadrim 0.571 0.667 0.615 Cd Peturbans 0.727 0.8 0.762 Cx Nigrip 0.889 0.8 0.842 Ma Titillans

Cultural Difference in Malaysia

What are cultural differences between Malaysia and other countries? In general culture can define as a way of life and process of development of the personality, spirit, mind, and the human effort in a community or cluster. Culture can be understood as the creation of human community in various forms, whether tangible or intangible. Culture it’s very important because it show the difference between societies to another society. The cultural features are obtained through a learning process, practices of sharing and cannot be separated from language. We cannot be denied that the Malaysian culture is very different compare to other countries.Malaysia is a multiracial country living in harmony compare to other countries. Among the three main races in Malaysia are Malays, Chinese and Indian. In addition, Malaysia is a homeland to approximately for 80 ethnic groups, especially in East Malaysia including Baba and Nyonya, Kadazan and Iban. The total of Malaysia’s population is 28. 3 million of which 91. 8 % are national citizens while 8. 2 % are non-citizen. Furthermore, indigenous people were 67. 4 %, of which 56. 4% are Malays ethnic and 11% of the other natives. Approximately, the total of Chinese ethnic in Malaysia’s population is 24. %, 7. 3% are Indian with a total of 1. 86 million people, while other race is 0. 7 %. Almost 85 % of Indians in Malaysia are Tamil community. In terms of religion, Islam is the official religion of the Government of Malaysia as subject under the Constitution and the most widely professed religion with the proportion of 61. 3 %. As multi-racial country, other religions are practiced are Buddhism, Christian and Hinduism. In terms of language, Bahasa Malaysia is the official language of the country. However, other races are free to use their mother tongue like Chinese and Tamil.English an international language which play role as a second language in Malaysia and widely used in the fields of education, trade and indu stry. Malaysia national population practices are vary according to their own religion. According to the practice of Muslim believers is to pray five times a day, fasting during the month of Ramadan and eat kosher food. Muslim believers not allowed to drink alcohol, eat pork, do not eat the flesh of slaughtered animals, stealing and committing sins. Buddhist faith practice is to remove their shoes before entering the temple and prostrate statue of Buddha.They are not allowed to kill all living beings, stealing, lying and drinking alcohol. Hindu people required to wash their feet before entering the temple, fasting, tying a banana tree during religious ceremony and wearing black mark (pottu) in the middle of forehead for unmarried women, while for married women must wear red mark called as kum kum. Hindu believer do not allowed to drink alcohol, stealing , lying, give respect for parents and do not allowed to eat beef because according to their religion cows considered as gods. In add ition, each religious wedding ceremony is different.In Malay wedding ceremony, marriage contract and enthronement is very important practice that must be done because it’s a part of wedding. For Buddhist, both bride and groom usually get the blessing from the divine. They also practiced tea drinking custom in wedding ceremony that aims to strengthen family relationship. In Hindu wedding ceremony, the primary witness of marriage is the sacred fire (agni). Both bride and groom need to turn around the sacred fire in circular motion for seven times. Malaysia culture is based on Malay culture as the original people of this region.Malay culture based on Sharia law and Islamic principles. Malay cultural give more important to values and preferred politeness, simplicity and harmony among family members, neighbours and society. Example; in general handshakes suffice for both men and women, although some Muslim ladies may acknowledge an introduction to a gentleman with a nod of her hea d and smile. The traditional greeting of â€Å"salam† resembles a handshake with both hands but without the grasp. The man offers both hands, lightly touches the visitor's outstretched hands, and then brings his hands to his chest to mean, â€Å"I greet you from my heart†.The visitor should reciprocate the â€Å"salam†. Furthermore, in Malaysia culture, the right hand is always used when giving and receiving objects like gift or money. Pointing is normally done using the thumb as using the right forefinger is considered rude. Here is some general gift giving etiquette. In Malay culture, if invited someone home, don’t ever give pig and dog toy to children and don’t give any products that made from pig skin. White and yellow wrapping should be avoided because white symbolizes death and mourning while yellow symbolizes colour of royalty.In Chinese culture, if going to visit relative home, bring a small gift of cake or fruits for children. Giving flowe r should be avoided because flower do not make good gift and usually used at funerals. Besides that, the gift should wrap with cheerful colour like red, pink or yellow except white, blue, or black because symbolizes mourning colours in Chinese culture. In Indian culture, the gift should be given with the right hand and not wrap the gift with white and black colour. In generally, the gift should not open when received to avoid people think you are greedy.The way names are used also varies between ethnicities. The Chinese people traditionally have 3 names. The surname is usually first name and two personal names. Example: Wong Jae Suk. Malay people do not have surnames. Men use term â€Å"bin†, so Kamarul bin Mohamad is Kamarul the son of Mohamad. Instead women add their father’s name to their own name with the term â€Å"binti†. Indian people doesn’t use surname. Men use s/o to refer themselves as the son of their father. Example: Ajay s/o Abishek. Culture and communication depends on each other and have a reciprocal relationship.According to Hall, â€Å"culture is communication, communication is culture†. This indicates that communication is two-way. Different culture held different values. The way we communicate depens on our culture, rules and norms. For example, American usually treats and exchanged with unknown person. Meanwhile, The Malaysia does not prefer to treats or exchanged with stranger. This showed that Malaysia cultural and American cultural are totally different. Malaysia practices courteous communication. For Malay society, interact courteously very important and showed our values of norms.For example, using proper words when talk to someone older than us like Sir or Madam. Besides that, culture is also shown through verbal and non-verbal communication. Speak rude and negative words are prohibited. Malaysian traditional cultural games are batu seremban, wau, mah-jong and kabadi. Batu Seremban and Wau is Malay traditional games, Mahjong is Chinese traditional game, while Kabadi is Indian Traditional game. Batu Seremban also known as a Selambut. This game often played in leisure time by girls, either individually or in groups. It’s usually using glass marbels or small stones.Wau is known as kite and normally played by rural people. Wau created in various design and has its own name like wau in shape of brid, stingray, peacock and many more in various colour. However, Mahjong played by four individual and it involves strategy, tactics, skill and calculation. In Asia, mah-jong is also popular as gambling. In kabadi, each team consists of five players on the reserve bench and this game played for 20 minutes. A team will be sent forward to the opponent’s team, which aims to touch and push one of the players before turning back to his team.The players who have been touched by the opposing team will be out and left the field. From aspect of the traditional dance, the various custom s and culture can be learned from various ethnic groups in Malaysia. Dance can be defined as a movement of the body, legs and arms to the beat of the music. The traditional dance has own purpose and tribes. Examples of traditional dance in Malaysia are gamelan, zapin, kathak, dragon dance, kathak, bharathanatyam and many more. Zapin dance is famous dance in the state of Johor and usually performed in both formal and informal events like wedding or opening ceremony.Kathak dance is one of the Indian classical dances which are originated from Kerala. Kathak means â€Å"art of the storytelling†. While, bharathanatyam it’s also Indian classical dance based on Indian epics in motion of dramatic and passionate. Amorous dragon dance is a traditional cultural dance in China. Lion dance and dragon dance often played on Chinese New Year. As a conclusion, this discussion showed dramatically that Malaysia’s cultural very different compare to other countries. It’s more follow to the traditional, principle of the religion and give more important to norms, values and rules. I am very proud to be a Malaysian.