Handwritten Character Recognition Using Bayesian Decision Theory

Handwritten Character Recognition Using Bayesian Decision Theory Abstract: Character recognition (CR) can solve more complex problem in handwritten character and make recognition easier. Handwriting character recognition (HCR) has received extensive attention in academic and production fields. The recognition system can be either online or offline. Offline handwritten character recognition is the sub fields of optical character recognition (OCR). The offline handwritten character recognition stages are preprocessing, segmentation, feature extraction and recognition. Our aim is to improve missing character rate of an offline character recognition using Bayesian decision theory. Keywords: Character recognition, Optical character recognition, Off-line Handwriting, Segmentation, Feature extraction, Bayesian decision theory. Introduction The recognition system can be either on-line or off-line. On-line handwriting recognition involves the automatic conversion of text as it is written on a special digitized or PDA, where a sensor picks up the pen-tip movements as well as pen-up/pen-down switching. That kind of data is known as digital ink and can be regarded as a dynamic representation of handwriting. Off-line handwriting recognition involves the automatic conversion of text in an image into letter codes which are usable within computer and text-processing applications. The data obtained by this form is regarded as a static representation of handwriting. The aim of character recognition is to translate human readable character to machine readable character. Optical character recognition is a process of translation of human readable character to machine readable character in optically scanned and digitized text. Handwritten character recognition (HCR) has received extensive attention in academic and production fields. Bayesian decision theory is a fundamental statistical approach that quantifies the tradeoffs between various decisions using probabilities and costs that accompany such decision. They divided the decision process into the following five steps: Identification of the problem. Obtaining necessary information. Production of possible solution. Evaluation of such solution. Selection of a strategy for performance. They also include a sixth stage implementation of the decision. In the existing approach missing data cannot be recognition which is useful in recognition historical data. In our approach we are recognition the missing words using Bayesian classifier. It first classifier the missing words to obtain minimize error. It can recover as much error as possible. Related Work The history of CR can be traced as early as 1900, when the Russian scientist Turing attempted to develop an aid for the visually handicapped [1]. The first character recognizers appeared in the middle of the 1940s with the development of digital computers. The early work on the automatic recognition of characters has been concentrated either upon machine-printed text or upon a small set of well-distinguished handwritten text or symbols. Machine-printed CR systems in this period generally used template matching in which an image is compared to a library of images. For handwritten text, low-level image processing techniques have been used on the binary image to extract feature vectors, which are then fed to statistical classifiers. Successful, but constrained algorithms have been implemented mostly for Latin characters and numerals. However, some studies on Japanese, Chinese, Hebrew, Indian, Cyrillic, Greek, and Arabic characters and numerals in both machine-printed and handwritten cas es were also initiated [2]. The commercial character recognizers were available in the 1950s, when electronic tablets capturing the x-y coordinate data of pen-tip movement was first introduced. This innovation enabled the researchers to work on the on-line handwriting recognition problem. A good source of references for on-line recognition until 1980 can be found in [3]. Studies up until 1980 suffered from the lack of powerful computer hardware and data acquisition devices. With the explosion of information technology, the previously developed methodologies found a very fertile environment for rapid growth addition to the statistical methods. The CR research was focused basically on the shape recognition techniques without using any semantic information. This led to an upper limit in the recognition rate, which was not sufficient in many practical applications. Historical review of CR research and development during this period can be found in [4] and [3] for off-line and on-line cases, respectively. The real progress on CR systems is achieved during this period, using the new development tools and methodologies, which are empowered by the continuously growing information technologies. In the early 1990s, image processing and pattern recognition techniques were efficiently combined with artificial intelligence (AI) methodologies. Researchers developed complex CR algorithms, which receive high-resolution input data and require extensive number crunching in the implementation phase. Nowadays, in addition to the more powerful computers and more accurate electronic equipments such as scanners, cameras, and electronic tablets, we have efficient, modern use of methodologies such as neural networks (NNs), hidden Markov models (HMMs), fuzzy set reasoning, and natural language processing. The recent systems for the machine-printed off-line [2] [5] and limited vocabulary, user-dependent on-line handwritten characters [2] [12] are quite satisfactory for restricted applications. However, there is still a long way to go in order to reach the ultimate goal of machine simulation of fluent human reading, especially for unconstrained on-line and off-line handwriting. Bayesian decision Theory (BDT), one of the statistical techniques for pattern classification, to identify each of the large number of black-and-white rectangular pixel displays as one of the 26 capital letters in the English alphabet. The character images were based on 20 different fonts and each letter within 20 fonts was randomly distorted to produce a file of 20,000 unique instances [6]. Existing System In this overview, character recognition (CR) is used as an umbrella term, which covers all types of machine recognition of characters in various application domains. The overview serves as an update for the state-of-the-art in the CR field, emphasizing the methodologies required for the increasing needs in newly emerging areas, such as development of electronic libraries, multimedia databases, and systems which require handwriting data entry. The study investigates the direction of the CR research, analyzing the limitations of methodologies for the systems, which can be classified based upon two major criteria: 1) the data acquisition process (on-line or off-line) and 2) the text type (machine-printed or handwritten). No matter in which class the problem belongs, in general, there are five major stages Figure1 in the CR problem: 1) Preprocessing 2) Segmentation 3) Feature Extraction 4) Recognition 5) Post processing 3.1. Preprocessing The raw data, depending on the data acquisition type, is subjected to a number of preliminary processing steps to make it usable in the descriptive stages of character analysis. Preprocessing aims to produce data that are easy for the CR systems to operate accurately. The main objectives of preprocessing are: 1) Noise reduction 2) Normalization of the data 3) Compression in the amount of information to be retained. In order to achieve the above objectives, the following techniques are used in the preprocessing stage. Preprocessing Segmentation Splits Words Feature Extraction Recognition Post processing Figure 1. Character recognition 3.1.1 Noise Reduction The noise, introduced by the optical scanning device or the writing instrument, causes disconnected line segments, bumps and gaps in lines, filled loops, etc. The distortion, including local variations, rounding of corners, dilation, and erosion, is also a problem. Prior to the CR, it is necessary to eliminate these imperfections. Hundreds of available noise reduction techniques can be categorized in three major groups [7] [8]: a) Filtering b) Morphological Operations c) Noise Modeling 3.1.2 Normalization Normalization methods aim to remove the variations of the writing and obtain standardized data. The following are the basic methods for normalization [4] [10][16]. a) Skew Normalization and Baseline Extraction b) Slant Normalization c) Size Normalization 3.1.3 Compression It is well known that classical image compression techniques transform the image from the space domain to domains, which are not suitable for recognition. Compression for CR requires space domain techniques for preserving the shape information. a) Threshold: In order to reduce storage requirements and to increase processing speed, it is often desirable to represent gray-scale or color images as binary images by picking a threshold value. Two categories of threshold exist: global and local. Global threshold picks one threshold value for the entire document image which is often based on an estimation of the background level from the intensity histogram of the image. Local (adaptive) threshold use different values for each pixel according to the local area information. b) Thinning: While it provides a tremendous reduction in data size, thinning extracts the shape information of the characters. Thinning can be considered as conversion of off-line handwriting to almost on-line like data, with spurious branches and artifacts. Two basic approaches for thinning are 1) pixel wise and 2) nonpareil wise thinning [1]. Pixel wise thinning methods locally and iteratively process the image until one pixel wide skeleton remains. They are very sensitive to noise and may deform the shape of the character. On the other hand, the no pixel wise methods use some global information about the character during the thinning. They produce a certain median or centerline of the pattern directly without examining all the individual pixels. In clustering-based thinning method defines the skeleton of character as the cluster centers. Some thinning algorithms identify the singular points of the characters, such as end points, cross points, and loops. These points are the source of problems. In a nonpareil wise thinning, they are handled with global approaches. A survey of pixel wise and nonpareil wise thinning approaches is available in [9]. 3.2. Segmentation The preprocessing stage yields a clean document in the sense that a sufficient amount of shape information, high compression, and low noise on a normalized image is obtained. The next stage is segmenting the document into its subcomponents. Segmentation is an important stage because the extent one can reach in separation of words, lines, or characters directly affects the recognition rate of the script. There are two types of segmentation: external segmentation, which is the isolation of various writing units, such as paragraphs, sentences, or words, and internal segmentation, which is the isolation of letters, especially in cursively written words. 1) External Segmentation: It is the most critical part of the document analysis, which is a necessary step prior to the off-line CR Although document analysis is a relatively different research area with its own methodologies and techniques, segmenting the document image into text and non text regions is an integral part of the OCR software. Therefore, one who works in the CR field should have a general overview for document analysis techniques. Page layout analysis is accomplished in two stages: The first stage is the structural analysis, which is concerned with the segmentation of the image into blocks of document components (paragraph, row, word, etc.), and the second one is the functional analysis, which uses location, size, and various layout rules to label the functional content of document components (title, abstract, etc.) [12]. 2) Internal Segmentation: Although the methods have developed remarkably in the last decade and a variety of techniques have emerged, segmentation of cursive script into letters is still an unsolved problem. Character segmentation strategies are divided into three categories [13] is Explicit Segmentation, Implicit Segmentation and Mixed Strategies. 3.3. Feature Extraction Image representation plays one of the most important roles in a recognition system. In the simplest case, gray-level or binary images are fed to a recognizer. However, in most of the recognition systems, in order to avoid extra complexity and to increase the accuracy of the algorithms, a more compact and characteristic representation is required. For this purpose, a set of features is extracted for each class that helps distinguish it from other classes while remaining invariant to characteristic differences within the class[14]. A good survey on feature extraction methods for CR can be found [15].In the following, hundreds of document image representations methods are categorized into three major groups are Global Transformation and Series Expansion, Statistical Representation and Geometrical and Topological Representation . 3.4. Recognition Techniques CR systems extensively use the methodologies of pattern recognition, which assigns an unknown sample into a predefined class. Numerous techniques for CR can be investigated in four general approaches of pattern recognition, as suggested in [16] are Template matching, Statistical techniques, and Structural techniques and Neural networks. 3.5. Post Processing Until this point, no semantic information is considered during the stages of CR. It is well known that humans read by context up to 60% for careless handwriting. While preprocessing tries to clean the document in a certain sense, it may remove important information, since the context information is not available at this stage. The lack of context information during the segmentation stage may cause even more severe and irreversible errors since it yields meaningless segmentation boundaries. It is clear that if the semantic information were available to a certain extent, it would contribute a lot to the accuracy of the CR stages. On the other hand, the entire CR problem is for determining the context of the document image. Therefore, utilization of the context information in the CR problem creates a chicken and egg problem. The review of the recent CR research indicates minor improvements when only shape recognition of the character is considered. Therefore, the incorporation of contex t and shape information in all the stages of CR systems is necessary for meaningful improvements in recognition rates. The proposed System Architecture The proposed research methodology for off-line cursive handwritten characters is described in this section as shown in Figure 2. 4.1 Preprocessing There exist a whole lot of tasks to complete before the actual character recognition operation is commenced. These preceding tasks make certain the scanned document is in a suitable form so as to ensure the input for the subsequent recognition operation is intact. The process of refining the scanned input image includes several steps that include: Binarization, for transforming gray-scale images in to black white images, scraping noises, Skew Correction- performed to align the input with the coordinate system of the scanner and etc., The preprocessing stage comprise three steps: (1) Binarization (2) Noise Removal (3) Skew Correction Scanned Document Image Feature Extraction Bayesian Decision Theory Training and Recognition Pre-processing Binarization Noise Removal Skew correction Segmentation Line Word Character Recognition o/p Figure 2. Proposed System Architecture 4.1.1 Binarization Extraction of foreground (ink) from the background (paper) is called as threshold. Typically two peaks comprise the histogram gray-scale values of a document image: a high peak analogous to the white background and a smaller peak corresponding to the foreground. Fixing the threshold value is determining the one optimal value between the peaks of gray-scale values [1]. Each value of the threshold is tried and the one that maximizes the criterion is chosen from the two classes regarded as the foreground and back ground points. 4.1.2 Noise Removal The presence of noise can cost the efficiency of the character recognition system; this topic has been dealt extensively in document analysis for typed or machine-printed documents. Noise may be due the poor quality of the document or that accumulated whilst scanning, but whatever is the cause of its presence it should be removed before further Processing. We have used median filtering and Wiener filtering for the removal of the noise from the image. 4.1.3 Skew Correction Aligning the paper document with the co-ordinate system of the scanner is essential and called as skew correction. There exist a myriad of approaches for skew correction covering correlation, projection, profiles, Hough transform and etc. For skew angle detection Cumulative Scalar Products (CSP) of windows of text blocks with the Gabor filters at different orientations are calculated. Alignment of the text line is used as an important feature in estimating the skew angle. We calculate CSP for all possible 50X50 windows on the scanned document image and the median of all the angles obtained gives the skew angle. 4.2 Segmentation Segmentation is a process of distinguishing lines, words, and even characters of a hand written or machine-printed document, a crucial step as it extracts the meaningful regions for analysis. There exist many sophisticated approaches for segmenting the region of interest. Straight-forward, may be the task of segmenting the lines of text in to words and characters for a machine printed documents in contrast to that of handwritten document, which is quiet difficult. Examining the horizontal histogram profile at a smaller range of skew angles can accomplish it. The details of line, word and character segmentation are discussed as follows. 4.2.1 Line Segmentation Obviously the ascenders and descanters frequently intersect up and down of the adjacent lines, while the lines of text might itself flutter up and down. Each word of the line resides on the imaginary line that people use to assume while writing and a method has been formulated based on this notion shown fig.3. Figure 3. Line Segmentation The local minima points are calibrated from each Component to approximate this imaginary baseline. To calculate and categorize the minima of all components and to recognize different handwritten lines clustering techniques are deployed. 4.2.2 Word and Character Segmentation The process of word segmentation succeeds the line separation task. Most of the word segmentation issues usually concentrate on discerning the gaps between the characters to distinguish the words from one another other. This process of discriminating words emerged from the notion that the spaces between words are usually larger than the spaces between the characters in fig 4. Figure 4. Word Segmentation There are not many approaches to word segmentation issues dealt in the literature. In spite of all these perceived conceptions, exemptions are quiet common due to flourishes in writing styles with leading and trailing ligatures. Alternative methods not depending on the one-dimensional distance between components, incorporates cues that humans use. Meticulous examination of the variation of spacing between the adjacent characters as a function of the corresponding characters themselves helps reveal the writing style of the author, in terms of spacing. The segmentation scheme comprises the notion of expecting greater spaces between characters with leading and trailing ligatures. Recognizing the words themselves in textual lines can itself help lead to isolation of words. Segmentation of words in to its constituent characters is touted by most recognition methods. Features like ligatures and concavity are used for determining the segmentation points. 4.3 Feature Extraction The size inevitably limited in practice, it becomes essential to exploit optimal usage of the information stored in the available database for feature extraction. Thanks to the sequence of straight lines, instead of a set of pixels, it is attractive to represent character images in handwritten character recognition. Whilst holding discriminated information to feed the classifier, considerable reduction on the amount of data is achieved through vector representation that stores only two pairs of ordinates replacing information of several pixels. Vectorization process is performed only on basis of bi-dimensional image of a character in off-line character recognition, as the dynamic level of writing is not available. Reducing the thickness of drawing to a single pixel requires thinning of character images first. Character before and after Thinning After streamlining the character to its skeleton, entrusting on an oriented search process of pixels and on a criterion of quality of represe ntation goes on the vectorization process. The oriented search process principally works by searching for new pixels, initially in the same direction and on the current line segment subsequently. The search direction will deviate progressively from the present one when no pixels are traced. The dynamic level of writing is retrieved of course with moderate level of accuracy, and that is object of oriented search. Starting the scanning process from top to bottom and from left to right, the starting point of the first line segment, the first pixel is identified. According to the oriented search principle, specified is the next pixel that is likely to be incorporated in the segment. Horizontal is the default direction of the segment considered for oriented search. Either if the distortion of representation exceeds a critical threshold or if the given number of pixels has been associated with the segment, the conclusion of line segment occurs. Computing the average distance between the l ine segment and the pixels associated with it will yield the distortion of representation. The sequence of straight lines being represented through ordinates of its two extremities character image representation is streamlined finally. All the ordinates are regularized in accordance to the initial width and height of character image to resolve scale Variance. 4.4 Bayesian Decision Theories The Bayesian decision theory is a system that minimizes the classification error. This theory plays a role of a prior. This is when there is priority information about something that we would like to classify. It is a fundamental statistical approach that quantifies the tradeoffs between various decisions using probabilities and costs that accompany such decisions. First, we will assume that all probabilities are known. Then, we will study the cases where the probabilistic structure is not completely known. Suppose we know P (wj) and p (x|wj) for j = 1, 2à ¢Ã¢â€š ¬Ã‚ ¦n. and measure the lightness of a fish as the value x. Define P (wj |x) as the a posteriori probability (probability of the state of nature being wj given the measurement of feature value x). We can use the Bayes formula to convert the prior probability to the posterior probability P (wj |x) = Where p(x) P (x|wj) is called the likelihood and p(x) is called the evidence. Probability of error for this decision P (w1 |x) if we decide w2 P (w2|x) if we decide w1 P (error|x) = { Average probability of error P (error) = P (error) = Bayes decision rule minimizes this error because P (error|x) = min {P (w1|x), P (w2|x)} Let {w1. . . wc} be the finite set of c states of nature (classes, categories). Let {ÃŽÂ ±1. . . ÃŽÂ ±a} be the finite set of a possible actions. Let ÃŽÂ » (ÃŽÂ ±i |wj) be the loss incurred for taking action ÃŽÂ ±i when the state of nature is wj. Let x be the D-component vector-valued random variable called the feature vector. P (x|wj) is the class-conditional probability density function. P (wj) is the prior probability that nature is in state wj. The posterior probability can be computed as P (wj |x) = Where p(x) Suppose we observe x and take action ÃŽÂ ±i. If the true state of nature is wj, we incur the loss ÃŽÂ » (ÃŽÂ ±i |wj). The expected loss with taking action ÃŽÂ ±i is R (ÃŽÂ ±i |x) = which is also called the conditional risk. The general decision rule ÃŽÂ ±(x) tells us which action to take for observation x. We want to find the decision rule that minimizes the overall risk R = Bayes decision rule minimizes the overall risk by selecting the action ÃŽÂ ±i for which R (ÃŽÂ ±i|x) is minimum. The resulting minimum overall risk is called the Bayes risk and is the best performance that can be achieved. 4.5 Simulations This section describes the implementation of the mapping and generation model. It is implemented using GUI (Graphical User Interface) components of the Java programming under Eclipse Tool and Database storing data in Microsoft Access. For given Handwritten image character and convert to Binarization, Noise Remove and Segmentation as shown in Figure 5(a). Then after perform Feature Extraction, Recognition using Bayesian decision theory as shown in Figure5(b). Figure 5(a) Binarization, Noise Remove and Segmentation Figure 5(b) Recognition using Bayesian decision theory 5. Results and Discussion This database contains 86,272 word instances from an 11,050 word dictionary written down in 13,040 text lines. We used the sets of the benchmark task with the closed vocabulary IAM-OnDB-t13. There the data is divided into four sets: one set for training; one set for validating the Meta parameters of the training; a second validation set which can be used, for example, for optimizing a language model; and an independent test set. No writer appears in more than one set. Thus, a writer independent recognition task is considered. The size of the vocabulary is about 11K. In our experiments, we did not include a language model. Thus the second validation set has not been used. Table1. Shows the results of the four individual recognition systems [17]. The word recognition rate is simply measured by dividing the number of correct recognized words by the number of words in the transcription. We presented a new Bayesian decision theory for the recognition of handwritten notes written on a whiteboard. We combined two off-line and two online recognition systems. To combine the output sequences of the recognizers, we incrementally aligned the word sequences using a standard string matching algorithm. Evaluation of proposed Bayesian decision theory with existing recognition systems with respect to graph is shown in figure 6. Table 1. Results of four individuals recognition systems System Method Recognition rate Accuracy 1st Offline Hidden Markov Method 66.90% 61.40% 1st Online ANN 73.40% 65.10% 2nd Online HMM 73.80% 65.20% 2nd Offline Bayesian Decision theory 75.20% 66.10% Figure 6 Evaluation of Bayesian decision theory with existing recognition systems Then each output position the word with the most occurrences has been used as the  ¬Ã‚ nal result. With the Bayesian decision theory could statistically signi ¬Ã‚ cantly increase the accuracy. 6. Conclusion We conclude that the proposed approach for offline character recognition, which fits the input character image for the appropriate feature and classifier according to the input image quality. In existing system missing characters cant be identified. Our approach using Bayesian Decision Theories which can classify missing data effectively which decrease error in compare to hidden Markova model. Significantly increases in accuracy levels will found in our method for character recognition

Health Promotions and Disease Prevention Paper Essay

Elder Mistreatment Elder mistreatment is a widespread problem in our society that is often under-recognized by health care professionals. As a result of growing public outcry over the past 20 years, all states now have abuse laws that are specific to older adults; most states have mandated reporting by all health care professionals. The term â€Å"mistreatment† includes physical abuse and neglect, psychological abuse, financial exploitation and violation of rights. Poor health, physical or cognitive impairment, alcohol abuse and a history of domestic violence are some of the risk factors for elder mistreatment. Diagnosis of elder mistreatment depends on acquiring a detailed history from the patient and the caregiver. It also involves performing a comprehensive physical examination. Only through awareness, a healthy suspicion and the performing of certain procedures are physicians able to detect elder mistreatment. Once it is suspected, elder mistreatment should be reported to adult protective se rvices (HHS fact sheet, 2005). It is estimated that over 2 million older adults are mistreated each year in the United States. Elder mistreatment first gained attention as a medical and social problem about 20 years ago, when the term â€Å"granny battering† first appeared in a British medical journal. Since that time, elder mistreatment has become a matter of concern not only in the United States, but throughout the world. This heightened awareness has followed a growing awareness of child and spousal abuse. Nevertheless, because of differing definitions, poor detection and under-reporting, the extent of elder mistreatment is unknown. These same factors make the collection of data difficult and its accuracy questionable. Published studies estimate that the prevalence of elder mistreatment ranges from 1 to 5 percent (Healthy people, 2010). Most health care professionals are reluctant to address domestic violence. However, physicians are in an ideal position to detect and manage mistreatment, as they may be the only person outside the family/caregiver role who regularly sees the older adult. In addition, the  physician is the most likely person to order the testing, hospital admissions and support services that are sometimes needed to correct elder mistreatment. This paper will discuss the clinical, ethical and legal issues regarding elder mistreatment. The various forms of elder mistreatment are defined, including ways to identify patient and caregiver risk factors, and history and physical findings that suggest a diagnosis of elder mistreatment. Finally, a systematic approach to patient evaluation, documentation and reporting of suspected cases will be reviewed. Reasons elder abuse may be missed or not reported by health care professionals include unfavorable attitude toward older adults (ageism), little information in medical literature about elder mistreatment, reluctance to attribute signs of mistreatment (disbelief),isolation of victims (patient not seen often by physicians/health care providers), subtle presentation (i.e., poor hygiene or dehydration), reluctance/fear of confronting the offender, reluctance to report mistreatment that is only suspected, mistreated person requests that abuse not be reported (patient/physician privilege), lack of knowledge about proper reporting procedure, fear of jeopardizing relationship with hospital or nursing facility Types of elder abuse Physical Abuse- occurs when a person is touched in an inappropriate way, such as hitting, punching, kicking, slapping, and pushing. Physical abuse often leaves marks on the person’s body: bite marks, bruises, welts, and burn marks. Marks are often left on arms, wrists, face, neck, and abdomen area; Emotional/Psychological Abuse- occurs when a person is demeaning to another person. A person may treat the elder like a child or call them names. An elder may seem unusually depressed or may talk bad about themselves; Sexual abuse- among an elder occurs when sexual contact is made without consent. It also occurs when an elder is incapable of making such a decision, and is rape; Financial abuse- occurs when a person or persons take advantage of an elderly person financially. This includes stealing money, lying about how much the elder needs for certain care, or cashing the elder’s checks without permission; Neglect/Abandonment- occurs when the elder is not being properly cared for, such as not being fed, bathed, and properly medicated. This is also when the elder is being ignored. The care  giver refuses to give care to the individual (Physical abuse of the elderly, 2005). Elder Mistreatment: Definitions and Classifications In an effort to increase physicians’ awareness, facilitate accurate detection and promote further research, the American Medical Association published a position paper on elder mistreatment in 1987. This paper proposed a standard definition: â€Å"‘Abuse’ shall mean an act or omission which results in harm or threatened harm to the health or welfare of an elderly person. Abuse includes intentional infliction of physical or mental injury; sexual abuse; or withholding of necessary food, clothing, and medical care to meet the physical and mental needs of an elderly person by one having the care, custody or responsibility of an elderly person† (HHS fact sheet, 2005). Elder mistreatment may take many forms. Types of elder mistreatment are often classified as physical abuse and neglect, psychological abuse, financial exploitation and violation of rights. A major obstacle to prevention of and intervention for elder mistreatment is a lack of awareness on the part o f physicians and other health care professionals (LA4Seniors, 2005). Risk Factors and Prevention Cognitive impairment and the need for assistance with activities of daily living are important risk factors for elder mistreatment. Caregiver burnout and frustration can lead to elder mistreatment. Substance abuse by the caregiver or the patient, especially abuse of alcohol, significantly increases the risk of physical violence and neglect. Psychological and character pathology in the caregiver and patient are also major risk factors. Prevention of elder mistreatment is difficult and depends as much on the social support network as on the medical network. Preventing elder mistreatment involves identifying high-risk patients and caregivers, and attempting to address the underlying issues. Screening patients and caregivers before placement can be helpful, when it is feasible. Helping patients obtain county or state assistance can also help reduce some high-risk situations. Risk Factors for Elder Mistreatment Older age, lack of access to resources, low income, social isolation, minority status, low level of education, functional debility, substance  abuse by caregiver or by elderly person, psychological disorders and character pathology, previous history of family violence, caregiver burnout and frustration, and Cognitive impairment. History- Recognizing mistreatment is often difficult. The older adult may be unable to provide information because of cognitive impairment. The history is sometimes difficult to obtain from the victim, for fear of retaliation by the abuser. This retaliation can come in the form of physical punishment or threats of violence and abandonment. Older adults are often fearful of being placed in a nursing facility, and some may prefer to be abused in their own home rather than be moved to such a facility (LA4Seniors, 2005). The mistreated older adult often presents with somatic complaints. Physicians should ask older patients about rough handling, confinement and verbal or emotional abuse. Subtle or confusing complaints may actually be indicative of mistreatment. It is important to recognize that abuse and neglect are most often discovered during routine visits at the physician’s office or in the long term care facility. Generally, the patient should be interviewed without the caregiver(s) present. Cognitive impairment may limit the ability to obtain an accurate history. It is important to ask general questions about conditions in the home or nursing facility. The physician should try to obtain an accurate view of the patient’s daily life, including meals, medication, shopping and social outlets (HHS fact sheet, 2005). It is also important to ask the patient about the nature and quality of the relationship with the caregiver. It may be helpful to ask questions such as, â€Å"How do you and the caregiver get along?† and â€Å"Is the caregiver taking good care of you?† It is critical to assess the patient’s mental status for indicators of depression or alcohol and substance abuse. A discussion of the patientâ €™s financial situation may be appropriate. If issues of mistreatment are raised, the caregiver should be interviewed as well. The physician must be careful not to over interpret or to make suggestive comments, especially when the patient is cognitively impaired. Essential Features of the History in the Assessment of Mistreated Elders Medical problems/diagnoses, detailed description of home environment (adequacy of food, shelter, supplies), accurate description of events related to injury or trauma (instances of rough handling, confinement, verbal or emotional abuse), history of prior violence, description of prior injuries and events  surrounding them, description of berating, threats or emotional abuse, improper care of medical problems, untreated injuries, poor hygiene, prolonged period before presenting for medical attention, depression or other mental illness, extent of confusion or dementia, drug or alcohol abuse, quality/nature of relationships with caregivers. Physical Examination and Laboratory Tests The physical examination is often used as legal evidence of mistreatment. Laboratory and imaging studies should be performed to confirm any suspicious findings in the history and physical examination. The presence of dehydration and malnutrition can be established with simple laboratory tests such as a complete blood count and measurement of blood urea nitrogen, creatinine, protein and albumin levels. Radiographic studies provide evidence of old and new fractures. Unfortunately, proving that a fracture was caused by abuse can be difficult (HHS fact sheet, 2005). Role of advance nurse and nursing intervention strategies- The nurses can play a vital role as a case finder, manager, advocate, educator, researcher and caregiver to physically abused elderly and family or caregiver in a given community. Inform the decision makers about the magnitude, trends and characteristics of violent deaths; and, evaluate and continue to improve by educating the patient and the care giver, and if is necessary reporting the abuser to the authorities. Nurses should involve the case managers and the social workers, document all the findings accurately and report the mistreatment case as soon as possible. Documentation Documentation of all findings may be entered as evidence in criminal trials or in guardianship hearings. Documentation must be complete, thorough and legible. Such circumstances dictate that there is a â€Å"chain of evidence.† This need mandates a careful collection of physical evidence, which is critical in cases of suspected sexual or physical abuse. Laboratory data and, when possible, photographs should be used for verification of written documentation. Management Management of elder mistreatment first involves discussing the situation with the patient, if feasible. The patient should be allowed to play a role  in the ultimate resolution, if he or she is able to do so. First, the competency of the patient should be determined. Local and state social services have different methods of addressing mistreatment. Social workers from hospitals, clinics or long term care facilities are valuable resources and should be able to assist with these services. Multidisciplinary teams can be very effective. These teams typically include geriatricians, social workers, case management nurses and representatives from legal, financial and adult protective services. Multidisciplinary teams are often more effective in problem-solving and provide a forum for discussion with participants involved in the older adult’s care. Senior advocacy volunteer groups are also helpful. A senior advocate can provide information to the abused person and enable access to resources from community programs and social services. Basic Features of the Physical Examination Head- Traumatic alopecia or other evidence of direct physical violence; poor oral hygiene; Skin- Hematomas, welts, bite marks, burns, decubitus ulcers; Musculoskeletal- fractures or signs of previous fractures; Neurological- cognitive impairment that is a risk factor for mistreatment and influences management decisions regarding competency; Genito rectal- poor hygiene, inguinal rash, impaction of feces; General- weight loss, dehydration, poor hygiene, unkempt appearance (LA4Seniors, 2005). Reporting All health care providers (physicians, nurses, social workers, etc.) and administrators are mandated by law to report suspected elder mistreatment. The laws differ from state to state; physicians should determine the specific requirements in their states. Any other person may also report suspected abuse and neglect. All reporters are immune from civil liability if they act in good faith and without malice. They are also protected from termination of employment. Health care providers can be found to be negligent if they fail to report the suspected mistreatment. Penalties can include fines, imprisonment or loss of licensure. Reports of suspected elder mistreatment should be given to the state or county division of adult protective services. In the absence of such services, the reporter should contact the county extension office or the state’s office of child and  family services. In addition, any Area Agency on Aging would be able to provide assistance in reporting suspected mistreatment. The National Domestic Violence Hotline (telephone: 800-799-SAFE) or the Older Women’s League (telephone: 800-825-3695) could also help. Contacting the police is always an option, especially in an urgent situation (HHS fact sheet, 2005). In the event that the older adult is a resident of a long term care facility, a separate mechanism often exists for investigating suspected mistreatment through the state agency that surveys these facilities. Identifying the appropriate avenue for investigation can be done through the available adult protective service agency or the state department of child and family services (Elder Abuse, 2005). Once suspected mistreatment has been reported, the responsible agency will assign a social worker to investigate the case. The social worker will provide an accurate description of the home or nursing-facility environment. After assessment, the social worker may provide insight into some possible solutions to the problem and information about available resources. Unlike cases of child abuse, if the older adult is competent to make decisions, he or she may refuse intervention. If the older adult is not competent to make decisions, a guardian can be appointed by the state. The guardian can then direct care as needed until the problem is satisfactorily resolved. Injury Prevention- In healthy people there is no precise data specific for elder abuse, but these are related data from that site. The target rate for physical assault by intimate partner is 3.3 physical assaults per 1,000, and the baseline is 4.4 physical assaults per 1,000. The target rate for annual rate of rape is 0.7 rapes or attempted rapes per 1,000 persons, and the base line is 0.8 rapes or attempted rapes per 1,000 (Health people, 2010). Objectives from Healthy People 2010 Reduce injuries, disabilities, and deaths due to injuries and violence, and educating the primary care givers about the signs and symptoms of abuse or mistreatment, and educating them about alternative coping mechanisms. Several themes become evident when examining reports on injury prevention and control, including acute care, treatment, and rehabilitation. First, injury comprises a group of complex problems involving many different sectors of society. No single force working alone can accomplish everything  needed to reduce the number of injuries. Improved outcomes require the combined efforts of many fields, including health, education, law, and safety sciences. Second, many of the factors that cause injuries are closely associated with violent and abusive behavior (Health people, 2010). Violence and Abuse Prevention Violence in the United States is pervasive and can change quality of life. Reports of children killing children in schools are shocking and cause parents to worry about the safety of their children at school, and if the problem is untreated the aggression later on will turn on the parents or older adults. Reports of gang violence make people fearful for their safety. Although suicide rates began decreasing in the mid-1990s, prior increases among youth aged 10 to 19 years and adults aged 65 years and older have raised concerns about the vulnerability of these population groups. Intimate partner violence and sexual assault threaten people in all walks of life (Elder Abuse, 2005). Violence claims the lives of many of the Nation’s young persons and threatens the health and well-being of many persons of all ages in the United States. On an average day in America, 53 people die from homicide, and a minimum of 18,000 persons survive interpersonal assaults, and as many as 3,000 persons attempt suicide (Elder Abuse, 2005). Elderly, females, and children continue to be targets of both physical and sexual assaults, which are frequently perpetrated by individuals they know. Examples of general issues that impede the public health response to progress in this area include the lack of comparable data sources, lack of standardized definitions and definitional issues, lack of resources to establish adequately consistent tracking systems, and lack of resources to fund promising prevention programs. Disparities Adults aged 65 years and older are at increased risk of death from fire because they are more vulnerable to smoke inhalation and burns and are less likely to recover. Sense impairment (such as blindness or hearing loss) may prevent older adults from noticing a fire, and mobility impairment may prevent them from escaping its consequences. Older adults also are less likely to have learned fire safety behavior and prevention information,  because they grew up at a time when little fire safety was taught in schools, and most current educational programs target children (Healthy people, 2010). Opportunities To reduce the number and severity of injuries, prevention activities must focus on the type of injury—drowning, fall, fire or burn, firearm, or motor vehicle. Understanding injuries allows for development and implementation of effective prevention interventions. Some interventions can reduce injuries from violence-related episodes. For instance, efforts to promote awareness in society can help reduce the risk of assault, intentional self-inflicted and elder neglect and abuse. Higher taxes on alcoholic beverages are associated with lower death rates for some categories of violent crime, including mistreatment, abuse, and rape (Healthy people, 2010). Healthy People Objectives This organization encourages the Individuals, groups, and other organizations to use this framework and integrate it into their current programs, events, publications, and meetings, schools, colleges, and civic organizations to undertake activities in order to further the health of all members of their community. It is a national initiative that aims to improve the health of all Americans and eliminate disparities in health. Reducing the prevalence and overall number of people who suffer from different variety of diseases, and increase concerns for the nation’s elderly, and to reduce the overall rate of elder abuse, prevent its associated health problems, and educating the care givers and enhancing their coping mechanisms and alternative modalities to deal with the related stress. Health care providers can encourage their patients to pursue healthier lifestyles and to participate in community-based programs. By following the national objectives, individuals and organizations c an build an agenda for community health improvement and can monitor results over time. Healthy People objectives have been specified by Congress as the measure for assessing the progress of the Indian Health Care Improvement Act, the Maternal and Child Health Block Grant, and the Preventive Health and Health  Services Block Grant. Healthy People objectives have been used in performance measurement activities (Healthy people, 2010). Many objectives focus on interventions designed to reduce or eliminate illness, disability, and premature death among individuals and communities, and to educate the care giver regarding recognizing elder abuse, and prevention modalities; others focus on broader issues, such as improving access to quality health care, strengthening public health services, and improving the availability and dissemination of health-related information. Each objective has a target for specific improvements to be achieved by the year 2010. Together, these objectives reflect the depth of scientific knowledge as well as the breadth of diversity in the Nation’s communities. More importantly, they are designed to help the Nation achieve Healthy People 2010’s two overarching goals and realize the vision of healthy people living in healthy communities (Healthy people, 2010). Interim Progress toward Year 2000 Objectives Numerous objectives addressed injury prevention in Healthy People 2010. Twenty-six objectives were specific for unintentional injuries, and 19 objectives were specific for violence prevention. By the end of the decade, targets had been met for 11 objectives. Unintentional injury objectives showing achievement were unintentional injury hospitalizations, residential fire deaths, nonfatal head injuries, spinal cord injuries, nonfatal homicide poisonings, and pedestrian deaths. Violence prevention objectives that met their targets were, suicide, weapon carrying by adolescents, conflict resolution in schools, and child death review systems. REFERENCES Elder Abuse and Neglect Statistics (2005). In search of solutions. Retrieved on 8/20/05 from http://www.apa.org/pi/aging/eldabuse.html Healthy people 2010(2005). Retrieved on 8/20/05 from http://www.healthypeople.gov LA4Seniors (2005). A public service website for seniors and their families. Retrieved on 8/20/05 from www.la4seniors.com/elder abuse.htm National center on elder abuse (2005). Retrieved on 8/20/05 from http://www.elderabusecenter.org HHS fact sheet (2005). US department of health and human services. Retrieved on 8/20/05 from www.hhs.gov/news/press/2000pres/20000503b.html Physical abuse of the elderly (2005). Physical abuse of the elderly: signs, descriptions, and what you can do about it. Retrieved on 8/21/05 from http://de.essortment.com/physicalabusee_rfjb.htm

Perception and reality of technology

Perception and Reality of Technology Nowadays, technology is popular in our lives and greatly improves as time is passing. Technology has brought many benefits, but in reality, many people perceive technology as having negatively impacted our lives. There are three things of technology which can provide to us a better knowledge; faster ways of locomotion and communication are smart phones, computers, and televisions.The perception is everyone knows smart phones can make life easier in many ays; Smart phones support a wide variety of other services such as text messages, emails, the Internet access, games, and photography; they allow us to reach anyone all around the world. By using smart phones for voice calls or video calls and text messages, people are able to keep in touch with others in a long distance. Today, with the development of cell phones, we can use them for our entertainment such as surfing the Internet, sending photos or downloading videos and games.Additionally, GPS in smart phones becomes an important part of our life. People use GPS in smart phones to find their way to home, work, restaurants, or shopping centers. Although we already have classical GPSs, smart phones are usually lighter, smaller, and faster than the classical GPSs. That is why smart phones are used by many people in around the world. According to a research, the number of people accessing the web through smart phones is increasing to over 17. 4% of worldwide internet usage (Fox). In the reality, smart phones have negative effects to students in the education environment.They distract students from their lessons and make them miss important notes. As students often text messages to each other; they may also interrupt other students. Most people do not control how much time they should use smart phones, they waste their time on smart phones: playing games, watching movies, and news updating. Moreover, smart phones disturb people when they focus on driving; and they have to take t heir eyes off the road to talk or text. It is very risky and dangerous to everyone in and around the car. According to a report in 2009 in he U.S, there were a reported 5,474 people killed by distracted drivers; 995 of those were considered killed by drivers distracted by smart phones (Distracted Driving 2009). Next, in perception, that is the computer is one of the important features of technology, which is very useful for people in order to widen their knowledge. Our current development is due to computers in many areas. People consider that computers provide better education; they provide as with many distant learning courses and online testing like McGraw-Hill Connect and MyltLab.Also, computers furnish a lot of necessary access to information such as news and emails. Moreover, people use computers to keep in touch with entertainment, looking for friends; or watch Glee comedy and America's Next Top Model shows. On the other hand, in reality, people sometimes abuse computers. Pla ying games on computer do not have benefit to adults and children; it wills a cause bad effect on people such as eye strains, wrist, neck and back pains. People, who use computers too otten in a long time, should take a snort break atter 1 or 2 hours.Computer can affect our mental health with the large amount of bad knowledge on the Internet; especially children and teenagers. They enjoy play video games and watch violent movies; it may also affect their psychologist and make them become a murder in school. According toa research in 2011 in the U. S, the Supreme Court struck down California's law did not allow to sale or rental of violent video games to people under 18 (Beresin). In perception, television is another kind of popular technology which many household have.They can be operated either by battery or electronics. Furthermore, television can improve vocabulary and language skills for people who want to learn second languages. After a long day of work, people love to spend ti me watching TV with their family members. With the help TV, people may learn more about different countries culture from entertainment, educates, and informs all functions of mass media. The reality is the disadvantage of televisions; they often stop people from following other good habits like reading books and socializing.

Definition and Examples of False Analogies

The fallacy,  or false analogy, is an argument  based on misleading, superficial, or implausible comparisons. It is also known as a  faulty analogy, weak analogy, wrongful comparison,  metaphor as argument, and analogical fallacy. The term comes from the Latin word  fallacia, meaning deception, deceit, trick, or artifice The analogical fallacy consists of supposing that things which are similar in one respect must be similar in others. It draws a comparison on the basis of what is known, and proceeds to assume that the unknown parts must also be similar, says Madsen Pirie, author of How to Win Every Argument. Analogies are commonly used for illustrative purposes to make a complex process or idea easier to understand. Analogies become false or faulty  when they are overextended or presented as conclusive proof. Commentary There are seven windows given to animals in the domicile of the head: two nostrils, two eyes, two ears, and a mouth...From this and many other similarities in Nature, too tedious to enumerate, we gather that the number of planets must necessarily be seven. – Francesco Sizzi, 17th-century Italian astronomer [F]alse analogy is central to jokes whose humour derives from ill-judged comparisons, as in the old joke where a mad scientist builds a rocket to the sun but plans to embark at night to avoid being cremated. Here a false analogy is created between the sun and a light bulb, suggesting that when the sun is not shining it is not turned on, and hence, not hot. – Tony Veale, Computability as a Test on Linguistic Theories, in Cognitive Linguistics: Current Applications and Future Perspectives, ed. by Gitte Kristiansen et al. Mouton de Gruyter, 2006 When you find yourself reasoning by analogy, ask yourself two questions: (1) are the basic similarities greater and more significant than the obvious differences? and (2) am I over-relying on surface similarities and ignoring more essential differences? – David Rosenwasser and Jill Stephen, Writing Analytically, 6th ed. Wadsworth, 2012 The Age of False Analogies We are living in the age of the false, and often shameless, analogy. A slick advertising campaign compares the politicians working to dismantle Social Security to Franklin D. Roosevelt. In a new documentary, Enron: The Smartest Guys in the Room, Kenneth Lay compares attacks on his company to the terrorist attacks on the United States. Intentionally misleading comparisons are becoming the dominant mode of public discourse... The power of an analogy is that it can persuade people to transfer the feeling of certainty they have about one subject to another subject about which they may not have formed an opinion. But analogies are often undependable. Their weakness is that they rely on the dubious principle that, as one logic textbook puts it, because two things are similar in some respects they are similar in some other respects. An error-producing fallacy of weak analogy results when relevant differences outweigh relevant similarities. – Adam Cohen, An SAT Without Analogies Is Like: (A) A Confused Citizenry... The New York Times, March 13, 2005 The Mind-As-Computer Metaphor The mind-as-computer metaphor helped [psychologists] to focus attention on questions of how the mind accomplishes various perceptual and cognitive tasks. The field of cognitive science grew up around such questions. However, the  mind-as-computer metaphor  drew attention away from questions of evolution... creativity, social interaction, sexuality, family life, culture, status, money, power... As long as you ignore most of human life, the computer metaphor is terrific. Computers are human artifacts  designed to fulfill human needs, such as increasing the value of Microsoft stock. They are not autonomous entities that evolved to  survive and reproduce. This makes the computer metaphor very poor at helping psychologists to identify mental adaptations that evolved through natural and sexual selection. – Geoffrey Miller, 2000; quoted by Margaret Ann Boden in Mind as Machine: A History of Cognitive Science. Oxford University Press, 2006 The Darker Side of False Analogies A false analogy occurs when the two things compared are not similar enough to warrant the comparison. Particularly common are inappropriate World War II analogies to Hitlers Nazi regime. For example, the Internet has more than 800,000 hits for the analogy animal Auschwitz, which compares the treatment of animals to the treatment of Jews, gays and other groups during the Nazi era. Arguably, the treatment of animals is terrible in some cases, but it is arguably different in degree and kind from what happened in Nazi Germany. – Clella Jaffe, Public Speaking: Concepts and Skills for a Diverse Society, 6th ed. Wadsworth, 2010 The Lighter Side of False Analogies Next, I said, in a carefully controlled tone, we will discuss False Analogy. Here is an example: Students should be allowed to look at their textbooks during examinations. After all, surgeons have X-rays to guide them during an operation, lawyers have briefs to guide them during a trial, carpenters have blueprints to guide them when they are building a house. Why, then, shouldn’t students be allowed to look at their textbooks during an examination? There now, [Polly] said enthusiastically, is the most marvy idea I’ve heard in years. Polly, I said testily, the argument is all wrong. Doctors, lawyers, and carpenters aren’t taking a test to see how much they have learned, but students are. The situations are altogether different, and you can’t make an analogy between them. I still think it’s a good idea, said Polly. Nuts, I muttered. – Max Shulman, The Many Loves of Dobie Gillis. Doubleday, 1951