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The past few years have seen marijuana become more accepted within the medical community as an increasingly useful pain treatment. But marijuana isn’t alone. Groups of researchers across the country have also been studying the potential benefits of psilocybin, the hallucinogenic compound that gives so-called “magic mushrooms” their power. Soon the drug could become an effective medical treatment.
Psilocybin is one of two classic hallucinogens, the other being LSD. Both drugs, when ingested, act on tiny receptors found throughout the brain called 5-HT2A receptors, igniting a flood of changes leading to symptoms like euphoria, hallucinations, and extreme emotions. Using psilocybin as a medical treatment isn’t a new idea. Back in the 1940s through 60s, psychedelics were a big part of psychiatry. A handful of studies were published at the time hinting at the possible benefits of this drug on anxiety and depression, as well as other conditions like alcoholism. But the controlled substance act of 1970 put a halt to this line if inquiry, and it wasn’t until recently that scientists started to study the drugs again.
Recent studies on healthy adults reflected those earlier positive findings. In 2011, research showed similar benefits in terminally ill cancer patients.
Typically, patients who are suffering from depression and anxiety in addition to a terminal illness will receive traditional treatments, like SSRI antidepressants, which often take a long time to work and come with a slew of unwanted side effects.
In the two studies published today in the Journal of Psychopharmacology, the researchers wanted to see the effects that a single dose of psilocybin could have on cancer patients with anxiety or depression related to their illness. In a living room-type setting, two psychiatrists gave participants a standard, moderate dose of psilocybin and allowed time for the effects to kick in. Then, after a number of hours had passed, the psychiatrists walked the participants through a structured therapy session.
The participants’ reactions (which were reported immediately after the exercise and again six months later) were profound: 80 percent of them had increased optimism and decreased anxiety, especially anxiety relating to death. In fact, the researchers report, many participants called the experience one of the most meaningful of their lives. One woman, who had been diagnosed with ovarian cancer in 2010, said that the hallucinogen helped her manifest her anxiety in a tangible way: “I visualized my fear as a physical mass in my body, i screamed at it and told it to get the fuck out, and it was gone. These fear and anxieties were still gone.”
“These results show [psilocybin] can produce deeply meaningful experiences and attribute positive changes in mood and behavior,” says Roland Griffiths, a psychiatrist at Johns Hopkins medical school and lead author of the Hopkins study.
But the researchers and others in the field caution that the positive benefits seen are not solely from psilocybin. The combination of the hallucinogen, the right environment, and a guided therapy session with psychiatrists is what did the trick. Any treatments that use psilocybin in the future should be executed in a similar way.
“The paradigm shift is not just with the drug itself,” says Leor Rossman, a PhD candidate at Imperial College London who studies psychedelic-assisted therapy but was not involved in the new studies. “But it’s together with the right environment and having a therapy session accompanying it.”
It’s not clear how long the mental health effects of a single dose of psilocybin last, so the researchers aren’t sure whether patients would need to repeat their shroom-aided therapy down the road. But while the researchers only followed their patients for six months, Griffiths says, they have reason to believe that the effects might be longterm. “We started research on healthy volunteers 15 years ago, who will say something like, I remember that experience for the rest of my life as a touchstone experience, and which they remember and continue to draw upon,” he says.
The researchers hope that their results will lay the groundwork for further studies on psilocybin. Eventually, they say, the drug could be used as a treatment for depression and alcoholism in patients with and without terminal illness.
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How To Deal with Android Keyboard Not Working Properly Best Ways To Fix Android Keyboard Has Stopped Working Ways To Check Android Keyboard Not Working Properly
There’s probably nothing more annoying than a faulty keyboard when you need to send an urgent and important message. Follow this guide which will help you fix the error Android keyboard has stopped the error.1. Your keyboard might not be working because of the accumulated cache
If you find your Android keyboard not working properly, it could be because of the overwhelming amount of cache files accumulated in your phone. Although cache files are harmless, too many of them can pile up on your phone storage, thus slowing it down.
You could install a third-party cache cleaner app
Smart Phone Cleaner is one of the best apps that can help your device get rid of all the cache. Not just that, the app even boosts your system’s performance by automatically optimizing the RAM.
Manually clean cache
You can take a slightly bigger route and manually clean your Android device’s cache by following the path mentioned below. Again, your settings may depend on the model you have –2. Check the language and input settings
For instance, for the device mentioned in the screenshot above here’s the path –3. Autocorrect on Android keyboard acting weird
However convenient the Autocorrect feature makes our lives, there are times when it makes our tasks hard. For instance, if what you are writing includes a lot of proper nouns or your text is full of latest lingo, you might consider turning the autocorrect option off.
Also Read: Best Apps to Remotely Access Android Device4. If your Android keyboard hangs or messes around
Just as in case of any app that’s playing dead, if you find your Android phone keyboard not working properly or messing around or hanging up for no specific reasons, a simple reboot can fix it easily.
Another way to address this issue is by tapping on “force stop”.
What Force Stop does is that it kills all the current instances of the app (which could be one of the reasons Android Keyboard not working in phone).
And, don’t forget to share this post with your friends and subscribe to Tweak Library. You can also find us on our YouTube channel that goes by the same name.
FAQ 1Why do some letters not work on my mobile keyboard?
One of the most common reasons some keys might not work on smartphones is that they might be piled up by dust. To fix this, simply tilt the laptop at an angle of 75 degrees and shake the laptop to get rid of the dust.
FAQ 2Why does my Android keyboard keep glitching?
Your keyboard may lag due to constantly copying on the clipboard. Sometimes, when the device setting becomes disorganized and not set properly, it affects the overall working of the keyboard.
FAQ 3Why is my phone keyboard lagging?
Some Android keyboards may experience constant delays and lags because of third-party application conflicts. Besides this, an overwhelming amount of cache files accumulating on the smartphone may lead to the Android keyboard not working properly.Quick Reaction:
About the author
Introduction to Work To Deadlines
Work to deadlines is something a professional needs to cope with at any stage of life. It’s a tough day, and you’re loaded with tough work. When you are loaded with assignments in school, now in the office or college, you are overloaded with pending work of your client, presentations, word documents, etc. Coming up with this stress requires you to be mentally and physically strong. You need to cope with some challenges. Work for the client is still pending, and assignments are yet to be reviewed –for this, you don’t need to panic.
Start Your Free Human Resource (HR) Course15 Tricks That Will Help You Cope with Your Pending Work
Here are some 15 tricks that will help you cope with your pending work and so much work pressure. This can help turn the pressure into a more positive force rather than a panicking poison.
Try To Keep The Desk Tidy
A tidy desk is a tidy mind. A free mind and a tidy environment help to work properly and efficiently. A tidy desk is a place where you can concentrate well. On a tidy desk, A to-do list is a tool that can help you meet your work. It helps you keep track of all the pending work and work pre-planned or postponed. Remember, tracking work left or pending can help you solve half your problems.
Try To Keep Distractions Away
Generally, we also log on to our Facebook or Twitter accounts during the peak crisis hour. Keep these distractions away. Try to concentrate on work, or you can plug in your earphones or concentrate better; they help you. Try to prevent procrastination. Make a plan to create real-time expectations. Consider all your productivity hacks and improve upon them. Motivate yourself and set up a procrastination equation. Logging on Facebook What’s Up chats can get you distracted. Keep yourself away, set targets, keep yourself motivated, and see how it keeps distractions away.
Use Task Manager Apps
Get a clear mind, and concentrate on the task. Use task manager apps to manage your tasks, make a list of to-do tasks, and set up a time limit for them. Task manager apps help you to keep track of all the pending tasks. An app installed in your system to help you find out work remaining, pending work left to do. This entirely helps a working professional to keep track of pending work. Task manager apps also help you manage the pending amounts to collect from friends. It helps to manage work easier and faster.
Working in Pairs
Divide Time for Each Slot of Work
This helps! If you allocate adequate work hours to each slot, it will help you finish beforehand, as time management is one of the key skills a project manager must possess. Think about allocating time accurately. It’s not to unnerve you …you’ll learn slowly how helpful time management is…Work your best hours. Work at the time which suits you the best. Try to be the most productive in 20 % of your leisure hours. Try to find new and smarter ways to do the same tedious work. Ditch clock–watching. Just keep up with self–set time guidelines.
Think Of Each Task as Unfamiliar
Sometimes, due to boredom, we delay starting a particular task. Think of each task as new, filled with fresh learning, and start it with zeal, passion, and enthusiasm. This will help to prevent work delays. The same kind of work seems monotonous; thus, different work helps to avoid boredom. This will help you to keep away stress. Removing boredom is one key parameter to finishing off a task faster. Fresh and new, challenging and exciting work excites us.
Create Self-Assigned Deadlines
This help to assist you in setting your work to deadlines. This can help you devote equal and ample time to each task without last-minute pending tasks. Try to do any work with perfection. It is the ultimate tool to help you stop re-doing or rework to ensure faster work. Share your yet-to-do list to list all the work you wish to do and complete on time. Manage a proper work breakdown structure for your work. Divide work based on its priority, prioritize your work, and complete it on time to meet the deadlines. If you are a project manager, designate tasks for the employees under you and try to complete them on time. Finish the work before the due date to complete the task.
Use Smart Work and Smart Tools
Check out other people in your project. Are they using smart tools or techniques to complete faster? Network with them, communicate, and find faster and easier ways to do the same work. Upgrade your technology. Use the best tools in the marketplace, keep up with technology, and finish your work faster. Keeping yourself upgraded with smart apps and tools and keeping pace with technology helps in doing smarter work and the same work faster and quicker.
Smart work is crucial for success. Managing time and work equally can help meet deadlines. Regaining control can reduce panic and stress. Communicating with Project Managers is efficient in deciding how to complete multiple projects. Alert them of any delays or resource needs. Ensure sufficient resources or hire on a contract basis if deadlines are near.
Communicate regularly and inform your project managers and team members about your progress. Also, inform them about the major issues you are facing. Your work might directly affect the work and progress of other employees, so communication is important for working together to complete multiple projects on time. Effective communication is the key to proper project management skills. Alert them of any issues or delays as soon as possible so everyone else can adjust their schedules and expectations. Communication is the key to any problem, or someone else can help you complete a task that takes longer than normal. Some key skills of a business manager should be taught to manage work faster.
This is, again, a very major skill that needs to be born within a business manager. A business Manager needs to make the right decisions at every phase of the business, whom to hire, how to hire, cost of resources, innovations, and changes in the business plan –all these decisions drive the business and greatly affect the success of the enterprise or organization. A business manager must make the right business skills decisions at the right time. Decision-taking capability, be it right or wrong, is very important.
Self-discipline is essential for success in any area of life, whether you’re an entrepreneur, student, or employee. It involves consistently setting and adhering to key principles or rules, even when no one is watching. Developing self-discipline requires hard work and dedication, but it can help you achieve anything you want in life. Remember, aiming high can still lead to significant success even if you don’t reach 100% of your goals.
Use the Right Tool
Effective managers generally do smart work. Choosing the right tool for faster work is important. Over the past 100 years, leadership and management research has come up with a single definitive conclusion when answering the question, “what’s the best approach?” The answer is, “it depends”. It depends on the situation, has been the best answers for business managers…the skills of the leader, the needs of the employees, and the unique interaction between the three, collaboration and integration of work in all spheres. Effective managers have an arsenal of tools to draw from, to use to work faster and collaborate with teams faster, and, most importantly, they have the performance analysis skills to know which tools to use. Coaching, counseling, feedback, information sharing, self-disclosing, encouragement, recognition, problem-solving, corrective action, and others are options that the effective manager can use at will.
Learn and Practice Your Craft
These skills, well practiced, can lead to a great business manager who can adequately manage work pressure and deadlines. Give yourself a given time regime to complete a task, but keep away from re-work as it will pile up more and more work for you even in the coming future.Recommended Articles
This has been a guide to Work to deadlines, which a working professional needs to cope with at any stage of life. These are the following external link related to work to deadlines.
We have several machine learning algorithms at our disposal for model building. Doing data based prediction is now easier like never before. Whether it is a regression or classification problem, one can effortlessly achieve a reasonably high accuracy using a suitable algorithm. But, this is not the case everytime. Classification problems can sometimes get a bit tricky.
ML algorithms tend to tremble when faced with imbalanced classification data sets. Moreover, they result in biased predictions and misleading accuracies. But, why does it happen ? What factors deteriorate their performance ?
The answer is simple. With imbalanced data sets, an algorithm doesn’t get the necessary information about the minority class to make an accurate prediction. Hence, it is desirable to use ML algorithms with balanced data sets. Then, how should we deal with imbalanced data sets ? The methods are simple but tricky as described in this article.
In this article, I’ve shared the important things you need to know to tackle imbalanced classification problems. In particular, I’ve kept my focus on imbalance in binary classification problems. As usual, I’ve kept the explanation simple and informative. Towards the end, I’ve provided a practical view of dealing with such data sets in R with ROSE package.What is Imbalanced Classification ?
Imbalanced classification is a supervised learning problem where one class outnumbers other class by a large proportion. This problem is faced more frequently in binary classification problems than multi-level classification problems.
The term imbalanced refer to the disparity encountered in the dependent (response) variable. Therefore, an imbalanced classification problem is one in which the dependent variable has imbalanced proportion of classes. In other words, a data set that exhibits an unequal distribution between its classes is considered to be imbalanced.
For example: Consider a data set with 100,000 observations. This data set consist of candidates who applied for Internship in Harvard. Apparently, harvard is well-known for its extremely low acceptance rate. The dependent variable represents if a candidate has been shortlisted (1) or not shortlisted (0). After analyzing the data, it was found ~ 98% did not get shortlisted and only ~ 2% got lucky. This is a perfect case of imbalanced classification.
In real life, does such situations arise more ? Yes! For better understanding, here are some real life examples. Please note that the degree of imbalance varies per situations:
An automated inspection machine which detect products coming off manufacturing assembly line may find number of defective products significantly lower than non defective products.
A test done to detect cancer in residents of a chosen area may find the number of cancer affected people significantly less than unaffected people.
In credit card fraud detection, fraudulent transactions will be much lower than legitimate transactions.
A manufacturing operating under six sigma principle may encounter 10 in a million defected products.
There are many more real life situations which result in imbalanced data set. Now you see, the chances of obtaining an imbalanced data is quite high. Hence, it’s important to learn to deal with such problems for every analyst.Why do standard ML algorithms struggle with accuracy on imbalanced data?
This is an interesting experiment to do. Try it! This way you will understand the importance of learning the ways to restructure imbalanced data. I’ve shown this in the practical section below.
ML algorithms struggle with accuracy because of the unequal distribution in dependent variable.
This causes the performance of existing classifiers to get biased towards majority class.
The algorithms are accuracy driven i.e. they aim to minimize the overall error to which the minority class contributes very little.
ML algorithms assume that the data set has balanced class distributions.
They also assume that errors obtained from different classes have same cost (explained below in detail).What are the methods to deal with imbalanced data sets ?
The methods are widely known as ‘Sampling Methods’. Generally, these methods aim to modify an imbalanced data into balanced distribution using some mechanism. The modification occurs by altering the size of original data set and provide the same proportion of balance.
These methods have acquired higher importance after many researches have proved that balanced data results in improved overall classification performance compared to an imbalanced data set. Hence, it’s important to learn them.
Below are the methods used to treat imbalanced datasets:
Synthetic Data Generation
Cost Sensitive Learning
Let’s understand them one by one.1. Undersampling
This method works with majority class. It reduces the number of observations from majority class to make the data set balanced. This method is best to use when the data set is huge and reducing the number of training samples helps to improve run time and storage troubles.
Undersampling methods are of 2 types: Random and Informative.
Random undersampling method randomly chooses observations from majority class which are eliminated until the data set gets balanced. Informative undersampling follows a pre-specified selection criterion to remove the observations from majority class.
Within informative undersampling, EasyEnsemble and BalanceCascade algorithms are known to produce good results. These algorithms are easy to understand and straightforward too.
EasyEnsemble: At first, it extracts several subsets of independent sample (with replacement) from majority class. Then, it develops multiple classifiers based on combination of each subset with minority class. As you see, it works just like a unsupervised learning algorithm.
BalanceCascade: It takes a supervised learning approach where it develops an ensemble of classifier and systematically selects which majority class to ensemble.
Do you see any problem with undersampling methods? Apparently, removing observations may cause the training data to lose important information pertaining to majority class.2. Oversampling
This method works with minority class. It replicates the observations from minority class to balance the data. It is also known as upsampling. Similar to undersampling, this method also can be divided into two types: Random Oversampling and Informative Oversampling.
Random oversampling balances the data by randomly oversampling the minority class. Informative oversampling uses a pre-specified criterion and synthetically generates minority class observations.3. Synthetic Data Generation
In simple words, instead of replicating and adding the observations from the minority class, it overcome imbalances by generates artificial data. It is also a type of oversampling technique.
In regards to synthetic data generation, synthetic minority oversampling technique (SMOTE) is a powerful and widely used method. SMOTE algorithm creates artificial data based on feature space (rather than data space) similarities from minority samples. We can also say, it generates a random set of minority class observations to shift the classifier learning bias towards minority class.
To generate artificial data, it uses bootstrapping and k-nearest neighbors. Precisely, it works this way:
Take the difference between the feature vector (sample) under consideration and its nearest neighbor.
Multiply this difference by a random number between 0 and 1
Add it to the feature vector under consideration
This causes the selection of a random point along the line segment between two specific features
R has a very well defined package which incorporates this techniques. We’ll look at it in practical section below.4. Cost Sensitive Learning (CSL)
It is another commonly used method to handle classification problems with imbalanced data. It’s an interesting method. In simple words, this method evaluates the cost associated with misclassifying observations.
It does not create balanced data distribution. Instead, it highlights the imbalanced learning problem by using cost matrices which describes the cost for misclassification in a particular scenario. Recent researches have shown that cost sensitive learning have many a times outperformed sampling methods. Therefore, this method provides likely alternative to sampling methods.
Let’s understand it using an interesting example: A data set of passengers in given. We are interested to know if a person has bomb. The data set contains all the necessary information. A person carrying bomb is labeled as positive class. And, a person not carrying a bomb in labeled as negative class. The problem is to identify which class a person belongs to. Now, understand the cost matrix.
There in no cost associated with identifying a person with bomb as positive and a person without negative. Right ? But, the cost associated with identifying a person with bomb as negative (False Negative) is much more dangerous than identifying a person without bomb as positive (False Positive).
Cost Matrix is similar of confusion matrix. It’s just, we are here more concerned about false positives and false negatives (shown below). There is no cost penalty associated with True Positive and True Negatives as they are correctly identified.
The goal of this method is to choose a classifier with lowest total cost.
Total Cost = C(FN)xFN + C(FP)xFP
FN is the number of positive observations wrongly predicted
FP is the number of negative examples wrongly predicted
Using clustering, divide the majority class into K distinct cluster. There should be no overlap of observations among these clusters. Train each of these cluster with all observations from minority class. Finally, average your final prediction.
Collect more data. Aim for more data having higher proportion of minority class. Otherwise, adding more data will not improve the proportion of class imbalance.Which performance metrics to use to evaluate accuracy ?
Choosing a performance metric is a critical aspect of working with imbalanced data. Most classification algorithms calculate accuracy based on the percentage of observations correctly classified. With imbalanced data, the results are high deceiving since minority classes hold minimum effect on overall accuracy.
The difference between confusion matrix and cost matrix is that, cost matrix provides information only about the misclassification cost, whereas confusion matrix describes the entire set of possibilities using TP, TN, FP, FN. In a cost matrix, the diagonal elements are zero. The most frequently used metrics are Accuracy & Error Rate.
Accuracy: (TP + TN)/(TP+TN+FP+FN)
Error Rate = 1 - Accuracy = (FP+FN)/(TP+TN+FP+FN)
As mentioned above, these metrics may provide deceiving results and are highly sensitive to changes in data. Further, various metrics can be derived from confusion matrix. The resulting metrics provide a better measure to calculate accuracy while working on a imbalanced data set:
Precision: It is a measure of correctness achieved in positive prediction i.e. of observations labeled as positive, how many are actually labeled positive.
Precision = TP / (TP + FP)
Recall: It is a measure of actual observations which are labeled (predicted) correctly i.e. how many observations of positive class are labeled correctly. It is also known as ‘Sensitivity’.
Recall = TP / (TP + FN)
F measure: It combines precision and recall as a measure of effectiveness of classification in terms of ratio of weighted importance on either recall or precision as determined by β coefficient.
F measure = ((1 + β)² × Recall × Precision) / ( β² × Recall + Precision )
β is usually taken as 1.
Though, these methods are better than accuracy and error metric, but still ineffective in answering the important questions on classification. For example: precision does not tell us about negative prediction accuracy. Recall is more interesting in knowing actual positives. This suggest, we can still have a better metric to cater to our accuracy needs.
Fortunately, we have a ROC (Receiver Operating Characteristics) curve to measure the accuracy of a classification prediction. It’s the most widely used evaluation metric. ROC Curve is formed by plotting TP rate (Sensitivity) and FP rate (Specificity).
Specificity = TN / (TN + FP)
Any point on ROC graph, corresponds to the performance of a single classifier on a given distribution. It is useful because if provides a visual representation of benefits (TP) and costs (FP) of a classification data. The larger the area under ROC curve, higher will be the accuracy.
There may be situations when ROC fails to deliver trustworthy performance. It has few shortcomings such as.
It may provide overly optimistic performance results of highly skewed data.
It does not provide confidence interval on classifier’s performance
It fails to infer the significance of different classifier performance.
As alternative methods, we can use other visual representation metrics include PR curve, cost curves as well. Specifically, cost curves are known to possess the ability to describe a classifier’s performance over varying misclassification costs and class distributions in a visual format. In more than 90% instances, ROC curve is known to perform quite well.Imbalanced Classification in R
Till here, we’ve learnt about some essential theoretical aspects of imbalanced classification. It’s time to learn to implement these techniques practically. In R, packages such as ROSE and DMwR helps us to perform sampling strategies quickly. We’ll work on a problem of binary classification.
ROSE (Random Over Sampling Examples) package helps us to generate artificial data based on sampling methods and smoothed bootstrap approach. This package has well defined accuracy functions to do the tasks quickly.
Let’s get started
The package ROSE comes with an inbuilt imbalanced data set named as hacide. It comprises of two files: hacide.train and hacide.test. Let’s load it in R environment:
$ x2 : num 0.678 1.5766 -0.5595 -0.0938 -0.2984 ...
As you can see, the data set contains 3 variable of 1000 observations. cls is the response variable. x1 and x2 are dependent variables. Let’s check the severity of imbalance in this data set:
As we see, this data set contains only 2% of positive cases and 98% of negative cases. This is a severely imbalanced data set. So, how badly can this affect our prediction accuracy ? Let’s build a model on this data. I’ll be using decision tree algorithm for modeling purpose.
Let’s check the accuracy of this prediction. To check accuracy, ROSE package has a function names accuracy.meas, it computes important metrics such as precision, recall & F measure.
Examples are labelled as positive when predicted is greater than 0.5
These metrics provide an interesting interpretation. With threshold value as 0.5, Precision = 1 says there are no false positives. Recall = 0.20 is very much low and indicates that we have higher number of false negatives. Threshold values can be altered also. F = 0.167 is also low and suggests weak accuracy of this model.
We’ll check the final accuracy of this model using ROC curve. This will give us a clear picture, if this model is worth. Using the function roc.curve available in this package:
Area under the curve (AUC): 0.600
AUC = 0.60 is a terribly low score. Therefore, it is necessary to balanced data before applying a machine learning algorithm. In this case, the algorithm gets biased toward the majority class and fails to map minority class.
We’ll use the sampling techniques and try to improve this prediction accuracy. This package provides a function named ovun.sample which enables oversampling, undersampling in one go.
Let’s start with oversampling and balance the data.
In the code above, method over instructs the algorithm to perform over sampling. N refers to number of observations in the resulting balanced set. In this case, originally we had 980 negative observations. So, I instructed this line of code to over sample minority class until it reaches 980 and the total data set comprises of 1960 samples.
Similarly, we can perform undersampling as well. Remember, undersampling is done without replacement.
Now the data set is balanced. But, you see that we’ve lost significant information from the sample. Let’s do both undersampling and oversampling on this imbalanced data. This can be achieved using method = “both“. In this case, the minority class is oversampled with replacement and majority class is undersampled without replacement.
p refers to the probability of positive class in newly generated sample.
This generated data has size equal to the original data set (1000 observations). Now, we’ve balanced data sets using 4 techniques. Let’s compute the model using each data and evaluate its accuracy.
It’s time to evaluate the accuracy of respective predictions. Using inbuilt function roc.curve allows us to capture roc metric.
Area under the curve (AUC): 0.989
Area under the curve (AUC): 0.798
Area under the curve (AUC): 0.867
Area under the curve (AUC): 0.798
Here is the resultant ROC curve where:
Black color represents ROSE curve
Red color represents oversampling curve
Green color represents undersampling curve
Blue color represents both sampling curve
Hence, we get the highest accuracy from data obtained using ROSE algorithm. We see that the data generated using synthetic methods result in high accuracy as compared to sampling methods. This technique combined with a more robust algorithm (random forest, boosting) can lead to exceptionally high accuracy.
This package also provide us methods to check the model accuracy using holdout and bagging method. This helps us to ensure that our resultant predictions doesn’t suffer from high variance.
seed = 1)
Holdout estimate of auc: 0.985
We see that our accuracy retains at ~ 0.98 and shows that our predictions aren’t suffering from high variance. Similarly, you can use bootstrapping by setting method.assess to “BOOT”. The parameter chúng tôi is a function which extracts the column of probabilities belonging to positive class.End Notes
In this article, I’ve discussed the important things one should know to deal with imbalanced data sets. For R users, dealing with such situations isn’t difficult since we are blessed with some powerful and awesome packages.You can test your skills and knowledge. Check out Live Competitions and compete with best Data Scientists from all over the world.
What to Look for?
If cancerous growth is commencing, some indications may be found.
The appearance of a new spot
Changing the shape, size, or color of an old spot
Itching and pain at a spot
Soreness with blood or crust
Red shining bump
Rough or scaly area
Growth like a wart
Growth with a protruding borderIs Skin Cancer Contagious?
Skin cancer cannot be spread through close contact like touching or through the air. Isolation is not required and contact with people will not harm them. The body’s immune system recognizes alien cells and destroys them. Cancer occurs when identification and destruction was not possible. Organ transplant raises cancer risk.Can Skin Cancer be Cured?
If detected early enough, almost every type of skin cancer can be cured. A full recovery is quite possible. While about 90 percent of basal cell skin cancer cancers find a cure, most deaths result from melanoma which may result in death within 5 years.Melanoma
Face or body, sun-exposed skin, or otherwise, melanoma can occur in diverse locations. Melanoma often occurs in the lower legs of women on normal skin or a mole. Dark and fair skins are both affected. In dark skins, melanoma may grow on soles and palms, and under the nails of hands and feet.
Melanoma appears sometimes like an irregular small lesion of different colors like pink and blue, red and white. The lesion may be paining, itching, and burning. Dark lesions may be found on the palms and soles. The lesions could occur on mucous membranes in the mouth and nose or private parts. It might be a big brown spot with darker specks. It could be a mole that keeps changing in size and color, sometimes bleeding.Basal Cell Carcinoma
Areas exposed to the sun like the face and neck are usually affected by BCC. The lesion may be brown like a scar or flat and the color of flesh. It could be a bump that looks like a pearl. It sometimes appears as a sore with blood and scabs that may heal but recurs.Squamous Cell Carcinoma
Face and ears along with the hands are the usual sun-exposed locations for SCC. Dark-skinned persons usually get it on areas not sun-exposed like the palms and soles. SCC may appear as a flat lesion with scales or a crust. It could look like a red node.Less Common Cancer Types
Sebaceous gland carcinoma is not common but aggressive. It occurs in the skin’s oil glands. While found anywhere, they appear on the eyelids and are mistaken for other medical conditions. They take the form of hard nodes but do not cause pain.
Kaposi sarcoma is rare and occurs in the blood vessels of the skin. It appears as red areas on the skin or in the mucous membranes. Weak immunity like in AIDS cases or organ transplants may suffer Kaposi carcinoma. Young African men and older Jews in Italy or Est Europe are prone to such cancers.
Merkel cell carcinoma results in hard and shining nodes in hair follicles or the skin and under it. it is mostly found in the trunk, head, or neck regions.Skin Cancer Treatments
Spreading through Stage 0 to Stage 4, the extent of cancer growth decides the treatment strategy. The higher number indicates a greater spread. A limited cancer growth could be deleted by a biopsy. What are the additional common cancer treatments?
Chemotherapy and immunotherapy kill cancer cells with a variety of medications. Topical chemotherapy uses medicines on the skin for cancers limited to the top layer. Intravenous or pill medications apply if cancer has spread elsewhere to different body regions. Immunotherapy is the process of using the immune system to destroy cancerous cells.
Radiation therapy makes use of strong energy beams called radiation to destroy cancer cells or restrict growth and division.
Photodynamic therapy first covers the skin with medication. A fluorescent light activates the medicine. Precancerous cells are killed. Normal cells are unharmed.
Cryotherapy freezes skin cancer cells with the use of liquid nitrogen. The dead cells fall off. Only small and beginning cancers confined to the skin’s top layer can be destroyed through such an approach.
Excisional surgery works by completely removing cancer and neighboring healthy skin.
Mohs surgery first removes the raised part of the lesion. Later, the surgeon removes a layer of skin cancer cells with a scalpel. After the tissue is studied under a microscope, further layers are removed until no more cancerous tissue is visible. This procedure mostly works on basal cell and squamous cell cancers. The normal skin is left untouched. The process works near sensitive areas such as the eyes and the nose.
Curettage and electrodesiccation scrape the tumor to remove cancerous cells. The process uses a sharp-looped edge. Later, an electric needle helps to destroy the remaining cancer cells. Basal cell cancers, squamous cell cancers, and precancerous skin tumors use this technique.Is Skin Cancer Prevention Possible?
Ultraviolet rays from the sun result in skin damage that may eventually lead to skin cancer. The best policy for prevention is to avoid too much sun exposure and sunburn. Avoid the midday harsh sun. Regular use of broad-spectrum sunscreen with SPF 30 or more is effective. Such a sunscreen protects against UVB and UVA rays and should be applied in all weather conditions. Protect the arms and legs with long shirts and trousers. Broad hats protect the face, neck, and ears better. Wear sunglasses against UVB and UVA rays. Look for similar dresses. Avoid tanning beds. Avoid medications that render the skin sensitive to sunlight.Skin Cancer Diagnosis
The diagnosis uses biopsies like excisional, shave and punch biopsies. CT scan or computed tomography scan may be used. Along with MRI or magnetic resonance imaging, X-rays are also used.Skin Cancer Risk Factors
Along with exposure to UV light from the sun, a few other factors pose risks −
Exposure to chemicals like arsenic, coal, and paraffin
Kaposi sarcoma-associated herpesvirus
Xeroderma pigmentosum and such inherited conditions
Prevention is the best policy with minimal sun exposure. If symptoms do occur, early detection and treatment minimize the risks and may result in a complete cure in a majority of cases. Start with a careful examination of the skin, and repeat that at regular intervals.
Snapheal’s software is so easy to use that it only requires a couple of minutes to be familiar with the most used controls. When you first launch Snapheal, you are asked to add a photo. You are presented with three options – load an image, drag and drop, and import.
The main screen has five modules to choose from. The first is erasing, the main feature of Snapheal. You also have clone and stamp. This helps after erasing when you want to smoothen out the remaining area. In addition, Snapheal offers the basic editing tools like rotate, retouch, adjust exposures, and cropping. The top region of the interface offers more importation options, saving, sharing, undoing and more.The Heavy Jobs
The application fared very well with doing some complicating jobs. I was actually surprised at how the image was able to repair itself after being partially erased. Unlike my previous assumption, Snapheal doesn’t leave you with a hole or any image quality reduction; it tries to match the erased portion with the rest of the image. To erase a section of the image:
1. Paint over the area you want erased
3. Bear the waiting time…
Voila! Your Image is complete! We must remember that Snapheal is software, and not the human touch, so it isn’t invincible. We will cover more of Snapheal’s flaws later on.Strengths Weaknesses
In addition, there were a couple of times when my edits seemed to make a black gaping hole in the image. Aside from other photo editing software where this is the norm, this wasn’t the case for Snapheal, so I was unpleasantly surprised to see this. Lastly, Snapheal crashed at least twice when I imported some DSLR images for editing. I expected this to happen with such a large image (in terms of file size), but it was still a flaw nonetheless.Our Verdict
During testing, Snapheal seemed to fare pretty well. I was able to make both minor and major edits with ease, despite a horrid waiting time. The application also was able to watch according to the surrounding image, though there were a couple of times when glitches occurred. But these flaws are more than likely going to disappear once the developer is able to get all the kinks out now that it’s public. At a price of $15, Snapheal is definitely an economically friendly application. While the heavy user will get too frustrated with the application, the every day and semi-professional will find Snapheal the right fit on their Mac dock.
Ari Simon has been a writer with Make Tech Easier since August 2011. Ari loves anything related to technology and social media. When Ari isn’t working, he enjoys traveling and trying out the latest tech gadget.
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