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#artificialintelligence
joeyimpoza · 5 months
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Hokusai Surf by https://www.instagram.com/mazepah/
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naturewondr · 5 days
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"The Earth has music for those who listen."
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mlearningai · 1 year
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roguetoo · 9 months
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Stop freaking out about robots
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o-link · 3 days
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humanly · 1 year
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#insect #butterfly #monarchbutterfly #fashion #headwear #vibrant #balaclava #hoodie #fashiondesign #fallwinter #ai #aiart #aiartwork #generativeart #aidesign #digitalart #conceptart #pixelart #artificialintelligence #midjourney (at Brooklyn, New York) https://www.instagram.com/p/Coaiiymu3wK/?igshid=NGJjMDIxMWI=
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brianshoe · 8 months
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And if the dam breaks open many years too soon
And if there is no room upon the hill
And if your head explodes with dark forebodings too I'll see you on the dark side of the moon
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coffeenuts · 3 months
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desipicasso · 10 months
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भैया अक्शर विदेश चले जाते हे और भाभी मोहल्ले में ऐसे ही घूमती रहती हे
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startappzblog · 10 months
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Using gyroscope-based hand gestures to improve user experience
All content producers are trying to innovate new hooks that grab human attention. The most recent trend is short-form videos (Reels, stories, and Youtube shorts), which are used heavily in almost all social and content apps because of the substantial impact of user interaction.
And this refers to allowing new policies that will enable individuals to post freely on the internet and utilize the available platforms.
The Challenge
Enjoying short-form videos can mean lots of time spent on the internet, which is associated with exhaustion from browsing the internet and, thus, an increasing need for an aiding tool to help the users.
For instance, some people have pain in their thumbs, which lessens their ability to scroll over the screen of an internet-enabled device, which happens after spending a long time using the device. The reflection will explore an innovative tool available for users who use one hand to navigate short-form content.
I believe the gyroscope is a feature that can be a pleasing alternative that offers a new experience for the targeted users.
When does this happen?
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Users continuously consume a lot of time while using their mobile devices, specifically for entertainment purposes like watching reels, which causes hand pain, especially for the thumb joint, especially when using the thumb for scrolling up and down for more than one hour.
Navigate Intuitively
The gyroscope is a microdevice that consists of a disc mounted to spin rapidly around an axis (Armenise & Ciminelli, 2020).
The gyroscopes' sensitivity is incredible; it feels like it is activated by your brain, not by your hand!
So moving the hand at a slight angle that is barely noticeable will crawl to reveal more content.
Gyroscope In Mobile Devices
Utilizing a gyroscope to measure the accelerometers' velocity along the x, y, and z axes. It was used in many different techniques and fields, like navigation, wellness, gaming, and many more. Here are some examples:
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Gyroscope In Content Browsing
The gyroscope can deliver a seamless experience by tilting the mobile device up and down along the Y axis, which offers the ability to scroll between screen components that have a specific height for each post, regardless of the content type, for the sake of reducing the use of the thumb heavily.
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Content Size
Determining the size of posts is essential to assist hand movement and the gyroscope sensitivity to measure how much the user needs to tilt their hand up or down to jump back and forth between different contents.
The social content depends on the following sizes to represent short video content, whether the label is a reel or any other similar video type. Those types usually have the same time duration, which is less than one minute, and they share the transition experience and functionality.
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Targeted users
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The findings indicated that 70% of the respondents used Facebook and Instagram, 40% used TikTok, 20% used Snapchat, and 40% used Pinterest. Moreover, 90% enjoyed watching the short-form video (reels)
Furthermore, 40% of the respondents were annoyed by the experience of sliding up and down to reveal content on their favorite social media platform. 70% of participants enjoyed watching short-form videos for more than 2 hours, and the rest were between 1 and 2 hours.
40% feel hand pain from browsing on their mobile devices, which makes them switch the device between hands to continue. Most of the participants enjoy browsing early morning and directly before sleeping.
A significant number of the respondents (80%) stopped using their devices because they were busy. 100% of the respondents use one hand and spend their time on social media applications.
How it works
By linking the gyroscope sensor with the desired content, The gyroscope can respond to changes with a hand gesture. Any movements can be calculated and connected with a particular output on the application.
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Thus, the size of the short-form videos is defined, and the gyroscope can move from one video to another without the old-fashioned experience that depends on using the to scroll up/down.
Moreover, by shaking the device, we can refresh the current content and download the newest one, and this is by taking the user to the top of the screen again.
Reference: Nader Al-azzeh CCO at Startappz
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renaissanceofthearts · 4 months
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devil-ayres-ai-art · 5 months
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journey-man-ai · 5 months
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The Grotesque Beauty of a Deceased Superman #viral #shorts https://youtube.com/shorts/h3dqBN7Riks?si=ysdORYX6RsO5qcX9 via @YouTube
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d0nutzgg · 8 months
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Predicting Alzheimer's With Machine Learning
Alzheimer's disease is a progressive neurodegenerative disorder that affects millions of people worldwide. Early diagnosis is crucial for managing the disease and potentially slowing its progression. My interest in this area is deeply personal. My great grandmother, Bonnie, passed away from Alzheimer's in 2000, and my grandmother, Jonette, who is Bonnie's daughter, is currently exhibiting symptoms of the disease. This personal connection has motivated me to apply my skills as a data scientist to contribute to the ongoing research in Alzheimer's disease.
Model Creation
The first step in creating the model was to identify relevant features that could potentially influence the onset of Alzheimer's disease. After careful consideration, I chose the following features: Mini-Mental State Examination (MMSE), Clinical Dementia Rating (CDR), Socioeconomic Status (SES), and Normalized Whole Brain Volume (nWBV).
MMSE: This is a commonly used test for cognitive function and mental status. Lower scores on the MMSE can indicate severe cognitive impairment, a common symptom of Alzheimer's.
CDR: This is a numeric scale used to quantify the severity of symptoms of dementia. A higher CDR score can indicate more severe dementia.
SES: Socioeconomic status has been found to influence health outcomes, including cognitive function and dementia.
nWBV: This represents the volume of the brain, adjusted for head size. A decrease in nWBV can be indicative of brain atrophy, a common symptom of Alzheimer's.
After selecting these features, I used a combination of Logistic Regression and Random Forest Classifier models in a Stacking Classifier to predict the onset of Alzheimer's disease. The model was trained on a dataset with these selected features and then tested on a separate dataset to evaluate its performance.
Model Performance
To validate the model's performance, I used a ROC curve plot (below), as well as a cross-validation accuracy scoring mechanism.
The ROC curve (Receiver Operating Characteristic curve) is a plot that illustrates the diagnostic ability of a model as its discrimination threshold is varied. It is great for visualizing the accuracy of binary classification models. The curve is created by plotting the true positive rate (TPR) against the false positive rate (FPR) at various threshold settings.
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The area under the ROC curve, often referred to as the AUC (Area Under the Curve), provides a measure of the model's ability to distinguish between positive and negative classes. The AUC can be interpreted as the probability that the model will rank a randomly chosen positive instance higher than a randomly chosen negative one.
The AUC value ranges from 0 to 1. An AUC of 0.5 suggests no discrimination (i.e., the model has no ability to distinguish between positive and negative classes), 1 represents perfect discrimination (i.e., the model has perfect ability to distinguish between positive and negative classes), and 0 represents total misclassification.
The model's score of an AUC of 0.98 is excellent. It suggests that the model has a very high ability to distinguish between positive and negative classes.
The model also performed extremely well in another test, which showed the model has a final cross-validation score of 0.953. This high score indicates that the model was able to accurately predict the onset of Alzheimer's disease based on the selected features.
However, it's important to note that while this model can be a useful tool for predicting Alzheimer's disease, it should not be the sole basis for a diagnosis. Doctors should consider all aspects of diagnostic information when making a diagnosis.
Conclusion
The development and application of machine learning models like this one are revolutionizing the medical field. They offer the potential for early diagnosis of neurodegenerative diseases like Alzheimer's, which can significantly improve patient outcomes. However, these models are tools to assist healthcare professionals, not replace them. The human element in medicine, including a comprehensive understanding of the patient's health history and symptoms, remains crucial.
Despite the challenges, the potential of machine learning models in improving early diagnosis leaves me and my family hopeful. As we continue to advance in technology and research, we move closer to a world where diseases like Alzheimer's can be effectively managed, and hopefully, one day, cured.
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