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GitHub Repo https://github.com/AatifAli64/DocMind-AI-Event-Driven-Multi-Document-RAG-Intelligence

AatifAli64/DocMind-AI-Event-Driven-Multi-Document-RAG-Intelligence

DocMind AI: Event-driven RAG system using FastAPI, Streamlit, and Inngest. It transforms PDFs into a searchable knowledge base via Qdrant vector search. Features multi-doc comparison, semantic retrieval, and GPT-4o-mini integration for high-accuracy answers. Built with durable background workflows and full source attribution for AI analysis.

GitHub Repo https://github.com/SebastianMoldovan/Meta

SebastianMoldovan/Meta

The first thing to understand is that we are NOT looking for anything overelaborate or that would be exceptionally difficult to implement. If there are aspects of what we are suggesting that appear to you to be technically complex or problematic, please advise. We are less concerned about absolute perfection or smoothness as we are about having a clean Web site that operates effectively, works with a broad spectrum of browsers and devices, of varying compute power, and isn't difficult for the user to navigate. The second thing is that the overall design - as you may have seen - is based on the 'Koch Curve', a fractal figure that at any scale has the same properties. What our thinking is, is that the site will be navigated for the most part by 'descending' through the curve, that is, effectively zooming in to the next level of detail. With that in mind, the animation effect we are trying to achieve is not one of constant-size figures moving around on a 2-D surface, but rather the impression of zooming into a static figure. One of the main advantages of this approach for us is that it is a completely scalable design, that is to say, as the company grows, we can add pages simply by adding another hierarchical level and zooming in to the next level. At the same time, because zooming in, at any level beyond the top, produces exactly the same figure, only a few graphical templates are required, which can be endlessly replicated as one descends down. For the moment, only the very top level and one level of hierarchical depth will exist, but later, this could be expanded, again, by replication of the existing templates. The top level is, obviously, different from all the other levels because it's where the recursion 'tops out'. This is the level that shows the 'star' surrounded by the first depth level below it as background 'leaves'. When entering the site at the homepage, these surrounding leaves should be grey; this is a graphical way of hinting at another level of expansion whilst indicating, through the fact that the star's points are in a saturated colour, that this is the level that's active. Whether at the top level or at subsequent levels, the idea is that the 'point' pointing straight up represents the currently active area. At the homepage, initially, there isn't an active area as such, it's just a means to navigate down to areas which have content. Whatever graphical elements are in the most saturated colour, are clickable, and each point that can be clicked represents a sub-page. If the point clicked is already on top, then the figure just does a straightforward 'zoom' into the next level. But if an off-axis point is clicked, it has to be placed on top. So the idea is that at any level of zoom, the figure rotates first so that the selected point is on top, then zooms into the next level. During the zoom process, the next level of hierarchy - the next group of 'leaves' - appears for the now-zoomed area. Note that because these figures are self-similar at all scales, all zooms can be achieved by simple rotations and scalings of the same figure set, it's not going to be a separate graphic template for each sub-page. Also, if you hover over a point at the 'active' level (the one that's in a saturated colour), the next level of subpoints (the next 'leaf') should appear over the point. This allows the user to hover and see what they could access by clicking a given point. All points have text labels, these go on the interior of the point. Meanwhile, once you're below the top level (home page), clicking on the exterior point (the largest one sitting at the bottom of the page, in a semi-saturated colour), lets you 'pop' a level, returning to the previous scale. This is nothing more than a reverse zoom, and since only the centre point is involved, there's never any rotation, the zoom-in effect is simply run 'backwards'. Once you reach a page with content, the screen splits into 2 panes; the left-hand pane contains the figure as always, at the active zoom level, the right-hand pane contains the text content (with any graphics, etc. that may be on it) set on a slate grey background. For mobile users, only the content page should appear, but with the black triangle (which could have a 'hamburger' symbol in its centre to make it clear what this does) in the top right. Tapping the triangle will bring you back to what would be the left pane on a desktop or laptop machine, essentially, the navigation section. It would be fine to have any 'hamburger' navigation in mobile mode let you navigate directly to a page via a menu-like series of options - this conforms to what users will expect. And so we get back to that black triangle, the one element that stays on every page. Its scale never changes, and it always serves the same purpose: global navigation. The idea is that clicking the black triangle gives you the option either to a) return to the main page or b) open a menu of all pages that lets you skip directly to them. (Later, we may also allow a third option, which would allow any form data to be cleared, but this only makes sense once the site reaches the point where there is even a form that could be filled, and for the moment we do NOT have any forms. That's potential future functionality). At the home page only, the black triangle is directly in the centre of the page, but on each subsequent level, it sits centred at the bottom of the page. All of these graphics should be vector format, not bitmaps, especially since we're scaling everything. A set of background SVGs should probably do it quite nicely. I have been very detailed in describing how this is intended to work to try to avoid ambiguity, but once again I want to emphasise that the overall effect and idea we are looking for is really quite simple - please do not be intimidated by the lengthy description, it's only long so as to be as precise as possible. Please contact me if you have further questions. Thank you,

GitHub Repo https://github.com/4fox123/LATEST-TRENDS-in-UI-UX---4Fox-Solutions

4fox123/LATEST-TRENDS-in-UI-UX---4Fox-Solutions

COVID has certainly changed the dynamics of the IT industry. As a businessman, one needs to cohere to the latest UI UX trends. UI/UX trends are altered based on the varying taste of the user. A user visits thousands of websites every day and as a businessman, everyone is trying to grab the attention of the visitor. Standing out from the competitors is hard to crack nowadays. The online platform that grabs the attention is the one that scores the game. A good design surely adds value and makes your customers visit you again. Though, the design is changing all the time. Each year, UI trends change, new features are introduced, and out-dated versions lose their way. So, you need to stay updated and keep your business up to date as well. In this article, we study the latest UI/UX trends that help you in your business growth. So let's dive into What is UI/UX? UI/UX is becoming a popular term and it’s also becoming better known in the industry. UX or User Experience essentially understands the customer their needs and designing an experience for the customer according to that which covers various aspects such as tech, such as understanding needs, what they see, what they experience with all their senses is what user experience is all about. A user experience with your site is known as User Experience. A UX/UI Designer is responsible for creating user-friendly interfaces and helps the clients to better understand the use of technological products UI or user interface is one part of the user experience, it’s one of the touch points the customer interfaces with, that is what they see and that’s what they touch now there is on touch devices or if it’s a computer then it’s something that you use online but that interface that communicated the brand or the service to the customer is the user interface and because that is a very important way or the very useful way to communicate with the customer, it plays a huge role in the communication of the service or the product to the customer. User Experience/ User Interface Design Trends Minimalism If we talk in terms of Design, then less is always more, a golden rule of website design. Limiting the number of colors, trying different proportions and compositions are good. It's because an average user sees many discount ads and gets constant notifications and the designer always try to simplify the way they present information. Simplified UX Avoid inserting extra steps which users found non-mandatory. Try to minimize the elements. Now the latest UX is Simplified registration and sign-in. Users don't want to remember extra passwords, so making use of their phone number as a password is a bit simplified. If you can omit unnecessary steps, do it. If you can omit some fields in the form, do it. Blurred, colorful background User Interface design helps to make the website more stylish, elegant, and eye-soothing. In earlier use, 2-3 colors in linear gradients were in trend but now it goes up to 10. In short, an overlay is trending now. However, you need to be careful while playing with the gradients. Neomorphism Neomorphism is gaining popularity because of its subtle yet original appearance that merges skeuomorphism with flat design. At present, the main purpose of products is to produce different user interface items utilizing geomorphic. Unique and absurd 2D illustrations 2D illustrations contribute to several aspects of design in production, including Environment design, Prop design, PreVis design, drawers, clean-up, rotations, and color design. Illustrations stay on top of user interface trends. It is getting fancier with time. The picture quality is in SVG format because like other formats such as PNG, GIF, and JPEG the picture quality is damaged when we increase the screen resolution and this is not the issue in SVG format. Because the vector format can be increased and decreased with no loss in quality. (VUI )Voice User Interface Users may use voice commands to engage with a UI or to talk. A voice-activated interface prevents consumers from using the interface. UX design teams compete with the latest upgrades and advances in this industry more than ever before. VUI is widely used in apps for translation. It hears in your language and translates into the language you want. ALEXA, SIRI is the best example of a voice user interface. With VUI you can easily communicate with people who do not know your language. Mobile-first approach Never ignore mobile accessibility as almost a 5.27billion people use a mobile phone and the chances of using social media platforms are 90%. So what if you ignore mobile visibility and more focus on the desktop appearance of your website? In this case, you lose a large amount of audience from your end. So always use the mobile approach in mind and create and design your website accordingly. Icons Icons are generally visually expressing objects, actions, and ideas. To communicate with the user we need different UI/UX. There are different picture representations of icons that are visually appealing. Conveying meaning in less space and creative ways is more powerful. Choose icons from the same family as they are the same size. It is also vital to understand that not all UX design trends are required for a product or website. The usefulness could only be complicated. We would rather keep up with the trends and only use them if they match the needs of your users and are likely to be working for your company. Synopsis: In 2021, design is something less complicated, more diverse, delightful, and satisfactory. Make sure you adhere to all these trends for reaching out to the million customers out there. Hope these trends are helpful for you to know where we stand in UI/UX nowadays. UI/UX design is to help users achieve their goals. Always ensure the design is relevant and valuable for consumers.

GitHub Repo https://github.com/swapnilchavan18901/FileMind

swapnilchavan18901/FileMind

Multi-tenant RAG-based Chatbot Builder using FastAPI, RabbitMQ, AWS S3, and Qdrant with background document processing and vector search.

GitHub Repo https://github.com/Aastha2104/Parkinson-Disease-Prediction

Aastha2104/Parkinson-Disease-Prediction

Introduction Parkinson’s Disease is the second most prevalent neurodegenerative disorder after Alzheimer’s, affecting more than 10 million people worldwide. Parkinson’s is characterized primarily by the deterioration of motor and cognitive ability. There is no single test which can be administered for diagnosis. Instead, doctors must perform a careful clinical analysis of the patient’s medical history. Unfortunately, this method of diagnosis is highly inaccurate. A study from the National Institute of Neurological Disorders finds that early diagnosis (having symptoms for 5 years or less) is only 53% accurate. This is not much better than random guessing, but an early diagnosis is critical to effective treatment. Because of these difficulties, I investigate a machine learning approach to accurately diagnose Parkinson’s, using a dataset of various speech features (a non-invasive yet characteristic tool) from the University of Oxford. Why speech features? Speech is very predictive and characteristic of Parkinson’s disease; almost every Parkinson’s patient experiences severe vocal degradation (inability to produce sustained phonations, tremor, hoarseness), so it makes sense to use voice to diagnose the disease. Voice analysis gives the added benefit of being non-invasive, inexpensive, and very easy to extract clinically. Background Parkinson's Disease Parkinson’s is a progressive neurodegenerative condition resulting from the death of the dopamine containing cells of the substantia nigra (which plays an important role in movement). Symptoms include: “frozen” facial features, bradykinesia (slowness of movement), akinesia (impairment of voluntary movement), tremor, and voice impairment. Typically, by the time the disease is diagnosed, 60% of nigrostriatal neurons have degenerated, and 80% of striatal dopamine have been depleted. Performance Metrics TP = true positive, FP = false positive, TN = true negative, FN = false negative Accuracy: (TP+TN)/(P+N) Matthews Correlation Coefficient: 1=perfect, 0=random, -1=completely inaccurate Algorithms Employed Logistic Regression (LR): Uses the sigmoid logistic equation with weights (coefficient values) and biases (constants) to model the probability of a certain class for binary classification. An output of 1 represents one class, and an output of 0 represents the other. Training the model will learn the optimal weights and biases. Linear Discriminant Analysis (LDA): Assumes that the data is Gaussian and each feature has the same variance. LDA estimates the mean and variance for each class from the training data, and then uses properties of statistics (Bayes theorem , Gaussian distribution, etc) to compute the probability of a particular instance belonging to a given class. The class with the largest probability is the prediction. k Nearest Neighbors (KNN): Makes predictions about the validation set using the entire training set. KNN makes a prediction about a new instance by searching through the entire set to find the k “closest” instances. “Closeness” is determined using a proximity measurement (Euclidean) across all features. The class that the majority of the k closest instances belong to is the class that the model predicts the new instance to be. Decision Tree (DT): Represented by a binary tree, where each root node represents an input variable and a split point, and each leaf node contains an output used to make a prediction. Neural Network (NN): Models the way the human brain makes decisions. Each neuron takes in 1+ inputs, and then uses an activation function to process the input with weights and biases to produce an output. Neurons can be arranged into layers, and multiple layers can form a network to model complex decisions. Training the network involves using the training instances to optimize the weights and biases. Naive Bayes (NB): Simplifies the calculation of probabilities by assuming that all features are independent of one another (a strong but effective assumption). Employs Bayes Theorem to calculate the probabilities that the instance to be predicted is in each class, then finds the class with the highest probability. Gradient Boost (GB): Generally used when seeking a model with very high predictive performance. Used to reduce bias and variance (“error”) by combining multiple “weak learners” (not very good models) to create a “strong learner” (high performance model). Involves 3 elements: a loss function (error function) to be optimized, a weak learner (decision tree) to make predictions, and an additive model to add trees to minimize the loss function. Gradient descent is used to minimize error after adding each tree (one by one). Engineering Goal Produce a machine learning model to diagnose Parkinson’s disease given various features of a patient’s speech with at least 90% accuracy and/or a Matthews Correlation Coefficient of at least 0.9. Compare various algorithms and parameters to determine the best model for predicting Parkinson’s. Dataset Description Source: the University of Oxford 195 instances (147 subjects with Parkinson’s, 48 without Parkinson’s) 22 features (elements that are possibly characteristic of Parkinson’s, such as frequency, pitch, amplitude / period of the sound wave) 1 label (1 for Parkinson’s, 0 for no Parkinson’s) Project Pipeline pipeline Summary of Procedure Split the Oxford Parkinson’s Dataset into two parts: one for training, one for validation (evaluate how well the model performs) Train each of the following algorithms with the training set: Logistic Regression, Linear Discriminant Analysis, k Nearest Neighbors, Decision Tree, Neural Network, Naive Bayes, Gradient Boost Evaluate results using the validation set Repeat for the following training set to validation set splits: 80% training / 20% validation, 75% / 25%, and 70% / 30% Repeat for a rescaled version of the dataset (scale all the numbers in the dataset to a range from 0 to 1: this helps to reduce the effect of outliers) Conduct 5 trials and average the results Data a_o a_r m_o m_r Data Analysis In general, the models tended to perform the best (both in terms of accuracy and Matthews Correlation Coefficient) on the rescaled dataset with a 75-25 train-test split. The two highest performing algorithms, k Nearest Neighbors and the Neural Network, both achieved an accuracy of 98%. The NN achieved a MCC of 0.96, while KNN achieved a MCC of 0.94. These figures outperform most existing literature and significantly outperform current methods of diagnosis. Conclusion and Significance These robust results suggest that a machine learning approach can indeed be implemented to significantly improve diagnosis methods of Parkinson’s disease. Given the necessity of early diagnosis for effective treatment, my machine learning models provide a very promising alternative to the current, rather ineffective method of diagnosis. Current methods of early diagnosis are only 53% accurate, while my machine learning model produces 98% accuracy. This 45% increase is critical because an accurate, early diagnosis is needed to effectively treat the disease. Typically, by the time the disease is diagnosed, 60% of nigrostriatal neurons have degenerated, and 80% of striatal dopamine have been depleted. With an earlier diagnosis, much of this degradation could have been slowed or treated. My results are very significant because Parkinson’s affects over 10 million people worldwide who could benefit greatly from an early, accurate diagnosis. Not only is my machine learning approach more accurate in terms of diagnostic accuracy, it is also more scalable, less expensive, and therefore more accessible to people who might not have access to established medical facilities and professionals. The diagnosis is also much simpler, requiring only a 10-15 second voice recording and producing an immediate diagnosis. Future Research Given more time and resources, I would investigate the following: Create a mobile application which would allow the user to record his/her voice, extract the necessary vocal features, and feed it into my machine learning model to diagnose Parkinson’s. Use larger datasets in conjunction with the University of Oxford dataset. Tune and improve my models even further to achieve even better results. Investigate different structures and types of neural networks. Construct a novel algorithm specifically suited for the prediction of Parkinson’s. Generalize my findings and algorithms for all types of dementia disorders, such as Alzheimer’s. References Bind, Shubham. "A Survey of Machine Learning Based Approaches for Parkinson Disease Prediction." International Journal of Computer Science and Information Technologies 6 (2015): n. pag. International Journal of Computer Science and Information Technologies. 2015. Web. 8 Mar. 2017. Brooks, Megan. "Diagnosing Parkinson's Disease Still Challenging." Medscape Medical News. National Institute of Neurological Disorders, 31 July 2014. Web. 20 Mar. 2017. Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection', Little MA, McSharry PE, Roberts SJ, Costello DAE, Moroz IM. BioMedical Engineering OnLine 2007, 6:23 (26 June 2007) Hashmi, Sumaiya F. "A Machine Learning Approach to Diagnosis of Parkinson’s Disease."Claremont Colleges Scholarship. Claremont College, 2013. Web. 10 Mar. 2017. Karplus, Abraham. "Machine Learning Algorithms for Cancer Diagnosis." Machine Learning Algorithms for Cancer Diagnosis (n.d.): n. pag. Mar. 2012. Web. 20 Mar. 2017. Little, Max. "Parkinsons Data Set." UCI Machine Learning Repository. University of Oxford, 26 June 2008. Web. 20 Feb. 2017. Ozcift, Akin, and Arif Gulten. "Classifier Ensemble Construction with Rotation Forest to Improve Medical Diagnosis Performance of Machine Learning Algorithms." Computer Methods and Programs in Biomedicine 104.3 (2011): 443-51. Semantic Scholar. 2011. Web. 15 Mar. 2017. "Parkinson’s Disease Dementia." UCI MIND. N.p., 19 Oct. 2015. Web. 17 Feb. 2017. Salvatore, C., A. Cerasa, I. Castiglioni, F. Gallivanone, A. Augimeri, M. Lopez, G. Arabia, M. Morelli, M.c. Gilardi, and A. Quattrone. "Machine Learning on Brain MRI Data for Differential Diagnosis of Parkinson's Disease and Progressive Supranuclear Palsy."Journal of Neuroscience Methods 222 (2014): 230-37. 2014. Web. 18 Mar. 2017. Shahbakhi, Mohammad, Danial Taheri Far, and Ehsan Tahami. "Speech Analysis for Diagnosis of Parkinson’s Disease Using Genetic Algorithm and Support Vector Machine."Journal of Biomedical Science and Engineering 07.04 (2014): 147-56. Scientific Research. July 2014. Web. 2 Mar. 2017. "Speech and Communication." Speech and Communication. Parkinson's Disease Foundation, n.d. Web. 22 Mar. 2017. Sriram, Tarigoppula V. S., M. Venkateswara Rao, G. V. Satya Narayana, and D. S. V. G. K. Kaladhar. "Diagnosis of Parkinson Disease Using Machine Learning and Data Mining Systems from Voice Dataset." SpringerLink. Springer, Cham, 01 Jan. 1970. Web. 17 Mar. 2017.