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Artificial Intelligence
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The Olympic Games in Tokyo 2020 should be used a big lot of new technologies in the communication and information areas. As an example, it should be the 12K video on the 50-meter screen in the Harbor, Augmented Reality in the Olympic pool, etc. But the PANDEMIC limitations can cancel these innovations. Do you participate in Olympic Games? What do you know about new technologies at this event?
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Thank you,
Muhammad Ziaul Hoque
, it is interesting news. I think that this technology can help China's sportsmen take a good start on the Olympic Games.
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For example AI can automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions. But what are the clinical challenges for its application?
#AI #cancer #clinical #oncology #biomedical
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Another challenge is that real clinical images can have quite a bit if noise for example images can be blurred where people aren’t perfectly still while they’re being taken. this distorts the image and makes it harder to detect, especially if the training data is only of nice clear images.
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Like other meta-heuristic algorithms, some algorithms tend to be trapped in low diversity, local optima and unbalanced exploitation ability.
1- Enhance its exploratory and exploitative performance.
2- Overcome premature convergence (increase the fast convergence) and ease of falling (trapped) into a local optimum.
3- Increase the diversity of population and alleviate the prematurity convergence problem
4- The algorithm suffers from an immature balance between exploitation and exploration.
5- Maintain the diversity of solutions during the search, so that the tendency of stagnation towards the sub-optimal solutions can be avoided and the convergence rate can be boosted to obtain more accurate optimal solutions.
6- Slow convergence speed, inability to jump out of local optima and fixed step length.
7- Improve its population diversity in the search space.
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Understand you mean "lack of diversity". Not sure if this is a genetic/evolution type algorithm or data driven. If you have a population where each individual is represented somehow, I would store the data at succesive check points (i.e. dataset, one record for each population member with key characteristics). Then you can analyze this and see the population distribution for different characteristics and at different times during the evolution. Once you know that, you can maybe introduce some functionality in the code to influence the population evolution and diversity, etc. ..
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I was recently advised it might be predatory and I struggle to decide whether I should still choose to publish with them, keeping in mind I am at the debut of my PhD and it would be problematic to start my research career by publishing with a controversial journal.
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One little correction. Journals indexed in ESCI are (nowadays) included in the JCR Report (see enclosed file).
The journal is too new to have an impact factor yet (it has to be indexed in either SCIE or SSCI), but looking at the perfect track record of this publisher this will happen soon.
Best regards.
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Hi, I am looking forward for collaborators (academic and research work) who are interested to work in the following area:
Quantum Attacks
Quantum Computing
Quantum Artificial Intelligence
Post-quantum Cryptography
Internet of Drones
Blockchain and Quantum Computing
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Hello Sir,
I am interested in the suggested topics. My domain is cloud computing security using cryptographic techniques. I have few publications in this domain. Please have a look.
Regards
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I would like to have a classification model to identify trends within my dataset. My dataset is a continuous data type (time series data of a variable that will increase/decrease over time). But the time series data will increase and decrease at different rates (and the rate of increase will determine the class of my model).what would be the more appropriate AI model? Do help thanks. I would think a decision tree model would be good. But would like to explore other types of models available.
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It is not clear to me from your scenario whether you want to classify:
  1. each individual/group within the panel series into a single class (i.e. to specify the trend of that individual/group)
  2. changing trends for the individual/group over time.
If the problem is #1, this seems fairly straightforward to do with (a) multiclass logistic regression or (b) a dense neural network with a final softmax layer, in either case with a feature for each time step.
If the problem is #2, some kind of recurrent network might be needed, but more information on the problem would likely be needed to choose a reasonable approach.
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Scientists agree that a pathogen is likely to cause a global pandemic in the near future. But which one? And how?
Engineering sciences are always based on
successive approximation; therefore the use of AI and ML is
quite justified to face the challenge of climate change and new
pandemic. The Question is:
How to strengthen our mastery of
these tools and create other complementary ones in order to face new challenge such as: Climate change and new pandemic like covid 19.
I do request financial support for bridging AI and ML.
I've got successful tool discovery based on bridging EDI with SGML thanks to financial support by Insurance companies in France.
Tool description with representative semantic web applications is detailed at the following link: http://www.ediaudit.fr/
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Dear Amin Elsaleh,
Many appreciations for your effort. The link is worth reading.
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Tell us how you perceive AI and its adoption.
AI- Future is here! Blurring the lines of reality and fiction.  A good 2 minute read on basics to help one start on their “pursuit of AI” Looking forward to the series @SandeepPandey Link to article:  https://lnkd.in/er-sWQK Link to post: https://lnkd.in/e7ANuJ7
Link to our paper on ROI computation for AI investments:
#AI #Artificialintelligence #transformation #datascience #RPA #ML #Deeplearning #machinelearning#future #enthusiasts
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Learning the hidden information in real time datasets related to economics and health. Both predicting and feature interpretation.
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I have been reading certain publication that mainly talks about Artificial Intelligence and how it affects behavioural capability in general (DOIs:10.1007/s10676-021-09598-8, 10.1177/2057047320950636, 10.2196/22845, 10.1007/s41649-018-0061-0, and so on). Along with the exponential progression of AIs in certain applications (Such as, but not limited to, Chatbots programmes).
It seemed somehow apparent to me, that there have been emotionalization towards AIs, such as employing it as friend. Of course the depicted AIs in popular culture references and sci-fis are, well of course still currently in the realm of "Sci-fi".
But seeing the progression, along with the prolonged isolation from social interaction (mainly due to pandemic) in many countries. How should we view such term? Especially towards the notion of self-consciousness, humanization, and of course biological realization in human-bots interaction?
Is it something that we must stay neutral, look forward upon, or perhaps an inevitable shift that will forever change how we view the norm.
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Barry Fox Thank you for the reply! I've seen Eliza some while ago by accident. Thank you for bringing it again!!
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Hello, research community,
I am looking for some open problems in bioinformatics specifically in the area of, but not limited to, proteomics, and genomics. Since I am new to this area, any useful suggestions, a discussion on open problems and relevant resources are welcome.
Thanks.
Rahul
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examples:
Protein structure prediction
single cell RNA/DNA unsupervised learning/clustering
correlation of gene expression & variation with clinical outcomes
many more open problems tbh
e.g. see DeepVariant by google research
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Like other meta-heuristic algorithms, some algorithms tend to be trapped in low diversity, local optima and unbalanced exploitation ability.
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Success-history based parameter adaptation is very useful.
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Greetings community,
I am looking for a non-open-access journal with an impact factor of less than 2.50 for publication related to satellite imagery (cloud classification/detection)-based solutions with AI.
Any assistance on this matter will be highly admired.
Thanks
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Artificial intelligence can assist doctors in finding the right treatments among many options for cancer, Capturing data from various databases relating to the condition, AI helps physicians identify & choose the right drugs for the right patients, AI supports the decision-making processes for existing drugs & expanded treatments for other conditions, as well as expediting the clinical trials process by finding the right patients from a number of data sources.
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There is available the preprint of the following paper
The methodical paper describes application of complexity measures and AI/ML methods on prediction of life threatening arrhytmias up to one hour before their onset. It is written in the form available to everyone, even to non-specialists. Entropy is explained in detail and easy to grasp way.
Feel free to comment the paper and your understanding of AI & complexity capabilities to uncover and predict physiology reactions of organs and bodies.
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As you know, artificial intelligence has become a trend these days, and with the development of embedded systems, it has become necessary to work on projects that combine the two topics. The first topic most of time is a trained model, while the second is a hardware controlled by a code in its memory, My question is how to integrate the two topics, for example, how to download a trained model after complete training it on a Drone or Raspberry Pi or other systems.
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First, if you want to do artificial intelligence, I advise you to learn some of the following Deep learning frameworks: Pytorch, TensorFlow, or Keras. All of them facilitate the design and training of the models, besides having a good integration in embedded systems like the raspberry pi or Jetson.
Normally when I have needed to perform an integration that combines embedded systems with Deep Learning, I prefer to use an NVIDIA Jetson embedded system (Jetson Nano). These embedded systems have an NVIDIA GPU that facilitates their integration with Deep learning frameworks and allows to improve the inference times of the models. For example, I have achieved more than 100FPS in classification tasks with no problem on a Jetson Nano. If you want to improve even more the inference time, you can use TensorRT on the already trained models. TensorRT has support for the three frameworks I mentioned at the beginning. I understand that your concern is how to implement an already trained model in an embedded system, in the case of PyTorch, is as simple as training and saving the model, then pass it to the memory of the embedded system either by USB or as you prefer, and ready the same codes you use in your computer to run the model will work in the Jetson embedded system.
In summary, these would be the steps to follow:
1- Use a good Deep learning framework (Pytorch, TensorFlow, Keras).
2- Design and train the model on your computer.
3- I recommend Jetson embedded systems for their GPU for tasks that require, for example, image analysis.
4- Use TensorRT to improve the trained models.
5-Install in the embedded system the Deep learning framework library in which you have trained your model.
6- Save the file of the trained model in your embedded system.
7-Design the code that captures the image, for example, using OpenCV, and then adapts the image so that it can enter the model and obtain the result. Usually, the same codes that you use in your computer to run the model will work in the Jetson or raspberry embedded system as long as you have all the libraries installed.
If you want to learn from scratch how to use PyTorch in combination with NVIDIA Jetson embedded systems, you can attend this course https://courses.nvidia.com/courses/course-v1:DLI+S-RX-02+V2/about and you will get a free certificate from NVIDIA.
cite:
"You'll learn how to:
  • Set up your Jetson Nano and camera
  • Collect image data for classification models
  • Annotate image data for regression models
  • Train a neural network on your data to create your own models
  • Run inference on the Jetson Nano with the models you create"
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Generally when it comes to assessing the performance of an ANN the most reliable approach is using a test set. However, as you further progress into the future there will be no more test data to use, as your model will provide the estimated values. In this regard, if you want to retrain your ANN using new incoming data you cannot test your adjusted model anymore. On what terms should you choose between two trained models without a test set? Validation error, loss etc. ? I look forward to any suggestions.
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Alessandro Ferrarini how often do you suggest retraining an ANN? For example, retraining each week on a dataset containing hourly data and using your percentages it would mean having 168 vectors of data, from which approximatively 100 for training, 33 for validation and 35 for testing. Is there any method you suggest for determining when retraining should be done, so its relevant (meaning that the trained model has something new to learn)? Thank you very much for your previous answer, it assured me that I was doing the comparison using the right metrics.
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Hi, I'm a beginner student looking for someone who is interested and passionate about the various subfields in AI, mostly computer vision. I'm really interested in deep learning and want to write a good research paper in computer vision (vehicle detection ) but don't have any friends with whom I can discuss and research on certain topics. I don't know if it's the right place to discuss this but please let me know if anyone is willing to work on research papers together as a team. Thanks
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I am doing a PhD research in deep CNNs, and I am about to publish my first article. Ready to collaborate!
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I would like to dive into the research domain of explainable AI. What are some of the recent trending methodologies in this domain? What can be a good start to dive into this field?
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Go to Google Scholar and type in some papers you know about, for example Shapley Values:
Review the papers that cite the "The many Shapley values for model explanation" paper.
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The gpsSensor System object models data output from a Global Positioning System (GPS) receiver.
To model a GPS receiver:
  1. Create the gpsSensor object and set its properties.
  2. Call the object with arguments, as if it were a function.
Syntax
GPS = gpsSensor
GPS = gpsSensor('ReferenceFrame',RF)
GPS = gpsSensor(___,Name,Value)
Description
GPS = gpsSensor returns a gpsSensor System object that computes a Global Positioning System receiver reading based on a local position and velocity input signal. The default reference position in geodetic coordinates is
  • latitude: 0^o N
  • longitude: 0^o E
  • altitude: 0 m
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I was exploring federated learning algorithms and reading this paper (https://arxiv.org/pdf/1602.05629.pdf). In this paper, they have average the weights that are received from clients as attached file. In the marked part, they have considered total client samples and individual client samples. As far I have learned that federated learning has introduced to keep data on the client-side to maintain privacy. Then, how come the server will know this information? I am confused about this concept.
Any clarification?
Thanks in advance.
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Thanks for your input. I have their codes. They have followed the same. I have attached their code below.
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If artificial intelligence is implemented for the online mobile banking, can this banking segment be deprived of employing human capital altogether?
Please reply
Best wishes
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Dear Justyna Żywiołek,
Yes, the current fourth technological revolution, in terms of increasing the efficiency and objectification of work, replacing human work with technology, etc., resembles analogous processes that took place during previous technological revolutions.
Thank you, Best regards,
Dariusz Prokopowicz
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I would appreciate the suggestion of the scientific community on this topic. Thank you so much.
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The effects of incentives, social norms, and employees' values on work performance
You can also read this article
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how to calculate number of computations and parameters of our customized deep learnig algorithm designed with MATLAB
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I want to know is there any AI based software available for windows which can detects image duplications in scientific journals.
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Have you used the article summarizer Scholarcy https://www.scholarcy.com/? What was your experience with it? Can you tell me how accurate the summarized information is?
Thanks in advance for your answers!
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Thanks for sharing your comments.
I am interested to know how accurate the summarized information provided by Scholarcy is. I was thinking more of an application in science dissemination to the general public, not academic purpouses.
Best regards
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Hi. I'm doing a classification problem using deep learning. so that need to train 512x512 images but when i trained my algorithm shows out of memory error. I want to know how much memory size needed to train 512x512 images in MATLAB.
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Dear Srinivas:
For classification and regression tasks, you can train various types of neural networks using the trainNetwork function.
i.e. you can train:
-- a convolutional neural network (ConvNet, CNN) for image data.
-- a recurrent neural network (RNN) such as a long short-term memory (LSTM) or a gated recurrent unit (GRU) network for sequence and time-series data
-- a multilayer perceptron (MLP) network for numeric feature data.
You can train on either a CPU or a GPU. For image classification and image regression, you can train a single network in parallel using multiple GPUs or a local or remote parallel pool. Training on a GPU or in parallel requires Parallel Computing Toolbox™. To use a GPU for deep learning, you must also have a supported GPU device. For information on supported devices, see GPU Support by Release (Parallel Computing Toolbox). To specify training options, including options for the execution environment, use the trainingOptions function.
When training a neural network, you can specify the predictors and responses as a single input or in two separate inputs.
Thus, the entirety of this process depends mainly on the properties of these two hardware (cpu or Gpu).
I hope it will be helpful..
With my best wishes ...
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I am highly interested to do a PhD in Artificial Intelligence, could any one suggest me which would be the challenging field in the coming future, and any ideas on the research projects related to Computer Vision, Machine learning, and Neural Networks sectors.
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In the field of neural networks, you can try in the field of Deep Neural Networks. There are a lot of different models of DNN like CNN, RNN, GAN which are suitable for different applications. These models are very efficient and are in use in classification problems.
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Is the quantum computing is the good choice for the artificial intelligence and the digital era!
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While quantum computing is still in the early phases, there have already been many innovations and breakthroughs. Companies like IBM, Microsoft, Google and Honeywell have been investing aggressively in the technology.
Regards,
Shafagat
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The purpose of this discussion is to find out if the AI and ML techniques are being used practically in geotechnical projects or their applications are still limited to academic studies. If they have been accepted as reliable design approaches, has any standard been developed for systematic application of these techniques?
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Nouman Amjad Raja Thanks for your helpful contribution to the discussion.
That's right, one of the major challenges in this area is the lack of reliable data in large scale. However, from my industry engagement experience, I can say that the data is available but the main issue is the lack of effective collaboration/communicate between universities and industry to use these value databases for AI/ML studies/analyses, which by itself could be another important topic for discussion.
I believe the Geotech industry should be well educated through simplified manuals and design methods to learn how to use AI and ML approaches in practice and also be able to review/verify the designs that have been done based on these techniques.
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Do you think artificial intelligence will be implemented for computer games?
What can be the effects of artificial intelligence implemented for computer games?
Please, answer, comments.
I invite you to the discussion.
Best wishes
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Uses in games beyond NPCs
Georgios N. Yannakakis suggests that academic AI developments may play roles in-game AI beyond the traditional paradigm of AI controlling NPC behavior. He highlights four other potential application areas:
  1. Player-experience modeling: Discerning the ability and emotional state of the player, so as to tailor the game appropriately. This can include dynamic game difficulty balancing, which consists of adjusting the difficulty in a video game in real-time based on the player's ability. Game AI may also help deduce player intent (such as gesture recognition).
  2. Procedural-content generation: Creating elements of the game environment like environmental conditions, levels, and even music in an automated way. AI methods can generate new content or interactive stories.
  3. Data mining on user behavior: This allows game designers to explore how people use the game, what parts they play most, and what causes them to stop playing, allowing developers to tune gameplay or improve monetization.
  4. Alternate approaches to NPCs: These include changing the game set-up to enhance NPC believability and exploring social rather than individual NPC behavior.
Rather than a procedural generation, some researchers have used generative adversarial networks (GANs) to create new content. In 2018 researchers at Cornwall University trained a GAN on a thousand human-created levels for DOOM (1993); following training, the neural net prototype was able to design new playable levels on its own. Similarly, researchers at the University of California prototyped a GAN to generate levels for Super Mario. In 2020 Nvidia displayed a GAN-created clone of Pac-Man; the GAN learned how to recreate the game by watching 50,000 (mostly bot-generated) playthroughs.
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How does one make their marketing mix be more agile to new channels, ever changing environment. what are the models used for this analysis and their interpretation.
our paper on MMM- complex models and interpretations to discuss the advertising effects, models- simple and complex to collate it all together.
Please read, review and suggest how we can add on to enhance our research going forward
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Well, intereyting approach, although I personally have a problem with "econometrisation" of marketing.
I like new perspectives on basic marketing concepts, for example SAVE concept instead of classic 4P
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During the last some years, artificial intelligence (AI) is impacting most areas of management, including the different functions of the management of the human resource. Which aspects of HRM, you think, are being affected the most in this regard and through what interventions?
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Dear Prof. Saini!
I did my best to search resources that address your question:
1) Saxena, M., Bagga, T. & Gupta, S. Fearless path for human resource personnel’s through analytics: a study of recent tools and techniques of human resource analytics and its implication. Int. j. inf. tecnol. (2021). https://doi.org/10.1007/s41870-021-00677-z, Free access:
2) Garg, S., Sinha, S., Kar, A.K. and Mani, M. (2021), "A review of machine learning applications in human resource management", International Journal of Productivity and Performance Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IJPPM-08-2020-0427 Available at: https://www.emerald.com/insight/content/doi/10.1108/IJPPM-08-2020-0427/full/html
3) Vogt, J. Where is the human got to go? Artificial intelligence, machine learning, big data, digitalisation, and human–robot interaction in Industry 4.0 and 5.0. AI & Soc (2021). https://doi.org/10.1007/s00146-020-01123-7 Open access:
Yours sincerely, Bulcsu Szekely
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I modeled a multi class predicting neural network I provided train data (800 entries)and prepared model for prediction. Now I used test data (100 entries) and predicted there corresponding label correctly. Is it possible to get a prediction for a single user input. Hand written digits prediction dataset, I loded from keras, I am unable predict the hand written digits captured from mobile camara and reduced to required pixel ratio and converted to gray scale then scaled and flattened then supplied to neural network.
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You need to apply the pipeline that you created using your training set. For instance, if you applied only standardization you would do the following:
- load the user input (an array with dimension 1 x number of features).
- for each column in the array, remove the corresponding training mean and divide by the corresponding training standard deviation.
- estimate the predictions for the standardized input array.
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Hello members,
I am about to write my proposal and I decided to work on the Attention mechanism in AI.
Does anyone have experience in this field?
which area of artificial intelligence not covered yet by the attention mechanism?
I need to know what is the latest work done in this field? Any problem facing it? suggesting some papers that could help me to explore it more?
Thanks in advance,
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Hello,I am a btech student. I want to do a project on artificial intelligence and machine learning. So,can anyone guide me on this.please ping me
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I am looking for a potential Matlab code of RNN and LSTM of the figure attached.
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For example: let us consider the machine learning and AI based algorithms. The way we are moving towards technology, will it be a problem in 20 years from now, that students, might not opt for research at all, provided the depth and volume of existing works would have increased tremendously. At a certain level of abstraction they might, but will they dig more deep?
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That would be an interesting question. The way how research goes depends on what we know and are interested in researching, while there are numerous gaps in the knowledge. Given that I see Ai would fill those gaps otherwise we do, research would speed up, not limit research but promote it, but with so many unknown parameters, it would be unpredictable.
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This may sound like a chicken and egg problem as new questions are usually based on previous answers. In my understanding, nowadays that many answers could be found via simple googling, asking (novel and good) questions are more important as they could hardly be generated by artificial intelligence. On the other hand, providing useful answers to such questions is challenging as well. What is your opinion?
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A challenging Q / A can lead to an interesting A / Q in response.
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The current technological revolution, known as Industry 4.0, is determined by the development of the following technologies of advanced information processing: Big Data database technologies, cloud computing, machine learning, Internet of Things, artificial intelligence, Business Intelligence and other advanced data mining technologies.
In connection with the above, I would like to ask you:
Which information technologies of the current technological revolution Industry 4.0 to the greatest extent support the logistics management process of the enterprise?
Please reply
Best wishes
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in my opinion, modern technologies, voice systems, artificial intelligence and the Internet of Things facilitate the work in logistics. However, it is worth remembering that the analysis of large amounts of data should support people, not just the process.
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Hi! I'm trying to train a convolutional neural network (CNN) using Keras for leaves disease classification from images. There are very few plant disease image datasets, so I need to use one of the available TensorFlow datasets for training my model: specifically, two TensorFlow datasets are suitable for this task: 'plant_village' dataset and 'plant_leaves' dataset.
The problem is I don't know how to explore that datasets for see the classes, features, labels... and I don't know how to split them in training, validation and test datasets. I've tried to use the code that is used in TensorFlow docs to explore and manage 'CIFAR10' or 'mnist' datasets, but it doesn't work with the plants image datasets...
Can someone suggest me how to explore and manage 'plant_village' and/or 'plant_leaves' datasets, please?
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Hello!
The procedure to explore a TensorFlow dataset should be the same regardless of the datasets. Do mind that some datasets are implemented as folders, so the process may vary slightly.
If you are looking for datasets for leaf disease classification, I'd suggest you go through Kaggle. Here's a link: Cassava Leaf Disease Classification | Kaggle
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I know this has no fixed answer. To give the question a direction, what are the best set of feature engg, data validation, data cleaning, feature extraction, feature selection (these cover data-centric approach), image data modeling, language modeling, hyper-parameter opt. and deployment techniques which should be adopted to get optimal models in less number of hit and trials. Answer to this questions demands experience and structuring best yielding methods you've learned till date. This question is open so that different domain experts can give their own views. E.g. A researcher who has extensively worked in NLP can suggest a pipeline which he/she may follow.
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Your very welcome Sahil Ahuja
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Hello Members,
I would appreciate if you could suggest to me a topic for a PhD research. Artificial Intelligence Algoritme that needs improvement. Or suggest me some interesting papers that could help to find some gaps and write a proposal.
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Automatic Code Generation using Deep Learning is trending these days. Also, simulations & physical modeling using GNN is also on the rise. IoT and BigData Analytics is an evergreen topic.
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Is there an automated technique to convert a text into its graphical form, so that it can be added to a knowledge base ? WikiData, Inquire and other systems have build their Knowledge Base manually
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Well, I don't think you have "the" answer. The text you mention describes whole areas within NLP: named entity extraction, relation extraction, coreference resolution, etc. None of them work as well as we want them to. There are dozens and dozens of systems for each of one these tasks and many more ways of putting them together.
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At the moment we hear a lot about artificial intelligence and machine learning. It is an awesome development, unimaginable completely or partially, now. How and when humans will gain the benefits of the Used Technology? We don't know yet. You can imagine many useful applications. For the benefit of all humanity. However: Everything in life has positive and negative aspects, has pros and cons. Recently I read the books: Cathy O’Neil Weapons of math Destruction: How Big data increases inequality and threatens democracy (recommended me by Marco Rao in another discussion here in RG) or published this year: Frank Schätzing: “Die Tyrannei des Schmetterlings” (“The tyrannies of the butterfly”. Inspired by that, I attended a lecture by a mathematician about machine learning yesterday. He talked about different methods and algorithms. I’m not a mathematician, but I learned a lot. A current application example for machine learning has made me think: With the methods of machine learning, it is decided for patients after a stroke, who gets a rehabilitation and with whom this is no longer worthwhile. Parameters such as age, various body parameters and the current state after the stroke are included in these forecasts. This is really being used in Germany. My mother (77 years) had a stroke in 2011. According to the survey of the health insurance company, she should only receive a three-week rehabilitation, but not for three months. We asked the surveyor of the health insurance why she does not get the longer and thus better rehabilitation program. He answered that the prognoses for this group of patients are worse A major rehabilitation would not be worthwhile. Now I understood what happened then: Machine learning, algorithms have decided how my mother is treated! By the way, the story went well: My brother is a doctor. He fought for my mother to get the big three-month rehabilitation. She was completely well again, could do everything alone again and lived well for four more years. Machine learning and algorithms would not have given her that opportunity. But I’m very, very thoughtful. Everything in life has positive and negative aspects, has pros and cons. What wins in the end depends on many circumstances! The positive can only succeed if the negative is reduced or eliminated. Therefore, one must also look at the negative, make aware, know. You cannot ignore it. Do not close one's eyes to potential problems. Possibilities and limitations of new technologies. How much control must and can humans keep about processes? Can models, computers and algorithms really everything as more and more is suggested? And is the result compelling in the sense of mankind? Or do the technologies create a reality that is logically and correct in the sense of artificial intelligence? May be artificial intelligence develops its own value frame and own rules? Because artificial intelligence has to learn and develop independently in order to be... What do you think?
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The development of artificial intelligence will significantly affect the labor markets in the perspective of the next min. over a dozen years. Part of the professions and professions performed by people will be carried out by artificial intelligence, besides technology learning machines, autonomous robots equipped with artificial intelligence, and also in connection with other Industry 4.0 technologies.
Best regards,
Dariusz Prokopowicz
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The 264 word lists are for historical reference, the 277 word lists are more recent and comprehensive.
The original lists were alphabetically sorted. The clustered lists organize the function words by grammatical type to optimize the attributes (in this case for use in a decision tree classifier). See paper for details.
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As stated, it's in the paper, but basically 277 word lists are more recent and comprehensive.
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How to define coherent "limits" of technology´s in AI ?
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Hello members,
I would appreciate it if someone can help me choose a topic in AI Deep Learning or Machine Learning.
I am looking for an Algorithm that can be used in different application and have some issues in terms of accuracy and result, to work on its improvement.
recommend me some papers that help me to find some gaps so I can write my proposal.
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An Essay: Next Steps for AI
  • Published on February 14, 2021
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Barry Fox
Senior Scientist at CastleWood Technical Services
3 articles
A recent article by Ben Dickson published at VentureBeat.com (January 2, 2021) carries the title Leading computer scientists debate the next steps for AI in 2021.
The article begins with the statement “a decade’s worth of research and application has made it clear that in its current state, deep learning is not the final solution to solving the ever-elusive challenge of creating human-level AI.
This statement is comparable to a statement by Karen Hao in the MIT Technology review, January 25, 2019, “Our study of 25 years of artificial-intelligence research suggests the era of deep learning may come to an end.”
This is not unlike the conclusion attributed to Yoshua Bengio in an article by Bill Knight in Wired (October 8, 2019) (https://www.wired.com/story/ai-pioneer-algorithms-understand-why/),
Now, Bengio says deep learning needs to be fixed. He believes it won’t realize its full potential, and won’t deliver a true AI revolution, until it can go beyond pattern recognition and learn more about cause and effect. In other words, he says, deep learning needs to start asking why things happen.
Nevertheless, it is easy to find many articles that promote what may be new applications of AI or new technologies within the field of AI. Quoting from an article by David Vergun in defense.gov January 22, 2021, “The first wave involved symbolic AI, which has explicit rules, such as if it's raining, then bring an umbrella, he said. Commercial income tax programs operate this way, using rules, logic and reasoning to reach a conclusion. The second wave involved neural nets, which Scherlis refers to as statistical AI. Neural nets attempt to replicate higher-order human thinking skills, such as problem solving. All AI relies on having good data. But although data is certainly important, the real game-changer for AI will be the third wave where symbolic is meshed with statistical to get the best of both worlds, Scherlis predicted.”
This is an overly simplistic statement. It fails to identify what variety of symbolic (reasoning), how it will be implemented, what relation it would have with statistics, and more important, in what field of use will that symbolic reasoning be applied.
The article by Dickson includes two important statements. First:  “Cognitive scientist Gary Marcus, who cohosted the debate, reiterated some of the key shortcomings of deep learning, including excessive data requirements, low capacity for transferring knowledge to other domains, opacity, and a lack of reasoning and knowledge representation.” (emphasis mine) and Second: “Marcus, who is an outspoken critic of deep learning–only approaches, published a paper in early 2020 in which he suggested a hybrid approach that combines learning algorithms with rules-based software.
This is reminiscent of a joke in the early ‘90s. Now that we have failed to produce anything of importance with artificial intelligence, we have now begun to study parallel artificial intelligence.
While the complaint of a lack of reasoning and knowledge representation, is likely true. The conclusion that rules-based software is needed is unsupported, given the historical record of “knowledge based systems” as promoted in “The Rise of the Expert Company” (Feigenbaum, McCorduck, and Nii, 1988).
The wired article cited above, includes the statement, “A small group of researchers is working to combine causality and machine learning.” With reference to the textbook by Judea Pearl entitled “Causality: Models, Reasoning, and Inference.” This is clearly a step in the right direction, but overlooks, the wide, technically complex, range of subjects in the 400-page textbook and how they would be applied to common everyday requirements.
An alternative that seeks to avoid the weakness of “rule-based expert systems” and the unbounded complexity of causal reasoning is to focus on what may be labeled: domain specific artificial intelligence. Where it is possible to create sound logical frameworks for commonly occurring subjects and circumstances.
The difference between rule-base-expert systems and domain-specific-artificial intelligence is that domain specific artificial intelligence is (or can be) composed of sound logical statements that, in effect, comprise a theory for the subject domain. And when complete, the integrity of the framework can be analyzed by humans and tested by machines to verify that they are complete and correct
For example, in the past, sequencing of factory operations based upon precedence constraints was accomplished by simple analysis of the combined (conjunctive) precedence constraints. This limited framework would lead to a feasible ordering of the work but fail to produce other more flexible alternative ordering. The problem is that analysis of a conjunction of precedence constraints will lead to only one solution. Strict logical analysis of conjunctive, disjunctive, and negative precedence constraints will (if feasible) lead to many alternative solutions. Moreover, analysis of conjunctive, disjunctive, and negative precedence constraints can be used to explain how the orderings were created, how the orderings compare, and how to modify the orderings during performance of work. In other words, logical analysis of conjunctive, disjunctive, and negative precedence provides both a framework for both knowledge representation and logical reasoning.
This is a small, simple example of domain specific artificial intelligence that can be employed in all domains where the ordering of steps or operations must be determined in advance of action. Other simple logical frameworks can be implemented for testing, monitoring, and diagnosis of physical systems.
More generally, logical frameworks can be developed for many other fields including some where the framework is understandable but the step by step analysis and inference is beyond the range of human ability.
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Bolisani, E. & Bratianu, C. The Elusive Definition of Knowledge Knowledge Management and Organizational Learning, 2018, 1-22, explores various definitions of knowledge.
If knowledge is acquired after solving a problem, then finding good problems may be an important prerequisite.
In that regard there are also these somewhat related articles:
What are your views? Are there examples illustrating your views?
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chapter attached
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I have started doing my research on Artificial Intelligence and Human Resource Functions.
The problem I am facing is in finding any standardized questionnaire for the same.
Can anyone help me with any questionnaire or provide me details on what kind of questions I can add to show the Impact of AI on HR functions, specifically in the IT industry.
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Use of AI in HRM is still an emerging topic and the related academic literature is limited. You may adopt questions used in the article following article:
I would also suggest you to collect data from HR managers instead of employees. Data provided by HR managers will be more appropriate for the theme you are trying to study. All the best.
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Hello,
I am working on my research and looking for an IoT simulator environment or software to provide the following capabilities or provide integration to other tools to provide capabilities.
I have already looked at NS-3 briefly and NetSim.
- IoT network system with simulation of different IoT architecture such as Star or Mesh
- AI including Reinforcement learning capabilities
- Monitoring and capturing network activities/packets
- Cyberattack scenario simulation
- Possible 5G capabilities
Appreciate your help,
Thanks,
Anthony
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Good question
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Demands for obtaining higher quality effluent from wastewater treatment plants (WWTP) are increasing day by day. In order to meet these demands, WWTPs need to be operated and controlled more efficiently. In modeling the treatment process dynamics, it is possible to monitor the pollution parameters of the treatment plant and to control these pollution parameters during treatment in a faster, more reliable and economical way. The purpose of these program models will be to predict the treatment problems that may occur in the future, to intervene in the facility on time and faster, to reduce or prevent environmental pollution in the future, to improve the ecosystem, to determine the treatment efficiency of the treatment plants by requiring less laboratory scale reactors and pilot plants.
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The above discussion very useful
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Intelligent Transportation Systems(ITS) are widely used in the world for traffic management. However, there are still many questions about the effective use of these systems. For example, are intelligent transportation systems used efficiently enough? Do different intelligent transportation systems have the ability to interact with each other? Is proactive control possible with traffic control centers? Can Artificial Intelligence be used for proactive work? Can the performance of the system or center be increased with artificial intelligence? I am curious about your ideas and/or experience on this subject.
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Totally agreed with above answers
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Naseer Bhat asked "What is consciousness? What is its nature and origin?" We do not know. We can speculate about nature and origin but for what should this be good? I think there is a necessitiy in data processing which forced the evolutionary process to create this phenomenon. I am sure anticipation, association and social interaction are part in this process. May be the analyssis of wet brains will bring some light in this question, but we should follow this question step by step in bottom up manner asking what an organism needs to process the environmental and inner data. To decide if there is consciousness we need a significant prove method. This would be a much harder problem then creating a consciousness automata.
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Hi. The proof is the behaviors.
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Dear Experts,
I want to make a predictive model with the problem classifications (two classes: 0 and 1). Therefore, it would be mind to receive a suggestion from you about any method in Artificial Intelligences (AIs) and others. Note that, for example, my target problems vary regarding the time variations and etc. I hope that you all will be kind in my attempt.
Thank you in advance for your help.
Gratefully,
Phimmasone
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You may take hourly based pollution control dataset and apply any one of the Deep Neural Network algorithm to develop a model for predicting any one/more pollutant(s). But to apply DNN, you require huge dataset.
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We are conducting a survey on colleagues' pereceptions and Expectations towards Artificial intelligence in Capsule Endoscopy
The survey can be found in the following link:
thank you, everyone
TK
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Will do thank you
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Have you ever taken part in empirical studies to assess effectiveness and goodness of factual and counterfactual explanations in the context of Explainable AI?
Please, contribute to our project by filling in the following survey (it should takes about 30 min) and then share with us your thoughts.
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Theoretical researches were done by many institutions and academicians in replication and scalable transaction management in geo-replicated cloud storage services but the implementation of different technologies to them is so rural. Therefore, I implement Machine Learning to said areas in cloud storage services which Machine Learning is a sub-group of Artificial Intelligence. I did not research only subjects, I created prototype solutions for future problems which I predict from the “quantity creates quality” statement because the quantity of users in cloud storage increase demands to quality. These demands can meat by data which were generated by a big amount of users or just by quantity. Am I right? :)
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Hi. Plz see the following link:
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When a computer calculates a sum, is the sum artificial or the calculating agent is?
When a computer uses the relation "true-and-false = false" which is artificial the underlying logic or the computer?
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Hi Juan. Now I think I have come to understand your question better. You are looking at the same output from different sources, man vs machine. It is quite unlike producing an artificial flower instead of a natural one. In your case, the output is the same and come under the same qualification. There is nothing natural or artificial about mathematical expressions. They are conventions to help us quantify phenomena regardless of the source. It has been very clearly pointed out that "intelligence" refers to the source of an activity not to the activity or output. Thus, 2+2=4 in itself Is not intelligence, but the ability to work it out.
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Mathematics is the basis of exact sciences. The development of mathematics consists in the fact that, among others, new phenomena of the surrounding world, which until recently were only described in the humanistic perspective, are also interpreted in mathematical terms.
However, is it possible to write down the essence of artistic creativity in mathematical models and create a pattern model for creating works of art, creative solutions and innovative inventions? If that was possible, then artificial intelligence could be programmed to create works of art, creative solutions and innovative inventions. Will it be possible in the future?
Do you agree with my opinion on this matter?
In view of the above, I am asking you the following question:
Will mathematics help to improve artificial intelligence so that it will achieve human qualities of artistic creativity and innovation?
Please reply
I invite you to the discussion
Best wishes
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Dear Stan Sykora, Boris Pérez-Cañedo, Baidaa Mohammed Ahmed,
Thank you for answering the above question and participating in this discussion.
Regards,
Dariusz Prokopowicz
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We have developed a framework for AI effectiveness measurement and ROI computation.
We are looking for response to expand our research.
link to our paper:
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There are varieties of framework available for the user depending upon your expertise like Amazon Machine Learning, Apache SINGA,TensorFlow, Scikit-Learn, MLlib Spark, Spark ML, Caffe, H2O, Torch, Keras, mlpack, Azure ML Studio, Google Cloud ML Engine, Theano, Veles are some of them.
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Dear all
I am doing MSc in project management and searching for a topic for my thesis.
My case study is construction projects in the steel industry. I have also studied in the following areas:
  1. Risk management
  2. Formulation for the project scheduling problem
  3. Application of artificial intelligence in estimating and predicting project time , cost and resources.
In above fields or any other field , all the hot topic suggestion would be much appreciated.
thank you in advance
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I think that it can be multi-project management not only under the umbrella of one program but also in cross -program environment with using AI for Decision Making.
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Hello,
I would appreciate it if you suggest me some public twitter sentiment analysis dataset during COVID-19 pandemic
Best regards,
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Sentimental Analysis of COVID-19 Tweets Using Deep ... - MDPI
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AI-based techniques such as DNN, Fuzzy, SVM, and ANN show different forecasting performances for different pollutants such as Particulate matter,SO2, NO2.
Say for e.g. - DNN outperforms other techniques in PM forecasting
- SVM outperforms other techniques in SO2 forecasting.
I am curious to know about the deep logic behind this response of AI techniques.
P.S.- I know the source and origin of the air pollutants might be having some important role to play in this matter.
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Different methods considers different algorithms and implementations, for example DNN are good for Image data, SVM in some other points and so on, and for DNN you need to tune a lot of Hyper parameters as well like other methods, but your noise or missing values also could impact on your solutions which some algorithms are much more sensitive to noise than others.
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If multiple deep learning (DL) algorithms are merged together to create models then the system will be complex.
To analyze this, how to calculate complexity?
Is there any formal way or mathematical proof to analyze this kind of complexity of DL models?
Thanks in advance.
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Conceptualizing a Research topic binding materials and space structures (architecture), Artificial intelligence and human health (astronaut) health in microgravity/ Mars.
I have presently thought of enhanced gravity-based architectures aiding astronaut health constructed by robonauts on Mars.
Let me know your views.
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Yes, a rotating structure in space could indeed mitigate some of the bad gravitational effects. But a rotating structure in a grounded or "gravity-based" architecture surely would have problems of scale that would render it impracticable for a structure of habitat size, no?
But who knows? It's still early days....
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Urbanization without consideration of Sustainable Development Framework, how impacted with the Eco-system?
In this regard I'm looking an AI tools which can measuring intensity, total effect, long run development impact in eco-system. Result may be statistically significant in 95% confidence interval and opposite in 5% interval.
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This link might be helpful, have a look:
Kind Regards
Qamar Ul Islam
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I am conducting a study to evaluate Google's responsible AI review process.
Context: When AI solutions and technologies are proposed, the Google Responsible AI team conducts a review to ensure the proposed solution/technology aligns with its established AI principles.
I would like to apply Rate (1994) Ethical Decision Making (EDM) framework to validate the integrity of the review process, and to establish if an EDM framework is useful in augmenting the level of confidence in the intended outcomes of the proposed AI solutions
I would welcome thoughts, suggestions, and ideas on an evaluation approach
Thank you
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This is certainly an interesting study. However, I was wondering that your study may have subjectivity in exploring Google’s ethical standard on the basis of its evaluation principles disclosed in the survey after the technology [email protected] AI is built. It’s because ethical issues are integrated into the codes/parameters and the algorithm built as well as how the algorithm was trained with what quality of data. unless you examine them thoroughly it will be hard to understand the reality as to how algorithm would behave ethically. Ethics are subjectively constructed and interpreted, and algorithm may interpret data (Fx. Word or name representing a gender) from one culture in a particular way while it may interpret the similar data from another culture quite differently. Anyway, I Wish you best of luck with your study.
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The WuDao 2.0 model of China Neural Network (NN) with 1,75 trillion parameters topped the 1.6 trillion that Google unveiled in a similar model in January 2021. Is it the start of Race to Quadrillion parameters NN? Do you have additional information about the structure and design of such ultra big NN?
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This reminds me of the Japanese Fifth Generation program. Working bigger and harder with the same methods will probably not lead to major leaps.
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I think this discussion would help a lot of people if you could share your finding and experiences:
According to the different options that a PhD student can have like Fellowship, Internship, Studentship, traineeship and about different positions that can catch like being a researcher at academic place or industry or work, what do you think a PhD student especially in the field of AI must follow during his/her PhD years of studying which could give him/her good experience and position for the future? what do you consider to choose a PhD student or postdoctoral for your positions? should a PhD student just focus on his/her research or do more research in other field as well? what would be the best plan for different years of doing PhD? please share your experiences about the different options and plans which a PhD student can catch and then continue it after that to have a good potential and use time efficiently.
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Thanks for your note.
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AI is no longer the future, it’s the present, what are your experiences?
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Prof. D A Gayan Nayanajith: I disagree with you. AI currently has many different aspects where its name is no longer spacious to hold these aspects. The same thing has happened with the "Computer" as a name. Its name is originally initiated to hold the four-basic-computing operations (+, -, *, and /). But now computer means logic, AI,...etc.
AI behind all the developments you see now. But there is another face to the same coin that you will see that at the following RG link:
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What are the industries that embraced blockchain technology?
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...BC could revolutionize the underlying technology of the payment clearing and credit information systems in banks, thus upgrading and transforming them. Blockchain applications also promote the formation of “multi-center, weakly intermediated” scenarios, which will enhance the efficiency of the banking industry. However, despite the permission-less and self-governing nature of blockchains, the regulation and actual implementation of a decentralized system are problems that remain to be resolved...Guo, Y., & Liang, C. (2016). Blockchain application and outlook in the banking industry. Financial Innovation, 2(1), 1-12.
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What are the factors that predispose patients to treatment-resistant depression?
What are the advances that predict antidepressant treatment response for depression?
Two articles about prediction of antidepressant treatment response by using artificial intelligence technology and machine learning algorithms:
  • An electroencephalographic signature predicts antidepressant response in major depression (doi:10.1038/s41587-019-0397-3)
  • Brain regulation of emotional conflict predicts antidepressant treatment response for depression (doi:10.1038/s41562-019-0732-1)
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Because it depends on several causes:
-The type and intensity of such Depression
-The one that, in addition, there is also an Anxiety Chart (Mixed Depressive / Anxious Disorder)
-Whoever is male or female
-The type of Antidepressant used: An MAOI is not the same as a Tricyclic type, an SSRI or a Selective Serotonin and Noradrenaline Reuptake Inhibitor or Epinephrine (SSNRI)
-The one who receives a Combined Treatment with, in addition to Antidepressants, Psychotherapy, eminently Cognitive Behavioral.
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There is a lot of research on AI-based air pollution forecasting, but very few have put up a reasonable explanation in this regard.
I want to know what might be the reasons for the performance drop ??
Is it a problem of data length or any other issue ??
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It's very simple. All forecasting methods are based on the search for patterns in the retrospective data and on the assumption (hypothesis) that these patterns will be valid in the future for the forecast period. In other words, it is assumed that the training sample is representative for a certain period in the future. This period is called the period of ergodicity. But this is an incorrect assumption. Sometimes the patterns in the modeled domain change. The period of ergodicity is violated. New patterns are being formed, although the old ones may remain. Therefore, the point of violation of ergodicity is called the bifurcation point. It is necessary to predict not only based on the patterns of the past period, but also to predict the risks of violating these patterns. I did it back in 1994: http://lc.kubagro.ru/aidos/aidos02/7.4.htm (see Figure 7.2).
Это очень просто. Все методы прогнозирования основаны на поиске закономерностей в ретроспективных данных и на предположении (гипотезе), что эти закономерности будут действовать и в будущем на период прогнозирования. Иначе говоря, предполагается, что обучающая выборка репрезентативна на определенный период в будущее. Этот период называется периодом эргодичности. Но это неверное предположение. Иногда закономерности в моделируемой предметной области меняются. Период эргодичности нарушается. Формируются новые закономерности, хотя могут оставаться и прежние. Поэтому точка нарушения эргодичности называется точка бифуркации. Надо прогнозировать не только основываясь на закономерностях прошлого периода, но и прогнозировать риски нарушения этих закономерностей. Я это делал еще в 1994 году: http://lc.kubagro.ru/aidos/aidos02/7.4.htm (см. рис. 7.2).
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Hi all,
I have found an association between social distancing attitudes and the willingness to do social distancing, as studied on a Likert type scale (Never to Always). Now there is also an association between what type of work they have and the same response.
My question is how can I isolate the influence of city from the influence of line of work? Wondering if there is a technique for nominal/ordinal variables similar to adjustment/control variables in regression. I would be interested in anything from AI / statistics, preferrably to be used in R or Excel.
And open for collaborations if anyone wants to co-author or similar
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It sounds like you could be analyzing 2-way and 3-way tables with large numbers of cells. What size is your sample?
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We call Donald Trump and Boris Johnston and his cat and the mice that the cat hunts and the fleas on the mice and the coronavirus etc LIVING. But Boris's desk and his TV and the computer program that beats him at chess and and his electronic android servant (soon!) and windmills (cf Don Quixote) and holograms and ghosts we call NOT LIVING. But what is the difference? As a mathematical kind of person I would really like to know ... and rather precisely... it matters for agent based modelling etc
Note -- the difference is surely not about reproduction -- computer programs and machines can easily generate and distribute copies of themselves.
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As was clearly described by Eugene Veniaminovich Lutsenko, material world is only part of the universe. Not the whole. Science is studying material things. The rest is expected to be non-existent.
The fact is that there is a piling evidence on the existence of the after-life or before-life where energies that we call spirits resides. When spirits enter the material world, they get accompanied by atoms.
Spirit can live without a body a.k.a. matter. Matter cannot become alive without a spirit that is making it alive.
Here we come to the important point: "What we spirits/souls?" Those are quantum fields that we are unable to detect by the current technology. Spirits are the oscillating specks within all encompassing field of the whole Universe.
After-life is a serious research subject that is working usually with deep hypnosis with regression of subjects into their past lives. Then researchers go and check all details and in the most cases find relatives of deceased who lived exactly same life as described by a person in regression. Such research was started by Dorothy Carron. There are followers like Newman and co-workers.
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Hello everybody,
I was wondering to ask you to share with me the works that used AI/machine learning for determining the parameters of a constitutive model. Thanks.
regards,
Faezeh
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See the attached paper
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We are in need of ECG data of covid19 patients in an effort to classify suspected patients who could possibly be infected by the coronavirus.
Other biodata like respiratory rate, body temperature, pulse or HRV should also prove helpful.
Any information on the possibility of future release of such data would be greatly appreciated.
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Hi, maybe this work would be helpful;
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Hello .. for PHD , i need topics for my research in the field of AI and machine learning , is there any suggestion, pls?
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The topics you mentioned are actually huge topics, you need to clearly focus on the research topic of your PhD based on your personal interests and that of your supervisor's. Discuss this with your co-workers and supervisor. They are the best persons to answer this question. TBH, I believe you should not ask anyone else about the topic selection, because at the end of the day, you'll have to do the work. So, you must ask yourself what do you want to do.
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The best simulator that can aid the design of a Bayesian Neural Network model.
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hi, you can find Bayesian machine learning models in python library scikit-learn https://scikit-learn.org/stable/modules/naive_bayes.html
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Hello everyone, we have been working on a very interesting AI hobby project for quite sometime now. It's about the work around of a data selection technique such that a predictor as low as the mixtures of gaussian model can be used for probability predictions on the image dataset. It has shown some success on MNIST dataset. We would like your criticism and corrections as there would be the implementation of the major AI techniques and other useful ones.
We would like the current progress of the project in source code to be made available to other hackers for any contributions at [email protected]
Thank you,
Yours sincerely,
Derrick Donkor
Felix Acquah
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Interested to Collaborate, get in touch at [email protected]
Kind Regards
Qamar Ul Islam
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This era is totally driven by information technology and lots of IT tools are developed day by day. Among that application of AI in agriculture sector has great scope. Kindly share your views. Regards
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Eugene Veniaminovich Lutsenko Sohel Rana John P Fox Mohammed Elmogy Salah Uddin Thank you sir for participating in the discussion. Your valuable word highly appreciated
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According to the nature of temporal data, strategies like k-fold cross validation is not an appropriate idea since we cannot remove the dimension of time. In this discussion we want to explore ideas about testing models for temporal data.
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Pooia Lalbakhsh you may want to employ deep learning models such as LSTM and GRU.
Good luck
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Hi everyone, in the sequel of the previous discussion for Artificial Intelligence Scenarios on Automotive Dealer where you helped a lot find out some useful scenarios, now it's the time to discuss how we can take the advantage of association rules. I am searching for possible applications in the Automotive Business.
Below there is a simple example of a basic scenario that I have discovered already and I would like to enhance it or discover new ones, that could help Automotive Industries on taking proper business actions:
1. Based on labor and item relations that are produced through each service basket (invoice) you can manage your shelves, shelves that containing items that are used frequently together need to be close, or labors/items that are used frequently together need to be in the area of specific labor type, or make promotions that can result in an increase of sales.
What do you think?
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“The interaction of a field with a thin scattering layer corresponds to multiplication with a diagonal matrix“
Original from:Wetzstein, Gordon, et al. "Inference in artificial intelligence with deep optics and photonics." Nature 588.7836 (2020): 39-47.
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Xiaohui Zhu , here is a short answer. For details, I suggest to read the book: "Introduction to Fourier Optics" by Goodman, J. W. (Roberts and Co, 2005), chapter 5 and Appendix B.
Answer:
A scattering layer is typically composed of an optically dense material, with a refractive index significantly different than the one of air, and then the propagation velocity of an optical disturbance is less than the velocity in air.
Since the layer is thin, the sole effect of it is to shift the phase of waves when they are passing through it. Such a phase shift, as compared to air, results in a phase delay
\Delta \phi = k(n-1)d
where n is the index of refraction of the layer's material, d its thickness.
Transformations involving phase shifts are associated with the diagonal elements of the transformation matrix.
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Call to participate in a research survey! for #Project #Management practitioners in the #UAE, please consider giving about 10 minutes to fill in the survey about the research that is aligned with UAE's vision of A.I. and Digital transformation. Survey: https://lnkd.in/d9BGUt9 Thank you so much in advance! #research #science #digitaltransformation #artificialintelliegence #UAE #agilemindset #artificialintelligencetechnology
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Done
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Dear Reader,
If you are related to Project Management by any means in the UAE, please consider giving about 10 minutes to fill in the survey about the research that is aligned with UAE's vision of A.I. and Digital transformation.
Thank you so much in advance!
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Done! good luck
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Hello everyone. Thank you for considering this question.
I have some tiff image stacks from MicroCT. I am trying to isolate porosity using a variety of tools available to me including FIJI ImageJ, Avizo, and Python: OpenCV and SciKit Image packages. Most of my X-ray stacks are easily segmented using the histogram, but these high resolution stacks are particularly difficult. Whilst edges are quite clearly defined, pore space and material solid are within identical intensity ranges, therefore using histograms to threshold is producing large pores as solid, or on the other side of the threshold, large pores within the solid material.
As preparation on these image stacks, I have used a median filter to preserve edges and then attempted a variety of thresholding techniques, all of which provide the errors mentioned above. AI is obviously the next step and I have used Tensorflow in Python to write my own U-net but unfortunately I cannot find a suitable thresholded microCT dataset to train the program. This left me in a catch 22. In order to use AI to threshold my dataset, I needed to threshold my dataset to train the AI. This would in turn threshold my already thresholded dataset.
I have included an example from the stack below. Please could anyone suggest an alternative method? As a last resort, I plan to use the histogram to threshold and then correct the pores by painting them in image by image. Time consuming, but at this stage, I am feeling like it is my only option.
Any help or suggestions welcome.
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Hi Ben,
I see two possibilities to solve your problem:
  1. You could use marker-based watershed segmentation instead of thresholding. If you use the gradient image as height map, edges in your data will be important, not absolute grey-values. You would need to set markers for your two materials (material and pore space). You could use thresholding for that. The markers for the pore-space you could put at the very low values, which are also occurring at the edges of the large pores, and the markers for the material you can set to the very high values. Or -- in Avizo -- you could use "2D Histogram Segmentation" for setting the markers and doing the watershed segmentation all within one module.
  2. The second option would be the better one in my opinion: Your problems arises from "phase contrast". If you have access to the projection images before reconstruction of the 3D data, you could apply a "single-distance phase-retrieval" algorithm and use the resulting images for reconstruction. A list of available software packages you can find, for instance, in this paper: https://arxiv.org/abs/2005.03660v2, in the introduction.
Kind regards,
Andreas