Friday, 25 March 2022

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Saturday, 8 January 2022

IOT enabled Plant leaf disease detection to make the farming smarter

IOT enabled Plant leaf disease detection to make the farming smarter 





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LUNG TUMOR DETECTION USING FPGA

LUNG TUMOR DETECTION USING FPGA



https://youtu.be/I990D6sZruc
 

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Thursday, 23 September 2021

Cross modal emotion Embedding model for Emotion analysis AI


Is it really possible to extract Real emotion Systematically?

            Yes, Many research works carried out to identify the real emotion and embed the emotion with the physiological signals to correlate the outcome. Such a model utilize various form of data source such as audio data, physiological signals, face expressions, unique face landmarks, gestures and body languages etc.

              Automatic extraction of emotion feature have the capability to effectively interact with the computer. Over the decades, optimum research is carried out to improvise the prediction accuracy, reducing the processing delay for robustness of Human-computer interface (HCI)

               Mono modal and Multi modal are the types of scenarios utilized for feature extraction prominently explore the uniqueness of the inputs. Deep learning algorithms give promising results on detection process. perhaps, Cross model approach combines the benefit of multi-modal and Mono-modal extraction process.
        
                Cross modal approach enables to read the combinational options to determine the exact emotion. using embedded systems. the real time EEG data also used. EEG determines the brain patterns, Audio is highly recommended input for emotion extraction.

Such Computing process is called Affective Computing  in Psychology. Some of the tools used are 
  • Python, 
  • Kiros, 
  • CrowdEmotion and measuring tool such as 
  • SAM, 
  • emoji model etc.
The below architecture is the sample Emotion detection process using Affective Computing.

Architecture of affective computing sample Model using CNN [1] 

References
[1] J. Han, Z. Zhang, Z. Ren and B. Schuller, "EmoBed: Strengthening Monomodal Emotion Recognition via Training with Crossmodal Emotion Embeddings," in IEEE Transactions on Affective Computing, vol. 12, no. 3, pp. 553-564, 1 July-Sept. 2021, doi: 10.1109/TAFFC.2019.2928297.

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Wednesday, 18 August 2021

Prediction of Unknown room data using Virtual sensors modeling through Machine learning | Python projects in chennai


Prediction of Unknown Location data using Virtual sensors modeling through Machine learning

What is Virtual Sensors?
Virtual sensors are created with the help of physical sensors that determine the information of the particular location using integrated embedded applications. These sensors physically record and fetch the recorded data for analysis and prediction of the invisible room

Is Virtual Sensors replace Physical sensors?
Virtual sensors are not the complete replacement of Physical sensors but it could replace few Physical sensors, so that it get the data from the existing physical sensors to create and model a new Virtual data

How Unknown Parameters are calculated?
Using machine learning algorithms, The existing physical sensors record the room parameters and create the training model that evaluate the time series data of  virtual data. The model is developed, Trained in such as way if the training data is acquired from the real time existing environment, using time series prediction, (future prediction model) the proposed Virtual data is being acquired and hence the Unknown parameters of the location is determined.

Example :Application to Determine the Unknown room Parameter
Consider Room A, Room B and Room C available in the Particular Building.
Room A and B is connected with Physical sensors Namely 
1. Illuminance Sensor
2. Temperature sensor
3. Humidity Sensor
  • Room A and B data is considered as the Physical data and Training data for the Machine Learning Model
  • Create a Time Series NARX model or any other Time Series regression Algorithm using Machine Learning ToolBox (Python or MATLAB)
  • At the end of the prediction state , The time State t+1 determine the Room C data (Which is unknown)
  • Each sensors are tuned like this and Predicted with t+1, 2t+1, 3t+1,4t+1 etc.....
  • Accumulated results at the time series data act as the Virtual Data and the Entire analysis Set up act as the Virtual Sensor
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Tuesday, 27 July 2021

FPGA for Machine learning Applications

FPGA for Machine learning Applications

The merging growth in information handling technology that enables the developers to understand the activity, evaluate the pattern and detect the anomalies using Machine Learning algorithms in almost all industries. We need high speed processing platforms at the other end to handle these data generated features and ensemble various other platforms too. 


Figure. AI Pre-processor Accelerator

 The FPGA are High speed, configurable platforms that can adopt the fast twisted operation speeds within the single integrated SOC. Many semiconductor manufacturing industries are focused on developing the artificial intelligence AI sensors that stacked inside the SOC modules itself to help the ML developers utilize such IP cores for real time predictions.

 These companies also incorporate references, design protocols, neural network IP cores, Software development tools and customized design services within the single SOC platforms. These FPGAs are high in performance, low power(starting from 1mW to 1W), flexible architecture and 5.5.mm package available [1] (ref. Lattice semiconductor)

The integrated FPGA accelerators helpful in developing lots of Smart applications enabled with machine learning frameworks. Where the data is larger, the device could take over the processing capability using the in-built accelerators. These devices are nowadays hardware adoptable and software adoptable too. evaluation of Tensor flow modules for training are accessed with the integrated environment itself.

Write to us for your machine learning based Research works qmostech@gmail.com

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References

[1] https://www.latticesemi.com/en/Solutions/Solutions/SolutionsDetails02/sensAI 

 

Wednesday, 21 October 2020

Ph.D. Research Ideas using Machine Learning


Ph.D. Research Ideas using Machine Learning

Robust prediction model for Nanosensor performance measure using Machine learning techniques
This is an interesting analysis idea with novelty in creating an efficient algorithm that measures the performance of so many nanosensors in the medical industry used for invasive testings for COVID-19. Machine learning algorithms such as random forest, decision tree algorithms are used to incorporate hybrid algorithms to measure the performance of the nanosensors.

Novel prediction model for Detection of Herbal plant disease using AI methods
The concept uses the live dataset of herbal plants that are used for so many ayurvedic treatments and used as raw material for medicines. the disease in such plants and spreading of the disease become evident on huge destroy of the plant hybrids. the proposed method uses the software model that captures the image of the herbal plants and detects the type of disease and root cause of the same. Novelty is produced through the IOT application and efficient usage of AI algorithms.

Low power FPGA architecture for neural Accelerators
Neural networks and machine learning become vital in any kind of software application and consumer software to improve the service better. usage of machine learning algorithms obviously needs a large space for processing. utilization of on-chip machine learning algorithms is interested in researchers. this proposed system derived from various literature studies to produce novel & Low power FPGA hardware-based Neural accelerators. 

For more Research idea, write to us
qmostech@gmail.com





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Wednesday, 7 October 2020

How to write a Journal paper 5 STEPS !!!

Step 1: Clearly define your Research Objective
The important step involved in writing a journal paper writing is finding a unique objective for the research work that needs to be conducted. The objective should be clear and unique. it defines your future scope and motivation factor to proceed. the objective selection will be done by comparing the future needs and already presented research works on the same stream.

Step 2: Identify the problem Statement
A novel idea is always worth publishing in a journal. your idea should be unique and that provides a solution to the previously faced problems that might be comparatively discussed in the literature survey. Through various research papers and their impacts on your present implementation, clearly identify the problem that is going to be addressed in your research work. The problem statement might be related to a better solution, choosing a better methodology, achieving higher accuracy, etc.

Step 3: Declare a solution statement
The next step of the research paper is declaring an apt solution that is going to solve the problem defined. The solution statement should be novel and comparatively unique with the previously done research works. Modification in the parameters, changes in the design methodology, creating a new method are some of the key factors to be considered while declaring the solution statement.

Step 4: Select Tool & Design technique
The selection of the best design technique that is suitable for achieving the proposed novelty is important. hence tool selection needs to be considered. The tools should be the latest and actively engaged in the current scenarios. The selection of new tools is recommended.  The design technique might be a small variation in the existing methodologies, tuning of existing processing steps, or hybridizing one or more methodology. A clearly explained block diagram and algorithm diagram with simple words are worth a mile to achieve good plagiarism and adding weightage to the journal paper.

Step 5: Implement Result & Derive Conclusion
Select the required design environment, implement the proposed plan, and obtain the results. Most of the results should be comparatively explained with the previous methodologies implemented. Adding more graphical representation included weightage to the paper. The comparative table should compare the previous research submission of the past 5 years minimum. Add the relevant future scope of the conducted research work. Check for plagiarism and Linguistic check before publishing.

Hope the Content is Useful !!!

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Wednesday, 30 September 2020

How to install Modelsim Software and How to download Modelsim Free software




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Monday, 10 August 2020

Emotion analysis on Pandemic situation due to Corona using Machine Learning - Data science


Emotion analysis on Pandemic situation due to COVID 19 using Machine Learning - Data science

The impact of COVID 19 throughout the country created unexpected lockdowns and situations that push the peoples to stay inside the home for an uncertain duration. the most impacting factors of the current situation and the reasons of emotional ups and downs during this period will be analyzed by using the communication data as input. the proposed study focused on analyzing the impacting factors and even small detailings of the life situations that enable the people to feel stressed and how far the stress affected their daily life. the study is implemented using machine learning algorithm that incorporated with data science and deep analysis for prediction of the pandemic situation in terms of emotions. the proposed model also provides the solution and suggestions based on the gathered data with the help of medical information collected from doctors and physiatrists. 

The proposed research work would be implemented for PhD / Mtech / Btech level.





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