Facial Emotion Recognition: A Deep Learning approachAshwinRachha
Neural Networks lie at the apogee of Machine Learning algorithms. With a large set of data and automatic feature selection and extraction process, Convolutional Neural Networks are second to none. Neural Networks can be very effective in classification problems.
Facial Emotion Recognition is a technology that helps companies and individuals evaluate customers and optimize their products and services by most relevant and pertinent feedback.
We will use 7 emotions namely - We have used 7 emotions namely - 'Angry', 'Disgust'濫, 'Fear', 'Happy', 'Neutral', 'Sad'☹️, 'Surprise' to train and test our algorithm using Convolution Neural Networks.
Facial Emotion Recognition: A Deep Learning approachAshwinRachha
Neural Networks lie at the apogee of Machine Learning algorithms. With a large set of data and automatic feature selection and extraction process, Convolutional Neural Networks are second to none. Neural Networks can be very effective in classification problems.
Facial Emotion Recognition is a technology that helps companies and individuals evaluate customers and optimize their products and services by most relevant and pertinent feedback.
We will use 7 emotions namely - We have used 7 emotions namely - 'Angry', 'Disgust'濫, 'Fear', 'Happy', 'Neutral', 'Sad'☹️, 'Surprise' to train and test our algorithm using Convolution Neural Networks.
Facial emotion detection on babies' emotional face using Deep Learning.Takrim Ul Islam Laskar
phase- 1
Face Detection.
Facial Landmark detection.
phase- 2
Neural Network Training and Testing.
validation and implementation.
phase - 1 has been completed successfully.
A comprehensive tutorial on Convolutional Neural Networks (CNN) which talks about the motivation behind CNNs and Deep Learning in general, followed by a description of the various components involved in a typical CNN layer. It explains the theory involved with the different variants used in practice and also, gives a big picture of the whole network by putting everything together.
Next, there's a discussion of the various state-of-the-art frameworks being used to implement CNNs to tackle real-world classification and regression problems.
Finally, the implementation of the CNNs is demonstrated by implementing the paper 'Age ang Gender Classification Using Convolutional Neural Networks' by Hassner (2015).
Emotion Detection using Artificial Intelligence presentation by Aryan Trisal.
In this ppt you will learn about emotion detection using AI and how will it change the world.
IF U WANT A PPT MADE AT VERY LOW PRICES CONTACT ME ON LINKEDIN -www.linkedin.com/in/aryan-trisal-420253190
Review of Natural Language Processing tasks and examples of why it is so hard. Then he describes in detail text categorization and particularly sentiment analysis. A few common approaches for predicting sentiment are discussed, going even further, explaining statistical machine learning algorithms.
It gives an overview of Sentiment Analysis, Natural Language Processing, Phases of Sentiment Analysis using NLP, brief idea of Machine Learning, Textblob API and related topics.
The project was started with a sole aim in mind that the design should be able to recognize the voice of a person by analyzing the speech signal. The simulation is done in MATLAB. The design of the project is based on using the Linear prediction filter coefficient (LPC) and Principal component analysis (PCA) on data (princomp) for the speech signal analysis. The Sample Collection process is accomplished by using the microphone to record the speech of male/female. After executing the program the speech is analyzed by the analysis part of our MATLAB program code and our design should be able to identify and give the judgment that the recorded speech signal is same as that of our desired output.
Emotion recognition using image processing in deep learningvishnuv43
User’s emotion using its facial expressions will be detected. These expressions can be derived from the live feed via system's camera or any pre-existing image available in the memory. Emotions possessed by humans can be recognized and has a vast scope of study in the computer vision industry upon which several researches have already been done.
We propose a compact CNN model for facial expression recognition.
The work has been implemented using Python Open Source Computer Vision Library (OpenCV) and NumPy,pandas,keras packages. The scanned image (testing dataset) is being compared to training dataset and thus emotion is predicted.
Automatic gender and age classification has become quite relevant in the rise of social media platforms. However, the existing methods have not been completely successful in achieving this. Through this project, an attempt has been made to determine the gender and age based on a frame of the person. This is done by using deep learning, OpenCV which is capable of processing the real-time frames. This frame is given as input and the predicted gender and age are given as output. It is difficult to predict the exact age of a person using one frame due the facial expressions, lighting, makeup and so on so for this purpose various age ranges are taken, and the predicted age falls in one of them. The Adience dataset is used as it is a benchmark for face photos and includes various real-world imaging conditions like noise, lighting etc.
Human Emotion Recognition using Machine Learningijtsrd
It is quite interesting to recognize the human emotions in the field of machine learning. Using a person's facial expression one can know his emotions or what the person wants to express. But at the same time it's not easy to recognize one's emotion easily its quite challenging at times. Facial expression consist of various human emotions such as sad, happy , excited, angry, frustrated and surprise. Few years back Natural language processing was used to detect the sentiment from the text and then it took a step forward towards emotion detection. Sentiments can be positive, negative or neutral where as emotions are more refined categories. There are many techniques used to recognize emotions. This paper provides a review of research work carried out and published in the field of human emotion recognition and various techniques used for human emotions recognition. Prof. Mrs. Dhanamma Jagli | Ms. Pooja Shetty "Human Emotion Recognition using Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25217.pdfPaper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/25217/human-emotion-recognition-using-machine-learning/prof-mrs-dhanamma-jagli
Deep Learning approaches for Hate speech detection. In this work we used the two deep learning approaches DCNN and MLP two separate classifier on four publicly available datasets.
The presentation is made on CNN's which is explained using the image classification problem, the presentation was prepared in perspective of understanding computer vision and its applications. I tried to explain the CNN in the most simple way possible as for my understanding. This presentation helps the beginners of CNN to have a brief idea about the architecture and different layers in the architecture of CNN with the example. Please do refer the references in the last slide for a better idea on working of CNN. In this presentation, I have also discussed the different types of CNN(not all) and the applications of Computer Vision.
There are several ways to detect emotion. We can briefly list them here:
EEG + BCI
ECG + Cardiovascular signals
Electrodermal activity
Speech + Voice intonation
Facial expressions
Body language
Now we can take a look at their applications!
Signal & Image Processing : An International Journal sipij
Signal & Image Processing : An International Journal is an Open Access peer-reviewed journal intended for researchers from academia and industry, who are active in the multidisciplinary field of signal & image processing. The scope of the journal covers all theoretical and practical aspects of the Digital Signal Processing & Image processing, from basic research to development of application.
Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Signal & Image processing.
Facial emotion detection on babies' emotional face using Deep Learning.Takrim Ul Islam Laskar
phase- 1
Face Detection.
Facial Landmark detection.
phase- 2
Neural Network Training and Testing.
validation and implementation.
phase - 1 has been completed successfully.
A comprehensive tutorial on Convolutional Neural Networks (CNN) which talks about the motivation behind CNNs and Deep Learning in general, followed by a description of the various components involved in a typical CNN layer. It explains the theory involved with the different variants used in practice and also, gives a big picture of the whole network by putting everything together.
Next, there's a discussion of the various state-of-the-art frameworks being used to implement CNNs to tackle real-world classification and regression problems.
Finally, the implementation of the CNNs is demonstrated by implementing the paper 'Age ang Gender Classification Using Convolutional Neural Networks' by Hassner (2015).
Emotion Detection using Artificial Intelligence presentation by Aryan Trisal.
In this ppt you will learn about emotion detection using AI and how will it change the world.
IF U WANT A PPT MADE AT VERY LOW PRICES CONTACT ME ON LINKEDIN -www.linkedin.com/in/aryan-trisal-420253190
Review of Natural Language Processing tasks and examples of why it is so hard. Then he describes in detail text categorization and particularly sentiment analysis. A few common approaches for predicting sentiment are discussed, going even further, explaining statistical machine learning algorithms.
It gives an overview of Sentiment Analysis, Natural Language Processing, Phases of Sentiment Analysis using NLP, brief idea of Machine Learning, Textblob API and related topics.
The project was started with a sole aim in mind that the design should be able to recognize the voice of a person by analyzing the speech signal. The simulation is done in MATLAB. The design of the project is based on using the Linear prediction filter coefficient (LPC) and Principal component analysis (PCA) on data (princomp) for the speech signal analysis. The Sample Collection process is accomplished by using the microphone to record the speech of male/female. After executing the program the speech is analyzed by the analysis part of our MATLAB program code and our design should be able to identify and give the judgment that the recorded speech signal is same as that of our desired output.
Emotion recognition using image processing in deep learningvishnuv43
User’s emotion using its facial expressions will be detected. These expressions can be derived from the live feed via system's camera or any pre-existing image available in the memory. Emotions possessed by humans can be recognized and has a vast scope of study in the computer vision industry upon which several researches have already been done.
We propose a compact CNN model for facial expression recognition.
The work has been implemented using Python Open Source Computer Vision Library (OpenCV) and NumPy,pandas,keras packages. The scanned image (testing dataset) is being compared to training dataset and thus emotion is predicted.
Automatic gender and age classification has become quite relevant in the rise of social media platforms. However, the existing methods have not been completely successful in achieving this. Through this project, an attempt has been made to determine the gender and age based on a frame of the person. This is done by using deep learning, OpenCV which is capable of processing the real-time frames. This frame is given as input and the predicted gender and age are given as output. It is difficult to predict the exact age of a person using one frame due the facial expressions, lighting, makeup and so on so for this purpose various age ranges are taken, and the predicted age falls in one of them. The Adience dataset is used as it is a benchmark for face photos and includes various real-world imaging conditions like noise, lighting etc.
Human Emotion Recognition using Machine Learningijtsrd
It is quite interesting to recognize the human emotions in the field of machine learning. Using a person's facial expression one can know his emotions or what the person wants to express. But at the same time it's not easy to recognize one's emotion easily its quite challenging at times. Facial expression consist of various human emotions such as sad, happy , excited, angry, frustrated and surprise. Few years back Natural language processing was used to detect the sentiment from the text and then it took a step forward towards emotion detection. Sentiments can be positive, negative or neutral where as emotions are more refined categories. There are many techniques used to recognize emotions. This paper provides a review of research work carried out and published in the field of human emotion recognition and various techniques used for human emotions recognition. Prof. Mrs. Dhanamma Jagli | Ms. Pooja Shetty "Human Emotion Recognition using Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25217.pdfPaper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/25217/human-emotion-recognition-using-machine-learning/prof-mrs-dhanamma-jagli
Deep Learning approaches for Hate speech detection. In this work we used the two deep learning approaches DCNN and MLP two separate classifier on four publicly available datasets.
The presentation is made on CNN's which is explained using the image classification problem, the presentation was prepared in perspective of understanding computer vision and its applications. I tried to explain the CNN in the most simple way possible as for my understanding. This presentation helps the beginners of CNN to have a brief idea about the architecture and different layers in the architecture of CNN with the example. Please do refer the references in the last slide for a better idea on working of CNN. In this presentation, I have also discussed the different types of CNN(not all) and the applications of Computer Vision.
There are several ways to detect emotion. We can briefly list them here:
EEG + BCI
ECG + Cardiovascular signals
Electrodermal activity
Speech + Voice intonation
Facial expressions
Body language
Now we can take a look at their applications!
Signal & Image Processing : An International Journal sipij
Signal & Image Processing : An International Journal is an Open Access peer-reviewed journal intended for researchers from academia and industry, who are active in the multidisciplinary field of signal & image processing. The scope of the journal covers all theoretical and practical aspects of the Digital Signal Processing & Image processing, from basic research to development of application.
Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Signal & Image processing.
ASERS-LSTM: Arabic Speech Emotion Recognition System Based on LSTM Modelsipij
The swift progress in the study field of human-computer interaction (HCI) causes to increase in the interest in systems for Speech emotion recognition (SER). The speech Emotion Recognition System is the system that can identify the emotional states of human beings from their voice. There are well works in Speech Emotion Recognition for different language but few researches have implemented for Arabic SER systems and that because of the shortage of available Arabic speech emotion databases. The most commonly considered languages for SER is English and other European and Asian languages. Several machine learning-based classifiers that have been used by researchers to distinguish emotional classes: SVMs, RFs, and the KNN algorithm, hidden Markov models (HMMs), MLPs and deep learning. In this paper we propose ASERS-LSTM model for Arabic Speech Emotion Recognition based on LSTM model. We extracted five features from the speech: Mel-Frequency Cepstral Coefficients (MFCC) features, chromagram, Melscaled spectrogram, spectral contrast and tonal centroid features (tonnetz). We evaluated our model using Arabic speech dataset named Basic Arabic Expressive Speech corpus (BAES-DB). In addition of that we also construct a DNN for classify the Emotion and compare the accuracy between LSTM and DNN model. For DNN the accuracy is 93.34% and for LSTM is 96.81%
A Study to Assess the Effectiveness of Planned Teaching Programme on Knowledg...ijtsrd
Suctioning is a common procedure performed by nurses to maintain the gas exchange, adequate oxygenation and alveolar ventilation in critical ill patients under mechanical ventilation and aim of this research is to provide knowledge regarding maintaining airway patency with suctioning care that will help in the implementation of the quality of nursing care, eventually it will lead to better results. The planned study is a pre experimental study to assess the effectiveness of planned teaching programme on knowledge regarding airway patency on patients with mechanical ventilator among the B.Sc. internship students of selected college of nursing at Moradabad. To assess the level of knowledge regarding maintaining airway patency in patients with mechanical ventilator among B.Sc. Nursing internship students. To assess the effectiveness of planned teaching programme in term of knowledge regarding airway patency among B.Sc. nursing internship students. The purpose of this study is to examine the association between knowledge and effectiveness regarding airway patency among B.Sc. Nursing internship demographic students and their selected partner variables. A pre experimental study was conducted among 86 participants, selected by non probability convenient sampling method. Demographic Performa and self structured questionnaire was used to collect the data from the B.Sc. internship students. Nafees Ahmed | Sana Usmani "A Study to Assess the Effectiveness of Planned Teaching Programme on Knowledge Regarding Maintaining Airway Patency in Patients with Mechanical Ventilator" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-1 , December 2021, URL: https://www.ijtsrd.com/papers/ijtsrd47917.pdf Paper URL: https://www.ijtsrd.com/medicine/nursing/47917/a-study-to-assess-the-effectiveness-of-planned-teaching-programme-on-knowledge-regarding-maintaining-airway-patency-in-patients-with-mechanical-ventilator/nafees-ahmed
Speech Emotion Recognition Using Neural Networksijtsrd
Speech is the most natural and easy method for people to communicate, and interpreting speech is one of the most sophisticated tasks that the human brain conducts. The goal of Speech Emotion Recognition SER is to identify human emotion from speech. This is due to the fact that tone and pitch of the voice frequently reflect underlying emotions. Librosa was used to analyse audio and music, sound file was used to read and write sampled sound file formats, and sklearn was used to create the model. The current study looked on the effectiveness of Convolutional Neural Networks CNN in recognising spoken emotions. The networks input characteristics are spectrograms of voice samples. Mel Frequency Cepstral Coefficients MFCC are used to extract characteristics from audio. Our own voice dataset is utilised to train and test our algorithms. The emotions of the speech happy, sad, angry, neutral, shocked, disgusted will be determined based on the evaluation. Anirban Chakraborty "Speech Emotion Recognition Using Neural Networks" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-1 , December 2021, URL: https://www.ijtsrd.com/papers/ijtsrd47958.pdf Paper URL: https://www.ijtsrd.com/other-scientific-research-area/other/47958/speech-emotion-recognition-using-neural-networks/anirban-chakraborty
ASERS-CNN: ARABIC SPEECH EMOTION RECOGNITION SYSTEM BASED ON CNN MODELsipij
When two people are on the phone, although they cannot observe the other person's facial expression and physiological state, it is possible to estimate the speaker's emotional state by voice roughly. In medical care, if the emotional state of a patient, especially a patient with an expression disorder, can be known, different care measures can be made according to the patient's mood to increase the amount of care. The system that capable for recognize the emotional states of human being from his speech is known as Speech emotion recognition system (SER). Deep learning is one of most technique that has been widely used in emotion recognition studies, in this paper we implement CNN model for Arabic speech emotion recognition. We propose ASERS-CNN model for Arabic Speech Emotion Recognition based on CNN model. We evaluated our model using Arabic speech dataset named Basic Arabic Expressive Speech corpus (BAES-DB). In addition of that we compare the accuracy between our previous ASERS-LSTM and new ASERS-CNN model proposed in this paper and we comes out that our new proposed mode is outperformed ASERS-LSTM model where it get 98.18% accuracy
ASERS-CNN: Arabic Speech Emotion Recognition System based on CNN Modelsipij
When two people are on the phone, although they cannot observe the other person's facial expression and
physiological state, it is possible to estimate the speaker's emotional state by voice roughly. In medical
care, if the emotional state of a patient, especially a patient with an expression disorder, can be known,
different care measures can be made according to the patient's mood to increase the amount of care. The
system that capable for recognize the emotional states of human being from his speech is known as Speech
emotion recognition system (SER). Deep learning is one of most technique that has been widely used in
emotion recognition studies, in this paper we implement CNN model for Arabic speech emotion
recognition. We propose ASERS-CNN model for Arabic Speech Emotion Recognition based on CNN
model. We evaluated our model using Arabic speech dataset named Basic Arabic Expressive Speech
corpus (BAES-DB). In addition of that we compare the accuracy between our previous ASERS-LSTM and
new ASERS-CNN model proposed in this paper and we comes out that our new proposed mode is
outperformed ASERS-LSTM model where it get 98.18% accuracy.
Signal & Image Processing : An International Journalsipij
Signal & Image Processing : An International Journal is an Open Access peer-reviewed journal intended for researchers from academia and industry, who are active in the multidisciplinary field of signal & image processing. The scope of the journal covers all theoretical and practical aspects of the Digital Signal Processing & Image processing, from basic research to development of application.
Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Signal & Image processing.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Emotion Recognition Based on Speech Signals by Combining Empirical Mode Decom...BIJIAM Journal
This paper proposes a novel method for speech emotion recognition. Empirical mode decomposition (EMD) is applied in this paper for the extraction of emotional features from speeches, and a deep neural network (DNN) is used to classify speech emotions. This paper enhances the emotional components in speech signals by using EMD with acoustic feature Mel-Scale Frequency Cepstral Coefficients (MFCCs) to improve the recognition rates of emotions from speeches using the classifier DNN. In this paper, EMD is first used to decompose the speech signals, which contain emotional components into multiple intrinsic mode functions (IMFs), and then emotional features are derived from the IMFs and are calculated using MFCC. Then, the emotional features are used to train the DNN model. Finally, a trained model that could recognize the emotional signals is then used to identify emotions in speeches. Experimental results reveal that the proposed method is effective.
H IDDEN M ARKOV M ODEL A PPROACH T OWARDS E MOTION D ETECTION F ROM S PEECH S...csandit
Emotions carry the token indicating a human’s menta
l state. Understanding the emotion
exhibited becomes difficult for people suffering fr
om autism and alexithymia. Assessment of
emotions can also be beneficial in interactions inv
olving a human and a machine. A system is
developed to recognize the universally accepted emo
tions such as happy, anger, sad, disgust,
fear and surprise. The gender of the speaker helps
to obtain better clarity for identifying the
emotion. Hidden Markov Model serves the purpose of
gender identification
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
3. [1] Speech Emotion Recognition using Neural Network and MLP Classifier (Jerry
Joy, Aparna Kannan, Shreya Ram, S. Rama)
● MLP Classifier
● 5 features extracted- MFCC, Contrast, Mel Spectrograph Frequency, Chroma
and Tonnetz
● Accuracy 70.28%
[2]Voice Emotion Recognition using CNN and Decision Tree (Navya Damodar,
Vani H Y, Anusuya M A.)
● Decision Tree , CNN
● MFCCs extracted
● Accuracy 72% CNN, 63% Decision Tree
Literature Review
4. ● To build a model to recognize emotion from speech using the librosa and
sklearn libraries and the RAVDESS dataset.
● To present a classification model of emotion elicited by speeches based on
deep neural networks MLP Classification based on acoustic features such as
Mel Frequency Cepstral Coefficient (MFCC). The model has been trained to
classify eight different emotions (calm, happy, fearful, disgust, angry, neutral,
surprised,sad).
Objective
6. ● As human beings speech is amongst the most natural way to express ourselves. We depend
so much on it that we recognize its importance when resorting to other communication
forms like emails and text messages where we often use emojis to express the emotions
associated with the messages. As emotions play a vital role in communication, the detection
and analysis of the same is of vital importance in today’s digital world of remote
communication.
● Emotion detection is a challenging task, because emotions are subjective. There is no
common consensus on how to measure them. We define a Speech Emotions Recognition
system as a collection of methodologies that process and classify speech signals to detect
emotions embedded in them.
Motivation
7. ● Human machine interaction is widely used nowadays in many applications. One of the medium
of interaction is speech. The main challenges in human machine interaction is detection of
emotion from speech.
● Emotion can play an important role in decision making. Emotion can be detected from different
physiological signal also. If emotion can be recognized properly from speech then a system can
act accordingly. Identification of emotion can be done by extracting the features or different
characteristics from the speech and training needed for a large number of speech database to
make the system accurate.
● An emotional speech RAVDESS dataset is selected then emotion specific features are extracted
from those speeches and finally a MLP classification model is used to recognize the emotions.
Introduction
10. 1.Preprocessing
The removal of unwanted noise signal from the speech.
➢Silent removal
➢Background Noise
removal
➢Windowing
➢Normalization
11. 2.Feature Extraction
● Extract the feature from audio file
● Used to identify How we speak
➢ Pitch
➢ Loudness
➢ Rhythm,etc
12. Dataset
Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) dataset.
● [3]RAVDESS dataset has recordings of 24 actors, 12 male actors and 12 female
actors, the actors are numbered from 01 to 24 in North American accent.
● All emotional expressions are uttered at two levels of intensity: normal and strong,
except for the ‘neutral’ emotion, it is produced only in normal intensity. Thus, the
portion of the RAVDESS, that we use contains 60 trials for each of the 24 actors,
thus making it 1440 files in total.
16. Multi-Layer Perceptron Classifier
● A multilayer perceptron (MLP) is a class of feedforward
artificial neural network (ANN).
● MLP consists of at least three layers of nodes-input
layer,hidden layer and output layer.
● MLPs are suitable for classification prediction problems
where inputs are assigned a class or label.
17. Building the MLP Classifier involves the following steps-
1. Initialisation MLP Classifier.
2. Neural Network.
3. Prediction.
4. Accuracy Calculation.
19. Feature Extraction
From the Audio data we have extracted three key features which have been used in this, namely:
● MFCC (Mel Frequency Cepstral Coefficients)
● Mel Spectrogram
● Chroma
21. Mel Spectrogram
A Fast Fourier Transform is computed on overlapping windowed segments of the signal,
and we get what is called the spectrogram. This is just a spectrogram that depicts amplitude
which is mapped on a Mel scale.
Chroma
A Chroma vector is typically a 12-element feature vector indicating how much energy of
each pitch class is present in the signal in a standard chromatic scale.
26. ● The proposed model achieved an accuracy of 66.67%.
● Calm was the best identified emotion.
● The model gets confused between similar emotions like calm-neutral, happy-surprised.
● We tested the model on our own voice file for the sentence “Dogs are sitting by the door” and it
identified the emotion correctly.
Conclusion
27. Future Work
● The system could take into consideration multiple speakers from different geographic locations
speaking with different accents.
● Though standard feed forward MLP is powerful tool for classification problems, we can use
CNN, RNN models with larger data sets and high computational power machines and compare
between them.
● Study shows that people suffering with autism have difficulty expressing their emotions
explicitly. Image based speech processing in real time can prove to be of great assistance.
28. References
[1] Jerry Joy, Aparna Kannan, Shreya Ram, S. Rama Speech Emotion Recognition using Neural
Network and MLP Classifier, IJESC, April 2020.
[2]Navya Damodar, Vani H Y, Anusuya M A. Voice Emotion Recognition using CNN and
Decision Tree. International Journal of Innovative Technology and Exploring Engineering
(IJITEE), October 2019.
[3]RAVDESS Dataset: https://zenodo.org/record/1188976#.X5r20ogzZPZ
[4]MLP/CNN/RNN Classification:
https://machinelearningmastery.com/when-to-use-mlp-cnn-and-rnn-neural-networks/
[5]MFCC:https://medium.com/prathena/the-dummys-guide-to-mfcc-aceab2450fd