This document presents a model for detecting mental fatigue based on eye blinking behavior and responses to a questionnaire. The model uses a webcam to track eye blinking rate rather than specialized eye tracking technology. Participants complete a questionnaire measuring mental fatigue and cognitive state while their eye blinks per minute are recorded. A convolutional neural network is trained on the questionnaire responses and blinking data to classify fatigue levels. The researchers found their simple, low-cost model could accurately detect mental fatigue based on natural viewing conditions and questionnaire responses.
IRJET- An Empirical Study on Effect of Sleep DeprivationIRJET Journal
This document summarizes past research on the effects of sleep deprivation. It discusses how lack of sleep can negatively impact job performance, health, cognitive abilities, and social/emotional functioning. The document then reviews 11 specific studies that examined these impacts on software developers and programmers. It assessed factors like productivity, code comprehension, stress levels, and introduction of bugs when sleep deprived. The studies utilized techniques like questionnaires, FMRI brain imaging, and sensors to measure impacts. The conclusion is that sleep deprivation is a major problem for employers and employees that can be addressed through techniques like this proposed model which uses machine learning algorithms and video to identify effects of sleep loss on software development work.
IRJET- Techniques for Analyzing Job Satisfaction in Working Employees – A...IRJET Journal
The document discusses techniques for analyzing job satisfaction in employees using deep learning algorithms. It proposes applying convolutional neural networks (CNNs) to facial images taken at regular intervals to classify emotions and determine if an employee is happy or stressed. CNNs would be trained to recognize emotions like surprise, fear, disgust, anger, happiness and sadness from facial expressions. The percentage of positive versus negative emotions detected over multiple images could indicate an employee's level of satisfaction. A literature review discusses limitations of existing approaches and supports using CNNs on facial expressions for more accurate analysis of employee mental health and work satisfaction.
International Journal of Humanities and Social Science Invention (IJHSSI) is an international journal intended for professionals and researchers in all fields of Humanities and Social Science. IJHSSI publishes research articles and reviews within the whole field Humanities and Social Science, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
We will discuss the key highlights and forecasts from SharpBrains’ latest market report “The Digital Brain Health Market 2012–2020: Web-based, mobile and biometrics-based technology to assess, monitor and enhance cognition and brain functioning.”
- Alvaro Fernandez, CEO of SharpBrains
Innovative partnerships to improve lifelong brain health and customer/ ...SharpBrains
(Session held at the 2014 SharpBrains Virtual Summit; October 28-30th, 2014)
10:00–11:30am. Innovative partnerships to improve lifelong brain health and customer/ patient satisfaction
- Bill Prenovitz, Global Product and Service Management at Philips Healthcare’s Aging-in-Place Program
- Dr. Michael Weiner, Lead Scientific Investigator of the Brain Health Registry
- Tommy Sagroun, CEO of CogniFit
- Chair: Rita Carter, Author, Broadcaster and BBC Contributor
Learn more here:
http://sharpbrains.com/summit-2014/agenda/
The document discusses the future of brain health and cognitive technologies. It describes 10 emerging brain technologies including wearables to monitor and enhance focus, meditation, and self-regulation. It also discusses electrical and magnetic brain stimulation, virtual reality treatments, brain-computer interfaces, and neuromonitoring technologies. Finally, it discusses how big data and machine learning can enhance diagnostics and treatments for various brain conditions like seizures, ALS, and more. Overall, the technologies described aim to transform brain health by enhancing functions like memory, learning, and skills training through personalized cognitive simulations and interfaces.
How can practitioners integrate emerging neuroplasticity-based interven...SharpBrains
A promising frontier of applied neuroscience lies in technologies that stimulate our brains in order to harness neuroplasticity and achieve positive outcomes. What are the practical Pros and Cons of different methodologies such as cognitive training, EEG/ QEEG biofeedback, virtual reality, and what are appropriate ways to integrate them with traditional interventions?
- Chair: Olivier Oullier, Professor of Behavioral and Brain Sciences at Aix-Marseille University
- Bruce Wexler, NIH Director’s Award Winner and Professor of Psychiatry at Yale University
- Kate Sullivan, Director of the Brain Fitness Center at Walter Reed National Military Medical Center
This session took place at the 2013 SharpBrains Virtual Summit: http://sharpbrains.com/summit-2013/agenda/
How to measure and improve brain-based outcomes that matter in health careSharpBrains
Pioneers advancing health research, prevention and treatment will help us understand emerging best practices where targeted assessments, monitoring and interventions can transfer into significant healthcare and quality of life outcomes.
-- Chair: Alvaro Fernandez, CEO & Co-Founder of SharpBrains
-- Dr. Madeleine S Goodkind, staff psychologist at New Mexico VA Health Care System
-- Dr. Randy McIntosh, Vice-president of Research and Director of Baycrest’s Rotman Research Institute
-- Chris Berka, CEO and Co-Founder of Advanced Brain Monitoring (ABM)
Presentation @ The 2015 SharpBrains Virtual Summit http://sharpbrains.com/summit-2015/agenda
IRJET- An Empirical Study on Effect of Sleep DeprivationIRJET Journal
This document summarizes past research on the effects of sleep deprivation. It discusses how lack of sleep can negatively impact job performance, health, cognitive abilities, and social/emotional functioning. The document then reviews 11 specific studies that examined these impacts on software developers and programmers. It assessed factors like productivity, code comprehension, stress levels, and introduction of bugs when sleep deprived. The studies utilized techniques like questionnaires, FMRI brain imaging, and sensors to measure impacts. The conclusion is that sleep deprivation is a major problem for employers and employees that can be addressed through techniques like this proposed model which uses machine learning algorithms and video to identify effects of sleep loss on software development work.
IRJET- Techniques for Analyzing Job Satisfaction in Working Employees – A...IRJET Journal
The document discusses techniques for analyzing job satisfaction in employees using deep learning algorithms. It proposes applying convolutional neural networks (CNNs) to facial images taken at regular intervals to classify emotions and determine if an employee is happy or stressed. CNNs would be trained to recognize emotions like surprise, fear, disgust, anger, happiness and sadness from facial expressions. The percentage of positive versus negative emotions detected over multiple images could indicate an employee's level of satisfaction. A literature review discusses limitations of existing approaches and supports using CNNs on facial expressions for more accurate analysis of employee mental health and work satisfaction.
International Journal of Humanities and Social Science Invention (IJHSSI) is an international journal intended for professionals and researchers in all fields of Humanities and Social Science. IJHSSI publishes research articles and reviews within the whole field Humanities and Social Science, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
We will discuss the key highlights and forecasts from SharpBrains’ latest market report “The Digital Brain Health Market 2012–2020: Web-based, mobile and biometrics-based technology to assess, monitor and enhance cognition and brain functioning.”
- Alvaro Fernandez, CEO of SharpBrains
Innovative partnerships to improve lifelong brain health and customer/ ...SharpBrains
(Session held at the 2014 SharpBrains Virtual Summit; October 28-30th, 2014)
10:00–11:30am. Innovative partnerships to improve lifelong brain health and customer/ patient satisfaction
- Bill Prenovitz, Global Product and Service Management at Philips Healthcare’s Aging-in-Place Program
- Dr. Michael Weiner, Lead Scientific Investigator of the Brain Health Registry
- Tommy Sagroun, CEO of CogniFit
- Chair: Rita Carter, Author, Broadcaster and BBC Contributor
Learn more here:
http://sharpbrains.com/summit-2014/agenda/
The document discusses the future of brain health and cognitive technologies. It describes 10 emerging brain technologies including wearables to monitor and enhance focus, meditation, and self-regulation. It also discusses electrical and magnetic brain stimulation, virtual reality treatments, brain-computer interfaces, and neuromonitoring technologies. Finally, it discusses how big data and machine learning can enhance diagnostics and treatments for various brain conditions like seizures, ALS, and more. Overall, the technologies described aim to transform brain health by enhancing functions like memory, learning, and skills training through personalized cognitive simulations and interfaces.
How can practitioners integrate emerging neuroplasticity-based interven...SharpBrains
A promising frontier of applied neuroscience lies in technologies that stimulate our brains in order to harness neuroplasticity and achieve positive outcomes. What are the practical Pros and Cons of different methodologies such as cognitive training, EEG/ QEEG biofeedback, virtual reality, and what are appropriate ways to integrate them with traditional interventions?
- Chair: Olivier Oullier, Professor of Behavioral and Brain Sciences at Aix-Marseille University
- Bruce Wexler, NIH Director’s Award Winner and Professor of Psychiatry at Yale University
- Kate Sullivan, Director of the Brain Fitness Center at Walter Reed National Military Medical Center
This session took place at the 2013 SharpBrains Virtual Summit: http://sharpbrains.com/summit-2013/agenda/
How to measure and improve brain-based outcomes that matter in health careSharpBrains
Pioneers advancing health research, prevention and treatment will help us understand emerging best practices where targeted assessments, monitoring and interventions can transfer into significant healthcare and quality of life outcomes.
-- Chair: Alvaro Fernandez, CEO & Co-Founder of SharpBrains
-- Dr. Madeleine S Goodkind, staff psychologist at New Mexico VA Health Care System
-- Dr. Randy McIntosh, Vice-president of Research and Director of Baycrest’s Rotman Research Institute
-- Chris Berka, CEO and Co-Founder of Advanced Brain Monitoring (ABM)
Presentation @ The 2015 SharpBrains Virtual Summit http://sharpbrains.com/summit-2015/agenda
Expo day: Digital Artefacts (BrainBaseline), HeartMath, Sleep Genius, The Al...SharpBrains
Expo Day (continued) @ 2014 SharpBrains Virtual Summit. Summit Sponsors announce and showcase their latest initiatives and solutions:
1–1.30pm. Digital Artefacts: Joan Severson, President
1.45–2.15pm. HeartMath: Catherine Calarco, Chief Marketing Officer
2.30-3pm. Sleep Genius: Colin House, CEO
3.15–3.45pm. The Alzheimer’s Research and Prevention Foundation: Dr. Dharma Singh Khalsa, President
Learn more here:
http://sharpbrains.com/summit-2014/agenda/
What are most promising lifestyle and tech options to harness lifelong neurop...SharpBrains
This document summarizes a panel discussion on harnessing lifelong neuroplasticity through lifestyle and technology options, and the challenges ahead. The panel was chaired by experts in neuroplasticity and brain health. Panelists discussed using noninvasive brain stimulation techniques like TMS to measure biomarkers of brain circuit dynamics and plasticity. They described ongoing studies measuring lifestyle factors' impacts on brain health through the Barcelona Brain Health Initiative. Another panelist discussed opportunities for brain augmentation through nanotechnology but called for responsible development and public engagement and ethics guidelines to ensure benefits for individuals and society.
How can Big Data help upgrade brain care?SharpBrains
Current standards of brain and mental care often rely on trials of insufficient scale, which not only limits our ability to diagnose, prevent, treat and personalize care but often leads to incorrect conclusions and undesirable results. What tools and data are becoming available via large-scale web-based and mobile applications, and how can researchers, innovators and practitioners connect with these initiatives?
- Chair: Alvaro Fernandez, CEO of SharpBrains, YGL Class of 2012
- Daniel Sternberg, Data Scientist at Lumosity
- Joan Severson, President of Digital Artefacts
- Robert Bilder, Chief of Medical Psychology-Neuropsychology at UCLA Semel Institute for Neuroscience
Machine Learning for Statisticians - IntroductionDr Ganesh Iyer
Introduction to Machine Learning for Statisticians. From the webinar given for Sacred Hearts College, Tevara, Ernakulam, India on 8/8/2020. It briefly introduces ML concepts and what does it mean for statisticians.
Best practices to assess and enhance brain function via mobile devices and ...SharpBrains
The document discusses best practices for assessing and enhancing brain function using mobile devices and wearables. It summarizes presentations from several speakers at a conference on this topic. Corinna Lathan discussed a mobile reaction time testing system called DANA that can help detect neurocognitive impairment. Eddie Martucci discussed his company Akili's approach of making medicine more engaging through digital games. Alex Doman talked about how wearables can provide personalized sleep reporting. Joan Severson presented on her company's BrainBaseline platform, which integrates cognitive performance measures with lifestyle data to track brain health over time.
At the frontier of Big Data and Brain HealthSharpBrains
During this session we will explore cutting-edge initiatives to accelerate research & development via Big Data, crowdsourcing, technologies for the extended mind, and a range of data-rich pervasive neurotechnologies such as virtual reality.
-Chair: Alison Fenney, Director of Industry Alliances at the Neurotechnology Industry Organization (NIO)
-Dr. Walter Greenleaf, Distinguished Visiting Scholar at Stanford University’s Virtual Human Interaction Lab
-Michael Meagher, President of Cogniciti
-José Barrios, Co-Founder & CEO of Cognilab
-Dr. Peter Reiner, Co-Founder, National Core for Neuroethics at the University of British Columbia
Presentation @ The 2015 SharpBrains Virtual Summit http://sharpbrains.com/summit-2015/agenda
Carving out Future Brain Health Directions + Expo DaySharpBrains
Expo Day (first part) @ 2014 SharpBrains Virtual Summit. Summit Sponsors announce and showcase their latest initiatives and solutions:
8–8.20am. Carving out future directions, by Dr. Ken Kosik, Co-Director of the UC Santa Barbara Neuroscience Research Institute
Expo Day
8:45–9.15am. Rosetta Stone/ FitBrains: Steve Quan, Head of Business Development & Strategic Partnerships
9.30-10am. Peak: Roy Zahut, Lead Scientist
10.15–10.45am. Baycrest/ Cogniciti: Mike Meagher, President of Cogniciti
11–11.30am. The Arrowsmith Program: Jessica Poulin, Managing Director
Learn more here:
http://sharpbrains.com/summit-2014/agenda/
Pervasive Neurotechnology: The Digital Revolution Meets the Human BrainSharpBrains
Slidedeck from June 30th, 2015 webinar, with Alvaro Fernandez and Nikhil Sriraman. to discuss the key take-aways from the new market report Pervasive Neurotechnology: A Groundbreaking Analysis of 10,000+ Patent Filings Transforming Medicine, Health, Entertainment and Business. To learn more: http://sharpbrains.com/pervasive-neurotechnology/
Agenda:
1–1.20pm ET: Five Key Trends Driving Neurotechnology to Become Pervasive
1.20–1.40pm ET: How Nielsen, Advanced Neuromodulation Systems, Medtronic, Microsoft and Brainlab emerged as leading IP Holders
1.40-2pm ET: Q&A
Various cataract detection methods-A surveyIRJET Journal
This document summarizes various methods for detecting cataracts. It discusses five different cataract detection methods proposed in previous research: 1) a mobile system using texture analysis and k-NN classification, 2) fundus image processing using histogram equalization, 3) a tri-training method that generates three classifiers, 4) analysis of automatic detection of nuclear and cortical cataracts using fundus images, and 5) enhanced texture features to classify cataractous and non-cataractous lenses. The document also reviews literature on diabetic retinopathy detection and classification. It concludes that while challenges remain, recent applications have potential for early cataract detection and classification.
Nero-IR is a novel area of research under cognitive psychology, neuro-physiological methods (eye tracking, EEG, EOG, and GSR) and machine learning to understand information searchers and to improve search experience. Neuro-IR is useful in investigating the search as a learning process and to employ these sensory data as assessment of reading, mind-wandering and in inferring metadata features for machine learning models. In this talk, I will introduce a unification framework for neuro-physiological data; practically these models provide context for user interactions. I will show how we can take advantage of many existing interactions combining various sensory platforms (e.g., PupilLabs, Emotiv, Empatica E4). Information fusion can provide numerous benefits in combining multiple-sources of neuro-physiological data. The most obvious among them is the expected performance gain due to combination of evidence from multiple cues. As a practical matter, acquisition of physiological metadata is a research frontier.
IRJET- Glaucoma Detection using Convolutional Neural NetworkIRJET Journal
This document describes a study that used a convolutional neural network (CNN) to detect glaucoma from eye images. The researchers:
1) Collected a database of 100 eye images, with 50 normal and 50 glaucoma cases, for training and testing the CNN model.
2) Pre-processed the images using Gaussian blur to remove noise before classification.
3) Trained a CNN on the images and tested it on a separate set of 100 images, achieving 97% accuracy, 96% precision, and 98% recall.
4) Concluded that CNNs provide an effective technique for early glaucoma detection that could help save vision.
Beyond the US college student’s brain: A survey of priorities across geo...SharpBrains
This document summarizes a survey of brain-based priorities across different geographies and age groups. It was chaired by Dr. Olivier Oullier and included presentations on growth mindset by Eduardo Briceño and how it can affect performance, health, and other outcomes. Graeme Moffat then discussed the Muse headband and how it can help with meditation. Finally, Tara Thiagarajan presented on a study analyzing brain activity data collected across India to understand differences based on factors like income, education, and technology usage.
Mano Vaidya: Gateway to Relaxation Via Machine LearningIRJET Journal
1. The document describes a mobile application called Mano Vaidya that aims to help reduce stress levels using machine learning techniques.
2. The app uses questionnaires from the SF-36 to classify users into stress level clusters (positive, tolerable, toxic) using k-means clustering. It then recommends stress-relieving activities based on the user's classification.
3. The application was developed using Android and evaluates users' mental health status through a series of questions. Machine learning algorithms like Naive Bayes, Decision Trees, and k-means are used to predict user's stress levels and suggest support activities.
This document describes a mobile application called the Mental Health Tracker app. The app aims to monitor and stabilize a user's mood by having them complete various activities like breathing exercises, listening to music, reading jokes, and maintaining a to-do list. The app also displays the user's mental state over time through graphs. It was created to help people struggling with mental health issues like anxiety and depression by providing simple activities and tracking their progress. The goal of the app is to only stabilize a user's mood, not diagnose or label them with any conditions. It also provides contact information for mental health experts if a user wants further assistance.
Healing Better Application for Mental Health AssessmentIRJET Journal
This document describes a proposed mobile application called Healing Better for assessing mental health issues like anxiety, depression, and stress. It would use the Depression, Anxiety and Stress Scale (DASS21) questionnaire to evaluate users based on their responses. Users would then receive their severity assessment levels and recommendations for relief techniques tailored to their needs, such as music therapy, motivational videos, or contacting a therapist for severe cases. The application aims to help users identify and address their mental health privately without direct interaction with medical professionals. It would use a login system to securely store user data and provide personalized assessments and support over time to monitor their progress in improving symptoms.
DETECTING PSYCHOLOGICAL INSTABILITY USING MACHINE LEARNINGIRJET Journal
This document discusses detecting psychological instability using machine learning algorithms. It proposes using various machine learning models like logistic regression, decision trees, KNN, SVM, and XGBoost to classify whether an individual exhibits signs of a mental disorder based on their behaviors and thoughts. The models will be trained on datasets containing examples of symptoms and tested using metrics like accuracy, precision, recall and F1-score. Previously most research used methods like questionnaires which have validity issues, or neural networks which can overfit. The proposed system applies a narrative review methodology to analyze literature and identify an appropriate machine learning approach to help diagnose mental illness.
This document describes a proposed mental health assistant that would use LSTM (long short-term memory) and NLP (natural language processing) to engage in conversation with users, analyze their voice or text input to study their emotion, and provide solutions. It discusses related work on similar mental health chatbots and assistants. The proposed system would take input through either speech or text, use speech recognition and text mining to extract emotion, and provide recommendations to improve the user's mood based on the recognized emotion.
Quantifying the efficacy of ML models at predicting mental health illnessesIRJET Journal
This document summarizes a research study that evaluated the efficacy of machine learning models at predicting mental health illnesses like depression. The study collected self-reported survey data from participants over two weeks to quantify emotional variability and depressive symptoms. It then used this data to train and compare the accuracy of logistic regression, random forest, and multi-layer perceptron models against a baseline model and participants' Beck Depression Inventory scores. The preliminary results found a positive correlation between emotional variability, amount of labeled data/features, and model accuracy. The random forest model was most accurate at predicting depression incidence compared to the other models and baseline. The research aims to assess the benefits and limitations of using ML to detect mental health issues like depression.
Mental Health Chatbot System by Using Machine LearningIRJET Journal
This document discusses the development of a mental health chatbot system using machine learning. The proposed chatbot would provide mental health services through a chat feature, voice input options in multiple languages, and a mood recommendation tool. Natural language processing and neural networks would be used to train the chatbot to understand language and respond appropriately. The goal is to make affordable and accessible mental healthcare available to more people.
1) The document describes methods for detecting depression using image processing and machine learning techniques applied to facial image analysis. It involves steps like face recognition, feature extraction from images, comparing features to labeled training data, and classifying images as depressed or not depressed.
2) Key algorithms discussed are face recognition through feature extraction and graph matching, as well as using classifiers like multiclass models to analyze facial features and recognize emotional states from images.
3) The goal is to develop an automated system that can help clinicians assess and monitor depression levels based on facial image analysis, in order to address the growing problem of depression diagnosis and treatment. The system aims to classify human depression and recognize facial expressions related to depressed emotional states.
Projective exploration on individual stress levels using machine learningIRJET Journal
This document describes a proposed system to predict stress levels in college students using machine learning. It discusses how stress is an increasing issue among college students and the challenges with current manual methods of assessing stress. The proposed system would use an automated approach based on student profiles and behaviors to identify those experiencing stress. It reviews several previous studies that developed machine learning models to detect stress using physiological sensor data. The objective of the proposed project is to build a real-time classification model to predict whether students are experiencing stress or not based on their answers to questions. It would provide personalized solutions and assistance to help manage student stress levels and mental well-being.
Expo day: Digital Artefacts (BrainBaseline), HeartMath, Sleep Genius, The Al...SharpBrains
Expo Day (continued) @ 2014 SharpBrains Virtual Summit. Summit Sponsors announce and showcase their latest initiatives and solutions:
1–1.30pm. Digital Artefacts: Joan Severson, President
1.45–2.15pm. HeartMath: Catherine Calarco, Chief Marketing Officer
2.30-3pm. Sleep Genius: Colin House, CEO
3.15–3.45pm. The Alzheimer’s Research and Prevention Foundation: Dr. Dharma Singh Khalsa, President
Learn more here:
http://sharpbrains.com/summit-2014/agenda/
What are most promising lifestyle and tech options to harness lifelong neurop...SharpBrains
This document summarizes a panel discussion on harnessing lifelong neuroplasticity through lifestyle and technology options, and the challenges ahead. The panel was chaired by experts in neuroplasticity and brain health. Panelists discussed using noninvasive brain stimulation techniques like TMS to measure biomarkers of brain circuit dynamics and plasticity. They described ongoing studies measuring lifestyle factors' impacts on brain health through the Barcelona Brain Health Initiative. Another panelist discussed opportunities for brain augmentation through nanotechnology but called for responsible development and public engagement and ethics guidelines to ensure benefits for individuals and society.
How can Big Data help upgrade brain care?SharpBrains
Current standards of brain and mental care often rely on trials of insufficient scale, which not only limits our ability to diagnose, prevent, treat and personalize care but often leads to incorrect conclusions and undesirable results. What tools and data are becoming available via large-scale web-based and mobile applications, and how can researchers, innovators and practitioners connect with these initiatives?
- Chair: Alvaro Fernandez, CEO of SharpBrains, YGL Class of 2012
- Daniel Sternberg, Data Scientist at Lumosity
- Joan Severson, President of Digital Artefacts
- Robert Bilder, Chief of Medical Psychology-Neuropsychology at UCLA Semel Institute for Neuroscience
Machine Learning for Statisticians - IntroductionDr Ganesh Iyer
Introduction to Machine Learning for Statisticians. From the webinar given for Sacred Hearts College, Tevara, Ernakulam, India on 8/8/2020. It briefly introduces ML concepts and what does it mean for statisticians.
Best practices to assess and enhance brain function via mobile devices and ...SharpBrains
The document discusses best practices for assessing and enhancing brain function using mobile devices and wearables. It summarizes presentations from several speakers at a conference on this topic. Corinna Lathan discussed a mobile reaction time testing system called DANA that can help detect neurocognitive impairment. Eddie Martucci discussed his company Akili's approach of making medicine more engaging through digital games. Alex Doman talked about how wearables can provide personalized sleep reporting. Joan Severson presented on her company's BrainBaseline platform, which integrates cognitive performance measures with lifestyle data to track brain health over time.
At the frontier of Big Data and Brain HealthSharpBrains
During this session we will explore cutting-edge initiatives to accelerate research & development via Big Data, crowdsourcing, technologies for the extended mind, and a range of data-rich pervasive neurotechnologies such as virtual reality.
-Chair: Alison Fenney, Director of Industry Alliances at the Neurotechnology Industry Organization (NIO)
-Dr. Walter Greenleaf, Distinguished Visiting Scholar at Stanford University’s Virtual Human Interaction Lab
-Michael Meagher, President of Cogniciti
-José Barrios, Co-Founder & CEO of Cognilab
-Dr. Peter Reiner, Co-Founder, National Core for Neuroethics at the University of British Columbia
Presentation @ The 2015 SharpBrains Virtual Summit http://sharpbrains.com/summit-2015/agenda
Carving out Future Brain Health Directions + Expo DaySharpBrains
Expo Day (first part) @ 2014 SharpBrains Virtual Summit. Summit Sponsors announce and showcase their latest initiatives and solutions:
8–8.20am. Carving out future directions, by Dr. Ken Kosik, Co-Director of the UC Santa Barbara Neuroscience Research Institute
Expo Day
8:45–9.15am. Rosetta Stone/ FitBrains: Steve Quan, Head of Business Development & Strategic Partnerships
9.30-10am. Peak: Roy Zahut, Lead Scientist
10.15–10.45am. Baycrest/ Cogniciti: Mike Meagher, President of Cogniciti
11–11.30am. The Arrowsmith Program: Jessica Poulin, Managing Director
Learn more here:
http://sharpbrains.com/summit-2014/agenda/
Pervasive Neurotechnology: The Digital Revolution Meets the Human BrainSharpBrains
Slidedeck from June 30th, 2015 webinar, with Alvaro Fernandez and Nikhil Sriraman. to discuss the key take-aways from the new market report Pervasive Neurotechnology: A Groundbreaking Analysis of 10,000+ Patent Filings Transforming Medicine, Health, Entertainment and Business. To learn more: http://sharpbrains.com/pervasive-neurotechnology/
Agenda:
1–1.20pm ET: Five Key Trends Driving Neurotechnology to Become Pervasive
1.20–1.40pm ET: How Nielsen, Advanced Neuromodulation Systems, Medtronic, Microsoft and Brainlab emerged as leading IP Holders
1.40-2pm ET: Q&A
Various cataract detection methods-A surveyIRJET Journal
This document summarizes various methods for detecting cataracts. It discusses five different cataract detection methods proposed in previous research: 1) a mobile system using texture analysis and k-NN classification, 2) fundus image processing using histogram equalization, 3) a tri-training method that generates three classifiers, 4) analysis of automatic detection of nuclear and cortical cataracts using fundus images, and 5) enhanced texture features to classify cataractous and non-cataractous lenses. The document also reviews literature on diabetic retinopathy detection and classification. It concludes that while challenges remain, recent applications have potential for early cataract detection and classification.
Nero-IR is a novel area of research under cognitive psychology, neuro-physiological methods (eye tracking, EEG, EOG, and GSR) and machine learning to understand information searchers and to improve search experience. Neuro-IR is useful in investigating the search as a learning process and to employ these sensory data as assessment of reading, mind-wandering and in inferring metadata features for machine learning models. In this talk, I will introduce a unification framework for neuro-physiological data; practically these models provide context for user interactions. I will show how we can take advantage of many existing interactions combining various sensory platforms (e.g., PupilLabs, Emotiv, Empatica E4). Information fusion can provide numerous benefits in combining multiple-sources of neuro-physiological data. The most obvious among them is the expected performance gain due to combination of evidence from multiple cues. As a practical matter, acquisition of physiological metadata is a research frontier.
IRJET- Glaucoma Detection using Convolutional Neural NetworkIRJET Journal
This document describes a study that used a convolutional neural network (CNN) to detect glaucoma from eye images. The researchers:
1) Collected a database of 100 eye images, with 50 normal and 50 glaucoma cases, for training and testing the CNN model.
2) Pre-processed the images using Gaussian blur to remove noise before classification.
3) Trained a CNN on the images and tested it on a separate set of 100 images, achieving 97% accuracy, 96% precision, and 98% recall.
4) Concluded that CNNs provide an effective technique for early glaucoma detection that could help save vision.
Beyond the US college student’s brain: A survey of priorities across geo...SharpBrains
This document summarizes a survey of brain-based priorities across different geographies and age groups. It was chaired by Dr. Olivier Oullier and included presentations on growth mindset by Eduardo Briceño and how it can affect performance, health, and other outcomes. Graeme Moffat then discussed the Muse headband and how it can help with meditation. Finally, Tara Thiagarajan presented on a study analyzing brain activity data collected across India to understand differences based on factors like income, education, and technology usage.
Mano Vaidya: Gateway to Relaxation Via Machine LearningIRJET Journal
1. The document describes a mobile application called Mano Vaidya that aims to help reduce stress levels using machine learning techniques.
2. The app uses questionnaires from the SF-36 to classify users into stress level clusters (positive, tolerable, toxic) using k-means clustering. It then recommends stress-relieving activities based on the user's classification.
3. The application was developed using Android and evaluates users' mental health status through a series of questions. Machine learning algorithms like Naive Bayes, Decision Trees, and k-means are used to predict user's stress levels and suggest support activities.
This document describes a mobile application called the Mental Health Tracker app. The app aims to monitor and stabilize a user's mood by having them complete various activities like breathing exercises, listening to music, reading jokes, and maintaining a to-do list. The app also displays the user's mental state over time through graphs. It was created to help people struggling with mental health issues like anxiety and depression by providing simple activities and tracking their progress. The goal of the app is to only stabilize a user's mood, not diagnose or label them with any conditions. It also provides contact information for mental health experts if a user wants further assistance.
Healing Better Application for Mental Health AssessmentIRJET Journal
This document describes a proposed mobile application called Healing Better for assessing mental health issues like anxiety, depression, and stress. It would use the Depression, Anxiety and Stress Scale (DASS21) questionnaire to evaluate users based on their responses. Users would then receive their severity assessment levels and recommendations for relief techniques tailored to their needs, such as music therapy, motivational videos, or contacting a therapist for severe cases. The application aims to help users identify and address their mental health privately without direct interaction with medical professionals. It would use a login system to securely store user data and provide personalized assessments and support over time to monitor their progress in improving symptoms.
DETECTING PSYCHOLOGICAL INSTABILITY USING MACHINE LEARNINGIRJET Journal
This document discusses detecting psychological instability using machine learning algorithms. It proposes using various machine learning models like logistic regression, decision trees, KNN, SVM, and XGBoost to classify whether an individual exhibits signs of a mental disorder based on their behaviors and thoughts. The models will be trained on datasets containing examples of symptoms and tested using metrics like accuracy, precision, recall and F1-score. Previously most research used methods like questionnaires which have validity issues, or neural networks which can overfit. The proposed system applies a narrative review methodology to analyze literature and identify an appropriate machine learning approach to help diagnose mental illness.
This document describes a proposed mental health assistant that would use LSTM (long short-term memory) and NLP (natural language processing) to engage in conversation with users, analyze their voice or text input to study their emotion, and provide solutions. It discusses related work on similar mental health chatbots and assistants. The proposed system would take input through either speech or text, use speech recognition and text mining to extract emotion, and provide recommendations to improve the user's mood based on the recognized emotion.
Quantifying the efficacy of ML models at predicting mental health illnessesIRJET Journal
This document summarizes a research study that evaluated the efficacy of machine learning models at predicting mental health illnesses like depression. The study collected self-reported survey data from participants over two weeks to quantify emotional variability and depressive symptoms. It then used this data to train and compare the accuracy of logistic regression, random forest, and multi-layer perceptron models against a baseline model and participants' Beck Depression Inventory scores. The preliminary results found a positive correlation between emotional variability, amount of labeled data/features, and model accuracy. The random forest model was most accurate at predicting depression incidence compared to the other models and baseline. The research aims to assess the benefits and limitations of using ML to detect mental health issues like depression.
Mental Health Chatbot System by Using Machine LearningIRJET Journal
This document discusses the development of a mental health chatbot system using machine learning. The proposed chatbot would provide mental health services through a chat feature, voice input options in multiple languages, and a mood recommendation tool. Natural language processing and neural networks would be used to train the chatbot to understand language and respond appropriately. The goal is to make affordable and accessible mental healthcare available to more people.
1) The document describes methods for detecting depression using image processing and machine learning techniques applied to facial image analysis. It involves steps like face recognition, feature extraction from images, comparing features to labeled training data, and classifying images as depressed or not depressed.
2) Key algorithms discussed are face recognition through feature extraction and graph matching, as well as using classifiers like multiclass models to analyze facial features and recognize emotional states from images.
3) The goal is to develop an automated system that can help clinicians assess and monitor depression levels based on facial image analysis, in order to address the growing problem of depression diagnosis and treatment. The system aims to classify human depression and recognize facial expressions related to depressed emotional states.
Projective exploration on individual stress levels using machine learningIRJET Journal
This document describes a proposed system to predict stress levels in college students using machine learning. It discusses how stress is an increasing issue among college students and the challenges with current manual methods of assessing stress. The proposed system would use an automated approach based on student profiles and behaviors to identify those experiencing stress. It reviews several previous studies that developed machine learning models to detect stress using physiological sensor data. The objective of the proposed project is to build a real-time classification model to predict whether students are experiencing stress or not based on their answers to questions. It would provide personalized solutions and assistance to help manage student stress levels and mental well-being.
RECOGNITION OF PSYCHOLOGICAL VULNERABILITIES USING MACHINE LEARNINGIRJET Journal
This document discusses recognizing psychological vulnerabilities using machine learning. It begins with an abstract that outlines using machine learning models and feature selection techniques on a dataset of mental health issues to identify the type of issue based on an individual's symptoms. Five machine learning algorithms (XG-Boost, SVM, logistic regression, decision tree, KNN) were used and evaluated based on accuracy, precision, and F1-score. The document then reviews related work applying machine learning to mental health areas like depression detection from social media posts. It presents the system architecture and compares the proposed system of automated diagnosis to existing rule-based systems. The proposed system is evaluated on a dataset of 1200 examples using SVM, decision tree, and random forest models, with random
IRJET- Development of Mobile Application to Monitor Mental WellbeingIRJET Journal
The document describes the development of a mobile application to monitor mental wellbeing. The application collects data through psychological questionnaires, tracks the user's mental health over time, and sends reports to friends, family, and doctors if it detects a decline in mental health. The application includes features like AI chatbots, calming exercises, positive quotes, and tests to help users manage anxiety and depression. It aims to serve as a companion for users struggling with their mental health.
Mental Health and Machine Learning in companiesRajviShah86
Machine learning algorithms can help predict mental health issues in employees by analyzing data from mental health surveys. Researchers used supervised learning algorithms like support vector machines, logistic regression, k-nearest neighbors, decision trees and random forest on survey data from tech and non-tech companies. Key factors identified for predicting mental disorders included whether the employee worked at a tech company, their age, gender, family history of mental health issues, personal history, and whether they discussed mental health with their employer. Related studies used logistic models and smartphones to predict anxiety disorders and monitor bipolar disorder.
Mental Health Prediction Using Machine LearningIRJET Journal
This document discusses using machine learning algorithms to predict mental health conditions like depression, anxiety, PTSD, and insomnia. It conducted a survey to collect data on symptoms from individuals, which was then used to train and test models. Several algorithms were tested, with random forest found to produce the best results. The goal is to help people recognize potential mental health issues and give doctors insight to better diagnose patients.
Neurotech devices can track brain activity and physiological signals to monitor fatigue, attention, and focus in the workplace. This allows for benefits like increased productivity and safety, but also risks like loss of privacy and employee trust. Methods of neurotech include EEG headbands that detect fatigue, earbuds that flag attention lapses, and adaptive systems that adjust workloads based on cognitive load. However, companies must consider employee rights to privacy and disclosure as well as create a high-trust environment for neurotech to achieve benefits without unintended consequences like disengagement.
waste management in daily management studyChandusandy4
This document outlines a project to develop a machine learning model for predicting stress levels in IT professionals. It will utilize physiological data like heart rate and skin conductivity as well as work-related factors like hours worked and meetings attended. The proposed model will use ensemble techniques like random forest, AdaBoost and extra trees to more accurately capture relationships between features. This aims to provide early stress detection and intervention for improved well-being and productivity compared to existing approaches. The document discusses the motivation, objectives, literature review, system architecture, requirements and algorithms to be used in building this stress prediction model.
This document is a synopsis submitted for a Master's degree in Business Administration. It discusses developing a cognitive expert system for evaluating employee performance in an industrial organization. The system aims to provide a more objective and accurate assessment compared to traditional appraisal methods. It will classify important evaluation features and use a cognitive inference methodology to calculate overall scores based on these weighted features. The methodology involves designing the feature dataset, developing the cognitive expert system to represent expert judgments, and testing the system using machine learning techniques.
IRJET- An Innovative Approach for Interviewer to Judge State of Mind of an In...IRJET Journal
This document presents a proposed system to analyze the state of mind of an interviewee during an interview using facial expression recognition and classification. The system would use Fisher Face algorithm to detect facial features from video frames and Naive Bayes classification to categorize the detected expressions as indicators of emotional states like happy, sad, angry etc. This automated analysis of facial expressions could provide feedback to improve the interview process and selection of candidates. The summarized system aims to identify an individual's state of mind during an interview through facial expression recognition using deep learning techniques.
This document discusses how a user's mood can impact software requirements and design. It proposes a model to assess a user's mood profile during the requirements gathering process. Key points:
- Mood is a generalized feeling that influences how users interact with and perceive software. Positive moods tend to produce more positive perceptions.
- The model involves gathering mood data on users and using this to modify the requirements specification process. Software developers would also self-report their moods.
- Implementing this mood-aware model could help produce higher quality software requirements that better meet user needs by accounting for psychological and emotional states. This could improve software design and reduce maintenance costs.
- In conclusion, the study supports incorporating
Depression Detection Using Various Machine Learning ClassifiersIRJET Journal
This document describes a study that uses machine learning classifiers to detect depression using data from Twitter posts. Several classifiers are trained and tested on a dataset of 20,000 tweets from various user profiles. Features like sentiment, word frequency, and user account data are extracted from the tweets. Various classifiers like Extra Tree Classifier, Logistic Regression, and Naive Bayes are compared for their ability to accurately detect depression. The Extra Tree Classifier is found to have the best performance with 94% accuracy and 97.29% precision.
IRJET - A Survey on Human Facial Expression Recognition TechniquesIRJET Journal
1. The document discusses several techniques for human facial expression recognition, including using facial animation parameters and Hidden Markov Models, support vector machines on facial feature displacements in video, AdaBoost with neural networks on reduced facial features, histograms of oriented gradients with further feature reduction, and ripplet transforms with least squares support vector machines.
2. It provides an overview of basic human facial expressions and the typical steps involved in a facial expression recognition system, including image preprocessing, face detection, feature extraction, and classification.
3. The techniques are evaluated on standard databases and achieve high recognition accuracies, typically above 90%.
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...IRJET Journal
This study compares the use of Stark Steel and TMT Steel as reinforcement materials in a two-way reinforced concrete slab. Mechanical testing is conducted to determine the tensile strength, yield strength, and other properties of each material. A two-way slab design adhering to codes and standards is executed with both materials. The performance is analyzed in terms of deflection, stability under loads, and displacement. Cost analyses accounting for material, durability, maintenance, and life cycle costs are also conducted. The findings provide insights into the economic and structural implications of each material for reinforcement selection and recommendations on the most suitable material based on the analysis.
Effect of Camber and Angles of Attack on Airfoil CharacteristicsIRJET Journal
This document discusses a study analyzing the effect of camber, position of camber, and angle of attack on the aerodynamic characteristics of airfoils. Sixteen modified asymmetric NACA airfoils were analyzed using computational fluid dynamics (CFD) by varying the camber, camber position, and angle of attack. The results showed the relationship between these parameters and the lift coefficient, drag coefficient, and lift to drag ratio. This provides insight into how changes in airfoil geometry impact aerodynamic performance.
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...IRJET Journal
This document reviews the progress and challenges of aluminum-based metal matrix composites (MMCs), focusing on their fabrication processes and applications. It discusses how various aluminum MMCs have been developed using reinforcements like borides, carbides, oxides, and nitrides to improve mechanical and wear properties. These composites have gained prominence for their lightweight, high-strength and corrosion resistance properties. The document also examines recent advancements in fabrication techniques for aluminum MMCs and their growing applications in industries such as aerospace and automotive. However, it notes that challenges remain around issues like improper mixing of reinforcements and reducing reinforcement agglomeration.
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...IRJET Journal
This document discusses research on using graph neural networks (GNNs) for dynamic optimization of public transportation networks in real-time. GNNs represent transit networks as graphs with nodes as stops and edges as connections. The GNN model aims to optimize networks using real-time data on vehicle locations, arrival times, and passenger loads. This helps increase mobility, decrease traffic, and improve efficiency. The system continuously trains and infers to adapt to changing transit conditions, providing decision support tools. While research has focused on performance, more work is needed on security, socio-economic impacts, contextual generalization of models, continuous learning approaches, and effective real-time visualization.
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...IRJET Journal
This document summarizes a research project that aims to compare the structural performance of conventional slab and grid slab systems in multi-story buildings using ETABS software. The study will analyze both symmetric and asymmetric building models under various loading conditions. Parameters like deflections, moments, shears, and stresses will be examined to evaluate the structural effectiveness of each slab type. The results will provide insights into the comparative behavior of conventional and grid slabs to help engineers and architects select appropriate slab systems based on building layouts and design requirements.
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...IRJET Journal
This document summarizes and reviews a research paper on the seismic response of reinforced concrete (RC) structures with plan and vertical irregularities, with and without infill walls. It discusses how infill walls can improve or reduce the seismic performance of RC buildings, depending on factors like wall layout, height distribution, connection to the frame, and relative stiffness of walls and frames. The reviewed research paper analyzes the behavior of infill walls, effects of vertical irregularities, and seismic performance of high-rise structures under linear static and dynamic analysis. It studies response characteristics like story drift, deflection and shear. The document also provides literature on similar research investigating the effects of infill walls, soft stories, plan irregularities, and different
This document provides a review of machine learning techniques used in Advanced Driver Assistance Systems (ADAS). It begins with an abstract that summarizes key applications of machine learning in ADAS, including object detection, recognition, and decision-making. The introduction discusses the integration of machine learning in ADAS and how it is transforming vehicle safety. The literature review then examines several research papers on topics like lightweight deep learning models for object detection and lane detection models using image processing. It concludes by discussing challenges and opportunities in the field, such as improving algorithm robustness and adaptability.
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...IRJET Journal
The document analyzes temperature and precipitation trends in Asosa District, Benishangul Gumuz Region, Ethiopia from 1993 to 2022 based on data from the local meteorological station. The results show:
1) The average maximum and minimum annual temperatures have generally decreased over time, with maximum temperatures decreasing by a factor of -0.0341 and minimum by -0.0152.
2) Mann-Kendall tests found the decreasing temperature trends to be statistically significant for annual maximum temperatures but not for annual minimum temperatures.
3) Annual precipitation in Asosa District showed a statistically significant increasing trend.
The conclusions recommend development planners account for rising summer precipitation and declining temperatures in
P.E.B. Framed Structure Design and Analysis Using STAAD ProIRJET Journal
This document discusses the design and analysis of pre-engineered building (PEB) framed structures using STAAD Pro software. It provides an overview of PEBs, including that they are designed off-site with building trusses and beams produced in a factory. STAAD Pro is identified as a key tool for modeling, analyzing, and designing PEBs to ensure their performance and safety under various load scenarios. The document outlines modeling structural parts in STAAD Pro, evaluating structural reactions, assigning loads, and following international design codes and standards. In summary, STAAD Pro is used to design and analyze PEB framed structures to ensure safety and code compliance.
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...IRJET Journal
This document provides a review of research on innovative fiber integration methods for reinforcing concrete structures. It discusses studies that have explored using carbon fiber reinforced polymer (CFRP) composites with recycled plastic aggregates to develop more sustainable strengthening techniques. It also examines using ultra-high performance fiber reinforced concrete to improve shear strength in beams. Additional topics covered include the dynamic responses of FRP-strengthened beams under static and impact loads, and the performance of preloaded CFRP-strengthened fiber reinforced concrete beams. The review highlights the potential of fiber composites to enable more sustainable and resilient construction practices.
Survey Paper on Cloud-Based Secured Healthcare SystemIRJET Journal
This document summarizes a survey on securing patient healthcare data in cloud-based systems. It discusses using technologies like facial recognition, smart cards, and cloud computing combined with strong encryption to securely store patient data. The survey found that healthcare professionals believe digitizing patient records and storing them in a centralized cloud system would improve access during emergencies and enable more efficient care compared to paper-based systems. However, ensuring privacy and security of patient data is paramount as healthcare incorporates these digital technologies.
Review on studies and research on widening of existing concrete bridgesIRJET Journal
This document summarizes several studies that have been conducted on widening existing concrete bridges. It describes a study from China that examined load distribution factors for a bridge widened with composite steel-concrete girders. It also outlines challenges and solutions for widening a bridge in the UAE, including replacing bearings and stitching the new and existing structures. Additionally, it discusses two bridge widening projects in New Zealand that involved adding precast beams and stitching to connect structures. Finally, safety measures and challenges for strengthening a historic bridge in Switzerland under live traffic are presented.
React based fullstack edtech web applicationIRJET Journal
The document describes the architecture of an educational technology web application built using the MERN stack. It discusses the frontend developed with ReactJS, backend with NodeJS and ExpressJS, and MongoDB database. The frontend provides dynamic user interfaces, while the backend offers APIs for authentication, course management, and other functions. MongoDB enables flexible data storage. The architecture aims to provide a scalable, responsive platform for online learning.
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...IRJET Journal
This paper proposes integrating Internet of Things (IoT) and blockchain technologies to help implement objectives of India's National Education Policy (NEP) in the education sector. The paper discusses how blockchain could be used for secure student data management, credential verification, and decentralized learning platforms. IoT devices could create smart classrooms, automate attendance tracking, and enable real-time monitoring. Blockchain would ensure integrity of exam processes and resource allocation, while smart contracts automate agreements. The paper argues this integration has potential to revolutionize education by making it more secure, transparent and efficient, in alignment with NEP goals. However, challenges like infrastructure needs, data privacy, and collaborative efforts are also discussed.
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.IRJET Journal
This document provides a review of research on the performance of coconut fibre reinforced concrete. It summarizes several studies that tested different volume fractions and lengths of coconut fibres in concrete mixtures with varying compressive strengths. The studies found that coconut fibre improved properties like tensile strength, toughness, crack resistance, and spalling resistance compared to plain concrete. Volume fractions of 2-5% and fibre lengths of 20-50mm produced the best results. The document concludes that using a 4-5% volume fraction of coconut fibres 30-40mm in length with M30-M60 grade concrete would provide benefits based on previous research.
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...IRJET Journal
The document discusses optimizing business management processes through automation using Microsoft Power Automate and artificial intelligence. It provides an overview of Power Automate's key components and features for automating workflows across various apps and services. The document then presents several scenarios applying automation solutions to common business processes like data entry, monitoring, HR, finance, customer support, and more. It estimates the potential time and cost savings from implementing automation for each scenario. Finally, the conclusion emphasizes the transformative impact of AI and automation tools on business processes and the need for ongoing optimization.
Multistoried and Multi Bay Steel Building Frame by using Seismic DesignIRJET Journal
The document describes the seismic design of a G+5 steel building frame located in Roorkee, India according to Indian codes IS 1893-2002 and IS 800. The frame was analyzed using the equivalent static load method and response spectrum method, and its response in terms of displacements and shear forces were compared. Based on the analysis, the frame was designed as a seismic-resistant steel structure according to IS 800:2007. The software STAAD Pro was used for the analysis and design.
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...IRJET Journal
This research paper explores using plastic waste as a sustainable and cost-effective construction material. The study focuses on manufacturing pavers and bricks using recycled plastic and partially replacing concrete with plastic alternatives. Initial results found that pavers and bricks made from recycled plastic demonstrate comparable strength and durability to traditional materials while providing environmental and cost benefits. Additionally, preliminary research indicates incorporating plastic waste as a partial concrete replacement significantly reduces construction costs without compromising structural integrity. The outcomes suggest adopting plastic waste in construction can address plastic pollution while optimizing costs, promoting more sustainable building practices.
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
Gas agency management system project report.pdfKamal Acharya
The project entitled "Gas Agency" is done to make the manual process easier by making it a computerized system for billing and maintaining stock. The Gas Agencies get the order request through phone calls or by personal from their customers and deliver the gas cylinders to their address based on their demand and previous delivery date. This process is made computerized and the customer's name, address and stock details are stored in a database. Based on this the billing for a customer is made simple and easier, since a customer order for gas can be accepted only after completing a certain period from the previous delivery. This can be calculated and billed easily through this. There are two types of delivery like domestic purpose use delivery and commercial purpose use delivery. The bill rate and capacity differs for both. This can be easily maintained and charged accordingly.
Applications of artificial Intelligence in Mechanical Engineering.pdfAtif Razi
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.
Supermarket Management System Project Report.pdfKamal Acharya
Supermarket management is a stand-alone J2EE using Eclipse Juno program.
This project contains all the necessary required information about maintaining
the supermarket billing system.
The core idea of this project to minimize the paper work and centralize the
data. Here all the communication is taken in secure manner. That is, in this
application the information will be stored in client itself. For further security the
data base is stored in the back-end oracle and so no intruders can access it.