A Self-Interpretable Soft Sensor Based on Deep Learning and Multiple Attention Mechanism From Data Selection to Sensor Modeling.pdf
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Module 04 Content· As a continuation to examining your policies, r
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· As a continuation to examining your policies, review for procedures that may relate to them.
· In a 4-page paper, describe the procedures for each of the two compliance plans. NOTE: procedures are not the same thing as policies. Policies (Module 03) sets the parameters for decision-making, while procedures explains the "how". The procedures should be step-by-step instructions, and may include a checklist or process steps to follow.
· Break each procedure section into 2 pages each.
· Remember to support your procedures for each of two plans with a total of three research sources (1-2 per procedure), cited at the end in APA format.
· Write your procedures in a way that all employees will understand at a large medical facility where you are the Compliance Officer.
· Remember, you chose two compliance policy plans under the key compliance areas of Compliance Standards, High-Level Responsibility, Education, Communication, Monitoring/Auditing (for Safety), Enforcement/Discipline, and Response/Prevention. (Check them out if you forget! Remember, you may have written about different policies for the two different compliance plans.)
·
· Submit your completed assignment by following the directions linked below. Please check the Course Calendar for specific due dates.
·
· Save your assignment as a Microsoft Word document. (Mac users, please remember to append the ".docx" extension to the filename.) The name of the file should be your first initial and last name, followed by an underscore and the name of the assignment, and an underscore and the date. An example is shown below:
· Jstudent_exampleproblem_101504
1
8
Southeast Missouri State University
Department of Computer Science
Name of the Instructor: Dr. Reshmi Mitra
CS – 609 Graduate Project
December 2021
Team Member List
Jing Ma
Manoj Thapa
Nagendra Mokara
Contents
Machine learning on Stock Predition 0
Abstract 4
1 INTRODUCTION 4
1.1 LSTM Prediction 5
2 BACKGROUND AND RELATED WORK 6
2.1 Overview of the design 7
3 RESEARCH DESIGN AND METHODS 8
3.1 Stock Price Prediction Project using LSTM 8
3.2 Build the dashboard using plotly 9
4 EVALUTION, VALIDATION ANF KEYRESULTS 9
4.1 Screenshot of Result 10
5 CONCLUSIONS 11
References 12
Sound Classification using Deep Learning ……………………………………………...13
Abstract ………………………………………………………………………………….14
6 INTRODUCTION …………………………………………………………………….14
6.1 Keywords ……………………………………………………………………………15
7 COLLECTION OF DATA FROM THE DATASET …………………………………..15
7.1 Dataset ……………………………………………………………………………….15
7.2 Segregation of Data into Various Folders ..………………………………………….15
8 WRITING CODE AND DATA MODELING/TRAINING……………………………16
8.1 Design Diagram of the Project……………………………………………………….16
9 RESULT ……………………………………………………………………………….17
10 CONCLUSION/FUTURE WORK …………………………………………………..18
Machine learning on stock prediction
Jing Ma
December 2021
Southeast Missouri State University
Advisor: Dr Robert Lowe
Course: Researc ...
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Over the last decade the world of wireless communications has been undergoing
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development interest, eventually resulting in the advent of a multitude of innovative
technologies and associated products such as WiFi, WiMax, 802.20, 802.22, wireless mesh
networks and Software Defined Radio. Such a highly varying radio environment calls for
intelligent management, allocation and usage of a scarce resource, namely the radio
spectrum. One of the most prominent emerging technologies that promise to handle such
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technology and utilize intelligent software packages that enrich their transceivers with the
highly attractive properties of self-awareness, adaptability and capability to learn. The
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learning, and optimization algorithms to control and adapt the radio system from the
physical layer and up the communication stack. The integration of a learning engine can be
very important for improving the stability and reliability of the discovery and evaluation of
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available and can be used by a Cognitive Radio ranging from pure lookup tables to
arbitrary combinations of soft Computing techniques, which include among others:
Artificial Neural Networks, evolutionary/Genetic Algorithms, reinforcement learning, fuzzy
systems, Hidden Markov Models, etc. The proposed work contributes in this direction,
aiming to develop a learning scheme and work towards solving problems related to learning
phase of Cognitive Radio systems. Interesting scenarios are to be mobilized for the
performance assessment work, conducted in order to design and use an appropriate
structure, while indicative results need to be presented and discussed in order to showcase
the benefits of incorporating such learning schemes into Cognitive Radio systems.
Subsequently feasibility of such learning schemes could be tested with simulations. In the
near future, such learning schemes are expected to assist a Cognitive Radio system to
compare among the whole of available, candidate radio configurations and finally select the
best one to operate in.
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Module 04 Content· As a continuation to examining your policies, r
Module 04 Content
· As a continuation to examining your policies, review for procedures that may relate to them.
· In a 4-page paper, describe the procedures for each of the two compliance plans. NOTE: procedures are not the same thing as policies. Policies (Module 03) sets the parameters for decision-making, while procedures explains the "how". The procedures should be step-by-step instructions, and may include a checklist or process steps to follow.
· Break each procedure section into 2 pages each.
· Remember to support your procedures for each of two plans with a total of three research sources (1-2 per procedure), cited at the end in APA format.
· Write your procedures in a way that all employees will understand at a large medical facility where you are the Compliance Officer.
· Remember, you chose two compliance policy plans under the key compliance areas of Compliance Standards, High-Level Responsibility, Education, Communication, Monitoring/Auditing (for Safety), Enforcement/Discipline, and Response/Prevention. (Check them out if you forget! Remember, you may have written about different policies for the two different compliance plans.)
·
· Submit your completed assignment by following the directions linked below. Please check the Course Calendar for specific due dates.
·
· Save your assignment as a Microsoft Word document. (Mac users, please remember to append the ".docx" extension to the filename.) The name of the file should be your first initial and last name, followed by an underscore and the name of the assignment, and an underscore and the date. An example is shown below:
· Jstudent_exampleproblem_101504
1
8
Southeast Missouri State University
Department of Computer Science
Name of the Instructor: Dr. Reshmi Mitra
CS – 609 Graduate Project
December 2021
Team Member List
Jing Ma
Manoj Thapa
Nagendra Mokara
Contents
Machine learning on Stock Predition 0
Abstract 4
1 INTRODUCTION 4
1.1 LSTM Prediction 5
2 BACKGROUND AND RELATED WORK 6
2.1 Overview of the design 7
3 RESEARCH DESIGN AND METHODS 8
3.1 Stock Price Prediction Project using LSTM 8
3.2 Build the dashboard using plotly 9
4 EVALUTION, VALIDATION ANF KEYRESULTS 9
4.1 Screenshot of Result 10
5 CONCLUSIONS 11
References 12
Sound Classification using Deep Learning ……………………………………………...13
Abstract ………………………………………………………………………………….14
6 INTRODUCTION …………………………………………………………………….14
6.1 Keywords ……………………………………………………………………………15
7 COLLECTION OF DATA FROM THE DATASET …………………………………..15
7.1 Dataset ……………………………………………………………………………….15
7.2 Segregation of Data into Various Folders ..………………………………………….15
8 WRITING CODE AND DATA MODELING/TRAINING……………………………16
8.1 Design Diagram of the Project……………………………………………………….16
9 RESULT ……………………………………………………………………………….17
10 CONCLUSION/FUTURE WORK …………………………………………………..18
Machine learning on stock prediction
Jing Ma
December 2021
Southeast Missouri State University
Advisor: Dr Robert Lowe
Course: Researc ...
Explanation of My Report in CMSC 411
The document discusses knowledge-based systems and artificial neural networks. It describes an early expert system developed in 1980 to approve credit applications. It also outlines the key components of expert systems, including the knowledge base and rules. Neural networks are discussed as being inspired by the human brain and capable of learning in a similar way. The multi-layer perception model is presented as a way to break tasks into smaller subtasks performed concurrently.
Soft Computing based Learning for Cognitive Radio
Over the last decade the world of wireless communications has been undergoing
some crucial changes, which have brought it at the forefront of international research and
development interest, eventually resulting in the advent of a multitude of innovative
technologies and associated products such as WiFi, WiMax, 802.20, 802.22, wireless mesh
networks and Software Defined Radio. Such a highly varying radio environment calls for
intelligent management, allocation and usage of a scarce resource, namely the radio
spectrum. One of the most prominent emerging technologies that promise to handle such
situations is Cognitive Radio. Cognitive Radio systems are based on Software Defined Radio
technology and utilize intelligent software packages that enrich their transceivers with the
highly attractive properties of self-awareness, adaptability and capability to learn. The
Cognitive Engine, the intelligent system behind the Cognitive Radio, combines sensing,
learning, and optimization algorithms to control and adapt the radio system from the
physical layer and up the communication stack. The integration of a learning engine can be
very important for improving the stability and reliability of the discovery and evaluation of
the configuration capabilities. To this effect, many different learning techniques are
available and can be used by a Cognitive Radio ranging from pure lookup tables to
arbitrary combinations of soft Computing techniques, which include among others:
Artificial Neural Networks, evolutionary/Genetic Algorithms, reinforcement learning, fuzzy
systems, Hidden Markov Models, etc. The proposed work contributes in this direction,
aiming to develop a learning scheme and work towards solving problems related to learning
phase of Cognitive Radio systems. Interesting scenarios are to be mobilized for the
performance assessment work, conducted in order to design and use an appropriate
structure, while indicative results need to be presented and discussed in order to showcase
the benefits of incorporating such learning schemes into Cognitive Radio systems.
Subsequently feasibility of such learning schemes could be tested with simulations. In the
near future, such learning schemes are expected to assist a Cognitive Radio system to
compare among the whole of available, candidate radio configurations and finally select the
best one to operate in.
SENTIMENT ANALYSIS FOR MOVIES REVIEWS DATASET USING DEEP LEARNING MODELS
Due to the enormous amount of data and opinions being produced, shared and transferred everyday across the internet and other media, Sentiment analysis has become vital for developing opinion mining systems. This paper introduces a developed classification sentiment analysis using deep learning networks and introduces comparative results of different deep learning networks. Multilayer Perceptron (MLP) was developed as a baseline for other networks results. Long short-term memory (LSTM) recurrent neural network, Convolutional Neural Network (CNN) in addition to a hybrid model of LSTM and CNN were developed and applied on IMDB dataset consists of 50K movies reviews files. Dataset was divided to 50% positive reviews and 50% negative reviews. The data was initially pre-processed using Word2Vec and word embedding was applied accordingly. The results have shown that, the hybrid CNN_LSTM model have outperformed the MLP and singular CNN and LSTM networks. CNN_LSTM have reported the accuracy of 89.2% while CNN has given accuracy of 87.7%, while MLP and LSTM have reported accuracy of 86.74% and 86.64 respectively. Moreover, the results have elaborated that the proposed deep learning models have also outperformed SVM, Naïve Bayes and RNTN that were published in other works using English datasets.
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Deep Neural Networks (DNNs) have shown to outperformtraditionalmethodsinvariousvisualrecognitiontasks including Facial Expression Recognition (FER). In spite of efforts made to improve the accuracy of FER systems using DNN, existing methods still are not generalizable enough in practical applications. This paper proposes a 3D Convolutional Neural Network method for FER in videos. This new network architecture consists of 3D Inception-ResNet layers followed by an LSTM unit that together extracts the spatial relations within facial images as well as the temporal relations between different frames in the video. Facial landmark points are also used as inputs to our network which emphasize on the importance of facial components rather than the facial regions that may not contribute significantly to generating facial expressions. Our proposed methodisevaluatedusingfourpubliclyavailabledatabases in subject-independent and cross-database tasks and outperforms state-of-the-art methods.
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=======================
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Facial Expression Recognition Using Enhanced Deep 3D Convolutional Neural Net...
Facial Expression Recognition Using Enhanced Deep 3D Convolutional Neural Net...Willy Marroquin (WillyDevNET)
The document describes a proposed method for facial expression recognition in videos using 3D convolutional neural networks and long short-term memory. Specifically, it proposes a 3D Inception-ResNet architecture to extract both spatial and temporal features from video sequences. It also incorporates facial landmarks to emphasize important facial components. The landmarks are used to generate filters during training. Finally, an LSTM unit is used to further extract temporal information from the enhanced feature maps output by the 3D Inception-ResNet layers. The proposed method is evaluated on several facial expression databases and is shown to outperform state-of-the-art methods.Classification of physiological signals for wheel loader operators using Mult...
This summarizes a research paper that presents a classification approach called MMSE-CBR to classify physiological parameters of wheel loader operators. It combines case-based reasoning (CBR) with multivariate multiscale entropy analysis (MMSE) to fuse data from multiple physiological sensors. The MMSE algorithm is used to extract features from signals measured by sensors like ECG, finger temperature, skin conductance, and respiration rate. These features are used to form cases in a case library. The CBR approach then classifies new cases by retrieving similar past cases. The approach was evaluated on data from 18 professional drivers and achieved 83.33% accuracy in classifying subjects as "stressed" or "healthy", comparable to an expert's
Data Analysis In The Cloud
This document discusses performance analysis and fault tolerance in software environments. It begins by introducing the importance of performance analysis and fault tolerance for software, as faults can lead to losses. It then discusses different fault tolerance techniques, which generally involve some type of replication to handle node and network failures. The two main approaches are replication and coordination, which rely on modeling computation as a deterministic state machine. The document will analyze performance and fault tolerance of software environments.
A Time Series ANN Approach for Weather Forecasting
Weather forecasting is most challenging problem around the world. There are various reason because of its experimented values in meteorology, but it is also a typical unbiased time series forecasting problem in scientific research. A lots of methods proposed by various scientists. The motive behind research is to predict more accurate. This paper contribute the same using artificial neural network (ANN) and simulated in MATLAB to predict two important weather parameters i.e. maximum and minimum temperature. The model has been trained using past 60 years of real data collected from(1901-1960) and tested over 40 years to forecast maximum and minimum temperature. The results based on mean square error function (MSE) confirm, this model which is based on multilayer perceptron has the potential to successful application to weather forecasting
MIDWAY PROJECT PRESENTATION.pptx
This presentation summarizes two papers on human activity recognition using smartphones and wearable sensors. The first paper uses a CNN-LSTM model to recognize both specific activities and transitions using a public dataset, achieving 95.87% accuracy. The second paper proposes a lightweight RNN-LSTM model for edge devices using a different dataset, focusing on capturing temporal dependencies with LSTM to recognize activities and transitions. The presentation then discusses the proposed project which will use the WISDM dataset and libraries like TensorFlow to build models for activity recognition.
IRJET - Heart Health Classification and Prediction using Machine Learning
1) The document discusses using machine learning models to classify heart health based on medical records and heart sounds.
2) Support vector machines, logistic regression, random forest, and ensemble learning algorithms were used to predict heart disease risk based on medical records.
3) Convolutional neural networks were used to develop models for heart sound segmentation and classification. The models classify heart sounds as normal, murmur, or extrasystole based on the timing of the S1 and S2 heart sounds.
The Design A Fuzzy System in Order to Schedule Sleep and Waking Of Sensors in...
Sensor networks are considered to be a standard technology in wireless communications and they are widely
used in military, surveillance, medicine, industry and houses as well. In sensor networks batteries with limited
amount of energy provide energy for the whole system. They are not rechargeable and as soon as the batteries
die the network life time will expire too. Using computational intelligence to schedule sleep and waking of the
sensor nodes is one of the suitable methods which helps the network to have a longer life. In this paper the focus
is on a fuzzy method to schedule sleeping and waking of sensor nodes. In this method the Environmental
conditions of each sensor (the number of neighbors, the remaining energy, and the distance to the next cluster
node) are considered as inputs by the application of a fuzzy system based on which the system creates an output
and the sleeping and waking time of each sensor is dynamically determined. The simulated results show that the
proposed algorithm is more efficient than other basic methods and consume less energy as well.
Face recognition using laplacianfaces
The document discusses a facial recognition system based on locality preserving projections (LPP). It begins by explaining that existing facial recognition systems using PCA and LDA aim to preserve global structure but local structure is more important. It then proposes a system using LPP, which aims to preserve local manifold structure by modeling the image space as a nearest-neighbor graph. The system represents faces as "Laplacianfaces" in a low-dimensional subspace that preserves local structure for more accurate identification. It provides theoretical analysis showing how PCA, LDA and LPP can be derived from different graph models.
Hybrid-e-greedy for mobile context-aware recommender system
This document proposes a hybrid-ε-greedy algorithm for mobile context-aware recommender systems that combines bandit algorithms and case-based reasoning. It summarizes related works that aim to follow the evolution of user interests or manage the user's context, but not both. The proposed approach uses case-based reasoning to consider the user's context in the bandit algorithm's exploration-exploitation strategy. It also uses content-based filtering with the ε-greedy algorithm.
Predictiveanalysisonactivityrecognitionsystem 190131212500
Activity Recognition dataset (UCI dataset) consists of various activities performed by the user (Bending, Walking, Lying etc.). Various data mining methods and classifiers (NaiveBayes, Random Forest, Random Tree, etc.) can be used to find out the predictive capability of the classifiers in classifying each activity.
Predictive analysis on Activity Recognition System
Predicted the activities performed by the user (Bending, Walking etc.) using Classification technique in Data mining.
Performed data cleaning, data pre-processing (removing false predictors, identifying important attributes) in Weka.
Created a model with 77% accuracy by performing Activity Recognition Classification and cross-checked it using Experimental design by adding noise, ROC Curves and Principal component analysis to create new attributes in Weka.
Time Series Forecasting Using Novel Feature Extraction Algorithm and Multilay...
Time series forecasting is important because it can often provide the foundation for decision making in a large variety of fields. A tree-ensemble method, referred to as time series forest (TSF), is proposed for time series classification. The approach is based on the concept of data series envelopes and essential attributes generated by a multilayer neural network... These claims are further investigated by applying statistical tests. With the results presented in this article and results from related investigations that are considered as well, we want to support practitioners or scholars in answering the following question: Which measure should be looked at first if accuracy is the most important criterion, if an application is time-critical, or if a compromise is needed? In this paper demonstrated feature extraction by novel method can improvement in time series data forecasting process
A Comparative Study of Various Data Mining Techniques: Statistics, Decision T...
In this paper we focus on some techniques for solving data mining tasks such as: Statistics, Decision Trees and Neural
Networks. The new approach has succeed in defining some new criteria for the evaluation process, and it has obtained valuable results
based on what the technique is, the environment of using each techniques, the advantages and disadvantages of each technique, the
consequences of choosing any of these techniques to extract hidden predictive information from large databases, and the methods of
implementation of each technique. Finally, the paper has presented some valuable recommendations in this field.
Wearable sensor-based human activity recognition with ensemble learning: a co...
The spectacular growth of wearable sensors has provided a key contribution to the field of human activity recognition. Due to its effective and versatile usage and application in various fields such as smart homes and medical areas, human activity recognition has always been an appealing research topic in artificial intelligence. From this perspective, there are a lot of existing works that make use of accelerometer and gyroscope sensor data for recognizing human activities. This paper presents a comparative study of ensemble learning methods for human activity recognition. The methods include random forest, adaptive boosting, gradient boosting, extreme gradient boosting, and light gradient boosting machine (LightGBM). Among the ensemble learning methods in comparison, light gradient boosting machine and random forest demonstrate the best performance. The experimental results revealed that light gradient boosting machine yields the highest accuracy of 94.50% on UCI-HAR dataset and 100% on single accelerometer dataset while random forest records the highest accuracy of 93.41% on motion sense dataset.
Introduction_PPT.pptx
This document summarizes an introductory seminar presentation on evaluating different machine learning algorithms in wireless sensor networks. The presentation discusses the problem statement, objectives, introduction, literature review, block diagram, expected outputs, future applications, and plan of work. The literature review summarizes past research evaluating neural networks, genetic algorithms, and other machine learning methods for optimizing data transmission and increasing sensor node lifetime in wireless sensor networks. The block diagram shows the overall framework. The objectives are to optimize data transmission using machine learning and increase sensor node lifetime.
Evaluation of Different Machine.pptx
This document summarizes an introductory seminar presentation on evaluating different machine learning algorithms in wireless sensor networks. The presentation discusses the problem statement, objectives, introduction, literature review, block diagram, expected outputs, future applications, and plan of work. The literature review summarizes past research evaluating neural networks, genetic algorithms, and machine learning methods for tasks like energy-efficient routing, localization, and reducing data transmission in wireless sensor networks. The objectives are to optimize information transmission using machine learning, detect redundancies, increase sensor lifetime, and extend application lifetime.
Image-Based Multi-Sensor Data Representation and Fusion Via 2D Non-Linear Con...
Sensor data fusion is the process of combining data collected from multi sensors of homogeneous or heterogeneous modalities to perform inferences that may not be possible using a single sensor. This process encompasses several stages to arrive at a sound reliable decision making end result. These stages include: senor-signal preprocessing, sub-object refinement, object refinement, situation refinement, threat refinement and process refinement. Every stage draws from different domains to achieve its requirements and goals. Popular methods for sensor data fusion include: ad-hock and heuristic-based, classical hypothesis-based, Bayesian inference, fuzzy inference, neural networks, etc. in this work, we introduce a new data fusion model that contributes to the area of multi-senor/source data fusion. The new fusion model relies on image processing theory to map stimuli from sensors onto an energy map and uses non-linear convolution to combine the energy responses on the map onto a single fused response map. This response map is then fed into a process of transformations to extract an inference that estimates the output state response as a normalized amplitude level. This new data fusion model is helpful to identify sever events in the monitored environment. An efficiency comparison with similar fuzzy-logic fusion model revealed that our proposed model is superior in time complexity as validated theoretically and experimentally.
LearningAG.ppt
The document discusses machine learning and learning agents in three main points:
1. It defines machine learning and discusses different types of machine learning tasks like supervised, unsupervised, and reinforcement learning.
2. It explains the key differences between traditional machine learning approaches and learning agents, noting that learning is one of many goals for agents and must be integrated with other agent functions.
3. It discusses different challenges of integrating machine learning into intelligent agents, such as balancing learning with recall of existing knowledge and addressing time constraints on learning from the environment.
Soft Computing Techniques_Part 1.pptx
The document discusses hard computing and soft computing. Hard computing uses precise mathematical models and algorithms, while soft computing uses techniques like neural networks and genetic algorithms to handle imprecise or complex problems. Soft computing is needed to solve real-world problems that involve uncertainty, incomplete information, noise, and non-linearity. It can provide approximate solutions and mimic human-like reasoning. The document then provides examples of applications of soft computing in various domains like image processing, automotive systems, bioinformatics, and power systems analysis.
Similar to A Self-Interpretable Soft Sensor Based on Deep Learning and Multiple Attention Mechanism From Data Selection to Sensor Modeling.pdf (20)
Facial Expression Recognition Using Enhanced Deep 3D Convolutional Neural Net...
Facial Expression Recognition Using Enhanced Deep 3D Convolutional Neural Net...
Classification of physiological signals for wheel loader operators using Mult...
Classification of physiological signals for wheel loader operators using Mult...
A Time Series ANN Approach for Weather Forecasting
A Time Series ANN Approach for Weather Forecasting
IRJET - Heart Health Classification and Prediction using Machine Learning
IRJET - Heart Health Classification and Prediction using Machine Learning
The Design A Fuzzy System in Order to Schedule Sleep and Waking Of Sensors in...
The Design A Fuzzy System in Order to Schedule Sleep and Waking Of Sensors in...
Hybrid-e-greedy for mobile context-aware recommender system
Hybrid-e-greedy for mobile context-aware recommender system
Predictiveanalysisonactivityrecognitionsystem 190131212500
Predictiveanalysisonactivityrecognitionsystem 190131212500
Predictive analysis on Activity Recognition System
Predictive analysis on Activity Recognition System
Time Series Forecasting Using Novel Feature Extraction Algorithm and Multilay...
Time Series Forecasting Using Novel Feature Extraction Algorithm and Multilay...
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A Comparative Study of Various Data Mining Techniques: Statistics, Decision T...
Wearable sensor-based human activity recognition with ensemble learning: a co...
Wearable sensor-based human activity recognition with ensemble learning: a co...
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