This document discusses using machine learning algorithms to analyze EEG data and predict a person's learning capabilities. It extracts features from raw EEG data, including delta, theta, alpha, beta, and gamma waves. It then applies logistic regression, XGBoost, RNN, and decision trees to classify if a student is confused while learning from videos. The highest accuracy was achieved using XGBoost. Overall, the study aims to develop a system to monitor learning using EEG and analyze the correlation between brain activity and learning capability.
Robot Motion Control Using the Emotiv EPOC EEG SystemjournalBEEI
Brain-computer interfaces have been explored for years with the intent of using human thoughts to control mechanical system. By capturing the transmission of signals directly from the human brain or electroencephalogram (EEG), human thoughts can be made as motion commands to the robot. This paper presents a prototype for an electroencephalogram (EEG) based brain-actuated robot control system using mental commands. In this study, Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) method were combined to establish the best model. Dataset containing features of EEG signals were obtained from the subject non-invasively using Emotiv EPOC headset. The best model was then used by Brain-Computer Interface (BCI) to classify the EEG signals into robot motion commands to control the robot directly. The result of the classification gave the average accuracy of 69.06%.
SUITABLE MOTHER WAVELET SELECTION FOR EEG SIGNALS ANALYSIS: FREQUENCY BANDS D...sipij
Wavelet transform (WT) is a powerful modern tool for time-frequency analysis of non-stationary signals such as electroencephalogram (EEG). The aim of this study is to choose the best and suitable mother wavelet function (MWT) for analyzing normal, seizure-free and seizured EEG signals. Several MWTs can be used, but the best MWT is the one that conserves the quasi-totality of information of the original signal on wavelet coefficients and gather more EEG rhythms in terms of frequency. In this study, Daubechies, Symlets and Coiflets orthogonal families were used as bsis mother wavelet functions. The percentage rootmeans square difference (PRD), the signal to noise ratio (SNR) and the simulated frequencies as the selection metrics. Simulation results indicate Daubechies wavelet at level 4 (Db4) as the most suitable MWT for EEG frequency bands decomposition.Furthermore, due to the redundancy of the extracted features, linear discriminant analysis (LDA) is applied for feature selection. Scatter plot showed that the selected feature vector represents the amount of changes in frequency distribution and carries most of the discriminative and representative information about their classes. Then, this study can provide a reference for the selection of a suitable MWT and discriminativefeatures.
IRJET- Human Emotions Detection using Brain Wave SignalsIRJET Journal
This document discusses detecting human emotions using brain wave signals captured through electroencephalography (EEG). It aims to provide a mobile system that can analyze EEG signals captured wirelessly from an EEG headset to classify emotions. The system architecture involves collecting raw EEG data, performing noise filtering, feature extraction using techniques like discrete wavelet transform and K-nearest neighbors, then classifying emotions using algorithms like support vector machines. The goal is to identify emotions in a cost-effective and mobile way to enable applications in healthcare, games, education, and more. Key challenges include designing stimuli to elicit single emotions, removing noise from EEG signals, and selecting the best machine learning techniques for emotion classification.
Bio-medical (EMG) Signal Analysis and Feature Extraction Using Wavelet TransformIJERA Editor
This document summarizes research on analyzing electromyography (EMG) signals using wavelet transforms to extract features for classification of muscle activity. A multi-channel EMG acquisition system was developed using surface electrodes to measure forearm muscle signals. Different wavelet families were used to analyze the EMG signals. Features like root mean square, logarithm of root mean square, centroid frequency, and standard deviation were extracted. Root mean square feature extraction performed best. In the future, this method could be used to control prosthetic or robotic arms for real-time processing based on muscle activity.
A novel neural network classifier for brain computerAlexander Decker
This document describes a novel neural network classifier for brain-computer interfaces. It proposes extracting EEG features using discrete cosine transforms and preprocessing signals with two-stage filtering, including a Butterworth filter and 15-point Spencer filter, to remove noise while maintaining a sharp step response. Classification is done using a proposed Semi Partial Recurrent Neural Network, which is claimed to have better accuracy than conventional neural network classifiers. The materials and methods section provides mathematical details on the discrete cosine transform, filtering approaches, and the proposed neural network structure.
11.a novel neural network classifier for brain computerAlexander Decker
This document describes a novel neural network classifier for brain-computer interfaces. It proposes extracting EEG features using discrete cosine transforms and preprocessing signals with two-stage filtering, including a Butterworth filter and 15-point Spencer filter. The signals would then be classified using a proposed Semi Partial Recurrent Neural Network, which is claimed to have better accuracy than conventional neural network classifiers. The document provides background on BCI systems and details on the proposed feature extraction and preprocessing methods, as well as introducing the novel classifier.
Design and implementation of image compression using set partitioning in hier...eSAT Journals
Abstract
To store digital image and video in raw form require large amount of memory space. Image compression means reducing the size of image file without degrading quality of image. Depending on the reconstructed image to be exactly same as the original or some unknown loss may incurred image compression divided into two techniques lossy and lossless techniques. In this paper we present hybrid model which is the combination of several compression techniques. This paper present DWT, DCT and SPIHT implementation. Simulation has been carried out on different images like Lena , Barbra, Cameraman, Test drive . Result analysis is done through parameters like MSE, PSNR and Elapsed time. Values of this parameters will go better than the old algorithms because here we apply SPIHT lossless technique and also due to hybrid combination of DWT and DCT we will get good level of compression. The result analysis shows that the proposed hybrid algorithm performs much better in terms of PSNR with a higher compression ratio as compared to standalone DWT and DCT techniques.
Keywords- DWT, DCT, SPIHT, PSNR, MSE
Classification of emotions induced by horror and relaxing movies using single-...IJECEIAES
It has been observed from recent studies that corticolimbic Theta rhythm from EEG recordings perceived as fear or threatening scene during neural processing of visual stimuli. In additions, neural oscillations’ patterns in Theta, Alpha and Beta sub-bands also play important role in brain’s emotional processing. Inspired from these findings, in this paper we attempt to classify two different emotional states by analyzing single-channel EEG recordings. A video clip that can evoke 3 different emotional states: neutral, relaxation and scary is shown to 19 college-aged subjects and they were asked to score their emotional outcome by giving a number between 0 to 10 (where 0 means not scary at all and 10 means the most scary). First, recorded EEG data were preprocessed by stationary wavelet transform (SWT) based artifact removal algorithm. Then power distribution in simultaneous time-frequency domain was analyzed using short-time Fourier transform (STFT) followed by calculating the average power during each 0.2s time-segment for each brain sub-band. Finally, 46 features, as the mean power of frequency bands between 4 and 50 Hz during each time-segment, containing 689 instances—for each subject —were collected for classification. We found that relaxation and fear emotions evoked during watching scary and relaxing movies can be classified with average classification rate of 94.208% using K-NN by applying methods and materials proposed in this paper. We also classified the dataset using SVM and we found out that K-NN classifier (when k = 1) outperforms SVM in classifying EEG dynamics induced by horror and relaxing movies, however, for K > 1 in K-NN, SVM has better average classification rate.
Robot Motion Control Using the Emotiv EPOC EEG SystemjournalBEEI
Brain-computer interfaces have been explored for years with the intent of using human thoughts to control mechanical system. By capturing the transmission of signals directly from the human brain or electroencephalogram (EEG), human thoughts can be made as motion commands to the robot. This paper presents a prototype for an electroencephalogram (EEG) based brain-actuated robot control system using mental commands. In this study, Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) method were combined to establish the best model. Dataset containing features of EEG signals were obtained from the subject non-invasively using Emotiv EPOC headset. The best model was then used by Brain-Computer Interface (BCI) to classify the EEG signals into robot motion commands to control the robot directly. The result of the classification gave the average accuracy of 69.06%.
SUITABLE MOTHER WAVELET SELECTION FOR EEG SIGNALS ANALYSIS: FREQUENCY BANDS D...sipij
Wavelet transform (WT) is a powerful modern tool for time-frequency analysis of non-stationary signals such as electroencephalogram (EEG). The aim of this study is to choose the best and suitable mother wavelet function (MWT) for analyzing normal, seizure-free and seizured EEG signals. Several MWTs can be used, but the best MWT is the one that conserves the quasi-totality of information of the original signal on wavelet coefficients and gather more EEG rhythms in terms of frequency. In this study, Daubechies, Symlets and Coiflets orthogonal families were used as bsis mother wavelet functions. The percentage rootmeans square difference (PRD), the signal to noise ratio (SNR) and the simulated frequencies as the selection metrics. Simulation results indicate Daubechies wavelet at level 4 (Db4) as the most suitable MWT for EEG frequency bands decomposition.Furthermore, due to the redundancy of the extracted features, linear discriminant analysis (LDA) is applied for feature selection. Scatter plot showed that the selected feature vector represents the amount of changes in frequency distribution and carries most of the discriminative and representative information about their classes. Then, this study can provide a reference for the selection of a suitable MWT and discriminativefeatures.
IRJET- Human Emotions Detection using Brain Wave SignalsIRJET Journal
This document discusses detecting human emotions using brain wave signals captured through electroencephalography (EEG). It aims to provide a mobile system that can analyze EEG signals captured wirelessly from an EEG headset to classify emotions. The system architecture involves collecting raw EEG data, performing noise filtering, feature extraction using techniques like discrete wavelet transform and K-nearest neighbors, then classifying emotions using algorithms like support vector machines. The goal is to identify emotions in a cost-effective and mobile way to enable applications in healthcare, games, education, and more. Key challenges include designing stimuli to elicit single emotions, removing noise from EEG signals, and selecting the best machine learning techniques for emotion classification.
Bio-medical (EMG) Signal Analysis and Feature Extraction Using Wavelet TransformIJERA Editor
This document summarizes research on analyzing electromyography (EMG) signals using wavelet transforms to extract features for classification of muscle activity. A multi-channel EMG acquisition system was developed using surface electrodes to measure forearm muscle signals. Different wavelet families were used to analyze the EMG signals. Features like root mean square, logarithm of root mean square, centroid frequency, and standard deviation were extracted. Root mean square feature extraction performed best. In the future, this method could be used to control prosthetic or robotic arms for real-time processing based on muscle activity.
A novel neural network classifier for brain computerAlexander Decker
This document describes a novel neural network classifier for brain-computer interfaces. It proposes extracting EEG features using discrete cosine transforms and preprocessing signals with two-stage filtering, including a Butterworth filter and 15-point Spencer filter, to remove noise while maintaining a sharp step response. Classification is done using a proposed Semi Partial Recurrent Neural Network, which is claimed to have better accuracy than conventional neural network classifiers. The materials and methods section provides mathematical details on the discrete cosine transform, filtering approaches, and the proposed neural network structure.
11.a novel neural network classifier for brain computerAlexander Decker
This document describes a novel neural network classifier for brain-computer interfaces. It proposes extracting EEG features using discrete cosine transforms and preprocessing signals with two-stage filtering, including a Butterworth filter and 15-point Spencer filter. The signals would then be classified using a proposed Semi Partial Recurrent Neural Network, which is claimed to have better accuracy than conventional neural network classifiers. The document provides background on BCI systems and details on the proposed feature extraction and preprocessing methods, as well as introducing the novel classifier.
Design and implementation of image compression using set partitioning in hier...eSAT Journals
Abstract
To store digital image and video in raw form require large amount of memory space. Image compression means reducing the size of image file without degrading quality of image. Depending on the reconstructed image to be exactly same as the original or some unknown loss may incurred image compression divided into two techniques lossy and lossless techniques. In this paper we present hybrid model which is the combination of several compression techniques. This paper present DWT, DCT and SPIHT implementation. Simulation has been carried out on different images like Lena , Barbra, Cameraman, Test drive . Result analysis is done through parameters like MSE, PSNR and Elapsed time. Values of this parameters will go better than the old algorithms because here we apply SPIHT lossless technique and also due to hybrid combination of DWT and DCT we will get good level of compression. The result analysis shows that the proposed hybrid algorithm performs much better in terms of PSNR with a higher compression ratio as compared to standalone DWT and DCT techniques.
Keywords- DWT, DCT, SPIHT, PSNR, MSE
Classification of emotions induced by horror and relaxing movies using single-...IJECEIAES
It has been observed from recent studies that corticolimbic Theta rhythm from EEG recordings perceived as fear or threatening scene during neural processing of visual stimuli. In additions, neural oscillations’ patterns in Theta, Alpha and Beta sub-bands also play important role in brain’s emotional processing. Inspired from these findings, in this paper we attempt to classify two different emotional states by analyzing single-channel EEG recordings. A video clip that can evoke 3 different emotional states: neutral, relaxation and scary is shown to 19 college-aged subjects and they were asked to score their emotional outcome by giving a number between 0 to 10 (where 0 means not scary at all and 10 means the most scary). First, recorded EEG data were preprocessed by stationary wavelet transform (SWT) based artifact removal algorithm. Then power distribution in simultaneous time-frequency domain was analyzed using short-time Fourier transform (STFT) followed by calculating the average power during each 0.2s time-segment for each brain sub-band. Finally, 46 features, as the mean power of frequency bands between 4 and 50 Hz during each time-segment, containing 689 instances—for each subject —were collected for classification. We found that relaxation and fear emotions evoked during watching scary and relaxing movies can be classified with average classification rate of 94.208% using K-NN by applying methods and materials proposed in this paper. We also classified the dataset using SVM and we found out that K-NN classifier (when k = 1) outperforms SVM in classifying EEG dynamics induced by horror and relaxing movies, however, for K > 1 in K-NN, SVM has better average classification rate.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
EEG SIGNAL IDENTIFICATION USING SINGLE-LAYER NEURAL NETWORKIJCI JOURNAL
EEG signal analysis is applied in various fields such as medicine, communication and control. To control based on EEG signals achieved good result, the system must identify effectively EEG signals. In this paper,
a novel approach proposes the EEG signal identification based on image with the EEG signal processing via Wavelet transform and the identification via single-layer neural network. The system model is designed and evaluated with the dataset of 21,000 samples. The accuracy rate can obtain 91.15%.
Introduction to Artificial Neural NetworksAdri Jovin
This presentation describes the various components, classification and application of Artificial Neural Networks. It also gives an outline on the other soft computing techniques also.
IRJET- Review on Depression Prediction using Different MethodsIRJET Journal
This document summarizes various methods that have been used to predict depression. It discusses using questionnaires and psychometric tests administered by psychiatrists, analyzing EEG signals through signal processing techniques, and using artificial intelligence and machine learning algorithms to analyze text, audio, and visual inputs. Specifically, it describes using standardized tests like the Hospital Anxiety and Depression Scale and Beck's Depression Inventory, extracting features from EEG frequency bands to classify subjects, and employing sentiment analysis and other text analysis on speech, facial expressions, and head movements to predict mental states. The document provides background on relevant concepts in artificial intelligence, machine learning, deep learning, and neural networks.
Using Brain Waves as New Biometric Feature for Authenticatinga Computer User...Buthainah Hamdy
This document presents a brain-computer interface (BCI) for person authentication using electroencephalography (EEG) signals. The BCI is used to lock and unlock a computer screen based on a user's brain activity. EEG data is collected from 14 sensors while users perform mental tasks. Power spectral density is calculated from the EEG signals and used as features for a two-stage classification system. The system achieves over 78% accuracy in authenticating users based on their brain activity patterns.
IRJET- Mental Workload Assessment using RnnIRJET Journal
This document describes a proposed system to assess mental workload using recurrent neural networks (RNNs) and electroencephalogram (EEG) data. The system aims to classify mental workload levels across different tasks with higher accuracy than existing methods. It involves preprocessing EEG signals, extracting features using clustering coefficients of variation, classifying the data with an RNN model, and evaluating the model's performance using various metrics like accuracy, sensitivity and specificity. The proposed RNN approach aims to learn representations from EEG data in multiple dimensions simultaneously, unlike existing methods that extract temporal and spectral features separately, to better distinguish brain dynamics across tasks.
This document discusses using complex wavelet transforms to analyze power signals and classify power quality disturbances. It generates simulated power signals containing different disturbance types and extracts features from the signal decompositions. A neural network is trained on these features to classify the disturbance types. The summary finds that complex wavelet transforms provide higher classification accuracy compared to discrete wavelet transforms, due to the complex wavelets' shift invariant nature.
IRJET- Depression Prediction System using Different MethodsIRJET Journal
This document summarizes a research paper that proposes a depression prediction system using different methods. The system would use three approaches: a question and answer part using standardized depression questionnaires; EEG signal processing to analyze brain activity; and sentiment analysis of social media posts. Machine learning algorithms like neural networks and naive Bayes would be used for classification. The goal is to help predict depression early through an online system that could be used by doctors and individuals. Key areas discussed include artificial intelligence, machine learning techniques for classification like support vector machines and logistic regression, and prior research analyzing EEG signals and social media posts to predict depression.
A New Approach to Denoising EEG Signals - Merger of Translation Invariant Wav...CSCJournals
In this paper we present a new algorithm using a merger of Independent Component Analysis and Translation Invariant Wavelet Transform. The efficacy of this algorithm is evaluated by applying contaminated EEG signals. Its performance was compared to three fixed-point ICA algorithms (FastICA, EFICA and Pearson-ICA) using Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Signal to Distortion Ratio (SDR), and Amari Performance Index. Experiments reveal that our new technique is the most accurate separation method.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
The document describes a wireless remote monitoring system based on sensor technology and GSM mobile communication. The system uses pyroelectric infrared sensors to detect changes in temperature caused by infrared light. When the sensor detects a temperature change, it produces a current based on the thermoelectric effect. This current is then transmitted wirelessly via GSM networks to notify users of monitoring events through text messages in real-time. The system allows remote monitoring of important places and items without needing a wired network. It was designed and implemented using wireless sensor modules, a receiver, decoder, microcontroller, alarm unit, and GSM communication module to achieve reliable wireless remote monitoring.
11.simulation of unified power quality conditioner for power quality improvem...Alexander Decker
1) The document discusses the simulation of a Unified Power Quality Conditioner (UPQC) using fuzzy logic and neural networks to improve power quality.
2) A UPQC consists of series and shunt active power filters connected back-to-back to compensate voltage sags and current quality problems affecting sensitive loads.
3) Fuzzy logic and neural network controllers are designed for the UPQC and their performance is compared in mitigating voltage sags using Matlab/Simulink simulations. The neural network controller is shown to compensate a higher percentage of voltage sags compared to the fuzzy logic controller.
- Artificial neural networks are inspired by biological neural networks and try to mimic their learning mechanisms by modifying synaptic strengths through an optimization process.
- Learning in neural networks can be formulated as a function approximation task where the network learns to approximate a function by minimizing an error measure through optimization of synaptic weights.
- A single hidden layer neural network is capable of learning nonlinear function approximations if general optimization methods are applied to update the synaptic weights.
The document discusses neural networks and their biological inspiration. It defines an artificial neural network as an information processing system modeled after the human brain. Neural networks can extract patterns from complex data, operate in parallel, and learn from experience. The document then covers biological neurons, characteristics of neural networks, popular neural network models, learning rules, and different types of learning.
Dsp lab report- Analysis and classification of EMG signal using MATLAB.Nurhasanah Shafei
This document discusses a study analyzing and classifying electromyogram (EMG) signals. The researchers developed a MATLAB-based system that can differentiate EMG signals coming from different patients. The system analyzes time and frequency domain characteristics of the EMG signals, including median value, average value, root mean square, maximum power, and minimum power. It then uses these characteristics to identify which patient a given EMG signal belongs to through a graphical user interface. The system was able to accurately classify EMG signals from two patients based on their power spectrum signatures.
This document compares the performance of feed-forward and Elman neural networks for face recognition using principal component analysis (PCA). PCA is used to extract features from face images. Both neural networks are then used as classifiers to identify faces from the ORL database. The recognition rate and total training time are calculated for different numbers of training images using both networks. Feed-forward neural network showed better performance than Elman neural network in terms of higher recognition rates and shorter training times.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Neural networks are computing systems inspired by biological neural networks. They are composed of interconnected nodes that process input data and transmit signals to each other. The document discusses various types of neural networks including feedforward, recurrent, convolutional, and modular neural networks. It also describes the basic architecture of neural networks including input, hidden, and output layers. Neural networks can be used for applications like pattern recognition, data classification, and more. They are well-suited for complex, nonlinear problems. The document provides an overview of neural networks and their functioning.
Neural networks are parallel information processing systems inspired by the brain. They can learn from historical data to make classifications and predictions. A neural network consists of interconnected nodes called neurons that receive inputs, perform operations, and output results. By training neural networks on large amounts of labeled data using techniques like backpropagation, they can learn complex patterns to solve problems like image and speech recognition.
Advanced applications of artificial intelligence and neural networksPraveen Kumar
The document summarizes a presentation given by Rajarajeshwari K Divate on advanced applications of artificial intelligence and neural networks. It provides an overview of artificial intelligence and neural networks, including definitions and key concepts. Specifically, it discusses biological neural networks, artificial neural networks, Hopfield networks, and an application of Hopfield networks for early lung cancer detection using sputum image segmentation. The presentation covers topics like network architecture, energy functions, features of Hopfield networks, and the Hopfield neural network segmentation algorithm.
Epileptic Seizure Detection using An EEG SensorIRJET Journal
This document presents a method for detecting epileptic seizures using an EEG sensor and signal processing techniques. It involves using an EEG headset to record raw brain wave data, filtering the signals to remove noise, applying discrete wavelet transform to extract features from different frequency bands, and using a support vector machine classifier to classify segments as normal, interictal, or ictal based on the extracted features. The proposed method aims to help doctors more accurately diagnose and monitor epilepsy in patients by objectively detecting seizures from EEG data in near real-time.
IRJET-A Survey on Effect of Meditation on Attention Level Using EEGIRJET Journal
This document summarizes a proposed study that investigates the effect of meditation on attention level using EEG data analysis. It begins with an introduction on attention and meditation, then reviews previous related studies that analyzed EEG data to measure attention. The proposed work will record EEG data from subjects using the 10-20 electrode placement system before and after an 8-week meditation program. The EEG data will be preprocessed to remove noise, features will be extracted using wavelet transforms, and a random forest classifier will be used to classify attention levels and analyze the effect of meditation. The goal is to objectively measure how meditation impacts attention to help students improve concentration.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
EEG SIGNAL IDENTIFICATION USING SINGLE-LAYER NEURAL NETWORKIJCI JOURNAL
EEG signal analysis is applied in various fields such as medicine, communication and control. To control based on EEG signals achieved good result, the system must identify effectively EEG signals. In this paper,
a novel approach proposes the EEG signal identification based on image with the EEG signal processing via Wavelet transform and the identification via single-layer neural network. The system model is designed and evaluated with the dataset of 21,000 samples. The accuracy rate can obtain 91.15%.
Introduction to Artificial Neural NetworksAdri Jovin
This presentation describes the various components, classification and application of Artificial Neural Networks. It also gives an outline on the other soft computing techniques also.
IRJET- Review on Depression Prediction using Different MethodsIRJET Journal
This document summarizes various methods that have been used to predict depression. It discusses using questionnaires and psychometric tests administered by psychiatrists, analyzing EEG signals through signal processing techniques, and using artificial intelligence and machine learning algorithms to analyze text, audio, and visual inputs. Specifically, it describes using standardized tests like the Hospital Anxiety and Depression Scale and Beck's Depression Inventory, extracting features from EEG frequency bands to classify subjects, and employing sentiment analysis and other text analysis on speech, facial expressions, and head movements to predict mental states. The document provides background on relevant concepts in artificial intelligence, machine learning, deep learning, and neural networks.
Using Brain Waves as New Biometric Feature for Authenticatinga Computer User...Buthainah Hamdy
This document presents a brain-computer interface (BCI) for person authentication using electroencephalography (EEG) signals. The BCI is used to lock and unlock a computer screen based on a user's brain activity. EEG data is collected from 14 sensors while users perform mental tasks. Power spectral density is calculated from the EEG signals and used as features for a two-stage classification system. The system achieves over 78% accuracy in authenticating users based on their brain activity patterns.
IRJET- Mental Workload Assessment using RnnIRJET Journal
This document describes a proposed system to assess mental workload using recurrent neural networks (RNNs) and electroencephalogram (EEG) data. The system aims to classify mental workload levels across different tasks with higher accuracy than existing methods. It involves preprocessing EEG signals, extracting features using clustering coefficients of variation, classifying the data with an RNN model, and evaluating the model's performance using various metrics like accuracy, sensitivity and specificity. The proposed RNN approach aims to learn representations from EEG data in multiple dimensions simultaneously, unlike existing methods that extract temporal and spectral features separately, to better distinguish brain dynamics across tasks.
This document discusses using complex wavelet transforms to analyze power signals and classify power quality disturbances. It generates simulated power signals containing different disturbance types and extracts features from the signal decompositions. A neural network is trained on these features to classify the disturbance types. The summary finds that complex wavelet transforms provide higher classification accuracy compared to discrete wavelet transforms, due to the complex wavelets' shift invariant nature.
IRJET- Depression Prediction System using Different MethodsIRJET Journal
This document summarizes a research paper that proposes a depression prediction system using different methods. The system would use three approaches: a question and answer part using standardized depression questionnaires; EEG signal processing to analyze brain activity; and sentiment analysis of social media posts. Machine learning algorithms like neural networks and naive Bayes would be used for classification. The goal is to help predict depression early through an online system that could be used by doctors and individuals. Key areas discussed include artificial intelligence, machine learning techniques for classification like support vector machines and logistic regression, and prior research analyzing EEG signals and social media posts to predict depression.
A New Approach to Denoising EEG Signals - Merger of Translation Invariant Wav...CSCJournals
In this paper we present a new algorithm using a merger of Independent Component Analysis and Translation Invariant Wavelet Transform. The efficacy of this algorithm is evaluated by applying contaminated EEG signals. Its performance was compared to three fixed-point ICA algorithms (FastICA, EFICA and Pearson-ICA) using Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Signal to Distortion Ratio (SDR), and Amari Performance Index. Experiments reveal that our new technique is the most accurate separation method.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
The document describes a wireless remote monitoring system based on sensor technology and GSM mobile communication. The system uses pyroelectric infrared sensors to detect changes in temperature caused by infrared light. When the sensor detects a temperature change, it produces a current based on the thermoelectric effect. This current is then transmitted wirelessly via GSM networks to notify users of monitoring events through text messages in real-time. The system allows remote monitoring of important places and items without needing a wired network. It was designed and implemented using wireless sensor modules, a receiver, decoder, microcontroller, alarm unit, and GSM communication module to achieve reliable wireless remote monitoring.
11.simulation of unified power quality conditioner for power quality improvem...Alexander Decker
1) The document discusses the simulation of a Unified Power Quality Conditioner (UPQC) using fuzzy logic and neural networks to improve power quality.
2) A UPQC consists of series and shunt active power filters connected back-to-back to compensate voltage sags and current quality problems affecting sensitive loads.
3) Fuzzy logic and neural network controllers are designed for the UPQC and their performance is compared in mitigating voltage sags using Matlab/Simulink simulations. The neural network controller is shown to compensate a higher percentage of voltage sags compared to the fuzzy logic controller.
- Artificial neural networks are inspired by biological neural networks and try to mimic their learning mechanisms by modifying synaptic strengths through an optimization process.
- Learning in neural networks can be formulated as a function approximation task where the network learns to approximate a function by minimizing an error measure through optimization of synaptic weights.
- A single hidden layer neural network is capable of learning nonlinear function approximations if general optimization methods are applied to update the synaptic weights.
The document discusses neural networks and their biological inspiration. It defines an artificial neural network as an information processing system modeled after the human brain. Neural networks can extract patterns from complex data, operate in parallel, and learn from experience. The document then covers biological neurons, characteristics of neural networks, popular neural network models, learning rules, and different types of learning.
Dsp lab report- Analysis and classification of EMG signal using MATLAB.Nurhasanah Shafei
This document discusses a study analyzing and classifying electromyogram (EMG) signals. The researchers developed a MATLAB-based system that can differentiate EMG signals coming from different patients. The system analyzes time and frequency domain characteristics of the EMG signals, including median value, average value, root mean square, maximum power, and minimum power. It then uses these characteristics to identify which patient a given EMG signal belongs to through a graphical user interface. The system was able to accurately classify EMG signals from two patients based on their power spectrum signatures.
This document compares the performance of feed-forward and Elman neural networks for face recognition using principal component analysis (PCA). PCA is used to extract features from face images. Both neural networks are then used as classifiers to identify faces from the ORL database. The recognition rate and total training time are calculated for different numbers of training images using both networks. Feed-forward neural network showed better performance than Elman neural network in terms of higher recognition rates and shorter training times.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Neural networks are computing systems inspired by biological neural networks. They are composed of interconnected nodes that process input data and transmit signals to each other. The document discusses various types of neural networks including feedforward, recurrent, convolutional, and modular neural networks. It also describes the basic architecture of neural networks including input, hidden, and output layers. Neural networks can be used for applications like pattern recognition, data classification, and more. They are well-suited for complex, nonlinear problems. The document provides an overview of neural networks and their functioning.
Neural networks are parallel information processing systems inspired by the brain. They can learn from historical data to make classifications and predictions. A neural network consists of interconnected nodes called neurons that receive inputs, perform operations, and output results. By training neural networks on large amounts of labeled data using techniques like backpropagation, they can learn complex patterns to solve problems like image and speech recognition.
Advanced applications of artificial intelligence and neural networksPraveen Kumar
The document summarizes a presentation given by Rajarajeshwari K Divate on advanced applications of artificial intelligence and neural networks. It provides an overview of artificial intelligence and neural networks, including definitions and key concepts. Specifically, it discusses biological neural networks, artificial neural networks, Hopfield networks, and an application of Hopfield networks for early lung cancer detection using sputum image segmentation. The presentation covers topics like network architecture, energy functions, features of Hopfield networks, and the Hopfield neural network segmentation algorithm.
Epileptic Seizure Detection using An EEG SensorIRJET Journal
This document presents a method for detecting epileptic seizures using an EEG sensor and signal processing techniques. It involves using an EEG headset to record raw brain wave data, filtering the signals to remove noise, applying discrete wavelet transform to extract features from different frequency bands, and using a support vector machine classifier to classify segments as normal, interictal, or ictal based on the extracted features. The proposed method aims to help doctors more accurately diagnose and monitor epilepsy in patients by objectively detecting seizures from EEG data in near real-time.
IRJET-A Survey on Effect of Meditation on Attention Level Using EEGIRJET Journal
This document summarizes a proposed study that investigates the effect of meditation on attention level using EEG data analysis. It begins with an introduction on attention and meditation, then reviews previous related studies that analyzed EEG data to measure attention. The proposed work will record EEG data from subjects using the 10-20 electrode placement system before and after an 8-week meditation program. The EEG data will be preprocessed to remove noise, features will be extracted using wavelet transforms, and a random forest classifier will be used to classify attention levels and analyze the effect of meditation. The goal is to objectively measure how meditation impacts attention to help students improve concentration.
The International Journal of Engineering and Science (IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
IRJET- Analysis of Electroencephalogram (EEG) SignalsIRJET Journal
The document analyzes EEG signals to classify emotions. EEG signals were collected from 30 subjects aged 18-25 using a Neurosky Mindwave sensor with electrodes on the forehead and ear. The analog EEG data was converted to digital by an Arduino UNO and sent to a computer. Feature extraction using continuous wavelet transform, probability distribution function, peak plot, and fast Fourier transform was performed on the EEG data in LabVIEW. The analysis classified emotions with 90.69% accuracy and 85.55% sensitivity.
Brain-computer interface of focus and motor imagery using wavelet and recurre...TELKOMNIKA JOURNAL
Brain-computer interface is a technology that allows operating a device without involving muscles and sound, but directly from the brain through the processed electrical signals. The technology works by capturing electrical or magnetic signals from the brain, which are then processed to obtain information contained therein. Usually, BCI uses information from electroencephalogram (EEG) signals based on various variables reviewed. This study proposed BCI to move external devices such as a drone simulator based on EEG signal information. From the EEG signal was extracted to get motor imagery (MI) and focus variable using wavelet. Then, they were classified by recurrent neural networks (RNN). In overcoming the problem of vanishing memory from RNN, was used long short-term memory (LSTM). The results showed that BCI used wavelet, and RNN can drive external devices of non-training data with an accuracy of 79.6%. The experiment gave AdaDelta model is better than the Adam model in terms of accuracy and value losses. Whereas in computational learning time, Adam's model is faster than AdaDelta's model.
Denoising Techniques for EEG Signals: A ReviewIRJET Journal
The document reviews techniques for denoising EEG signals contaminated with artifacts. It discusses regression, blind source separation (BSS) including principal component analysis (PCA) and independent component analysis (ICA), wavelet transform, and empirical mode decomposition (EMD). Each method has benefits and limitations. Combining multiple current approaches can address individual constraints and provide superior outcomes than single algorithms alone by overcoming each other's limitations.
This document describes an experiment that used EEG signals to detect mental stress in human subjects. EEG signals were collected from subjects using electrodes placed according to the 10-20 international system. Stress was induced using images from the IAPS dataset. Machine learning algorithms like ICA, DWT, and PCA were used to preprocess the signals, extract features, and reduce dimensions. SVM and neural networks were then used to classify states as stressed or calm, achieving accuracies of 82% and 80% respectively. The study aimed to determine a subject's mental state as stressed or not stressed, rather than determining causes or levels of stress.
Wavelet Based Feature Extraction Scheme Of Eeg Waveformshan pri
This document presents a project on wavelet based feature extraction of electroencephalography (EEG) signals. It discusses using wavelet transforms as an alternative to discrete Fourier transforms for feature extraction from EEG data. The objectives are to improve quality of life for those with disabilities through neuroprosthetics applications of brain-computer interfaces. Wavelet transforms provide advantages over short-time Fourier transforms like multi-resolution analysis and the ability to analyze non-stationary signals. The document outlines the methodology, which includes EEG signal acquisition, wavelet decomposition, coefficient computation, and signal reconstruction in MATLAB.
Biomedical Signals Classification With Transformer Based Model.pptxSandeep Kumar
The document summarizes research using a transformer model to classify biomedical signals, specifically EEG signals. Key points:
- A transformer model was used to classify EEG signals from epilepsy patients using statistical features extracted from wavelet decompositions of the signals.
- The model was tested on the Bonn and CHB-MIT EEG datasets, achieving accuracy above 97% on binary, tertiary and 5-class problems from the Bonn data and above 95% accuracy for individual patients in the CHB-MIT data.
- The transformer model outperformed previous methods like MLP, SVM, ANN and CNN in classifying the EEG signals, demonstrating its effectiveness for biomedical signal classification tasks.
Transfer learning for epilepsy detection using spectrogram imagesIAESIJAI
Epilepsy stands out as one of the common neurological diseases. The neural activity of the brain is observed using electroencephalography (EEG). Manual inspection of EEG brain signals is a slow and arduous process, which puts heavy load on neurologists and affects their performance. The aim of this study is to find the best result of classification using the transfer learning model that automatically identify the epileptic and the normal activity, to classify EEG signals by using images of spectrogram which represents the percentage of energy for each coefficient of the continuous wavelet. Dataset includes the EEG signals recorded at monitoring unit of epilepsy used in this study to presents an application of transfer learning by comparing three models Alexnet, visual geometry group (VGG19) and residual neural network ResNet using different combinations with seven different classifiers. This study tested the models and reached a different value of accuracy and other metrics used to judge their performances, and as a result the best combination has been achieved with ResNet combined with support vector machine (SVM) classifier that classified EEG signals with a high success rate using multiple performance metrics such as 97.22% accuracy and 2.78% the value of the error rate.
Prediction Model for Emotion Recognition Using EEGIRJET Journal
The document describes a study that compares different machine learning models for emotion recognition using EEG data. The study uses EEG data collected from patients with depression and healthy controls. It extracts features from the EEG data, including linear and nonlinear parameters from different frequency bands. It then uses classifiers like random forest, KNN, CNN, and LSTM to classify emotions as positive, negative or neutral. The random forest model achieved the best accuracy for identifying depression patients' emotions. The study provides a framework for EEG data collection, preprocessing, feature extraction and applying different machine learning models to optimize emotion recognition from EEG signals.
ENERGY COMPUTATION FOR BCI USING DCT AND MOVING AVERAGE WINDOW FOR NOISE SMOO...IJCSEA Journal
Brain computer interface (BCI) is a fast evolving field of research enabling computers and machines to be directly controlled by the human neural system. This enables people with muscular disability to directly control machines using their thought process. The brain signals are recorded using Electroencephalography (EEG) and patterns extracted so that the BCI system should be able to classify various patterns of brain signal accurately to perform different tasks. The raw EEG signal contains different kinds of interference waveforms (artifacts) and noise. Thus raw signals cannot be directly used for classification, the EEG signals has to undergo preprocessing, to remove artifacts and to extract the right attributes for classification. In this paper it is proposed to extract the energies in the EEG signal and classify the signal using Naïve Bayes and Instance based learners. The proposed method performs well for the two class problem in the multiple datasets used..
This document discusses a method for detecting epilepsy in EEG signals using discrete wavelet transforms and neural networks. It first preprocesses EEG data using wavelet decomposition to extract features in different frequency subbands (delta, theta, alpha, beta, gamma). Features like covariance, energy, minimum/maximum power spectral density, and entropy are then calculated from the subband signals. These features are input to a neural network for training to classify EEG patterns as epileptic or non-epileptic. The method aims to automatically detect seizures in a more time-efficient manner compared to visual inspection of long EEG recordings. It presents wavelet decomposition and feature extraction techniques, and uses neural networks for classification of epileptic vs. non-epileptic
Analysis of EEG data Using ICA and Algorithm Development for Energy Comparisonijsrd.com
This Electroencephalogram (EEG) signal analysis very useful in clinical research and brain computer interface application. EEG signal (brain wave) recordings are highly susceptible from artifacts which are originated from the non-cerebral origin of the brain. EEG detection and rejection of artifacts are necessary for acquiring correct information from EEG signal. Emotiv, Epoc headset can record 16 channels from the scalp of the electrode. EEGLAB allows analysis of EEG signal through Event related potential (ERP) analysis, Independent component analysis (ICA), and time/frequency analysis. Independent component analysis (ICA) may be suitable method for detecting artifacts. We analyzed EEG data which are recorded using emotiv epoc in a different situation for a single person. EEG data are preprocessed by EEGLAB and decomposes the data by the ICA. Using statistical method, analyzed the all the dataset and finding the relationship among the dataset. T- Test shows that EEG pattern is unique in a person. EEG data is divided into different frequency band to find the relationship between the dataset. Also develop the algorithm for calculating energy of dataset for each channel. Comparing the energy for each dataset and each channel to find the maximum and minimum value of energy. In higher frequency range (13-100 Hz) dataset D (meditation) contains maximum value of energy for most channels among all datasets.
5. detection and separation of eeg artifacts using wavelet transform nov 11, ...IAESIJEECS
This document summarizes a study on using wavelet transforms to detect and separate artifacts in EEG signals. The study aimed to minimize artifacts and noise in EEG signals without affecting the original signal. Wavelet transforms were found to be effective for analyzing non-stationary EEG signals. The results showed that wavelet transforms significantly reduced input size without compromising performance. Decomposing EEG signals using wavelet transforms extracted different frequency bands and resolved signals at different resolutions. This allowed artifacts and noise to be detected and the original signal to be recovered. Simulation results demonstrated the wavelet transform's ability to denoise EEG signals and extract key frequency components.
Feature Extraction Techniques and Classification Algorithms for EEG Signals t...Editor IJCATR
This document reviews techniques for extracting features and classifying EEG signals to detect human stress levels. It discusses EEG signals and how they can provide information about mental states. It also reviews common feature extraction methods like DCT and DWT that can preprocess EEG data by transforming it from the time to frequency domain. Classification algorithms like KNN, LDA, and Naive Bayes that can classify EEG data are also examined. The document proposes a system to use a Neurosky Mindwave EEG headset to record raw EEG signals, preprocess them with DWT, and classify stress levels using a combination of classifiers.
EEG S IGNAL Q UANTIFICATION B ASED ON M ODUL L EVELS sipij
This article proposes a contribution to quantify EE
G signals outline. This technique uses two tools fo
r EEG
signal characteristics extraction. Our tests were r
ealized on the basis of 32 canals EEG canals using
Neuroscan software. EEG example demonstration is re
ferenced CZ and is sampled at 1000HZ. The
principal aim of this technique is to reduce the im
portant volume of EEG signal data Without losing an
y
information. EEG signals are quantified on the basi
s of a whole predefined levels The obtained results
show that an EEG alignment can be posted in a quant
ified form.
IRJET-Electromyogram Signals for Multiuser Interface- A ReviewIRJET Journal
This document reviews various methods for feature extraction and classification of electromyogram (EMG) signals for multi-user myoelectric interfaces. It surveys previous work that used techniques like discrete wavelet transform (DWT) and support vector machines (SVM) for feature extraction and classification of EMG signals. The document concludes that DWT is well-suited for extracting both time and frequency domain features from non-stationary EMG signals. It also finds that SVM performed accurately for classification of features from multi-user EMG signals. The review aims to determine the best methods for a project using DWT for feature extraction and SVM for classification of EMG signals from multiple users.
EEG Signal Classification using Deep Neural NetworkIRJET Journal
This document discusses using deep neural networks to classify EEG signals. It proposes using a convolutional neural network to analyze recurrence plots generated from EEG data in order to distinguish between focal and non-focal EEG signals. The recurrence plots are generated from EEG data collected from epilepsy patients. The convolutional neural network is then used to classify the recurrence plots to identify patterns associated with focal versus non-focal EEG signals. This classification of EEG signals could help doctors locate epileptic foci to inform treatment decisions.
IRJET-Estimation of Meditation Effect on Attention Level using EEGIRJET Journal
This document discusses a study that investigates the effect of meditation on attention level using EEG data analysis. EEG data was collected from subjects during meditation and non-meditation states. The data was preprocessed to remove noise and artifacts. Statistical features were then extracted from the EEG data, including standard deviation, relative power, average power spectral density, and entropy. A random forest classification method was used to analyze the data and detect attention states, achieving 90% accuracy. The study aims to objectively measure attention levels and the impact of meditation using EEG analysis to better understand cognitive disorders like ADHD.
Similar to IRJET- Disentangling Brain Activity from EEG Data using Logistic Regression, XGBOOST, RNN and Classification Trees. (20)
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.
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.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)