Sentimental analysis or opinion mining is the process of obtaining sentiments about a given textual data using various methods of deep learning algorithms. The analysis is used to determine the polarity of the data as either positive or negative. This classifications can help automate data representation in various sectors which has a public feedback structure. In this paper, we are going to perform sentiment analysis on the infamous IMDB database which consists of 50000 movie reviews, in which we perform training on 25000 instances and test it on 25000 to determine the performance of the model. The model uses a variant of RNN algorithm which is LSTM Long Short Term Memory which will help us a make a model which will decide the polarity between 0 and 1. This approach has an accuracy of 88.04 Nirsen Amal A | Vijayakumar A "Movie Sentiment Analysis using Deep Learning - RNN" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42414.pdf Paper URL: https://www.ijtsrd.comcomputer-science/other/42414/movie-sentiment-analysis-using-deep-learning--rnn/nirsen-amal-a
This document summarizes Dongang Wang's speech emotion recognition project which compares feature selection and classification methods. Wang selects mel-frequency cepstral coefficients (MFCCs) and energy as features. For methods, Wang tests Gaussian mixture models (GMMs), discrete hidden Markov models (HMMs), and continuous HMMs including Kalman filters. Testing on German and English corpora, continuous HMMs achieved the best average accuracy of 61.67%, outperforming GMMs and discrete HMMs. While results are promising, Wang notes challenges in recognizing emotion across languages and speakers.
This document describes a deep neural network model for predicting emotions in images based on high-level semantic features like objects and backgrounds. The model was trained on a new image emotion dataset consisting of over 10,000 images labeled with valence and arousal values obtained through crowdsourcing. The model uses features like object detection, background semantics, and color statistics as inputs to a feedforward neural network that predicts continuous valence and arousal values, providing a more nuanced representation of emotions than discrete categories. Experiments showed the model could effectively predict emotions in images.
IRJET- Facial Emotion Detection using Convolutional Neural NetworkIRJET Journal
This document describes a system for facial emotion detection using convolutional neural networks. The system uses Haar cascade classifiers to detect faces in images and then applies a convolutional neural network to recognize seven basic emotions (happiness, sadness, anger, fear, disgust, surprise, contempt) from facial expressions. The convolutional neural network architecture includes convolutional layers to extract features, ReLU layers for non-linearity, pooling layers for dimensionality reduction, and fully connected layers for emotion classification. The system is described as having potential applications in security systems, driver monitoring systems, and other real-time emotion detection use cases.
This document describes a project to build a convolutional neural network (CNN) model to recognize six basic human emotions (angry, fear, happy, sad, surprise, neutral) from facial expressions. The CNN architecture includes convolutional, max pooling and fully connected layers. Models are trained on two datasets - FERC and RaFD. Experimental results show that Model C achieves the best testing accuracy of 71.15% on FERC and 63.34% on RaFD. Visualizations of activation maps and a prediction matrix are provided to analyze the model's performance and confusions between emotions. A live demo application is also developed using OpenCV to demonstrate real-time emotion recognition from video frames.
Facial Emotion Recognition using Convolution Neural NetworkYogeshIJTSRD
Facial expression plays a major role in every aspect of human life for communication. It has been a boon for the research in facial emotion with the systems that give rise to the terminology of human computer interaction in real life. Humans socially interact with each other via emotions. In this research paper, we have proposed an approach of building a system that recognizes facial emotion using a Convolutional Neural Network CNN which is one of the most popular Neural Network available. It is said to be a pattern recognition Neural Network. Convolutional Neural Network reduces the dimension for large resolution images and not losing the quality and giving a prediction output whats expected and capturing of the facial expressions even in odd angles makes it stand different from other models also i.e. it works well for non frontal images. But unfortunately, CNN based detector is computationally heavy and is a challenge for using CNN for a video as an input. We will implement a facial emotion recognition system using a Convolutional Neural Network using a dataset. Our system will predict the output based on the input given to it. This system can be useful for sentimental analysis, can be used for clinical practices, can be useful for getting a persons review on a certain product, and many more. Raheena Bagwan | Sakshi Chintawar | Komal Dhapudkar | Alisha Balamwar | Prof. Sandeep Gore "Facial Emotion Recognition using Convolution Neural Network" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd39972.pdf Paper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/39972/facial-emotion-recognition-using-convolution-neural-network/raheena-bagwan
The document describes a deep learning model called Deep-Emotion that uses an attentional convolutional network for facial expression recognition. The model focuses on important facial regions like the eyes and mouth that are most relevant to detecting emotions. The model achieves state-of-the-art accuracy on several facial expression datasets, outperforming previous methods. Visualization techniques show the model learns to focus on different facial regions for different emotions.
This document discusses using a deep neural network with vectorized facial features for emotion recognition. It introduces a novel method of vectorizing facial landmarks extracted from images to represent facial expressions. A deep neural network is trained on the vectorized facial feature data to classify emotions. The network achieves 84.33% accuracy on an emotion recognition test set. Compared to convolutional neural networks, the proposed deep neural network requires less training time and data.
Emotion recognition using image processing in deep learningvishnuv43
User’s emotion using its facial expressions will be detected. These expressions can be derived from the live feed via system's camera or any pre-existing image available in the memory. Emotions possessed by humans can be recognized and has a vast scope of study in the computer vision industry upon which several researches have already been done.
We propose a compact CNN model for facial expression recognition.
The work has been implemented using Python Open Source Computer Vision Library (OpenCV) and NumPy,pandas,keras packages. The scanned image (testing dataset) is being compared to training dataset and thus emotion is predicted.
This document summarizes Dongang Wang's speech emotion recognition project which compares feature selection and classification methods. Wang selects mel-frequency cepstral coefficients (MFCCs) and energy as features. For methods, Wang tests Gaussian mixture models (GMMs), discrete hidden Markov models (HMMs), and continuous HMMs including Kalman filters. Testing on German and English corpora, continuous HMMs achieved the best average accuracy of 61.67%, outperforming GMMs and discrete HMMs. While results are promising, Wang notes challenges in recognizing emotion across languages and speakers.
This document describes a deep neural network model for predicting emotions in images based on high-level semantic features like objects and backgrounds. The model was trained on a new image emotion dataset consisting of over 10,000 images labeled with valence and arousal values obtained through crowdsourcing. The model uses features like object detection, background semantics, and color statistics as inputs to a feedforward neural network that predicts continuous valence and arousal values, providing a more nuanced representation of emotions than discrete categories. Experiments showed the model could effectively predict emotions in images.
IRJET- Facial Emotion Detection using Convolutional Neural NetworkIRJET Journal
This document describes a system for facial emotion detection using convolutional neural networks. The system uses Haar cascade classifiers to detect faces in images and then applies a convolutional neural network to recognize seven basic emotions (happiness, sadness, anger, fear, disgust, surprise, contempt) from facial expressions. The convolutional neural network architecture includes convolutional layers to extract features, ReLU layers for non-linearity, pooling layers for dimensionality reduction, and fully connected layers for emotion classification. The system is described as having potential applications in security systems, driver monitoring systems, and other real-time emotion detection use cases.
This document describes a project to build a convolutional neural network (CNN) model to recognize six basic human emotions (angry, fear, happy, sad, surprise, neutral) from facial expressions. The CNN architecture includes convolutional, max pooling and fully connected layers. Models are trained on two datasets - FERC and RaFD. Experimental results show that Model C achieves the best testing accuracy of 71.15% on FERC and 63.34% on RaFD. Visualizations of activation maps and a prediction matrix are provided to analyze the model's performance and confusions between emotions. A live demo application is also developed using OpenCV to demonstrate real-time emotion recognition from video frames.
Facial Emotion Recognition using Convolution Neural NetworkYogeshIJTSRD
Facial expression plays a major role in every aspect of human life for communication. It has been a boon for the research in facial emotion with the systems that give rise to the terminology of human computer interaction in real life. Humans socially interact with each other via emotions. In this research paper, we have proposed an approach of building a system that recognizes facial emotion using a Convolutional Neural Network CNN which is one of the most popular Neural Network available. It is said to be a pattern recognition Neural Network. Convolutional Neural Network reduces the dimension for large resolution images and not losing the quality and giving a prediction output whats expected and capturing of the facial expressions even in odd angles makes it stand different from other models also i.e. it works well for non frontal images. But unfortunately, CNN based detector is computationally heavy and is a challenge for using CNN for a video as an input. We will implement a facial emotion recognition system using a Convolutional Neural Network using a dataset. Our system will predict the output based on the input given to it. This system can be useful for sentimental analysis, can be used for clinical practices, can be useful for getting a persons review on a certain product, and many more. Raheena Bagwan | Sakshi Chintawar | Komal Dhapudkar | Alisha Balamwar | Prof. Sandeep Gore "Facial Emotion Recognition using Convolution Neural Network" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd39972.pdf Paper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/39972/facial-emotion-recognition-using-convolution-neural-network/raheena-bagwan
The document describes a deep learning model called Deep-Emotion that uses an attentional convolutional network for facial expression recognition. The model focuses on important facial regions like the eyes and mouth that are most relevant to detecting emotions. The model achieves state-of-the-art accuracy on several facial expression datasets, outperforming previous methods. Visualization techniques show the model learns to focus on different facial regions for different emotions.
This document discusses using a deep neural network with vectorized facial features for emotion recognition. It introduces a novel method of vectorizing facial landmarks extracted from images to represent facial expressions. A deep neural network is trained on the vectorized facial feature data to classify emotions. The network achieves 84.33% accuracy on an emotion recognition test set. Compared to convolutional neural networks, the proposed deep neural network requires less training time and data.
Emotion recognition using image processing in deep learningvishnuv43
User’s emotion using its facial expressions will be detected. These expressions can be derived from the live feed via system's camera or any pre-existing image available in the memory. Emotions possessed by humans can be recognized and has a vast scope of study in the computer vision industry upon which several researches have already been done.
We propose a compact CNN model for facial expression recognition.
The work has been implemented using Python Open Source Computer Vision Library (OpenCV) and NumPy,pandas,keras packages. The scanned image (testing dataset) is being compared to training dataset and thus emotion is predicted.
Face Emotion Analysis Using Gabor Features In Image Database for Crime Invest...Waqas Tariq
The face is the most extraordinary communicator, which plays an important role in interpersonal relations and Human Machine Interaction. . Facial expressions play an important role wherever humans interact with computers and human beings to communicate their emotions and intentions. Facial expressions, and other gestures, convey non-verbal communication cues in face-to-face interactions. In this paper we have developed an algorithm which is capable of identifying a person’s facial expression and categorize them as happiness, sadness, surprise and neutral. Our approach is based on local binary patterns for representing face images. In our project we use training sets for faces and non faces to train the machine in identifying the face images exactly. Facial expression classification is based on Principle Component Analysis. In our project, we have developed methods for face tracking and expression identification from the face image input. Applying the facial expression recognition algorithm, the developed software is capable of processing faces and recognizing the person’s facial expression. The system analyses the face and determines the expression by comparing the image with the training sets in the database. We have followed PCA and neural networks in analyzing and identifying the facial expressions.
Three-dimensional multimodal models of objective classes are a great tool in modeling and recognition. The multimodal involuntary emotion recognition during a mentally challenged-based communication is presented. We have easily found the mentally disorder people without a doctor. The features are built upon the emotion, motion and frequency to identifying the percentage of mentally disorder peoples. Using Different categories of an image, video, audio and emotions can be discriminated. An image using an algorithms for classification is 3DMM (Three-dimensional morph able models) used to fit the model to images, and a framework for face emotion recognition. GPSO (Guided Particle Swarm Optimization) the emotion finding problem is basically an exploration problem, where at every point; we are pointed to recognize which of the thinkable emotions ensures the current facial expression denotes and GA (Genetic Algorithm) has the virtues of overflowing coding, and decoding, assigning complex information flexibly. GA is calculating the percentage of mental disorder. We proposed using different algorithm to identify the mentally challenged persons.
We seek to classify images into different emotions using a first 'intuitive' machine learning approach, then training models using convolutional neural networks and finally using a pretrained model for better accuracy.
EEG Based Classification of Emotions with CNN and RNNijtsrd
Emotions are biological states associated with the nervous system, especially the brain brought on by neurophysiological changes. They variously cognate with thoughts, feelings, behavioural responses, and a degree of pleasure or displeasure and it exists everywhere in daily life. It is a significant research topic in the development of artificial intelligence to evaluate human behaviour that are primarily based on emotions. In this paper, Deep Learning Classifiers will be applied to SJTU Emotion EEG Dataset SEED to classify human emotions from EEG using Python. Then the accuracy of respective classifiers that is, the performance of emotion classification using Convolutional Neural Network CNN and Recurrent Neural Networks are compared. The experimental results show that RNN is better than CNN in solving sequence prediction problems. S. Harshitha | Mrs. A. Selvarani "EEG Based Classification of Emotions with CNN and RNN" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30374.pdf Paper Url :https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/30374/eeg-based-classification-of-emotions-with-cnn-and-rnn/s-harshitha
A Dualistic Sub-Image Histogram Equalization Based Enhancement and Segmentati...inventy
This document presents a dualistic sub-image histogram equalization technique for medical image enhancement and segmentation. The technique divides an image histogram into two parts based on mean and median, then equalizes each sub-histogram independently. It enhances images effectively while constraining average luminance shift. For segmentation, canny edge detection and neural networks are used. The technique is tested on medical images and shows improved completeness and correctness over previous methods, with neural networks increasing accuracy to 98.3257%.
This document provides a literature review of deep reinforcement learning applications in medical imaging. It begins with introducing deep reinforcement learning and reinforcement learning concepts. It then discusses several medical imaging applications of deep reinforcement learning, including landmark detection, image registration, object lesion localization and detection, view plane localization, and plaque tracking. Deep reinforcement learning has also been used for optimization tasks in medical imaging like hyperparameter tuning, data augmentation, and neural architecture search. While promising results have been shown, the document notes that deep reinforcement learning has not been fully utilized to meet clinical image segmentation and classification requirements.
improving Profile detection using Deep LearningSahil Kaw
The document discusses how deep learning methods have revolutionized human profile detection. It describes using convolutional neural networks (CNNs) to accurately classify features like faces and ages from images. CNN models achieve higher accuracy than previous models for tasks like face recognition, verification and age estimation. The paper also evaluates different CNN architectures for image retrieval and selects an optimal architecture with 99.63% accuracy. It discusses how using deep convolutional networks instead of bottleneck layers and deep learned aging algorithms with CNNs improve precision for classifying human ages.
EMOTIONAL LEARNING IN A SIMULATED MODEL OF THE MENTAL APPARATUScsandit
How a human being learns is a wide field and not fully understood until now. This paper should give an alternative attempt to get closer to the answer how human beings learn something and what the relation to emotions is. Therefore, the cognitive architecture of the project “Simulation of Mental Apparatus and Applications (SiMA)” is used to fulfill two tasks. One is to give an answer to the question above and the other one is to enhance the functional model of the mental apparatus with learning. For that reason, the functions of the model are analyzed in detail for their ability to enhance them with a learning ability. The focus of the analysis lay on emotions and their impact on the ability to change memories in the model to determine a different behavior than without learning.
This document provides a review of different techniques for segmenting brain MRI images to detect tumors. It compares the K-means and Fuzzy C-means clustering algorithms. K-means is an exclusive clustering algorithm that groups data points into distinct clusters, while Fuzzy C-means is an overlapping clustering algorithm that allows data points to belong to multiple clusters. The document finds that Fuzzy C-means requires more time for brain tumor detection compared to other methods like hierarchical clustering or K-means. It also reviews related work applying these clustering algorithms to segment brain MRI images.
Passive Image Forensic Method to Detect Resampling Forgery in Digital Imagesiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Development of video-based emotion recognition using deep learning with Googl...TELKOMNIKA JOURNAL
Emotion recognition using images, videos, or speech as input is considered as a hot topic in the field of research over some years. With the introduction of deep learning techniques, e.g., convolutional neural networks (CNN), applied in emotion recognition, has produced promising results. Human facial expressions are considered as critical components in understanding one's emotions. This paper sheds light on recognizing the emotions using deep learning techniques from the videos. The methodology of the recognition process, along with its description, is provided in this paper. Some of the video-based datasets used in many scholarly works are also examined. Results obtained from different emotion recognition models are presented along with their performance parameters. An experiment was carried out on the fer2013 dataset in Google Colab for depression detection, which came out to be 97% accurate on the training set and 57.4% accurate on the testing set.
This document summarizes an algorithm for measuring the quality of an image. It begins with an overview of objective image quality assessment and the goal of developing metrics that can automatically predict perceived image quality. It then describes the traditional approach of using error sensitivity models based on the human visual system (HVS) to measure differences between a reference image and distorted image. However, it notes limitations of this approach, such as how it defines quality, operates in suprathreshold ranges, and accounts for natural image complexity. The document proposes using structural similarity as an alternative and concludes by discussing future work areas like improved HVS modeling.
IRJET- Intelligent Image Recognition Technology based on Neural NetworkIRJET Journal
1) The document discusses an intelligent image recognition technology based on neural networks. It uses neural networks to analyze images obtained through digital image acquisition devices and recognize patterns.
2) A typical neural network model is described with an input layer, hidden layer, and output layer. The backpropagation algorithm is used to minimize error and adjust weights between layers during training.
3) The document demonstrates the application of this neural network for image recognition. 36 sample images of the letter 'A' rotated in 10 degree increments are used to train the network. Test results showed the feasibility and effectiveness of the neural network approach for image recognition.
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.
This document summarizes and evaluates various rule extraction algorithms from trained artificial neural networks. It begins with an introduction explaining the importance of explanation capabilities for neural networks. It then provides a taxonomy for classifying rule extraction approaches based on the expressiveness of the extracted rules, whether the approach takes an open-box or black-box view of the neural network, any specialized training regimes used, the quality of explanations generated, and computational complexity. The document discusses sensitivity analysis as a basic method for understanding neural network relationships before focusing on decompositional and pedagogical rule extraction approaches.
Facial emotion detection on babies' emotional face using Deep Learning.Takrim Ul Islam Laskar
phase- 1
Face Detection.
Facial Landmark detection.
phase- 2
Neural Network Training and Testing.
validation and implementation.
phase - 1 has been completed successfully.
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.
This document proposes an automatic emotion recognition system that analyzes audio information to classify human emotions. It uses spectral features and MFCC coefficients for feature extraction from voice signals. Then, a deep learning-based LSTM algorithm is used for classification. The system is evaluated on three audio datasets. Recurrent convolutional neural networks are proposed to capture temporal and frequency dependencies in speech spectrograms. The system aims to improve on existing methods which have lower accuracy and require more computational resources for implementation.
I developed a Convolutional Neural Network using Python. This particular CNN is able to identify the correct individual based solely off of a photo with the knowledge of facial recognition.
MUSIC RECOMMENDATION THROUGH FACE RECOGNITION AND EMOTION DETECTIONIRJET Journal
This document describes a system for music recommendation based on facial emotion recognition using convolutional neural networks. It analyzes facial images using CNN models to detect seven basic emotions. Based on the detected emotion, it recommends songs from predefined playlists that match the user's mood. The system architecture inputs images of the user's face, uses a CNN for emotion detection, and then selects appropriate music from playlists organized by emotion. It was developed to provide personalized music recommendations based on a user's real-time facial expressions and emotional state, unlike other systems that rely only on search queries or prior listening history.
Face Emotion Analysis Using Gabor Features In Image Database for Crime Invest...Waqas Tariq
The face is the most extraordinary communicator, which plays an important role in interpersonal relations and Human Machine Interaction. . Facial expressions play an important role wherever humans interact with computers and human beings to communicate their emotions and intentions. Facial expressions, and other gestures, convey non-verbal communication cues in face-to-face interactions. In this paper we have developed an algorithm which is capable of identifying a person’s facial expression and categorize them as happiness, sadness, surprise and neutral. Our approach is based on local binary patterns for representing face images. In our project we use training sets for faces and non faces to train the machine in identifying the face images exactly. Facial expression classification is based on Principle Component Analysis. In our project, we have developed methods for face tracking and expression identification from the face image input. Applying the facial expression recognition algorithm, the developed software is capable of processing faces and recognizing the person’s facial expression. The system analyses the face and determines the expression by comparing the image with the training sets in the database. We have followed PCA and neural networks in analyzing and identifying the facial expressions.
Three-dimensional multimodal models of objective classes are a great tool in modeling and recognition. The multimodal involuntary emotion recognition during a mentally challenged-based communication is presented. We have easily found the mentally disorder people without a doctor. The features are built upon the emotion, motion and frequency to identifying the percentage of mentally disorder peoples. Using Different categories of an image, video, audio and emotions can be discriminated. An image using an algorithms for classification is 3DMM (Three-dimensional morph able models) used to fit the model to images, and a framework for face emotion recognition. GPSO (Guided Particle Swarm Optimization) the emotion finding problem is basically an exploration problem, where at every point; we are pointed to recognize which of the thinkable emotions ensures the current facial expression denotes and GA (Genetic Algorithm) has the virtues of overflowing coding, and decoding, assigning complex information flexibly. GA is calculating the percentage of mental disorder. We proposed using different algorithm to identify the mentally challenged persons.
We seek to classify images into different emotions using a first 'intuitive' machine learning approach, then training models using convolutional neural networks and finally using a pretrained model for better accuracy.
EEG Based Classification of Emotions with CNN and RNNijtsrd
Emotions are biological states associated with the nervous system, especially the brain brought on by neurophysiological changes. They variously cognate with thoughts, feelings, behavioural responses, and a degree of pleasure or displeasure and it exists everywhere in daily life. It is a significant research topic in the development of artificial intelligence to evaluate human behaviour that are primarily based on emotions. In this paper, Deep Learning Classifiers will be applied to SJTU Emotion EEG Dataset SEED to classify human emotions from EEG using Python. Then the accuracy of respective classifiers that is, the performance of emotion classification using Convolutional Neural Network CNN and Recurrent Neural Networks are compared. The experimental results show that RNN is better than CNN in solving sequence prediction problems. S. Harshitha | Mrs. A. Selvarani "EEG Based Classification of Emotions with CNN and RNN" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30374.pdf Paper Url :https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/30374/eeg-based-classification-of-emotions-with-cnn-and-rnn/s-harshitha
A Dualistic Sub-Image Histogram Equalization Based Enhancement and Segmentati...inventy
This document presents a dualistic sub-image histogram equalization technique for medical image enhancement and segmentation. The technique divides an image histogram into two parts based on mean and median, then equalizes each sub-histogram independently. It enhances images effectively while constraining average luminance shift. For segmentation, canny edge detection and neural networks are used. The technique is tested on medical images and shows improved completeness and correctness over previous methods, with neural networks increasing accuracy to 98.3257%.
This document provides a literature review of deep reinforcement learning applications in medical imaging. It begins with introducing deep reinforcement learning and reinforcement learning concepts. It then discusses several medical imaging applications of deep reinforcement learning, including landmark detection, image registration, object lesion localization and detection, view plane localization, and plaque tracking. Deep reinforcement learning has also been used for optimization tasks in medical imaging like hyperparameter tuning, data augmentation, and neural architecture search. While promising results have been shown, the document notes that deep reinforcement learning has not been fully utilized to meet clinical image segmentation and classification requirements.
improving Profile detection using Deep LearningSahil Kaw
The document discusses how deep learning methods have revolutionized human profile detection. It describes using convolutional neural networks (CNNs) to accurately classify features like faces and ages from images. CNN models achieve higher accuracy than previous models for tasks like face recognition, verification and age estimation. The paper also evaluates different CNN architectures for image retrieval and selects an optimal architecture with 99.63% accuracy. It discusses how using deep convolutional networks instead of bottleneck layers and deep learned aging algorithms with CNNs improve precision for classifying human ages.
EMOTIONAL LEARNING IN A SIMULATED MODEL OF THE MENTAL APPARATUScsandit
How a human being learns is a wide field and not fully understood until now. This paper should give an alternative attempt to get closer to the answer how human beings learn something and what the relation to emotions is. Therefore, the cognitive architecture of the project “Simulation of Mental Apparatus and Applications (SiMA)” is used to fulfill two tasks. One is to give an answer to the question above and the other one is to enhance the functional model of the mental apparatus with learning. For that reason, the functions of the model are analyzed in detail for their ability to enhance them with a learning ability. The focus of the analysis lay on emotions and their impact on the ability to change memories in the model to determine a different behavior than without learning.
This document provides a review of different techniques for segmenting brain MRI images to detect tumors. It compares the K-means and Fuzzy C-means clustering algorithms. K-means is an exclusive clustering algorithm that groups data points into distinct clusters, while Fuzzy C-means is an overlapping clustering algorithm that allows data points to belong to multiple clusters. The document finds that Fuzzy C-means requires more time for brain tumor detection compared to other methods like hierarchical clustering or K-means. It also reviews related work applying these clustering algorithms to segment brain MRI images.
Passive Image Forensic Method to Detect Resampling Forgery in Digital Imagesiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Development of video-based emotion recognition using deep learning with Googl...TELKOMNIKA JOURNAL
Emotion recognition using images, videos, or speech as input is considered as a hot topic in the field of research over some years. With the introduction of deep learning techniques, e.g., convolutional neural networks (CNN), applied in emotion recognition, has produced promising results. Human facial expressions are considered as critical components in understanding one's emotions. This paper sheds light on recognizing the emotions using deep learning techniques from the videos. The methodology of the recognition process, along with its description, is provided in this paper. Some of the video-based datasets used in many scholarly works are also examined. Results obtained from different emotion recognition models are presented along with their performance parameters. An experiment was carried out on the fer2013 dataset in Google Colab for depression detection, which came out to be 97% accurate on the training set and 57.4% accurate on the testing set.
This document summarizes an algorithm for measuring the quality of an image. It begins with an overview of objective image quality assessment and the goal of developing metrics that can automatically predict perceived image quality. It then describes the traditional approach of using error sensitivity models based on the human visual system (HVS) to measure differences between a reference image and distorted image. However, it notes limitations of this approach, such as how it defines quality, operates in suprathreshold ranges, and accounts for natural image complexity. The document proposes using structural similarity as an alternative and concludes by discussing future work areas like improved HVS modeling.
IRJET- Intelligent Image Recognition Technology based on Neural NetworkIRJET Journal
1) The document discusses an intelligent image recognition technology based on neural networks. It uses neural networks to analyze images obtained through digital image acquisition devices and recognize patterns.
2) A typical neural network model is described with an input layer, hidden layer, and output layer. The backpropagation algorithm is used to minimize error and adjust weights between layers during training.
3) The document demonstrates the application of this neural network for image recognition. 36 sample images of the letter 'A' rotated in 10 degree increments are used to train the network. Test results showed the feasibility and effectiveness of the neural network approach for image recognition.
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.
This document summarizes and evaluates various rule extraction algorithms from trained artificial neural networks. It begins with an introduction explaining the importance of explanation capabilities for neural networks. It then provides a taxonomy for classifying rule extraction approaches based on the expressiveness of the extracted rules, whether the approach takes an open-box or black-box view of the neural network, any specialized training regimes used, the quality of explanations generated, and computational complexity. The document discusses sensitivity analysis as a basic method for understanding neural network relationships before focusing on decompositional and pedagogical rule extraction approaches.
Facial emotion detection on babies' emotional face using Deep Learning.Takrim Ul Islam Laskar
phase- 1
Face Detection.
Facial Landmark detection.
phase- 2
Neural Network Training and Testing.
validation and implementation.
phase - 1 has been completed successfully.
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.
This document proposes an automatic emotion recognition system that analyzes audio information to classify human emotions. It uses spectral features and MFCC coefficients for feature extraction from voice signals. Then, a deep learning-based LSTM algorithm is used for classification. The system is evaluated on three audio datasets. Recurrent convolutional neural networks are proposed to capture temporal and frequency dependencies in speech spectrograms. The system aims to improve on existing methods which have lower accuracy and require more computational resources for implementation.
I developed a Convolutional Neural Network using Python. This particular CNN is able to identify the correct individual based solely off of a photo with the knowledge of facial recognition.
MUSIC RECOMMENDATION THROUGH FACE RECOGNITION AND EMOTION DETECTIONIRJET Journal
This document describes a system for music recommendation based on facial emotion recognition using convolutional neural networks. It analyzes facial images using CNN models to detect seven basic emotions. Based on the detected emotion, it recommends songs from predefined playlists that match the user's mood. The system architecture inputs images of the user's face, uses a CNN for emotion detection, and then selects appropriate music from playlists organized by emotion. It was developed to provide personalized music recommendations based on a user's real-time facial expressions and emotional state, unlike other systems that rely only on search queries or prior listening history.
IRJET- Automated Detection of Gender from Face ImagesIRJET Journal
1) The document describes a system to automatically detect gender from face images using convolutional neural networks and Python. The system was developed to help address problems like security, fraud, and criminal identification.
2) The system uses a CNN classifier trained on the UTKFace dataset of facial images. The CNN model contains convolutional, activation, max pooling, flatten, dense and dropout layers to analyze image features and predict the gender of an unknown input face image.
3) The goal of the system is to identify gender from images faster than traditional criminal identification methods in order to help solve crimes and security issues more efficiently.
Emotion Recognition through Speech Analysis using various Deep Learning Algor...IRJET Journal
This document summarizes a research paper on emotion recognition through speech analysis using deep learning algorithms. The researchers used datasets containing speech samples labeled with seven emotions to train and test convolutional neural network (CNN), support vector machine (SVM), recurrent neural network (RNN), and random forest models. They found that the RNN model achieved the highest testing accuracy at 75.31% for emotion recognition. The researchers concluded that speech emotion recognition systems could be useful for applications like dialogue systems, call centers, student voice reviews, and building healthy relationships.
IRJET- Techniques for Analyzing Job Satisfaction in Working Employees – A...IRJET Journal
The document discusses techniques for analyzing job satisfaction in employees using deep learning algorithms. It proposes applying convolutional neural networks (CNNs) to facial images taken at regular intervals to classify emotions and determine if an employee is happy or stressed. CNNs would be trained to recognize emotions like surprise, fear, disgust, anger, happiness and sadness from facial expressions. The percentage of positive versus negative emotions detected over multiple images could indicate an employee's level of satisfaction. A literature review discusses limitations of existing approaches and supports using CNNs on facial expressions for more accurate analysis of employee mental health and work satisfaction.
International Journal of 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.
https://ijitce.com/index.php
Our journal maintains rigorous peer review standards. Each submitted article undergoes a thorough evaluation by experts in the respective field. This stringent review process helps ensure that only high-quality and scientifically sound research is accepted for publication. Researchers can trust that the articles they find in IJITCE have been critically assessed for validity, significance, and originality.
Scene Description From Images To SentencesIRJET Journal
This document presents an approach for generating sentences to describe images using distributed intelligence. It involves detecting objects in images using YOLO detection, finding relative positions of objects, labeling background scenes, generating tuples of objects/scenes/relations, extracting candidate sentences from Wikipedia containing tuple elements, searching images for each sentence and selecting the sentence whose images most closely match the input image. The approach is compared to the Babytalk model using BLEU and ROUGE scores, showing comparable performance. Future work to improve object detection and use larger knowledge sources is discussed.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
This document describes a novel approach to automated classification of brain tumors using probabilistic neural networks (PNN). It discusses how principal component analysis (PCA) can be used to reduce the dimensionality of magnetic resonance (MR) brain images, and then a PNN can classify the tumors. The proposed method involves using PCA for feature extraction and a PNN for classification. This is intended to provide faster and more accurate classification of brain tumors in MR images than conventional human-based methods.
Face Detection and Recognition using Back Propagation Neural Network (BPNN)IRJET Journal
1) The document discusses face detection and recognition using a back propagation neural network. It aims to recognize faces from images and determine if individuals are authorized.
2) Face detection is used to locate and crop face areas from images. Principal component analysis extracts features for dimension reduction. A back propagation neural network and radial basis function network are then used for classification.
3) The system was tested and achieved high recognition rates. Individual information was stored in a database. The document reviews related work on neural networks and previous implementations of face recognition.
IRJET-Breast Cancer Detection using Convolution Neural NetworkIRJET Journal
This document discusses using a convolutional neural network (CNN) to detect breast cancer from medical images. CNNs are a type of deep learning model that can learn image features without manual feature engineering. The proposed system would take a sample medical image as input, preprocess it, and compare it to images in a database labeled as cancerous or non-cancerous. If cancer is detected, the system would determine the cancer stage and recommend appropriate treatment. The CNN model would be built and trained using libraries like Keras, TensorFlow, and Numpy to classify images and detect breast cancer at early stages for better treatment outcomes.
Meta analysis of convolutional neural networks for radiological images - PubricaPubrica
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A Neural Network-Inspired Approach For Improved And True Movie RecommendationsAmy Roman
This research article proposes a neural network-inspired approach for improved movie recommendations. It presents a recurrent multivariate movie recommendation system that analyzes user movie reviews using recurrent neural networks (RNNs/LSTMs) with attention to extract sentiment. It evaluates multiple variables (ratings, votes, likes, reviews) from different sources to generate more personalized movie recommendations for users on a mobile app in an efficient way. The system addresses challenges with traditional recommendation approaches like sparsity and cold starts by using a distributed architecture with Apache Hadoop and implicate ratings.
IRJET- Visual Question Answering using Combination of LSTM and CNN: A SurveyIRJET Journal
This document discusses using a combination of long short-term memory (LSTM) and convolutional neural networks (CNN) for visual question answering (VQA). It proposes extracting image features from CNNs and encoding question semantics with LSTMs. A multilayer perceptron would then combine the image and question representations to predict answers. The methodology aims to reduce statistical biases in VQA datasets by focusing attention on relevant image regions. It was implemented in Keras with TensorFlow using pre-trained CNNs for images and word embeddings for questions. The proposed approach analyzes local image features and question semantics to improve VQA classification accuracy over methods relying solely on language.
The objective of emotion recognition is
identifying emotions of a human. The emotion can be
captured either from face or from verbal communication. In
this work we focus on identifying human emotion from
facial expressions. Facial emotion recognition is one of the
useful task and can be used as a base for many real-time
applications.
AI Therapist – Emotion Detection using Facial Detection and Recognition and S...ijtsrd
This paper presents an integrated system for emotion detection using facial detection and recognition. we have taken into account the fact that emotions are most widely represented with eyes and mouth expressions. In this research effort, we implement a general convolutional neural network CNN building framework for designing real time CNNs. We validate our models by creating a real time vision system that accomplishes the tasks of face detection, emotion classification, and generating the content according to the emotion or mood of the person simultaneously in one blended step using our proposed CNN architecture. Our proposed model consisted of modules such as image processing, Feature extraction, feature classification, and recommendation process. The images used in the experiment are pre processed with various image processing methods like canny edge detection, histogram equalization, fit ellipse, and FER dataset is mediated for conducting the experiments. With a trained profile that can be updated flexibly, a user can detect his her behavior on a real time basis. It utilizes the state of the art of face detection and recognition algorithms. Sanket Godbole | Jaivardhan Shelke "AI Therapist – Emotion Detection using Facial Detection and Recognition & Showing Content According to Emotions" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33267.pdf Paper Url: https://www.ijtsrd.com/computer-science/artificial-intelligence/33267/ai-therapist-–-emotion-detection-using-facial-detection-and-recognition-and-showing-content-according-to-emotions/sanket-godbole
This document describes a student project on speech-based emotion recognition. The project uses convolutional neural networks (CNN) and mel-frequency cepstral coefficients (MFCC) to classify emotions in speech into categories like happy, sad, fearful, calm and angry. The proposed system provides advantages over existing systems by allowing variable length audio inputs, faster processing, and real-time classification of more emotion categories. It achieves a test accuracy of 91.04% according to the document.
Semantic-based visual emotion recognition in videos-a transfer learning appr...IJECEIAES
Automatic emotion recognition is active research in analyzing human’s emotional state over the past decades. It is still a challenging task in computer vision and artificial intelligence due to its high intra-class variation. The main advantage of emotion recognition is that a person’s emotion can be recognized even if he is extreme away from the surveillance monitoring since the camera is far away from the human; it is challenging to identify the emotion with facial expression alone. This scenario works better by adding visual body clues (facial actions, hand posture, body gestures). The body posture can powerfully convey the emotional state of a person in this scenario. This paper analyses the frontal view of human body movements, visual expressions, and body gestures to identify the various emotions. Initially, we extract the motion information of the body gesture using dense optical flow models. Later the high-level motion feature frames are transferred to the pre-trained convolutional neural network (CNN) models to recognize the 17 various emotions in Geneva multimodal emotion portrayals (GEMEP) dataset. In the experimental results, AlexNet exhibits the architecture's effectiveness with an overall accuracy rate of 96.63% for the GEMEP dataset is better than raw frames and 94% for visual geometry group-19 VGG-19, and 93.35% for VGG-16 respectively. This shows that the dense optical flow method performs well using transfer learning for recognizing emotions.
Sign Language Recognition using Deep LearningIRJET Journal
The document discusses using deep learning techniques like MobileNet V2 to develop a model for sign language recognition. It aims to classify sign language gestures to help communicate with deaf people. The model was trained on a dataset of sign language images and achieved an accuracy of 70% in recognizing letters, numbers, and gestures.
Similar to Movie Sentiment Analysis using Deep Learning RNN (20)
‘Six Sigma Technique’ A Journey Through its Implementationijtsrd
The manufacturing industries all over the world are facing tough challenges for growth, development and sustainability in today’s competitive environment. They have to achieve apex position by adapting with the global competitive environment by delivering goods and services at low cost, prime quality and better price to increase wealth and consumer satisfaction. Cost Management ensures profit, growth and sustainability of the business with implementation of Continuous Improvement Technique like Six Sigma. This leads to optimize Business performance. The method drives for customer satisfaction, low variation, reduction in waste and cycle time resulting into a competitive advantage over other industries which did not implement it. The main objective of this paper ‘Six Sigma Technique A Journey Through Its Implementation’ is to conceptualize the effectiveness of Six Sigma Technique through the journey of its implementation. Aditi Sunilkumar Ghosalkar "‘Six Sigma Technique’: A Journey Through its Implementation" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64546.pdf Paper Url: https://www.ijtsrd.com/other-scientific-research-area/other/64546/‘six-sigma-technique’-a-journey-through-its-implementation/aditi-sunilkumar-ghosalkar
Edge Computing in Space Enhancing Data Processing and Communication for Space...ijtsrd
Edge computing, a paradigm that involves processing data closer to its source, has gained significant attention for its potential to revolutionize data processing and communication in space missions. With the increasing complexity and data volume generated by modern space missions, traditional centralized computing approaches face challenges related to latency, bandwidth, and security. Edge computing in space, involving on board processing and analysis of data, offers promising solutions to these challenges. This paper explores the concept of edge computing in space, its benefits, applications, and future prospects in enhancing space missions. Manish Verma "Edge Computing in Space: Enhancing Data Processing and Communication for Space Missions" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64541.pdf Paper Url: https://www.ijtsrd.com/computer-science/artificial-intelligence/64541/edge-computing-in-space-enhancing-data-processing-and-communication-for-space-missions/manish-verma
Dynamics of Communal Politics in 21st Century India Challenges and Prospectsijtsrd
Communal politics in India has evolved through centuries, weaving a complex tapestry shaped by historical legacies, colonial influences, and contemporary socio political transformations. This research comprehensively examines the dynamics of communal politics in 21st century India, emphasizing its historical roots, socio political dynamics, economic implications, challenges, and prospects for mitigation. The historical perspective unravels the intricate interplay of religious identities and power dynamics from ancient civilizations to the impact of colonial rule, providing insights into the evolution of communalism. The socio political dynamics section delves into the contemporary manifestations, exploring the roles of identity politics, socio economic disparities, and globalization. The economic implications section highlights how communal politics intersects with economic issues, perpetuating disparities and influencing resource allocation. Challenges posed by communal politics are scrutinized, revealing multifaceted issues ranging from social fragmentation to threats against democratic values. The prospects for mitigation present a multifaceted approach, incorporating policy interventions, community engagement, and educational initiatives. The paper conducts a comparative analysis with international examples, identifying common patterns such as identity politics and economic disparities. It also examines unique challenges, emphasizing Indias diverse religious landscape, historical legacy, and secular framework. Lessons for effective strategies are drawn from international experiences, offering insights into inclusive policies, interfaith dialogue, media regulation, and global cooperation. By scrutinizing historical epochs, contemporary dynamics, economic implications, and international comparisons, this research provides a comprehensive understanding of communal politics in India. The proposed strategies for mitigation underscore the importance of a holistic approach to foster social harmony, inclusivity, and democratic values. Rose Hossain "Dynamics of Communal Politics in 21st Century India: Challenges and Prospects" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64528.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/history/64528/dynamics-of-communal-politics-in-21st-century-india-challenges-and-prospects/rose-hossain
Assess Perspective and Knowledge of Healthcare Providers Towards Elehealth in...ijtsrd
Background and Objective Telehealth has become a well known tool for the delivery of health care in Saudi Arabia, and the perspective and knowledge of healthcare providers are influential in the implementation, adoption and advancement of the method. This systematic review was conducted to examine the current literature base regarding telehealth and the related healthcare professional perspective and knowledge in the Kingdom of Saudi Arabia. Materials and Methods This systematic review was conducted by searching 7 databases including, MEDLINE, CINHAL, Web of Science, Scopus, PubMed, PsycINFO, and ProQuest Central. Studies on healthcare practitioners telehealth knowledge and perspectives published in English in Saudi Arabia from 2000 to 2023 were included. Boland directed this comprehensive review. The researchers examined each connected study using the AXIS tool, which evaluates cross sectional systematic reviews. Narrative synthesis was used to summarise and convey the data. Results Out of 1840 search results, 10 studies were included. Positive outlook and limited knowledge among providers were seen across trials. Healthcare professionals like telehealth for its ability to improve quality, access, and delivery, save time and money, and be successful. Age, gender, occupation, and work experience also affect health workers knowledge. In Saudi Arabia, healthcare professionals face inadequate expert assistance, patient privacy, internet connection concerns, lack of training courses, lack of telehealth understanding, and high costs while performing telemedicine. Conclusions Healthcare practitioners telehealth perceptions and knowledge were examined in this systematic study. Its collection of concerned experts different personal attitudes and expertise would help enhance telehealths implementation in Saudi Arabia, develop its healthcare delivery alternative, and eliminate frequent problems. Badriah Mousa I Mulayhi | Dr. Jomin George | Judy Jenkins "Assess Perspective and Knowledge of Healthcare Providers Towards Elehealth in Saudi Arabia: A Systematic Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64535.pdf Paper Url: https://www.ijtsrd.com/medicine/other/64535/assess-perspective-and-knowledge-of-healthcare-providers-towards-elehealth-in-saudi-arabia-a-systematic-review/badriah-mousa-i-mulayhi
The Impact of Digital Media on the Decentralization of Power and the Erosion ...ijtsrd
The impact of digital media on the distribution of power and the weakening of traditional gatekeepers has gained considerable attention in recent years. The adoption of digital technologies and the internet has resulted in declining influence and power for traditional gatekeepers such as publishing houses and news organizations. Simultaneously, digital media has facilitated the emergence of new voices and players in the media industry. Digital medias impact on power decentralization and gatekeeper erosion is visible in several ways. One significant aspect is the democratization of information, which enables anyone with an internet connection to publish and share content globally, leading to citizen journalism and bypassing traditional gatekeepers. Another aspect is the disruption of conventional media industry business models, as traditional organizations struggle to adjust to the decrease in advertising revenue and the rise of digital platforms. Alternative business models, such as subscription models and crowdfunding, have become more prevalent, leading to the emergence of new players. Overall, the impact of digital media on the distribution of power and the weakening of traditional gatekeepers has brought about significant changes in the media landscape and the way information is shared. Further research is required to fully comprehend the implications of these changes and their impact on society. Dr. Kusum Lata "The Impact of Digital Media on the Decentralization of Power and the Erosion of Traditional Gatekeepers" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64544.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/political-science/64544/the-impact-of-digital-media-on-the-decentralization-of-power-and-the-erosion-of-traditional-gatekeepers/dr-kusum-lata
Online Voices, Offline Impact Ambedkars Ideals and Socio Political Inclusion ...ijtsrd
This research investigates the nexus between online discussions on Dr. B.R. Ambedkars ideals and their impact on social inclusion among college students in Gurugram, Haryana. Surveying 240 students from 12 government colleges, findings indicate that 65 actively engage in online discussions, with 80 demonstrating moderate to high awareness of Ambedkars ideals. Statistically significant correlations reveal that higher online engagement correlates with increased awareness p 0.05 and perceived social inclusion. Variations across colleges and a notable effect of college type on perceived social inclusion highlight the influence of contextual factors. Furthermore, the intersectional analysis underscores nuanced differences based on gender, caste, and socio economic status. Dr. Kusum Lata "Online Voices, Offline Impact: Ambedkar's Ideals and Socio-Political Inclusion - A Study of Gurugram District" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64543.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/political-science/64543/online-voices-offline-impact-ambedkars-ideals-and-sociopolitical-inclusion--a-study-of-gurugram-district/dr-kusum-lata
Problems and Challenges of Agro Entreprenurship A Studyijtsrd
Noting calls for contextualizing Agro entrepreneurs problems and challenges of the agro entrepreneurs and for greater attention to the Role of entrepreneurs in agro entrepreneurship research, we conduct a systematic literature review of extent research in agriculture entrepreneurship to overcome the study objectives of complications of agro entrepreneurs through various factors, Development of agriculture products is a key factor for the overall economic growth of agro entrepreneurs Agro Entrepreneurs produces firsthand large scale employment, utilizes the labor and natural resources, This research outlines the problems of Weather and Soil Erosions, Market price fluctuation, stimulates labor cost problems, reduces concentration of Price volatility, Dependency on Intermediaries, induces Limited Bargaining Power, and Storage and Transportation Costs. This paper mainly devoted to highlight Problems and challenges faced for the sustainable of Agro Entrepreneurs in India. Vinay Prasad B "Problems and Challenges of Agro Entreprenurship - A Study" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64540.pdf Paper Url: https://www.ijtsrd.com/other-scientific-research-area/other/64540/problems-and-challenges-of-agro-entreprenurship--a-study/vinay-prasad-b
Comparative Analysis of Total Corporate Disclosure of Selected IT Companies o...ijtsrd
Disclosure is a process through which a business enterprise communicates with external parties. A corporate disclosure is communication of financial and non financial information of the activities of a business enterprise to the interested entities. Corporate disclosure is done through publishing annual reports. So corporate disclosure through annual reports plays a vital role in the life of all the companies and provides valuable information to investors. The basic objectives of corporate disclosure is to give a true and fair view of companies to the parties related either directly or indirectly like owner, government, creditors, shareholders etc. in the companies act, provisions have been made about mandatory and voluntary disclosure. The IT sector in India is rapidly growing, the trend to invest in the IT sector is rising and employment opportunities in IT sectors are also increasing. Therefore the IT sector is expected to have fair, full and adequate disclosure of all information. Unfair and incomplete disclosure may adversely affect the entire economy. A research study on disclosure practices of IT companies could play an important role in this regard. Hence, the present research study has been done to study and review comparative analysis of total corporate disclosure of selected IT companies of India and to put forward overall findings and suggestions with a view to increase disclosure score of these companies. The researcher hopes that the present research study will be helpful to all selected Companies for improving level of corporate disclosure through annual reports as well as the government, creditors, investors, all business organizations and upcoming researcher for comparative analyses of level of corporate disclosure with special reference to selected IT companies. Dr. Vaibhavi D. Thaker "Comparative Analysis of Total Corporate Disclosure of Selected IT Companies of India" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64539.pdf Paper Url: https://www.ijtsrd.com/other-scientific-research-area/other/64539/comparative-analysis-of-total-corporate-disclosure-of-selected-it-companies-of-india/dr-vaibhavi-d-thaker
The Impact of Educational Background and Professional Training on Human Right...ijtsrd
This study investigated the impact of educational background and professional training on human rights awareness among secondary school teachers in the Marathwada region of Maharashtra, India. The key findings reveal that higher levels of education, particularly a master’s degree, and fields of study related to education, humanities, or social sciences are associated with greater human rights awareness among teachers. Additionally, both pre service teacher training and in service professional development programs focused on human rights education significantly enhance teacher’s knowledge, skills, and competencies in promoting human rights principles in their classrooms. Baig Ameer Bee Mirza Abdul Aziz | Dr. Syed Azaz Ali Amjad Ali "The Impact of Educational Background and Professional Training on Human Rights Awareness among Secondary School Teachers" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64529.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/education/64529/the-impact-of-educational-background-and-professional-training-on-human-rights-awareness-among-secondary-school-teachers/baig-ameer-bee-mirza-abdul-aziz
A Study on the Effective Teaching Learning Process in English Curriculum at t...ijtsrd
“One Language sets you in a corridor for life. Two languages open every door along the way” Frank Smith English as a foreign language or as a second language has been ruling in India since the period of Lord Macaulay. But the question is how much we teach or learn English properly in our culture. Is there any scope to use English as a language rather than a subject How much we learn or teach English without any interference of mother language specially in the classroom teaching learning scenario in West Bengal By considering all these issues the researcher has attempted in this article to focus on the effective teaching learning process comparing to other traditional strategies in the field of English curriculum at the secondary level to investigate whether they fulfill the present teaching learning requirements or not by examining the validity of the present curriculum of English. The purpose of this study is to focus on the effectiveness of the systematic, scientific, sequential and logical transaction of the course between the teachers and the learners in the perspective of the 5Es programme that is engage, explore, explain, extend and evaluate. Sanchali Mondal | Santinath Sarkar "A Study on the Effective Teaching Learning Process in English Curriculum at the Secondary Level of West Bengal" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd62412.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/education/62412/a-study-on-the-effective-teaching-learning-process-in-english-curriculum-at-the-secondary-level-of-west-bengal/sanchali-mondal
The Role of Mentoring and Its Influence on the Effectiveness of the Teaching ...ijtsrd
This paper reports on a study which was conducted to investigate the role of mentoring and its influence on the effectiveness of the teaching of Physics in secondary schools in the South West Region of Cameroon. The study adopted the convergent parallel mixed methods design, focusing on respondents in secondary schools in the South West Region of Cameroon. Both quantitative and qualitative data were collected, analysed separately, and the results were compared to see if the findings confirm or disconfirm each other. The quantitative analysis found that majority of the respondents 72 of Physics teachers affirmed that they had more experienced colleagues as mentors to help build their confidence, improve their teaching, and help them improve their effectiveness and efficiency in guiding learners’ achievements. Only 28 of the respondents disagreed with these statements. With majority respondents 72 agreeing with the statements, it implies that in most secondary schools, experienced Physics teachers act as mentors to build teachers’ confidence in teaching and improving students’ learning. The interview qualitative data analysis summarized how secondary school Principals use meetings with mentors and mentees to promote mentorship in the school milieu. This has helped strengthen teachers’ classroom practices in secondary schools in the South West Region of Cameroon. With the results confirming each other, the study recommends that mentoring should focus on helping teachers employ social interactions and instructional practices feedback and clarity in teaching that have direct measurable impact on students’ learning achievements. Andrew Ngeim Sumba | Frederick Ebot Ashu | Peter Agborbechem Tambi "The Role of Mentoring and Its Influence on the Effectiveness of the Teaching of Physics in Secondary Schools in the South West Region of Cameroon" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64524.pdf Paper Url: https://www.ijtsrd.com/management/management-development/64524/the-role-of-mentoring-and-its-influence-on-the-effectiveness-of-the-teaching-of-physics-in-secondary-schools-in-the-south-west-region-of-cameroon/andrew-ngeim-sumba
Design Simulation and Hardware Construction of an Arduino Microcontroller Bas...ijtsrd
This study primarily focuses on the design of a high side buck converter using an Arduino microcontroller. The converter is specifically intended for use in DC DC applications, particularly in standalone solar PV systems where the PV output voltage exceeds the load or battery voltage. To evaluate the performance of the converter, simulation experiments are conducted using Proteus Software. These simulations provide insights into the input and output voltages, currents, powers, and efficiency under different state of charge SoC conditions of a 12V,70Ah rechargeable lead acid battery. Additionally, the hardware design of the converter is implemented, and practical data is collected through operation, monitoring, and recording. By comparing the simulation results with the practical results, the efficiency and performance of the designed converter are assessed. The findings indicate that while the buck converter is suitable for practical use in standalone PV systems, its efficiency is compromised due to a lower output current. Chan Myae Aung | Dr. Ei Mon "Design Simulation and Hardware Construction of an Arduino-Microcontroller Based DC-DC High-Side Buck Converter for Standalone PV System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64518.pdf Paper Url: https://www.ijtsrd.com/engineering/mechanical-engineering/64518/design-simulation-and-hardware-construction-of-an-arduinomicrocontroller-based-dcdc-highside-buck-converter-for-standalone-pv-system/chan-myae-aung
Sustainable Energy by Paul A. Adekunte | Matthew N. O. Sadiku | Janet O. Sadikuijtsrd
Energy becomes sustainable if it meets the needs of the present without compromising the ability of future generations to meet their own needs. Some of the definitions of sustainable energy include the considerations of environmental aspects such as greenhouse gas emissions, social, and economic aspects such as energy poverty. Generally far more sustainable than fossil fuel are renewable energy sources such as wind, hydroelectric power, solar, and geothermal energy sources. Worthy of note is that some renewable energy projects, like the clearing of forests to produce biofuels, can cause severe environmental damage. The sustainability of nuclear power which is a low carbon source is highly debated because of concerns about radioactive waste, nuclear proliferation, and accidents. The switching from coal to natural gas has environmental benefits, including a lower climate impact, but could lead to delay in switching to more sustainable options. “Carbon capture and storage” can be built into power plants to remove the carbon dioxide CO2 emissions, but this technology is expensive and has rarely been implemented. Leading non renewable energy sources around the world is fossil fuels, coal, petroleum, and natural gas. Nuclear energy is usually considered another non renewable energy source, although nuclear energy itself is a renewable energy source, but the material used in nuclear power plants is not. The paper addresses the issue of sustainable energy, its attendant benefits to the future generation, and humanity in general. Paul A. Adekunte | Matthew N. O. Sadiku | Janet O. Sadiku "Sustainable Energy" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64534.pdf Paper Url: https://www.ijtsrd.com/engineering/electrical-engineering/64534/sustainable-energy/paul-a-adekunte
Concepts for Sudan Survey Act Implementations Executive Regulations and Stand...ijtsrd
This paper aims to outline the executive regulations, survey standards, and specifications required for the implementation of the Sudan Survey Act, and for regulating and organizing all surveying work activities in Sudan. The act has been discussed for more than 5 years. The Land Survey Act was initiated by the Sudan Survey Authority and all official legislations were headed by the Sudan Ministry of Justice till it was issued in 2022. The paper presents conceptual guidelines to be used for the Survey Act implementation and to regulate the survey work practice, standardizing the field surveys, processing, quality control, procedures, and the processes related to survey work carried out by the stakeholders and relevant authorities in Sudan. The conceptual guidelines are meant to improve the quality and harmonization of geospatial data and to aid decision making processes as well as geospatial information systems. The established comprehensive executive regulations will govern and regulate the implementation of the Sudan Survey Geomatics Act in all surveying and mapping practices undertaken by the Sudan Survey Authority SSA and state local survey departments for public or private sector organizations. The targeted standards and specifications include the reference frame, projection, coordinate systems, and the guidelines and specifications that must be followed in the field of survey work, processes, and mapping products. In the last few decades, there has been a growing awareness of the importance of geomatics activities and measurements on the Earths surface in space and time, together with observing and mapping the changes. In such cases, data must be captured promptly, standardized, and obtained with more accuracy and specified in much detail. The paper will also highlight the current situation in Sudan, the degree to which survey standards are used, the problems encountered, and the errors that arise from not using the standards and survey specifications. Kamal A. A. Sami "Concepts for Sudan Survey Act Implementations - Executive Regulations and Standards" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63484.pdf Paper Url: https://www.ijtsrd.com/engineering/civil-engineering/63484/concepts-for-sudan-survey-act-implementations--executive-regulations-and-standards/kamal-a-a-sami
Towards the Implementation of the Sudan Interpolated Geoid Model Khartoum Sta...ijtsrd
The discussions between ellipsoid and geoid have invoked many researchers during the recent decades, especially during the GNSS technology era, which had witnessed a great deal of development but still geoid undulation requires more investigations. To figure out a solution for Sudans local geoid, this research has tried to intake the possibility of determining the geoid model by following two approaches, gravimetric and geometrical geoid model determination, by making use of GNSS leveling benchmarks at Khartoum state. The Benchmarks are well distributed in the study area, in which, the horizontal coordinates and the height above the ellipsoid have been observed by GNSS while orthometric heights were carried out using precise leveling. The Global Geopotential Model GGM represented in EGM2008 has been exploited to figure out the geoid undulation at the benchmarks in the study area. This is followed by a fitting process, that has been done to suit the geoid undulation data which has been computed using GNSS leveling data and geoid undulation inspired by the EGM2008. Two geoid surfaces were created after the fitting process to ensure that they are identical and both of them could be counted for getting the same geoid undulation with an acceptable accuracy. In this respect, statistical operation played an important role in ensuring the consistency and integrity of the model by applying cross validation techniques splitting the data into training and testing datasets for building the geoid model and testing its eligibility. The geometrical solution for geoid undulation computation has been utilized by applying straightforward equations that facilitate the calculation of the geoid undulation directly through applying statistical techniques for the GNSS leveling data of the study area to get the common equation parameters values that could be utilized to calculate geoid undulation of any position in the study area within the claimed accuracy. Both systems were checked and proved eligible to be used within the study area with acceptable accuracy which may contribute to solving the geoid undulation problem in the Khartoum area, and be further generalized to determine the geoid model over the entire country, and this could be considered in the future, for regional and continental geoid model. Ahmed M. A. Mohammed. | Kamal A. A. Sami "Towards the Implementation of the Sudan Interpolated Geoid Model (Khartoum State Case Study)" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63483.pdf Paper Url: https://www.ijtsrd.com/engineering/civil-engineering/63483/towards-the-implementation-of-the-sudan-interpolated-geoid-model-khartoum-state-case-study/ahmed-m-a-mohammed
Activating Geospatial Information for Sudans Sustainable Investment Mapijtsrd
Sudan is witnessing an acceleration in the processes of development and transformation in the performance of government institutions to raise the productivity and investment efficiency of the government sector. The development plans and investment opportunities have focused on achieving national goals in various sectors. This paper aims to illuminate the path to the future and provide geospatial data and information to develop the investment climate and environment for all sized businesses, and to bridge the development gap between the Sudan states. The Sudan Survey Authority SSA is the main advisor to the Sudan Government in conducting surveying, mappings, designing, and developing systems related to geospatial data and information. In recent years, SSA made a strategic partnership with the Ministry of Investment to activate Geospatial Information for Sudans Sustainable Investment and in particular, for the preparation and implementation of the Sudan investment map, based on the directives and objectives of the Ministry of Investment MI in Sudan. This paper comes within the framework of activating the efforts of the Ministry of Investment to develop technical investment services by applying techniques adopted by the Ministry and its strategic partners for advancing investment processes in the country. Kamal A. A. Sami "Activating Geospatial Information for Sudan's Sustainable Investment Map" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63482.pdf Paper Url: https://www.ijtsrd.com/engineering/information-technology/63482/activating-geospatial-information-for-sudans-sustainable-investment-map/kamal-a-a-sami
Educational Unity Embracing Diversity for a Stronger Societyijtsrd
In a rapidly changing global landscape, the importance of education as a unifying force cannot be overstated. This paper explores the crucial role of educational unity in fostering a stronger and more inclusive society through the embrace of diversity. By examining the benefits of diverse learning environments, the paper aims to highlight the positive impact on societal strength. The discussion encompasses various dimensions, from curriculum design to classroom dynamics, and emphasizes the need for educational institutions to become catalysts for unity in diversity. It highlights the need for a paradigm shift in educational policies, curricula, and pedagogical approaches to ensure that they are reflective of the diverse fabric of society. This paper also addresses the challenges associated with implementing inclusive educational practices and offers practical strategies for overcoming barriers. It advocates for collaborative efforts between educational institutions, policymakers, and communities to create a supportive ecosystem that promotes diversity and unity. Mr. Amit Adhikari | Madhumita Teli | Gopal Adhikari "Educational Unity: Embracing Diversity for a Stronger Society" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64525.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/education/64525/educational-unity-embracing-diversity-for-a-stronger-society/mr-amit-adhikari
Integration of Indian Indigenous Knowledge System in Management Prospects and...ijtsrd
The diversity of indigenous knowledge systems in India is vast and can vary significantly between different communities and regions. Preserving and respecting these knowledge systems is crucial for maintaining cultural heritage, promoting sustainable practices, and fostering cross cultural understanding. In this paper, an overview of the prospects and challenges associated with incorporating Indian indigenous knowledge into management is explored. It is found that IIKS helps in management in many areas like sustainable development, tourism, food security, natural resource management, cultural preservation and innovation, etc. However, IIKS integration with management faces some challenges in the form of a lack of documentation, cultural sensitivity, language barriers legal framework, etc. Savita Lathwal "Integration of Indian Indigenous Knowledge System in Management: Prospects and Challenges" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63500.pdf Paper Url: https://www.ijtsrd.com/management/accounting-and-finance/63500/integration-of-indian-indigenous-knowledge-system-in-management-prospects-and-challenges/savita-lathwal
DeepMask Transforming Face Mask Identification for Better Pandemic Control in...ijtsrd
The COVID 19 pandemic has highlighted the crucial need of preventive measures, with widespread use of face masks being a key method for slowing the viruss spread. This research investigates face mask identification using deep learning as a technological solution to be reducing the risk of coronavirus transmission. The proposed method uses state of the art convolutional neural networks CNNs and transfer learning to automatically recognize persons who are not wearing masks in a variety of circumstances. We discuss how this strategy improves public health and safety by providing an efficient manner of enforcing mask wearing standards. The report also discusses the obstacles, ethical concerns, and prospective applications of face mask detection systems in the ongoing fight against the pandemic. Dilip Kumar Sharma | Aaditya Yadav "DeepMask: Transforming Face Mask Identification for Better Pandemic Control in the COVID-19 Era" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64522.pdf Paper Url: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/64522/deepmask-transforming-face-mask-identification-for-better-pandemic-control-in-the-covid19-era/dilip-kumar-sharma
Streamlining Data Collection eCRF Design and Machine Learningijtsrd
Efficient and accurate data collection is paramount in clinical trials, and the design of Electronic Case Report Forms eCRFs plays a pivotal role in streamlining this process. This paper explores the integration of machine learning techniques in the design and implementation of eCRFs to enhance data collection efficiency. We delve into the synergies between eCRF design principles and machine learning algorithms, aiming to optimize data quality, reduce errors, and expedite the overall data collection process. The application of machine learning in eCRF design brings forth innovative approaches to data validation, anomaly detection, and real time adaptability. This paper discusses the benefits, challenges, and future prospects of leveraging machine learning in eCRF design for streamlined and advanced data collection in clinical trials. Dhanalakshmi D | Vijaya Lakshmi Kannareddy "Streamlining Data Collection: eCRF Design and Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63515.pdf Paper Url: https://www.ijtsrd.com/biological-science/biotechnology/63515/streamlining-data-collection-ecrf-design-and-machine-learning/dhanalakshmi-d
How to Setup Default Value for a Field in Odoo 17Celine George
In Odoo, we can set a default value for a field during the creation of a record for a model. We have many methods in odoo for setting a default value to the field.
Elevate Your Nonprofit's Online Presence_ A Guide to Effective SEO Strategies...TechSoup
Whether you're new to SEO or looking to refine your existing strategies, this webinar will provide you with actionable insights and practical tips to elevate your nonprofit's online presence.
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A Free 200-Page eBook ~ Brain and Mind Exercise.pptxOH TEIK BIN
(A Free eBook comprising 3 Sets of Presentation of a selection of Puzzles, Brain Teasers and Thinking Problems to exercise both the mind and the Right and Left Brain. To help keep the mind and brain fit and healthy. Good for both the young and old alike.
Answers are given for all the puzzles and problems.)
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Andreas Schleicher presents PISA 2022 Volume III - Creative Thinking - 18 Jun...EduSkills OECD
Andreas Schleicher, Director of Education and Skills at the OECD presents at the launch of PISA 2022 Volume III - Creative Minds, Creative Schools on 18 June 2024.
2. International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
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Fig.2. 1 Graphical Representation of best movies over
a century
Hence a solution is required to improve the quality of the
movies by providing deep analysis of the movies using
reviews to the filmmakers so that they can recalibrate on
their methods on creating a good movie.Agenericsentiment
analysis consists of variousparametersincludingall humane
emotions. This gives a overall view on the sentiments rather
than a binary view of the sentiments, As to work on the
simplifications of the model only twopolarityofpositive and
negative is used in this paper.
Fig.2.2 Sentiment analysis
A. Convolutional Neural Networks
Convolutional Neural Networks or CNN is one type of deep
neural network. It is basically designed for visual imagery.
This type of neural network isprominentincomputervision.
It enables us to classify images better like a human brain.
The ability of image classification is a strength of this neural
networks. However it was used in Sentiment analysisaslo.It
has achieved an accuracy of 85 percent. The CNN
architecture consists of an input layer, hidden layer and an
output layer. In convolusion network the hidden layer are
crucial as they perform convolutions. Generally this layer
will perform dot product on the convolution kernel fromthe
input layer. The kernel goes along the input matrix of the
layer while generating a feature map.
A feature map, or activation map, is the outputactivationsfor
a given filter and the definition is thesameregardlessofwhat
layer we are on. This follows by other layers such as pooling
layer, normalization layers, fully connected layers. This
constitutes the architecture of a typical CNN. In CNN the
results of one layer is passed onto the next layer. Before
convolusions to take place the input layer takes in the input
and transforms them into tensors which will have shapes :
(number of inputs) x (input height) x (input width) x (input
channels). Then it goes under feature mapping process so
that the network can process the given input. There are
various parameters to the process such as weights, channels
etc. All these parameters constitute to the architecture of the
model. As a part of Neural Networks, it isdesignedinawayto
imitate human brain connectivities. It imitates the processof
how neurons communicate with each other. CNN is feed
forward based and it is efficient in processing images, it used
various methods to implement it on text classificationaswell
using the maxpooling layers etc. The size of the output
volume is determined by three hyper-parameters known as
padding size, stride, depth. The depth of the volume is the
dense neurons which are collectively packed in the same
region as the input volume. Stride influences the column
heights and weights. Paddinghelps in controllingthevolume
of output. While input volume taken as W, kernel field size K,
stride taken as S and zero padding on the border taken as P
The number of neurons that can fit in a given volume is
A parameter sharing scheme is used in convolutional layers
to control the number of free parameters. It relies on the
assumption that if a patch feature is useful to compute at
some spatial position, then it should also be useful to
compute at other positions. Denoting a single 2-dimensional
slice of depth as a depth slice, the neurons in each depth slice
are constrained to use the same weights and bias.Another
important concept of CNNs ispooling, which is a formofnon-
linear down-sampling.Thereareseveralnon-linearfunctions
to implement pooling, where max pooling is the most
common. It partitions the input image intoa set of rectangles
and, for each such sub-region, outputs the maximum.
Intuitively, the exact location of a feature is less important
than its rough location relative to other features. This is the
idea behind the use of pooling in convolutional neural
networks. The pooling layer serves to progressively reduce
the spatial size of the representation,toreducethenumberof
parameters, memoryfootprintandamountofcomputationin
the network, and hence to also control over fitting. This is
known as down-sampling It is common to periodically insert
a pooling layer between successive convolutional layers
(each one typically followedbyanactivationfunction,suchas
a ReLU layer) in a CNN architecture. While pooling layers
contribute to local translationinvariance,theydonotprovide
global translation invariance in a CNN,unlessaformofglobal
pooling is used All these combined influence the working of
the neural network of CNN. CNN is based on the convolution
kernel or filter which plays a prominent role in the entire
network. In text classification, the text is passed to the CNN
where it is passed over to the embedding layer of embedding
matrix.GlobalMaxPooling1D layers are applied to eachlayer.
All the outputs are then concatenated. A Dropout layer then
Dense then Dropout and then Final Dense layer is applied.
This is how a CNN can handle textual sequence data. Various
filters are used on the textual data for mapping different
vocabularies. This method has been experimented over
sentiment analysis as well. But the disadvantages of CNN
prevented accurate models and it is quite difficult to
implement. It became quite tricky after a while. Hence a
better and efficient algorithmissoughtafter.Thisbringsusto
modern RNN like LSTM which has back propagation feed
which is quite effective as it can hold memory for a longer
time. This proved quite useful in the field of sentiment
analysis. CNNs use more hyper parameters than a standard
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multilayer perceptron (MLP). While the usual rules for
learning rates and regularization constants still apply, the
following should be kept in mind when optimizing. Since
feature map size decreases with depth, layers near the input
layer tend to have fewer filters while higher layers can have
more. To equalize computation at each layer, the product of
feature values va with pixel position is kept roughlyconstant
across layers. Preserving more information about the input
would require keeping the total number of activations
(number of feature maps times number of pixel positions)
non-decreasing from one layer to the next. The number of
feature maps directly controls the capacity and depends on
the number of available examples and task complexity.
Regularization is a process of introducing additional
information to solve an ill-posed problem or to prevent over
fitting. CNNs use various types of regularization.
Fig2.3 CNN Architecture
The applications of CNN varies from field to field. They are
basically bedrock of deep learning. They became the
predecessors for modern deep learning algorithms. In image
recognition systems CNNs are predominantely used. Using
MNIST and NORB database it is stated that the prediction is
really fast, similarly a CNN named AlexNet also performed
equally compared with the above. The facial recognition is
also one of the crucial area in which CNN can be used. It is
proven that the error rate has been decreased while using
CNNs. This laid a foundation for modern facial recognition
systems. 97.6 % recognition rate has been confirmed on 10
subjects of nearly 5600 still images. The video quality is
observed manually by the CNN so that the system had a less
root mean square error. The benchmark for object
classification and detection is the ImageNet Large Scale
Image Recognition challenge which consists of millions of
images and object classes. GoogleLeNet so far has the best
performance of average precision of 0.439329, and the
classification error is reduced to 0.06656. CNN used on
ImageNet is close to human’s performance for detection.
However there are problems which affecttheperformanceof
CNNs such as imagesdistortedwithfilterswhichisacommon
phenomenon in modern images. In contrast, humans cannot
accurately classify breeds of dogs and species of flowers as
accurate as the CNN. Hence CNN has been better classifier of
objects till date. Many layered CNN in 2015 has been usedfor
face detection from almost every angles and proved to be
efficient in detection of faces. A database of 200,000 images
with various angles and orientations with another 2 million
images without faces has been used to train the network. A
batch of 128 images of over 50,000 iterations has been used.
CNNs also play a vital role in the video domain. There are
comparitivelylessstudymadeonthevideoclassificationthan
the image domain. Oneapproachtowardsvideoclassification
is to have time and space as equal dimensions of the input.
There is also another method in which we use use two CNNs
i.e one for the spatial stream and another for the temporal
stream. LSTM infused with themodeltoaccountforinter-clip
dependenciesistypicallyusedtoachievethis.Theapplication
of CNN has also evolvedtowardsnaturallanguageprocessing
as well. It is by this means a predominantly image
classification and detection algorithm is introduced to the
world of Natural Language Processing. It has been successful
so far in regards to semantic parsing, search query retrieval,
sentence modeling, classification, prediction and other
traditional NLP tasks. It is also used in anamoly detection in
videos as well. In order to achieve this, a CNN with 1D
convolutions was used over time series in frequency domain
with unsupervised model to detect anamolies in the time
domain. It is also an useful tool on drug discovery as the
model will be trained on identifying the reaction between
biological proteins and molecules. This has helped in
discovering potential treatment to a disease. A simple CNN
was combined with Cox-Gompertz proportional hazards
model and used to produce a proof-of-concept example of
digital biomarkers ofagingintheformofall-causes-mortality
predictor. The application of CNN is very vast and
researchers are using deep learning algorithms to further
develop systems that will help us in every aspect of science
and technology. In our experiment, the use of CNN for text
classification has achieved an accuracy less than Recurrent
Neural Networks or RNN. We have realised the CNN’s
limitations on processing Natural Language Processing.
III. PROPOSED SYSTEM FOR MOVIE SENTIMENT
ANALYSIS
RNN is the best approach towards modellingsequential data
as it processes basic inputs with its internal memory states
ht = f(ht-1, xt) = tanh(whhht-1 + wxh xt)
RNN can be used in the long sequent data theoretically they
cannot effectivelywork with real timeapplications,thiscould
be because of the inadequate gradient disadvantage. It uses
back propagation through time(BPTT) For addressing the
problems in RNN in real time applications LSTM or Long
Short Term Memory is introduced, as it can effectively used
to make models which has longersequencesatinterval.Four
gates are used in the data flow of LSTM. It uses different
gates to see what proportion of the new information should
be supplemental to the state cell (input(i)), the previous cell
is forgotten (forget(f)), gate(g) and output(o)gateattheside
of the cell (c) and hidden state (st) state. σ represents
provision sigmoid.
Following formulas are the state values at each gate.
i = σ( + −1 )
f = σ( + −1 )
o = σ( + −1 )
g = tanhσ( + −1 )
= −1o + o
= tanh( )o
4. International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
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Fig 3.1 LSTM cell
When comparing with other neural networks such as CNN,
GRU, LSTM proves to be more accurate than the above
mentioned algorithms hence it has been decided for the
model. Word embedding process is looked over by the
word2vec embedding. Word2vec is basically a technique
used in NLP (Natural Language Processing), which involves
vectorizing the words by creating a neural network which
will consist of word associations that are derived from a
large set of textual data. As machines cannot words like
humans, it is necessary to parse them into values which can
be understood by machines. Hence word embeddings are
used as the input to the model. Word2vec consists of a two
layer neural link which will contain a vectore sapce created
from the large set of textual dataset which will be used a
linguistic model for the machine. It can be done with two
model architecture for word distributions namely CROW
(Continoious Bag of words) and Continious skip gram. In
CROW the current word is predicted from the surrounding
word corpus regardless of the order of the words. In
continuous skip gram the surrounding words are predicted
using the current word, in this method also the order is not
an influencing factor. While CBOW is faster skip gram canbe
used for uncommon words. A tokenisor is also used in this
model. The model will consist of 4 layers namelyEmbedding
layer, 2 LSTM cell layer and a Dense Layer. This model will
be trained with 15 epochs over IMDB dataset in order to
achieve a precise model.
A. Word Tokenisation
Tokenization is the process of seperating textual data as
tokens inorder to conduct natural language processing
efficiently. Without tokenisation it is hard for themachine to
understand textual data. Most deep learning algorithms like
RNN, GRU process the sequencial data as tokens. This token
level approach is essential for data processing.RNN recieves
and processes tokens under a given time step. The
tokenization outputs a vocabulary from the corpus.
Vocabulary refers to the set of unique tokens. It can be
constructed by considering each unique token in the corpus
or by considering the top K Frequently Occurring Words. In
this experiment the word tokenisation is used as the
tokenization technique in which a pretrained word
tokenization like word2vec is used. It consists of pretrained
data that is obtained from conducting tokenization on a
larger word corpus. This is not completely fool proof. The
problem arises when an OOV occurs. The OOV refers to the
Out of Vocabulary i.e which is not encountered during the
training phase. The OOV will create a huge impact on the
testing set as it is not aware of the new vocabulary used in
the test data. This could even affect the accuracy of the
process. Hence a rich word tokenizaton is used in thismodel
to prevent such scenarios. The character tokenization
prevents the OOV probem yet it is not suitable for the above
model as the length of input and output sentences increases
a lot since we are using this method to characterise the
entire sentence, This will prevent the model to bring
meaningful sentences. Hence word tokeization i.e word2vec
is used inorder to be efficient in sentence vectorization.
Fig.3.2 Architecture of CNN for text processing
The application of tokenization in deep learning is vast as it
is primarily co-related with the natural language processing
which serves as one of the bedrock for neural networks.
Using this method the sequence data of text can be
vectorized and hence it is compatible for the machines to
process human language. Since machines communicate in
vectors this method is suitable for the experiment.
IV. IMPLEMENTATION
The implementation includestheIMDBdatasetwhichserves
a benchmark for sentiment analysis, of about 50000records
in which a 50/50 split will be made for training and testing.
The 25000 records of well reviewed data will be used for
training on the model. Once the trained model is ready it is
passed with the testing set to determine the performance of
the model. The goal is to use this model to determine the
polarity of the reviews as either positive or negative. We
tend to compare models of planned models with existing
models hence a comparison study has been carried out on
existing models. The result states that LSTM with word2vec
embedding offers higher performance than the existing
differnent models.SVM offers the lowest performance when
put next to other models, DNN offers slightly similar
performance with LSTM models as well. Hence with all this
comparisons, LSTM is decided to be the effective neural
network algorithm that can be used for this move sentiment
analysis. Before all this processes commences a word
preprocessing must be carried out before loading it onto the
model as the review data consists of unsanitised data which
will hinder the training process. The data from IMDB could
be web scraping result as it consists of various tags such as
<br></br>. Once the word preprocessing is done, the result
data is passed to the word2vec embedding which will
tokenise and vectorise the data as per the rules mentioned.
Using this as an input to the model, the training fit starts
with 15 epoch to achieve an accuracy of 88.04%. 0.001 is set
as the regulation parameter to avoid overfitting. The model
will be then loaded to a prediction program in which the
user will input reviews to predict the polarity of the reviews
as either positive or negative. The threshold value is set to
0.5.The value obtained above is stated as postive reviewand
below the threshold is predicted as a negative review. A
comparision study can be used to compare the
performances.
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TABLE.1 Performance of the model
This table shows the accuracy of models with different
architectures From the table, we are able to observe that
with a hundred LSTM units and when the model is trained
with fifty epochs we have a tendency to reached the most
effective performance when put nexttotheoppositemodels.
When the review length is about to a thousandweareable to
observe there's a dip in performance. once the quantity of
LSTM units is redoubled to 200 then additionallyweareable
to observe the deterioration in performance. This could
result to overfitting drawback. This gives us a comparative
study of performance of different classification models on
the benchmark IMDB movie review dataset. This model is
compared with logistic regression, SVM, MLP and CNN.
Except CNN all the other models area unit shallow models
and SVM is that the strong classification model compared to
the other models.To compare the planned model with the
present model a comparison study has been done used on
totally different existing models. Table shows that the
planned LSTM primarily based model with word2vec
embedding offers higher performance compared to
alternative models. statistical regression is best than SVM.
This could be thanks to the linear kernel used with the SVM
model. it's a great deal evident that the planned model is
giving higher performance when put next to the other
models.
Fig.4.1. Perfomances of different models
Fig.4.2Architecture of LSTM model
V. RESULTS AND DISCUSSION
The result shows that the accuracy has reached 88
precentage and the loss is comparitively less considering
previous attempts. The trained model is loaded to a
prediction program in which the model is tested with real
time reviews. The prediction program has a threshold value
of 0.5. The values predicted for the review relates to the
sentiments of the textual data. From this we canassumethat
the experiment is a success as the program can successfully
predict the sentiments as either a positive or a negative
review. The problem is that there is vanishing gradient
problem which needs to be addressed so algorithms such as
GRU can be utilised to avoid such issues.
Fig.5.1Accuracy and loss of the model
VI. CONCLUSION AND FUTURE ENHANCEMENT
This proposed system shows that LSTM is effective in the
sentiment analysis process and the resulting model has
achieved an accuracy of 88.04%.In future implementation,
the model will be tested on other reliabledatasetsandbetter
algorithms will be used to increasetheaccuracyofthemodel
and hence a more precise sentiment analysis model can be
achieved. There will be classificationsonvariousaspectsofa
film such as screenplay, music, acting, comedy etc as the
resulting prediction to further have a deep analysis of the
review. This reviews serve as the most crucial feedback for
the movie makers and hence this tool will be used to
automate the feedback process in the future. With the big
data combined this approach can be used in opinion mining
of large scale datasets and hence a report can be generated
on the diversified emotions of the people on a particular
topic or trend. This will be an important role in digital
marketing as it will give feedback on the performance of the
products/services on the market. This can be used to
6. International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
@ IJTSRD | Unique Paper ID – IJTSRD42414 | Volume – 5 | Issue – 4 | May-June 2021 Page 896
provide quality products/services as per the mass’s needs
and requirements in the current age. Sentiment analysisasa
whole serves as powerful tool in every aspects of the
commerce. So more and more deep learning techniques
should be used in order to improve the existing models in
the world. The applications of sentiment analysis is diverse
and is a powerful way to understand public sentiments
towards a subject.
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