Pub Date : 2023-11-30DOI: 10.54105/ijcgm.d6601.083223
Mr. Tsega Asresa, Mr. Getahun Tigistu, Mr. Melaku Bayih
Computer vision is a sub branch of artificial intelligence (AI) that enables computers and systems to derive substitutive information from digital images and Video. Artificial intelligence plays a significant role in the area of security and surveillance, image processing and machine learning. In computer vision and image processing object detection algorisms are used to detect objects from certain classes of images or video. There is a scope identification of human face emotion Facial emotion recognition is done using computer vision algorism whether the person’s emotion is Happy, sad, fear, disgust, neutral and so on. Object detection algorism are used in deep learning used to classify the detected the regions. Facial emotion recognition is an emerging research area for improving human and computer interaction. It plays a crucial role in security, social communication commercial enterprise and law enforcement. In this research project CNN is used for training the data and predicting seven emotions such as anger, happy, sad, disgust, fear neutral and surprise. In this paper the experiment will be conduct using convolutional neural network as classifier, since it is multi class classification relu, softmax (activation function), categorical cross entropy(loss function) dropout max pooling conducted. The researcher tried to train the model by 80/20, 70/30, 90/10 train test split. However 70/30 train test split out performs over the other. The performance of the model is measured by using the epoch 10 and dropout 0.3. Totally the model is performed 93.8% in the training accuracy and it 75% for the testing.
{"title":"Convolutional Neural Network Driven Computer Vision Based Facial Emotion Detection and Recognition","authors":"Mr. Tsega Asresa, Mr. Getahun Tigistu, Mr. Melaku Bayih","doi":"10.54105/ijcgm.d6601.083223","DOIUrl":"https://doi.org/10.54105/ijcgm.d6601.083223","url":null,"abstract":"Computer vision is a sub branch of artificial intelligence (AI) that enables computers and systems to derive substitutive information from digital images and Video. Artificial intelligence plays a significant role in the area of security and surveillance, image processing and machine learning. In computer vision and image processing object detection algorisms are used to detect objects from certain classes of images or video. There is a scope identification of human face emotion Facial emotion recognition is done using computer vision algorism whether the person’s emotion is Happy, sad, fear, disgust, neutral and so on. Object detection algorism are used in deep learning used to classify the detected the regions. Facial emotion recognition is an emerging research area for improving human and computer interaction. It plays a crucial role in security, social communication commercial enterprise and law enforcement. In this research project CNN is used for training the data and predicting seven emotions such as anger, happy, sad, disgust, fear neutral and surprise. In this paper the experiment will be conduct using convolutional neural network as classifier, since it is multi class classification relu, softmax (activation function), categorical cross entropy(loss function) dropout max pooling conducted. The researcher tried to train the model by 80/20, 70/30, 90/10 train test split. However 70/30 train test split out performs over the other. The performance of the model is measured by using the epoch 10 and dropout 0.3. Totally the model is performed 93.8% in the training accuracy and it 75% for the testing.","PeriodicalId":130599,"journal":{"name":"Indian Journal of Computer Graphics and Multimedia","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139207396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-30DOI: 10.54105/ijcgm.h9683.083223
Mona A. Elzuway, Hend M. Farkash, Amani M. Shatshat
Finding solutions to nonlinear equations is not only a matter for mathematicians but is essential in many branches such as physics, statistics, and others. However, some of the nonlinear equations in numerical analysis require a lot of complex calculations to achieve convergence. This leads to many arithmetic errors and is consumed a great effort to solve them. Hence, researchers in numerical analysis use computer programs to find approximate solutions. This study used Matlab and Artificial Neural Networks and applied two different numerical analysis methods. The results from training artificial neural networks by utilizing the Backpropagation algorithm and MATLAB have been compared. The importance of this study lies in shedding light on the capabilities of Matlab and its strength in the field of methods for solving mathematical series, and helps students in mathematics in solving complex equations faster and more accurately, also studying the utilization of Artificial Neural Network algorithms in solving these methods, and clarifying the difference between them and programming Ordinary Matlab and comparing them with ordinary mathematical methods. The findings revealed that Traditional methods need more effort. MATLAB helps. On the other hand, solving numerical analysis problems is easier, faster, more accurate, and more effective. Furthermore, in the case of the Matlab application, the Newton method gave faster and less in the number of steps. Additionally, in training, the neural network based on the Newton method gave results faster depending on the Bisection method.
{"title":"Effectiveness of MATLAB and Neural Networks for Solving Nonlinear Equations by Repetitive Methods","authors":"Mona A. Elzuway, Hend M. Farkash, Amani M. Shatshat","doi":"10.54105/ijcgm.h9683.083223","DOIUrl":"https://doi.org/10.54105/ijcgm.h9683.083223","url":null,"abstract":"Finding solutions to nonlinear equations is not only a matter for mathematicians but is essential in many branches such as physics, statistics, and others. However, some of the nonlinear equations in numerical analysis require a lot of complex calculations to achieve convergence. This leads to many arithmetic errors and is consumed a great effort to solve them. Hence, researchers in numerical analysis use computer programs to find approximate solutions. This study used Matlab and Artificial Neural Networks and applied two different numerical analysis methods. The results from training artificial neural networks by utilizing the Backpropagation algorithm and MATLAB have been compared. The importance of this study lies in shedding light on the capabilities of Matlab and its strength in the field of methods for solving mathematical series, and helps students in mathematics in solving complex equations faster and more accurately, also studying the utilization of Artificial Neural Network algorithms in solving these methods, and clarifying the difference between them and programming Ordinary Matlab and comparing them with ordinary mathematical methods. The findings revealed that Traditional methods need more effort. MATLAB helps. On the other hand, solving numerical analysis problems is easier, faster, more accurate, and more effective. Furthermore, in the case of the Matlab application, the Newton method gave faster and less in the number of steps. Additionally, in training, the neural network based on the Newton method gave results faster depending on the Bisection method.","PeriodicalId":130599,"journal":{"name":"Indian Journal of Computer Graphics and Multimedia","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115057190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-30DOI: 10.54105/ijcgm.c7264.082222
Usman Ibrahim Musa, A. Roy
The visibility of items in water is lower than that of those on land. Light waves from a source don't have enough time to reach an item before it vanishes beneath the surface because light waves in water travel more quickly than they do in air. As a result, it can be challenging for people to deal with water properly due to certain of its physical characteristics. In light of this, object detection underwater has a wide range of uses, including environmental monitoring, surveillance, search and rescue, and navigation. This might enhance the precision, efficiency, and safety of undersea activities. In light of the aforementioned, we proposed a deep-learning technique that can detect and classify a variety of underwater objects.
{"title":"Innovations in Marine Robotics: Object Detection and Localization Underwater","authors":"Usman Ibrahim Musa, A. Roy","doi":"10.54105/ijcgm.c7264.082222","DOIUrl":"https://doi.org/10.54105/ijcgm.c7264.082222","url":null,"abstract":"The visibility of items in water is lower than that of those on land. Light waves from a source don't have enough time to reach an item before it vanishes beneath the surface because light waves in water travel more quickly than they do in air. As a result, it can be challenging for people to deal with water properly due to certain of its physical characteristics. In light of this, object detection underwater has a wide range of uses, including environmental monitoring, surveillance, search and rescue, and navigation. This might enhance the precision, efficiency, and safety of undersea activities. In light of the aforementioned, we proposed a deep-learning technique that can detect and classify a variety of underwater objects.","PeriodicalId":130599,"journal":{"name":"Indian Journal of Computer Graphics and Multimedia","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117000845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Identification of medicinal plants automatically in the environments is necessary to know about their existence around us. Recently, there are many techniques followed to recognize plants automatically such as through leaves and flowers with their shape and texture. Leaf-based plant species identification systems are widely used nowadays. This proposed research work uses a deep learning approach using Convolutional Neural Networks (CNN) to recognize medicinal plants through leaves with high accuracy. For this research, leaf images are collected from nature and used as the experimental dataset. The authors have collected leaf items from 5 different medicinal plants. After the collection of images and have to pre-process them which plays an important role in the classification steps. Deep learning model and algorithm are used for classification purposes among them, VGG16 worked pretty well and got an accuracy level of 95.48%. In real life, this paper can well affect the medical sector and learn more about medicinal plants.
{"title":"Automatic Recognition of Medicinal Plants: Based on Multispectral and Texture Features using Hidden Deep Learning Model","authors":"Murad Kabir Md. Rakib, Himanish Debnath Himu, Md. Omar Faruq Fahim, Ms. Zahura Zaman, MD. Jalal Uddin Rumi Palak","doi":"10.54105/ijcgm.d4089.023123","DOIUrl":"https://doi.org/10.54105/ijcgm.d4089.023123","url":null,"abstract":"Identification of medicinal plants automatically in the environments is necessary to know about their existence around us. Recently, there are many techniques followed to recognize plants automatically such as through leaves and flowers with their shape and texture. Leaf-based plant species identification systems are widely used nowadays. This proposed research work uses a deep learning approach using Convolutional Neural Networks (CNN) to recognize medicinal plants through leaves with high accuracy. For this research, leaf images are collected from nature and used as the experimental dataset. The authors have collected leaf items from 5 different medicinal plants. After the collection of images and have to pre-process them which plays an important role in the classification steps. Deep learning model and algorithm are used for classification purposes among them, VGG16 worked pretty well and got an accuracy level of 95.48%. In real life, this paper can well affect the medical sector and learn more about medicinal plants.","PeriodicalId":130599,"journal":{"name":"Indian Journal of Computer Graphics and Multimedia","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127674966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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