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Journal articles on the topic 'Deep learning CNN model'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

Dr., Rekha Patil, Kumar Katrabad Vidya, Mahantappa, and Kumar Sunil. "Image Classification Using CNN Model Based on Deep Learning." Journal Of Scientific Research And Technology (JSRT) 1, no. 2 (2023): 60–71. https://doi.org/10.5281/zenodo.7965526.

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In this work, we will use a convolutional neural network to classify images. In the field of visual image analysis, CNNs (a subset of deep neural networks) are the norm. Multilayer perceptron is used to develop CNN; it is based on a hierarchical model that works on network construction and then delivers to a fully linked layer. All the neurons are linked together and their output is processed in this layer. Here, we demonstrate how our system can get the job done in challenging domains like computer vision by using a deep learning approach. Convolutional Neural Networks (CNNs) are a machine le

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Srinivas, Dr Kalyanapu, and Reddy Dr.B.R.S. "Deep Learning based CNN Optimization Model for MR Braing Image Segmentation." Journal of Advanced Research in Dynamical and Control Systems 11, no. 11 (2019): 213–20. http://dx.doi.org/10.5373/jardcs/v11i11/20193190.

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Aysuh, Jaggi, and Vinod Sharma Prof. "Classification of Healthy Seeds Using Deep Learning." Journal of Scientific Research and Technology (JSRT) 1, no. 4 (2023): 10–23. https://doi.org/10.5281/zenodo.8222793.

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With the increasing demand for healthy and high-quality seeds in agriculture, accurate and efficient seed classification methods are essential for seed quality control and optimisation of crop production. This work utilises a deep learning-based approach for healthy seed classification. It proposes a deep learning-based approach for beneficial seed classification, leveraging the power of neural networks to learn discriminative features from seed images automatically. The proposed method involves a multi-step pipeline that includes Image preprocessing, and Classification. The seed images are in

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Santosh, Giri1 and Basanta Joshi. "TRANSFER LEARNING BASED IMAGE VISUALIZATION USING CNN." International Journal of Artificial Intelligence and Applications (IJAIA) 10, July (2019): 47–55. https://doi.org/10.5281/zenodo.3371299.

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Image classification is a popular machine learning based applications of deep learning. Deep learning techniques are very popular because they can be effectively used in performing operations on image data in large-scale. In this paper CNN model was designed to better classify images. We make use of featureextraction part of inception v3 model for feature vector calculation and retrained the classification layer with these feature vector. By using the transfer learning mechanism the classification layer of the CNN model was trained with 20 classes of Caltech101 image dataset and 17 classes of

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K, Gayathri, and Thangavelu S. "Novel deep learning model for vehicle and pothole detection." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 3 (2021): 1576–82. https://doi.org/10.11591/ijeecs.v23.i3.pp1576-1582.

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The most important aspect of automatic driving and traffic surveillance is vehicle detection. In addition, poor road conditions caused by potholes are the cause of traffic accidents and vehicle damage. The proposed work uses deep learning models. The proposed method can detect vehicles and potholes using images. The faster region-based convolutional neural network (CNN) and the inception network V2 model are used to implement the model. The proposed work compares the performance, accuracy numbers, detection time, and advantages and disadvantages of the faster region-based convolution neural ne

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Zahraa, Najm Abdullah, Abdulridha Abutiheen Zinah, A. Abdulmunem Ashwan, and A. Harjan Zahraa. "Official logo recognition based on multilayer convolutional neural network model." TELKOMNIKA (Telecommunication, Computing, Electronics and Control) 20, no. 5 (2022): 1083–90. https://doi.org/10.12928/telkomnika.v20i5.23464.

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Deep learning has gained high popularity in the field of image processing and computer vision applications due to its unique feature extraction property. For this characteristic, deep learning networks used to solve different issues in computer vision applications. In this paper the issue has been raised is classification of logo of formal directors in Iraqi government. The paper proposes a multi-layer convolutional neural network (CNN) to classify and recognize these official logos by train the CNN model on several logos. The experimental show the effectiveness of the proposed method to recog

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Swetha, NG, Himasri Allu, Chandana KP Hari, Ujwal Kumar, Naga Sushwar, and BL Swathi. "Emotion Detection using Deep Learning CNN Model." International Journal of Microsystems and IoT 2, no. 9 (2024): 1187–96. https://doi.org/10.5281/zenodo.14099594.

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Facial Emotion Recognition (FER) is crucial in domains like human-computer interaction, mental health assessment, and marketing. This paper details the design and implementation of a FER model using Deep Convolutional Neural Networks (DCNNs) on the FER2013 dataset, which contains grayscale images labeled with seven emotions. Data augmentation and feature extraction are employed to enhance dataset diversity and reduce dimensionality. The DCNN architecture includes ReLU and Softmax activations for efficient non-linearity and multiclass classification, respectively, with Tanh and LeakyReLU showin

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Mohebbanaaz, Mohebbanaaz, Y. Padma Sai, and L. V. Rajani Kumari. "Detection of cardiac arrhythmia using deep CNN and optimized SVM." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 1 (2021): 217–25. https://doi.org/10.11591/ijeecs.v24.i1.pp217-225.

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Deep learning (DL) has become a topic of study in various applications, including healthcare. Detection of abnormalities in an electrocardiogram (ECG) plays a significant role in patient monitoring. It is noted that a deep neural network when trained on huge data, can easily detect cardiac arrhythmia. This may help cardiologists to start treatment as early as possible. This paper proposes a new deep learning model adapting the concept of transfer learning to extract deep-CNN features and facilitates automated classification of electrocardiogram (ECG) into sixteen types of ECG beats using an op

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Anbarasi., A., and S. Ravi. "Optimal Deep Learning based Classification Model for Mitral Valve Diagnosis System." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 4 (2020): 315–21. https://doi.org/10.35940/ijeat.C6530.049420.

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In present days, the domain of mitral valve (MV) diagnosis so common due to the changing lifestyle in day to day life. The increased number of MV disease necessitates the development of automated disease diagnosis model based on segmentation and classification. This paper makes use of deep learning (DL) model to develop a MV classification model to diagnose the severity level. For the accurate classification of ML, this paper applies the DL model called convolution neural network (CNN-MV) model. And, an edge detection based segmentation model is also applied which will helps to further enhance

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Yue, Wang, and Li Lei. "Sentiment Analysis using a CNN-BiLSTM Deep Model Based on Attention Classification." Information 26, no. 3 (2023): 117–62. http://dx.doi.org/10.47880/inf2603-02.

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With the rapid development of the Internet, the number of social media and e-commerce platforms increased dramatically. Users from all over world share their comments and sentiments on the Internet become a new tradition. Applying natural language processing technology to analyze the text on the Internet for mining the emotional tendencies has become the main way in the social public opinion monitoring and the after-sale feedback of manufactory. Thus, the study on text sentiment analysis has shown important social significance and commercial value. Sentiment analysis is a hot research topic in

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Kumar, P. V. Punith, K. M. Kiran Raj, and Harish Kunder. "Monkeypox Disease Detection using Deep Learning Techniques." Indian Journal Of Science And Technology 18, no. 26 (2025): 2135–47. https://doi.org/10.17485/ijst/v18i26.833.

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Objectives: To strengthen diagnostic precision and slow the transmission of the illness by creating an AI-based system to speed up monkeypox detection via an improved VGG-19 Convolutional Neural Network (CNN) system. Method: In order to maximize performance, bottleneck layers were integrated into a CNN model centered on the VGG-19 design. An openly accessible dataset of monkeypox photos was used to train and evaluate the algorithm. Accuracy, F1 Score, Precision, and Recall criteria were used to assess performance. Findings: With 95% accuracy, the VGG-19-based model showed promise in monkeypox

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Smithu, B. S., D. R. Janardhana, C. P. Leela, and G. Pushpa. "Forest Fire Risk Assessment and Detection using Deep Learning Models." Indian Journal Of Science And Technology 17, no. 46 (2024): 4921–28. https://doi.org/10.17485/ijst/v17i46.2138.

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Background: There is a severe need to detect any kind of fire in a faster and accurate method, especially forest fires to stop huge losses to the human community and the environment losses. The main purpose of the proposal is to identify and evaluate the accuracy of the existing Artificial Intelligence (AI) methods for detecting fire and improve the methods to detect fire in real-world scenarios in faster and accurate methods. Methods: The proposal uses a dataset to train a model, and in addition uses a few test images from an existing database to test the models developed. We develop and test

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Thi, Ha Phan, Chung Tran Duc, and Fadzil Hassan Mohd. "Vietnamese character recognition based on CNN model with reduced character classes." Bulletin of Electrical Engineering and Informatics 10, no. 2 (2021): 962~969. https://doi.org/10.11591/eei.v10i2.2810.

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This article will detail the steps to build and train the convolutional neural network (CNN) model for Vietnamese character recognition in educational books. Based on this model, a mobile application for extracting text content from images in Vietnamese textbooks was built using OpenCV and Canny edge detection algorithm. There are 178 characters classes in Vietnamese with accents. However, within the scope of Vietnamese character recognition in textbooks, some classes of characters only differ in terms of actual sizes, such as “c” and “C”, “o” and “O&r

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Harkulkar, Nilam. "Heart Disease Prediction using CNN, Deep Learning Model." International Journal for Research in Applied Science and Engineering Technology 8, no. 12 (2020): 875–81. http://dx.doi.org/10.22214/ijraset.2020.32671.

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ORAL, Serhat, İrfan ÖKTEN, and Uğur YÜZGEÇ. "Fungus Classification Based on CNN Deep Learning Model." Bitlis Eren Üniversitesi Fen Bilimleri Dergisi 12, no. 1 (2023): 226–41. http://dx.doi.org/10.17798/bitlisfen.1225375.

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Artificial intelligence has been developing day by day and has started to take a more prominent place in human life. As computer technologies advance, research on artificial intelligence has also increased in this direction. One of the main goals of this research is to examine how real problems in human life can be solved using artificial intelligence-based deep learning, and to present a case study. Poisoning from the consumption of poisonous fungi is a common problem worldwide. To prevent these poisonings, a mobile application has been developed using Convolutional Neural Networks (CNNs) and

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Ibrahim, Amin, Adel Refky, and Abdelghany M. Abdelghany. "Self-Driving Car Based CNN Deep Learning Model." International Journal of Advanced Engineering and Business Sciences 4, no. 3 (2023): 0. http://dx.doi.org/10.21608/ijaebs.2023.208574.1081.

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Solar, Mauricio, and Pablo Aguirre. "Deep learning techniques to process 3D chest CT." JUCS - Journal of Universal Computer Science 30, no. (6) (2024): 758–78. https://doi.org/10.3897/jucs.112977.

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The idea of using X–rays and Computed Tomography (CT) images as diagnostic method has been explored in several studies. Most of these studies work with slices of CT image in 2D, requiring less computational capacity and less time to process them than 3D. The processing of volumetric data (the complete CT images in 3D) adds an extra dimension of information. However, the magnitude of the data is considerably larger than working with slices in 2D, so extra computational processing is required. In this study a model capable of performing a classification of a 3D input that represents the vo

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Janarthanan Sekar. "Human and Object Detection Deep Learning Model Using R-CNN." Journal of Information Systems Engineering and Management 10, no. 30s (2025): 748–56. https://doi.org/10.52783/jisem.v10i30s.4897.

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Human and object detection is deep learning model. Which identifies and detects human(people) and object from image. For implementing the human and object detection there many popular algorithms like YOLO (You Only Look Once ), SSD (Single Shot Multi-Box Detector, CNN and R- CNN family. R-CNN family has R-CNN (Region Based Convolution Neural Network), FAST R-CNN, FASTER R- CNN. This time the R-CNN is very popular in market for more accuracy and efficient machine and deep learning object detection model. In this paper we have explain about algorithm and implementation of the human and object de

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Wang, Yipu, and Stuart Perrin. "Deep Chinese Teaching and Learning Model Based on Deep Learning." International Journal of Languages, Literature and Linguistics 10, no. 1 (2024): 32–35. http://dx.doi.org/10.18178/ijlll.2024.10.1.479.

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Deep learning is a more situational and reflective way of learning that integrates complex knowledge and skills into intuitive thinking. As a language that closely combines sound, form and meaning, Chinese teaching and learning from the perspective of deep learning can help break through the limitations of the current teaching model that only focuses on certain language knowledge or cultural behaviors. This paper combines deep learning with international Chinese education, creates deep Chinese teaching and learning model including “four stages and ten steps”, and carries out practical applicat

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B N, Ramya, Ranganath C, Bhuvanesh Kumar G, N. R. Eshwar, and Tejaswi M. "Colorization of Black-and-White Videos Using Deep Learning with Caffe-based CNN Model." International Journal of Research Publication and Reviews 6, no. 5 (2025): 1067–71. https://doi.org/10.55248/gengpi.6.0525.1625.

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Bhanja, Samit, and Abhishek Das. "A hybrid deep learning model for air quality time series prediction." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 3 (2021): 1611–18. https://doi.org/10.11591/ijeecs.v22.i3.pp1611-1618.

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Air quality (mainly PM2.5) forecasting plays an important role in the early detection and control of air pollution. In recent times, numerous deep learning-based models have been proposed to forecast air quality more accurately. The success of these deep learning models heavily depends on the two key factors viz. proper representation of the input data and preservation of temporal order of the input data during the feature’s extraction phase. Here we propose a hybrid deep neural network (HDNN) framework to forecast the PM2.5 by integrating two popular deep learning architectures, viz. Co

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K, Manimekalai, and A. Kavitha Dr. "Deep Learning Methods in Classification of Myocardial Infarction by employing ECG Signals." Indian Journal of Science and Technology 13, no. 28 (2020): 2823–32. https://doi.org/10.17485/IJST/v13i28.445.

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Abstract <strong>Background/Objectives:</strong> To automatically classify and detect the Myocardial Infarction using ECG signals.<strong> Methods/Statistical analysis:</strong> Deep Learning algorithms Convolutional Neural Network(CNN), Long Short Term Memory(LSTM) and Enhanced Deep Neural Network(EDN) were implemented. The proposed model EDN, comprises the techniques CNN and LSTM. Vector operations like matrix multiplication and gradient decent were applied to large matrices of data that are executed in parallel with GPU support. Because of parallelism EDN faster the execution

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Neetha, Alex, Lal Abhijith, S. Saranya, Anna Sunil Sharon, and S. Sreejith. "Prediction of Chronic Disease using Deep Learning." Recent Trends in Androids and IOS Applications 6, no. 1 (2023): 16–22. https://doi.org/10.5281/zenodo.10205658.

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<i>In present times, people are exposed to various illnesses due to their lifestyle and the state of the environment. It is crucial to identify and predict these diseasesat an early stage to prevent their progression to a more severe state. The goal of this proposed work is to identify and predict the patientswith more common chronicillness. The prediction gives thebenefit of early disease detection. In this proposed work, prediction is done by deep learning. The paper proposesa d

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Amit, Prakash Sen, Kumar Rout Nirmal, Pradhan Tuhinansu, and Mukherjee Amrit. "Hybrid Deep CNN Model for the Detection of COVID-19." Indian Journal of Science and Technology 15, no. 41 (2022): 2121–28. https://doi.org/10.17485/IJST/v15i41.1421.

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Abstract <strong>Objectives:</strong> To propose a model which will pre-process the dataset for the removal of any noise before the training of the network. <strong>Methods:</strong> Reported literature does not focus on the pre-processing of the dataset before the training of the network. A noise removal scheme called Probabilistic Decision Based Adaptive Improved Trimmed Median Filter (PDAITMF) is implemented as a pre-processing tool before the developed model. The PDAITMF de-noises the dataset. <strong>Findings:</strong> This supports an effective learning process b

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Mohamed, Maher Ben Ismail. "Insult detection using a partitional CNN-LSTM model." Computer Science and Information Technologies 1, no. 2 (2020): 84–92. https://doi.org/10.11591/csit.v1i2.p84-92.

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Recently, deep learning has been coupled with notice- able advances in Natural Language Processing related research. In this work, we propose a general framework to detect verbal offense in social networks comments. We introduce a partitional CNN-LSTM architecture in order to automatically recognize ver- bal offense patterns in social network comments. Specifically, we use a partitional CNN along with a LSTM model to map the social network comments into two predefined classes. In particular, rather than considering a whole document/comments as input as performed using typical CNN, we partition

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Shivarudraiah, Prof. "CNN Model for Smart Agriculture." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47576.

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Abstract— Precision farming is being revolutionized by the integration of innovative machine learning and computer vision methods. Identifying and classifying weeds and crops accurately remains a major challenge in this field, which has a direct effect on optimizing the yield as well as sustainability. In this work, an approach to smart weed detection based on deep learning using Convolutional Neural Networks (CNN) for feature learning followed by comparison of classifiers to select the best-performing model is introduced. In our research, InceptionV3 was utilized to extract features, and four

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Sharma, Amit, Dr V. K. Singh, and Dr Pushpendra Singh. "Deep CNN Based Hybrid Model for Image Retrieval." International Journal of Innovative Technology and Exploring Engineering 11, no. 9 (2022): 23–28. http://dx.doi.org/10.35940/ijitee.g9203.0811922.

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The popularity of deep features based image retrieval and classification task has grown a lot in the recent years. Feature representation based on Convolutional Neural Networks (CNNs) found to be very effective in terms of accuracy by various researchers in the field of visual content based image retrieval. The features which are neutral to their domain knowledge with automatic learning capability from their images are in demand in various image applications. For improving accuracy and expressive power, pre-trained CNN models with the use of transfer learning can be utilized by training them o

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Sri, Hari Nallamala, Polireddy Yashwanthini, Naga Thanujeswary Peram Venkata, Narra Devika, Krishna Teja Maddali Paavana, and Naga Kiran Botlagunta Hari. "Pneumonia detection using CNN: A deep learning approach." i-manager's Journal on Information Technology 14, no. 1 (2025): 17. https://doi.org/10.26634/jit.14.1.21936.

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Pneumonia is a highly contagious lung infection characterized by inflammation of air sacs in one or both the lungs. The air sacs get filled with fluid resulting in fever, cough and difficult breathing. Chest X-ray images are used to detect pneumonia. The manual identification of pneumonia using chest X-ray images is typically time-consuming and prone to errors, which may delay diagnosis and treatment. So, a deep learning model is used for detecting the pneumonia without any delays. A Convolutional Neural Network is a type of deep learning model specifically designed for processing images. Two

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Toqa, A. Sadoon, and H. Ali Mohammed. "Deep learning model for glioma, meningioma and pituitary classification." International Journal of Advances in Applied Sciences (IJAAS) 10, no. 1 (2021): 88–89. https://doi.org/10.11591/ijaas.v10.i1.pp88-98.

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One of the common causes of death is a brain tumor. Because of the above mentioned, early detection of a brain tumor is critical for faster treatment, and therefore there are many techniques used to visualize a brain tumor. One of these techniques is magnetic resonance imaging (MRI). On the other hand, machine learning, deep learning, and convolutional neural network (CNN) are the state of art technologies in the recent years used in solving many medical image-related problems such as classification. In this research, three types of brain tumors were classified using magnetic resonance imaging

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Amit, Sharma, V.K. Singh Dr., and Pushpendra Singh Dr. "Deep CNN Based Hybrid Model for Image Retrieval." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 11, no. 9 (2023): 23–28. https://doi.org/10.35940/ijitee.G9203.0811922.

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<strong>Abstract: </strong>The popularity of deep features based image retrieval and classification task has grown a lot in the recent years. Feature representation based on Convolutional Neural Networks (CNNs) found to be very effective in terms of accuracy by various researchers in the field of visual content based image retrieval. The features which are neutral to their domain knowledge with automatic learning capability from their images are in demand in various image applications. For improving accuracy and expressive power, pre-trained CNN models with the use of transfer learning can be

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Gandhar, Abhishek, Prakhar Priyadarshi, Shashi Gandhar, S. B. Kumar, Arvind Rehalia, and Mohit Tiwari. "An Effective Deep Learning Model Design for Cyber Intrusion Prevention System." Indian Journal Of Science And Technology 18, no. 10 (2025): 811–15. https://doi.org/10.17485/ijst/v18i10.318.

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Objectives: The increasing frequency of cyber threats necessitates the advancement of Intrusion Prevention Systems (IPS). However, existing IPS models suffer from high false positive rates, inefficiencies in real-time detection, and suboptimal accuracy levels. Methods: This study presents a CNN-LSTM hybrid model optimized for real-time cyber intrusion detection. The CICIDS2018 dataset was utilized for training, incorporating feature selection, hyper-parameter tuning, and dropout-based regularization to improve efficiency and prevent over-fitting. Findings: The proposed system achieved an F1-sc

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Zhan, Zhiwei, Guoliang Liao, Xiang Ren, et al. "RA-CNN." International Journal of Software Science and Computational Intelligence 14, no. 1 (2022): 1–14. http://dx.doi.org/10.4018/ijssci.311446.

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Emotion is a feeling that can be expressed by different mediums. Emotion analysis is a key task in NLP which is responsible for judging the emotional tendency of texts. Currently, in a complex multi-semantic environment, it still suffers from poor performance. Traditional methods usually require human intervention, while deep learning always has a trade-off between local and global features. To solve the problem that deep learning models generalize poorly for emotion analysis, this article proposed a semantic-enhanced method called RA-CNN, a classification model under a multi-semantic environm

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Alkhatib, Bassel, and Mohammad Madian Kamal Eddin. "Deep Learning Model CNN With LSTM For Speaker Recognition." Journal of Digital Information Management 20, no. 4 (2022): 131–47. http://dx.doi.org/10.6025/jdim/2022/20/4/131-147.

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Potnuru, Samanvi, Agrawal Shruti, Ranjan Mallick Soubhagya, et al. "Alzheimer's disease diagnosis using convolutional neural networks model." International Journal of Informatics and Communication Technology 13, no. 2 (2024): 206–13. https://doi.org/10.11591/ijict.v13i2.p206-213.

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The global healthcare system and related fields are experiencing extensive transformations, taking inspiration from past trends to plan for a technologically advanced society. Neurodegenerative diseases are among the illnesses that are hardest to treat. Alzheimer’s disease is one of these conditions and is one of the leading causes of dementia. Due to the lack of permanent treatment and the complexity of managing symptoms as the severity grows, it is crucial to catch Alzheimer’s disease early. The objective of this study was to develop a convolutional neural network (CNN)-based mod

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Nehal, Mohamed Ali, Mostafa Abd El Hamid Marwa, and Youssif Aliaa. "Sentiment Analysis for Movies Reviews Dataset Using Deep Learning Models." International Journal of Data Mining & Knowledge Management Process (IJDKP) 9, no. 2/3 (2019): 19–27. https://doi.org/10.5281/zenodo.3340668.

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Due to the enormous amount of data and opinions being produced, shared and transferred everyday across the internet and other media, Sentiment analysis has become vital for developing opinion mining systems. This paper introduces a developed classification sentiment analysis using deep learning networks and introduces comparative results of different deep learning networks. Multilayer Perceptron (MLP) was developed as a baseline for other networks results. Long short-term memory (LSTM) recurrent neural network, Convolutional Neural Network (CNN) in addition to a hybrid model of LSTM and CNN we

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Pan, Cong, Minyan Lu, Biao Xu, and Houleng Gao. "An Improved CNN Model for Within-Project Software Defect Prediction." Applied Sciences 9, no. 10 (2019): 2138. http://dx.doi.org/10.3390/app9102138.

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To improve software reliability, software defect prediction is used to find software bugs and prioritize testing efforts. Recently, some researchers introduced deep learning models, such as the deep belief network (DBN) and the state-of-the-art convolutional neural network (CNN), and used automatically generated features extracted from abstract syntax trees (ASTs) and deep learning models to improve defect prediction performance. However, the research on the CNN model failed to reveal clear conclusions due to its limited dataset size, insufficiently repeated experiments, and outdated baseline

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Kothari, Sonali, Shwetambari Chiwhane, Shruti Jain, and Malti Baghel. "Cancerous brain tumor detection using hybrid deep learning framework." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 3 (2022): 1651–61. https://doi.org/10.11591/ijeecs.v26.i3.pp1651-1661.

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Computational models based on deep learning (DL) algorithms have multiple processing layers representing data at multiple levels of abstraction. Deep learning has exploded in popularity in recent years, particularly in medical image processing, medical image analysis, and bioinformatics. As a result, deep learning has effectively modified and strengthened the means of identification, prediction, and diagnosis in several healthcare fields, including pathology, brain tumours, lung cancer, the abdomen, cardiac, and retina. In general, brain tumours are among the most common and aggressive maligna

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Abed Maeedi, Ahmed, Dalal Abdulmohsin Hammood, and Shatha Mezher Hasan. "Breast Cancer Detection Using Deep Learning." Iraqi Journal for Computers and Informatics 50, no. 2 (2024): 122–31. https://doi.org/10.25195/ijci.v50i2.500.

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This research aims to develop an image classification model by integrating long short-term memory (LSTM) with a convolutional neural network (CNN). LSTM, which is a type of neural network, can retain and retrieve long-term dependencies and improves the feature extraction capabilities of CNN when used in a multi-layer setting. The proposed approach outperforms typical CNN classifiers in image classification. The model’s high accuracy is due to the data passing through two stages and multiple layers: first the LSTM layer, followed by the CNN layer for accurate classification. Convolutional and r

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Thilagavathy, C. "Leveraging Machine and Deep Learning Models for Load Balancing Strategies in Cloud Computing." Indian Journal Of Science And Technology 17, no. 45 (2024): 4722–31. https://doi.org/10.17485/ijst/v17i45.2728.

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Objectives: To evaluate the efficiency of task prediction and resource allocation for load balancing (LB) in the cloud environment using the combined approach like random Forest(RF) for task prediction and Particle Swarm optimization for optimization and Convolutional Neural Networks (PSO-CNN) for resource prediction and allocation. Methods: The ensemble approach in the present study uses Random Forest (RF), a machine learning (ML) model for task prediction and Particle Swarm Optimization (PSO+CNN), a bio-inspired algorithm and Deep Learning (DL) model for optimization and resource allocation.

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Shaikh, Mohd Salim, Lucky Nirankari, Vasant Pardeshi, Rupesh Sharma, and Prof Sunil Kale. "DEEPFAKE DETECTION USING DEEP LEARNING (CNN+LSTM)." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 11 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem26808.

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Artificial intelligence advancements have led to the development of deepfake technology, which seriously jeopardises the integrity of visual media material. Robust detection algorithms are becoming more and more necessary as deepfake creation techniques become more complex. This study combines Long Short-Term Memory (LSTM) networks with Convolutional Neural Networks (CNNs) to present a novel method for deepfake identification. The suggested CNN+LSTM architecture makes use of LSTMs' temporal modelling capabilities and CNNs' spatial feature extraction capabilities. While the LSTM component analy

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Bindushree, S., and A. N. Rakshitha. "Face Recognition Using Deep Learning." International Journal of Advanced Scientific Inovation 01, no. 01 (2021): 12–18. https://doi.org/10.5281/zenodo.4641691.

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<strong><em>Today face recognition and its usage are  developing at a remarkable rate. Researches are at present building up different strategies in which facial recognition framework works. In circumstances like accidents, normal disasters, missing cases, clashes between nations, kidnappings and numerous different circumstances individuals are regularly isolated by their families. Recognizing the relatives of those refugees is essential to arrive at their family for refugee’s security and backing. Everyday polices are enrolling with missing cases, a portion of those enlisted cases

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Srilakshmi, Ch, K. Kiruthika, Priya R. Bharathi, and J. Jayalakshmi. "Detection of Human Facial Expression using CNN Model." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 5 (2020): 629–34. https://doi.org/10.35940/ijeat.E9615.069520.

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<strong>Facial expression is the most effective and herbal non verbal emotional conversation method People can range indoors the way they display their expressions Even pics of the same character within the identical countenance can vary in brightness historical past and pose and these variations are emphasized if thinking about particular subjects because of versions in shape ethnicity amongst others Hence countenance recognition remains a challenging trouble in PC vision To advise a solution for expression reputation that uses a combination of Convolutional Neural Network and precise picture

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M, Venkata Krishna Reddy, and Pradeep S. "Envision Foundational of Convolution Neural Network." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 10, no. 6 (2021): 54–60. https://doi.org/10.35940/ijitee.F8804.0410621.

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Profound learning's goes to the achievement of spurs in a large number and understudies to find out about the energizing innovation. At this regular process of novices to venture the multifaceted nature of comprehension and applying profound learning. We present Convolution Neural Network (CNN) EXPLAINER, an intelligent representation instrument intended for non-specialists to learn and inspect (CNN)-Convolution Neural Network a fundamental profound learning model engineering. Our apparatus tends to key difficulties that fledglings face in finding out about Convolution Neural Network, it c

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Sriwong, Kittipat, Kittisak Kerdprasop, and Nittaya Kerdprasop. "The Study of Noise Effect on CNN-Based Deep Learning from Medical Images." International Journal of Machine Learning and Computing 11, no. 3 (2021): 202–7. http://dx.doi.org/10.18178/ijmlc.2021.11.3.1036.

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Currently, computational modeling methods based on machine learning techniques in medical imaging are gaining more and more interests from health science researchers and practitioners. The high interest is due to efficiency of modern algorithms such as convolutional neural networks (CNN) and other types of deep learning. CNN is the most popular deep learning algorithm because of its prominent capability on learning key features from images that help capturing the correct class of images. Moreover, several sophisticated CNN architectures with many learning layers are available in the cloud comp

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Muhammad, Zulqarnain, Ghazali Rozaida, Mazwin Mohmad Hassim Yana, and Rehan Muhammad. "A comparative review on deep learning models for text classification." Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) 19, no. 1 (2020): 325–35. https://doi.org/10.11591/ijeecs.v19.i1.pp325-335.

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Text classification is a fundamental task in several areas of natural language processing (NLP), including words semantic classification, sentiment analysis, question answering, or dialog management. This paper investigates three basic architectures of deep learning models for the tasks of text classification: Deep Belief Neural (DBN), “Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), these three main types of deep learning architectures, are largely explored to handle various classification tasks. DBN have excellent learning capabilities to extracts highly distingu

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Jagadesh, K. "Dog’s Breed Prediction using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 12, no. 6 (2024): 1161–66. http://dx.doi.org/10.22214/ijraset.2024.61626.

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Abstract: Dog Breed Prediction from Images using Deep Learning, in this project, we propose a Convolutional Neural Network (CNN) based approach for predicting dog breeds from images. With the increasing popularity of dogs as pets and the need for proper care and maintenance, it is essential to identify the breed of a dog accurately. However, manual identification can be time-consuming and prone to errors. Therefore, we propose a deep learning-based solution that can predict the breed of a dog with high accuracy. We train a CNN model on a large dataset of dog images, where each image is labeled

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Hosni Mahmoud, Hanan A. "Diabetic Retinopathy Progression Prediction Using a Deep Learning Model." Axioms 11, no. 11 (2022): 614. http://dx.doi.org/10.3390/axioms11110614.

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Diabetes is an illness that happens with a high level of glucose in the body, and can harm the retina, causing permanent loss vision or diabetic retinopathy. The fundus oculi method comprises detecting the eyes to perform a pathology test. In this research, we implement a method to predict the progress of diabetic retinopathy. There is a research gap that exists for the detection of diabetic retinopathy progression employing deep learning models. Therefore, in this research, we introduce a recurrent CNN (R-CNN) model to detect upcoming visual field inspections to predict diabetic retinopathy p

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Altun, Murat, Hüseyin Gürüler, Osman Özkaraca, Faheem Khan, Jawad Khan, and Youngmoon Lee. "Monkeypox Detection Using CNN with Transfer Learning." Sensors 23, no. 4 (2023): 1783. http://dx.doi.org/10.3390/s23041783.

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Monkeypox disease is caused by a virus that causes lesions on the skin and has been observed on the African continent in the past years. The fatal consequences caused by virus infections after the COVID pandemic have caused fear and panic among the public. As a result of COVID reaching the pandemic dimension, the development and implementation of rapid detection methods have become important. In this context, our study aims to detect monkeypox disease in case of a possible pandemic through skin lesions with deep-learning methods in a fast and safe way. Deep-learning methods were supported with

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Jinendra, Anchalia, and S. Srividhya Dr. "Building CNN Model for Autonomous Car using Udacity Simulator." Recent Trends in Computer Graphics and Multimedia Technology 4, no. 1 (2022): 1–7. https://doi.org/10.5281/zenodo.6627001.

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<em>Recent times has seen a rising trend in the area of automation. One of the most trending topics in the field of automation is self-driving cars which shows the culmination of artificial intelligence, machine learning, deep learning, internet of things and many other in-demand domains and technologies which have improved significantly in the last decade. These cars use complex artificial intelligence and machine learning algorithms. This paper aims at explaining how to build an architecture for autonomous cars in Udacity simulator and adjust parameters to get a deeper understanding of how t

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A., Sasi Kumar, and S. Aithal P. "DeepQ Residue Analysis of Brain-Computer Classification and Prediction using Deep CNN." International Journal of Applied Engineering and Management Letters (IJAEML) 7, no. 2 (2023): 144–63. https://doi.org/10.5281/zenodo.8104434.

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<strong>Purpose: </strong><em>During this</em><em> article, we are going to consistently explore the kinds of brain signals for Brain Computer Interface (BCI) and discover the related ideas of the in-depth learning of brain signal analysis. We talk review recent machine Associate in Nursing deep learning approaches within the detection of two brain unwellness just like Alzheimer' disease (AD), brain tumor. In addition, a quick outline of the varied marker extraction techniques that want to characterize brain diseases is provided. Project work, the automated tool for tumor classification su

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