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Journal articles on the topic 'Convolu- tional Neural Networks'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

G, Madhuri. "Early Detection of Alzheimers Disease using Deep Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem31392.

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Alzheimer’s disease (AD) poses a significant challenge to global healthcare systems due to its progressive nature and impact on patient’s lives. Accurate and early detection of AD is crucial for timely intervention and management. In this paper, we propose the use of deep learning models, including Convolutional Neural Networks (CNNs), MobileNet, and VGG16 for the classification of Magnetic Resonance Imaging (MRI) scans into different AD stages. Index Terms—Alzheimer’s disease , Deep Learning , Convolu- tional Neural Networks(CNN) , MobileNet , VGG16 , MRI scans

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Ashok Kumar, K., Vamsi Pulikonda, and Narendarnath Sai. "Road Fault Detection by Using Convolutional Neural Networks." Journal of Computational and Theoretical Nanoscience 17, no. 8 (2020): 3374–77. http://dx.doi.org/10.1166/jctn.2020.9188.

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Bad conditions of road due to the potholes are one of the major cause of road damage and accidents to vehicles. Recently, with the increase in pollution and vehicular traffic, most of roads are being filled with many small and large potholes in most of places in the country. Detecting potholes manually is a time-consuming task and labour-intensive task, automating this process which saves a lot of time and money. Hence, Many different methodologies have been implemented that is from reporting to authorities manually to the use of laser imaging. Though all of these techniques have some disadvan

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Neeti Yadav. "Deep Learning-Based Detection and Classification of Rice Diseases Using Residual Networks (ResNet50)." International Journal of Latest Technology in Engineering Management & Applied Science 14, no. 4 (2025): 567–73. https://doi.org/10.51583/ijltemas.2025.140400060.

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Abstract—Timely detection and classification of crop diseases are essential for maintaining agricultural productivity as well as the quality of food. Many traditional disease identification methods are labor-intensive, time-consuming, and human-error- prone. Recently developed techniques based on computer vision and deep learning add efficient, automated alternatives for disease detection. This work proposes a deep learning-based system utilizing a Residual Network (ResNet50) for automatically diagnosing and classifying rice diseases, a specialized Convolu- tional Neural Network (CNN) form. Ri

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Tian, Hongyu, Wenming Cao, and Qiyu Ran. "Residual Network with Triple-Attention Mechanisms for Knee Osteoarthritis Severity Classification." BIO Web of Conferences 174 (2025): 03023. https://doi.org/10.1051/bioconf/202517403023.

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As the quality of life continues to improve, people are more concerned about all types of diseas- es. Knee osteoarthritis (KOA) is a type of arthritis that is characterized by limited movement, joint stiffness and pain. This degenerative disease leads to gradual wear and tear of the knee joint and in severe cases, dis- ability. Conventional radiographic diagnosis remains challenging due to the subtle morphological changes in early-stage KOA that often resemble age-related physiological variations. Meanwhile, applying convolu- tional neural networks to the prediction of KOA has become an effect

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Muhammad Husnul Hayat. "Klasifikasi Citra Klon Teh Seri GMB Menggunakan Convolu-tional Neural Network (CNN) dengan Arsitektur Resnet, Vggnet, dan Alexnet." Jurnal Sains Teh dan Kina 1, no. 2 (2022): 26–39. http://dx.doi.org/10.22302/pptk.jur.jstk.v1i2.168.

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Produktivitas daun teh di Indonesia dari waktu ke waktu semakin menurun. Hal ini disebabkan oleh ketidakcocokan dalam memilih klon teh seri GMB yang digunakan. Klon teh seri GMB terdiri dari GMB 1 sampai dengan GMB 11. Keterbatasan karyawan Pusat Penelitian Teh dan Kina (PPTK) dan para petani teh dalam mengklasifikasi klon teh seri GMB untuk membedakan jenis antar klon yang secara morfologi memiliki tingkat kemiripan yang begitu dekat menjadi alasan kurang optimal dalam memilih klon teh seri GMB yang cocok untuk digunakan. Pada penelitian ini, dirancang suatu sistem secara visual yang mampu me

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Torralba, Edwin M. "Fibonacci Numbers as Hyperparameters for Image Dimension of a Convolu-tional Neural Network Image Prognosis Classification Model of COVID X-ray Images." International Journal of Multidisciplinary: Applied Business and Education Research 3, no. 9 (2022): 1703–16. http://dx.doi.org/10.11594/ijmaber.03.09.11.

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In recent years, convolutional neural networks (CNNs) have achieved amazing success in a variety of image categorization tasks. However, the architecture of CNNs has a significant impact on their performance. The designs of the most cutting-edge CNNs are frequently hand-crafted by experts in both CNNs and the topics under investigation. As a result, it's tough for users who don't have a lot of experience with CNNs to come up with the best CNN architecture for their individual image categorization challenges. This work investigates the application of the Fibonacci numbers to efficiently solve p

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Berezsky, Oleh M., and Petro B. Liashchynskyi. "Comparison of generative adversarial networks architectures for biomedical images synthesis." Applied Aspects of Information Technology 4, no. 3 (2021): 250–60. http://dx.doi.org/10.15276/aait.03.2021.4.

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The article analyzes and compares the architectures of generativeadversarialnetworks. These networks are based on convolu-tional neural networks that are widely used for classification problems. Convolutional networks require a lot of training data to achieve the desired accuracy. Generativeadversarialnetworks are used for the synthesis of biomedical images in this work. Biomedi-cal images are widely used in medicine, especially in oncology. For diagnosis in oncology biomedical images are divided into three classes: cytological, histological, and immunohistochemical. Initial samples of biomedi

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Selvarajah, Jarashanth, and Ruwan Nawarathna. "Identifying Tweets with Personal Medication Intake Mentions using Attentive Character and Localized Context Representations." JUCS - Journal of Universal Computer Science 28, no. (12) (2022): 1312–29. https://doi.org/10.3897/jucs.84130.

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Individuals with health anomalies often share their experiences on social media sites, such as Twitter, which yields an abundance of data on a global scale. Nowadays, social media data constitutes a leading source to build drug monitoring and surveillance systems. However, a proper assessment of such data requires discarding mentions which do not express drug-related personal health experiences. We automate this process by introducing a novel deep learning model. The model includes character-level and word-level embeddings, embedding-level attention, convolu- tional neural networks (CNN), bidi

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Selvarajah, Jarashanth, and Ruwan Nawarathna. "Identifying Tweets with Personal Medication Intake Mentions using Attentive Character and Localized Context Representations." JUCS - Journal of Universal Computer Science 28, no. 12 (2022): 1312–29. http://dx.doi.org/10.3897/jucs.84130.

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Individuals with health anomalies often share their experiences on social media sites, such as Twitter, which yields an abundance of data on a global scale. Nowadays, social media data constitutes a leading source to build drug monitoring and surveillance systems. However, a proper assessment of such data requires discarding mentions which do not express drug-related personal health experiences. We automate this process by introducing a novel deep learning model. The model includes character-level and word-level embeddings, embedding-level attention, convolu- tional neural networks (CNN), bidi

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Aakash, S. Amutha, D. Nandhini, and Ansh. "Classification and Validation of Tomato Leaf Disease Using Deep Learning Techniques." International Journal on Engineering Artificial Intelligence Management, Decision Support, and Policies 1, no. 1 (2024): 41–60. https://doi.org/10.63503/j.ijaimd.2024.9.

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Tomatoes are regarded as fruits since they fit the botanical definition of a fruit because they are the fleshy parts of a plant that enclose its seeds. There are approximately 10 different kinds of diseases for a tomato plant, which is huge in number and can create huge losses for the farmers. This paper focuses on the classification of tomato plant leaf diseases using Convolution Neural Net-work (CNN) a deep learning technique that is especially employed for image recognition and pixel data processing activities. CNN has been used to identify whether the given photo of the plant is of a healt

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Vedant, Kumar, Kumar Siddhant, Shingala Parth, Singh Pradhuman, and Kalbande Dhananjay. "AI Powered Holistic Solution for Travelersduring Pandemic." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 6 (2020): 302–7. https://doi.org/10.35940/ijeat.F1428.089620.

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As the world is engulfed with COVID-19 pandemic and the glimpse of vaccine is still a distant dream, taking precautions and maintaining the norms suggested by WHO will keep us safe. With this, we present in this paper a solution that would help travelers induce confidence in traveling while keeping in mind the guidelines that must be followed. The solution focuses on an end to end service that will not only help the travelers to make informed and safe decisions but also allow the hospitality industry to monetize from this application. This paper is focused on a detailed analysis of the solutio

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Vaf, Sina, and Tadeusz Antoni Wysocki. "Application of convolutional interleavers in turbo codes with unequal error protection." Journal of Telecommunications and Information Technology, no. 1 (March 30, 2006): 17–23. http://dx.doi.org/10.26636/jtit.2006.1.362.

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This paper deals with an application of convolu- tional interleavers in unequal error protection (UEP) turbo codes. The constructed convolutional interleavers act as block interleavers by inserting a number of stuff bits into the interleaver memories at the end of each data block. Based on the properties of this interleaver, three different models of UEP turbo codes are suggested. Simulation results confirm that uti- lizing UEP can provide better protection for important parts of each data block, while significantly decreasing the number of stuff bits.

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Shivanya Shomir Dutta, Aakash Kumar, Amutha S, and R Dhanush. "Enhancing Diabetes Mellitus Prediction: Integrating Hybrid Deep Learning Model with Sampling Techniques." International Journal on Engineering Artificial Intelligence Management, Decision Support, and Policies 1, no. 1 (2024): 29–40. https://doi.org/10.63503/j.ijaimd.2024.8.

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Diabetes, characterized by high blood glucose levels, is a leading cause of liver, eye, kidney, and heart diseases. This study evaluates various deep learning models, combined with machine learning classifiers, for predicting diabetes mellitus using the BRFSS dataset. The dataset's imbalance posed a challenge for binary classification, common in medical diagnostics. To address this, different sampling techniques were tested. Hybrid models combining Convolu-tional Long Short Term Memory (Conv LSTM) networks with traditional classi-fiers were also explored. The Conv LSTM model combined with Adab

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Sirohi, Anant. "Research Paper on Text to Audio Converter using NLP." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 1313–16. https://doi.org/10.22214/ijraset.2025.70467.

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The development of text-to-speech (TTS) systems has advanced significantly with the introduc on of deep learningbased models. This paper inves gates the impact of various deep learning architectures, such as WaveNet and Tacotron 2, on the naturalness of synthesized speech. By leveraging convolu onal neural networks (CNNs) and recurrent neural networks (RNNs), we explore techniques for improving prosody, intona on, and speech quality. Our experiments show that the integra on of a en on mechanisms and vocoder models leads to more accurate and human-like speech output, par cularly in complex sent

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Singh, Hrithik, Shambhavi Kaushik, Shruti Talyan, and Kartikeya Dwivedi. "Skin Cancer Detection Using Deep Learning techniques." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 4296–305. http://dx.doi.org/10.22214/ijraset.2022.43090.

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Abstract: Skin cancer detection is one of the major prob-lems across the world. Early detection of the skin cancer and its diagnosis is very important for the further treatment of it. Artificial Intelligence has progressed a lot in the field of healthcare and diagnosis and hence skin cancer can also be detected using Machine Leaning and AI. In this research, we have used convolutional neural network for image processing and recognition. The models implemented are Vgg-16, mobilenet, inceptionV3. The paper also reviewed different AI based skin cancer detection models. Here we have used transfer

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Gavrilov, D. A., E. I. Zakirov, E. V. Gameeva, V. Yu Semenov, and O. Yu Aleksandrova. "Automated skin melanoma diagnostics based on mathematical model of artificial convolutional neural network." Research'n Practical Medicine Journal 5, no. 3 (2018): 110–16. http://dx.doi.org/10.17709/2409-2231-2018-5-3-11.

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In the last 10 years there has been a revolu on in the ﬁ eld of computer image analysis and pa ern recogni on. Modern algorithms of computer vision equaled and even in some problems surpassed human capabili es. This jerk is largely due to the emergence and development of the technology of deep convolu onal neural networks.Recent developments in the ﬁ eld of image processing and machine learning open up the prospect of crea ng systems based on ar ﬁ cial neural convolu onal networks, superior to humans in problems of image classiﬁ ca on, in par cular, in solving problems of analysis of various m

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Veerman, Menno A., Robert Pincus, Robin Stoffer, Caspar M. van Leeuwen, Damian Podareanu, and Chiel C. van Heerwaarden. "Predicting atmospheric optical properties for radiative transfer computations using neural networks." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379, no. 2194 (2021): 20200095. http://dx.doi.org/10.1098/rsta.2020.0095.

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The radiative transfer equations are well known, but radiation parametrizations in atmospheric models are computationally expensive. A promising tool for accelerating parametrizations is the use of machine learning techniques. In this study, we develop a machine learning-based parametrization for the gaseous optical properties by training neural networks to emulate a modern radiation parametrization (RRTMGP). To minimize computa- tional costs, we reduce the range of atmospheric conditions for which the neural networks are applicable and use machine-specific optimized BLAS functions to accelera

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Bondarenko, Bogdan, and Yuri Samokhvalov. "SEARCH FOR MULTIMEDIA INFORMATION BASED ON NEURAL NETWORKS." Information systems and technologies security, no. 1 (3-4) (2020): 57–62. http://dx.doi.org/10.17721/ists.2020.4.58-62.

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The article considers approaches to the use of neural networks in multimedia information retrieval. The develop ment of methods for multimedia information retrieval is necessary due to the large amount of such information. Tradi tional methods of multimedia information retrieval have a high speed of data processing, but low accuracy due to the inability of semantic search. The use of neural networks allows for semantic search, which increases its accuracy and completeness. Approaches to the use of neural networks at the stages of indexing and retrieval of multimedia infor mation are considered

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Feigelman, Artyom M. "Self-willed assistant: neural networks as an artist’s tool." Digital Scholar: Philosopher`s Lab 7, no. 1 (2024): 15–21. https://doi.org/10.32326/2618-9267-2024-7-1-15-21.

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The article discusses issues related to the use of artificial intelligence (AI) within the frame-work of artistic creativity. Cases are presented demonstrating the influence of AI on modern artistic practices and institutions. It is argued that the capabilities of AI problematize the very subjectivity of the artist and his or her ontological status. The article emphasizes that works created using neural networks should be classified as generative art. An example is given in John Conway’s The Game of Life. The rest of the article discusses neural networks that work based on the “text-to-image”

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Pangesti, Witriana Endah, Indah Ariyati, Priyono Priyono, Sugiono Sugiono, and Rachmat Suryadithia. "Utilizing Genetic Algorithms To Enhance Student Graduation Prediction With Neural Networks." Sinkron 9, no. 1 (2024): 276–84. http://dx.doi.org/10.33395/sinkron.v9i1.13161.

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The prediction of student graduation plays a crucial role in improving higher education efficiency and as-sisting students in graduating on time. Neural networks have been used for predicting student graduation; however, the performance of neural network models can still be enhanced to make predictions more accurate. Genetic algorithms are optimization methods used to improve the performance of neural network models by optimizing their parameters. The problem at hand is the suboptimal performance of neural networks in predict-ing student graduation. Thus, the objective is to leverage genetic a

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Rohan Vaghela and Jigar Sarda. "Optimized Symmetric Positive Definite Neural Networks: A Novel Approach to Weather Prediction." International Journal on Computational Modelling Applications 2, no. 1 (2025): 1–14. https://doi.org/10.63503/j.ijcma.2025.47.

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A critical area is weather prediction, which has a direct application in agriculture, transport, disaster response, and energy control. The traditional techniques for weather forecasting sometimes fail to produce reliable results and are hardly scalable. This paper introduces an OSP-DNN fluid dynamics-based methodology for meteorological purposes, such as reliable and efficient weather prediction. Compared with asymmetric or indefinite matrices that can introduce adverse im pacts on neural networks, the proposed model takes advantage of the properties of SPD matrices to improve the learning ef

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Costiris, N., E. Mavrommatis, K. A. Gernoth, J. W. Clark та H. Li. "Statistical Global Modeling of β−-Decay Halflives Systematics Using Multilayer Feedforward Neural Networks and Support Vector Machines". HNPS Proceedings 16 (1 січня 2020): 243. http://dx.doi.org/10.12681/hnps.2602.

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In this work, the beta-decay halflives problem is dealt as a nonlinear optimiza- tion problem, which is resolved in the statistical framework of Machine Learning (LM). Continuing past similar approaches, we have constructed sophisticated Artificial Neural Networks (ANNs) and Support Vector Regression Machines (SV Ms) for each class with even-odd character in Z and N to global model the systemat- ics of nuclei that decay 100% by the β−-mode in their ground states. The arising large-scale lifetime calculations generated by both types of machines are discussed and compared with each other, with t

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Arjun Varma, Jampana Venkata. "YOLOv8-Enabled Real-Time Crop Health Monitoring with Conversational Diagnosis and Geospatial Support." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47655.

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Abstract—Agriculture is a cornerstone of global economies, supplying food, employment, and raw materials for numerous industries. Yet, one of the sector’s enduring challenges is crop disease, which can drastically reduce yields and threaten food security. Traditional approaches to identifying plant diseases rely on manual inspections and expert evaluations, which are often slow, costly, and vulnerable to human error. Without early diagnosis, diseases can spread uncontrollably, leading to major economic setbacks for farmers and decreased crop output. To overcome these issues, this project intro

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Shiju, Melgibson, Mathew George, Navaneeth AS, Muhammed Sinan PT, and Sr Reema Jose. "Review of Object Detection Techniques and AI-Driven Recipe Generation for Smart Kitchens." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem43554.

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The integration of object detection and conversa- tional AI in smart kitchen systems presents a promising avenue for enhancing cooking convenience. This review explores object detection techniques for identifying available ingredients and their application in generating personalized recipe suggestions. The system leverages computer vision to detect and catalog kitchen items and employs conversational AI to engage with users and recommend recipes tailored to their preferences, dietary restrictions, and available ingredients. By combining advanced object detection methods with natural language p

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Molodets, B., Т. Bulana, and D. Boldyriev. "Analysis of existing architectures for the development of an information system of air quality assessment." System technologies 5, no. 160 (2025): 3–10. https://doi.org/10.34185/1562-9945-5-160-2025-01.

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The paper focuses on the development of information technology for weather modeling and forecasting using modern deep learning methods. The relevance of the topic is driven by growing climate risks, an increase in the frequency of extreme weather events, and the need for highly accurate meteorological analytics for the agricultural sector, transportation, en-ergy, and civil defense systems. FourCastNet, a modern architecture based on deep neural networks and transformers that combines high performance with the ability to process large-scale historical meteorological data, was chosen as the mai

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Liu, Rui, Minghao Wang, Huan Wang, et al. "Recognition of NiCrAlY coating based on convolutional neural network." npj Materials Degradation 6, no. 1 (2022). http://dx.doi.org/10.1038/s41529-021-00213-1.

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AbstractThis paper established an eight-layer convolu-tional neural network to automatically recognize the characteristic phases of the NiCrAlY coating, the coating/substrate interface, and the oxide layer. Using this neural network, the Cr-rich phase, the coating/substrate interface, and the oxide layer, as the features of the NiCrAlY coating, were successfully identified and retrieved at different constant oxidation temperatures. Based on this achievement, the variations of the Cr-rich phase distribution and the changes of the oxide layer thickness calculated by the network were obtained, wh

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Shi, Shaowei, Felicito Caluyo, Rowell Hernandez, Jeffrey Sarmiento, and Cristina Amor Rosales. "Automatic classification and identification of Plant Disease Identification by using a convolu-tional neural network." Natural and Engineering Sciences, October 18, 2024. http://dx.doi.org/10.28978/nesciences.1569560.

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The prompt detection of plant diseases mitigates adverse effects on plants. Convolutional neural net-works (CNN) and intense learning are extensively utilized in computer vision and recognition of pat-tern tasks. Scientists presented several DL algorithms for the detection of plant illnesses. Deep learn-ing (DL) models need many parameters, resulting in extended training durations and complicated implementation on compact devices. This research presents a unique DL model utilizing the incep-tion tier and residual connections. Depthwise differentiated convolution is employed to decrease the var

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Liu, Baoguo, Eric B. Blancaflor, and Mideth Abisado. "Research on Improved LSTM and Deep Learning Intrusion Detection Algorithms." Journal of Machine and Computing, January 5, 2025, 076–88. https://doi.org/10.53759/7669/jmc202505006.

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These days, network security concerns are becoming more and more important due to the Internet's quick de-velopment. The goal of this article is to enhance the feature extraction and classification accuracy of network intrusion detection models by addressing the issues of low classification accuracy and weak generalization ability of current models in the field. A deep learning network intrusion detection model and an LSTM model based on convolutional neural networks (CNN) and weight dropout, abbreviated as AWD-CNN-LSTM, are creatively proposed. This model effectively extracts nonlinear featur

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LI, Junxian, Zhizhou WU, and Zhoubiao SHEN. "Open the Black Box – Visualising CNN to Understand Its Decisions on Road Network Performance Level." Promet 34, no. 4 (2022). http://dx.doi.org/10.7307/ptt.v34i4.4037.

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Visualisation helps explain the operating mechanisms of deep learning models, but its applications are rarely seen in traffic analysis. This paper employs a convolu-tional neural network (CNN) to evaluate road network performance level (NPL) and visualises the model to en-lighten how it works. A dataset of an urban road network covering a whole year is used to produce performance maps to train a CNN. In this process, a pretrained network is introduced to overcome the common issue of inadequa-cy of data in transportation research. Gradient weighted class activation mapping (Grad-CAM) is applied

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R, Nagaraj, D. Thirugnana Murthy, and Manik Murthy Rajput. "Modeling Renewables Based Hybrid Power System with Desalination Plant Load Using Neural Networks." Distributed Generation & Alternative Energy Journal, January 24, 2019, 32–46. http://dx.doi.org/10.13052/dgaej2156-3306.3412.

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Hybrid power system is seen as a viable alternative to the conven -tional systems. Estimating the potential of these hybrid power systemsfor a selected site is a major input required for making informed deci -sions. Often, estimation of the kWhr production is a very elaborate andtedious exercise due to lack of a reliable model for the same. This articleproposed an Artificial Neural Network based model that can be used toeasily estimate the total kWhr/year for a given combination of Solar PV,Wind generator and Battery. The variable load considered for the modelis a desalination plant load. The

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Noah, M. Kenney. "An Analysis of the Increasing Processing Power of Modern Microprocessors." December 20, 2022. https://doi.org/10.5281/zenodo.7747642.

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Traditional computational power calculations rely on the assumption that addi- tional processing power is best achieved by a quantatative increase in transistors, and particularly through a quantatative increase in the number of metal-oxide- semiconductor field-effect transistors (MOSFETs). However, advancements in microprocessor architecture could fundamentally alter the method in which ad- ditional processing power is achieved, thus rendering traditional computational power calculations meritless. Modern technologies such as Artificial Intelligence, deep learning, and neural networks all req

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