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762 articles for “deep learning”
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Prostate Cancer Detection Using Deep Learning
Abstract: Prostate Cancer is a cancer occurs in prostate gland which is located in male reproductive system. According to the WHO (World Health Organization) the estimated cancer cases in the year 2020 is about 1.4 Million. Prostate cancer is one of the reason for death in men. In this paper we have presented Secondary verification tool for doctors or for normal users to check patient have a cancer or not. And …
Published in International Journal of Biomedical Innovations and Engineering · Vol. 1, Issue 2, 2023 · pp. 21–26 Read article
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A Review of Various Deep Learning Models for Classification of BrainTumor
Abstract: Brain Tumor is most challenging and life threading decease now these days. Timely accurately detection of brain tumor is play important role for patient health improvement and it increase life survival rate. Traditional of brain tumor detection relay heavily on manual interpretation of medicalimage, it is very time consuming and chance to human error in detection of brain tumor. Now in recent advancement of Artificial intelligence (AI), Machine learning (ML) …
Published in International Journal of Brain Sciences · Vol. 3, Issue 2, 2026 Read article
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A Study of Various Machine Learning and Deep Learning Libraries/Platforms with Their Usage Perspective
Abstract: Learning is a complex multi-faceted phenomenon. While we do not understand how learning happens entirely, we understand it well enough to devise models that excel at narrow domain learning. Machine learning and deep learning enable us to create models that learn and implement that learning. There is an abundance of tools that achieve the same goals in different levels of abstraction, amount of work done and flexibility to tailor learning …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 5, Issue 3, 2018 · pp. 1–15 Read article
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Transfer Learning in Deep Learning Models for Medical Imaging: Utilizing Pretrained Models to Improve Performance in Medical Image Analysis
Abstract: Transfer learning is now a trending technique in deep learning, especially in medical imaging. This technique solves landmark problems by utilizing the pre-trained models, including the limited availability of the annotated medical data and the time-consuming computational costs of training deep learning models from scratch. The generalizability of deep models could increase diagnostic precision for specific medical tasks, require fewer samples to train, and take less time to train due …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 12, Issue 1, 2025 · pp. 67–85 Read article
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Overview of Deep Learning: Applications in Various Areas
Abstract: Deep learning is emerging field in the sense of artificial intelligence. Deep-learning innovation powers numerous parts of current society: from web quests to content sifting on interpersonal organizations to proposals on web-based business sites, and it is progressively present in shopper items, for example, cameras and cell phones. Deep learning offers effective procedures to discover designs in information for taking care of testing prescient issues. Deep learning is widely used …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 5, Issue 3, 2018 · pp. 39–48 Read article
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Transcript Summarizer of YouTube Videos Using Deep Learning
Abstract: Transcriber Sum is a deep learning-based YouTube transcript summarization tool. It employs advanced machine learning techniques to automatically generate concise summaries of YouTube video transcripts, enabling users to quickly grasp the key content and insights of videos without the need to watch or read the entire transcript. This Transcriber Sum addresses the challenge of providing users with concise and informative summaries of video content by harnessing the power of deep …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 3, 2024 · pp. 119–126 Read article
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Advancements in Intrusion Detection: Tackling Imbalanced Network Traffic with Machine Learning and Deep Learning Techniques
Abstract: Malicious cyberattacks can frequently hide enormous amounts of typical data in unbalanced network traffic. It is very stealthy and obfuscating in cyberspace, which makes it challenging for Network Intrusion Detection Systems (NIDS) to guarantee the precision and promptness of detection. This essay investigates. Machine learning and deep learning are utilized for intrusion detection in imbalanced network traffic. It offers a novel method for addressing the problem of class imbalance termed …
Published in Recent Trends in Electronics Communication Systems · Vol. 11, Issue 2, 2024 · pp. 18–24 Read article
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Network Intrusion Detection System using Machine Learning and Deep Learning Approach
Abstract: Networks play a significant part in today’s world; fast internet and communication industries result in vast network size and data expansion. Furthermore, attackers aiming to launch various cyberattacks inside the system cannot be neglected. An IDS keeps track of the network’s software and hardware security to preserve its privacy, integrity, and accessibility. Despite the significant efforts of the researchers, current IDS continue to confront challenges in terms of accuracy rate, …
Published in Journal Of Network security · Vol. 10, Issue 1, 2022 · pp. 7–34 Read article
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A Comparative Study of Transfer Learning-Based Deep Learning Models for Breast Cancer Detection
Abstract: Breast cancer is a major concern in the world today, and early and accurate diagnosis is most crucial in the case of breast cancer, as it is among the disorders where the total cost of loss of life is high. Traditional screening processes are subjective and vulnerable to inter-observer reliability issues and diagnostic errors, being primarily based on manual interpretation of medical images. To address these limitations, Deep Learning (DL) …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 15, Issue 1, 2026 · pp. 24–34 Read article
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Forecasting Commodity Prices Using Deep Learning Techniques: An Empirical Evidence from India
Abstract: Commodity price forecasting is instrumental in financial markets, providing framework for investment choices and risk management practices. Traditional models, including statistical and machine learning approaches, have limitations in capturing the nonlinear and volatile nature of commodity prices. Deep learning (DL) techniques have emerged as promising alternatives, leveraging advanced neural networks to enhance predictive accuracy. This study presents a thorough and comprehensive examination of deep learning applications in commodity price prediction, …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 3, 2025 · pp. 08–12 Read article
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Hybrid Approach for Community Detection Using Deep Learning Techniques
Abstract: Community detection in complex networks is a fundamental problem with applications across diverse domains, ranging from social networks to biological systems and beyond. Traditional methods based on graph theory have been widely used for identifying communities within networks. However, the intricate and evolving nature of modern networks demands more sophisticated approaches. This research work proposes a hybrid approach that combines the strengths of deep learning techniques with traditional community detection …
Published in Recent Trends in Electronics Communication Systems · Vol. 11, Issue 3, 2024 · pp. 18–26 Read article
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Pneumonia Detection and Classification Using Deep Learning
Abstract: Pneumonia, an infectious lung disease primarily caused by bacteria, often exacerbated by environmental factors, leads to the accumulation of pus in the lung’s alveoli. Accurate diagnosis through chest X-rays, ultrasounds, or lung biopsies is crucial to avoid misdiagnosis and ensure proper treatment, crucial for patients’ quality of life. Diagnostic capacities have been greatly improved by deep learning advances, especially with convolutional neural networks (CNNs). This research presents a robust CNN-based …
Published in Journal of Microwave Engineering and Technologies · Vol. 11, Issue 3, 2024 · pp. 9–19 Read article
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Deep Learning Approaches for Nanoplasmonics: Expanding Horizons
Abstract: Deep learning, a subfield of machine learning, has revolutionized various domains by providing powerful tools to process and analyze complex data. In the realm of nanoplasmonics, deep learning techniques have emerged as promising approaches to tackle the challenges associated with large-scaledata analysis, modeling, and optimization of plasmonic systems. This article presents an in-depth exploration of deep learning approaches applied to nanoplasmonics research, focusing on the advances, opportunities, and future directions …
Published in Journal of Nanoscience, NanoEngineering & Applications · Vol. 13, Issue 1, 2023 · pp. 30–38 Read article
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Data Anonymnization for Privacy Protection Using Deep Learning Techniques
Abstract: The majority of real data are unbalanced, which makes them challenging to classify. Unbalanced data has been the subject of some research. However, deep learning has not taken into account how to manage such data. In this paper, we suggest deep learning-based data mining with imbalanced data for privacy protection. We discuss and analyse the aspects of current privacy-protection data mining as well as a deep learning based anonymizing tool. …
Published in Recent Trends in Parallel Computing · Vol. 9, Issue 3, 2022 · pp. 26–35 Read article
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Deep Learning Algorithms for Medical Image Encryption to Ensure Secure Data Transfer
Abstract: Deep learning has significantly impacted various fields, including medical imaging, by offering new ways to encrypt medical images for secure data transfer. This research work examines how deep learning algorithms are used to enhance medical image security during transmission. Given the high sensitivity and privacy requirements of medical data, it’s crucial to maintain its confidentiality. Traditional encryption techniques, while reliable, often struggle with issues like scalability, computational efficiency, and the …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 11, Issue 3, 2024 · pp. 28–36 Read article
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Sentiment Analysis on Financial News Using Deep Learning Algorithm
Abstract: Sentiment analysis is the technique of computationally figuring out and categorizing reviews or comments expressed in a bit of textual content, especially a good way to decide whether or not the writer's mind-set in the direction and also very helpful to identify the customer’s opinion about the particular product or content. It is one of the active and wanted research areas in natural language processing. In existing work, machine learning …
Published in Journal of Computer Technology & Applications · Vol. 12, Issue 1, 2021 · pp. 24–27 Read article
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Leveraging Deep Learning and Cloud Computing for Water Usage Optimization in Agriculture: A Study
Abstract: Water scarcity and inefficient irrigation practices are significant challenges in modern agriculture. This research investigates how deep learning and cloud computing can be combined to enhance water efficiency in agricultural practices. Leveraging advancements in deep learning and cloud computing, researchers have developed innovative solutions for optimizing water usage. This review examines the state-of-the-art methodologies, technologies, and applications in smart irrigation systems. It explores how deep learning models and cloud platforms …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 2, 2025 · pp. 83–91 Read article
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Cutting-edge Deep Learning Methods for Predicting and Detecting Cardiovascular Diseases
Abstract: Cardiovascular diseases (CVDs) remain a major global health issue, highlighting the need for improved early detection and risk assessment methods. This research investigates the efficacy of both deep learning and traditional machine learning methods in forecasting cardiovascular diseases (CVDs). We evaluate a variety of models, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Multilayer Perceptrons (MLPs), Long Short-Term Memory (LSTM) networks, as well as Logistic Regression (LR), Decision Trees …
Published in Recent Trends in Parallel Computing · Vol. 11, Issue 2, 2024 · pp. 36–42 Read article
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An Efficient Deep Learning Classifiers Algorithm for Examining Public Perception of Covid SOPs
Abstract: In December 2019, Wuhan, China, reported the first coronavirus case, confirming the current pandemic. Following that, it swept across the entire world. The leading cause of this disease is still unknown, though. Governments are emphasizing physical distance and wearing masks in public places. The people mostly ignore the safety SOPs, which results in a surge in infected people rates and a healthcare burden on the national economy. Through the identification …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 10, Issue 1, 2023 · pp. 49–70 Read article
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Deep Learning Applications: An Overview
Abstract: It is essential that the data be handled with accuracy, and certain exercises are carried out so that the computers can think and act on their own, which is where the advanced time of Deep Learning comes in. A computer needs access to earlier information and interactions in order to think. The use of Deep Learning in diverse geographies will be discussed in this study. Many experiments have been conducted …
Published in Journal of Advancements in Robotics · Vol. 9, Issue 2, 2022 · pp. 8–15 Read article