Search
142 articles for “Deep Feature Extraction”
-
Improved Melanoma Recognition using Score Fusion Framework based on Deep Classifiers
Abstract: Melanoma is a fast-growing and malignant cancer that affects neural crest-derived cells of the skin. Early detection is the key to survival and rapid recovery; however, typical diagnosis is based on visual inspection by expert dermatologists. Many melanoma recognition methods have been proposed in the literature that can classify lesions, based on hand-crafted as well as deep learning-based features. However, an accurate, automated method for melanoma is still required. This …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 8, Issue 2, 2021 · pp. 25–33 Read article
-
Real-Time YOLOv8l Contraband Detection in X- Ray Security Screening with Automated Identification of Weapons and Hazardous Objects for Threat Alerting
Abstract: A real-time contraband detection system based on YOLOv8l has been proposed for use in very high security installations, such as airports and public screening facilities. The contraband detection system has been trained with curated X-ray contraband datasets containing twelve different object types like firearms, knives, bullets, etc., with the datasets having been constructed to provide an equal number of examples for each object class to support robust detection. The methodology …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 13, Issue 3, 2026 Read article
-
AI-Driven Handwriting Identification and Verification Using Textural Features
Abstract: The last few decades have seen handwriting recognition and verification earn their mark in areas like forensics, healthcare, education, and digital security. This study delves into the role of artificial intelligence (AI), machine learning (ML), and deep learning techniques in handwriting analysis. It highlights the extraction of textural features as a precursor to identifying narrows between original handwriting and its forgery, whereby a few distinctive patterns such as stroke width, …
Published in International Journal of Electronics Automation · Vol. 3, Issue 1, 2025 · pp. 35–44 Read article
-
Recent Advances in Content-based Image Retrieval: Techniques and Applications
Abstract: Content-based image retrieval (CBIR) plays a vital role in computer vision, driven by the increasing need for fast and accurate image retrieval across fields like healthcare, e-commerce, and digital libraries. This study offers a detailed review of CBIR methodologies, charting their progression from traditional feature extraction techniques, such as Local Binary Patterns (LBP), to contemporary deep learning-driven methods. The transformative impact of convolution neural networks (CNNs) is highlighted, emphasizing their …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 1, 2025 · pp. 67–71 Read article
-
Artificial Intelligence in Image Recognition: Context of Machine Vision
Abstract: The machine learning discipline is as old as decades, but some problems such as image recognition, location detection, image classification, image generation, speech recognition, and natural language processing cannot be solved. Image classification studies are another basic, most classic and essential line of research in deep learning. Computer intelligent recognition of the images technology has enabled a gradual reaction (updating) to foreign measurement trends, which promotes advancement of different areas …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 12, Issue 3, 2025 · pp. 01–06 Read article
-
Detection of Phishing Website Using URL
Abstract: Phishing attacks are one of the greatest threats to online security, where fraud websites deceive users into giving out sensitive information. Traditional methods of detection, such as blacklists and heuristic-based systems, often fail in identifying newly created or sophisticated phishing websites. This study proposes an intelligent phishing website detection system using Convolutional Neural Networks (CNNs) in analyzing URLs and associated features. Using labeled URLs, the system employs such attributes such …
Published in Journal Of Network security · Vol. 13, Issue 1, 2025 · pp. 10–15 Read article
-
Sentiment Analysis Using Emojis
Abstract: Sentiment analysis is a fast-growing research part in NLP (Natural Language Processing). It is fully focused on categorizing customer’s opinion about a particular product, blogs or comments etc. Public opinion has a significant impact on people's desire to contact with businesses, as well as overall brand perception. According to a Podium research, 93% of buyers believe online reviews affect their shopping decisions. Users may not give you another chance after …
Published in Journal of Computer Technology & Applications · Vol. 13, Issue 1, 2022 · pp. 43–46 Read article
-
Development of Intensity-based Segmentation Technique for Meningioma Tumor Detection in MRI Images
Abstract: There are many types of brain tumors. Some brain tumors detection system using segmentation and classification of MRI images. Brain tumors can have any shape or cut. This encourages us to use high-capacity deep neural networks. Segmentation task and 8000 images for classification task of our neural network and found the best architecture to use. convolutional neural network. In recent years, the three most common forms of brain tumours—glioma, meningioma, …
Published in Research and Reviews : Journal of Computational Biology · Vol. 11, Issue 03, 2022 · pp. 50–54 Read article
-
A Hybrid Multi-Physics Ensemble Deep Learning Framework for Simultaneous Prediction of Thermal and Electrical Conductivity in Functional Polymer-Based Nanocomposites
Abstract: Polymer-based nanocomposites have become promising materials for applications in energy storage, flexible electronics, biomedical devices, aerospace components, and advanced engineering because of their tunable thermal and electrical properties. Reliable prediction of these properties is essential for accelerating material design; however, existing analytical models and conventional machine learning techniques often fail to represent the complex interactions among filler characteristics, polymer matrices, processing conditions, and interfacial transport phenomena. This work presents HMEP-Net, …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 1062–1082 Read article
-
Early Lung Cancer Prediction using deep Learning
Abstract: Lung cancer is a global killer because it’s often found late. Finding it early is key to treatment and survival so computer assisted diagnostics are essential. This research uses deep learning to spot early stage lung cancer from CT scans. We trained and fine-tuned three convolutional neural networks—ResNet50, Dense Net 201 and EfficientNet-B0—using transfer learning. We preprocessed the lung CT images by resizing, normalizing and augmenting them to enhance the …
Published in Research and Reviews: Journal of Oncology and Hematology · Vol. 15, Issue 2, 2026 Read article
-
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
-
Cascade CNN Framework for Low Resolution Image Classification
Abstract: Abstract: Low resolution images contain less visual information, so classifications of these images are difficult. For overcoming this problem, cascade CNN framework for low resolution image classification is proposed. In this framework, super resolution CNN (SRCNN) enlarges low resolution image into super resolution image. The convolutional features of these super resolution images are fused with low resolution convolutional features which are extracted by low resolution CNN (LRCNN) feature extractor. A …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 6, Issue 1, 2019 · pp. 39–43 Read article
-
Salient Region Guided Deep Network for Violence Detection in Surveillance Systems
Abstract: Abstract: It is significant to detect violent actions in video surveillance systems automatically, for example, bus stands, malls and railway stations. Though, the earlier detection techniques generally extract statistic features around the spatiotemporal interest points or extract descriptor in the regions where movement takes place, leading to limited abilities to successfully detect violence activities in video surveillance systems. To solve this problem, a new approach for the automatic detection of …
Published in Journal of Computer Technology & Applications · Vol. 10, Issue 3, 2019 · pp. 19–28 Read article
-
Comparative Analysis Between Librosa and OpenSMILE
Abstract: This research work focuses on comparative study of Librosa, a python-based library, and openSMILE, a C++ toolkit, with python bindings used in audio speech analysis. Librosa is ideal for beginners due to its simple structure and flexibility with strong integration with machine learning frameworks like TensorFlow and PyTorch. On the other hand, OpenSMILE is ideal for speech-centric tasks like speech-emotion recognition or paralinguistic studies, offering a wide range of pre-defined …
Published in Journal of Open Source Developments · Vol. 12, Issue 3, 2025 · pp. 06–10 Read article
-
Sign Language Recognition Using Deep Learning
Abstract: People with hearing/speech disabilities use sign language to communicate their thoughts and feelings, but non-disabled people often find it hard to comprehend these hand gestures since they do not know sign language. This project aims to create a system that uses MediaPipe, LSTM and Keras for sign language recognition. The webcam will capture video footage of people performing sign language gestures, then MediaPipe will extract and identify the hand landmarks …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 10, Issue 2, 2023 · pp. 35–43 Read article
-
Disaster Impact Assessment Using Multi-Sensor Satellite Data: An AI-Based Remote Sensing Approach
Abstract: Natural disasters such as floods, earthquakes, and wildfires cause significant damage to human life and infrastructure every year. Rapid and accurate assessment of the affected areas is essential for effective disaster response and recovery planning. Traditional image-based analysis using single-sensor data often fails under adverse conditions such as cloud cover, smoke, or poor lighting. To overcome these limitations, this study proposes a novel framework for disaster impact assessment using multi-sensor …
Published in International Journal of Satellite Remote Sensing · Vol. 4, Issue 1, 2026 · pp. 10–22 Read article
-
Detecting Phishing Websites Using Hybrid Methodologies
Abstract: In the digital era, personal information theft has become a widespread and increasingly severe crime. Cybercriminals, often known as hackers, use deceptive strategies, with phishing websites being a major method for stealing confidential data. These fake websites imitate legitimate ones, tricking users into revealing sensitive personal and financial information, which has led to a rise in fraud cases. To address this escalating threat, a comprehensive research paper is proposed. This …
Published in Journal of Computer Technology & Applications · Vol. 15, Issue 3, 2024 · pp. 59–65 Read article
-
Sustainable Cotton Crop Productivity through Precision Weed Detection: A Deep Learning-Based Approach with UAV Integration
Abstract: Weeds present a major challenge to crop productivity by competing with crops for vital resources, including water, sunlight, and nutrients, often resulting in significant yield reductions. On a global scale, weeds are responsible for approximately 13.2% of annual crop losses, a quantity sufficient to feed nearly one billion people. These invasive plants disrupt agricultural systems and adversely impact crop yields. Given their uneven distribution in fields, ground or aerial robots …
Published in Journal of Aerospace Engineering & Technology · Vol. 15, Issue 1, 2025 · pp. 19–26 Read article
-
A Review on Artificial Intelligence Techniques for Analyzing Deforestation and Illegal Logging Using Satellite Imagery
Abstract: Deforestation and illegal logging remain critical environmental threats, driving biodiversity loss, climate change, and socio-economic disruption. Conventional monitoring techniques frequently do not yield real-time, large-scale insights. Recent developments in Artificial Intelligence (AI), especially in deep learning and computer vision, have revolutionized the ability to analyze high-resolution satellite images for detecting deforestation and monitoring illegal logging. This review synthesizes recent developments in AI-driven approaches, highlighting convolutional neural networks (CNNs), anomaly detection …
Published in International Journal of Satellite Remote Sensing · Vol. 4, Issue 1, 2026 · pp. 1–9 Read article
-
Convolution Neural Network Model for Intrusion Detection in Network
Abstract: The evolution of the internet has made protecting information a necessity. Network intrusion and prevention plays an integral role in network-based security. The Intrusion technologies primarily used in today’s world deploy various machine learning algorithms and train models based on them resulting in effectively low detection rates. A technical advancement from machine learning, Deep Learning employs complex mechanisms to extract features from samples. As observed that conventional intrusion detection systems …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 8, Issue 1, 2021 · pp. 7–13 Read article