Pattern Recognition Letters aims at rapid publication of concise articles of a broad interest in pattern recognition.
Subject areas include all the current fields of interest represented by the Technical Committees of the International Association of Pattern Recognition, and other developing themes involving learning and recognition. Examples include:

• Statistical, structural, syntactic pattern recognition;
• Neural networks, machine learning, data mining;
• Discrete geometry, algebraic, graph-based techniques for pattern recognition;
• Signal analysis, image coding and processing, shape and texture analysis;
• Computer vision, robotics, remote sensing;
• Document processing, text and graphics recognition, digital libraries;
• Speech recognition, music analysis, multimedia systems;
• Natural language analysis, information retrieval;
• Biometrics, biomedical pattern analysis and information systems;
• Scientific, engineering, social and economical applications of pattern recognition;
• Special hardware architectures, software packages for pattern recognition.

We invite contributions as research reports or commentaries.

Research reports should be concise summaries of methodological inventions and findings, with strong potential of wide applications.
Alternatively, they can describe significant and novel applications of an established technique that are of high reference value to the same application area and other similar areas.

Commentaries can be lecture notes, subject reviews, reports on a conference, or debates on critical issues that are of wide interests.

To serve the interests of a diverse readership, the introduction should provide a concise summary of the background of the work in an accepted terminology in pattern recognition, state the unique contributions, and discuss broader impacts of the work outside the immediate subject area. All contributions are reviewed on the basis of scientific merits and breadth of potential interests.

Special Issue on Pattern recognition in multimodal information analysis: observation, extraction, classification, and interpretation
Día de Entrega: 2024-09-20

In the information age, we grapple with a flood of diverse data types like text, images, audio, and video. AI's strides in single-modal analysis are notable, but the challenge lies in efficiently handling massive multimodal data to enhance machines' understanding of the world through pattern recognition. Advancements, in this area have led to techniques. For example, the use of image matching in scenarios involving modes is crucial in diagnostics, remote sensing, and computer vision. Coordinating the retrieval of data from modes improves the accuracy of pattern recognition while integrating audio video data enhances speech recognition and strengthens accident monitoring capabilities. In other words, multimodal learning and representation yield convincingly better results with confidence. However, there are still challenges that need to be addressed, such as handling types of data transforming data effectively enhancing datasets and ensuring interpretability of models, for processing data.

In this context, this special issue outlines recent advances in the pattern recognition field, intending to bring together the work of scholars in this multidisciplinary subject, drawing on the different skills and knowledge of pattern recognition approaches applied in the multimodal information analyzing from the perspective of observing, extraction, classifying and interpretation.

Guest editors:
Jingsha He, PhDBeijing University of Technology, Beijing, Chinajhe@bjut.edu.cn
Danilo Avola, PhDSapienza University of Rome, Roma, Italyavola@di.uniroma1.it
KC Santosh, PhDUniversity of South Dakota, Vermillion, USAsantosh.kc@usd.edu
Mario Molinara, PhDUniversity of Cassino and Southern Lazio, Cassino, Italym.molinara@unicas.it
Daniele Salvati, PhDUniversity of Udine, Udine, Italydaniele.salvati@uniud.it

Manuscript submission information:
The PRL's submission system (Editorial Manager®) will be open for submissions to our Special Issue from September 1st, 2024. When submitting your manuscript please select the article type VSI:PRMIA. Both the Guide for Authors and the submission portal could be found on the Journal Homepage: Guide for authors - Pattern Recognition Letters - ISSN 0167-8655 | ScienceDirect.com by Elsevier.

The submissions should be original and technically sound, and they should not have been published previously, nor be under consideration for publication elsewhere. If the submissions are extended works of previously published papers, the original works should be quoted in the References and a description of the changes that have been made should be provided.

Important dates 
Submission Portal Open: September 1st, 2024
Submission Deadline: September 20th, 2024
Acceptance Deadline: December 15th, 2024 

Special Issue on Trusty Visual Intelligence for Industry
Día de Entrega: 2024-10-20

Visual intelligence (VI) has revolutionized industries with their remarkable capabilities in image understanding and analysis. In recent years, there are many successful applications of VI technologies in industries, for example, using deep learning to train computers to monitor product quality. However, a salient fact is that the trustiness of visual technologies directly affects industrial production efficiency, product quality, safety, and traceability. Trusty VI may make the industrial operations much more efficient, improve resource (including human and material resources) utility and energy efficiency, and even help economic, environmental, and social sustainability.The motivation of this special issue is to advance trusty visual intelligence of industries, which connects to the industrial processes directly. We invite contributions that explore innovative methodologies and effective applications of visual analytics methods in industries.

Topics of interest:

Trusty imbalanced learning for industry
Interpretable deep learning models for industry
Knowledge embedded methods for industry
Trusty visual intelligence technologies for process monitoring
Trusty visual intelligence technologies for manufacturing
Trusty visual intelligence technologies for quality inspection
Trusty visual intelligence technologies for preventive maintenance
Trusty visual intelligence technologies for robotics
Automatic Annotation Tools for Image Data
Other trusty visual intelligence techniques and applications 

Special Issue on Deep Learning Models for Computer Vision in Medical Diagnosis
Día de Entrega: 2024-11-20

Convolutional Neural Networks (CNNs) serve as the cornerstone of contemporary deep learning methods for computer vision, fundamentally transforming the analysis of visual data. This revolution stems from the incorporation of convolutional layers, pooling layers, and fully connected layers, working collaboratively to progressively develop a nuanced understanding of input images. In the realm of medical applications, computer vision algorithms play a pivotal role in diagnosing imaging disorders, leveraging deep learning architectures to learn from both non-image and picture data through conventional deep networks and convolutional networks, respectively. The integration of deep learning in medical imaging and analysis empowers physicians and surgeons to gain clearer insights into a patient's body, facilitating the identification of potential issues or anomalies. This application spans various medical imaging modalities, including endoscopy, MRI, ultrasound, X-ray radiography, and more. Object detection algorithms, a key component of medical picture analysis, are frequently employed to identify initial abnormality symptoms in patients. Noteworthy examples include the identification of lung nodules on chest CT or X-ray images and the detection of breast lesions on mammography and ultrasound pictures.

In radiology, deep learning algorithms are applied to identify anomalies or diseases from X-ray images, categorizing them into different illness types or severity levels. This work often leverages various machine learning algorithms that have been optimized either theoretically or empirically. Within the domain of medical imaging, deep learning algorithms exhibit unparalleled precision, effectively segmenting organs or structures, classifying images, identifying anomalies, and even forecasting the course of diseases. Deep learning has emerged as a critical technique for ultrasonic image recognition, significantly enhancing diagnostic accuracy and providing valuable guidance to medical professionals assessing a patient's condition. Moreover, deep learning contributes significantly to drug discovery by aiding in the development and discovery of medicines. Patient medical histories are meticulously scrutinized, and treatment plans are formulated based on the findings. These applications extend across various industries, including medical devices and automated driving. Initial research has primarily focused on identifying conditions such as glaucoma, age-related macular degeneration, and referable diabetic retinopathy. In the automated image analysis of fundus photos and optical coherence tomography pictures, deep learning has demonstrated promising outcomes. Computer vision, empowered by deep learning, excels in diagnosing medical images with superior precision, speed, and accuracy, making fewer mistakes by identifying intricate patterns in the images. Computer vision algorithms extract imperceptible information from medical photographs, contributing to tasks involving prediction or decision-making. Currently, convolutional neural network models, limited Boltzmann machine models, and sparse models are the most frequently utilized deep learning models in computer vision. Although these models share similarities in image recognition and classification, nuances exist in feature extraction. In light of these advancements, we invite submissions and articles for a thematic article collection dedicated to Deep Learning Models for Computer Vision in Medical Diagnosis.

Potential topics include but are not limited to the following:

Explainable AI Techniques in Deep Learning Models for Interpretable Medical Image Diagnosis
Transfer Learning Approaches for Enhancing Generalization in Medical Computer Vision Models
Imbalanced Datasets on the Performance of Deep Learning Models in Medical Imaging
Multi-Modal Data for Comprehensive Medical Diagnosis Using Deep Learning Architectures
Robustness and Security of Deep Learning Models in Medical Image Classification Tasks
Novel Hybrid Architectures by Integrating Classical Image Processing Techniques with Deep Learning Models
Adversarial Attacks on the Reliability of Deep Learning Models for Medical Image Analysis
Generalization Capabilities of Pre-trained Models for Cross-Domain Medical Image Diagnosis
Scalability and Efficiency of Deep Learning Models for Real-time Medical Diagnosis Applications
Domain Adaptation Techniques to Enhance Robustness of Computer Vision-Based Medical Models: A future Perspective
Future of Clinical Metadata for Holistic Patient Diagnosis using Deep Learning Approaches in Medical Imaging

Guest editors:
Dr. Roseline Oluwaseun Ogundokun, PhDLandmark University Omu Aran, Kwara State, Nigeriaogundokun.roseline@lmu.edu.ng; dr.roselineogundokun@gmail.com
Dr. Akinbowale Nathaniel Babatunde, PhDKwara State University, Malete, Kwara State, Nigeriaakinbowale.babatunde@kwasu.edu.ng
Dr. Micheal Olaolu Arowolo, PhDBond Life Sciences Centre University of Missouri, Columbia, USAmoacvf@missouri.edu

Manuscript submission information:

The PRL's submission system (Editorial Manager®) will be open for submissions to our Special Issue from November 1st, 2024. When submitting your manuscript please select the article type VSI: DLMCVMD. Both the Guide for Authors and the submission portal could be found on the Journal Homepage: Guide for authors - Pattern Recognition Letters - ISSN 0167-8655 | ScienceDirect.com by Elsevier.

Important dates 
Submission Portal Open: November 1st, 2024
Submission Deadline: November 20th, 2024
Acceptance Deadline: March 25th, 2025