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TRANSFER LEARNING: INCEPTION-V3 BASED CUSTOM CLASSIFICATION APPROACH FOR FOOD IMAGES

Vishwanath C Burkapalli, Priyadarshini C Patil • ICTACT Journal on Image and Video Processing (2020)

Deep-learning approach has become more popular in the field of image processing. When it is concerned with health issues, there are lots of improvements in the applications of food image classification by deep learning methods. Transfer lea...

DOAJ Open Access (DOAJ) DOI: 10.21917/ijivp.2020.0321

ACHENY: A standard Chenopodiaceae image dataset for deep learning models

Ahmad Heidary-Sharifabad, Mohsen Sardari Zarchi, Sima Emadi, Gholamreza Zarei • Data in Brief (2021)

This paper contains datasets related to the “Efficient Deep Learning Models for Categorizing Chenopodiaceae in the wild” (Heidary-Sharifabad et al., 2021). There are about 1500 species of Chenopodiaceae that are spread worldwide and often a...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.dib.2021.107478

Deep learning-based multimodal data fusion in bone tumor management: Advances in clinical decision support

Tongtong Huo, Wei Wu, Xiaoliang Chen, Mingdi Xue et al. • Intelligent Oncology (2025)

Bone tumors (BTs)—including osteosarcoma, Ewing sarcoma, and chondrosarcoma—are rare but biologically complex malignancies characterized by pronounced heterogeneity in anatomical location, histological subtype, and molecular alterations. Re...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.intonc.2025.06.005

Deep learning prediction of pathological complete response in breast cancer using Mamba architecture

Wenchuan Zhang, Shuwan Zhang, Fengling Li, Yuanyuan Zhao et al. • npj Digital Medicine (2026)

Abstract Deep learning is capable of efficiently predicting the therapeutic efficacy of neoadjuvant chemotherapy (NAC) in breast cancer. However, current methods predominantly rely on convolutional neural networks or transformer architectur...

DOAJ Open Access (DOAJ) DOI: 10.1038/s41746-026-02849-2

The deep learning applications in IoT-based bio- and medical informatics: a systematic literature review

Zahra Amiri, Arash Heidari, Nima Jafari Navimipour, Mansour Esmaeilpour et al. • Neural Computing and Applications (2024)

Abstract Nowadays, machine learning (ML) has attained a high level of achievement in many contexts. Considering the significance of ML in medical and bioinformatics owing to its accuracy, many investigators discussed multiple solutions for ...

OPENALEX cc-by DOI: https://doi.org/10.1007/s00521-023-09366-3

Medical Informatics as a Concept and Field-Based Medical Informatics Research: The Case of Turkey

Muhammet Damar, Tuncay Küme, İbrahim Yüksel, Ali Emre Çetinkol et al. • Düzce Tıp Fakültesi Dergisi (2024)

Aim: This study aimed to evaluate the position of Turkey in the field of Medical Informatics and assess the general structure of research by analyzing Medical Informatics research with bibliometric methods. Material and Methods: In this stu...

OPENALEX cc-by-nc-nd DOI: https://doi.org/10.18678/dtfd.1410276

Software Engineering in Medical Informatics

Gonca Gökçe Menekşe Dalveren, Deepti Mishra • International Conference on Information Communication and Management (ICICM) (2019)

This study presents a systematic literature review to provide overall view of the application of Software Engineering (SE) in Medical Informatics (MI) field. Articles published from 2010 to 2019 from seven selected databases (Emerald, PubMe...

OPENALEX DOI: https://doi.org/10.1145/3357419.3357444

A Deep Learning Harmonization of Multi-Vendor MRI for Robust Intervertebral Disc Segmentation

Chaewoo Kim, Sang-Min Park, Sanghoon Lee, Deukhee Lee • IEEE Access (2024)

Magnetic resonance imaging (MRI) provides enhanced soft tissue contrast and high spatial resolution. However, the relationship between intensity values among soft tissues in MRI is inconsistent, even when obtained under the same conditions ...

DOAJ Open Access (DOAJ) DOI: 10.1109/ACCESS.2024.3360272

Is a Cambrian Explosion Coming for Robotics?

Gill A. Pratt • The Journal of Economic Perspectives (2015)

About half a billion years ago, life on earth experienced a short period of very rapid diversification called the “Cambrian Explosion.” Many theories have been proposed for the cause of the Cambrian Explosion, one of the most provocative be...

OPENALEX DOI: https://doi.org/10.1257/jep.29.3.51

Fast‐Tracker 2.0: Improving autonomy of aerial tracking with active vision and human location regression

Neng Pan, Ruibin Zhang, Tiankai Yang, Can Cui et al. • IET Cyber-systems and Robotics (2021)

Abstract In recent years, several progressive studies promote the development of aerial tracking. One of the representative studies is our previous work Fast‐Tracker which is applicable to various challenging tracking scenarios. However, it...

DOAJ Open Access (DOAJ) DOI: 10.1049/csy2.12033

Boosting precision crop protection towards agriculture 5.0 via machine learning and emerging technologies: A contextual review

Gustavo A. Mesías-Ruiz, Gustavo A. Mesías-Ruiz, María Pérez-Ortiz, José Dorado et al. • Frontiers in Plant Science (2023)

Crop protection is a key activity for the sustainability and feasibility of agriculture in a current context of climate change, which is causing the destabilization of agricultural practices and an increase in the incidence of current or in...

DOAJ Open Access (DOAJ) DOI: 10.3389/fpls.2023.1143326

Quantum Computing in Cybersecurity

• Wiley eBooks (2023)

Machine learning, deep learning, probabilistic neural networks, blockchain, and other new technologies all demand extremely high processing speeds. A quantum computer is an example of such a system. Quantum computers may be accessed over th...

OPENALEX DOI: https://doi.org/10.1002/9781394167401

Fundus imaging based quantum computing: Grading of severity of vision threatening diabetic retinopathy

Amna Ikram, Azhar Imran • Informatics in Medicine Unlocked (2026)

Diabetic Retinopathy (DR) is a major complication of diabetes that can result in irreversible vision loss if not detected early. Manual ophthalmologic screening is time-consuming and subject to subjectivity, underscoring the need for accura...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.imu.2026.101744

Multimodal deep learning for automated atomic qubits fabrication in silicon

Yuwei Cui, Kunrong Wu, Ping Wu, Luyan Yang et al. • Machine Learning: Science and Technology (2026)

Donor-based spin qubits in silicon are a promising platform for scalable quantum computing due to their long coherence times and high-fidelity gate operations. A viable path for fabricating donor qubit arrays with atomic precision is scanni...

DOAJ Open Access (DOAJ) DOI: 10.1088/2632-2153/ae8e30

Quantum transfer learning for cross-domain cybersecurity threat detection and categorization

Shtwai Alsubai, Mohamed Ayari, Natalia Kryvinska, Ahmad Almadhor et al. • Scientific Reports (2026)

Abstract Cybersecurity challenges have become increasingly complex and widespread, and the risks associated with these problems are substantial, affecting thousands of individuals and organisations and being crucial to national security. As...

DOAJ Open Access (DOAJ) DOI: 10.1038/s41598-026-40634-z

Using an Inspired Quantum-Classical Sequential Neural Network to Segment Liver Tumor Images Accurately

Sonwane Suchitra Shivaji Rao, K. Gangadhara Rao • Journal of Applied Science and Engineering (2026)

Medical analytics based on multimodal intelligence are essential to contemporary healthcare, especially in the diagnosis and detection of liver cancer. However, low contrast, varied textures, and irregular tumor forms in medical imaging con...

DOAJ Open Access (DOAJ) DOI: 10.6180/jase.202701_36.052

Recurrent quantum embedding neural network and its application in vulnerability detection

Zhihui Song, Xin Zhou, Jinchen Xu, Xiaodong Ding et al. • Scientific Reports (2024)

Abstract In recent years, deep learning has been widely used in vulnerability detection with remarkable results. These studies often apply natural language processing (NLP) technologies due to the natural similarity between code and languag...

DOAJ Open Access (DOAJ) DOI: 10.1038/s41598-024-63021-y