Results 161 to 170 of about 272,857 (301)

A Hybrid Transfer Learning Framework for Brain Tumor Diagnosis

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
A novel hybrid transfer learning approach for brain tumor classification achieves 99.47% accuracy using magnetic resonance imaging (MRI) images. By combining image preprocessing, ensemble deep learning, and explainable artificial intelligence (XAI) techniques like gradient‐weighted class activation mapping and SHapley Additive exPlanations (SHAP), the ...
Sadia Islam Tonni, Md. Alif Sheakh, Mst. Sazia Tahosin, Md. Zahid Hasan, Taslima Ferdaus Shuva, Touhid Bhuiyan, Muhammad Ali Abdullah Almoyad, Nabil Anan Orka, Md. Tanvir Rahman, Risala Tasin Khan, M. Shamim Kaiser, Mohammad Ali Moni   +11 more
wiley   +1 more source

Cross-order detection of bacteriophage transduction in microbial communities using RNA barcoding. [PDF]

Nat Commun
LaTurner ZW, Dysart MJ, Schwartz SK, Zeng E, Chappell J, Silberg JJ, Stadler LB.   +6 more
europepmc   +1 more source

Zero Watermarking Using Convolutional Additive Self‐Attention Vision Transformer and Discrete Wavelet Transform‐Variance‐Based Feature Descriptor for Medical Image Security in Mobile Healthcare Services

Advanced Intelligent Systems, EarlyView.
A zero‐watermarking algorithm that combines a refined convolutional additive self‐attention vision transformer (CAS‐ViT) with a discrete wavelet transform variance‐based feature descriptor (DVFD) is proposed for protecting the privacy of medical images in mobile healthcare services.
Pei Liu, Yan Yan, Hao Huang, Ping Wang, Weirong Qin, Xiangjian Zuo, Yongqin Li   +6 more
wiley   +1 more source

Novel 3‑Aza-Bicyclo[3.3.0]octane Compounds as ERK5 Inhibitors for Treating Cancer. [PDF]

ACS Med Chem Lett
Sabnis RW.
europepmc   +1 more source

Adaptive Autonomy in Microrobot Motion Control via Deep Reinforcement Learning and Path Planning Synergy

Advanced Intelligent Systems, EarlyView.
This study introduces a data‐driven framework that combines deep reinforcement learning with classical path planning to achieve adaptive microrobot navigation. By training a surrogate neural network to emulate microrobot dynamics, the approach improves learning efficiency, reduces training time, and enables robust real‐time obstacle avoidance in ...
Amar Salehi, Bairong Zhu, Haoyu Liu, Tingting Yu   +3 more
wiley   +1 more source

Impact of extended equilibration periods on in vitro post-thaw sperm quality in rams. [PDF]

Vet Res Commun
Korkmaz F, Gungor S, Inanc ME, Herdogan M, Cay HA, Mart F, Kahraman D, Kaya U.   +7 more
europepmc   +1 more source

Ram Mandir: Cultural production or cultural renaissance?

The consecration of the Ram Lalla idol in Ayodhya was unparalleled in that the entire nation united in welcoming it. Across the length and breadth of the country, the majority Hindu community rose as one, disregarding divisions of sect, class, caste, etc.
Batra, Jagdish
core  

An Attention‐Assisted Machine Learning System for Deep Microorganism Image Classification

Advanced Intelligent Systems, EarlyView.
An attention‐assisted DenseNet201 framework was developed for the classification of eight microorganism classes from microscopic images. The proposed model improved classification performance and achieved an accuracy of 87.38%. Advances in microbiology and environmental health fundamentally depend on precise and timely microorganism identification ...
Yujie Li, Ailing Gao, Guoxu Liu, Chunlei Chen, Yonghui Zhang, Sunkyoung Kang, M. Khosravi   +6 more
wiley   +1 more source

Filio Fratrique M. Stephano Ram, Provinciam Ecclesiasticam Tychepolitanam urbicam, quam ... quia meruerat, accepisse Viris Gravibus credimus, Sub auspicium anni vertentis adeunti, antiquum Feliciter dicimus: Functi sic & Pietatis & Amoris officio. ...

FILIO FRATRIQUE M. STEPHANO RAM, PROVINCIAM ECCLESIASTICAM TYCHEPOLITANAM URBICAM, QUAM ... QUIA MERUERAT, ACCEPISSE VIRIS GRAVIBUS CREDIMUS, SUB AUSPICIUM ANNI VERTENTIS ADEUNTI, ANTIQUUM FELICITER DICIMUS: FUNCTI SIC & PIETATIS & AMORIS OFFICIO ...
Ram, Stephanus
core  

Lidar‐Based Object Tracking of Traffic Participants with Sensor Nodes in Existing Urban Infrastructure

Advanced Intelligent Systems, EarlyView.
This paper presents a lidar‐based sensor node design and a rule‐based state observer for edge‐based traffic participant tracking. Unlike other state‐of‐the‐art methods, this state observer enables real‐time, CPU‐only edge processing without relying on machine learning approaches.
Simon Schäfer, Bassam Alrifaee, Ehsan Hashemi   +2 more
wiley   +1 more source