Fuzzy Enhanced Kidney Tumor Detection: Integrating Machine Learning Operations for a Fusion of Twin Transferable Network and Weighted Ensemble Machine Learning Classifier by Ananya Ghosh, Jyotismita Chaki

“…The proposed approach leverages the power of image enhancement using fuzzy systems, deep learning, and machine learning for automated kidney tumor detection in CT images. …”
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A Review: CRISPR Cas System and the Mechanism With an Inhibition of Binding of CRISPR Cas‐9 by Akram Hosen, Md Nakibul Hasan Nishat, MD. Mohaimin Hossain Soaib, Omar Sharif Sharkar, Md. Sahabuddin, Imdadul Haque Sharif, Sujay Kumar Bhajan

“…We have analyzed recent kinds of literature in this work to integrate a deep learning way that contributes over the next time with some important discussion about viral resistance in eukaryotes along with some cancer treatment strategies, and crop improvement by the CRISPR Cas system.…”
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ChatGPT Conversations on Oral Cancer: Unveiling ChatGPT's Potential and Pitfalls by Nikunj Maniyar, Gargi S. Sarode, Sachin C. Sarode, Shruti Thakkar

“…Background: The present research investigates the application of Chat Generative Pre-trained Transformer (ChatGPT), an artificial intelligence (AI)-based language processing model that utilize deep learning programs, in responding to various questions related to oral cancer. …”
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Machine Learning-Based Normal White Blood Cell Multi-Classification Optimization by Taeyeon Gil, Sukjun Lee, Onseok Lee

“…Several studies have employed deep learning (DL) or machine learning (ML) methods; no significant difference in performance between the two methods has been demonstrated. …”
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PlantAIM: A new baseline model integrating global attention and local features for enhanced plant disease identification by Abel Yu Hao Chai, Sue Han Lee, Fei Siang Tay, Hervé Goëau, Pierre Bonnet, Alexis Joly

“…Conventionally, detection has relied on plant pathologists, but recent advances in deep learning, particularly the Vision Transformer (ViT) and Convolutional Neural Network (CNN), have made it feasible for automated plant disease identification. …”
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DC-NNMN: Across Components Fault Diagnosis Based on Deep Few-Shot Learning by Juan Xu, Pengfei Xu, Zhenchun Wei, Xu Ding, Lei Shi

“…In recent years, deep learning has become a popular topic in the intelligent fault diagnosis of industrial equipment. …”
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Dual-Granularity Feature Alignment for Change Detection in Remote Sensing Images by Feng Zhou, Xinyu Zhang, Hui Shuai, Renlong Hang, Shanshan Zhu, Tianyu Geng

“…Deep learning has emerged as the preferred method for remote sensing change detection owing to its ability to automatically extract discriminative features from bitemporal images. …”
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Analysis of the integrated role of the Yangtze River Delta based on the industrial economic resilience of cities during COVID-19 by Cai Jia, Zini Cao, Jinkang Hu, Xudong Wang, Long Zhao, Junjun Zhi, Wangbing Liu, Gaohua Zhang, Shilong Ding, Yan Li, Luzhou Lin

“…The study employed the UNet deep learning method to detect the land use types in Wu-ma-Chu. …”
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Advanced generative adversarial network for optimizing layout of wireless sensor networks by S. Praveen Kumar, Setu Garg, Eatedal Alabdulkreem, Achraf Ben Miled

“…Therefore, this research paper presents a layout optimization problem using novel intelligent deep learning-based optimization methodology. Here, the major objective is to cover numerous objectives associated with optimal layouts of homogeneous WSNs that involves connectivity, coverage, energy consumption, lifetime, and the number of sensor nodes. …”
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An Attention-Based Residual U-Net for Tumour Segmentation Using Multi-Modal MRI Brain Images by Najme Zehra Naqvi, K. R. Seeja

“…Hence, this research proposes a novel automated deep-learning approach based on U-Net for segmenting Glioma tumours. …”
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TepiSense: A Social Computing-Based Real-Time Epidemic Surveillance System Using Artificial Intelligence by Bilal Tahir, Muhammad Amir Mehmood

“…TepiSense compares the performance of 3 feature extraction techniques, 9 machine/deep learning models, and 3 Large Language Models (LLMs). …”
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Personalized client-edge-cloud hierarchical federated learning in mobile edge computing by Chunmei Ma, Xiangqian Li, Baogui Huang, Guangshun Li, Fengyin Li

“…Federated learning in mobile edge computing is a forward-looking distributed framework for deploying deep learning algorithms in many application scenarios. …”
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Hybridizing Evolutionary Computation and Deep Neural Networks: An Approach to Handwriting Recognition Using Committees and Transfer Learning by Alejandro Baldominos, Yago Saez, Pedro Isasi

“…The field was introduced in the late-1980s, but only in the latest years the field has become mature enough to enable the optimization of deep learning models, such as convolutional neural networks. …”
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Bathymetry estimation for coastal regions using self-attention by Xiaoxiong Zhang, Maryam R. Al Shehhi

“…In this work, we present TransBathy, a deep-learning-based satellite-derived bathymetric model, to solve the coastal bathymetric mapping for different unknown challenging terrains. …”
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Improving Health Through Indoor Environmental Quality Monitoring: A Review of Data-Driven Models and Smart Sensor Innovations by Kidari Rachid, Tilioua Amine

“…Numerous cutting-edge deep learning techniques, including convolutional neural networks (CNNs), long short-term memory networks (LSTMs), decision trees (DTs), support vector machines (SVMs), artificial neural networks (ANNs), and deep neural networks (DNNs), are incorporated into the hybrid framework. …”
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Infrared thermography based fault diagnosis of diesel engines using convolutional neural network and image enhancement by Wang Rongcai, Yan Hao, Dong Enzhi, Cheng Zhonghua, Li Yuan, Jia Xisheng

“…In recent years, there has been a growing interest in deep learning (DL) for fault diagnosis in various industries. …”
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Vision-Based Underwater Docking Guidance and Positioning: Enhancing Detection with YOLO-D by Tian Ni, Can Sima, Wenzhong Zhang, Junlin Wang, Jia Guo, Lindan Zhang

“…A cascaded detection and positioning strategy incorporating fused active and passive markers enabled real-time detection of the relative position and pose between the UUV and docking station (DS). A novel deep learning-based network model, YOLO-D, was developed to detect docking markers in real time. …”
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Research progress of MRI-based radiomics in hepatocellular carcinoma by Xiao-Yun Xie, Rong Chen

“…Key search terms included Hepatocellular carcinoma, HCC, Liver cancer, Magnetic resonance imaging, MRI, radiomics, deep learning, machine learning, and artificial intelligence.ResultsA comprehensive analysis of 93 articles underscores the efficacy of MRI radiomics, a noninvasive imaging analysis modality, across various facets of HCC management. …”
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Biomedical named entity recognition using improved green anaconda-assisted Bi-GRU-based hierarchical ResNet model by Ram Chandra Bhushan, Rakesh Kumar Donthi, Yojitha Chilukuri, Ulligaddala Srinivasarao, Polisetty Swetha

“…To overcome these challenges, deep learning (DL) methods have emerged. However, DL-based NER methods may need help identifying long-distance relationships within text and require significant annotated datasets. …”
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Maritime Small Object Detection Algorithm in Drone Aerial Images Based on Improved YOLOv8 by Peng Ling, Yihong Zhang, Shuai Ma

“…Combining unmanned aerial vehicles (UAVs) with deep learning algorithms offers an efficient, safe and inexpensive alternative to maritime search and rescue (mSAR) missions. …”
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