Universal conditional networks (UniCoN) for multi-age embryonic cartilage segmentation with Sparsely annotated data by Nishchal Sapkota, Yejia Zhang, Zihao Zhao, Maria Jose Gomez, Yuhan Hsi, Jordan A. Wilson, Kazuhiko Kawasaki, Greg Holmes, Meng Wu, Ethylin Wang Jabs, Joan T. Richtsmeier, Susan M. Motch Perrine, Danny Z. Chen

“…Tackling this segmentation task with deep learning (DL) methods is laborious due to the big burden of manual image annotation, expensive due to the high acquisition costs of 3D micro-CT images, and difficult due to embryonic cartilage’s complex and rapidly changing shapes. …”
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Adversarial measurements for convolutional neural network-based energy theft detection model in smart grid by Santosh Nirmal, Pramod Patil, Sagar Shinde

“…Recent studies reveal that machine learning and deep learning models are vulnerable. Day by day, different attack techniques are coming up in different fields, including energy, financial, etc. …”
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Association between the subclinical level of problematic internet use and habenula volume: a look at mediation effect of neuroticism by Toshiya Murai, Hironobu Fujiwara, Qi Dai, Halwa Zakia, Yusuke Kyuragi, Naoya Oishi, Yuzuki Ishikawa, Lichang Yao, Morio Aki

“…Hb segmentation was performed using a deep learning technique. The Internet Addiction Test (IAT) and the NEO Five-Factor Inventory were used to assess the PIU level and personality, respectively. …”
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Advancements and trends in mangrove species mapping based on remote sensing: A comprehensive review and knowledge visualization by Yuqi Wu, Chunyan Lu, Kexin Wu, Wenna Gao, Nuocheng Yang, Jingwen Lin

“…Classification algorithm development has evolved four stages, from pixel-based methods to object-oriented approaches, progressing to approaches incorporating machine learning algorithms, and currently advancing towards ensemble learning and deep learning. Research in this field still faces several challenges in data fusion, classification algorithm enhancement, increased number of classification species, and large-scale long-term mapping. …”
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Comparison of 1D and 3D volume measurement techniques in NF2-associated vestibular schwannoma monitoring by Isabel Gugel, Nuran Aboutaha, Bianca Pfluegler, Ulrike Ernemann, Martin Ulrich Schuhmann, Marcos Tatagiba, Florian Grimm

“…For this reason, they are not recommended for monitoring off-label therapy with Bevacizumab or for treatment decisions depending on a precise assessment of tumor volume and growth. Developing deep learning-based volume determinations in the future is essential to reduce SVA’s time intensity.…”
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A Comprehensive Review of Direction-of-Arrival Estimation and Localization Approaches in Mixed-Field Sources Scenario by Amir Masoud Molaei, Bijan Zakeri, Seyed Mehdi Hosseini Andargoli, Muhammad Ali Babar Abbasi, Vincent Fusco, Okan Yurduseven

“…The review also identifies promising future research directions, such as the exploration of advanced signal processing techniques like compressive sensing and deep learning, exact NF modeling, estimation based on one-bit measurements, the integration of polarization diversity, employing metasurface antennas, tracking parameters, and the utilization of full-wave or experimental data for a more realistic representation of the challenges. …”
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Plant Disease Classifier: Detection of Dual-Crop Diseases Using Lightweight 2D CNN Architecture by Hasibul Islam Peyal, Md. Nahiduzzaman, Md. Abu Hanif Pramanik, Md. Khalid Syfullah, Saleh Mohammed Shahriar, Abida Sultana, Mominul Ahsan, Julfikar Haider, Amith Khandakar, Muhammad E. H. Chowdhury

“…The purpose of this work is to categorize 14 classes for both cotton and tomato crops, with 12 diseased classes and two healthy classes using a deep learning-based lightweight 2D CNN architecture and to implement the model in an android application named “Plant Disease Classifier” for smartphone-assisted plant disease diagnosis system, the results of the experiments reveal that the proposed model outperforms the pre-trained models VGG16, VGG19 and InceptionV3 despite having fewer parameters. …”
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An explainable Bi-LSTM model for winter wheat yield prediction by Abhasha Joshi, Biswajeet Pradhan, Subrata Chakraborty, Subrata Chakraborty, Renuganth Varatharajoo, Abdullah Alamri, Shilpa Gite, Chang-Wook Lee

“…Accurate, reliable and transparent crop yield prediction is crucial for informed decision-making by governments, farmers, and businesses regarding food security as well as agricultural business and management. Deep learning (DL) methods, particularly Long Short-Term Memory networks, have emerged as one of the most widely used architectures in yield prediction studies, providing promising results. …”
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Working-memory load decoding model inspired by brain cognition based on cross-frequency coupling by Jing Zhang, Tingyi Tan, Yuhao Jiang, Congming Tan, Liangliang Hu, Daowen Xiong, Yikang Ding, Guowei Huang, Junjie Qin, Yin Tian

“…Therefore, identifying working memory load is an essential area of research. Deep learning models have demonstrated remarkable potential in identifying the intensity of working memory load. …”
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Segmentation of Laser Marks of Diabetic Retinopathy in the Fundus Photographs Using Lightweight U-Net by Yukang Jiang, Jianying Pan, Ming Yuan, Yanhe Shen, Jin Zhu, Yishen Wang, Yewei Li, Ke Zhang, Qingyun Yu, Huirui Xie, Huiting Li, Xueqin Wang, Yan Luo

“…In this study, we develop a deep learning algorithm based on the lightweight U-Net to segment laser marks from the color fundus photos, which could help indicate a stage or providing valuable auxiliary information for the care of DR patients. …”
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Transformers for Neuroimage Segmentation: Scoping Review by Maya Iratni, Amira Abdullah, Mariam Aldhaheri, Omar Elharrouss, Alaa Abd-alrazaq, Zahiriddin Rustamov, Nazar Zaki, Rafat Damseh

“…Transformers are a promising deep learning approach for automated medical image segmentation. …”
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BananaImageBD: A comprehensive banana image dataset for classification of banana varieties and detection of ripeness stages in BangladeshMendeley Data by Md Hasanul Ferdaus, Rizvee Hassan Prito, Ahmed Abdal Shafi Rasel, Masud Ahmed, Md. Jahid Hassan Saykot, Shanjida Sultan Shanta, Sonali Akter, Ankan Chandra Das, Mohammad Manzurul Islam, Mahamudul Hasan, Md Sawkat Ali

“…Machine Learning (ML) and Deep Learning (DL) models can be trained on this dataset to accurately categorize banana varieties and determine their ripeness stages. …”
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Comparison of the Efficacy of Artificial Intelligence-Powered Software in Crown Design: An In Vitro Study by Ziqiong Wu, Chengqi Zhang, Xinjian Ye, Yuwei Dai, Jing Zhao, Wuyuan Zhao, Yuanna Zheng

“…AI-powered software requires further research and extensive deep learning to improve the morphological accuracy and stability of the crown design.…”
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Monitoring Moso bamboo (Phyllostachys pubescens) forests damage caused by Pantana phyllostachysae Chao considering phenological differences between on-year and off-year using UAV h... by Anqi He, Zhanghua Xu, Yifan Li, Bin Li, Xuying Huang, Huafeng Zhang, Xiaoyu Guo, Zenglu Li

“…The results demonstrate that classical machine learning and deep learning models can effectively detect P. phyllostachysae damage, with the 1D-CNN algorithm achieving the best performance. …”
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Neoplasms in the Nasal Cavity Identified and Tracked with an Artificial Intelligence-Assisted Nasal Endoscopic Diagnostic System by Xiayue Xu, Boxiang Yun, Yumin Zhao, Ling Jin, Yanning Zong, Guanzhen Yu, Chuanliang Zhao, Kai Fan, Xiaolin Zhang, Shiwang Tan, Zimu Zhang, Yan Wang, Qingli Li, Shaoqing Yu

“…Using Deep Snake, U-Net, and Att-Res2-UNet, we developed a nasal neoplastic detection network based on endoscopic images. After deep learning, the optimal network was selected as the initialization model and trained to optimize the SiamMask online tracking algorithm. …”
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Sentiment Analysis Twitter Bahasa Indonesia Berbasis WORD2VEC Menggunakan Deep Convolutional Neural Network by Hans Juwiantho, Esther Irawati Setiawan, Joan Santoso, Mauridhi Hery Purnomo

“…Penggunaan metode classical machine learning yang sudah banyak diterapkan pada sentiment analysis, tetapi metode tersebut tidak memperhatikan pentingnya urutan kata pada suatu kalimat. Metode deep learning dengan algoritme Deep Convolutional Neural Network ditawarkan untuk menjawab permasalahan tersebut dengan melakukan operasi convolution menggunakan filter sebesar ukuran window untuk mendapatkan fitur berdasarkan urutan kata. …”
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A novel early stage drip irrigation system cost estimation model based on management and environmental variables by Masoud Pourgholam-Amiji, Khaled Ahmadaali, Abdolmajid Liaghat

“…Then, different machine learning models such as Multivariate Linear Regression, Support Vector Regression, Artificial Neural Networks, Gene Expression Programming, Genetic Algorithms, Deep Learning, and Decision Trees, were used to estimate the costs of each of the of the aforementioned sections. …”
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Characterizing immune biomarkers and effector CD8+ T-cell exhaustion in pancreatic adenocarcinoma via single-cell RNA sequencing profiling by Rawaa AlChalabi, Raghda Makia, Semaa A. Shaban, Ahmed AbdulJabbar Suleiman

“…Virtual screening using a deep learning framework, GNINA, explored the inhibitory features of the anti-inflammatory drugs oxaprozin and celecoxib on IL7R. …”
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Positional embeddings and zero-shot learning using BERT for molecular-property prediction by Medard Edmund Mswahili, JunHa Hwang, Jagath C. Rajapakse, Kyuri Jo, Young-Seob Jeong

“…Abstract Recently, advancements in cheminformatics such as representation learning for chemical structures, deep learning (DL) for property prediction, data-driven discovery, and optimization of chemical data handling, have led to increased demands for handling chemical simplified molecular input line entry system (SMILES) data, particularly in text analysis tasks. …”
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Pathological and radiological assessment of benign breast lesions with BIRADS IVc/V subtypes. should we repeat the biopsy? by Wesam Rjoop, Anwar Rjoop, Alia Almohtaseb, Lama Bataineh, Zeina Nser Joubi, Maha Gharaibeh, Abdalrahman Al-Qwabah, Yousef Alasheh, Ismail Matalka

“…There is a need for continuous research to improve the diagnosis and treatment of breast lesions and reduce false-positive rates by incorporating other methodologies such as sonoelastography and incorporating deep learning and artificial intelligence in the decision-making to eliminate unnecessary procedures.…”
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