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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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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Advancing arabic dialect detection with hybrid stacked transformer models by Hager Saleh, Hager Saleh, Hager Saleh, Abdulaziz AlMohimeed, Rasha Hassan, Mandour M. Ibrahim, Saeed Hamood Alsamhi, Moatamad Refaat Hassan, Sherif Mostafa

“…Recent advances in deep learning (DL) models have shown promise in overcoming potential challenges in identifying Arabic dialects. …”
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Роль искусственного интеллекта в прогнозировании трудных дыхательных путей у взрослых: обзор литературы... by Андрей Юрьевич Зайцев, А. Б. Сорокин, Ю. А. Зайцев, К. В. Дубровин, Э. Г. Усикян

“…Поисковыми словами для англоязычных баз данных были artificial intelligence, deep learning, difficult airways; для русскоязычных — искусственный интеллект, глубокое машинное обучение, трудные дыхательные пути. …”
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In-Season Automated Mapping of Xinjiang Cotton Based on Cumulative Spectral and Phenological Characteristics by Yongsheng Huang, Yaozhong Pan, Yu Zhu, Xiufang Zhu, Xingsheng Xia, Qiong Chen, Jufang Hu, Hongyan Che, Xuechang Zheng, Lingang Wang

“…Methods based on machine learning, and deep learning, rely on a large number of training samples, which is time-consuming and laborious. …”
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A comprehensive environmental index for monitoring ecological quality of typical alpine wetlands in Central Asia by Jiudan Zhang, Junli Li, Changming Zhu, Anming Bao, Amaury Frankl, Philippe De Maeyer, Tim Van de Voorde

“…This study employed a deep-learning semantic segmentation model to map the structural changes of the Bayanbulak alpine wetland using Landsat imagery from 1977 to 2022. …”
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Inflamed immune phenotype predicts favorable clinical outcomes of immune checkpoint inhibitor therapy across multiple cancer types by Jeanne Shen, Sergio Pereira, Chan-Young Ock, Yung-Jue Bang, Seulki Kim, Sehhoon Park, Se-Hoon Lee, George A Fisher, Young Kwang Chae, Yoon-La Choi, Jin-Haeng Chung, Tony S K Mok, Leeseul Kim, Jun-Eul Hwang, Gahee Park, Sanghoon Song, Seunghwan Shin, Yoojoo Lim, Wonkyung Jung, Heon Song, Hyojin Kim, Taebum Lee, Sukjun Kim, Chang Ho Ahn, Seokhwi Kim, Ben W Dulken, Stephanie Bogdan, Maggie Huang, Chiyoon Oum, Siraj M. Ali

“…Here, we investigate artificial intelligence (AI)-based immune phenotypes capable of predicting ICI clinical outcomes in multiple solid tumor types.Methods Lunit SCOPE IO is a deep learning model which determines the immune phenotype of the tumor microenvironment based on TIL analysis. …”
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Swarm learning with weak supervision enables automatic breast cancer detection in magnetic resonance imaging by Oliver Lester Saldanha, Jiefu Zhu, Gustav Müller-Franzes, Zunamys I. Carrero, Nicholas R. Payne, Lorena Escudero Sánchez, Paul Christophe Varoutas, Sreenath Kyathanahally, Narmin Ghaffari Laleh, Kevin Pfeiffer, Marta Ligero, Jakob Behner, Kamarul A. Abdullah, Georgios Apostolakos, Chrysafoula Kolofousi, Antri Kleanthous, Michail Kalogeropoulos, Cristina Rossi, Sylwia Nowakowska, Alexandra Athanasiou, Raquel Perez-Lopez, Ritse Mann, Wouter Veldhuis, Julia Camps, Volkmar Schulz, Markus Wenzel, Sergey Morozov, Alexander Ciritsis, Christiane Kuhl, Fiona J. Gilbert, Daniel Truhn, Jakob Nikolas Kather

“…Results Upon systematically benchmarking various weakly supervised two-dimensional (2D) and three-dimensional (3D) deep learning (DL) methods, we find that the 3D-ResNet-101 demonstrates superior performance. …”
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Integrated Framework and Technical Path for Multi-level Nested Assessment of Landscape Character by Yuncai WANG, Qizhen DONG

“…By combining multi-scale segmentation and spatial clustering techniques of deep learning, a technical path for multi-level nested landscape character assessment is constructed as a new idea for characterizing the local characteristics of landscape at multiple scales.ConclusionIn the process of developing landscape character assessment systems, there have been numerous methodological systems. …”
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Single-cell transcriptome-wide Mendelian randomization and colocalization reveals immune-mediated regulatory mechanisms and drug targets for COVID-19Research in context by Hui Ying, Xueyan Wu, Xiaojing Jia, Qianqian Yang, Haoyu Liu, Huiling Zhao, Zhihe Chen, Min Xu, Tiange Wang, Mian Li, Zhiyun Zhao, Ruizhi Zheng, Shuangyuan Wang, Hong Lin, Yu Xu, Jieli Lu, Weiqing Wang, Guang Ning, Jie Zheng, Yufang Bi

“…For pathway analyses, we found the putative causal genes were enriched in natural killer (NK) recruiting cells but de-enriched in NK cells. Using a deep learning model, we found 107 (81%) of the putative causal genes (41 novel genes) were predicted to interact with SARS-COV-2 proteins. …”
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