Optimizing Neuro-Oncology Imaging: A Review of Deep Learning Approaches for Glioma Imaging by Madeleine M. Shaver, Paul A. Kohanteb, Catherine Chiou, Michelle D. Bardis, Chanon Chantaduly, Daniela Bota, Christopher G. Filippi, Brent Weinberg, Jack Grinband, Daniel S. Chow, Peter D. Chang

“…This translates into varying degrees of enhancement, edema, and necrosis, making reliable imaging assessment challenging. Deep learning, a subset of machine learning artificial intelligence, has gained traction as a method, which has seen effective employment in solving image-based problems, including those in medical imaging. …”
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SSN: stockwell scattering network for SAR image change detection by Chen, Gong, Zhao, Yanan, Wang, Yi, Yap, Kim-Hui

“…Although both traditional and modern learning-driven methods attempted to overcome this challenge, deep convolutional neural networks (DCNNs)-based methods are still hindered by the lack of interpretability and the requirement of large computation power. …”
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Deep Dual-Resolution Road Scene Segmentation Networks Based on Decoupled Dynamic Filter and Squeeze–Excitation Module by Hongyin Ni, Shan Jiang

“…In order to meet the above challenges, Deep Dual-resolution Road Scene Segmentation Networks based on Decoupled Dynamic Filter and Squeeze–Excitation (DDF&SE-DDRNet) are proposed in this paper. …”
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Role of Deep Learning in Loop Closure Detection for Visual and Lidar SLAM: A Survey by Saba Arshad, Gon-Woo Kim

“…Also, the major challenges of conventional approaches are identified. Based on those challenges, deep learning-based methods were reviewed where the identified challenges are tackled focusing on the methods providing long-term autonomy in various conditions such as changing weather, light, seasons, viewpoint, and occlusion due to the presence of mobile objects. …”
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A Comparative Analysis of Deep Reinforcement Learning-Enabled Freeway Decision-Making for Automated Vehicles by Teng Liu, Yuyou Yang, Wenxuan Xiao, Xiaolin Tang, Mingzhu Yin

“…In application, advanced autonomous driving technologies still face numerous challenges. Deep Reinforcement Learning (DRL) has emerged as a widespread and effective approach to address artificial intelligence challenges, due to its substantial potential for autonomous learning and self-improvement. …”
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Deep learning approach for segmentation and classification of blood cells using enhanced CNN by B Hemalatha, B Karthik, C.V Krishna Reddy, A Latha

“…This technique both time-consuming and challenging. Deep Learning (DL) is an artificial intelligence subset of machine learning that can examine unsupervised information. …”
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LSTM-Based Autoencoder with Maximal Overlap Discrete Wavelet Transforms Using Lamb Wave for Anomaly Detection in Composites by Syed Haider Mehdi Rizvi, Muntazir Abbas, Syed Sajjad Haider Zaidi, Muhammad Tayyab, Adil Malik

“…However, due to the complexity of Lamb wave signals, especially after interacting with structural components and defects, interpreting these waves and extracting useful information about the structure’s health is still a major challenge. Deep-learning-based strategy offers a great opportunity to address such challenges as the algorithm can operate directly on raw discrete time-domain signals. …”
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Explainable deep transfer learning model for disease risk prediction using high-dimensional genomic data. by Long Liu, Qingyu Meng, Cherry Weng, Qing Lu, Tong Wang, Yalu Wen

“…While high-dimensional genomic data provides valuable data resources for the investigations of disease risk, their huge amount of noise and complex relationships between predictors and outcomes have brought tremendous analytical challenges. Deep learning model is the state-of-the-art methods for many prediction tasks, and it is a promising framework for the analysis of genomic data. …”
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Phish Responder: A Hybrid Machine Learning Approach to Detect Phishing and Spam Emails by Molly Dewis, Thiago Viana

“…This research investigated the threat of phishing and spam and developed a detection solution to address this challenge. Deep learning and natural language processing are two techniques that have been employed in related research, which has illustrated improvements in the detection of phishing. …”
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Multifunctional croconaine nanoparticles for efficient optoacoustic imaging of deep tumors and photothermal therapy by Liu Nian, O’Connor Patrick, Gujrati Vipul, Anzenhofer Pia, Klemm Uwe, Kleigrewe Karin, Sattler Michael, Plettenburg Oliver, Ntziachristos Vasilis

“…This work introduces a promising novel agent for cancer theranostics of challenging deep-seated tumors.…”
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Application of Deep Learning in Multitemporal Remote Sensing Image Classification by Xinglu Cheng, Yonghua Sun, Wangkuan Zhang, Yihan Wang, Xuyue Cao, Yanzhao Wang

“…In response to this challenge, deep learning methods have become prevalent in machine learning and have been widely applied in remote sensing due to their ability to handle large datasets. …”
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TDPP-Net : achieving three-dimensional path planning via a deep neural network architecture by Wu, Keyu, Mahdi Abolfazli Esfahani, Yuan, Shenghai, Wang, Han

“…However, the computational time of most existing methods are dependent on the scale and complexity of environment, which leads to the compromise between time efficiency and path quality. To tackle this challenge, deep neural network based (DNN-based) planning methods have been actively explored. …”
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DeepNav: Joint View Learning for Direct Optimal Path Perception in Cochlear Surgical Platform Navigation by Majid Zamani, Andreas Demosthenous

“…Although much research has been conducted in the field of automated cochlear implant navigation, the problem remains challenging. Deep learning techniques have recently achieved impressive results in a variety of computer vision problems, raising expectations that they might be applied in other domains, such as identifying the optimal navigation zone (OPZ) in the cochlear. …”
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Challenges in internet of things towards the security using deep learning techniques by K.C. Ravikumar, Pandi Chiranjeevi, N. Manikanda Devarajan, Chamandeep Kaur, Ahmed I. Taloba

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A Comparison of Hybrid and End-to-End ASR Systems for the IberSpeech-RTVE 2020 Speech-to-Text Transcription Challenge by Juan M. Perero-Codosero, Fernando M. Espinoza-Cuadros, Luis A. Hernández-Gómez

“…This paper describes a comparison between hybrid and end-to-end Automatic Speech Recognition (ASR) systems, which were evaluated on the IberSpeech-RTVE 2020 Speech-to-Text Transcription Challenge. Deep Neural Networks (DNNs) are becoming the most promising technology for ASR at present. …”
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Modeling of intracranial tumor treating fields for the treatment of complex high-grade gliomas by David J. Segar, Joshua D. Bernstock, Omar Arnaout, Wenya Linda Bi, Gregory K. Friedman, Robert Langer, Giovanni Traverso, Sumientra M. Rampersad

“…The reconceptualization of TTF as a targeted, intracranial therapy has the potential to provide a meaningful survival benefit to patients with HGG and other brain tumors, including those in surgically challenging, deep, or anatomically eloquent locations which may preclude surgical resection. …”
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Deep transfer learning for intelligent vehicle perception: A survey by Xinyu Liu, Jinlong Li, Jin Ma, Huiming Sun, Zhigang Xu, Tianyun Zhang, Hongkai Yu

“…As a solution to this challenge, deep transfer learning can handle situations excellently by transferring the knowledge from one domain to another. …”
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WildFishNet: Open Set Wild Fish Recognition Deep Neural Network With Fusion Activation Pattern by Xiaoya Zhang, Baoxiang Huang, Ge Chen, Milena Radenkovic, Guojia Hou

“…Moreover, the unrestricted marine environment makes the problem even more challenging. Deep learning has been demonstrated as a powerful paradigm in image classification tasks. …”
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Self-Writer: Clusterable Embedding Based Self-Supervised Writer Recognition from Unlabeled Data by Zabir Mohammad, Muhammad Mohsin Kabir, Muhammad Mostafa Monowar, Md Abdul Hamid, Muhammad Firoz Mridha

“…Writer recognition based on a small amount of handwritten text is one of the most challenging deep learning problems because of the implicit characteristics of handwriting styles. …”
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Modeling of intracranial tumor treating fields for the treatment of complex high-grade gliomas by Segar, David J., Bernstock, Joshua D., Arnaout, Omar, Bi, Wenya Linda, Friedman, Gregory K., Langer, Robert, Traverso, Giovanni, Rampersad, Sumientra M.

“…The reconceptualization of TTF as a targeted, intracranial therapy has the potential to provide a meaningful survival benefit to patients with HGG and other brain tumors, including those in surgically challenging, deep, or anatomically eloquent locations which may preclude surgical resection. …”
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