BRAIN TUMOR DIAGNOSIS BASED ON MEDICAL IMAGES USING VISION TRANSFORMER by Masuma Mammadova, Fargana Abdullayeva

“… Brain tumor is one of the most common causes of death in modern times. Early and accurate detection of this disease can save the lives of a large part of the world’s population. …”
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A hierarchical reinforcement learning approach for energy‐aware service function chain dynamic deployment in IoT by Shuyi Wang, Haotong Cao, Longxiang Yang

“…Given the desire to minimize energy consumption and carbon emissions, one of the most essential concerns of future communication networks is ensuring rigorous performance restrictions of IoT services while improving energy efficiency. …”
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4D hypercomplex-valued neural network in multivariate time series forecasting by Radosław Kycia, Agnieszka Niemczynowicz

“…We evaluate different architectures, varying the input layers to include convolutional, Long Short-Term Memory (LSTM), or dense hypercomplex layers for 4D algebras. …”
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SKINVGG-NET: A MODIFIED AND FINE-TUNED VGG19-BASED DEEP LEARNING ARCHITECTURE FOR SKIN CANCER CLASSIFICATION by Maysaa R. Naeemah, Mohammed Y. Kamil

“…Skin cancer, one of the most common and potentially fatal cancers, requires early and correct diagnosis to improve patient outcomes. …”
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BN-SNN: Spiking neural networks with bistable neurons for object detection. by Siddiqui Muhammad Yasir, Hyun Kim

“…Spiking neural networks (SNNs) are emerging as a promising evolution in neural network paradigms, offering an alternative to conventional convolutional neural networks (CNNs). One of the most effective methods for SNN development is the CNN-to-SNN conversion process. …”
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Data‐Driven Predictions of Peak Warming Under Rapid Decarbonization by Noah S. Diffenbaugh, Elizabeth A. Barnes

“…Abstract The severe impacts associated with recent record‐setting annual global temperatures elevate the need to accurately predict the hottest conditions that could occur even if the most ambitious decarbonization goals are achieved. …”
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Input-output optics as a causal time series mapping: A generative machine learning solution by Abhijit Sen, Bikram Keshari Parida, Kurt Jacobs, Denys I. Bondar

“…For the example that generated the most complex mapping, the variational autoencoder produces outputs that have less than 10% error for more than 90% of inputs across our test data.…”
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Glaucoma identification with retinal fundus images using deep learning: Systematic review by Dulani Meedeniya, Thisara Shyamalee, Gilbert Lim, Pratheepan Yogarajah

“…The findings of this study, including comparisons of existing methods and key insights, will assist researchers and developers in identifying the most suitable techniques for glaucoma detection.…”
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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

“…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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A deep machine learning model development for the biomarkers of the anatomical and functional anti-VEGF therapy outcome detection on retinal OCT images by B.E. Malyugin, S.N. Sakhnov, L.E. Axenova, K.D. Axenov, E.V. Kozina, V.V. Vronskaya, V.V. Myasnikova

“…The neovascular form of age-related macular degeneration is the most common cause of such a complication as rupture of the pigment epithelium. …”
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Deep learning-based evaluation of the severity of mitral regurgitation in canine myxomatous mitral valve disease patients using digital stethoscope recordings by Soh-Yeon Lee, Sully Lee, Se-Hoon Kim, HyeSun Chang, Won-Yang Cho, Min-Ok Ryu, Jihye Choi, Hwa-Young Yoon, Kyoung-Won Seo

“…Abstract Background Myxomatous mitral valve disease (MMVD) represents the most prevalent cardiac disorder in dogs, frequently resulting in mitral regurgitation (MR) and congestive heart failure. …”
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Performance of externally validated machine learning models based on histopathology images for the diagnosis, classification, prognosis, or treatment outcome prediction in female b... by Ricardo Gonzalez, Peyman Nejat, Ashirbani Saha, Clinton J.V. Campbell, Andrew P. Norgan, Cynthia Lokker

“…Three studies externally validated ML models for diagnosis, 4 for classification, 2 for prognosis, and 1 for both classification and prognosis. Most studies used Convolutional Neural Networks and one used logistic regression algorithms. …”
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Cutis verticis gyrata in a patient with multiple basal cell carcinomas; case presentation and review of the literature by Simona-Roxana Georgescu, Maria Isabela Sârbu, Cristina-Iulia Mitran, Mădălina-Irina Mitran, Alice Rusu, Vasile Benea, Mircea Tampa

“…Basal cell carcinoma is the most frequent cancer in Caucasians, patients frequently presenting multiple tumors. …”
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Hierarchical Knowledge Transfer: Cross-Layer Distillation for Industrial Anomaly Detection by Junning Xu, Sanxin Jiang

“…There are two problems with traditional knowledge distillation methods in industrial anomaly detection: first, traditional methods mostly use feature alignment between the same layers. …”
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A Configurable Accelerator for CNN-Based Remote Sensing Object Detection on FPGAs by Yingzhao Shao, Jincheng Shang, Yunsong Li, Yueli Ding, Mingming Zhang, Ke Ren, Yang Liu

“…The results show that, under INT16 or INT8 precision, the system achieves remarkable throughput in most convolutional layers of the network, with an average performance of 153.14 giga operations per second (GOPS) or 301.52 GOPS, which is close to the system’s peak performance, taking full advantage of the platform’s parallel computing capabilities.…”
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Efficient BFCN for Automatic Retinal Vessel Segmentation by Yun Jiang, Falin Wang, Jing Gao, Wenhuan Liu

“…Retinal vessel segmentation has high value for the research on the diagnosis of diabetic retinopathy, hypertension, and cardiovascular and cerebrovascular diseases. Most methods based on deep convolutional neural networks (DCNN) do not have large receptive fields or rich spatial information and cannot capture global context information of the larger areas. …”
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Identification of diabetic retinopathy lesions in fundus images by integrating CNN and vision mamba models. by Zenglei Liu, Ailian Gao, Hui Sheng, Xueling Wang

“…Empirical findings demonstrate that the suggested methodology surpasses the most advanced algorithms on the datasets that are accessible openly. …”
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Usage of Neural-Based Predictive Modeling and IIoT in Wind Energy Applications by Adrian-Nicolae Buturache, Stelian Stancu

“…At the time of this study, no prior research studies have presented a direct comparison between feedforward, recurrent, and convolutional neural networks ‒ these being the most important in the field of supervised learning.…”
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Image-Based Malware Detection Using Deep CNN Models by hawraa omran musa, Muhanad Tahrir Younis

“…Malware or malicious software represents one of the most remarkable threats to cybersecurity, as it compromises the integrity, confidentiality, and availability of computer systems and networks. …”
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Machine-learning-based reconstruction of long-term global terrestrial water storage anomalies from observed, satellite and land-surface model data by N. Mandal, P. Das, K. Chanda, K. Chanda

“…Climate indices, like the Oceanic Niño Index and Dipole Mode Index, are selected as optimal predictors for a large number of grid cells globally, along with TWSAs from LSM outputs. The most effective machine learning (ML) algorithms among convolutional neural network (CNN), support vector regression (SVR), extra trees regressor (ETR) and stacking ensemble regression (SER) models are evaluated at each grid cell to achieve optimal reproducibility. …”
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