TomaFDNet: A multiscale focused diffusion-based model for tomato disease detection by Rijun Wang, Rijun Wang, Yesheng Chen, Fulong Liang, Xiangwei Mou, Xiangwei Mou, Guanghao Zhang, Hao Jin

“…IntroductionTomatoes are one of the most economically significant crops worldwide, with their yield and quality heavily impacted by foliar diseases. …”
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A ubiquitous and interoperable deep learning model for automatic detection of pleomorphic gastroesophageal lesions by Miguel Martins, Miguel José Mascarenhas, Maria João Almeida, João Afonso, Tiago Ribeiro, Pedro Cardoso, Francisco Mendes, Joana Mota, Patrícia Andrade, Hélder Cardoso, Miguel Mascarenhas-Saraiva, João Ferreira, Guilherme Macedo

“…We included 59,482 E-G frames, from 774 CE procedures of 5 centers, to develop a Convolutional Neural Network (CNN). The dataset was divided following an exam-based split, with 90% allocated for training – including a 5-fold cross validation – while the remaining was used for testing. …”
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Next-generation cutting-edge hybrid AI frameworks for predicting rheological properties and CO₂ emissions in alkali-activated concrete by Lu Zhang, Kangning Liu, Ali H. AlAteah, Sadiq Alinsaif, Muhammad Sufian, Ayaz Ahmad

“…To address this challenge, this study presents a next-generation AI-based predictive framework utilizing three hybrid machine learning techniques: adaptive neuro-fuzzy inference system with genetic algorithm (ANFIS-GA), convolutional neural networks with long short-term memory (CNN-LSTM), and multi-objective optimization (MOO). …”
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An Explainable Bayesian TimesNet for Probabilistic Groundwater Level Prediction by Zechen Peng, Shaoxing Mo, Alexander Y. Sun, Jichun Wu, Xiankui Zeng, Miao Lu, Xiaoqing Shi

“…BTimesNet transforms 1D time series data into 2D matrices based on periodicity, enhancing the capture of temporal patterns through convolutional filters. A Bayesian framework using Stein Variational Gradient Descent is implemented to quantify predictive uncertainties. …”
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Comparing efficiency of an attention-based deep learning network with contemporary radiological workflow for pulmonary embolism detection on CTPA: A retrospective study by Gagandeep Singh, Annie Singh, Tejasvi Kainth, Sudhir Suman, Nicole Sakla, Luke Partyka, Tej Phatak, Prateek Prasanna

“…Rational and objectives: Pulmonary embolism (PE) is the third most fatal cardiovascular disease in the United States. …”
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Investigation into the prognostic factors of early recurrence and progression in previously untreated diffuse large B-cell lymphoma and a statistical prediction model for POD12 by Ke Lian, Wenyao Zhu, Zhihui Hu, Fang Su, CaiXia Xu, Hui Wang

“…Comparatively, the CNN-LSTM and PSO-GRNN models are the most suitable to predict the risk level of the POD12 in the future.…”
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Multimodal Emotion Recognition Based on Facial Expressions, Speech, and EEG by Jiahui Pan, Weijie Fang, Zhihang Zhang, Bingzhi Chen, Zheng Zhang, Shuihua Wang

“…For work on the speech branch, this paper proposes a lightweight fully convolutional neural network (LFCNN) for the efficient extraction of speech emotion features. …”
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An exploratory analysis of longitudinal artificial intelligence for cognitive fatigue detection using neurophysiological based biosignal data by Sameer Nooh, Mahmoud Ragab, Rania Aboalela, Abdullah AL-Malaise AL-Ghamdi, Omar A. Abdulkader, Ghadah Alghamdi

“…The graph convolutional autoencoder (GCA) classifier is employed to classify cognitive fatigue detection. …”
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Frontotemporal dementia: a systematic review of artificial intelligence approaches in differential diagnosis by Serena Dattola, Augusto Ielo, Giuseppe Varone, Giuseppe Varone, Alberto Cacciola, Angelo Quartarone, Lilla Bonanno

“…Deep learning methods, particularly convolutional neural networks (CNNs), have also been increasingly adopted, demonstrating high accuracy in distinguishing FTD from other dementias. …”
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A multitask framework based on CA-EfficientNetV2 for the prediction of glioma molecular biomarkers by Qian Xu, Feng Ning Liang, Ya Ru Cao, Jin Duan, Teng Cui, Teng Zhao, Hong Zhu

“…IntroductionGlioma is the most common primary malignant tumor of the central nervous system. …”
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Internet of things driven object detection framework for consumer product monitoring using deep transfer learning and hippopotamus optimization by Amnah Alshahrani, Mukhtar Ghaleb, Hany Mahgoub, Achraf Ben Miled, Nojood O. Aljehane, Mohammed Yahya Alzahrani, Hasan Beyari, Sultan Alanazi

“…Object detection (OD) is the most significant and challenging problem in computer vision (CV). …”
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FinSafeNet: securing digital transactions using optimized deep learning and multi-kernel PCA(MKPCA) with Nyström approximation by Ahmad Raza Khan, Shaik Shakeel Ahamad, Shailendra Mishra, Mohd Abdul Rahim Khan, Sunil Kumar Sharma, Abdullah AlEnizi, Osama Alfarraj, Majed Alowaidi, Manoj Kumar

“…FinSafeNet is based on a Bi-Directional Long Short-Term Memory (Bi-LSTM), a Convolutional Neural Network (CNN) and an additional dual attention mechanism to study the transaction data and influence the observation of various security threats. …”
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Metaparameter optimized hybrid deep learning model for next generation cybersecurity in software defined networking environment by C. Labesh Kumar, Suresh Betam, Denis Pustokhin, E. Laxmi Lydia, Kanchan Bala, Rajanikanth Aluvalu, Bhawani Sankar Panigrahi

“…Furthermore, the binary narwhal optimizer (BNO)-based feature selection is accomplished to classify the most related features. For the DDoS attack classification process, the attention mechanism with convolutional neural network and bidirectional gated recurrent units (CNN-BiGRU-AM) is employed. …”
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Quantifying leaf symptoms of sorghum charcoal rot in images of field‐grown plants using deep neural networks by Emmanuel M. Gonzalez, Ariyan Zarei, Sebastian Calleja, Clay Christenson, Bruno Rozzi, Jeffrey Demieville, Jiahuai Hu, Andrea L. Eveland, Brian Dilkes, Kobus Barnard, Eric Lyons, Duke Pauli

“…EfficientNet‐B3 and a fully convolutional network emerged as the top‐performing models for image classification and segmentation tasks, respectively. …”
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Impact of Artificial Intelligence in Nursing for Geriatric Clinical Care for Chronic Diseases: A Systematic Literature Review by Mahdieh Poodineh Moghadam, Zabih Allah Moghadam, Mohammad Reza Chalak Qazani, Pawel Plawiak, Roohallah Alizadehsani

“…Our findings reveal that Random Forest, logistic regression, and convolutional neural network (CNN) are the most frequently used AI techniques, typically evaluated by accuracy metrics and the area under the curve (AUC). …”
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Novel hybrid transfer neural network for wheat crop growth stages recognition using field images by Aisha Naseer, Madiha Amjad, Ali Raza, Kashif Munir, Aseel Smerat, Henry Fabian Gongora, Carlos Eduardo Uc Rios, Imran Ashraf

“…Abstract Wheat is one of the world’s most widely cultivated cereal crops and is a primary food source for a significant portion of the population. …”
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A Combined Deep Learning Method with Attention-Based LSTM Model for Short-Term Traffic Speed Forecasting by Pan Wu, Zilin Huang, Yuzhuang Pian, Lunhui Xu, Jinlong Li, Kaixun Chen

“…Results show that the proposed method outperforms other deep learning algorithms (such as recurrent neural network (RNN) and convolutional neural network (CNN)) in terms of both calculating efficiency and prediction accuracy. …”
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Multiclass leukemia cell classification using hybrid deep learning and machine learning with CNN-based feature extraction by Sazzli Kasim, Sorayya Malek, JunJie Tang, Xue Ning Kiew, Song Cheen, Bryan Liew, Norashikin Saidon, Raja Ezman, Raja Shariff

“…Abstract Leukemia is the most prevalent form of blood cancer, affecting individuals across all age groups. …”
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Enhanced MRI brain tumor detection using deep learning in conjunction with explainable AI SHAP based diverse and multi feature analysis by Asif Rahman, Maqsood Hayat, Nadeem Iqbal, Fawaz Khaled Alarfaj, Salem Alkhalaf, Fahad Alturise

“…In addition, The SHAP analysis was used to identify the most important features in classification. In a small dataset, CNN obtained 97.8% accuracy while SVC yielded 98.06% accuracy. …”
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Application of Deep Learning Techniques in Uranium Microparticle Fission Track Detection by ZHAO Xiong, REN Fangda, SHEN Yan

“…To address the issue of long-distance dependencies in convolutional operations, a window multi-head attention mechanism (swin transformer) was integrated to design the uranium microparticle detection network. …”
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