Automated Tomato Leaf Disease Detection Using Image Processing: An SVM-Based Approach with GLCM and SIFT Features by Rashid Khan, Nasir Ud Din, Asim Zaman, Bingding Huang

“…Future research can explore integrating advanced deep learning algorithms to enhance the system’s accuracy in this multiclass classification challenge.…”
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Robust Face Detection and Identification under Occlusion using MTCNN and RESNET50 by Eiman Wahab, Wajeeha Shafique, Habiba Amir, Sameena Javed, Muhammad Marouf

“…By leveraging the capabilities in the deep learning algorithms, this project provides a robust solution for automating the recognition of partially occluded faces. …”
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A Full Stage Data Augmentation Method in Deep Convolutional Neural Network for Natural Image Classification by Qinghe Zheng, Mingqiang Yang, Xinyu Tian, Nan Jiang, Deqiang Wang

“…Nowadays, deep learning has achieved remarkable results in many computer vision related tasks, among which the support of big data is essential. …”
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Construction of Value Chain E-Commerce Model Based on Stationary Wavelet Domain Deep Residual Convolutional Neural Network by Chenyuan Wang

“…The original loss function based on the residual learning model deep learning is modified to solve the original model fuzzy problem, which improves the effect and has good robustness. …”
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Predicting the Shanghai Composite Index Using Chinese TikTok Self-Media Data and Machine Learning Model in China by Li Zhiming, Han Huijian, Li Zongwei, Zhang Rui

“…In addition, we compared and analyzed the prediction results of several machine learning and deep learning models in the short term, medium term, and long term. …”
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Survey on explainable knowledge graph reasoning methods by Yi XIA, Mingjng LAN, Xiaohui CHEN, Junyong LUO, Gang ZHOU, Peng HE

“…In recent years, deep learning models have achieved remarkable progress in the prediction and classification tasks of artificial intelligence systems.However, most of the current deep learning models are black box, which means it is not conducive to human cognitive reasoning process.Meanwhile, with the continuous breakthroughs of artificial intelligence in the researches and applications, high-performance complex algorithms, models and systems generally lack the transparency and interpretability of decision making.This makes it difficult to apply the technologies in a wide range of fields requiring strict interpretability, such as national defense, medical care and cyber security.Therefore, the interpretability of artificial intelligence should be integrated into these algorithms and systems in the process of knowledge reasoning.By means of carrying out explicit explainable intelligence reasoning based on discrete symbolic representation and combining technologies in different fields, a behavior explanation mechanism can be formed which is an important way for artificial intelligence to realize data perception to intelligence perception.A comprehensive review of explainable knowledge graph reasoning was given.The concepts of explainable artificial intelligence and knowledge reasoning were introduced briefly.The latest research progress of explainable knowledge graph reasoning methods based on the three paradigms of artificial intelligence was introduced.Specifically, the ideas and improvement process of the algorithms in different scenarios of explainable knowledge graph reasoning were explained in detail.Moreover, the future research direction and the prospect of explainable knowledge graph reasoning were discussed.…”
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Offline English Handwritten Character Recognition using Sequential Convolutional Neural Network by Muhammad Iqbal, Muniba Humayun, Raheel Siddiqi, Christopher J. Harrison, Muneeb Abid Malik

“…The main idea of this research paper is to propose a deep learning CNN method to help recognize English characters. …”
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Stock price prediction with attentive temporal convolution-based generative adversarial network by Ying Liu, Xiaohua Huang, Liwei Xiong, Ruyu Chang, Wenjing Wang, Long Chen

“…Furthermore, when compared to the deep learning method GRU, ACTGAN reduced the Mean Absolute Error (MAE) by 51.01%. …”
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Sistem Isyarat Bahasa Indonesia (SIBI) Metode Convolutional Neural Network Sequential secara Real Time by Oky Dwi Nurhayati, Dania Eridani, Muhammad Hafiz Tsalavin

“…Perangkat tambahan yang dikembangkan dalam penelitian ini melibatkan teknologi visi komputer deep learning sehingga menghasilkan tools untuk menerjemahkan bahasa isyarat tangan. …”
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Automated Detection of Galactic Rings from Sloan Digital Sky Survey Images by Linn Abraham, Sheelu Abraham, Ajit K. Kembhavi, N. S. Philip, A. K. Aniyan, Sudhanshu Barway, Harish Kumar

“…Abraham et. al. with deep-learning techniques, we identify a set of 2087 galaxies with bars as well as rings. …”
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Similarity-Based Summarization of Music Files for Support Vector Machines by Jan Jakubik, Halina Kwaśnicka

“…Recent advancements in the area rely on the use of deep learning, which allows researchers to operate on a low-level description of the music. …”
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A large-scale dataset for Chinese historical document recognition and analysis by Yongxin Shi, Dezhi Peng, Yuyi Zhang, Jiahuan Cao, Lianwen Jin

“…Recently, researchers have tried to utilize deep-learning techniques to automate recognition and analysis. …”
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Enhanced ResNet-50 for garbage classification: Feature fusion and depth-separable convolutions. by Lingbo Li, Runpu Wang, Miaojie Zou, Fusen Guo, Yuheng Ren

“…However, existing deep learning-based garbage image classification models generally suffer from low classification accuracy, insufficient robustness, and slow detection speed due to the large number of model parameters. …”
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Current Situation and Developing Trend of Fatigue Life Prediction of Components based on Data-driven by Xiuhua Zhang, Huaiju Liu, Caichao Zhu, Peitang Wei, Shaojie Wu

“…With the rapid development of machine learning，deep learning and other technologies，data-driven model has become a hot topic in the research of components fatigue life prediction of due to the advantages of no need to know detailed failure mechanism and accurate prediction results. …”
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Developing an Hourly Water Level Prediction Model for Small- and Medium-Sized Agricultural Reservoirs Using AutoML: Case Study of Baekhak Reservoir, South Korea by Jeongho Han, Joo Hyun Bae

“…Using these collected data, we compared widely used individual machine learning and deep learning models with the pipeline models generated by TPOT. …”
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IQNet: Image Quality Assessment Guided Just Noticeable Difference Prefiltering for Versatile Video Coding by Yu-Han Sun, Chiang Lo-Hsuan Lee, Tian-Sheuan Chang

“…Our method demonstrates higher perceptual quality and a model size that is an order of magnitude smaller than previous deep learning methods.…”
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A lightweight power quality disturbance recognition model based on CNN and Transformer by ZHANG Bide, QIU Jie, LOU Guangxin, ZHOU Can, LUO Qingqing, LI Tianqian

“…Its lightweight, end-to-end design also results in shorter inference times compared to other deep learning models.…”
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Enhancing Classification Models With Sophisticated Counterfactual Images by Xiang Li, Ren Togo, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama

“…In deep learning, training data, which are mainly from realistic scenarios, often carry certain biases. …”
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DNNobfus: a study on obfuscation-based edge-side model protection framework by SONG Feiyang, ZHAO Xinmiao, YAN Fei, CHENG Binlin, ZHANG Liqiang, YANG Xiaolin, WANG Yang

“…The extensive integration of deep learning models into edge devices has introduced novel security challenges. …”
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Data-Driven Approach for Modeling the Nonlane-Based Mixed Traffic Conditions by Narayana Raju, Shriniwas S. Arkatkar, Said Easa, Gaurang Joshi

“…The results show that a deep learning model and k-NN tend to replicate better-mixed traffic conditions than random forest and regression trees.…”
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