Software Defect Prediction Based On Deep Learning Algorithms : A Systematic Literature Review by Akhlas Hasan, Shayma Mohi-Aldeen

“…The top three DL algorithms used in building SDP models and used in predicting software bugs were convolutional neural network (CNN), long-short-term memory (LSTM), and bidirectional LSTM. …”
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Region-Focusing Data Augmentation via Salient Region Activation and Bitplane Recombination for Target Detection by Huan Zhang, Xiaolin Han, Weidong Sun

“…As the performance of a convolutional neural network is logarithmically proportional to the amount of training data, data augmentation has attracted increasing attention in recent years. …”
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Impact of Pretrained Deep Neural Networks for Tomato Leaf Disease Prediction by Mohamed Bouni, Badr Hssina, Khadija Douzi, Samira Douzi

“…This article identifies tomato leaf disease using a deep convolutional neural network (CNN) and transfer learning. …”
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Robot Ground Media Classification Based on Hilbert–Huang Transform and Attention-Based Spatiotemporal Coupled Network by Jixiang Niu, Han Li, Zhenxiong Liu, Wei Liu, Hejun Xu

“…Then the features in the expanded dataset were extracted by the convolutional neural network, long short-term memory, and attention module. …”
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Artificial Neural Networks for Image Processing in Precision Agriculture: A Systematic Literature Review on Mango, Apple, Lemon, and Coffee Crops by Christian Unigarro, Jorge Hernandez, Hector Florez

“…This review identifies Convolutional Neural Networks (CNNs), including commonly employed architectures such as VGG16 and ResNet50, as highly effective, achieving accuracies ranging between 83% and 99%. …”
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Integrating Color and Contour Analysis with Deep Learning for Robust Fire and Smoke Detection by Abror Shavkatovich Buriboev, Akmal Abduvaitov, Heung Seok Jeon

“…This study suggests a unique concatenated convolutional neural network (CNN) model that combines deep learning with hybrid preprocessing methods, such as contour-based algorithms and color characteristics analysis, to provide reliable and accurate fire and smoke detection. …”
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Optimized aspect level sentiment analysis of tweet data using deep learning and rule-based techniques by S. N. Enemuo, O. N. Akande, M. O. Lawrence, I. C. Saidu

“…Towards coming up with a localized sentiment analysis system, this research explored the use of rules and optimized Convolutional Neural Network (CNN) Deep Learning (DL) technique for aspect-level sentiment analysis of users’ tweets. …”
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CSL-SFNet for Cooperative Spectrum Sensing in Cognitive Satellite Network with GEO and LEO Satellites by Kai Yang, Shengbo Hu, Xin Zhang, Tingting Yan, Manqin Zhu

“…Thus, we propose a novel CSS scheme, which uses convolutional neural networks (CNNs), self-attention (SA) modules, long short-term memory networks (LSTMs), and soft fusion networks, called CSL-SFNet. …”
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Analysis of Internet Marketing Forecast Model Based on Parallel K-Means Algorithm by Xiaolei Chen, Sikun Ge

“…Secondly, the weights in the K-means algorithm are mostly only applicable to target detection tasks. …”
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Human Action Recognition Based on The Skeletal Pairwise Dissimilarity by E.E. Surkov, O.S. Seredin, A.V. Kopylov

“…The paper conducts frame-by-frame annotation of activities in the TST Fall Detection v2 database, such as standing, sitting, lying, walking, falling, post-fall lying, grasp, ungrasp. A convolutional neural network based on the ResNetV2 with the SE-block is proposed to solve the activity recognition problem. …”
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Performance Enhancement of EEG Signatures for Person Authentication Using CNN BiLSTM Method by Ashish Ranjan Mishra, Rakesh Kumar, Rajkumar Saini

“…We propose a multiscale convolutional neural network (CNN) and a Bidirectional LSTM (BiLSTM) model called CNN-BiLSTM to extract features and classify raw EEG data. …”
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Evaluation and prediction of coal seam mining mode: Coefficient of Variation-TOPSIS and CNN-NGO methods by Haixiong Li, Fei Wang

“…The weights of the evaluation indicators for coal seam mining were allocated using the coefficient of variation method, followed by a comprehensive evaluation using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). In addition, a Convolutional Neural Network (CNN) regression model was constructed and optimized with the Northern Goshawk Optimization (NGO) algorithm, resulting in a more precise CNN-NGO prediction model. …”
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Prediction of COVID-19 cases by multifactor driven long short-term memory (LSTM) model by Yanwen Shao, Tsz Kin Wan, Kei Hang Katie Chan

“…Under the metric of explained variance scores (EVS), the prediction performances were the most accurate for Germany (0.864), Italy (0.860) and the United States (0.766). …”
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Semantic ECG hash similarity graph by Yixian Fang, Shilin Zhang, Yuwei Ren

“…Abstract Graph-based methods have made significant progress in addressing the dependent correlations among ECG time series variables. However, most existing graph structures primarily focus on local similarity while overlooking global semantic correlation. …”
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Advancing Hate Speech Detection in Indonesian Language Using Graph Neural Networks and TF-IDF by Syaikha Amirah Zikrina, Fitriyani

“…Most of the hate speech and abusive content on social media, particularly in the Indonesian language, presents significant challenges for content moderation systems. …”
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Integrating the CNN Model with the Web for Indonesian Sign Language (BISINDO) Recognition by Enisda Libra Kelana, Muhammad Riko Anshori Prasetya, Mambang ., Muhammad Zulfadhilah

“…Effective communication is challenging for deaf individuals in Indonesia, most of whom use Indonesian Sign Language (BISINDO). …”
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Comparison of CNN Architectures for Pre-Cancerous Cervical Lesion Classification Based on Colposopy Images Using IARC and AnnoCerv Datasets by Sigit Prasetyo Noprianto, Siti Nurmaini, Dian Palupi Rini

“…This research investigates and compares the effectiveness of various Convolutional Neural Network (CNN) models in classifying colposcopic images according to the severity of cervical lesions. …”
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Lighting System Control for Cataract Maturity Imaging Device by Katon Bagus Setiawan, I Komang Somawirata, Irmalia Suryani Faradisa

“…In cataract detection, artificial intelligence-based methods such as Convolutional Neural Networks (CNN) have proven very effective in detecting cataracts. …”
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MRSNet: Multi-Resolution Scale Feature Fusion-Based Universal Density Counting Network by Yi Zhang, Wei Song, Mingyue Shao, Xiangchun Liu

“…The current methods, whether CNNs with fixed convolutional kernel sizes or Transformers with fixed attention sizes, struggle to handle such variability effectively. …”
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Pilot Maneuvering Performance Analysis and Evaluation with Deep Learning by Shiwen Zhang, Zhimei Huo, Yanjin Sun, Fujuan Li, Bo Jia

“…To study the relationship between QAR data and pilot performance, this paper puts forward one-dimensional convolutional neural networks (1-D CNN) which consider QAR metrics in an integrated manner. …”
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