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

“…Deep learning techniques are widely used in SDP, which can produce accurate and exceptional results in different fields.The study aims to systematically review models, techniques, datasets, and performance evaluation metrics to gain a complete understanding of current methodologies related to SDP, and the use of DL in software defect prediction research between 2019 and 2024. …”
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Expanding the Reach of Structured EHR Data with Clinical Notes by Seda Bilaloglu, Vincent J Major, Himanshu Grover, Isabel Metzger, Yindalon Aphinyanaphongs

“…Patients of both groups do die in the following months suggesting the two approaches identify different patient phenotypes which supplement one another. …”
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SiNC: Saliency-injected neural codes for representation and efficient retrieval of medical radiographs. by Jamil Ahmad, Muhammad Sajjad, Irfan Mehmood, Sung Wook Baik

“…Neuronal activation features termed as neural codes from different CNN layers are comprehensively studied to identify most appropriate features for representing radiographs. …”
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Multi-task deep learning framework for enhancing Mayo endoscopic score classification in ulcerative colitis by Jaehyuk Lee, Eunchan Kim

“…Future studies should explore integrating multiple convolutional neural network-based models to further boost classification accuracy.…”
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Bridging neuroscience and AI: a survey on large language models for neurological signal interpretation by Sreejith Chandrasekharan, Jisu Elsa Jacob

“…Traditional deep neural networks, such as convolutional networks, sequence-to-sequence networks, and hybrids of such neural networks were proven to be effective for a wide range of neurological disease classifications. …”
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Automatic serving method of volleyball training robot based on improved YOLOv5 and improved Hough transform by Tao Sun, Xiaolong He, Jiajun Zhang

“…Abstract Volleyball training robots play an important role in modern sports training, and their automatic serving technology can simulate serving modes in different scenarios, providing athletes with diverse training programs. …”
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Wind energy system fault classification using deep CNN and improved PSO‐tuned extreme gradient boosting by Chun‐Yao Lee, Edu Daryl C. Maceren

“…The effectiveness of the proposed method is demonstrated through validation conducted on a different imbalanced dataset showing superior performance metrics in terms of accuracy. …”
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Predictive Modeling of Dairy Sales Using Multi-Perspective Fusion Bi-LSTM Integrated with Universal Scale CNN: Insights from the Dairy Supply Chain by Naveen D. Chandavarkar, Dr. Soumya S

“…The performance of proposed Multi-Perspective Fusion Bi-LSTM with Universal Scale CNN is evaluated by different performance metrics which includes RMSE (Root Mean Squared Error), MAE (Mean Absolute Error), MSE (Mean Square Error) and R2 (R Square).   …”
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Diagnosis Retinal Disease by using Deep Learning Models by attallh salih, Manar Kashmoola

“…The proposed model consists of three different convolutional neural network (CNN) models to be used in this approach and compare the results of each one with others. …”
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EEG Depression Recognition Based on Multi-domain Features Combined with CBAM Model by CHEN Yu, HU Xiuxiu, WANG Sheng

“…Firstly, the continuous wavelet transform (CWT) is used to extract time-frequency domain features, and combined with the spatial information of EEG electrodes to form a 2D feature image, which jointly retains the spatial, time and frequency information of EEG; then the convolutional neural network (CNN) is used) to extract spatial and frequency domain features, and then input bidirectional long and short-term memory ( BiLSTM) to capture time information; finally combined with attention mechanism (AM) , different weights are assigned to the multi-domain features extracted from the network, enabling the selection of more representative depressive features, thereby improving the accuracy of identifying depression. …”
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Single-level Discrete Two Dimensional Wavelet Transform Based Multiscale Deep Learning Framework for Two-Wheeler Helmet Detection by Amrutha Annadurai, Manas Ranjan Prusty, Trilok Nath Pandey, Subhra Rani Patra

“…In particular, four different modes are used for segmenting a single image namely approximation, horizontal detail, vertical detail and diagonal detail. …”
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Multimodal fusion based few-shot network intrusion detection system by Congyuan Xu, Yong Zhan, Zhiqiang Wang, Jun Yang

“…Existing few-shot learning methods, while reducing reliance on large datasets, mostly handle single-modality data and fail to fully exploit complementary information across different modalities, limiting detection performance. …”
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Artificial Intelligence for Earthquake Prediction: A Preliminary System Based on Periodically Trained Neural Networks Using Ionospheric Anomalies by Sergio Baselga

“…The use of three-dimensional data matrices, having spatiotemporal information to feed a convolutional neural network, is proposed in this contribution. …”
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RETRACTED PAPER: Enhancing 3D human pose estimation through multi-feature fusion by Xianlei GE, Vladimir MARIANO

“…The proposed model utilizes convolutional kernels of different sizes to extract feature maps with diverse resolutions and dimensions. …”
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Brain Representation in Conscious and Unconscious Vision by Ning Mei, David Soto

“…Moreover, this pattern of results generalised when the models were trained and tested with different participants. Remarkably, these observations results held even when the analysis was restricted to observers that showed null perceptual sensitivity. …”
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UMEDNet: a multimodal approach for emotion detection in the Urdu language by Adil Majeed, Hasan Mujtaba

“…In the end, we analyzed the impact of UMEDNet and found that our model integrates data on different modalities and leads to better performance.…”
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Intelligent Model for Brain Tumor Identification Using Deep Learning by Abdul Hannan Khan, Sagheer Abbas, Muhammad Adnan Khan, Umer Farooq, Wasim Ahmad Khan, Shahan Yamin Siddiqui, Aiesha Ahmad

“…The processing of medical images plays a crucial role in assisting humans in identifying different diseases. The classification of brain tumors is a significant part that depends on the expertise and knowledge of the physician. …”
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Multiple sclerosis diagnosis with brain MRI retrieval: A deep learning approach by R.M. Haggag, Eman M. Ali, M.E. Khalifa, Mohamed Taha

“…We experiment with Nine different distance metrics to measure query and database image similarity. …”
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Ambiguous facial expression detection for Autism Screening using enhanced YOLOv7-tiny model by Akhil Kumar, Ambrish Kumar, Dushantha Nalin K. Jayakody

“…Children with autism spectrum disorder show ambiguous facial expressions which are different from the facial attributes of normal children. …”
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Development of Ai-Based Crop Quality Grading Systems using Image Recognition by Dusi Prerna, Sharma Pooja

“…The data given is 10,000 labeled images across five quality grades from various crop types under different lighting conditions for robustness. The system is then evaluated and doesn't perform well, showing that Transfer Learning outperforms other baselines with 95.8% of accuracy, whereas CNN, Random Forest, and SVM get 92.1%, 87.4% and 85.9% respectively. …”
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