Image-Based Face Recognition Techniques Used in Disease Detection Approaches: A Survey by Ghalib Ahmed Salman, Inas Jawad Kadhim, Ahmed Husham Al-Badri

“…FR applications relying on Artificial Neural Network (ANN) techniques have recorded higher accuracy rates in diagnosing facial diseases. …”
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A Unified Approach to Voice Classification: Leveraging Spectrograms, Mel Spectrograms, and Statistical Features by Muhammad Talha, Huma Ghafoor, Seung Yeob Nam

“…This study presents a multi-input neural network architecture for voice classification that integrates two parallel convolutional neural networks (CNNs) for spectrogram and Mel spectrogram images, along with a fully connected dense network for six handpicked numerical statistical features from time domain signal. …”
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International Natural Uranium Price Prediction Based on TF-CNN-BiLSTM Model by YANG Jingzhe, XUE Xiaogang

“…The dataset was split into training (80%) and testing (20%) sets to evaluate the model’s performance. The proposed TF-CNN-BiLSTM model was designed with three core modules: 1) a Transformer encoder to capture long-term global dependencies via self-attention mechanisms; 2) a 1D CNN layer to extract localized price fluctuations; 3) a BiLSTM network to model bidirectional temporal relationships. …”

Pears Internal Quality Inspection Based on X-Ray Imaging and Multi-Criteria Decision Fusion Model by Zeqing Yang, Jiahui Zhang, Zhimeng Li, Ning Hu, Zhengpan Qi

“…The proposed method combines manual feature-based classifiers, including Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG), with a deep convolutional neural network (DCNN) model within an MCD-based fusion framework. …”
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Epileptic seizure detection in EEG signals via an enhanced hybrid CNN with an integrated attention mechanism by Sakorn Mekruksavanich, Wikanda Phaphan, Anuchit Jitpattanakul

“…Despite advances in machine learning and deep learning, capturing the intricate spatial and temporal patterns in EEG data remains challenging. This study introduced a novel deep learning framework combining a convolutional neural network (CNN), bidirectional gated recurrent unit (BiGRU), and convolutional block attention module (CBAM). …”
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A Machine Learning Approach for User Behavior Analysis in Developing Websites by Aeesha Shaheen

“…After training, the accuracy of network assesses using test data, and evaluate the effectiveness of forecasting. …”
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State of health prediction of lithium-ion batteries in charging chambers based on multi-modal deep learning by ZHAO Yinghua, CHEN Anbi, ZHANG Zengyu, LI Wenzhong, HAN Yu

“…A multi-modal deep learning network model, TCN-BiLSTM-Transformer, was constructed, leveraging a multi-level feature extraction mechanism for efficient processing of temporal signals. …”
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Predictive modeling and optimization of noise emissions in a palm oil methyl ester-fueled diesel engine using response surface methodology and artificial neural network integrated... by J.M. Zikri, M.S.M. Sani, M.F.F.A. Rashid, J. Muriban, G.S. Prayogo

“…This research examines the predictive performance of two modeling techniques—Response Surface Methodology (RSM) and an Artificial Neural Network enhanced by a Genetic Algorithm (ANN-GA)—in relation to noise emission levels from a single-cylinder diesel engine running on palm oil methyl ester (POME). …”
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A map of human protein interactions derived from co‐expression of human mRNAs and their orthologs by Arun K Ramani, Zhihua Li, G Traver Hart, Mark W Carlson, Daniel R Boutz, Edward M Marcotte

“…Abstract The human protein interaction network will offer global insights into the molecular organization of cells and provide a framework for modeling human disease, but the network's large scale demands new approaches. …”
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Scene Semantic Recognition Based on Modified Fuzzy C-Mean and Maximum Entropy Using Object-to-Object Relations by Ahmad Jalal, Abrar Ahmed, Adnan Ahmed Rafique, Kibum Kim

“…An Artificial Neural Network recognizes the multiple objects using the different patterns of objects. …”
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Adversarial denoising of EEG signals: a comparative analysis of standard GAN and WGAN-GP approaches by Imad Eddine Tibermacine, Samuele Russo, Francesco Citeroni, Giuseppe Mancini, Abdelaziz Rabehi, Amal H. Alharbi, El-Sayed M. El-kenawy, El-Sayed M. El-kenawy, Christian Napoli, Christian Napoli, Christian Napoli

“…The results further suggest that GAN-based frameworks can be easily scaled to next-generation wireless networks and complex electrophysiological datasets, offering robust and dynamic solutions to long-standing challenges in EEG denoising.…”
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Computer-aided diagnosis of lung nodule classification between benign nodule, primary lung cancer, and metastatic lung cancer at different image size using deep convolutional neura... by Mizuho Nishio, Osamu Sugiyama, Masahiro Yakami, Syoko Ueno, Takeshi Kubo, Tomohiro Kuroda, Kaori Togashi

“…For the DCNN method, CADx was evaluated using the VGG-16 convolutional neural network with and without transfer learning, and hyperparameter optimization of the DCNN method was performed by random search. …”
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Smart intrusion detection model to identify unknown attacks for improved road safety and management by Faisal Alshammari, Abdullah Alsaleh

“…ACIDS integrates convolutional neural networks (CNN) for hierarchical feature extraction, the synthetic minority over-sampling technique (SMOTE) to address class imbalance and an open-set classification framework to detect novel attack patterns. …”
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Can attention-deficit/hyperactivity disorder be considered a form of cerebellar dysfunction? by Valeria Isaac, Vladimir Lopez, Maria Josefina Escobar

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Forecasting temperature and rainfall using deep learning for the challenging climates of Northern India by Syed Nisar Hussain Bukhari, Kingsley A. Ogudo

“…Accurate temperature and rainfall (T&R) forecasting is vital for the climate-sensitive regions of Northern India, particularly Jammu, Kashmir, and Ladakh, where volatile weather patterns significantly affect livelihoods, socio-economic development, and disaster management efforts. …”
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Estimation of Fractal Dimensions and Classification of Plant Disease with Complex Backgrounds by Muhammad Hamza Tariq, Haseeb Sultan, Rehan Akram, Seung Gu Kim, Jung Soo Kim, Muhammad Usman, Hafiz Ali Hamza Gondal, Juwon Seo, Yong Ho Lee, Kang Ryoung Park

“…Furthermore, to improve the capability of our proposed system, we performed fractal dimension estimation to evaluate the structural complexity of healthy and diseased leaf patterns. …”
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TFTformer: A novel transformer based model for short-term load forecasting by Ahmad Ahmad, Xun Xiao, Huadong Mo, Daoyi Dong

“…A linear transformation layer post embedding improves feature representation, aligning and standardising features across sequences for improved pattern recognition. Additionally, a Temporal Convolutional Network is integrated within the Transformer’s encoder, employing causal convolutions and dilation to adapt to the sequential nature of data with an expanded receptive field. …”
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Continuous Glucose Monitoring (CGM) use in patients with diabetes mellitus type 1 is associated with higher healthcare costs, increased hospitalizations, and social disparities in... by Ameer Khowaja, Lina Adil, Teigan Dwyer, Tyson Barrett, Jamil Alkhaddo

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Enhanced detection of accounting fraud using a CNN-LSTM-Attention model optimized by Sparrow search by Peifeng Wu, Yaqiang Chen

“…Experimental results demonstrate that the proposed model outperforms conventional methods across various evaluation metrics, offering superior accuracy and robustness in recognizing fraudulent patterns in corporate accounting data.…”
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Navigating the road ahead: using concept mapping to assess Clinical and Translational Science Award (CTSA) program goals by Cathleen Kane, William Trochim, Haim Bar, Andie Vaught, Heather Baker, Munziba Khan, Robin Wagner, Kristi Holmes, Keith Herzog, Jamie Mihoko Doyle

“…The results also revealed a pattern where long-term impacts were ranked among the highest in importance but among the lowest in feasibility, particularly for measures tied to the Translational Science Benefits Model (TSBM), a new evaluation framework gaining popularity across the CTSA. …”
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