DeepMiRBP: a hybrid model for predicting microRNA-protein interactions based on transfer learning and cosine similarity by Sasan Azizian, Juan Cui

“…The first component employs bidirectional long short-term memory (Bi-LSTM) neural networks to capture sequential dependencies and context within RNA sequences, attention mechanisms to enhance the model’s focus on the most relevant features and transfer learning to apply knowledge gained from a large dataset of RNA-protein binding sites to the specific task of predicting microRNA-protein interactions. …”
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Development of a deep learning model for automated detection of calcium pyrophosphate deposition in hand radiographs by Thomas Hügle, Elisabeth Rosoux, Guillaume Fahrni, Deborah Markham, Tobias Manigold, Fabio Becce

“…CPPD presence was then predicted using a convolutional neural network. We tested seven CPPD models, each with a different combination of sites out of TFCC, MCP-2 and MCP-3. …”
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Prevalence, associated risk factors and satellite imagery analysis in predicting soil-transmitted helminth infection in Nakhon Si Thammarat Province, Thailand by Jarawadee Muenjak, Jutarat Thongrod, Chanakan Choodamdee, Pongphan Pongpanitanont, Manachai Yingklang, Tongjit Thanchomnang, Sakhone Laymanivong, Penchom Janwan

“…Mono-infections predominated, with Trichuris trichiura (5.02%) and hookworm (3.49%) being the most frequent. Mixed infections accounted for 1.25%, primarily co-infections of hookworm with T. trichiura (0.94%) or Strongyloides stercoralis (0.31%). …”
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Short-term and long-term inertia forecasting with low-inertia event prediction in IBR-integrated power systems using a deep learning approach by Santosh Diggikar, Arunkumar Patil, Katkar Siddhant Satyapal, Kunal Samad

“…Accurate inertia forecasting is essential for ensuring grid stability, particularly in systems such as the Great Britain (GB) power system, where inertia levels occasionally fall below critical thresholds. However, most traditional and online estimation techniques provide reactive inertia assessments, limiting their effectiveness for proactive grid management. …”
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Comparing acoustic representations for deep learning-based classification of underwater acoustic signals: A case study on orca (Orcinus orca) vocalizations by Fabio Frazao, Ruth Joy, Michael Dowd

“…This statement is also true for most other ways of representing acoustic information. …”
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A hybrid CNN-BILSTM deep learning framework for signal detection of a massive MIMONOMA system by Mohamed A. Abdelhamed, Mennatalla Samy, Bassem E. Elnaghi, Ahmed Magdy

“…Non-orthogonal multiple access (NOMA) has been proposed as a replacement for orthogonal multiple access (OMA) in 6G networks to reduce latency, improve throughput and increase data rates. However, the most common technique for detecting NOMA in receivers, known as successive interference cancellation (SIC), has limitations in error detection. …”
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Detection and classification of long terminal repeat sequences in plant LTR-retrotransposons and their analysis using explainable machine learning by Jakub Horvath, Pavel Jedlicka, Marie Kratka, Zdenek Kubat, Eduard Kejnovsky, Matej Lexa

“…Previous experimental and sequence studies have provided only limited information about LTR structure and composition, mostly from model systems. To enhance our understanding of these key sequence modules, we focused on the contrasts between LTRs of various retrotransposon families and other genomic regions. …”
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Vibration Signal Analysis for Intelligent Rotating Machinery Diagnosis and Prognosis: A Comprehensive Systematic Literature Review by Ikram Bagri, Karim Tahiry, Aziz Hraiba, Achraf Touil, Ahmed Mousrij

“…In the context of fault detection, support vector machines (SVMs), convolutional neural networks (CNNs), Long Short-Term Memory (LSTM) networks, k-nearest neighbors (KNN), and random forests have been identified as the five most frequently employed algorithms. …”
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Improving timing resolution of BGO for TOF-PET: a comparative analysis with and without deep learning by Francis Loignon-Houle, Nicolaus Kratochwil, Maxime Toussaint, Carsten Lowis, Gerard Ariño-Estrada, Antonio J. Gonzalez, Etiennette Auffray, Roger Lecomte

“…Deep learning, particularly convolutional neural networks (CNNs), can also enhance CTR by training with digitized waveforms. …”
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Comparison of Deep Learning-Based Auto-Segmentation Results on Daily Kilovoltage, Megavoltage, and Cone Beam CT Images in Image-Guided Radiotherapy by Zhixing Wang PhD, Chengyu Shi PhD, Carson Wong BS, Seyi M Oderinde PhD, William T Watkins PhD, Kun Qing PhD, Bo Liu PhD, Terence M Williams MD, PhD, An Liu PhD, Chunhui Han PhD

“…Deep learning auto-segmentation models using a convolutional neural network algorithm were used to generate organs-at-risk contours. …”
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AI in Medical Questionnaires: Innovations, Diagnosis, and Implications by Xuexing Luo, Yiyuan Li, Jing Xu, Zhong Zheng, Fangtian Ying, Guanghui Huang

“…Overall, 24 AI technologies were identified, covering traditional algorithms such as random forest, support vector machine, and k-nearest neighbor, as well as deep learning models such as convolutional neural networks, Bidirectional Encoder Representations From Transformers, and ChatGPT. …”
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Prediction of Alzheimer’s Disease Based on Multi-Modal Domain Adaptation by Binbin Fu, Changsong Shen, Shuzu Liao, Fangxiang Wu, Bo Liao

“…However, the structure and semantics of different modal data are different, and the distribution between different datasets is prone to the problem of domain shift. Most of the existing methods start from the single-modal data and assume that different datasets meet the same distribution, but they fail to fully consider the complementary information between the multi-modal data and fail to effectively solve the problem of domain distribution difference. …”
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Tree Species Detection and Enhancing Semantic Segmentation Using Machine Learning Models with Integrated Multispectral Channels from PlanetScope and Digital Aerial Photogrammetry i... by Arun Gyawali, Mika Aalto, Tapio Ranta

“…For semantic segmentation, the CatBoost model with 20 bands outperformed other models, achieving 85% accuracy, 80% Kappa, and 81% MCC, with CHM, EVI, NIRPlanet, GreenPlanet, NDGI, GNDVI, and NDVI being the most influential variables. These results indicate that a simple boosting model like CatBoost can outperform more complex CNNs for semantic segmentation in young forests.…”
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Automatic Identification of Amharic Text Idiomatic Expressions Using a Deep Learning Approach by Habtamu Hunegnaw Limenih, Abebe Belay Adege, Abrham Yaregal Alene, Habtamu Tariku Demasu, Habtamu Molla Belachew

“…Natural Language Processing (NLP) is a tract of artificial intelligence and linguistics devoted to making computers understand the statements or words written in human languages. Amharic, the most widely spoken language in Ethiopia, uses a lot of idiomatic expressions and proverbs to emphasize the message of the text. …”
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MHRA-MS-3D-ResNet-BiLSTM: A Multi-Head-Residual Attention-Based Multi-Stream Deep Learning Model for Soybean Yield Prediction in the U.S. Using Multi-Source Remote Sensing Data by Mahdiyeh Fathi, Reza Shah-Hosseini, Armin Moghimi, Hossein Arefi

“…Recent advances have highlighted the effectiveness and ability of Machine Learning (ML) models in analyzing Remote Sensing (RS) data for this purpose. However, most of these models do not fully consider multi-source RS data for prediction, as processing these increases complexity and limits their accuracy and generalizability. …”
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Expression Dynamics and Genetic Compensation of Cell Cycle Paralogues in <i>Saccharomyces cerevisiae</i> by Gabriele Schreiber, Facundo Rueda, Florian Renner, Asya Fatima Polat, Philipp Lorenz, Edda Klipp

“…Due to the duplication of the yeast genome during evolution, most of the cyclins are present as a pair of paralogues, which are considered to have similar functions and periods of expression. …”
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Accurate modeling and simulation of the effect of bacterial growth on the pH of culture media using artificial intelligence approaches by Suleiman Ibrahim Mohammad, Hamza Abu Owida, Asokan Vasudevan, Suhas Ballal, Shaker Al-Hasnaawei, Subhashree Ray, Naveen Chandra Talniya, Aashna Sinha, Vatsal Jain, Ahmad Abumalek

“…Sensitivity analysis using Monte Carlo simulations revealed bacterial cell concentration as the most influential factor, followed by time, culture medium type, initial pH, and bacterial type. …”
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Pengembangan Deep Learning untuk Sistem Deteksi Dini Komplikasi Kaki Diabetik Menggunakan Citra Termogram by Medycha Emhandyksa, Indah Soesanti, Rina Susilowati

“…Selain arsitektur deep convolutional neural network model 4, kontribusi penelitian yang didapatkan dari penelitian ini adalah penggunaan variasi ukuran filter 3x3, 2x2, dan 1x1 dengan jumlah convolutional layer yang tetap dan pengurangan jumlah hidden layer pada struktur algoritma mampu menurunkan jumlah parameter model dengan tetap mempertahankan kemampuan deteksi yang tinggi. …”
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Leveraging AI for early cholera detection and response: transforming public health surveillance in Nigeria by Adamu Muhammad Ibrahim, Mohamed Mustaf Ahmed, Shuaibu Saidu Musa, Usman Abubakar Haruna, Mohammed Raihanatu Hamid, Olalekan John Okesanya, Aishat Muhammad Saleh, Don Eliso Lucero-Prisno III

“…Since the first epidemic in 1972, Nigeria has experienced recurrent outbreaks, with the most severe in 1991, resulting in over 7,000 deaths. …”
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Deep Learning-Based Super-Resolution of Remote Sensing Images for Enhanced Groundwater Quality Assessment and Environmental Monitoring in Urban Areas by Peng Shu, Rana Waqar Aslam, Iram Naz, Bushra Ghaffar, Dmitry E. Kucher, Abdul Quddoos, Danish Raza, M. Abdullah-Al-Wadud, Rana Muhammad Zulqarnain

“…Using the super-resolved images alongside traditional water quality parameters (pH, hardness, TDS) analyzed through fuzzy analytic hierarchy process, we calculated the groundwater quality index (GWQI) for 33 areas across four years (2008&#x2013;2020). Results showed most areas achieved &#x201C;Better water&#x201D; quality status by 2020, though two regions (Old City and Anarkali) were classified as &#x201C;Poor water&#x201D; quality. …”
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