Deciphering the Role of Artificial Intelligence in Medical Sciences: An Update by Siddhant Khanna, Merajul Haque Siddiqui, Shalini Bhushan, Rahul Saxena

“…While humans are still faster at some jobs than computers, the accuracy of modern computer algorithms has lately surpassed that of human experts in the medical sciences. …”
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Persian SMS Spam Detection using Machine Learning and Deep Learning Techniques by Roya Khorashadizade, Somayyeh Jafarali Jassbi, Alireza Yari

“…This work sought to develop an effective strategy for identifying SMS spam through utilizing combining machine learning classification algorithms together with deep learning models. After applying preprocessing on our gathered dataset, the suggested technique applies two convolutional neural network layers, the first of which being an LSTM layer, and the second one which is a fully connected layer to extract the data characteristics, thereby implementing the suggested deep learning approach. …”
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Detection and Analysis of Burned Areas with Sentinel-2 MSI and Landsat-8 OLI: Çanakkale / Gelibolu Forest Fire by Beyza Yılmaz, Mehveş Demirel, Filiz Bektaş Balçık

“…Remote sensing technologies and various algorithms are used to determine and analyze the burned forest areas. …”
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Hardware Efficient Speech Enhancement With Noise Aware Multi-Target Deep Learning by Salinna Abdullah, Majid Zamani, Andreas Demosthenous

“…When processing NOISEX-92 noises, the system achieved an average short-time objective intelligibility (STOI) and perceptual evaluation of speech quality (PESQ) scores of 0.81 and 1.62, respectively, outperforming SE algorithms trained with only a single learning target. …”
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A Comparative Study of Theoretical Graph Models for Characterizing Structural Networks of Human Brain by Xiaojin Li, Xintao Hu, Changfeng Jin, Junwei Han, Tianming Liu, Lei Guo, Wei Hao, Lingjiang Li

“…Afterwards, the global and local graph properties of the constructed structural brain networks are measured using the state-of-the-art graph analysis algorithms and tools and are further compared with seven popular theoretical graph models. …”
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A novel knowledge distillation framework for enhancing small object detection in blurry environments with unmanned aerial vehicle-assisted images by Sayed Jobaer, Xue-song Tang, Yihong Zhang, Gaojian Li, Foysal Ahmed

“…Using image deblurring before object detection is an option, but it demands significant computing power and relies heavily on the accuracy of the deblurring algorithms. Another common issue is the suitable dataset for the specific problem. …”
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Assessment of Rainfall Frequencies from Global Precipitation Datasets by Xueyi Yin, Ziyang Zhang, Zhi Lin, Jun Yin

“…These differences across most regions of the world should be interpreted as the inherent biases associated with the model structure or algorithms used for deriving precipitation data and cannot be reduced simply by increasing the data resolutions. …”
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MLAS: Machine Learning-Based Approach for Predicting Abiotic Stress-Responsive Genes in Chinese Cabbage by Xiong You, Yiting Shu, Xingcheng Ni, Hengmin Lv, Jian Luo, Jianping Tao, Guanghui Bai, Shusu Feng

“…For the selected feature set, six distinct machine learning binary classification algorithms were employed. The results demonstrate that the constructed models can effectively predict SRGs associated with the four types of abiotic stresses, with the area under the receiver operating characteristic curve (auROC) for the models being 81.42%, 87.92%, 80.85%, and 88.87%, respectively. …”
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Deep learning in microbiome analysis: a comprehensive review of neural network models by Piotr Przymus, Krzysztof Rykaczewski, Adrián Martín-Segura, Jaak Truu, Enrique Carrillo De Santa Pau, Mikhail Kolev, Mikhail Kolev, Irina Naskinova, Aleksandra Gruca, Alexia Sampri, Alexia Sampri, Marcus Frohme, Alina Nechyporenko, Alina Nechyporenko

“…These computational techniques have become essential for addressing the inherent complexity and high-dimensionality of microbiome data, which consist of different types of omics datasets. Deep learning algorithms have shown remarkable capabilities in pattern recognition, feature extraction, and predictive modeling, enabling researchers to uncover hidden relationships within microbial ecosystems. …”
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From Data to Decisions: A Smart IoT and Cloud Approach to Environmental Monitoring by Guerbaoui Mohammed, El Faiz Samira, Ed-Dahhak Abdelali, Lachhab Abdeslam, Benhala Bachir, Bakziz Zakaria, Ichou Ismail, Selmani Abdelouahed

“…Future enhancements could include the integration of additional sensors and the application of machine learning algorithms for predictive analytics. Overall, the project demonstrates the potential of IoT and data analytics in addressing real-world challenges related to environmental monitoring.…”
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Efficient guided inpainting of larger hole missing images based on hierarchical decoding network by Xiucheng Dong, Yaling Ju, Dangcheng Zhang, Bing Hou, Jinqing He

“…Abstract When dealing with images containing large hole-missing regions, deep learning-based image inpainting algorithms often face challenges such as local structural distortions and blurriness. …”
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Tongue Color Analysis and Diseases Detection Based on a Computer Vision System by Abdulghafor Khudhaer Abdullah, Saleem Lateef Mohammed, Ali Al-Naji, Mohammed Sameer Alsabah

“…Quantitative diagnosis of the tongue requires some devices, including image acquisition devices such as cameras, light sources, filters, color checkers, image analysis and processing devices through the application of some algorithms or image processing and color correction software, as well as a computer. …”
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Graph Deep Active Learning Framework for Data Deduplication by Huan Cao, Shengdong Du, Jie Hu, Yan Yang, Shi-Jinn Horng, Tianrui Li

“…To fill this research gap, we propose a novel graph deep active learning framework for data deduplication, which is based on similarity algorithms combined with the bidirectional encoder representations from transformers (BERT) model to extract the deep similarity features of multi-source data records, and first introduce the graph active learning strategy to build a clean graph to filter the data that needs to be labeled, which is used to delete the duplicate data that retain the most information. …”
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Evaluation of Object-Based and Pixel-Based Technique for Extracting Snow Cover Surface Using Landsat 8 Satellite Images (Case Study Damavand Mountain Range) by Behnam Sedaghat, Ali Javadzade khiavi, Babak Naeim, Erfan Khajavi, Amir Reza Taghavi Khanghah

“…The SAM classification method, a pixel-based technique, and object-oriented classification methods, along with NDSI, NDVI, and LST algorithms, were utilized to process the images. Thematic maps were derived from each classification, and their overall accuracy was evaluated in the post-processing stage. …”
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Resilience of Machine Learning Models in Anxiety Detection: Assessing the Impact of Gaussian Noise on Wearable Sensors by Abdulrahman Alkurdi, Jean Clore, Richard Sowers, Elizabeth T. Hsiao-Wecksler, Manuel E. Hernandez

“…This analysis highlights the need for more robust algorithms capable of maintaining accuracy in noisy, real-world environments and emphasizes the importance of considering environmental factors in the development of wearable anxiety detection systems.…”
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Improving Industrial Quality Control: A Transfer Learning Approach to Surface Defect Detection by Ângela Semitela, Miguel Pereira, António Completo, Nuno Lau, José P. Santos

“…Three decision-making algorithms were tested for implementation: a new model built and trained from scratch and transfer learning of pre-trained networks (ResNet-50 and Inception V3). …”
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Machine learning prediction of obesity-associated gut microbiota: identifying Bifidobacterium pseudocatenulatum as a potential therapeutic target by Hao Wu, Yuan Li, Yuxuan Jiang, Xinran Li, Shenglan Wang, Changle Zhao, Changle Zhao, Ximiao Yang, Baocheng Chang, Juhong Yang, Juhong Yang, Jianjun Qiao, Jianjun Qiao

“…Utilizing machine learning (ML) algorithms to predict obesity-associated gut microbiota and validating their efficacy with specific bacterial strains could significantly enhance obesity management strategies.MethodsWe leveraged gut microbiome data from 1,563 healthy individuals and 2,043 overweight patients sourced from the GMrepo database. …”
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Cancer phylogenetic tree inference at scale from 1000s of single cell genomes by Salehi, Sohrab, Dorri, Fatemeh, Chern, Kevin, Kabeer, Farhia, Rusk, Nicole, Funnell, Tyler, Williams, Marc J., Lai, Daniel, Andronescu, Mirela, Campbell, Kieran R., McPherson, Andrew, Aparicio, Samuel, Roth, Andrew, Shah, Sohrab P., Bouchard-Côté, Alexandre

“…The sitka transformation allows us to design novel scalable Markov chain Monte Carlo (MCMC) algorithms. Moreover, we introduce a novel point mutation calling method that incorporates the CN data and the underlying phylogenetic tree to overcome the low per-cell coverage of scWGS. …”
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A New Dual Steering System in a Compact Tractor by Giorgio Paolo Massarotti, German Filippini, Gustavo Raush Alviach, Pedro Javier Gamez-Montero, Esteban Codina Macia

“…The goal of this work is to provide guidance on design methods and, through the Bond Graph-3D model, suggest the best control algorithms to minimize steering noise and enhance driving comfort. …”
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GFTT: Geographical Feature Tokenization Transformer for SAR-to-Optical Image Translation by Hongbo Liang, Xuezhi Yang, Xiangyu Yang, Jinjin Luo, Jiajia Zhu

“…Through qualitative and quantitative experimental evaluations, we verify the reliability of the proposed GIT that aligns with authentic expressions of the optical observation scenario, and indicates the superiority of GFTT in contrast to the state-of-the-art algorithms.…”
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