Optimisation of the adaptive neuro-fuzzy inference system for adjusting low-cost sensors PM concentrations by Martina Casari, Piotr A. Kowalski, Laura Po

“…Driven by the urgent necessity for accurate environmental data in urban settings, this research leverages the Adaptive Neuro-Fuzzy Inference System (ANFIS) as a machine learning-based approach to refine SPS30 low-cost sensor data influenced by hygroscopicity in Turin, Italy. …”
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Navigating Ethical Dilemmas Of Generative AI In Medical Writing by Qurrat Ulain Hamdan, Waleed Umar, Mahnoor Hasan

“…Generative AI in Medical Writing Generative AI tools or “chatbots” combine the adaptive learning capabilities of deep learning algorithms and natural language processing, resulting in a virtual assistant or aide that is capable of answering queries, following commands, and improving its responses according to the vast data available on the Internet in addition to user responses.3 This has allowed the accomplishment of various complex tasks within seconds that would otherwise require hours of trial and error. …”
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ANALISIS PROSES BERPIKIR KOMPUTASI SISWA PADA MATERI BARISAN ARITMETIKA BERDASARKAN KECERDASAN MAJEMUK by Rosevita Melati, Toto Nusantara, Mochammad Hafiizh

“…Visual-spatial intelligence students excelled at pattern detection throughout the algorithmic thinking level. …”
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Proteomic analysis of blood plasma as a tool for personalized diagnosis of lung adenocarcinoma by D. N. Korobkov, A. S. Kononikhin, S. D. Semenov, H. L. Kordzaya, A. G. Brzhozovskiy, A. E. Bugrova, E. Yu. Vasilieva, D. Yu. Kanner, E. N. Nikolaev, A. A. Komissarov

“…The study included 30 healthy donors and 30 patients with diagnosed LAC. using a combination of liquid chromatography and tandem mass spectrometry in combination with the method of multiple reactions monitoring, we analyzed the representation of a wide range of proteins in the blood plasma of the study participants. The data obtained were analyzed using modern methods of biological statistics, including machine learning algorithms.Results. …”
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Deep Multi-Modal Skin-Imaging-Based Information-Switching Network for Skin Lesion Recognition by Yingzhe Yu, Huiqiong Jia, Li Zhang, Suling Xu, Xiaoxia Zhu, Jiucun Wang, Fangfang Wang, Lianyi Han, Haoqiang Jiang, Qiongyan Zhou, Chao Xin

“…The diagnostic potential of recent multi-modal skin lesion detection algorithms is limited because they ignore dynamic interactions and information sharing across modalities at various feature scales. …”
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Evaluating anti-VEGF responses in diabetic macular edema: A systematic review with AI-powered treatment insights by S Tamilselvi, M Suchetha, Dhanashree Ratra, Janani Surya, S Preethi, Rajiv Raman

“…According to a review of 50 relevant papers published between 2016 and 2023, the algorithms achieved an average automated sensitivity of 74% (95% CI: 0.55–0.92) in detecting treatment responses.…”
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Study on Rapid Inversion Method for Chlorophyll Content in Ginseng Leaves Based on Reflectance Spectroscopy by Jinyu Wang, Jin Yang, Jiaqi Chen, Shulong Feng, Zitong Zhao, Mingjia Wang, Nan Song, Wei Zhang, Ci Sun

“…To address the problem that the existing means of detecting chlorophyll in ginseng leaves are time-consuming and disruptive and cannot meet the demand for rapid detection of chlorophyll in ginseng leaves, this study firstly establishes a variety of prediction models for chlorophyll in ginseng leaves based on the hyperspectral reflectance data and the vegetation index, respectively, and determines the strengths and weaknesses of the models by comparing the RMSE and the MAE of the test sets; Secondly, the analytical model construction process used for ginseng leaf chlorophyll content prediction was obtained through comparison and summary, and a fast and non-destructive ginseng leaf chlorophyll prediction method based on hyperspectral imaging technology and combining vegetation indices with machine learning algorithms was proposed; The experimental results showed that the final VI-SPA-RFR ginseng leaf chlorophyll prediction model had the best prediction performance, which had an RMSE of 1.1568 and an MAE of 0.9936 in the test set. …”
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“Diwan”: Constructing the Largest Annotated Corpus for Arabic Poetry by Badriyya B. Al-Onazi, Wadee A. Nashir, Asma A. Al-Shargabi

“…By leveraging intelligent annotation algorithms, Diwan serves as a foundational resource and benchmark dataset for advancing research in fields such as automatic poetry generation, metrical analysis, thematic classification, and plagiarism detection. …”
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Rapid identification of foodborne pathogenic bacteria using hyperspectral imaging combined with convolutional neural networks(高光谱结合卷积神经网络对食源性致病菌的快速识别)... by 周贯旭(ZHOU Guanxu), 姜红(JIANG Hong), 徐雪芳(XU Xuefang)

“…It adopts hyperspectral analysis to detect Shigella, Salmonella, Clostridium perfringens, and Streptococcus suis, and collects hyperspectral data. …”
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Predicting Mesothelioma Using Artificial Intelligence: A Scoping Review of Common Models and Applications by Malihe Ram MS, Mohammad Reza Afrash PhD, Khadijeh Moulaei PhD, Erfan Esmaeeli, Mohadeseh Sadat Khorashadizadeh, Ali Garavand PhD, Parastoo Amiri PhD, Azam Sabahi PhD

“…Conclusion Artificial intelligence, particularly machine learning models such as neural networks, decision trees, support vector machines, and random forests, holds promise in predicting and managing mesothelioma, potentially enhancing early detection and improving patient outcomes.…”
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Stellar Variability toward the Galactic Open Cluster NGC 7209 by Dorothy M. Mwanzia, Sneh Lata, W. P. Chen, Soumen Mondal, Geoffrey Okeng’o, John Buers, Samrat Ghosh, Athul Dileep, Arjav Jain, Alisher S. Hojaev, Santosh Joshi

“…Using the machine learning algorithm HDBSCAN technique and Gaia DR3 data, we identified 246 cluster members with membership probability greater than 60%, among which 17 were identified in our study as variable stars. …”
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The State of Artificial Intelligence and its Prospects in Pakistan's Medical Sector by Rohail Akhtar Habib, Yumna Sattar Khan

“…Predictive analytics using machine learning algorithms has become more popular in Pakistani healthcare. …”
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HI4HC and AAAAD: Exploring a hierarchical method and dataset using hybrid intelligence for remote sensing scene captioning by Jiaxin Ren, Wanzeng Liu, Jun Chen, Shunxi Yin

“…To address these shortcomings, we propose HI4HC (hybrid intelligence for remote sensing scene hierarchical captioning), a novel method that combines deep learning algorithms with expert knowledge to generate hierarchical captions for remote sensing scenes. …”
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Quantifying leaf symptoms of sorghum charcoal rot in images of field‐grown plants using deep neural networks by Emmanuel M. Gonzalez, Ariyan Zarei, Sebastian Calleja, Clay Christenson, Bruno Rozzi, Jeffrey Demieville, Jiahuai Hu, Andrea L. Eveland, Brian Dilkes, Kobus Barnard, Eric Lyons, Duke Pauli

“…The objective of this work was to implement various machine learning algorithms to evaluate their ability to accurately detect and quantify CRS in red‐green‐blue images of sorghum plants exhibiting symptoms of infection. …”
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Intelligent SDN to enhance security in IoT networks by Safi Ibrahim, Aya M. Youssef, Mahmoud Shoman, Sanaa Taha

“…This study employs multiclassification algorithms to precisely detect and categorise diverse security threats in SDN. …”
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The Effect of Hearing Aid Amplification on Gait Parameters: A Pilot Study Using Ear-Worn Motion Sensors by Ann-Kristin Seifer, Arne Küderle, Kaja Strobel, Ronny Hannemann, Björn M. Eskofier

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Crop classification with deep convolutional neural network based on crop feature by Mohamad Reza Gili, Davoud Ashourloo, Hosein Aghighi, Ali Akbar Matkan, Alireza SHakiba

“…In the next step, the functions that were developed as phenological features for crops were applied on the time series of the bands, and for each crop, a feature channel was obtained as the special feature of that crop. Then the algorithm was implemented using these feature channels in the test area and the overall accuracy was upgraded to 86% and the kappa coefficient to 0.82 compared to which indicated a significant improvement in the results compared to the previous case.Conclusion:The deep convolutional neural network is very sensitive to the type of input channels for detecting agricultural crops and selecting the channels with suitable tempo-spectral characteristics for different types of crops, has a great impact on the accuracy of network training and can reduce the loss of training network and increase its efficiency in the classification of various crops.…”
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Emerging Diagnostic Approaches for Musculoskeletal Disorders: Advances in Imaging, Biomarkers, and Clinical Assessment by Rahul Kumar, Kiran Marla, Kyle Sporn, Phani Paladugu, Akshay Khanna, Chirag Gowda, Alex Ngo, Ethan Waisberg, Ram Jagadeesan, Alireza Tavakkoli

“…Artificial intelligence and machine learning algorithms now automate image interpretation, predict clinical outcomes, and enhance clinical decision support, complementing conventional clinical evaluations. …”
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Advances in the application of nomograms for patients with gastric cancer associated with peritoneal metastasis by Shiyang Jin, Zeshen Wang, Qiancheng Wang, Zhenglong Li, Xirui Liu, Kuan Wang

“…Imaging-based models leverage CT radiomics and deep learning algorithms to detect occult PM, with Huang et al.’s deep learning model attaining an AUC of 0.900. …”
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Deeper insight into speech characteristics of patients at ultra-high risk using classification and explainability models by Deok-Hee Kim-Dufor, Michel Walter, Marie-Odile Krebs, Yannis Haralambous, Philippe Lenca, Christophe Lemey, Christophe Lemey

“…Different language features analyzed using natural language processing and machine learning have been reported to differentiate patients at ultra-high risk for psychosis. …”
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