Identification of Plasma Proteins Associated with Alzheimer's Disease Using Feature Selection Techniques and Machine Learning Algorithms by Zakaria Mokadem, Mohamed Djerioui, Bilal Attallah, Youcef Brik

“…This study aims to use computational algorithms to explore the relationship between plasma proteins and AD progression by identifying a panel of plasma proteins that can serve as biomarkers for tracking and diagnosing AD. …”
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Dataset of Centella Asiatica leaves for quality assessment and machine learning applicationsMendeley Data by Rohini Jadhav, Mayuri Molawade, Amol Bhosle, Yogesh Suryawanshi, Kailas Patil, Prawit Chumchu

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Explainable Machine Learning Models for Colorectal Cancer Prediction Using Clinical Laboratory Data by Rui Li MS, Xiaoyan Hao MS, Yanjun Diao MD, Liu Yang MS, Jiayun Liu MD

“…This study aims to develop machine learning (ML) models for CRC risk prediction using clinical laboratory data. …”
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Comparison of Machine Learning Models for Classification of Breast Cancer Risk Based on Clinical Data by Haniyeh Rafiepoor, Alireza Ghorbankhanloo, Kazem Zendehdel, Zahra Zangeneh Madar, Sepideh Hajivalizadeh, Zeinab Hasani, Ali Sarmadi, Behzad Amanpour‐Gharaei, Mohammad Amin Barati, Mozafar Saadat, Seyed‐Ali Sadegh‐Zadeh, Saeid Amanpour

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Spectroscopic photoacoustic denoising framework using hybrid analytical and data-free learning method by Fangzhou Lin, Shang Gao, Yichuan Tang, Xihan Ma, Ryo Murakami, Ziming Zhang, John D. Obayemi, Winston O. Soboyejo, Haichong K. Zhang

“…Advanced methods, including learning-based approaches and analytical algorithms, have demonstrated promise but often require extensive training data and parameter tuning. …”
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Predicting Pineapple Quality from Hyperspectral Data of Plant Parts Applied to Machine Learning by Vitória Carolina Dantas Alves, Sebastião Ferreira de Lima, Dthenifer Cordeiro Santana, Rafael Ferreira Barreto, Roger Augusto da Cunha, Ana Carina da Silva Cândido Seron, Larissa Pereira Ribeiro Teodoro, Paulo Eduardo Teodoro, Rita de Cássia Félix Alvarez, Cid Naudi Silva Campos, Carlos Antonio da Silva Junior, Fábio Luíz Checchio Mingotte

“…Food quality detection by machine learning (ML) is more practical and sustainable as it does not require sample preparation and reagents. …”
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Enhancing sentiment analysis in tourism reviews: A comparative study of algorithms in ASPECT-BASED SENTIMENT ANALYSIS and EMOTION DETECTION by Viktor Handrianus Pranatawijaya, Putu Bagus Adidyana Anugrah Putra, Ressa Priskila, Novera Kristianti

“…Review data was taken from Google Maps and analyzed using BoW, LDA, NRC Emotion Lexicon, machine learning, and deep learning algorithms such as Logistic Regression (LR), Naïve Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Gradient Boosting (GB), Decision Tree (DT), and BERT. …”
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Advanced object detection for smart accessibility: a Yolov10 with marine predator algorithm to aid visually challenged people by Mahir Mohammed Sharif Adam, Hussah Nasser AlEisa, Samah Al Zanin, Radwa Marzouk

“…This study proposes a novel Advanced Object Detection for Smart Accessibility using the Marine Predator Algorithm to aid visually challenged people (AODSA-MPAVCP) model. …”
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Federated learning framework for IoT intrusion detection using tab transformer and nature-inspired hyperparameter optimization by Mohamed Abd Elaziz, Mohamed Abd Elaziz, Mohamed Abd Elaziz, Ibrahim A. Fares, Abdelghani Dahou, Mansour Shrahili

“…Whereas it enhances the processing and detection capability of huge amounts of data generated from IoT devices. …”
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Adversarial attacks dataset for low light image enhancementMendeley Data by Axel Martinez, Matthieu Olague, Gustavo Olague, Emilio Hernandez, Julio Cesar Lopez-Arredondo

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Tuberculosis Detection using Gray Level Co-Occurrence Matrix (GLCM) and K-Nearest Neighbor (K-NN) Algorithms by Fuad Anwar, Mohtar Yunianto*, Rahmanisya Fani Aisha Putri

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Modified Whale Optimization Algorithm for Multiclass Skin Cancer Classification by Abdul Majid, Masad A. Alrasheedi, Abdulmajeed Atiah Alharbi, Jeza Allohibi, Seung-Won Lee

“…Our method outperforms the genetic algorithm (GA), Particle Swarm Optimization (PSO), and the slime mould algorithm (SMA), as well as deep learning-based skin cancer classification models, which have reported accuracies of 87% to 94% in previous studies. …”
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Utilizing Machine Learning Techniques for Cancer Prediction and Classification based on Gene Expression Data by Mariwan Mahmood Hama Aziz, Sozan Abdullah Mahmood

“…Lately, several studies have delved into cancer classification by leveraging data mining techniques, machine learning algorithms, and statistical methods to thoroughly analyze high-dimensional datasets. …”
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Deepfake Audio Detection for Urdu Language Using Deep Neural Networks by Omair Ahmad, Muhammad Sohail Khan, Salman Jan, Inayat Khan

“…The main goal of the research presented in this paper is to evaluate the effectiveness of deep learning neural networks in detecting Deepfake audios in the Urdu language. …”
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Yield prediction, pest and disease diagnosis, soil fertility mapping, precision irrigation scheduling, and food quality assessment using machine learning and deep learning algorith... by S. Ajith, S. Vijayakumar, N. Elakkiya

“…Artificial intelligence, particularly machine learning and deep learning, is revolutionizing agricultural practices by enabling data-driven, precise, and sustainable solutions. …”
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Intelligent Data Processing Methods for the Atypical Values Correction of Stock Quotes by T. V. Zolotova, D. A. Volkova

“…The results of machine learning algorithms are demonstrated for sets of real statistical data representing the closing prices of shares of three Russian companies “Sberbank”, “Aeroflot”, “Gazprom” in the period from 01.12.2019 to 30.11.2020, obtained from the website of the Investment Company “FINAM”. …”
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Multi-LiDAR-Based 3D Object Detection via Data-Level Fusion Method by Yu Luo, Tao Wang, Shuai Lu, Xuerui Dai, Zhi Li

“…Secondly, a new point cloud detection model based on the deep learning framework is proposed, which enhances the feature extraction ability of small targets with sparse point clouds at the intersection of perception stations by focusing on features by attention mechanism. …”
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Target image detection algorithm of complex road scene based on improved multi-scale adaptive feature fusion technology by Xu Zhaosheng, Liao Zhongming, Xiao Xiaoyong, Ahmad Suzana, Mat Diah Norizan, Ismail Azlan

“…In addition, a semantic recognition algorithm for a road scene based on image data is suggested. …”
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A novel hybrid sand and dust storm detection method using MODIS data on GEE platform by Wei Wang, Alim Samat, Jilili Abuduwaili, Yongxiao Ge, Philippe De Maeyer, Tim Van de Voorde

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Adversarial example defense algorithm for MNIST based on image reconstruction by Zhongyuan QIN, Zhaoxiang HE, Tao LI, Liquan CHEN

“…With the popularization of deep learning, more and more attention has been paid to its security issues.The adversarial sample is to add a small disturbance to the original image, which can cause the deep learning model to misclassify the image, which seriously affects the performance of deep learning technology.To address this challenge, the attack form and harm of the existing adversarial samples were analyzed.An adversarial examples defense method based on image reconstruction was proposed to effectively detect adversarial examples.The defense method used MNIST as the test data set.The core idea was image reconstruction, including central variance minimization and image quilting optimization.The central variance minimization was only processed for the central area of the image.The image quilting optimization incorporated the overlapping area into the patch block selection.Considered and took half the size of the patch as the overlap area.Using FGSM, BIM, DeepFool and C＆amp;W attack methods to generate adversarial samples to test the defense performance of the two methods, and compare with the existing three image reconstruction defense methods (cropping and scaling, bit depth compression and JPEG compression).The experimental results show that the central variance minimization and image quilting optimization algorithms proposed have a satisfied defense effect against the attacks of existing common adversarial samples.Image quilting optimization achieves over 75% classification accuracy for samples generated by the four attack algorithms, and the defense effect of minimizing central variance is around 70%.The three image reconstruction algorithms used for comparison have unstable defense effects on different attack algorithms, and the overall classification accuracy rate is less than 60%.The central variance minimization and image quilting optimization proposed achieve the purpose of effectively defending against adversarial samples.The experiments illustrate the defense effect of the proposed defense algorithm in different adversarial sample attack algorithms.The comparison between the reconstruction algorithm and the algorithm shows that the proposed scheme has good defense performance.…”
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