Navigating the Challenges and Opportunities of Tiny Deep Learning and Tiny Machine Learning in Lung Cancer Identification by Yasir Salam Abdulghafoor, Auns Qusai Al-Neami, Ahmed Faeq Hussein

“…More than 70 state-of-the-art articles (from 2019 to 2024) were extensively explored to highlight the different machine learning and deep learning (DL) techniques of different models used for the detection, classification, and prediction of cancerous lung tumors. …”
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Application of artificial intelligence technologies for the detection of early childhood caries by Priyanka A, Rishi Sreekumar, S Namasivaya Naveen

“…This study mainly focuses on the different risk factors, dental caries indexes, and the importance of early caries prediction and treatment. …”
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Are baboons learning "orthographic" representations? Probably not. by Maja Linke, Franziska Bröker, Michael Ramscar, Harald Baayen

“…The ability of Baboons (papio papio) to distinguish between English words and nonwords has been modeled using a deep learning convolutional network model that simulates a ventral pathway in which lexical representations of different granularity develop. …”
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A Deep Reinforcement Learning Approach for Portfolio Management in Non-Short-Selling Market by Ruidan Su, Chun Chi, Shikui Tu, Lei Xu

“…Moreover, stock spatial interrelation representing the correlation between two different stocks is captured by a graph convolution network based on fundamental data. …”
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A hybrid model based on CNN-LSTM for assessing the risk of increasing claims in insurance companies by Walaa Gamaleldin, Osama Attayyib, Mrim M. Alnfiai, Faiz Abdullah Alotaibi, Ruixing Ming

“…The results demonstrate that the model effectively classifies insurance risks in different market environments, highlighting its potential for real-world applications. …”
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An example of the application of artificial intelligence models in human resources processes by Mustafa Kemal Aydın, Berk Küçük, Selim Sürücü

“…In the second stage, the resumes of the applicants are analyzed using three different deep learning models such as CNN (Convolutional Neural Network), GRU (Gated Recurrent Unit), and LSTM (Long Short-Term Memory) for classification purposes. …”
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Driver Steering Intention Prediction for Human-Machine Shared Systems of Intelligent Vehicles Based on CNN-GRU Network by Chen Zhou, Fan Zhang, Edric John Cruz Nacpil, Zheng Wang, Fei-Xiang Xu

“…The proposed prediction method also possesses adaptability to different driver behaviors.…”
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Bridging the Gap in Facial Age Progression: An Attention Mechanism Approach by Taoli Liu, Yubin Liang, Wenchen Wu, Yize Tang

“…Our model effectively captures the subtleties of facial aging across different demographics. Extensive experiments and ablation studies demonstrate that our approach excels in preserving identity, ensuring racial consistency, and generating realistic aging effects. …”
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The Role of ChatGPT in Dermatology Diagnostics by Ziad Khamaysi, Mahdi Awwad, Badea Jiryis, Naji Bathish, Jonathan Shapiro

“…Artificial intelligence (AI), especially large language models (LLMs) like ChatGPT, has disrupted different medical disciplines, including dermatology. …”
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Adjusting U-Net for the aortic abdominal aneurysm CT segmentation case by R.U. Epifanov, N.A. Nikitin, A.A. Rabtsun, L.N. Kurdyukov, A.A. Karpenko, R.I. Mullyadzhanov

“…As a result of our study, macro dice score for classes of interest reaches 83.12% ± 4.27%. We explored different augmentation styles and showed the importance of applying intensity augmentation style to improve segmentation algorithm robustness in conditions of clinical data diversity. …”
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Plant disease detection with generative adversarial networks by Garam Han, Derek Kwaku Pobi Asiedu, Kwabena Ebo Bennin

“…To empirically validate the effectiveness of GANs on the performance of binary and multi-class PDD, we train GANs on diverse plant species and disease symptoms, enabling the classification of different plant diseases. To achieve this, we trained two GAN models, namely Deep convolutional GAN (DCGAN) and alpha beta GAN (αβGAN), on different groups and numbers of plant species and disease classes to generate synthetic im-ages. …”
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Fusion of Deep Features of Wavelet Transform for Wildfire Detection by Akbar Asgharzadeh-Bonab, Salar Ghamati, Farid Ahmadi, Hashem Kalbkhani

“…Forests uniquely deliver different vital resources, particularly oxygen and carbon dioxide purification. …”
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Advanced investing with deep learning for risk-aligned portfolio optimization. by Minh Duc Nguyen

“…This study introduces a deep learning-based framework for portfolio optimization tailored to different investor risk preferences. We combine two prediction models, Long Short-Term Memory (LSTM) and One-Dimensional Convolutional Neural Network (1D-CNN), with three portfolio frameworks: Mean-Variance with Forecasting (MVF), Risk Parity Portfolio (RPP), and Maximum Drawdown Portfolio (MDP). …”
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Study of the Current–Voltage Characteristics of Membrane Systems Using Neural Networks by Evgenia Kirillova, Anna Kovalenko, Makhamet Urtenov

“…During this work, several different neural network architectures were developed and tested. …”
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Hybrid Backbone-Based Deep Learning Model for Early Detection of Forest Fire Smoke by Gökalp Çınarer

“…A total of 30 different object detection models, including the proposed model, were run with the extended Wildfire Smoke dataset, and the results were compared. …”
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Detection of IPv6 routing attacks using ANN and a novel IoT dataset by Murat Emec

“…Using artificial intelligence and machine-learning techniques, a performance evaluation was performed on four different artificial neural network models (convolutional neural network, deep neural network, multilayer perceptron structure, and routing attack detection-fed forward neural network [RaD-FFNN]). …”
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Detection of Welding Defects Tracked by YOLOv4 Algorithm by Yunxia Chen, Yan Wu

“…The improvements include optimizing the stacking method of residual blocks, modifying the activation functions for different convolutional layers, and eliminating the downsampling layer in the PANet (Pyramid Attention Network) to preserve edge information. …”
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An Improved CEEMDAN-FE-TCN Model for Highway Traffic Flow Prediction by Heyao Gao, Hongfei Jia, Lili Yang

“…The fuzzy entropy (FE) is then calculated to recombine subsequences, highlighting traffic flow dynamics in different frequencies and improving prediction efficiency. …”
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Adversarial sample generation algorithm for vertical federated learning by Xiaolin CHEN, Daoguang ZAN, Bingchao WU, Bei GUAN, Yongji WANG

“…To adapt to the scenario characteristics of vertical federated learning (VFL) applications regarding high communication cost, fast model iteration, and decentralized data storage, a generalized adversarial sample generation algorithm named VFL-GASG was proposed.Specifically, an adversarial sample generation framework was constructed for the VFL architecture.A white-box adversarial attack in the VFL was implemented by extending the centralized machine learning adversarial sample generation algorithm with different policies such as L-BFGS, FGSM, and C＆amp;W.By introducing deep convolutional generative adversarial network (DCGAN), an adversarial sample generation algorithm named VFL-GASG was designed to address the problem of universality in the generation of adversarial perturbations.Hidden layer vectors were utilized as local prior knowledge to train the adversarial perturbation generation model, and through a series of convolution-deconvolution network layers, finely crafted adversarial perturbations were produced.Experiments show that VFL-GASG can maintain a high attack success while achieving a higher generation efficiency, robustness, and generalization ability than the baseline algorithm, and further verify the impact of relevant settings for adversarial attacks.…”
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D2D cooperative caching strategy based on graph collaborative filtering model by Ningjiang CHEN, Linming LIAN, Pingjie OU, Xuemei YUAN

“…A D2D cooperative caching strategy based on graph collaborative filtering model was proposed for the problem of difficulty in obtaining sufficient data to predict user preferences in device-to-device (D2D) caching due to the limited signal coverage of base stations.Firstly, a graph collaborative filtering model was constructed, which captured the higher-order connectivity information in the user-content interaction graph through a multilayer graph convolutional neural network, and a multilayer perceptron was used to learn the nonlinear relationship between users and content to predict user preferences.Secondly, in order to minimize the average access delay, considering user preference and cache delay benefit, the cache content placement problem was modeled as a Markov decision process model, and a cooperative cache algorithm based on deep reinforcement learning was designed to solve it.Simulation experiments show that the proposed caching strategy achieves optimal performance compared with existing caching strategies for different content types, user densities, and D2D communication distance parameters.…”
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