KNEE OSTEOARTHRITIS STAGE CLASSIFICATION BASED ON HYBRID FUSION DEEP LEARNING FRAMEWORK by Delveen Luqman Abd Alnabi, Shereen Sh Ahmed, Nisreen Luqman Abd Alnabi

“… Knee osteoarthritis severity detection is one of the most challenging applications in computer vision due to the similarity between X-ray images of the adjacent stages. …”
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CNN‐based off‐angle iris segmentation and recognition by Ehsaneddin Jalilian, Mahmut Karakaya, Andreas Uhl

“…Within the framework of these approaches, a series of experiments is carried out to determine whether (i) improving the segmentation outputs and/or correcting the output iris images before or after the segmentation can compensate for some off‐angle distortions, (ii) a CNN trained on frontal eye images is capable of detecting and extracting the learnt features on the corrected images, or (iii) the generalisation capability of the network can be improved by training it on iris images of different gaze angles. …”
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Model-based Bayesian Fusion-Net for infrared and visible image fusion by Wang Li, Kuang Yafang, Cai Ziyi, Chu Ning, Mohammad-Djafari Ali

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Re-identification assistance and multi-stage association for pedestrian multi-object tracking by Ye Li, Li Zhan, Lei Wu, Hongkun Liu, Jianli Min, Xinzhong Wang

“…This method innovatively focuses on the role of low confidence bounding boxes in MOT, and introduces a separately trained pedestrian re-identification model to extract discriminative features of pedestrians, then adds this feature to the multi-stage data association algorithm to improve the accuracy of multi-object tracking. …”
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A Tunnel Crack Detection Method Based on an Unmanned Aerial Vehicle (UAV) Equipped with a High-Speed Camera and Crack Recognition Algorithm Using Improved Multi-Scale Retinex and P... by Wei Sun, Xiaohu Liu, Zhiyong Lei

“…In order to solve the problems of low efficiency and accuracy in the traditional detection of tunnel cracks, this paper proposes a tunnel crack detection method based on a UAV (unmanned aerial vehicle) equipped with a high-speed camera and a crack recognition algorithm using the improved multi-scale Retinex (MSR) algorithm and the Prewitt–Otsu algorithm, aiming to improve the accuracy and efficiency of detection. …”
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Analysis and prediction of land use changes: the case study of coastal areas of Gilan province by Sahar Abdollahi, Hashem Dadashpoor

“…Introduction: Land use changes in coastal areas of Gilan Province in recent decades have caused problems such as forest and wetland degradation, soil erosion, biodiversity reduction, and increased environmental pollution. …”
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Optimizing UAV performance with IoT and fuzzy linear fractional transportation models by Samaher Al-Janabi, Nawras G. Seyhood

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Research progress in globular fruit picking recognition algorithm based on deep learning by LI Hui, ZHANG Jun, YU Shuochen, LI Zhixin

“…This method achieves fast detection while maintaining high accuracy, transforms the problem of target detection into a regression problem, and completes the location and classification of the target directly. …”
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Key frame extraction based abnormal vehicle identification technique using statistical distribution analysis by M. A. Y. Peer Mohamed Appa, V. Vanitha, Priti Rishi, Shrddha Sagar, M. Asha Paul

“…Though many vehicle identification works have been done by applying machine and deep learning approaches, still there is some problem with handling repetition frames and identifying the abnormal vehicles among vehicles in a camera. …”
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Perbandingan Kinerja Naïve Bayes dan Random Forest dalam Mendeteksi Berita Palsu by William William, Teny Handhayani

“… Fake news has become a serious problem in today's digital era. The existence of fake news can have various negative impacts, including the spread of misinformation, social unrest, and economic losses. …”
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Multi-Model Attentional Fusion Ensemble for Accurate Skin Cancer Classification by Iftekhar Ahmed, Biggo Bushon Routh, Md. Saidur Rahman Kohinoor, Shadman Sakib, Md Mahfuzur Rahman, Farag Azzedin

“…Artifacts like hair can further obscure important features. This research addresses the problem and introduces a novel deep learning approach for accurate skin cancer classification by combining ResNet50V2, MobileNetV2, and EfficientNetV2 models. …”
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Low-Damage Grasp Method for Plug Seedlings Based on Machine Vision and Deep Learning by Fengwei Yuan, Gengzhen Ren, Zhang Xiao, Erjie Sun, Guoning Ma, Shuaiyin Chen, Zhenlong Li, Zhenhong Zou, Xiangjiang Wang

“…Currently, most research focuses on the reduction of substrate loss, while ignoring damage to the hole tray seedling itself. Targeting the problem of high damage rate during transplantation of plug seedlings, we have proposed an adaptive grasp method based on machine vision and deep learning, and designed a lightweight real-time grasp detection network (LRGN). …”
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Pest classification: Explainable few-shot learning vs. convolutional neural networks vs. transfer learning by Nitiyaa Ragu, Jason Teo

“…This study addresses the problem of limited datasets in pest detection by exploring the potential of Explainable Few-Shot Learning (FSL), a machine learning approach that not only enables learning from a small amount of data but also provides interpretable insights into the decision-making process. …”
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ResCalib: Joint LiDAR and Camera Calibration Based on Geometrically Supervised Deep Neural Networks by Jiahui CHAI, Minglei LI, Min LI, Dazhou WEI, Guangyong CHEN

“…Light Detection And Ranging (LiDAR) systems lack texture and color information, while cameras lack depth information. …”
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Orchard Navigation Method Based on RS-SC Loop Frame Search Method and SLAM Technology by Ning Xu, Qingshan Meng, Fengping Liu, Zhihe Li, Guangming Wang, Na Guo, Wenxuan Wu

“…In order to solve the problem that existing synchronous localization and map construction techniques are not accurate enough in this kind of environment, a loopback detection algorithm based on neighborhood search and scanning context fusion is proposed. …”
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A text classification method based on a convolutional and bidirectional long short-term memory model by Hai Huan, Zelin Guo, Tingting Cai, Zichen He

“…In response to this problem, a text classification method based on the CBM (Convolutional and Bi-LSTM Model) model, which can extract shallow local semantic features and deep global semantic features, is proposed. …”
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A Novel Tree-Based Combined Test for Seasonality by Karsten Webel, Daniel Ollech

“…For that reason, we show how conditional inference trees can be used to devise more sophisticated alternatives. Treating the detection of seasonality as a classification problem and the tests’ p-values as correlated predictors, the first step is to identify the most important tests in the ensemble via recursive feature elimination in multiple random forests of such trees; the second step is to grow and prune a single tree based upon information from only these identified tests. …”
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Deep Residual Transfer Ensemble Model for mRNA Gene-Expression-Based Breast Cancer by Job Prasanth Kumar Chinta Kunta, Vijayalakshmi A. Lepakshi

“…Here, ResNet101 avoided gradient vanishing, while AlexNet provided 4096-dimensional features to train the ensemble-of-ensemble (E2E) classifier for consensus-based breast cancer detection. …”
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When Two are Better Than One: Synthesizing Heavily Unbalanced Data by Francisco Ferreira, Nuno Lourenco, Bruno Cabral, Joao Paulo Fernandes

“…The data used to create these solutions is usually highly structured and contains categorical and continuous features characterised by complex distributions. One of the main challenges of fraud detection is concerned with the scarcity of fraudulent instances which results in highly unbalanced datasets. …”
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Deep Fusion Intelligence: Enhancing 5G Security Against Over-the-Air Attacks by Mohammadreza Amini, Ghazal Asemian, Burak Kantarci, Cliff Ellement, Melike Erol-Kantarci

“…The true positive and negative rates are reported as 93.5% and 91.9%, respectively, showcasing strong performance for scenarios with CFO and channel impairments and outperforming the other compared methods by at least 12%. An optimization problem is formulated and solved based on the level of uncertainty observed in the experimental set-up and the optimum TEDA configuration is derived for the target false-alarm and miss-detection probability. …”
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