Development of a CNN-based decision support system for lung disease diagnosis using chest radiographs by B. T. Magar, M. A. Rahman, P. K. Saha, M. Ahmad, M. A. Rashid, H. Higa

“…Extensive testing under three different data distribution conditions demonstrated the model’s superior performance, achieving an average accuracy of 95.7%, precision of 95.3%, recall of 95.3%, and an F1-score of 95.3% for multi-class classification. …”
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Comparative Analysis of Deep Learning Models for Intrusion Detection in IoT Networks by Abdullah Waqas, Sultan Daud Khan, Zaib Ullah, Mohib Ullah, Habib Ullah

“…This study addresses the problem of detecting intrusions in IoT environments by evaluating the performance of deep learning (DL) models under different data and algorithmic conditions. We conducted a comparative analysis of three widely used DL models—Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Bidirectional LSTM (biLSTM)—across four benchmark IoT intrusion detection datasets: BoTIoT, CiCIoT, ToNIoT, and WUSTL-IIoT-2021. …”
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A New Hybrid Wavelet Transform-Deep Learning for Smart Resilient Inverters in Microgrids Against Cyberattacks by Chou-Mo Yang, Pei-Min Huang, Chun-Lien Su, Mahmoud Elsisi

“…While the model shows high performance, further research is needed to validate its generalizability across different inverter hardware and against novel, zero-day attack variants.…”
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Automatic Detection of Tiny Drainage Outlets and Ventilations on Flat Rooftops from Aerial Imagery by L. Arzoumanidis, W. Li, W. Li, J. Knechtel, Y. Kosmayadi, Y. Dehbi

“…This paper presents an automated approach to detecting drainage outlets and ventilation systems on flat rooftops, using a custom-labeled dataset of highresolution aerial imagery. We evaluated two different object detection methods, with FCOS (Fully Convolutional One-Stage Object Detection) outperforming Faster R-CNN in identifying these small utilities. …”
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Integration of Nuclear, Clinical, and Genetic Features for Lung Cancer Subtype Classification and Survival Prediction Based on Machine- and Deep-Learning Models by Bin Xie, Mingda Mo, Haidong Cui, Yijie Dong, Hongping Yin, Zhe Lu

“…<b>Conclusions:</b> Our study was the first to incorporate the characteristics of nuclei and the genetic information of patients to predict the subtypes and OS of patients with lung cancer. The combination of different factors and the usage of artificial intelligence methods achieved a small breakthrough in the results of previous studies regarding AUC values.…”
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Investigation of a transformer-based hybrid artificial neural networks for climate data prediction and analysis by Shangke Liu, Ke Liu, Zheng Wang, Yuanyuan Liu, Bin Bai, Rui Zhao

“…Lastly, LSTM is adept at handling long-term dependencies, ensuring the model can remember and utilize information over extended time spans.Results and DiscussionExperiments conducted on temperature data from Guangdong Province in China validate the performance of the proposed model. Compared to four different climate prediction decomposition methods, the proposed hybrid model with the Transformer method performs the best. …”
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Enhancing FMCW Radar Gesture Classification With Physically Interpretable Data Augmentation by Alessandra Fusco, Zain Amir Zaman, Souvik Hazra, Lorenzo Servadei, Robert Wille

“…The augmentation techniques employed in this research include time scaling, range and angle transformation, and noise injection, effectively simulating different gesture speeds, orientations, distances, and interference levels. …”
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Improving subpixel impervious surface estimation based on point of interest (POI) data by Junzhe Wang, Wang Jin, Zheng Cao, Zhiyi Pan, Guang Yang, Yaolong Zhao

“…The proposed method was tested in two study areas with distinctly different urban land patterns: Shenzhen, China, and Chicago, USA. …”
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Real-time monitoring and optimization of machine learning intelligent control system in power data modeling technology by Qiong Wang, Zuohu Chen, Yongbo Zhou, Zhiyuan Liu, Zhenguo Peng

“…The average response times in three different testing methods were 139.8 ms, 151 ms, and 140.6 ms, respectively. …”
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A Review of Open Remote Sensing Data with GIS, AI, and UAV Support for Shoreline Detection and Coastal Erosion Monitoring by Demetris Christofi, Christodoulos Mettas, Evagoras Evagorou, Neophytos Stylianou, Marinos Eliades, Christos Theocharidis, Antonis Chatzipavlis, Thomas Hasiotis, Diofantos Hadjimitsis

“…Despite the developments seen with these tools, issues relating to atmosphere such as cloud cover, data fusion, and model generalizability in different coastal environments continue to require resolutions to be addressed by future studies in terms of enhanced sensors and adaptive learning approaches with the rise of AI technology the recent years.…”
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Unlocking transcranial FUS-EEG feature fusion for non-invasive sleep staging in next-gen clinical applications by Suneet Gupta, Praveen Gupta, Bechoo Lal, Aniruddha Deka, Hirakjyoti Sarma, Sheifali Gupta

“…Addressing the variations in electroencephalogram (EEG) and electrooculogram (EOG) signals across different sleep stages, this study introduces a transcranial focused ultrasound (tFUS) based multimodal feature fusion deep learning model (MFDL) for automated sleep staging. …”
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A deep learning based multiple RNA methylation sites prediction across species by Sajid Shah, Saima Jabeen, Mohammed ElAffendi, Ishrat Khan, Muhammad Almas Anjum, Mohamed A. Bahloul

“…Secondly, this work investigates the effect of different encoding techniques on model performance, including one-hot encoding, Gene2Vec, and position encoding, as well as their combinations using concatenation, summation, and multiplication. …”
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Deep learning enhanced light sheet fluorescence microscopy for in vivo 4D imaging of zebrafish heart beating by Meng Zhang, Renjian Li, Songnian Fu, Sunil Kumar, James Mcginty, Yuwen Qin, Lingling Chen

“…With the fast generation of appropriate training data via flexible switching between confocal line-scanning LSFM (LS-LSFM) and conventional LSFM, this method achieves a three- to five-fold signal-to-noise ratio (SNR) improvement and ~1.8 times contrast improvement in ex vivo zebrafish heart imaging and long-term in vivo 4D (3D morphology + time) imaging of heartbeat dynamics at different developmental stages with ultra-economical acquisitions in terms of light dosage and acquisition time.…”
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Deep Learning and Recurrence Information Analysis for the Automatic Detection of Obstructive Sleep Apnea by Daniele Padovano, Arturo Martinez-Rodrigo, José M. Pastor, José J. Rieta, Raul Alcaraz

“…The present paper addresses this gap by integrating convolutional neural networks (CNNs) with HRV recurrence analysis. …”
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Cross-Visual Style Change Detection for Remote Sensing Images via Representation Consistency Deep Supervised Learning by Jinjiang Wei, Kaimin Sun, Wenzhuo Li, Wangbin Li, Song Gao, Shunxia Miao, Yingjiao Tan, Wei Cui, Yu Duan

“…Change detection techniques, which extract different regions of interest from bi-temporal remote sensing images, play a crucial role in various fields such as environmental protection, damage assessment, and urban planning. …”
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Federated Learning and EEL-Levy Optimization in CPS ShieldNet Fusion: A New Paradigm for Cyber–Physical Security by Nalini Manogaran, Yamini Bhavani Shankar, Malarvizhi Nandagopal, Hui-Kai Su, Wen-Kai Kuo, Sanmugasundaram Ravichandran, Koteeswaran Seerangan

“…This involves the incorporation of the Federated Residual Convolutional Network into an optimization method called EEL-Levy Fusion Optimization. …”
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EHAFF-NET: Enhanced Hybrid Attention and Feature Fusion for Pedestrian ReID by Jun Yang, Yan Wang, Haizhen Xie, Jiayue Chen, Shulong Sun, Xiaolan Zhang

“…This study addresses the cross-scenario challenges in pedestrian re-identification for public safety, including perspective differences, lighting variations, occlusions, and vague feature expressions. …”
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UCrack-DA: A Multi-Scale Unsupervised Domain Adaptation Method for Surface Crack Segmentation by Fei Deng, Shaohui Yang, Bin Wang, Xiujun Dong, Siyuan Tian

“…Additionally, by integrating a Mix-Transformer encoder, a multi-scale dilated attention module, and a mixed convolutional attention decoder, we effectively solve the challenges of cross-domain data distribution differences and complex scene crack segmentation. …”
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Leveraging sentiment analysis of food delivery services reviews using deep learning and word embedding by Dheya Mustafa, Safaa M. Khabour, Mousa Al-kfairy, Ahmed Shatnawi

“…In addition, the article investigated different effective approaches for word embedding and stemming techniques. …”
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Enhanced Workload Prediction in Data Centers Using Two-Stage Decomposition and Hybrid Parallel Deep Learning by Dalal Alqahtani, Hamidreza Imani, Tarek El-Ghazawi

“…These layers extract patterns from the input data, capturing short-term, medium-term, and long-term workload information, allowing the model to learn variations at different scales. Following this, Bi-LSTM layers capture the temporal dependencies within the patterns identified by the Conv1D layers. …”
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