L2D2: A Novel LSTM Model for Multi-Class Intrusion Detection Systems in the Era of IoMT by Gokhan Akar, Shaaban Sahmoud, Mustafa Onat, Unal Cavusoglu, Emmanuel Malondo

“…In this paper, we propose a novel approach for detecting various intrusion attacks targeting Internet of Medical Things (IoMT) devices, utilizing an enhanced version of the LSTM deep learning algorithm. To evaluate and compare the proposed algorithm with other methods, we used the CICIoMT2024 dataset, which encompasses various types of equipment and corresponding attacks. …”
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A Multi-Granularity Features Representation and Dimensionality Reduction Network for Website Fingerprinting by Yaojun Ding, Bingxuan Hu

“…Website fingerprinting (WF) refers to the identification of target websites accessed by users in anonymous communication scenarios, playing a critical role in cybercrime investigation and forensics. In recent years, deep learning-based WF has become a research focus in this field. …”
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Exploring transition states of protein conformational changes via out-of-distribution detection in the hyperspherical latent space by Bojun Liu, Jordan G. Boysen, Ilona Christy Unarta, Xuefeng Du, Yixuan Li, Xuhui Huang

“…Here, we introduce Transition State identification via Dispersion and vAriational principle Regularized neural networks (TS-DAR), a deep learning framework inspired by out-of-distribution (OOD) detection in trustworthy artificial intelligence (AI). …”
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Breast cancer classification based on hybrid CNN with LSTM model by Mourad Kaddes, Yasser M. Ayid, Ahmed M. Elshewey, Yasser Fouad

“…Medical image analysis methods and computer-aided diagnosis can enhance this process, providing training and assistance to less experienced clinicians. Deep Learning (DL) models play a great role in accurately detecting and classifying cancer in the huge dataset, especially when dealing with large medical images. …”
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Harnessing artificial intelligence role in oral cancer diagnosis and prediction: A comprehensive exploration by Archana Behera, N. Aravindha Babu, Remya Rajan Renuka, Mukesh Kumar Dharmalingam Jothinathan

“…The systems involving collaboration pathologists witness higher abnormality detection and treatment planning, thereby bettering the diagnosis precision and the quality of care. Deep learning techniques, particularly neural networks are an important aspect of oral cancer early detection, and they drastically minimize human error. …”
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Mapping urban green structures using object-based analysis of satellite imagery: A review by Shivesh Kishore Karan, Bjørn Tobias Borchsenius, Misganu Debella-Gilo, Jonathan Rizzi

“…Future directions for UGS mapping include the integration of deep learning algorithms, advancements in satellite data technologies, and the development of standardized classification frameworks. …”
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A lung nodule segmentation model based on the transformer with multiple thresholds and coordinate attention by Tianjiao Hu, Yihua Lan, Yingqi Zhang, Jiashu Xu, Shuai Li, Chih-Cheng Hung

“…With the rapid development of deep learning, lung nodule segmentation models based on the encoder-decoder structure have become the mainstream research approach. …”
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Synchronization-based graph spatio-temporal attention network for seizure prediction by Jie Xiang, Yanan Li, Xubin Wu, Yanqing Dong, Xin Wen, Yan Niu

“…In recent years, a large number of studies have been conducted using deep learning models on epileptic open electroencephalogram (EEG) datasets with good results, but due to individual differences there are still some subjects whose seizure features cannot be accurately captured and are more difficult to differentiate, with poor prediction results. …”
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Real-time discrimination of earthquake signals by integrating artificial intelligence technology into IoT devices by Zhi Geng, Yanfei Wang, Wenyong Pan, Caixia Yu, Zhijing Bai, Hongzhou Zhang

“…We present an evolutionary deep learning method that yields a model named MCU-Quake. …”
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Convolutional neural networks for diabetic retinopathy detection by Darwin Patiño-Pérez, Luis Armijos-Valarezo, Luis Chóez-Acosta, Freddy Burgos-Robalino

“… The early detection of diabetic retinopathy remains a critical challenge in medical diagnostics, with deep learning techniques in artificial intelligence offering promising solutions for identifying pathological patterns in retinal images. …”
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A Marine Object Detection Algorithm Based on SSD and Feature Enhancement by Kai Hu, Feiyu Lu, Meixia Lu, Zhiliang Deng, Yunping Liu

“…Firstly, according to the spiny-edge characteristics of a sea urchin, a multidirectional edge detection algorithm is proposed to enhance the feature, which is taken as the 4th channel of image and the original 3 channels of underwater image together as the input for the further deep learning. Then, in order to improve the shortcomings of SSD algorithm’s poor ability to detect small targets, resnet 50 is used as the basic framework of the network, and the idea of feature cross-level fusion is adopted to improve the feature expression ability and strengthen semantic information. …”
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Pre-trained artificial intelligence-aided analysis of nanoparticles using the segment anything model by Gabriel A. A. Monteiro, Bruno A. A. Monteiro, Jefersson A. dos Santos, Alexander Wittemann

“…The presented method, which employs a pre-trained deep learning model, outperforms traditional techniques by circumventing systemic errors and human bias. …”
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Transfer learning for multi-material classification of transition metal dichalcogenides with atomic force microscopy by Isaiah A Moses, Wesley F Reinhart

“…Deep learning models based on atomic force microscopy enhance efficiency in inverse design and characterization of materials. …”
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GuardianMPC: Backdoor-Resilient Neural Network Computation by Mohammad Hashemi, Domenic Forte, Fatemeh Ganji

“…The rapid growth of deep learning (DL) has raised serious concerns about users’ data and neural network (NN) models’ security and privacy, particularly the risk of backdoor insertion when outsourcing the training or employing pre-trained models. …”
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Long short-term memory autoencoder based network of financial indices by Kamrul Hasan Tuhin, Ashadun Nobi, Mahmudul Hasan Rakib, Jae Woo Lee

“…Abstract We present a novel approach for analyzing financial time series data using a Long Short-Term Memory Autoencoder (LSTMAE), a deep learning method. Our primary objective is to uncover intricate relationships among different stock indices, leading to the extraction of stock networks. …”
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Interval Prediction of Photovoltaic Power Using Improved NARX Network and Density Peak Clustering Based on Kernel Mahalanobis Distance by Wen-He Chen, Long-Sheng Cheng, Zhi-Peng Chang, Han-Ting Zhou, Qi-Feng Yao, Zhai-Ming Peng, Li-Qun Fu, Zong-Xiang Chen

“…Comparative research of point forecasting is implemented to evaluate the machine learning and deep learning methods, with the proposed SAE-BRNARX under four different periods. …”
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Designing Channel Attention Fully Convolutional Networks with Neural Architecture Search for Customer Socio-Demographic Information Identification Using Smart Meter Data by Zhirui Luo, Qingqing Li, Ruobin Qi, Jun Zheng

“…<b>Results:</b> The performance of the proposed method was evaluated and compared with a set of machine learning and deep learning baseline methods using a smart meter dataset widely used in this research area. …”
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Indoor Positioning System in Learning Approach Experiments by Dodo Zaenal Abidin, Siti Nurmaini, Erwin, Errissya Rasywir, Yovi Pratama

“…The test was conducted with a deep learning approach using a deep neural network (DNN) algorithm. …”
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IM- LTS: An Integrated Model for Lung Tumor Segmentation using Neural Networks and IoMT by Jayapradha J, Su-Cheng Haw, Naveen Palanichamy, Kok-Why Ng, Senthil Kumar Thillaigovindhan

“…In recent days, Internet of Medical Things (IoMT) and Deep Learning (DL) techniques are broadly used in medical data processing in decision-making. …”
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Cells Grouping Detection and Confusing Labels Correction on Cervical Pathology Images by Wenbo Pang, Yi Ma, Huiyan Jiang, Qiming Yu

“…Differences in assessment criteria among pathologists result in ambiguously labeled cells, which poses a significant challenge for deep learning networks. To address this issue, we perform a labels correction module with feature similarity by constructing feature centers for typical cells in each category. …”
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