TepiSense: A Social Computing-Based Real-Time Epidemic Surveillance System Using Artificial Intelligence by Bilal Tahir, Muhammad Amir Mehmood

“…TepiSense compares the performance of 3 feature extraction techniques, 9 machine/deep learning models, and 3 Large Language Models (LLMs). …”
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RETRACTED: An Infrared Small Target Detection Method Based on a Weighted Human Visual Comparison Mechanism for Safety Monitoring by Yuanyuan Chen, Huiqian Wang, Yu Pang, Jinhui Han, En Mou, Enling Cao

“…In addition, unlike deep learning, this method is appropriate for small sample sizes and is easy to implement on FPGA hardware.…”
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Blockchain-Powered Secure and Scalable Threat Intelligence System With Graph Convolutional Autoencoder and Reinforcement Learning Feedback Loop by Mohamad Khayat, Ezedin Barka, Mohamed Adel Serhani, Farag Sallabi, Khaled Shuaib, Heba M. Khater

“…This paper proposes an approach that integrates secure blockchain technology with data preprocessing, deep learning, and reinforcement learning to enhance threat detection and response capabilities. …”
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Peningkatan Performa Pengenalan Wajah pada Gambar Low-Resolution Menggunakan Metode Super-Resolution by Muhammad Imaduddin Abdur Rohim, Auliati Nisa, Muhammad Nurkhoiri Hindratno, Radhiyatul Fajri, Gembong Satrio Wibowanto, Nova Hadi Lestriandoko, Pesigrihastamadya Normakristagaluh

“…Kami menginvestigasi penggunaan metode super-resolution (SR) berbasis deep learning, termasuk DFDNet, LapSRN, GFPGAN, Real-ESRGAN, Real-ESRGAN+GFPGAN, dan FaceSPARNet. …”
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Transitioning from wet lab to artificial intelligence: a systematic review of AI predictors in CRISPR by Ahtisham Fazeel Abbasi, Muhammad Nabeel Asim, Andreas Dengel

“…Within the landscape of AI predictors in CRISPR-Cas9 multi-step process, it provides insights of representation learning methods, machine and deep learning methods trends, and performance values of existing 50 predictive pipelines. …”
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Prediction models for cognitive impairment in middle-aged patients with cerebral small vessel disease by Wei Zheng, Xiaoyan Qin, Ronghua Mu, Peng Yang, Bingqin Huang, Bingqin Huang, Zhixuan Song, Xiqi Zhu, Xiqi Zhu

“…PurposeThis study aims to develop hippocampal texture model for predicting cognitive impairment in middle-aged patients with cerebral small vessel disease (CSVD).MethodsThe dataset included 145 CSVD patients (Age, 52.662 ± 5.151) and 99 control subjects (Age, 52.576±4.885). An Unet-based deep learning neural network model was developed to automate the segmentation of the hippocampus. …”
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Advances in colorectal cancer diagnosis using optimal deep feature fusion approach on biomedical images by Sultan Refa Alotaibi, Manal Abdullah Alohali, Mashael Maashi, Hamed Alqahtani, Moneerah Alotaibi, Ahmed Mahmud

“…Lately, computer-aided diagnosis (CAD) based on HI has progressed rapidly with the increase of machine learning (ML) and deep learning (DL) based models. This study introduces a novel Colorectal Cancer Diagnosis using the Optimal Deep Feature Fusion Approach on Biomedical Images (CCD-ODFFBI) method. …”
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SVFRH: A Growth Stage-Based Compartmental Model for Predicting the Disease Incident in Tomato (Solanum lycopersicum) by Athira P. Shaji, S. Hemalatha

“…Over decades, research has primarily focused on addressing these challenges through computer vision and deep learning techniques. In this work, we employ a comprehensive modelling approach that combines compartmental and logistic regression models to thoroughly address disease dynamics in tomato crops, with a particular focus on tomato early blight diseases. …”
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Comparative analysis of the DCNN and HFCNN Based Computerized detection of liver cancer by Sandeep Dwarkanth Pande, Pala Kalyani, S Nagendram, Ala Saleh Alluhaidan, G Harish Babu, Sk Hasane Ahammad, Vivek Kumar Pandey, G Sridevi, Abhinav Kumar, Ebenezer Bonyah

“…Researchers have explored numerous machine learning (ML) techniques and deep learning (DL) approaches aimed at the automated recognition of liver disease by analysing computed tomography (CT) images. …”
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MAEMC-NET: a hybrid self-supervised learning method for predicting the malignancy of solitary pulmonary nodules from CT images by Tianhu Zhao, Tianhu Zhao, Yong Yue, Hang Sun, Jingxu Li, Yanhua Wen, Yudong Yao, Wei Qian, Yubao Guan, Shouliang Qi, Shouliang Qi

“…This study aims to address this diagnostic challenge by developing a novel deep learning model.MethodsThis study proposes MAEMC-NET, a model integrating generative (Masked AutoEncoder) and contrastive (Momentum Contrast) self-supervised learning to learn CT image representations of intra- and inter-solitary nodules. …”
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Advanced artificial intelligence with federated learning framework for privacy-preserving cyberthreat detection in IoT-assisted sustainable smart cities by Mahmoud Ragab, Ehab Bahaudien Ashary, Bandar M. Alghamdi, Rania Aboalela, Naif Alsaadi, Louai A. Maghrabi, Khalid H. Allehaibi

“…Nevertheless, the possibility of FL regarding IoT forensics remains mostly unexplored. Deep learning (DL) focused cyberthreat detection has developed as a powerful and effective approach to identifying abnormal patterns or behaviours in the data field. …”
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The utility of artificial intelligence in identifying radiological evidence of lung cancer and pulmonary tuberculosis in a high-burden tuberculosis setting by Z Z Nxumalo, E M Irusen, B W Allwood, M Tadepalli, J Bassi, C F N Koegelenberg

“…Artificial intelligence (AI), using deep learning (DL) systems, can be utilised to detect radiological changes of various pulmonary diseases. …”
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Artificial intelligence links CT images to pathologic features and survival outcomes of renal masses by Ying Xiong, Linpeng Yao, Jinglai Lin, Jiaxi Yao, Qi Bai, Yuan Huang, Xue Zhang, Risheng Huang, Run Wang, Kang Wang, Yu Qi, Pingyi Zhu, Haoran Wang, Li Liu, Jianjun Zhou, Jianming Guo, Feng Chen, Chenchen Dai, Shuo Wang

“…Here we show that the deep learning models can non-invasively predict the likelihood of malignant and aggressive pathology of a renal mass based on preoperative multi-phase CT images.…”
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Machine Learning in the Management of Patients Undergoing Catheter Ablation for Atrial Fibrillation: Scoping Review by Aijing Luo, Wei Chen, Hongtao Zhu, Wenzhao Xie, Xi Chen, Zhenjiang Liu, Zirui Xin

“…In terms of model type, deep learning, represented by convolutional neural networks, was most frequently applied (14/23, 61%). …”
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Automated on-site broiler live weight estimation through YOLO-based segmentation by Mahmoud Y. Shams, Wael M. Elmessery, Awad Ali Tayoush Oraiath, Ahmed Elbeltagi, Ali Salem, Pankaj Kumar, Tamer M. El-Messery, Tarek Abd El-Hafeez, Mohamed F. Abdelshafie, Gomaa G. Abd El-Wahhab, Ibrahim S. El-Soaly, Abdallah Elshawadfy Elwakeel

“…The study utilizes YOLO version 8, a deep learning-based network segmentation technique, for precise broiler segmentation, significantly improving weight accuracy in complex environments. …”
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Inhibition of tumour necrosis factor alpha by Etanercept attenuates Shiga toxin-induced brain pathology by Robin Christ, Devon Siemes, Shuo Zhao, Lars Widera, Philippa Spangenberg, Julia Lill, Stephanie Thiebes, Jenny Bottek, Lars Borgards, Andreia G. Pinho, Nuno A. Silva, Susana Monteiro, Selina K. Jorch, Matthias Gunzer, Bente Siebels, Hannah Voss, Hartmut Schlüter, Olga Shevchuk, Jianxu Chen, Daniel R. Engel

“…Analysis of microglial populations using a novel human-in-the-loop deep learning algorithm for the segmentation of microscopic imaging data indicated specific morphological changes, which were reduced to healthy condition after inhibition of TNF-α. …”
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FoxA1 knockdown promotes BMSC osteogenesis in part by activating the ERK1/2 signaling pathway and preventing ovariectomy-induced bone loss by Lijun Li, Renjin Lin, Yang Xu, Lingdi Li, Zhijun Pan, Jian Huang

“…Abstract The influence of deep learning in the medical and molecular biology sectors is swiftly growing and holds the potential to improve numerous crucial domains. …”
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A multi-model feature fusion based transfer learning with heuristic search for copy-move video forgery detection by Hessa Alfraihi, Muhammad Swaileh A. Alzaidi, Hamed Alqahtani, Abdulbasit A. Darem, Ali M. Al-Sharafi, Ahmad A. Alzahrani, Menwa Alshammeri, Abdulwhab Alkharashi

“…In contrast, methods that depend on deep learning (DL) have exposed good performance and suggested outcomes. …”
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Association between the subclinical level of problematic internet use and habenula volume: a look at mediation effect of neuroticism by Toshiya Murai, Hironobu Fujiwara, Qi Dai, Halwa Zakia, Yusuke Kyuragi, Naoya Oishi, Yuzuki Ishikawa, Lichang Yao, Morio Aki

“…Hb segmentation was performed using a deep learning technique. The Internet Addiction Test (IAT) and the NEO Five-Factor Inventory were used to assess the PIU level and personality, respectively. …”
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A Comprehensive Review of Direction-of-Arrival Estimation and Localization Approaches in Mixed-Field Sources Scenario by Amir Masoud Molaei, Bijan Zakeri, Seyed Mehdi Hosseini Andargoli, Muhammad Ali Babar Abbasi, Vincent Fusco, Okan Yurduseven

“…The review also identifies promising future research directions, such as the exploration of advanced signal processing techniques like compressive sensing and deep learning, exact NF modeling, estimation based on one-bit measurements, the integration of polarization diversity, employing metasurface antennas, tracking parameters, and the utilization of full-wave or experimental data for a more realistic representation of the challenges. …”
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