Hybrid Gradient Descent Grey Wolf Optimizer for Machine Learning Performance Enhancement by Sri Rossa Aisyah Puteri Baharie, Sugiyarto Surono, Aris Thobirin

“…SVM is a robust machine learning algorithm for classification and regression. …”
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Using deep learning for thyroid nodule risk stratification from ultrasound images by Yasaman Sharifi, Morteza Danay Ashgzari, Susan Shafiei, Seyed Rasoul Zakavi, Saeid Eslami

“…This study proposes a computer-aided diagnosis system (CAD) for thyroid nodule risk classification and management recommendations based on the American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TIRADS), which uses a deep learning framework to increase diagnostic accuracy and reliability. …”
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Efficient Real-Time Object Detection and Classification Using mmWave Radar and Jetson Xavier NX by Mohamed Lamane, Abdessamad Klilou, Mohamed Tabaa

“…This paper associates a mmWave radar with a real-time embedded computing platform and proposes a real-time implementation of deep learning algorithms in order to classify the detected objects. …”
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A neuronal imaging dataset for deep learning in the reconstruction of single-neuron axons by Liya Li, Liya Li, Ying Hu, Ying Hu, Xiaojun Wang, Pei Sun, Tingwei Quan

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A framework of partial discharge detection for gas-insulated switchgear based on LSTM and multi-head attention by YANG Zhengsheng, LIU Fang

“…Finally， a classification layer was utilized for fault detection in GIS.ResultsThe experimental results were as follows： Linear SVM performed the worst， indicating that traditional machine learning classification algorithms were not effective in capturing subtle differences in the data. …”
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Radiomics capabilities in the interpretation of ultrasound and CT data in patients with chronic kidney disease: A review by Alexandra V. Proskura, Khalil M. Ismailov, Alexander G. Smoleevskiy, Amina I. Salpagarova, Irina N. Bobkova, Andrei M. Shestiuk

“…The article discusses the basics of radiomic methods, including texture analysis of images and the creation of diagnostic models using machine learning algorithms. The advantages of radiomic characteristics, in particular statistical features of order II and higher orders, in assessing interstitial fibrosis and other abnormal changes in the renal parenchyma are discussed in detail. …”
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DDoSNet: Detection and prediction of DDoS attacks from realistic multidimensional dataset in IoT network environment by Goda Srinivasa Rao, P. Santosh Kumar Patra, V.A. Narayana, Avala Raji Reddy, G.N.V. Vibhav Reddy, D. Eshwar

“…Several preprocessing approaches, including data-cleaning algorithms and imputation methods, are used to improve the accuracy and dependability of the data. …”
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Multi-Sensor Fusion and Machine Learning for Forest Age Mapping in Southeastern Tibet by Zelong Chi, Kaipeng Xu

“…Based on the Google Earth Engine platform and using the topographically complex and climatically diverse Southeastern Tibet as the study area, we propose a new method for forest age estimation that integrates multi-source remote-sensing data with machine learning. The study employs the Continuous Degradation Detection (CODED) algorithm combined with spectral unmixing models and Normalized Difference Fraction Index (NDFI) time series analysis to update forest disturbance information and provide annual forest distribution, mapping young forest distribution. …”
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Vision-Based Branch Road Detection for Intersection Navigation in Unstructured Environment Using Multi-Task Network by Joonwoo Ahn, Yangwoo Lee, Minsoo Kim, Jaeheung Park

“…Therefore, this study proposes a method for detecting the branch roads at the intersection using deep learning. …”
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An abnormal traffic detection method for chain information management system network based on convolutional neural network by Chao Liu, Chunxiang Liu, Changrong Liu

“…CBAM AE-CRF integrates the convolutional block attention module (CBAM) into convolutional neural network to enhance the model’s ability to learn anomalous features in network traffic. CBAM improves the detection accuracy of abnormal traffic in chain information management system by adaptively adjusting channel attention. …”
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Transfer Learning of High-Dimensional Stochastic Frontier Model via Elastic Net by Jiahao Chen, Wenjun Chen, Yunquan Song

“…In addition, based on the prior knowledge of the parameters, this paper introduces linear constraints to improve the estimation accuracy in transfer learning. When the transferable source is unknown, this paper designs a corresponding algorithm to detect the transferable source. …”
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Improving the efficiency and security of passport control processes at airports by using the R-CNN object detection model by Elhoucine Ouassam, Yassine Dabachine, Nabil Hmina, Belaid Bouikhalene

“…To automate and optimize these procedures, AI algorithms such as character recognition, facial recognition, predictive algorithms and automatic data processing can be implemented. …”
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A Convolutional Neural Network–Based Approach for Detecting Solar System Objects in Wide-field Imaging by Aram Lee, J. J. Kavelaars, Hossen Teimoorinia, Wesley Fraser, Edward Ashton

“…We demonstrate that deep learning object detection algorithms can aid in TNO and SSO discovery, supporting future discovery pipeline development.…”
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Deep multi-modal imaging temperature measurement method for detecting temperature rise in electrical equipment faults by Jinxuan Wen, Jiangjiang Li, Yachao Zhang

“…The method integrates data from multiple imaging modalities and uses deep learning algorithms to fuse and analyze this information. …”
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Edge-based detection and localization of adversarial oscillatory load attacks orchestrated by compromised EV charging stations by Khaled Sarieddine, Mohammad Ali Sayed, Sadegh Torabi, Ribal Atallah, Chadi Assi

“…The edge-based detection relies on the behavioral characteristics of the power grid in the presence of interconnected EVCS while combining cyber and physical layer features to implement deep learning algorithms for the effective detection of oscillatory load attacks at the EVCS. …”
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Enhancing Autonomous Driving Perception: A Practical Approach to Event-Based Object Detection in CARLA and ROS by Jingxiang Feng, Peiran Zhao, Haoran Zheng, Jessada Konpang, Adisorn Sirikham, Phuri Kalnaowakul

“…Additionally, we developed a real-time object detection pipeline optimized for event-based data, integrating it into the CARLA simulation environment and ROS for system prototyping. …”
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Towards automatic farrowing monitoring-A Noisy Student approach for improving detection performance of newborn piglets. by Martin Wutke, Clara Lensches, Ulrich Hartmann, Imke Traulsen

“…Although object detection algorithms are increasingly being used in various scenarios in the field of livestock farming, their usability for detecting newborn piglets has so far been limited. …”
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Artificial intelligence-driven label-free detection of chronic myeloid leukemia cells using ghost cytometry by Kohjin Suzuki, Naoki Watanabe, Yutaka Tsukune, Tadaaki Inano, Shintaro Kinoshita, Sayuri Tomoda, Kohei Yamada, Yusuke Konishi, Takuya Kuwana, Takeshi Sugiyama, Kenji Fukada, Kazuhiro Yamada, Miki Ando, Tomoiku Takaku

“…We concluded that the multidimensional morphological information of single cells obtained using GC, combined with machine learning algorithms, enables the quantitative detection of label-free CML cells. …”
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An Investigation of Suicidal Ideation from Social Media Using Machine Learning Approach by Soumyabrata Saha, Suparna Dasgupta, Adnan Anam, Rahul Saha, Sudarshan Nath, Surajit Dutta

“…The machine learning algorithms showed high accuracy, precision, recall, and F1-score in detecting suicide patterns on social media data whereas SVM has the highest performance with an accuracy of 0.886.       …”
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Time Frequency Analysis Based Fault Detection in PV Array Using Scaling Basis Chirplet Transform by S Ramana Kumar Joga, Chidurala SaiPrakash, Srikanth Velpula, Alivarani Mohapatra, Theophilus A. T. Kambo Jr.

“…To evaluate the effectiveness of the proposed method, extensive simulations and experiments are conducted using real‐world PV array data. The SBCT with combination of various machine learning algorithms is proposed to detect faults in PV array. …”
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