Tea Disease Detection Method Based on Improved YOLOv8 in Complex Background by Junchen Ai, Yadong Li, Shengxiang Gao, Rongsheng Hu, Wengang Che

“…Finally, the map values of 89.7% and 68.5% were obtained on a self-made tea data set and a public tea disease data set, which were improved by 3.9% and 4.3%, respectively, compared with the original benchmark model, and the reasoning speed of the model was 164.3 fps. …”
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Early detection of heart failure using in-patient longitudinal electronic health records. by Ignat Drozdov, Benjamin Szubert, Clare Murphy, Katriona Brooksbank, David J Lowe

“…Despite its considerable health economic burden, techniques for early detection of HF in the general population are sparse. …”
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SEM Deep Learning Multiclass Noise Level Classification With Data Augmentation by Kai Liang Lew, Kok Swee Sim, Shing Chiang Tan

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AN APPROACH HYBRID RECURRENT NEURAL NETWORK AND RULE-BASE FOR INTRUSION DETECTION SYSTEM by Trần Thị Hương, Phạm Văn Hạnh

“…A comparison of the detection efficiency of our model with the previous detection models on the same data set, KDD CUP 99, shows that the proposed model is effective for detecting network intrusions at rates higher than 99%.…”
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Automated Detection of Connective Tissue by Tissue Counter Analysis and Classification and Regression Trees by Josef Smolle, Peter Kahofer

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Few-Shot Graph Anomaly Detection via Dual-Level Knowledge Distillation by Xuan Li, Dejie Cheng, Luheng Zhang, Chengfang Zhang, Ziliang Feng

“…In anomaly detection tasks, collecting plenty of annotated data tends to be costly and laborious. …”
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Leveraging environmental microbial indicators in wastewater for data-driven disease diagnostics by Gayatri Gogoi, Gayatri Gogoi, Sarangthem Dinamani Singh, Sarangthem Dinamani Singh, Devpratim Koch, Devpratim Koch, Emon Kalyan, Rashmi Rani Boro, Aradhana Devi, Hridoy Jyoti Mahanta, Hridoy Jyoti Mahanta, Pankaj Bharali, Pankaj Bharali

“…Key physicochemical parameters, heavy metals, and minerals were measured, and viral presence was detected using RT-qPCR. After data preprocessing, correlation analyses identified 19 relevant environmental parameters. …”
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Urine microbiota in non-muscle-invasive bladder cancer and approaches to its detection by M. I. Kogan, Yu. L. Naboka, A. V. Ryzhkin, I. A. Gudima, A. G. Ivanov, S. N. Ivanov, O. N. Vasiliev, V. P. Glukhov, A. V. Ilyash, D. V. Sizyakin

“…Moreover, Peptococcus spp. was more often found among anaerobic taxa. (70%), Eubacterium spp., Propionibacterium spp. (45% each, respectively), among representatives of the aerobes — Corynebacterium spp. (60%), S. lentus (up to 45%), S. haemolyticus (35%) and E. faecalis (30%). Data from a comparative analysis of the detection frequencies of microorganisms depending on the method of collecting material and the set of media for cultivation showed that more isolates are isolated in the midstream urine samples than in the study of catheter urine (2.9- and 1.9-fold with the standard and extended sets of media, respectively). …”
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Quantitative Detection of Water Content of Winter Jujubes Based on Spectral Morphological Features by Yabei Di, Huaping Luo, Hongyang Liu, Huaiyu Liu, Lei Kang, Yuesen Tong

“…The optimal model was the fusion model at a center wavelength of 1146 nm with a correlation coefficient of 0.9942 for the calibration set and 0.8698 for the prediction set. The overall results showed that the wave valley is more reflective of the fruit quality, and the morphological characteristics of the wave valley are more suitable than those of the wave peak for the quantitative detection of the moisture content of winter jujubes.…”
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Simulation of automatic intrusion detection in university networks by using neural network algorithms by Houdun Xu

“…This article collects a large amount of network intrusion data from universities and obtains data on normal network traffic for comparison. …”
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Auto forensic detecting algorithms of malicious code fragment based on TensorFlow by Binglong LI, Jinlong TONG, Yu ZHANG, Yifeng SUN, Qingxian WANG, Chaowen CHANG

“…In order to auto detect the underlying malicious code fragments in complex，heterogeneous and massive evidence data about digital forensic investigation, a framework for malicious code fragment detecting algorithm based on TensorFlow was proposed by analyzing TensorFlow model and its characteristics.Back-propagation training algorithm was designed through the training progress of deep learning.The underlying binary feature pre-processing algorithm of malicious code fragment was discussed and proposed to address the problem about different devices and heterogeneous evidence sources from storage media and such as AFF forensic containers.An algorithm which used to generate data set about code fragments was designed and implemented.The experimental results show that the comprehensive evaluation index F₁of the method can reach 0.922, and compared with CloudStrike, Comodo, FireEye antivirus engines, the algorithm has obvious advantage in dealing with the underlying code fragment data from heterogeneous storage media.…”
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A neural network classifier for detecting diabetic retinopathy from retinal images by M. M. Lukashevich

“…In this paper, the research focuses on the development of an efficient method for DR detection based on the EfficientNet convolutional neural network, self-learning technology and data augmentation operations. …”
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Anomaly Detection in Wireless Sensor Networks Using Immune-Based Bioinspired Mechanism by Ramsha Rizwan, Farrukh Aslam Khan, Haider Abbas, Sajjad Hussain Chauhdary

“…Then the random packets are tested and matched with the detector set and anomalies are identified. Anomalous data packets are used for further processing to identify specific anomalies. …”
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Beyond Classical AI: Detecting Fake News with Hybrid Quantum Neural Networks by Volkan Altıntaş

“…In this study, we propose a novel hybrid model, the HQDNN (Hybrid Quantum–Deep Neural Network), designed for the automatic detection of fake news. The model integrates classical fully connected neural layers with a parameterized quantum circuit, enabling the processing of textual data within both classical and quantum computational domains. …”
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An Automatic Damage Detection Method Based on Adaptive Theory-Assisted Reinforcement Learning by Chengwen Zhang, Qing Chun, Yijie Lin

“…The convergence ratio of damage detection on the training set is approximately 97%, and that on the test set is in the range of 86.2%–91.9%. …”
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A Detection Method of Lentinus Edodes Based on Improved YOLOv4 Algorithm by HUANG Ying-lai, LI Da-ming, LU Xin, YANG Liu-song

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Detection of Burnt Areas by Remote Sensing Techniques: Antalya Manavgat Forest Fire by Orhun Soydan

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A Fused Multiscale Pictorial Sequence Learning Mechanism Applied to Pavement Detection by Zhang Jiazhen, Wang Xiulai, Zhao Shuxu

“…Considering that most pavement anomaly detection algorithms are difficult to play a stable role in data related to different distributions of pavement anomalies, this paper proposes a pavement anomaly detection algorithm based on multi-scale fusion time series information. …”
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Machine Learning Model for Early Detection of COVID-19 by Heart Rhythm Abnormalities by M. S. Mezhov, V. O. Kozitsin, Iu. D. Katser

“…Electronic devices capable of collecting individual telemetry data have opened up prospects for preclinical detection of COVID-19 signs. …”
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Internet of Things Security Detection Technology Based on Grey Association Decision Algorithm by Jie Chang, Xiaojun Zuo, Botao Hou, Lipeng Shi, Guanghua Zhang

“…Finally, the characteristics of grey relational analysis and rough set theory attribute reduction are used to form the basis of grey correlation decision-level fusion algorithm, to achieve effective processing of the data of the Internet of Things security monitoring system.…”
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