Optimised hybrid deep learning classification model for kidney stone diagnosis by Y Jini Jacob, Bethanney Janney J, Hemalatha RJ, Preethi S

“…An optimized AlexNet-GRU model is introduced in this work for detection of kidney stone, feature extraction, and classification. …”
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An Intelligent Model for Facial Skin Colour Detection by Chih-Huang Yen, Pin-Yuan Huang, Po-Kai Yang

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Detection and visualisation of terrain edges in slope failures by Slámová Martina, Sitko Roman, Kadlečík Roman, Skurčák Ľuboš, Chudý František

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Robustness evaluation of commercial liveness detection platform by Pengcheng WANG, Haibin ZHENG, Jianfei ZOU, Ling PANG, Hu LI, Jinyin CHEN

“…Liveness detection technology has become an important application in daily life, and it is used in scenarios including mobile phone face unlock, face payment, and remote authentication.However, if attackers use fake video generation technology to generate realistic face-swapping videos to attack the living body detection system in the above scenarios, it will pose a huge threat to the security of these scenarios.Aiming at this problem, four state-of-the-art Deepfake technologies were used to generate a large number of face-changing pictures and videos as test samples, and use these samples to test the online API interfaces of commercial live detection platforms such as Baidu and Tencent.The test results show that the detection success rate of Deepfake images is generally very low by the major commercial live detection platforms currently used, and they are more sensitive to the quality of images, and the false detection rate of real images is also high.The main reason for the analysis may be that these platforms were mainly designed for traditional living detection attack methods such as printing photo attacks, screen remake attacks, and silicone mask attacks, and did not integrate advanced face-changing detection technology into their liveness detection.In the algorithm, these platforms cannot effectively deal with Deepfake attacks.Therefore, an integrated live detection method Integranet was proposed, which was obtained by integrating four detection algorithms for different image features.It could effectively detect traditional attack methods such as printed photos and screen remakes.It could also effectively detect against advanced Deepfake attacks.The detection effect of Integranet was verified on the test data set.The results show that the detection success rate of Deepfake images by proposed Integranet detection method is at least 35% higher than that of major commercial live detection platforms.…”
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Plant disease detection with generative adversarial networks by Garam Han, Derek Kwaku Pobi Asiedu, Kwabena Ebo Bennin

“…Generative Adversarial Networks (GANs) have emerged as a promising approach for enhancing image detection and facilitating image classification. Deep learning models, which exhibit high classification accuracy, have proven advantageous over conventional approaches for Plant disease detection (PDD). …”
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PLANT DISEASE DETECTION USING DEEP LEARNING by Amrita, Sanjay Kumar Nayak, Rajiv Kumar, Sakshi Tomar

“…In the proposed method, five deep CNN models such as Sequential, ResNet50, InceptionV3, VGG16, and VGG19 are used. Comparative analysis of the implemented models suggested that DL helps in extracting the significant features and biomarkers related to these diseases. …”
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Anomaly Detection Dataset for Industrial Control Systems by Alireza Dehlaghi-Ghadim, Mahshid Helali Moghadam, Ali Balador, Hans Hansson

“…Although a few commonly used datasets may not reflect realistic ICS network data, lack necessary features for effective anomaly detection, or be outdated. …”
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Methods for Detecting Subvisible Particles in Medicines (Review) by S. I. Kuleshova, M. L. Goryunova, O. N. Vysochanskaya

“…The microscopic method with flow visualization and the scanning electron microscopy method are not used for routine control of medicines, but are important additional tools for determining the features of the granulometric composition and detecting undesirable contamination by foreign particles in medicines.CONCLUSION. …”
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Detecting tropical freshly-opened swidden fields using a combined algorithm of continuous change detection and support vector machine by Ningsang Jiang, Peng Li, Zhiming Feng

“…The development of data-, information-, and knowledge-based algorithms for monitoring swidden agriculture requires integration of multi-dimensional features. The first part of the Continuous Change Detection and Classification (CCDC) algorithm holds promising potential in capturing abrupt changes. …”
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An approach to malignant mammary phyllodes tumors detection by Ilić Ivan, Ranđelović Pavle, Ilić Ratko, Katić Vuka, Milentijević Maja, Veličković Ljubinka, Krstić Miljan

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基于脑影像及临床特征的机器学习模型预测缺血性卒中后心房颤动 A Machine Learning Model Based on Brain Imaging and Clinical Features for Predicting Atrial Fibrillation Detected after Stroke... by 张栗源1，刘涛2，姜勇1，李子孝1，3，王拥军1，3，杨晓萌1，3(ZHANG Liyuan1, LIU Tao2, JIANG Yong1, LI Zixiao1,3, WANG Yongjun1,3, YANG Xiaomeng1,3 )

“…During the feature engineering stage, the Spearman’s rank correlation coefficient analysis was applied (preset threshold |ρ|>0.8) to eliminate highly collinear features. …”
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A Novel Deep Learning Model for Human Skeleton Estimation Using FMCW Radar by Parma Hadi Rantelinggi, Xintong Shi, Mondher Bouazizi, Tomoaki Ohtsuki

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Multimodal malware classification using proposed ensemble deep neural network framework by Sadia Nazim, Muhammad Mansoor Alam, Safdar Rizvi, Jawahir Che Mustapha, Syed Shujaa Hussain, Mazliham Mohd Su’ud

“…This study presents a state-of-the-art malware analysis framework that employs a multimodal approach by integrating malware images and numeric features for effective malware classification. …”
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Concept of natural genome reconstruction. Part 3. Analysis of changes in the amount of telomeric DNA in colony cells as a new amplified feature that arose during the processing of... by V. S. Ruzanova, S. G. Oshikhmina, G. S. Ritter, E. V. Dolgova, S. S. Kirikovich, E. V. Levites, Y. R. Efremov, T. V. Karamysheva, A. G. Bogomolov, M. I. Meschaninova, A. L. Mamaev, O. S. Taranov, S. V. Sidorov, S. D. Nikonov, O. Y. Leplina, A. A. Ostanin, E. R. Chernykh, N. A. Kolchanov, A. S. Proskurina, S. S. Bogachev

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Enhancing Traffic Accident Severity Prediction: Feature Identification Using Explainable AI by Jamal Alotaibi

“…This study aims to explore critical features related to traffic accident detection and prevention. …”
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Deep Learning-Based Speech Emotion Recognition Using Multi-Level Fusion of Concurrent Features by Samuel, Kakuba, Alwin, Poulose, Dong, Seog Han, Senior Member, Ieee

“…The detection and classification of emotional states in speech involves the analysis of audio signals and text transcriptions. …”
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Highway subgrade stability prediction model based on depth separation convolutional fusion network by Yubian Wang

“…To promote the ability of high-precision highway maintenance and detection and solve the situation of false detection or missing detection of road defects, it is necessary to establish a monitoring mechanism of multi-scale feature fusion. …”
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Automated facial feature evaluation system to prevent stress of head fixed mice. by Anna Nasr, Gianna Rettinger, Huibert D Mansvelder, Robert N S Sachdev, Matthew E Larkum

“…In this study, we present MouseCare, an automated software solution for immediate stress detection by real-time facial feature video analysis in head-fixed mice. …”
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Application of a Texture Appearance Model for Segmentation of Lung Nodules on Computed Tomography of the Chest by F. Shariaty, V. A. Pavlov, S. V. Zavjalov, M. Orooji, T. M. Pervunina

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ST-CFI: Swin Transformer with convolutional feature interactions for identifying plant diseases by Sheng Yu, Li Xie, Liang Dai

“…This paper introduces the Swin Transformer with Convolutional Feature Interactions (ST-CFI), a state-of-the-art deep learning framework designed for detecting plant diseases through the analysis of leaf images. …”
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