Machine Learning Strategies for Forecasting Mannosylerythritol Lipid Production Through Fermentation: A Proof-of-Concept by Carolina A. Vares, Sofia P. Agostinho, Ana L. N. Fred, Nuno T. Faria, Carlos A. V. Rodrigues

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Robust face forgery detection integrating local texture and global texture information by Rongrong Gong, Ruiyi He, Dengyong Zhang, Arun Kumar Sangaiah, Mohammed J. F. Alenazi

“…By examining forgery traces from multiple perspectives, we have developed an adaptive feature fusion module to facilitate interactive feature fusion between the two streams. …”
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YOLO-v4 Small Object Detection Algorithm Fused With L-α by ZHANG Ning, YU Ming, REN Honge, AO Rui, ZHAO Long

“… The detection ability for small object is still need to be improved urgently in spite of the rapidly developing object detection technology based on deep learning at present.Compared with large objects, small object detection tasks hold drawbacks of low resolution and feature loss which leads to that many general algorithms cannot be directly applied to small object detection.The feature pyramid fusion can effectively combine the features of deep and shallow layers to enhance the performance.To solve the problem most models existing ignoring the imbalance of information during the feature fusion between adjacent layers, it is proposed to integrate the idea of fusion factor into the PANet of YOLOv4, use the fusion factor L-αto control the amount of information transmitted from the deep layer to the shallow, so as to effectively improve the efficiency of information fusion and enhance the ability of YOLO-v4 for small objects detection.With the addition of L-αin YOLO- V4 model, the experiment results show that the APtiny50and APsmall50on the TinyPerson are improved by 2.14% and 1.85% respectively, while the AP and APS on the MS COCO are separately increased by 1.4% and 2.7%.It is proved that this improved method is effective for small object detection with the evidence of better result than other small object detection algorithms.…”
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Robust Adversarial Example Detection Algorithm Based on High-Level Feature Differences by Hua Mu, Chenggang Li, Anjie Peng, Yangyang Wang, Zhenyu Liang

“…A range of detection algorithms has been developed to differentiate between benign samples and adversarial examples. …”
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Intelligent anti-jamming communication technology with electromagnetic spectrum feature cognition. by Hui Zhao, Guobin Zhao, Xichun Wang, Zhonghui Zhang, Xianchao Xun

“…By extracting and processing signal features through deep neural networks, and dynamically adjusting communication strategies with near-end optimization, the model effectively addresses the recognition and prediction of signal transmission feature parameters in target communication systems, generates interference signals with the same feature parameters, and achieves effective interference suppression. …”
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Feature of Ocular-Ischemic Syndrome in Patients with Cardiovascular Pathology. Literature Review by M. A. Frolov, N. A. Sakhovskaya, A. M. Frolov, A. D. Pryamikov

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On Image Ｒecognition Using Bidirectional Feature Pyramid and Deep Neural Network by ZHAO Sheng, ZHAO Li

“…Object recognition is one of the fundamental tasks in the area of computer vision. The development of deep neural networks advances the object recognition. …”
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Zinc protection of fertilized eggs is an ancient feature of sexual reproduction in animals. by Katherine L Wozniak, Rachel E Bainbridge, Dominique W Summerville, Maiwase Tembo, Wesley A Phelps, Monica L Sauer, Bennett W Wisner, Madelyn E Czekalski, Srikavya Pasumarthy, Meghan L Hanson, Melania B Linderman, Catherine H Luu, Madison E Boehm, Steven M Sanders, Katherine M Buckley, Daniel J Bain, Matthew L Nicotra, Miler T Lee, Anne E Carlson

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Research on filter-based adversarial feature selection against evasion attacks by Qimeng HUANG, Miaomiao WU, Yun LI

“…With the rapid development and widespread application of machine learning technology, its security has attracted increasing attention, leading to a growing interest in adversarial machine learning.In adversarial scenarios, machine learning techniques are threatened by attacks that manipulate a small number of samples to induce misclassification, resulting in serious consequences in various domains such as spam detection, traffic signal recognition, and network intrusion detection.An evaluation criterion for filter-based adversarial feature selection was proposed, based on the minimum redundancy and maximum relevance (mRMR) method, while considering security metrics against evasion attacks.Additionally, a robust adversarial feature selection algorithm was introduced, named SDPOSS, which was based on the decomposition-based Pareto optimization for subset selection (DPOSS) algorithm.SDPOSS didn’t depend on subsequent models and effectively handles large-scale high-dimensional feature spaces.Experimental results demonstrate that as the number of decompositions increases, the runtime of SDPOSS decreases linearly, while achieving excellent classification performance.Moreover, SDPOSS exhibits strong robustness against evasion attacks, providing new insights for adversarial machine learning.…”
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Digital Art Feature Association Mining Based on the Machine Learning Algorithm by Zhiying Wu, Yuan Chen

“…With the development of computer hardware and software, digital art is a new discipline. …”
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An Indoor Scene Classification Method for Service Robot Based on CNN Feature by Shaopeng Liu, Guohui Tian

“…To solve this problem, an indoor scene classification method is proposed in this paper, which utilizes CNN feature of scene images to generate scene category features to classify scenes by a novel feature matching algorithm. …”
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Explorative Binary Gray Wolf Optimizer with Quadratic Interpolation for Feature Selection by Yijie Zhang, Yuhang Cai

“…The high dimensionality of large datasets can severely impact the data mining process. Therefore, feature selection becomes an essential preprocessing stage, aimed at reducing the dimensionality of the dataset by selecting the most informative features while improving classification accuracy. …”
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A feature enhancement FCOS algorithm for dynamic traffic object detection by Tuqiang Zhou, Wei Liu, Haoran Li

“…The development of object detection plays an important role in the realisation of fully autonomous driving, and the feature extraction is the key step for object detection. …”
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Feature Generation with Genetic Algorithms for Imagined Speech Electroencephalogram Signal Classification by Edgar Lara-Arellano, Andras Takacs, Saul Tovar-Arriaga, Juvenal Rodríguez-Reséndiz

“…This algorithm can efficiently explore ample feature space and identify the most relevant features for the task. …”
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Odor classification: Exploring feature performance and imbalanced data learning techniques. by Durgesh Ameta, Surendra Kumar, Rishav Mishra, Laxmidhar Behera, Aniruddha Chakraborty, Tushar Sandhan

“…Our explainable model sheds further light on features and odour relations. The results hold the potential to guide the development of the electric nose and our dataset will be made publicly available.…”
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Densenet Model for Binary Glaucoma Classification Performance Assessment with Texture Feature by Wildan Jameel Hadi, Amal Sufiuh Ajrash, Sahar Muneam Salman, Mays jalal jasim, Mina Taha Ibrahim

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Optimizing Feature Selection for IOT Intrusion Detection Using RFE and PSO by zahraa mehssen agheeb Alhamdawee

“…The best results were obtained using RF and kNN classifiers that were trained with features selected by RFE. kNN benefits from the smaller feature space since it focuses on distance measures, which are more successful with a refined set of features. …”
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FEATURE SELECTION AND CLASSIFICATION OF LEUKEMIC CELLS USING IOT AND MACHINE LEARNING by K. R.Vineetha, Kovvuri N. Bhargavi, G. L. Narasamba Vanguri, Jenifer Mahilraj, V. Kannan

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