With the prevalence of machine learning in malware defense,hackers have tried to attack machine learning models to evade *** is generally difficult to explore the details of malware detection models,hackers can adopt ...
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With the prevalence of machine learning in malware defense,hackers have tried to attack machine learning models to evade *** is generally difficult to explore the details of malware detection models,hackers can adopt fuzzing attack to manipulate the features of the malware closer to benign programs on the premise of retaining their *** this paper,attack and defense methods on malware detection models based on machine learning algorithms were ***,we designed a fuzzing attack method by randomly modifying features to evade *** fuzzing attack can effectively descend the accuracy of machine learning model with single *** an adversarial malware detection model MaliFuzz is proposed to defend fuzzing *** from the ordinary single feature detection model,the combined features by static and dynamic analysis to improve the defense ability are *** experiment results show that the adversarial malware detection model with combined features can deal with the *** methods designed in this paper have great significance in improving the security of malware detection models and have good application prospects.
In the field of computer-aided drug discovery, identifying promising drug candidates from small molecule libraries requires meaningful molecular embeddings for downstream tasks, such as property prediction. However, o...
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In the era of advanced machine learning techniques,the development of accurate predictive models for complex medical conditions,such as thyroid cancer,has shown remarkable *** predictivemodels for thyroid cancer enhan...
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In the era of advanced machine learning techniques,the development of accurate predictive models for complex medical conditions,such as thyroid cancer,has shown remarkable *** predictivemodels for thyroid cancer enhance early detection,improve resource allocation,and reduce ***,the widespread adoption of these models in clinical practice demands predictive performance along with interpretability and *** paper proposes a novel association-rule based feature-integratedmachine learning model which shows better classification and prediction accuracy than present *** study also focuses on the application of SHapley Additive exPlanations(SHAP)values as a powerful tool for explaining thyroid cancer prediction *** the proposed method,the association-rule based feature integration framework identifies frequently occurring attribute combinations in the *** original dataset is used in trainingmachine learning models,and further used in generating SHAP values *** the next phase,the dataset is integrated with the dominant feature sets identified through association-rule based *** new integrated dataset is used in re-training the machine learning *** new SHAP values generated from these models help in validating the contributions of feature sets in predicting *** conventional machine learning models lack interpretability,which can hinder their integration into clinical decision-making *** this study,the SHAP values are introduced along with association-rule based feature integration as a comprehensive framework for understanding the contributions of feature sets inmodelling the *** study discusses the importance of reliable predictive models for early diagnosis of thyroid cancer,and a validation framework of *** proposed model shows an accuracy of 93.48%.Performance metrics such as precision,recall,F1-score,and the area un
The earthquake early warning (EEW) system provides advance notice of potentially damaging ground shaking. In EEW, early estimation of magnitude is crucial for timely rescue operations. A set of thirty-four features is...
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The earthquake early warning (EEW) system provides advance notice of potentially damaging ground shaking. In EEW, early estimation of magnitude is crucial for timely rescue operations. A set of thirty-four features is extracted using the primary wave earthquake precursor signal and site-specific information. In Japan's earthquake magnitude dataset, there is a chance of a high imbalance concerning the earthquakes above strong impact. This imbalance causes a high prediction error while training advanced machine learning or deep learning models. In this work, Conditional Tabular Generative Adversarial Networks (CTGAN), a deep machine learning tool, is utilized to learn the characteristics of the first arrival of earthquake P-waves and generate a synthetic dataset based on this information. The result obtained using actual and mixed (synthetic and actual) datasets will be used for training the stacked ensemble magnitude prediction model, MagPred, designed specifically for this study. There are 13295, 3989, and 1710 records designated for training, testing, and validation. The mean absolute error of the test dataset for single station magnitude detection using early three, four, and five seconds of P wave are 0.41, 0.40, and 0.38 MJMA. The study demonstrates that the Generative Adversarial Networks (GANs) can provide a good result for single-station magnitude prediction. The study can be effective where less seismic data is available. The study shows that the machine learning method yields better magnitude detection results compared with the several regression models. The multi-station magnitude prediction study has been conducted on prominent Osaka, Off Fukushima, and Kumamoto earthquakes. Furthermore, to validate the performance of the model, an inter-region study has been performed on the earthquakes of the India or Nepal region. The study demonstrates that GANs can discover effective magnitude estimation compared with non-GAN-based methods. This has a high potential
In recent years, deep neural networks (DNNs) have made significant progress on face recognition (FR). However, DNNs have been found to be vulnerable to adversarial examples, leading to fatal consequences in real-world...
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Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and ***,achieving precise segmentation remains a challenge due to various factors,in...
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Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and ***,achieving precise segmentation remains a challenge due to various factors,including scattering noise,low contrast,and limited resolution in ultrasound *** existing segmentation models have made progress,they still suffer from several limitations,such as high error rates,low generalizability,overfitting,limited feature learning capability,*** address these challenges,this paper proposes a Multi-level Relation Transformer-based U-Net(MLRT-UNet)to improve thyroid nodule *** MLRTUNet leverages a novel Relation Transformer,which processes images at multiple scales,overcoming the limitations of traditional encoding *** transformer integrates both local and global features effectively through selfattention and cross-attention units,capturing intricate relationships within the *** approach also introduces a Co-operative Transformer Fusion(CTF)module to combine multi-scale features from different encoding layers,enhancing the model’s ability to capture complex patterns in the ***,the Relation Transformer block enhances long-distance dependencies during the decoding process,improving segmentation *** results showthat the MLRT-UNet achieves high segmentation accuracy,reaching 98.2% on the Digital Database Thyroid Image(DDT)dataset,97.8% on the Thyroid Nodule 3493(TG3K)dataset,and 98.2% on the Thyroid Nodule3K(TN3K)*** findings demonstrate that the proposed method significantly enhances the accuracy of thyroid nodule segmentation,addressing the limitations of existing models.
In this paper,a robust and consistent COVID-19 emergency decision-making approach is proposed based on q-rung linear diophantine fuzzy set(q-RLDFS),differential evolutionary(DE)optimization principles,and evidential r...
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In this paper,a robust and consistent COVID-19 emergency decision-making approach is proposed based on q-rung linear diophantine fuzzy set(q-RLDFS),differential evolutionary(DE)optimization principles,and evidential reasoning(ER)*** proposed approach uses q-RLDFS in order to represent the evaluating values of the alternatives corresponding to the *** optimization is used to obtain the optimal weights of the attributes,and ER methodology is used to compute the aggregated q-rung linear diophantine fuzzy values(q-RLDFVs)of each *** the score values of alternatives are computed based on the aggregated *** alternative with the maximum score value is selected as a better *** applicability of the proposed approach has been illustrated in COVID-19 emergency decision-making system and sustainable energy planning ***,we have validated the proposed approach with a numerical ***,a comparative study is provided with the existing models,where the proposed approach is found to be robust to perform better and consistent in uncertain environments.
Emotion recognition is crucial in human-computer interaction and psychological research, utilizing modalities such as facial expressions, voice intonations, and EEG signals. This research investigates AI-driven techni...
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Although lots of research has been done in recognizing facial expressions,there is still a need to increase the accuracy of facial expression recognition,particularly under uncontrolled *** use of Local Directional Pa...
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Although lots of research has been done in recognizing facial expressions,there is still a need to increase the accuracy of facial expression recognition,particularly under uncontrolled *** use of Local Directional Patterns(LDP),which has good characteristics for emotion detection has yielded encouraging *** innova-tive end-to-end learnable High Response-based Local Directional Pattern(HR-LDP)network for facial emotion recognition is implemented by employing fixed convolutional filters in the proposed *** combining learnable convolutional layers with fixed-parameter HR-LDP layers made up of eight Kirsch filters and derivable simulated gate functions,this network considerably minimizes the number of network *** cost of the parameters in our fully linked layers is up to 64 times lesser than those in currently used deep learning-based detection *** seven well-known databases,including JAFFE,CK+,MMI,SFEW,OULU-CASIA and MUG,the recognition rates for seven-class facial expression recognition are 99.36%,99.2%,97.8%,60.4%,91.1%and 90.1%,*** results demonstrate the advantage of the proposed work over cutting-edge techniques.
Predicting the metastatic direction of primary breast cancer (BC), thus assisting physicians in precise treatment, strict follow-up, and effectively improving the prognosis. The clinical data of 293,946 patients with ...
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