Journal articles on the topic 'Naive Bayes fusion'
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Zhou, Xiaoliang, Donghua Wu, Zitong You, Dongyang Wu, Ning Ye, and Li Zhang. "Adaptive Two-Index Fusion Attribute-Weighted Naive Bayes." Electronics 11, no. 19 (September 29, 2022): 3126. http://dx.doi.org/10.3390/electronics11193126.
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Naive Bayes (NB) is one of the essential algorithms in data mining. However, it is rarely used in reality because of the attribute independence assumption. Researchers have proposed many improved NB methods to alleviate this assumption. Among these methods, due to its high efficiency and easy implementation, the filter-attribute-weighted NB methods have received great attentions. However, there still exist several challenges, such as the poor representation ability for a single index and the fusion problem of two indexes. To overcome the above challenges, we propose a general framework of an adaptive two-index fusion attribute-weighted NB (ATFNB). Two types of data description category are used to represent the correlation between classes and attributes, the intercorrelation between attributes and attributes, respectively. ATFNB can select any one index from each category. Then, we introduce a regulatory factor β to fuse two indexes, which can adaptively adjust the optimal ratio of any two indexes on various datasets. Furthermore, a range query method is proposed to infer the optimal interval of regulatory factor β. Finally, the weight of each attribute is calculated using the optimal value β and is integrated into an NB classifier to improve the accuracy. The experimental results on 50 benchmark datasets and a Flavia dataset show that ATFNB outperforms the basic NB and state-of-the-art filter-weighted NB models. In addition, the ATFNB framework can improve the existing two-index NB model by introducing the adaptive regulatory factor β. Auxiliary experimental results demonstrate the improved model significantly increases the accuracy compared to the original model without the adaptive regulatory factor β.
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Ou, Guiliang, Yulin He, Philippe Fournier-Viger, and Joshua Zhexue Huang. "A Novel Mixed-Attribute Fusion-Based Naive Bayesian Classifier." Applied Sciences 12, no. 20 (October 17, 2022): 10443. http://dx.doi.org/10.3390/app122010443.
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The Naive Bayesian classifier (NBC) is a well-known classification model that has a simple structure, low training complexity, excellent scalability, and good classification performances. However, the NBC has two key limitations: (1) it is built upon the strong assumption that condition attributes are independent, which often does not hold in real-life, and (2) the NBC does not handle continuous attributes well. To overcome these limitations, this paper presents a novel approach for NBC construction, called mixed-attribute fusion-based NBC (MAF-NBC). It alleviates the two aforementioned limitations by relying on a mixed-attribute fusion mechanism with an improved autoencoder neural network for NBC construction. MAF-NBC transforms the original mixed attributes of a data set into a series of encoded attributes with maximum independence as a pre-processing step. To guarantee the generation of useful encoded attributes, an efficient objective function is designed to optimize the weights of the autoencoder neural network by considering both the encoding error and the attribute’s dependence. A series of persuasive experiments was conducted to validate the feasibility, rationality, and effectiveness of the designed MAF-NBC approach. Results demonstrate that MAF-NBC has superior classification performance than eight state-of-the-art Bayesian algorithms, namely the discretization-based NBC (Dis-NBC), flexible naive Bayes (FNB), tree-augmented naive (TAN) Bayes, averaged one-dependent estimator (AODE), hidden naive Bayes (HNB), deep feature weighting for NBC (DFW-NBC), correlation-based feature weighting filter for NBC (CFW-NBC), and independent component analysis-based NBC (ICA-NBC).
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Zhang, Yinghui, Hongjun Wang, Hanxiong Liu, Yan Yang, and Qin Chen. "Medical Image Segmentation Fusion Based on Finite Naive Bayes Mixture Model." Journal of Medical Imaging and Health Informatics 6, no. 8 (December 1, 2016): 1865–71. http://dx.doi.org/10.1166/jmihi.2016.1939.
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Chen, Xiaoping, Jiamin Lin, Han Huang, and Yunpeng Huang. "Impact Factors on Posterior Modified Transfacet Debridement for Thoracic Spinal Tuberculosis Basing on Regression and Classification Analysis." Scientific Programming 2020 (August 1, 2020): 1–6. http://dx.doi.org/10.1155/2020/8892815.
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Posterior transfacet approach has been proved to be a safe and effective access to treat thoracic disc herniation. However, the influencing factors of posterior modified transarticular debridement for thoracic tuberculosis have not been reported in the clinical literature. From 2009 to 2014, 37 patients with TST underwent a posterior modified transfacet debridement, interbody fusion following posterior instrumentation, under the cover of 18 months of antituberculosis chemotherapy. The patients were evaluated preoperatively and postoperatively in terms of Frankel Grade, visual analog scale (VAS) pain score, kyphotic Cobb angle, and bone fusion. Blood loss (positive correlation) and focal debridement (positive correlation) could affect operative time. Operative time (positive correlation) could affect blood loss. While, age (positive correlation), PostE (negative correlation), and T_FocalDebridement (positive correlation) could affect bone fusion. The accuracy of naive bayes classifier model is 86.11%. Our preliminary results show that blood loss and focal debridement could affect operative time; operative time could affect blood loss; age, PostE, and T_FocalDebridement could affect bone fusion; the naive Bayes classifier model can predict the KirkaldyWillis accurately.
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Sun, Shuang, Li Liang, Ming Li, and Xin Li. "Multidamage Detection of Bridges Using Rough Set Theory and Naive-Bayes Classifier." Mathematical Problems in Engineering 2018 (May 27, 2018): 1–13. http://dx.doi.org/10.1155/2018/6752456.
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This paper is intended to introduce a two-stage detection method to solve the multidamage problem in bridges. Vibration analysis is conducted to acquire the dynamic fingerprints which are regarded as information sources. Bayesian fusion is used to integrate these sources and preliminarily locate the damage. Then, the RSNB method which combines rough set theory and Naive-Bayes classifier is proposed to simplify the sample dimensions and fuse the remaining attributes for damage extent detection. A numerical simulation of a real structure, the Sishui Bridge in Shenyang, China, is conducted to validate the effectiveness of the proposed detection method. Data fusion based method is compared with single-valued index method at the damage localization stage. The proposed RSNB method is compared with the Back Propagation Neural Network (BPNN) method at the damage qualification stage. The results show that the proposed two-stage damage detection method has better performances in regard to transparency, accuracy, efficiency, noise robustness, and stability. Furthermore, an ambient excitation modal test was carried out on the bridge to obtain the vibration responses and assess the damage condition with the proposed method. This novel approach is applicable for early damage detection and provides a basis for bridge management and maintenance.
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Wang, Yi, Yuhao Huang, Kai Yang, Zhihan Chen, and Cheng Luo. "Generator Fault Classification Method Based on Multi-Source Information Fusion Naive Bayes Classification Algorithm." Energies 15, no. 24 (December 19, 2022): 9635. http://dx.doi.org/10.3390/en15249635.
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The existing motor fault classification methods mostly use sensors to detect a single fault feature, which makes it difficult to ensure high diagnostic accuracy. In this paper, a motor fault classification method based on multi-source information fusion Naive Bayes classification algorithm is proposed. Firstly, this paper introduces the concept and advantages of multi-source information fusion, as well as its problems of miscellaneous information and inconsistent data magnitude. For example, as this paper classifies the fault of generators, there are many physical quantities, such as voltage, current and temperature, which are not in the same dimension, therefore it is difficult to fuse. Then, aiming at the corresponding problems, this paper uses a PCA dimension reduction method to remove redundant information and reduce the dimension of multi-dimensional complex information. Aiming at the problem of unequal data magnitude, the interval mapping method is adopted to effectively solve the misjudgment caused by unequal data magnitude. After the initial multi-source information processing, the classical Naive Bayes classification algorithm is used for fault classification, and the algorithm diagnosis and verification are carried out according to the statistical fault data. Use of the algorithm increases accuracy to more than 97%.
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Femina, Bahari T., and Sudheep Elayidom M. "A Novel Fuzzy Linguistic Fusion Approach to Naive Bayes Classifier for Decision Making Applications." International Journal on Advanced Science, Engineering and Information Technology 10, no. 5 (October 19, 2020): 1889. http://dx.doi.org/10.18517/ijaseit.10.5.8186.
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Anisuzzaman, D. M., and Abdus Salam. "Authorship Attribution for Bengali Language Using the Fusion of N-Gram and Naive Bayes Algorithms." International Journal of Information Technology and Computer Science 10, no. 10 (October 8, 2018): 11–21. http://dx.doi.org/10.5815/ijitcs.2018.10.02.
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Yang, Qiufen, and Yan Lu. "Driving Detection Based on the Multifeature Fusion." Journal of Control Science and Engineering 2022 (June 30, 2022): 1–7. http://dx.doi.org/10.1155/2022/8266295.
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In order to solve the problems of facial feature localization and driver fatigue state identification methods in driving fatigue detection, a driving detection method based on the multifeature fusion was proposed. This method uses a supervised descent algorithm to simultaneously locate multiple facial features of drivers. On the basis of blink, yawn and nod judgment, multiple characteristic values of blink frequency, yawn frequency, and nod frequency of drivers were extracted to establish a fatigue detection sample database, and a naive Bayes classifier was constructed to judge fatigue. When the driver appears fatigue driving, warning information is given in time in order to prevent traffic accidents. The experimental results show that two sample videos were selected for testing. The accuracy rate of video sample 1 and video sample 2 was 94.74% and 95.00%, respectively. Conclusion. In the actual driving environment video test results, the discriminant average accuracy of a driver fatigue state reaches 94.87%, which has a good performance.
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Soares, Elaine Anita De Melo Gomes, and Ronei Marcos Moraes. "Fusion of Online Assessment Methods for Gynecological Examination Training: a Feasibility Study." TEMA (São Carlos) 19, no. 3 (December 17, 2018): 423. http://dx.doi.org/10.5540/tema.2018.019.03.423.
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The objective of this paper was to determine if a fusion of online assessment methods is a feasible methodology for online assessment of performance of users inside virtual reality simulators. Three different forms of the Fuzzy Naive Bayes method based on statistical distributions were used to assess specific tasks and the fusion of information was performed by a Weighted Majority Voting system. Data was compiled representing a portion of the Gynecological Exam, which is a checkup examination that is routinely performed for women and is paramount in finding earlier cases of cervical cancer. Confusion matrices and Kappa coefficients were obtained using a Monte Carlo simulation for this method. From the analysis of these results, it is possible to confirm that this method performed well, with a substantial agreement degree.
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Mahjoub, Amel Ben, and Mohamed Atri. "A Flexible High-Level Fusion for an Accurate Human Action Recognition System." Journal of Circuits, Systems and Computers 29, no. 12 (February 19, 2020): 2050190. http://dx.doi.org/10.1142/s021812662050190x.
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Action recognition is a very effective method of computer vision areas. In the last few years, there has been a growing interest in Deep learning networks as the Long Short–Term Memory (LSTM) architectures due to their efficiency in long-term time sequence processing. In the light of these recent events in deep neural networks, there is now considerable concern about the development of an accurate action recognition approach with low complexity. This paper aims to introduce a method for learning depth activity videos based on the LSTM and the classification fusion. The first step consists in extracting compact depth video features. We start with the calculation of Depth Motion Maps (DMM) from each sequence. Then we encode and concatenate contour and texture DMM characteristics using the histogram-of-oriented-gradient and local-binary-patterns descriptors. The second step is the depth video classification based on the naive Bayes fusion approach. Training three classifiers, which are the collaborative representation classifier, the kernel-based extreme learning machine and the LSTM, is done separately to get classification scores. Finally, we fuse the classification score outputs of all classifiers with the naive Bayesian method to get a final predicted label. Our proposed method achieves a significant improvement in the recognition rate compared to previous work that has used Kinect v2 and UTD-MHAD human action datasets.
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Shaikh, Zuhaib Ahmed, David Van Hamme, Peter Veelaert, and Wilfried Philips. "Probabilistic Fusion for Pedestrian Detection from Thermal and Colour Images." Sensors 22, no. 22 (November 9, 2022): 8637. http://dx.doi.org/10.3390/s22228637.
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Pedestrian detection is an important research domain due to its relevance for autonomous and assisted driving, as well as its applications in security and industrial automation. Often, more than one type of sensor is used to cover a broader range of operating conditions than a single-sensor system would allow. However, it remains difficult to make pedestrian detection systems perform well in highly dynamic environments, often requiring extensive retraining of the algorithms for specific conditions to reach satisfactory accuracy, which, in turn, requires large, annotated datasets captured in these conditions. In this paper, we propose a probabilistic decision-level sensor fusion method based on naive Bayes to improve the efficiency of the system by combining the output of available pedestrian detectors for colour and thermal images without retraining. The results in this paper, obtained through long-term experiments, demonstrate the efficacy of our technique, its ability to work with non-registered images, and its adaptability to cope with situations when one of the sensors fails. The results also show that our proposed technique improves the overall accuracy of the system and could be very useful in several applications.
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Yang, Liangyu, Tianyu Shi, Jidong Lv, Yan Liu, Yakang Dai, and Ling Zou. "A multi-feature fusion decoding study for unilateral upper-limb fine motor imagery." Mathematical Biosciences and Engineering 20, no. 2 (2022): 2482–500. http://dx.doi.org/10.3934/mbe.2023116.
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<abstract><p>To address the fact that the classical motor imagination paradigm has no noticeable effect on the rehabilitation training of upper limbs in patients after stroke and the corresponding feature extraction algorithm is limited to a single domain, this paper describes the design of a unilateral upper-limb fine motor imagination paradigm and the collection of data from 20 healthy people. It presents a feature extraction algorithm for multi-domain fusion and compares the common spatial pattern (CSP), improved multiscale permutation entropy (IMPE) and multi-domain fusion features of all participants through the use of decision tree, linear discriminant analysis, naive Bayes, a support vector machine, k-nearest neighbor and ensemble classification precision algorithms in the ensemble classifier. For the same subject, the average classification accuracy improvement of the same classifier for multi-domain feature extraction relative to CSP feature results went up by 1.52%. The average classification accuracy improvement of the same classifier went up by 32.87% relative to the IMPE feature classification results. This study's unilateral fine motor imagery paradigm and multi-domain feature fusion algorithm provide new ideas for upper limb rehabilitation after stroke.</p></abstract>
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Candanedo, Inés Sittón, Sarah Rodríguez González, and Lilia Muñoz. "Diseño de un Modelo Predictivo en el Contexto Industria 4.0." KnE Engineering 3, no. 1 (February 11, 2018): 543. http://dx.doi.org/10.18502/keg.v3i1.1458.
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The Internet of Things (IoT), the development and installation of advanced sensors for data collection, computer solutions for remote connection and other disruptive technologies are marking a transformation process in the industry; giving rise to what various sectors have called the fourth industrial revolution or Industry 4.0. With this process of change, organizations face both new opportunities and challenges. This article focuses on the modeling and integration of industrial data, generated by sensors installed in machines. The extraction of patterns is proposed, using data fusion techniques that allow the design of a predictive maintenance model. Finally, a case study is presented with a database that is applied to the Naive Bayes Algorithm to obtain predictions.Keywords: Industry 4.0, Sensors, Internet of Things, Pattern Extraction, Omnibus Models.
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Sumesh, A., Dinu Thomas Thekkuden, Binoy B. Nair, K. Rameshkumar, and K. Mohandas. "Acoustic Signature Based Weld Quality Monitoring for SMAW Process Using Data Mining Algorithms." Applied Mechanics and Materials 813-814 (November 2015): 1104–13. http://dx.doi.org/10.4028/www.scientific.net/amm.813-814.1104.
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The quality of weld depends upon welding parameters and exposed environment conditions. Improper selection of welding process parameter is one of the important reasons for the occurrence of weld defect. In this work, arc sound signals are captured during the welding of carbon steel plates. Statistical features of the sound signals are extracted during the welding process. Data mining algorithms such as Naive Bayes, Support Vector Machines and Neural Network were used to classify the weld conditions according to the features of the sound signal. Two weld conditions namely good weld and weld with defects namely lack of fusion, and burn through were considered in this study. Classification efficiencies of machine learning algorithms were compared. Neural network is found to be producing better classification efficiency comparing with other algorithms considered in this study.
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Sun, Shousheng, and Shaoping Li. "Values of Intelligent Alarm System Under Photoelectric Sensor Networks." Journal of Nanoelectronics and Optoelectronics 16, no. 1 (January 1, 2021): 54–63. http://dx.doi.org/10.1166/jno.2021.2905.
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This work was aimed to construct the intelligent alarm system with multiple photoelectric sensors as the core in this study. The system is first designed the circuit with microprocessor as the core, and then, there was a principle analysis of photoelectric measurement in the height, speed, and temperature to design a network mode of photoelectric sensor, circuits to control security doors and manage password, substation, and the monitoring center. The fusion approach based on deep learning is designed for the data collected by security alarm system. The 1-dimensional (1-D) representation of 2-dimensional (2-D) data is also designed according to the most of key information represented by the eigenvalue set of singular value decomposition of data matrix. The original 1-D signal sequence and the characteristics after 1-D were for data fusion, which is applied to identify, thus improving the accuracy of the alarm system and reducing its labor cost. During the experiment, the data fusion method proposed in this study is compared with naive bayes (NB) method and the weighted majority voting (WMV) method. The random data sets are generated with the help of a Gaussian function. The extreme learning machine (ELM) neural network classifier and k-nearest neighbors (KNN) classifier are carried in the alarm system designed in this study, respectively. The simulation analysis shows that WMV can obtain better performance of information classification compared with NB and data fusion methods, so the accuracy of classification is improved obviously. Besides, the fusion results accuracy of WMV is greatly higher than the other two.
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Liao, Mengyi, Hengyao Duan, and Guangshuai Wang. "Application of Machine Learning Techniques to Detect the Children with Autism Spectrum Disorder." Journal of Healthcare Engineering 2022 (March 25, 2022): 1–10. http://dx.doi.org/10.1155/2022/9340027.
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Early detection of autism spectrum disorder (ASD) is highly beneficial to the health sustainability of children. Existing detection methods depend on the assessment of experts, which are subjective and costly. In this study, we proposed a machine learning approach that fuses physiological data (electroencephalography, EEG) and behavioral data (eye fixation and facial expression) to detect children with ASD. Its implementation can improve detection efficiency and reduce costs. First, we used an innovative approach to extract features of eye fixation, facial expression, and EEG data. Then, a hybrid fusion approach based on a weighted naive Bayes algorithm was presented for multimodal data fusion with a classification accuracy of 87.50%. Results suggest that the machine learning classification approach in this study is effective for the early detection of ASD. Confusion matrices and graphs demonstrate that eye fixation, facial expression, and EEG have different discriminative powers for the detection of ASD and typically developing children, and EEG may be the most discriminative information. The physiological and behavioral data have important complementary characteristics. Thus, the machine learning approach proposed in this study, which combines the complementary information, can significantly improve classification accuracy.
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Li, Zhiqiang, Juning Huang, and Weixuan Zhong. "Design of Computer-Aided Translation System Based on Naive Bayesian Algorithm." Computational Intelligence and Neuroscience 2022 (September 6, 2022): 1–10. http://dx.doi.org/10.1155/2022/1348991.
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With the progress of society and the rapid development of science and technology, computer translation technology has become an important auxiliary tool in the fields of software localization and technical translation. This realistic demand has prompted translators to pay more attention to computer translation and have made some useful explorations on this basis. This paper aims to study and discuss computer-aided translation systems based on the fusion of naive Bayesian algorithms. This paper theoretically analyzes some key technologies in computer-aided translation. Computer-aided translation refers to helping translators to translate texts with a series of tools and then proposes a Bayesian classification algorithm. Translation memory technology can solve many practical problems, especially in the machinery manufacturing industry, processing some sentences in documents, which can reduce repetitive labor, unify vocabulary, and make translation styles more coordinated. The experimental results of this paper show that applying the naive Bayes method to the computer-aided translation system can better classify the documents in the translation system, thereby improving the ability of computer-aided translation. When the proportion of professional terms in the article reaches 85%, computer-aided translation has an auxiliary role for the translator. When the proportion of professional terms in the article reaches about 95%, computer-assisted translation can efficiently speed up the work speed and quality of translators. Due to the prosperity of computer translation systems, the duplication of labor for translators has been significantly reduced, and this ensures the consistency of terminology and translation style, so that the fruits of labor are fully utilized.
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Guharoy, Rabel, Nanda Dulal Jana, and Suparna Biswas. "An Efficient Epileptic Seizure Detection Technique using Discrete Wavelet Transform and Machine Learning Classifiers." Journal of Physics: Conference Series 2286, no. 1 (July 1, 2022): 012013. http://dx.doi.org/10.1088/1742-6596/2286/1/012013.
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Abstract This paper presents an epilepsy detection method based on discrete wavelet transform (DWT) with Machine learning classifiers. Here DWT has been used for feature extraction as it provides a better decomposition of the signals in different frequency bands. At first, DWT has been applied to the EEG signal to extract the detail and approximate coefficients or different sub-bands. After the extraction of the coefficients, Principal component analysis (PCA) has been applied on different sub-bands and then a feature level fusion technique is used to extract the main features in low dimensional feature space. Three classifiers name: Support Vector Machine (SVM) classifier, K-Nearest-Neighbor (KNN) classifier, and Naive Bayes (NB) classifier have been used in the proposed work for classifying the EEG signals. The raised method is tested over Bonn databases and provides a maximum of 100% recognition accuracy for KNN, SVM, NB classifiers.
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Zhang, S. W., Q. Pan, H. C. Zhang, Z. C. Shao, and J. Y. Shi. "Prediction of protein homo-oligomer types by pseudo amino acid composition: Approached with an improved feature extraction and Naive Bayes Feature Fusion." Amino Acids 30, no. 4 (May 15, 2006): 461–68. http://dx.doi.org/10.1007/s00726-006-0263-8.
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Kumar, Sharan, Dr D.Jayadevappa, and Mamata V Shetty. "Fuzzy Deformable Based Fusion Approach for Tumor Segmentation and Classification in Brain MRI Images." International Journal of Engineering & Technology 7, no. 4.7 (September 27, 2018): 171. http://dx.doi.org/10.14419/ijet.v7i4.7.20538.
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In recent years, the automatic identification and classification of tumor regions have gained more interest due to accuracy and reduced time complexity. One of the important strategies in tumor identification is segmenting the image as tumor and nontumor region, and this helps the researchers more significantly, as the MRI image comes in different modalities. This work introduces novel optimization based strategy for segmenting and classifying the image. Initially, the MRI images in the database are subjected to pre-processing and given to the segmentation process. For segmentation, this work utilizes the deformable model, and Fuzzy C Means (FCM) algorithm and the resultant segmented images are hybridized through proposed Dolphin based Sine Cosine Algorithm, preferred to be Dolphin-SCA. After segmentation, the tumor and non tumor-related features are extracted using the power LBP operator. The extracted features are subjected to Fuzzy Naive Bayes classifier for the classification, and finally, the classifier finds the suitable tumor class labels. Here, the entire experimentation is done by taking the MRI images from the BRATS database, and evaluated based on sensitivity, specificity, accuracy and ROC metrics. The simulation results reveal the dominance of proposed scheme over other comparative models, and the proposed scheme achieved 95.249% accuracy.
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Sun, Hongjun, Feihong Yu, and Haiyan Xu. "Discriminating the Nature of Thyroid Nodules Using the Hybrid Method." Mathematical Problems in Engineering 2020 (August 7, 2020): 1–13. http://dx.doi.org/10.1155/2020/6147037.
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Prompt and correct diagnosis of benign and malignant thyroid nodules has always been a core issue in the clinical practice of thyroid nodules. Ultrasound imaging is one of the most common visualizing tools used by radiologists to identify the nature of thyroid nodules. However, visual assessment of nodules is difficult and often affected by inter- and intraobserver variabilities. This paper proposes a novel hybrid approach based on machine learning and information fusion to discriminate the nature of thyroid nodules. Statistical features are extracted from the B-mode ultrasound image while deep features are extracted from the shear-wave elastography image. Classifiers including logistic regression, Naive Bayes, and support vector machine are adopted to train classification models with statistical features and deep features, respectively, for comparison. A voting system with certain criteria is used to combine two classification results to obtain a better performance. Experimental and comparison results demonstrate that the proposed method classifies the thyroid nodules correctly and efficiently.
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Zhu, Wenjing, Shoufeng Shen, and Zhijun Zhang. "Improved Multiclassification of Schizophrenia Based on Xgboost and Information Fusion for Small Datasets." Computational and Mathematical Methods in Medicine 2022 (July 19, 2022): 1–11. http://dx.doi.org/10.1155/2022/1581958.
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To improve the performance in multiclass classification for small datasets, a new approach for schizophrenic classification is proposed in the present study. Firstly, the Xgboost classifier is introduced to discriminate the two subtypes of schizophrenia from health controls by analyzing the functional magnetic resonance imaging (fMRI) data, while the gray matter volume (GMV) and amplitude of low-frequency fluctuations (ALFF) are extracted as the features of classifiers. Then, the D-S combination rule of evidence is used to achieve fusion to determine the basic probability assignment based on the output of different classifiers. Finally, the algorithm is applied to classify 38 healthy controls, 16 deficit schizophrenic patients, and 31 nondeficit schizophrenic patients. 10-folds cross-validation method is used to assess classification performance. The results show the proposed algorithm with a sensitivity of 73.89%, which is higher than other classification algorithms, such as supported vector machine (SVM), logistic regression (LR), K -nearest neighbor (KNN) algorithm, random forest (RF), BP neural network (NN), classification and regression tree (CART), naive Bayes classifier (NB), extreme gradient boosting (Xgboost), and deep neural network (DNN). The accuracy of the fusion algorithm is higher than that of classifier based on the GMV or ALFF in the small datasets. The accuracy rate of the improved multiclassification method based on Xgboost and fusion algorithm is higher than that of other machine learning methods, which can further assist the diagnosis of clinical schizophrenia.
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Liu, Tao, Chaoyang Shen, Jingfa Lei, Zhiqiang Yin, Hong Sun, and Jingxiong Wu. "Magnetic-Acoustic Feature Extraction and Damage Fusion Evaluation of 45 Steel Specimens during Fatigue Process for Remanufacturing." Advances in Materials Science and Engineering 2022 (August 12, 2022): 1–13. http://dx.doi.org/10.1155/2022/1966794.
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To clarify the changes of the magnetic-acoustic features of 45 steel specimens during fatigue damage, an experimental platform was built to carry out magnetic memory and acoustic emission detection. The magnetic memory and acoustic emission signals of specimens in different damage states were collected, and the multi-scale entropy characteristics of magnetic memory signals, as well as the wavelet packet energy spectrum and singularity index characteristics of acoustic emission signals, were further extracted. A magnetic-acoustic feature fusion and damage assessment model was constructed by using Naive Bayes method. Results show that the average value of multi-scale entropy of normal magnetic field intensity Hp (y) increases gradually with the increase of fatigue cycles, and the average value of multi-scale entropy of magnetic field intensity gradient K gradually decreases. The cumulative ringing count and energy spectrum (proportion of frequency band 1) of acoustic emission signals decrease with the increase of fatigue cycles, while the amplitude singularity index gradually increases. The established model has high evaluation accuracy, and the conclusions of this paper can provide basic methods and data support for fatigue damage evaluation of remanufactured components.
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Wang, Chuncheng. "AI-Based Heterogenous Large-Scale English Translation Strategy." Mobile Information Systems 2022 (February 9, 2022): 1–10. http://dx.doi.org/10.1155/2022/8344814.
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English has become one of the most widely used languages in the world. If there is no good translation mechanism for such a widely used language, it will bring trouble to both study and life. At present, the world’s major platforms are committed to the study of English translation strategies. There are translation platforms from different regions and different translation mechanisms. These translation data from different translation platforms have the characteristics of large-scale, multisource, heterogeneity, high dimensions, and poor quality. However, such inconsistent translation data will increase the translation difficulty and translation time. Therefore, it is necessary to improve the quality of translation data to achieve a better translation effect. How to provide a large-scale and efficient translation strategy needs to integrate the translation strategies of various platforms to perform heterogeneous translation data cleaning and fusion based on machine learning. At first, this paper represents the multisource, heterogeneous translation data model as tree-augmented naive Bayes networks (TANs) and naturally captures the relationship between the datasets through the learning of TANs structure and the probability distribution of input attributes and tuples, using data probability value to complete the classification of translation data cleaning. Then, a multisource, heterogeneous translation data fusion model based on recurrent neural network (RNN) is constructed, and RNN is used to control the node data of hidden layer to enhance the fault-tolerant ability in the fusion process and complete the construction of fusion model. Finally, experimental results show that TANs-based translation data cleaning method can effectively improve the cleaning rate with an average improvement of approximately 10% and cleaning time with an average reduce about 5%. In addition, RNN-based multisource translation data fusion method improves the shortcomings of the traditional fusion model and improves the practicability of the fusion model in terms of root mean square error (RMSE), mean absolute percentage error (MAPE), fusion time, and integrity.
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Li, Gang, Qiangwei Liu, Shanmeng Zhao, Wenting Qiao, and Xueli Ren. "Automatic crack recognition for concrete bridges using a fully convolutional neural network and naive Bayes data fusion based on a visual detection system." Measurement Science and Technology 31, no. 7 (May 5, 2020): 075403. http://dx.doi.org/10.1088/1361-6501/ab79c8.
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Kamhi, Souha, Shuai Zhang, Mohamed Ait Amou, Mohamed Mouhafid, Imran Javaid, Isah Salim Ahmad, Isselmou Abd El Kader, and Ummay Kulsum. "Multi-Classification of Motor Imagery EEG Signals Using Bayesian Optimization-Based Average Ensemble Approach." Applied Sciences 12, no. 12 (June 7, 2022): 5807. http://dx.doi.org/10.3390/app12125807.
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Motor Imagery (MI) classification using electroencephalography (EEG) has been extensively applied in healthcare scenarios for rehabilitation aims. EEG signal decoding is a difficult process due to its complexity and poor signal-to-noise ratio. Convolutional neural networks (CNN) have demonstrated their ability to extract time–space characteristics from EEG signals for better classification results. However, to discover dynamic correlations in these signals, CNN models must be improved. Hyperparameter choice strongly affects the robustness of CNNs. It is still challenging since the manual tuning performed by domain experts lacks the high performance needed for real-life applications. To overcome these limitations, we presented a fusion of three optimum CNN models using the Average Ensemble strategy, a method that is utilized for the first time for MI movement classification. Moreover, we adopted the Bayesian Optimization (BO) algorithm to reach the optimal hyperparameters’ values. The experimental results demonstrate that without data augmentation, our approach reached 92% accuracy, whereas Linear Discriminate Analysis, Support Vector Machine, Random Forest, Multi-Layer Perceptron, and Gaussian Naive Bayes achieved 68%, 70%, 58%, 64%, and 40% accuracy, respectively. Further, we surpassed state-of-the-art strategies on the BCI competition IV-2a multiclass MI database by a wide margin, proving the benefit of combining the output of CNN models with automated hyperparameter tuning.
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Xu, Lijuan, Lihong Zhang, and Zhenhua Du. "Coastal Ecological Environment Monitoring and Protection System Based on Multisource Information Fusion Decision." Journal of Sensors 2021 (October 28, 2021): 1–15. http://dx.doi.org/10.1155/2021/5194700.
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With the problem of nuclear leakage being concerned by more and more industries, the research of coastal ecological environment monitoring has become more and more important. Therefore, it is necessary to study the current unsystematic coastal ecological environment monitoring and protection system. Aiming at the accuracy of feature fusion and representation of single short environment information, this paper compares the classification effects of the three fusion methods on four classifiers: logistic regression, SVM, random forest, and naive Bayes, to verify the effectiveness of LDA and DS model fusion and determine the consistency vector representation method of short environment information data. This paper collects and analyzes the coastal data in recent years using multisource information fusion decision-making. In this paper, DS (Dempster Shafer) evidence algorithm is used to collect the data of coastal salinization degree and air relative humidity, and then, the DS feature matching model is introduced to fuse the whole index system. The method in the article completes the standardized and standardized processing of monitoring data digital conversion, quality control, and data classification, forms interrelated four-dimensional spatiotemporal data, and establishes a distributed, object-oriented, Internet-oriented dynamic management real-time and delayed database. Finally, this paper carries out tree decision processing on the coastal ecological environment monitoring data of multisource information fusion, to achieve the extraction and intuitive analysis of special data, and puts forward targeted protection strategies for the coastal ecological environment according to the data results of the DS algorithm. The research shows that the number of indicators in multisource information fusion in this paper is 16, a total of 3251 data, 2866 meaningful information, and 1869 data including ecological cycle. These data are the results of the collection of multi-information data. Based on the multilevel nature of the existing marine environment three-dimensional monitoring system, the study established a comprehensive resource-guaranteed framework and divided it into four levels according to the level of the marine monitoring system: country, sea area, locality, and data access point. In specific analysis, the guarantee resources involved in each level are introduced. On the basis of in-depth analysis of the requirements of the marine environment three-dimensional monitoring system operation guarantee and the guarantee resource structure, the marine environment three-dimensional monitoring operation comprehensive guarantee system is described from the internal structure and the external connection. The DS algorithm extracts the status information resources of various marine environment three-dimensional monitoring systems, through the interaction of various subsystems, realizes the operation and maintenance of the monitoring system, and provides various technical supports such as system evaluation and failure analysis. After multisource information fusion and decision-making, it is obtained that the index equilibrium module in the DS algorithm in this paper is 0.52, the sensitivity is 0.68, and the independence is 0.42. Among them, the range of sensitivity is the largest. In the simulation results, the eco-economic coefficient can be increased from 12% to 36%. Therefore, using the method of multisource information fusion for quantitative index analysis can provide data support for coastal ecological environment detection, to establish a more perfect protection system.
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Tchakounté, Franklin, Athanase Esdras Yera Pagor, Jean Claude Kamgang, and Marcellin Atemkeng. "CIAA-RepDroid: A Fine-Grained and Probabilistic Reputation Scheme for Android Apps Based on Sentiment Analysis of Reviews." Future Internet 12, no. 9 (August 27, 2020): 145. http://dx.doi.org/10.3390/fi12090145.
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To keep its business reliable, Google is concerned to ensure the quality of apps on the store. One crucial aspect concerning quality is security. Security is achieved through Google Play protect and anti-malware solutions. However, they are not totally efficient since they rely on application features and application execution threads. Google provides additional elements to enable consumers to collectively evaluate applications providing their experiences via reviews or showing their satisfaction through rating. The latter is more informal and hides details of rating whereas the former is textually expressive but requires further processing to understand opinions behind it. Literature lacks approaches which mine reviews through sentiment analysis to extract useful information to improve the security aspects of provided applications. This work goes in this direction and in a fine-grained way, investigates in terms of confidentiality, integrity, availability, and authentication (CIAA). While assuming that reviews are reliable and not fake, the proposed approach determines review polarities based on CIAA-related keywords. We rely on the popular classifier Naive Bayes to classify reviews into positive, negative, and neutral sentiment. We then provide an aggregation model to fusion different polarities to obtain application global and CIAA reputations. Quantitative experiments have been conducted on 13 applications including e-banking, live messaging and anti-malware apps with a total of 1050 security-related reviews and 7,835,322 functionality-related reviews. Results show that 23% of applications (03 apps) have a reputation greater than 0.5 with an accent on integrity, authentication, and availability, while the remaining 77% has a polarity under 0.5. Developers should make a lot of effort in security while developing codes and that more efforts should be made to improve confidentiality reputation. Results also show that applications with good functionality-related reputation generally offer a bad security-related reputation. This situation means that even if the number of security reviews is low, it does not mean that the security aspect is not a consumer preoccupation. Unlike, developers put much more time to test whether applications work without errors even if they include possible security vulnerabilities. A quantitative comparison against well-known rating systems reveals the effectiveness and robustness of CIAA-RepDroid to repute apps in terms of security. CIAA-RepDroid can be associated with existing rating solutions to recommend developers exact CIAA aspects to improve within source codes.
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Rastiveis, H. "DECISION LEVEL FUSION OF LIDAR DATA AND AERIAL COLOR IMAGERY BASED ON BAYESIAN THEORY FOR URBAN AREA CLASSIFICATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1-W5 (December 11, 2015): 589–94. http://dx.doi.org/10.5194/isprsarchives-xl-1-w5-589-2015.
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Airborne Light Detection and Ranging (LiDAR) generates high-density 3D point clouds to provide a comprehensive information from object surfaces. Combining this data with aerial/satellite imagery is quite promising for improving land cover classification. In this study, fusion of LiDAR data and aerial imagery based on Bayesian theory in a three-level fusion algorithm is presented. In the first level, pixel-level fusion, the proper descriptors for both LiDAR and image data are extracted. In the next level of fusion, feature-level, using extracted features the area are classified into six classes of “Buildings”, “Trees”, “Asphalt Roads”, “Concrete roads”, “Grass” and “Cars” using Naïve Bayes classification algorithm. This classification is performed in three different strategies: (1) using merely LiDAR data, (2) using merely image data, and (3) using all extracted features from LiDAR and image. The results of three classifiers are integrated in the last phase, decision level fusion, based on Naïve Bayes algorithm. To evaluate the proposed algorithm, a high resolution color orthophoto and LiDAR data over the urban areas of Zeebruges, Belgium were applied. Obtained results from the decision level fusion phase revealed an improvement in overall accuracy and kappa coefficient.
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Aftab, Shabib, Sagheer Abbas, Taher M. Ghazal, Munir Ahmad, Hussam Al Hamadi, Chan Yeob Yeun, and Muhammad Adnan Khan. "A Cloud-Based Software Defect Prediction System Using Data and Decision-Level Machine Learning Fusion." Mathematics 11, no. 3 (January 26, 2023): 632. http://dx.doi.org/10.3390/math11030632.
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This research contributes an intelligent cloud-based software defect prediction system using data and decision-level machine learning fusion techniques. The proposed system detects the defective modules using a two-step prediction method. In the first step, the prediction is performed using three supervised machine learning techniques, including naïve Bayes, artificial neural network, and decision tree. These classification techniques are iteratively tuned until the maximum accuracy is achieved. In the second step, the final prediction is performed by fusing the accuracy of the used classifiers with a fuzzy logic-based system. The proposed fuzzy logic technique integrates the predictive accuracy of the used classifiers using eight if–then fuzzy rules in order to achieve a higher performance. In the study, to implement the proposed fusion-based defect prediction system, five datasets were fused, which were collected from the NASA repository, including CM1, MW1, PC1, PC3, and PC4. It was observed that the proposed intelligent system achieved a 91.05% accuracy for the fused dataset and outperformed other defect prediction techniques, including base classifiers and state-of-the-art ensemble techniques.
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T, Senthil Kumar. "Construction of Hybrid Deep Learning Model for Predicting Children Behavior based on their Emotional Reaction." March 2021 3, no. 1 (May 11, 2021): 29–43. http://dx.doi.org/10.36548/jitdw.2021.1.004.
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Emotion prediction, the sub-domain of sentiment analysis helps to analyze the emotion. Recently, the prediction of children’s behavior based on their present emotional activities is remaining as a challenging task. Henceforth, the deep learning algorithms are used to support and assist in the process of children’s behavior prediction by considering the emotional features with a good accuracy rate. Besides, this article presents the prediction of children’s behavior based on their emotion with the deep learning classifiers method. To analyze the performance, decision tree and naïve Bayes probability model are compared. Totally, 35 sample emotions are considered in the prediction process of deep learning classifier with a probability model. Furthermore, the hybrid emotions are incorporated in the proposed dataset. The comparison between both the decision tree and the Naïve Bayes method has been performed to predict the children’s emotions after the classification. Based on the probability model of naïve Bayes method and decision tree, naïve Bayes method provides good results in terms of recognition rate and prediction accuracy when compared to the decision tree method. Therefore, a fusion of these two algorithms is proposed for predicting the emotions involved in children’s behavior. This research article includes the combined algorithm mathematical proof of prediction based on the emotion samples. This article discusses about the future scope of the proposal and the obtained prediction results.
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Hamzah, Raseeda, Nursuriati Jamil, and Rosniza Roslan. "Development of Acoustical Feature Based Classifier Using Decision Fusion Technique for Malay Language Disfluencies Classification." Indonesian Journal of Electrical Engineering and Computer Science 8, no. 1 (October 1, 2017): 262. http://dx.doi.org/10.11591/ijeecs.v8.i1.pp262-267.
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<p>Speech disfluency such as filled pause (FP) is a hindrance in Automated Speech Recognition as it degrades the accuracy performance. Previous work of FP detection and classification have fused a number of acoustical features as fusion classification is known to improve classification results. This paper presents new decision fusion of two well-established acoustical features that are zero crossing rates (ZCR) and speech envelope (ENV) with eight popular acoustical features for classification of Malay language filled pause (FP) and elongation (ELO). Five hundred ELO and 500 FP are selected from a spontaneous speeches of a parliamentary session and Naïve Bayes classifier is used for the decision fusion classification. The proposed feature fusion produced better classification performance compared to single feature classification with the highest F-measure of 82% for both classes.</p>
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Chen, Fu-Chen, and Mohammad R. Jahanshahi. "NB-CNN: Deep Learning-Based Crack Detection Using Convolutional Neural Network and Naïve Bayes Data Fusion." IEEE Transactions on Industrial Electronics 65, no. 5 (May 2018): 4392–400. http://dx.doi.org/10.1109/tie.2017.2764844.
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Dabrowski, Joel Janek, Johan Pieter de Villiers, and Conrad Beyers. "Naïve Bayes switching linear dynamical system: A model for dynamic system modelling, classification, and information fusion." Information Fusion 42 (July 2018): 75–101. http://dx.doi.org/10.1016/j.inffus.2017.10.002.
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DENG, YONG, D. FRANK HSU, ZHONGHAI WU, and CHAO-HSIEN CHU. "COMBINING MULTIPLE SENSOR FEATURES FOR STRESS DETECTION USING COMBINATORIAL FUSION." Journal of Interconnection Networks 13, no. 03n04 (September 2012): 1250008. http://dx.doi.org/10.1142/s0219265912500089.
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Physiological sensors have been used to detect different stress levels in order to improve human health and well-being. When analyzing these sensor data, sensor features are generated in the experiment and a subset of the features are selected and then combined using a host of informatics techniques (machine learning, data mining, or information fusion). Our previous work studied feature selection using correlation and diversity as well as feature combination using five methods C4.5, Naïve Bayes, Linear Discriminant Function, Support Vector Machine, and k-Nearest Neighbors. In this paper, we use combinatorial fusion, based on performance criterion (CF-P) and cognitive diversity (CF-CD), to combine those multiple sensor features. Our results showed that: (a) sensor feature combination method is distinctly much better than CF-CD and other algorithms, and (b) CF-CD is as good as other five feature combination methods, and is better in most of the cases.
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Chandran, Bhuvaneswari. "An Image based Diagnostic System for Lung Disease Classification." Journal of Communications Technology, Electronics and Computer Science 3 (December 29, 2015): 6. http://dx.doi.org/10.22385/jctecs.v3i0.6.
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Model-based detection and classification of nodules are two major steps in CAD systems design and evaluation. A common health problem, lung diseases are the most prevailing medical conditions throughout the world. In this paper, Lung diseases are automatically classified as Emphysema, Bronchitis, Pleural effusion and normal lung.The lung CT images are taken as input, preprocessing is applied, feature extraction is done by various methods such as Gabor filter extracts the texture features, walsh hadamard transform extracts the pixel co-efficient values, and a fusion method is proposed in this work which extracts the median absolute deviation values. Feature selection including statistical correlation based methods and Genetic Algorithm for searching in feature vector space are investigated. Four types of the classifiers are used where the Multi-Layer Perceptron Neural Network (MLP-NN) classifier with proposed fusion feature extraction method, genetic algorithm feature selection method gives promising result of 91% accuracy than J48, K- Nearest Neighbour and Naïve bayes classifiers.
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Lee, Posen, Tai-Been Chen, Chin-Hsuan Liu, Chi-Yuan Wang, Guan-Hua Huang, and Nan-Han Lu. "Identifying the Posture of Young Adults in Walking Videos by Using a Fusion Artificial Intelligent Method." Biosensors 12, no. 5 (May 3, 2022): 295. http://dx.doi.org/10.3390/bios12050295.
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Many neurological and musculoskeletal disorders are associated with problems related to postural movement. Noninvasive tracking devices are used to record, analyze, measure, and detect the postural control of the body, which may indicate health problems in real time. A total of 35 young adults without any health problems were recruited for this study to participate in a walking experiment. An iso-block postural identity method was used to quantitatively analyze posture control and walking behavior. The participants who exhibited straightforward walking and skewed walking were defined as the control and experimental groups, respectively. Fusion deep learning was applied to generate dynamic joint node plots by using OpenPose-based methods, and skewness was qualitatively analyzed using convolutional neural networks. The maximum specificity and sensitivity achieved using a combination of ResNet101 and the naïve Bayes classifier were 0.84 and 0.87, respectively. The proposed approach successfully combines cell phone camera recordings, cloud storage, and fusion deep learning for posture estimation and classification.
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Rahman, Atta-ur, Sagheer Abbas, Mohammed Gollapalli, Rashad Ahmed, Shabib Aftab, Munir Ahmad, Muhammad Adnan Khan, and Amir Mosavi. "Rainfall Prediction System Using Machine Learning Fusion for Smart Cities." Sensors 22, no. 9 (May 4, 2022): 3504. http://dx.doi.org/10.3390/s22093504.
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Precipitation in any form—such as rain, snow, and hail—can affect day-to-day outdoor activities. Rainfall prediction is one of the challenging tasks in weather forecasting process. Accurate rainfall prediction is now more difficult than before due to the extreme climate variations. Machine learning techniques can predict rainfall by extracting hidden patterns from historical weather data. Selection of an appropriate classification technique for prediction is a difficult job. This research proposes a novel real-time rainfall prediction system for smart cities using a machine learning fusion technique. The proposed framework uses four widely used supervised machine learning techniques, i.e., decision tree, Naïve Bayes, K-nearest neighbors, and support vector machines. For effective prediction of rainfall, the technique of fuzzy logic is incorporated in the framework to integrate the predictive accuracies of the machine learning techniques, also known as fusion. For prediction, 12 years of historical weather data (2005 to 2017) for the city of Lahore is considered. Pre-processing tasks such as cleaning and normalization were performed on the dataset before the classification process. The results reflect that the proposed machine learning fusion-based framework outperforms other models.
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Zhuang, Kai-Xiang, and I.-Ching Hsu. "Knowledge Fusion Based on Cloud Computing Environment for Long-Term Care." International Journal of Healthcare Information Systems and Informatics 15, no. 4 (October 2020): 38–55. http://dx.doi.org/10.4018/ijhisi.2020100103.
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Globally, aging is now a societal trend and challenge in many developed and developing countries. A key medical strategy that a fast-paced aging society must consider is the provision of quality long-term care (LTC) services. Even so, the lack of LTC caregivers is a persistent global problem. Herein, attention is called to the increasing need for identifying appropriate LTC caregivers and delivering client-specific LTC services to the elderly via emerging and integrative technologies. This paper argues for the use of an intelligent cloud computing long-term care platform (ICCLCP) that integrates statistical analysis, machine learning, and Semantic Web technologies into a cloud-computing environment to facilitate LTC services delivery. The Term frequency-inverse document frequency is a numerical statistic adopted to automatically assess the professionalism of each LTC caregiver's services. The machine learning method adopts naïve Bayes classifier to estimate the LTC services needed for the elderly. These two items of LTC information are integrated with the Semantic Web to provide an intelligent LTC framework. The deployed ICCLCP will then aid the elderly in the recommendation of LTC caregivers, thereby making the best use of available resources for LTC services.
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Wady, Shakhawan Hares. "Classification of Acute Lymphoblastic Leukemia through the Fusion of Local Descriptors." UHD Journal of Science and Technology 6, no. 1 (February 26, 2022): 21–33. http://dx.doi.org/10.21928/uhdjst.v6n1y2022.pp21-33.
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Leukemia is characterized by an abnormal proliferation of leukocytes in the bone marrow and blood, which is usually detected by pathologists using a microscope to examine a blood smear. Leukemia identification and diagnosis in advance are a trending topic in medical applications for decreasing the death toll of individuals with Acute Lymphoblastic Leukemia (ALL). It is critical to analyze the white blood cells for the identification of ALL for which the blood smear images are utilized. This paper discusses and presents a micro-pattern descriptor, called Local Directional Number Pattern along with Multi-scale Weber Local Descriptor for feature extraction task to determine cancerous and noncancerous blood cells. A balanced dataset with 260 blood smear images from the ALL-IDB2 dataset was used as training data. Consequently, a proposed model was constructed by applying different individual and combined feature extraction methods, and fed into the machine learning classifiers (Decision Tree, Ensemble, K-Nearest Neighbors, Naïve Bayes, and Random Forest) to determine cancerous and noncancerous blood cells. Experimental results indicate that the developed feature fusion technique assured a reasonable performance compared to other existing works with a testing average accuracy of 97.69 ± 1.83% using Ensemble classifier.
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Surowy, C. S., V. L. van Santen, S. M. Scheib-Wixted, and R. A. Spritz. "Direct, sequence-specific binding of the human U1-70K ribonucleoprotein antigen protein to loop I of U1 small nuclear RNA." Molecular and Cellular Biology 9, no. 10 (October 1989): 4179–86. http://dx.doi.org/10.1128/mcb.9.10.4179-4186.1989.
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We have studied the interaction of two of the U1 small nuclear ribonucleoprotein (snRNP)-specific proteins, U1-70K and U1-A, with U1 small nuclear RNA (snRNA). The U1-70K protein is a U1-specific RNA-binding protein. Deletion and mutation analyses of a beta-galactosidase/U1-70K partial fusion protein indicated that the central portion of the protein, including the RNP sequence domain, is both necessary and sufficient for specific U1 snRNA binding in vitro. The highly conserved eight-amino-acid RNP consensus sequence was found to be essential for binding. Deletion and mutation analyses of U1 snRNA showed that both the U1-70K fusion protein and the native HeLa U1-70K protein bound directly to loop I of U1 snRNA. Binding was sequence specific, requiring 8 of the 10 bases in the loop. The U1-A snRNP protein also interacted specifically with U1 snRNA, principally with stem-loop II.
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Surowy, C. S., V. L. van Santen, S. M. Scheib-Wixted, and R. A. Spritz. "Direct, sequence-specific binding of the human U1-70K ribonucleoprotein antigen protein to loop I of U1 small nuclear RNA." Molecular and Cellular Biology 9, no. 10 (October 1989): 4179–86. http://dx.doi.org/10.1128/mcb.9.10.4179.
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We have studied the interaction of two of the U1 small nuclear ribonucleoprotein (snRNP)-specific proteins, U1-70K and U1-A, with U1 small nuclear RNA (snRNA). The U1-70K protein is a U1-specific RNA-binding protein. Deletion and mutation analyses of a beta-galactosidase/U1-70K partial fusion protein indicated that the central portion of the protein, including the RNP sequence domain, is both necessary and sufficient for specific U1 snRNA binding in vitro. The highly conserved eight-amino-acid RNP consensus sequence was found to be essential for binding. Deletion and mutation analyses of U1 snRNA showed that both the U1-70K fusion protein and the native HeLa U1-70K protein bound directly to loop I of U1 snRNA. Binding was sequence specific, requiring 8 of the 10 bases in the loop. The U1-A snRNP protein also interacted specifically with U1 snRNA, principally with stem-loop II.
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Spinelli, Roberta, Rocco Piazza, Hima Raman, Alessandra Pirola, Simona Valletta, Alessandra Stasia, and Carlo Gambacorti-Passerini. "Identification of Novel Point Mutations in Splicing Sites by the Integration of Exome and RNA Sequencing Data in Myeloproliferative Diseases." Blood 118, no. 21 (November 18, 2011): 2462. http://dx.doi.org/10.1182/blood.v118.21.2462.2462.
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Abstract Abstract 2462 Point mutations in intronic regions near mRNA splice junctions can affect mRNA splicing, altering the resulting RNA sequence. The molecular characterization of in-frame or out-of-frame splicing variants in cancer samples can potentially assist in the molecular characterization of tumors. The aim of this study was to identify mutations located in the 5' or 3' exon-intron borders that affect RNA splicing using whole-exome sequencing analysis, a technique that targets coding sequences but also include the nearby intronic regions. In order to identify novel (in-frame and out-of-frame) splicing variants in myeloproliferative disorders we developed a bioinformatics procedure ‘Splice-Site Prediction Procedure to analyze Next Generation Sequencing data’ (SSPP-NGS). The SSPP-NGS bioinformatics method is an integration of two functional annotation tools for high-throughput sequencing data, ANNOVAR and MutationTaster and two canonical splice-site analysis tools, NetGene2 and Neural Network Promoter Prediction Tool (NNPPT). In addition, to assess the phenotypic effects of intronic mutations on mRNA splicing we combined DNA mutational screening analysis with RNA-Seq mediated gene expression profiling. Whole genome expression analysis was performed by using TopHat and Cufflinks: the first one is a splice junction mapper for RNA-Seq experiments able to mapp the reads against the junction to confirm them; the second one estimates gene expression, isoform-level expression, transcript abundance, differential gene expression and splicing. We used ANNOVAR and MutationTaster based on statistical Naive Bayes classifier to predict the non-coding mutations that affected physiological splicing. We then confirmed the results by queering NetGene2 and NNPPT using default parameters. Only the predictions found in all three programs were accepted as putative splicing variants and sequenced by Sanger method. We applied the entire procedure to whole exome sequencing data from 1 Ph+ leukemic patient sample (>80% myeloid cells) matched to autologous normal lymphocytes: on average, 70 million of paired-end reads and 5.2 gigabases (Gb) of sequences were generated per sample. A total of 177 candidate somatic point mutations (with minimum read depth of 20, minimum percent of substitution equal to 25% and minimum average Phred quality score of 30, corresponding to an accuracy of 99.9%, confirmed by at least 6 individual sequences) were found: 82/177 annotated in coding regions and 95/177 in non-coding regions. In particular 5/95 were located within 10-bp from a splicing junction. SSPS-NGS prediction analysis suggested the presence of 1/5 potential splicing site (predicting a loss of physiologic donor splicing site), while 4/5 were annotated as polymorphisms. The hypothetical splicing variant was located near the 5' donor splice site at position +1 in the intron between exons 5 and 6 of the GNAQ gene (IVS5+1C->T); it was present with a frequency of mutation of 35%, corresponding to its heterozygous presence in 88% of cells. The presence of this heterozygous mutation was confirmed by Sanger method. SSPS-NGS allowed us to focus on transcriptional analysis of this gene. RNA-seq analysis showed that 73% of GNAQ mRNA effectively skipped the upstream exon 5, resulting in a 4 to 6 frameshift fusion, which likely destroys the GTPase activity of GNAQ. No evidence of GNAQ exon 5 deleted RNA was found in additional 7 patients analyzed who lacked the intronic mutation. We extended the SSPN-NGS analysis to 7 myeloproliferative patients analyzed by exome sequencing. Three novel heterozygous splicing variants were identified, affecting the HOOK1, SMAD9 and DNAH9 genes. All mutations were confirmed by Sanger method. SSPS-NSG analysis predicted 1 loss of donor site in-frame (DNAH9) and 2 loss of acceptor splice site out of frame (HOOK1 and SMAD9), in one case with an activation of a new cryptic splicing site (HOOK1). RNA-seq analysis is in progress. In conclusion, the work presented here showed the applicability of SSPPs-NGS to whole-exome sequencing data as a tool to complement exome analysis, in order to identify novel splicing variants. Disclosures: No relevant conflicts of interest to declare.
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Bui, Toan Huy, Kazuhiko Hamamoto, and May Phu Paing. "Deep Fusion Feature Extraction for Caries Detection on Dental Panoramic Radiographs." Applied Sciences 11, no. 5 (February 24, 2021): 2005. http://dx.doi.org/10.3390/app11052005.
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Caries is the most well-known disease and relates to the oral health of billions of people around the world. Despite the importance and necessity of a well-designed detection method, studies in caries detection are still limited and show a restriction in performance. In this paper, we proposed a computer-aided diagnosis (CAD) method to detect caries among normal patients using dental radiographs. The proposed method mainly consists of two processes: feature extraction and classification. In the feature extraction phase, the chosen 2D tooth image was employed to extract deep activated features using a deep pre-trained model and geometric features using mathematic formulas. Both feature sets were then combined, called fusion feature, to complement each other defects. Then, the optimal fusion feature set was fed into well-known classification models such as support vector machine (SVM), k-nearest neighbor (KNN), decision tree (DT), Naïve Bayes (NB), and random forest (RF) to determine the best classification model that fit the fusion features set and perform the most preeminent result. The results show 91.70%, 90.43%, and 92.67% for accuracy, sensitivity, and specificity, respectively. The proposed method has outperformed the previous state-of-the-art and shows promising results when none of the measured factors is less than 90%; therefore, the method is promising for dentists and capable of wide-scale implementation caries detection in hospitals.
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Khade, Smita, Shilpa Gite, Sudeep D. Thepade, Biswajeet Pradhan, and Abdullah Alamri. "Detection of Iris Presentation Attacks Using Feature Fusion of Thepade’s Sorted Block Truncation Coding with Gray-Level Co-Occurrence Matrix Features." Sensors 21, no. 21 (November 8, 2021): 7408. http://dx.doi.org/10.3390/s21217408.
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Iris biometric detection provides contactless authentication, preventing the spread of COVID-19-like contagious diseases. However, these systems are prone to spoofing attacks attempted with the help of contact lenses, replayed video, and print attacks, making them vulnerable and unsafe. This paper proposes the iris liveness detection (ILD) method to mitigate spoofing attacks, taking global-level features of Thepade’s sorted block truncation coding (TSBTC) and local-level features of the gray-level co-occurrence matrix (GLCM) of the iris image. Thepade’s SBTC extracts global color texture content as features, and GLCM extracts local fine-texture details. The fusion of global and local content presentation may help distinguish between live and non-live iris samples. The fusion of Thepade’s SBTC with GLCM features is considered in experimental validations of the proposed method. The features are used to train nine assorted machine learning classifiers, including naïve Bayes (NB), decision tree (J48), support vector machine (SVM), random forest (RF), multilayer perceptron (MLP), and ensembles (SVM + RF + NB, SVM + RF + RT, RF + SVM + MLP, J48 + RF + MLP) for ILD. Accuracy, precision, recall, and F-measure are used to evaluate the performance of the projected ILD variants. The experimentation was carried out on four standard benchmark datasets, and our proposed model showed improved results with the feature fusion approach. The proposed fusion approach gave 99.68% accuracy using the RF + J48 + MLP ensemble of classifiers, immediately followed by the RF algorithm, which gave 95.57%. The better capability of iris liveness detection will improve human–computer interaction and security in the cyber-physical space by improving person validation.
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Hillmann, Dietmar, Iris Eschenbacher, Anja Thiel, and Michael Niederweis. "Expression of the Major Porin Gene mspA Is Regulated in Mycobacterium smegmatis." Journal of Bacteriology 189, no. 3 (December 1, 2006): 958–67. http://dx.doi.org/10.1128/jb.01474-06.
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ABSTRACT MspA is the major porin of Mycobacterium smegmatis and is important for diffusion of small and hydrophilic solutes across its unique outer membrane. The start point of transcription of the mspA gene was mapped by primer extension and S1 nuclease experiments. The main promoter driving transcription of mspA was identified by single point mutations in lacZ fusions and resembled σA promoters of M. smegmatis. However, a 500-bp upstream fragment including P mspA in a transcriptional fusion with lacZ yielded only low β-galactosidase activity, whereas activity increased 12-fold with a 700-bp fragment. Activation of P mspA by the 200-bp element was almost eliminated by increasing the distance by 14 bp, indicating binding of an activator protein. The chromosomal mspA transcript had a size of 900 bases and was very stable with a half-life of 6 minutes, whereas the stabilities of episomal mspA transcripts with three other 5′ untranslated region (UTRs) were three- to sixfold reduced, indicating a stabilizing role of the native 5′ UTR of mspA. Northern blot experiments revealed that the amount of mspA mRNA was increased under nitrogen limitation but reduced under carbon and phosphate limitation at 42°C in stationary phase in the presence of 0.5 M sodium chloride, 18 mM hydrogen peroxide, and 10% ethanol and at acidic pH. These results show for the first time that M. smegmatis regulates porin gene expression to optimize uptake of certain nutrients and to protect itself from toxic solutes.
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Cao, Feng, Fei Liu, Han Guo, Wenwen Kong, Chu Zhang, and Yong He. "Fast Detection of Sclerotinia Sclerotiorum on Oilseed Rape Leaves Using Low-Altitude Remote Sensing Technology." Sensors 18, no. 12 (December 17, 2018): 4464. http://dx.doi.org/10.3390/s18124464.
Full textAbstract:
Sclerotinia sclerotiorum, one of the major diseases infecting oilseed rape leaves, has seriously affected crop yield and quality. In this study, an indoor unmanned aerial vehicle (UAV) low-altitude remote sensing simulation platform was built for disease detection. Thermal, multispectral and RGB images were acquired before and after being artificially inoculated with Sclerotinia sclerotiorum on oilseed rape leaves. New image registration and fusion methods based on scale-invariant feature transform (SIFT) were presented to construct a fused database using multi-model images. The changes of temperature distribution in different sections of infected areas were analyzed by processing thermal images, the maximum temperature difference (MTD) on a single leaf reached 1.7 degrees Celsius 24 h after infection. Four machine learning models were established using thermal images and fused images respectively, including support vector machine (SVM), random forest (RF), K-nearest neighbor (KNN) and naïve Bayes (NB). The results demonstrated that the classification accuracy was improved by 11.3% after image fusion, and the SVM model obtained a classification accuracy of 90.0% on the task of classifying disease severity. The overall results indicated the UAV low-altitude remote sensing simulation platform equipped with multi-sensors could be used to early detect Sclerotinia sclerotiorum on oilseed rape leaves.
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Tang, Heng, Hanwei Xu, Xiaoping Rui, Xuebiao Heng, and Ying Song. "The Identification and Analysis of the Centers of Geographical Public Opinions in Flood Disasters Based on Improved Naïve Bayes Network." International Journal of Environmental Research and Public Health 19, no. 17 (August 30, 2022): 10809. http://dx.doi.org/10.3390/ijerph191710809.
Full textAbstract:
The increasing frequency of floods and the lack of protective measures have the potential to cause severe damage. Working from the perspective of network public opinion is an effective way to understand flood disasters. However, the existing research tends to focus on a single perspective, such as the characteristics of the text, algorithm optimization, or spatial location recognition, while scholars have paid much less attention to the impact of social-psychological differences in space on network public opinion. This research is based on the following hypothesis: When public opinions break out, the differences of network public opinions in geography will form spatially different centers of geographical public opinions in flood disasters (CGeoPOFDs). These centers represent the cities that receive the most attention from network public opinion. Based on this hypothesis, this study proposes a new way of identifying and analyzing CGeoPOFDs. First, two optimization strategies were applied to enhance a naïve Bayes network: syntactic parsing, which was used to optimize the selection of feature word vectors, and ensemble learning, which enabled multi-classifier fusion optimization. Social media data were classified through the improved algorithm, and then, various methods (hotspot analysis, geographic mapping, and sentiment analysis) were used to identify CGeoPOFDs. Finally, analysis was performed in terms of spatiotemporal, virtual, and real dimensions. In addition, microblog social data and real disaster data were used to arrive at empirical results. According to the study findings, the identified CGeoPOFDs offered traditional characteristics of network public opinion while also featuring unique spatiotemporal characteristics. Over time, CGeoPOFDs demonstrated spatial aggregation and bias diffusion and an overall positive emotional tendency.
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Zhang, Chu, Chang Wang, Fei Liu, and Yong He. "Mid-Infrared Spectroscopy for Coffee Variety Identification: Comparison of Pattern Recognition Methods." Journal of Spectroscopy 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/7927286.
Full textAbstract:
The potential of using mid-infrared transmittance spectroscopy combined with pattern recognition algorithm to identify coffee variety was investigated. Four coffee varieties in China were studied, including Typica Arabica coffee from Yunnan Province, Catimor Arabica coffee from Yunnan Province, Fushan Robusta coffee from Hainan Province, and Xinglong Robusta coffee from Hainan Province. Ten different pattern recognition methods were applied on the optimal wavenumbers selected by principal component analysis loadings. These methods were classified as highly effective methods (soft independent modelling of class analogy, support vector machine, back propagation neural network, radial basis function neural network, extreme learning machine, and relevance vector machine), methods of medium effectiveness (partial least squares-discrimination analysis,Knearest neighbors, and random forest), and methods of low effectiveness (Naive Bayes classifier) according to the classification accuracy for coffee variety identification.