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Lever, James, Robert Brkljača, Colin Rix, and Sylvia Urban. "Application of Networking Approaches to Assess the Chemical Diversity, Biogeography, and Pharmaceutical Potential of Verongiida Natural Products." Marine Drugs 19, no. 10 (October 18, 2021): 582. http://dx.doi.org/10.3390/md19100582.
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This study provides a review of all isolated natural products (NPs) reported for sponges within the order Verongiida (1960 to May 2020) and includes a comprehensive compilation of their geographic and physico-chemical parameters. Physico-chemical parameters were used in this study to infer pharmacokinetic properties as well as the potential pharmaceutical potential of NPs from this order of marine sponge. In addition, a network analysis for the NPs produced by the Verongiida sponges was applied to systematically explore the chemical space relationships between taxonomy, secondary metabolite and drug score variables, allowing for the identification of differences and correlations within a dataset. The use of scaffold networks as well as bipartite relationship networks provided a platform to explore chemical diversity as well as the use of chemical similarity networks to link pharmacokinetic properties with structural similarity. This study paves the way for future applications of network analysis procedures in the field of natural products for any order or family.
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Igumnov, Sergey, Konstantin Zbarousky, Pavel Lapanau, Ruslan Popok, and Ekaterina Grinevich. "ALEXITHYMIA AS RISK FACTOR OF THE DEVELOPMENT OF ADDICTIVE BEHAVIOR OF YOUNG PEOPLE IN THE REPUBLIC OF BELARUS." Visuomenės sveikata 28, no. 4 (November 20, 2018): 47–50. http://dx.doi.org/10.5200/sm-hs.2018.049.
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A socio-psychological survey and clinical-psychological research were conducted (level of alexithymia; intensity of internet addiction; individual-characterological personality traits; individual style of coping strategies, behavioral patterns and resources of personality) in the cohort of 150 people at the age of 15-24, average age: 18.9±1.64, M:F= 87:63). As a result of the randomization of subjects, 3 groups were detected: themain group (people with traces of developing anaddiction from “new psychoactive substances” (NPS)) (MG, 50 people); a comparison group (people with “non-chemical” forms of addiction (internet addiction) (CG, 50 people) and a reference group (50 people, without addictions and deviant behavior). The research of coping behavior methods, in the category “Search for social support” groups MG and RG significantly differ statistically (probability of passing similarity p = 0.016, t =2.45, df = 147) has shown the significance of asocial network support as in the case of NPS addiction. The total score of the Toronto Alexithymia Scale-26-R showed that people from MG (average number 67.66±8.01) and RG (average number 58.92±8.36) statistically differ significantly, with a probability of passing similarity being p<0.001, t=5.3, df=147. Indicators of expressed alexithymia noticeably prevailed in MG and CG, in comparison with the RG. While researching internet addiction using a subject oriented scale, there was a statistically significant difference between CG and RG. The probability of a passing similarity is p = 0.006, t=2.82, df =147. The received and analyzed study materials are the basis for the development of acombined preventive and rehabilitation program for those with “NPS” addiction and “internet addiction” among teenagers and youth.
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Bharti, Puja, and Deepti Mittal. "An Ultrasound Image Enhancement Method Using Neutrosophic Similarity Score." Ultrasonic Imaging 42, no. 6 (October 5, 2020): 271–83. http://dx.doi.org/10.1177/0161734620961005.
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Ultrasound images, having low contrast and noise, adversely impact in the detection of abnormalities. In view of this, an enhancement method is proposed in this work to reduce noise and improve contrast of ultrasound images. The proposed method is based on scaling with neutrosophic similarity score (NSS), where an image is represented in the neutrosophic domain through three membership subsets T, I, and F denoting the degree of truth, indeterminacy, and falseness, respectively. The NSS measures the belonging degree of pixel to the texture using multi-criteria that is based on intensity, local mean intensity and edge detection. Then, NSS is utilized to extract the enhanced coefficient and this enhanced coefficient is applied to scale the input image. This scaling reflects contrast improvement and denoising effect on ultrasound images. The performance of proposed enhancement method is evaluated on clinical ultrasound images, using both subjective and objective image quality measures. In subjective evaluation, with proposed method, overall best score of 4.3 was obtained and that was 44% higher than the score of original images. These results were also supported by objective measures. The results demonstrated that the proposed method outperformed the other methods in terms of mean brightness preservation, edge preservation, structural similarity, and human perception-based image quality assessment. Thus, the proposed method can be used in computer-aided diagnosis systems and to visually assist radiologists in their interactive-decision-making task.
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Cai, Xinlu, Yongming Wang, Hanyu Zhou, Jia Huang, Simon S. Y. Lui, Arne Møller, Henry K. F. Mak, Eric F. C. Cheung, and Raymond C. K. Chan. "M158. ASSOCIATIONS OF NEUROLOGICAL SOFT SIGNS AND CEREBELLAR-CEREBRAL FUNCTIONAL CONNECTIVITY IN PATIENTS WITH FIRST-EPISODE SCHIZOPHRENIA AND THEIR UNAFFECTED SIBLINGS." Schizophrenia Bulletin 46, Supplement_1 (April 2020): S196. http://dx.doi.org/10.1093/schbul/sbaa030.470.
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Abstract Background Neurological softs signs (NSS) are defined as subtle neurological abnormalities with manifestations of motor coordination, sensory integration and disinhibition. Evidence has suggested NSS as one of the most promising endophenotypes for schizophrenia spectrum disorders. Moreover, accumulating evidence also suggest that NSS may be associated with specific functional connectivity. The present study aimed to examine the cerebellar-cerebral resting-state functional connectivity (rsFC) of NSS in patients with first-episode schizophrenia (FES) and their unaffected siblings (SB). Methods We administered the abridge version of the Cambridge Neurological Inventory (CNI) to 51 FES patients, 20 unaffected SB, and 50 healthy controls (HC) to assess the severity of NSS. All the participants also underwent a resting-state functional magnetic resonance imaging (MRI) scan. Ten regions of interest (ROIs) in the cerebellum were selected to represent cerebellar motor network (MN) and cerebellar executive control network (EN), which corresponded to the “sensorimotor-cognitive” dichotomy of NSS. rsFC between each ROI and the whole brain voxels were constructed, and the linear regression analysis was conducted to examine the cerebellar-cerebral rsFC patterns of NSS in each group. Results Regarding the cerebellar MN, there were positive correlations observed between the rsFC of the cerebellar MN with the default mode network (DMN) and NSS in FES patients group (CNI total score and the motor coordination subscale) and the SB group (CNI total score and the motor coordination and sensory integration subscales). The rsFC of the cerebellar MN and the sensorimotor network were significantly and positively correlated with NSS (CNI total score and the motor coordination and sensory integration subscales) in the SB group. Regarding the cerebellar EN, we found that both the FES and the SB groups exhibited significantly negative correlations between NSS (CNI total score and the motor coordination subscale) and the rsFC of the cerebellar EN with the DMN. Moreover, the rsFC between the cerebellar EN and the sensorimotor network was positively correlated with NSS (CNI total score and the motor coordination and disinhibition subscales) in the SB group. Discussion We found inverse correlations between NSS and the rsFC of the cerebellar EN/MN and the DMN in both FES patients and their unaffected SB, suggesting that altered cerebellar-cerebral rsFC between these networks is correlated with the NSS. Moreover, the SB group exhibited a unique correlational pattern that NSS were correlated with the cerebellar-sensorimotor network rsFC, suggesting that such a network connectivity may serve as a potential biomarker for schizophrenia.
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Luo, Jiasai, Sen Zhou, Yiming Li, Yu Pang, Zhengwen Wang, Yi Lu, Huiqian Wang, and Tong Bai. "Polarization Orientation Method Based on Remote Sensing Image in Cloudy Weather." Remote Sensing 15, no. 5 (February 22, 2023): 1225. http://dx.doi.org/10.3390/rs15051225.
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Autonomous navigation technology is a core technology for intelligent operation, allowing the vehicles to perform tasks without relying on external information, which effectively improves the concealability and reliability. In this paper, based on the previous research on the bionic compound eye, a multi-channel camera array with different polarization degrees was used to construct the atmospheric polarization state measurement platform. A polarization trough threshold segmentation algorithm was applied to study the distribution characteristics and characterization methods of polarization states in atmospheric remote sensing images. In the extracted polarization feature map, the tilting suggestion box was obtained based on the multi-direction window extraction network (similarity-based region proposal networks, SRPN) and the rotation of the suggestion box (Rotation Region of interests, RRoIs). Fast Region Convolutional Neural Networks (RCNN) was used to screen the suggestion boxes, and the Non-maximum suppression (NMS) method was used to select the angle, corresponding to the label of the suggestion box with the highest score, as the solar meridian azimuth in the vehicle coordinate system. The azimuth angle of the solar meridian in the atmospheric coordinate system can be calculated by the astronomical formula. Finally, the final heading angle can be obtained according to the conversion relationship between the coordinate systems. By fitting the measured data based on the least Square method, the slope K value is −1.062, RMSE (Root Mean Square Error) is 6.984, and the determination coefficient R-Square is 0.9968. Experimental results prove the effectiveness of the proposed algorithm, and this study can construct an autonomous navigation algorithm with high concealment and precision, providing a new research idea for the research of autonomous navigation technology.
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Tuli, Shikhar, Bhishma Dedhia, Shreshth Tuli, and Niraj K. Jha. "FlexiBERT: Are Current Transformer Architectures too Homogeneous and Rigid?" Journal of Artificial Intelligence Research 77 (May 6, 2023): 39–70. http://dx.doi.org/10.1613/jair.1.13942.
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The existence of a plethora of language models makes the problem of selecting the best one for a custom task challenging. Most state-of-the-art methods leverage transformer-based models (e.g., BERT) or their variants. However, training such models and exploring their hyperparameter space is computationally expensive. Prior work proposes several neural architecture search (NAS) methods that employ performance predictors (e.g., surrogate models) to address this issue; however, such works limit analysis to homogeneous models that use fixed dimensionality throughout the network. This leads to sub-optimal architectures. To address this limitation, we propose a suite of heterogeneous and flexible models, namely FlexiBERT, that have varied encoder layers with a diverse set of possible operations and different hidden dimensions. For better-posed surrogate modeling in this expanded design space, we propose a new graph-similarity-based embedding scheme. We also propose a novel NAS policy, called BOSHNAS, that leverages this new scheme, Bayesian modeling, and second-order optimization, to quickly train and use a neural surrogate model to converge to the optimal architecture. A comprehensive set of experiments shows that the proposed policy, when applied to the FlexiBERT design space, pushes the performance frontier upwards compared to traditional models. FlexiBERT-Mini, one of our proposed models, has 3% fewer parameters than BERT-Mini and achieves 8.9% higher GLUE score. A FlexiBERT model with equivalent performance as the best homogeneous model has 2.6× smaller size. FlexiBERT-Large, another proposed model, attains state-of-the-art results, outperforming the baseline models by at least 5.7% on the GLUE benchmark.
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Ding, Jiujie, Jiahuan Zhang, Zongqian Zhan, Xiaofang Tang, and Xin Wang. "A Precision Efficient Method for Collapsed Building Detection in Post-Earthquake UAV Images Based on the Improved NMS Algorithm and Faster R-CNN." Remote Sensing 14, no. 3 (January 29, 2022): 663. http://dx.doi.org/10.3390/rs14030663.
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The results of collapsed building detection act as an important reference for damage assessment after an earthquake, which is crucial for governments in order to efficiently determine the affected area and execute emergency rescue. For this task, unmanned aerial vehicle (UAV) images are often used as the data sources due to the advantages of high flexibility regarding data acquisition time and flying requirements and high resolution. However, collapsed buildings are typically distributed in both connected and independent pieces and with arbitrary shapes, and these are generally more obvious in the UAV images with high resolution; therefore, the corresponding detection is restricted by using conventional convolutional neural networks (CNN) and the detection results are difficult to evaluate. In this work, based on faster region-based convolutional neural network (Faster R-CNN), deformable convolution was used to improve the adaptability to the arbitrarily shaped collapsed buildings. In addition, inspired by the idea of pixelwise semantic segmentation, in contrast to the intersection over union (IoU), a new method which estimates the intersected proportion of objects (IPO) is proposed to describe the degree of the intersection of bounding boxes, leading to two improvements: first, the traditional non-maximum suppression (NMS) algorithm is improved by integration with the IPO to effectively suppress the redundant bounding boxes; second, the IPO is utilized as a new indicator to determine positive and negative bounding boxes, and is introduced as a new strategy for precision and recall estimation, which can be considered a more reasonable measurement of the degree of similarity between the detected bounding boxes and ground truth bounding boxes. Experiments show that compared with other models, our work can obtain better precision and recall for detecting collapsed buildings for which an F1 score of 0.787 was achieved, and the evaluation results from the suggested IPO are qualitatively closer to the ground truth. In conclusion, the improved NMS with the IPO and Faster R-CNN in this paper is feasible and efficient for the detection of collapsed buildings in UAV images, and the suggested IPO strategy is more suitable for the corresponding detection result’s evaluation.
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Galphat, Yugchhaya. "TD score: Time Aware Domain Similarity based Link Prediction." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 3621–27. http://dx.doi.org/10.22214/ijraset.2022.43176.
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Abstract: Online Social Network has gained immense traction of users in past decade. Link prediction across social networks has become a new exploration area for researchers, where existing links are investigated and new links are anticipated among billions of online customers. Majority of work in this area focusses on exploring the current status of a particular network at a specific time, without exploring the behavior of the network links as time goes by. Only a Small amount of work has been performed with the consideration of temporal aspect of network. As the interests and interactions of user change over time, the links among nodes become weaker or noisy which affects the prediction accuracy. This paper intend to explore a new integrated temporal method TD score which includes time stamp of interaction and domain similarity information for each pair of unconnected nodes to predict links. Experiment over co-authorship network reveals that link prediction covering time aware domain similarity is effective and efficient approach than traditional ones. Keywords: Co-authorship network; link prediction; node similarity; global feature; temporal feature; domain similarity.
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Ghosh, Sambit, Vasundhara Gadiyaram, and Saraswathi Vishveshwara. "Validation of protein structure models using network similarity score." Proteins: Structure, Function, and Bioinformatics 85, no. 9 (June 27, 2017): 1759–76. http://dx.doi.org/10.1002/prot.25332.
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Gupta, Anand Kumar, and Neetu Sardana. "Performance Analysis of Naïve Bayes Classifier Over Similarity Score-Based Techniques for Missing Link Prediction in Ego Networks." Journal of Information Technology Research 14, no. 1 (January 2021): 110–22. http://dx.doi.org/10.4018/jitr.2021010107.
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Zhu, Ning, Mohammad Najafi, Bin Han, Steven Hancock, and Dimitre Hristov. "Feasibility of Image Registration for Ultrasound-Guided Prostate Radiotherapy Based on Similarity Measurement by a Convolutional Neural Network." Technology in Cancer Research & Treatment 18 (January 1, 2019): 153303381882196. http://dx.doi.org/10.1177/1533033818821964.
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Purpose: Registration of 3-dimensional ultrasound images poses a challenge for ultrasound-guided radiation therapy of the prostate since ultrasound image content changes significantly with anatomic motion and ultrasound probe position. The purpose of this work is to investigate the feasibility of using a pretrained deep convolutional neural network for similarity measurement in image registration of 3-dimensional transperineal ultrasound prostate images. Methods: We propose convolutional neural network-based registration that maximizes a similarity score between 2 identical in size 3-dimensional regions of interest: one encompassing the prostate within a simulation (reference) 3-dimensional ultrasound image and another that sweeps different spatial locations around the expected prostate position within a pretreatment 3-dimensional ultrasound image. The similarity score is calculated by (1) extracting pairs of corresponding 2-dimensional slices (patches) from the regions of interest, (2) providing these pairs as an input to a pretrained convolutional neural network which assigns a similarity score to each pair, and (3) calculating an overall similarity by summing all pairwise scores. The convolutional neural network method was evaluated against ground truth registrations determined by matching implanted fiducial markers visualized in a pretreatment orthogonal pair of x-ray images. The convolutional neural network method was further compared to manual registration and a standard commonly used intensity-based automatic registration approach based on advanced normalized correlation. Results: For 83 image pairs from 5 patients, convolutional neural network registration errors were smaller than 5 mm in 81% of the cases. In comparison, manual registration errors were smaller than 5 mm in 61% of the cases and advanced normalized correlation registration errors were smaller than 5 mm only in 25% of the cases. Conclusion: Convolutional neural network evaluation against manual registration and an advanced normalized correlation -based registration demonstrated better accuracy and reliability of the convolutional neural network. This suggests that with training on a large data set of transperineal ultrasound prostate images, the convolutional neural network method has potential for robust ultrasound-to-ultrasound registration.
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Li, Yun-Lun, Hao-Ting Li, and Chen-Kuo Chiang. "Multi-Camera Vehicle Tracking Based on Deep Tracklet Similarity Network." Electronics 11, no. 7 (March 24, 2022): 1008. http://dx.doi.org/10.3390/electronics11071008.
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Multi-camera vehicle tracking at the city scale has received lots of attention in the last few years. It has large-scale differences, frequent occlusion, and appearance differences caused by the viewing angle differences, which is quite challenging. In this research, we propose the Tracklet Similarity Network (TSN) for a multi-target multi-camera (MTMC) vehicle tracking system based on the evaluation of the similarity between vehicle tracklets. In addition, a novel component, Candidates Intersection Ratio (CIR), is proposed to refine the similarity. It provides an associate scheme to build the multi-camera tracking results as a tree structure. Based on these components, an end-to-end vehicle tracking system is proposed. The experimental results demonstrate that an 11% improvement on the evaluation score is obtained compared to the conventional similarity baseline.
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Nguyen, Nam-Ninh, Sriganesh Srihari, Hon Wai Leong, and Ket-Fah Chong. "EnzDP: Improved enzyme annotation for metabolic network reconstruction based on domain composition profiles." Journal of Bioinformatics and Computational Biology 13, no. 05 (October 2015): 1543003. http://dx.doi.org/10.1142/s0219720015430039.
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Determining the entire complement of enzymes and their enzymatic functions is a fundamental step for reconstructing the metabolic network of cells. High quality enzyme annotation helps in enhancing metabolic networks reconstructed from the genome, especially by reducing gaps and increasing the enzyme coverage. Currently, structure-based and network-based approaches can only cover a limited number of enzyme families, and the accuracy of homology-based approaches can be further improved. Bottom-up homology-based approach improves the coverage by rebuilding Hidden Markov Model (HMM) profiles for all known enzymes. However, its clustering procedure relies firmly on BLAST similarity score, ignoring protein domains/patterns, and is sensitive to changes in cut-off thresholds. Here, we use functional domain architecture to score the association between domain families and enzyme families (Domain-Enzyme Association Scoring, DEAS). The DEAS score is used to calculate the similarity between proteins, which is then used in clustering procedure, instead of using sequence similarity score. We improve the enzyme annotation protocol using a stringent classification procedure, and by choosing optimal threshold settings and checking for active sites. Our analysis shows that our stringent protocol EnzDP can cover up to 90% of enzyme families available in Swiss-Prot. It achieves a high accuracy of 94.5% based on five-fold cross-validation. EnzDP outperforms existing methods across several testing scenarios. Thus, EnzDP serves as a reliable automated tool for enzyme annotation and metabolic network reconstruction. Available at: www.comp.nus.edu.sg/~nguyennn/EnzDP .
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Singh, Sharad Pratap, Shahanaz Ayub, and J. P. Saini. "Analysis and comparison of normal and altered fingerprint using artificial neural networks." International Journal of Knowledge-based and Intelligent Engineering Systems 25, no. 2 (July 26, 2021): 243–49. http://dx.doi.org/10.3233/kes-210068.
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Fingerprint matching is based on the number of minute matches between two fingerprints. Implementation mainly includes image enhancement, segmentation, orientation histogram, etc., extraction (completeness) and corresponding minutiae. Finally, a matching score is generated that indicates whether two fingerprints coincide with the help of coding with MATLAB to find the matching score and simulation of Artificial Neural Network extending the feedback of the network. Using the artificial neural network tool, an important advantage is the similarity index between the sample data or unknown data. A neural network is a massively parallel distributed processor consisting of simple processing units that have a natural property to store knowledge and computer experiences are available for use. A fingerprint comparison essentially consists of two fingerprints to generate a fingerprint match score the match score is used to determine whether the two impressions they are of the same finger. The decision is made this study shows the comparison of normal and altered fingerprints using MATLAB coding and data used to study in the self-generated data using biometric scanner also the open source data available on the web is used for finding out matching score or similarity index, The study shows that there is hardly any matching between normal and altered fingerprints of the same person.
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Xie, Chunli, Xia Wang, Cheng Qian, and Mengqi Wang. "A Source Code Similarity Based on Siamese Neural Network." Applied Sciences 10, no. 21 (October 26, 2020): 7519. http://dx.doi.org/10.3390/app10217519.
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Finding similar code snippets is a fundamental task in the field of software engineering. Several approaches have been proposed for this task by using statistical language model which focuses on syntax and structure of codes rather than deep semantic information underlying codes. In this paper, a Siamese Neural Network is proposed that maps codes into continuous space vectors and try to capture their semantic meaning. Firstly, an unsupervised pre-trained method that models code snippets as a weighted series of word vectors. The weights of the series are fitted by the Term Frequency-Inverse Document Frequency (TF-IDF). Then, a Siamese Neural Network trained model is constructed to learn semantic vector representation of code snippets. Finally, the cosine similarity is provided to measure the similarity score between pairs of code snippets. Moreover, we have implemented our approach on a dataset of functionally similar code. The experimental results show that our method improves some performance over single word embedding method.
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Hua, Wenqiang, Yurong Zhang, Cong Zhang, and Xiaomin Jin. "PolSAR Image Classification Based on Relation Network with SWANet." Remote Sensing 15, no. 8 (April 11, 2023): 2025. http://dx.doi.org/10.3390/rs15082025.
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Deep learning and convolutional neural networks (CNN) have been widely applied in polarimetric synthetic aperture radar (PolSAR) image classification, and satisfactory results have been obtained. However, there is one crucial issue that still has not been solved. These methods require abundant labeled samples and obtaining the labeled samples of PolSAR images is usually time-consuming and labor-intensive. To obtain better classification results with fewer labeled samples, a new attention-based 3D residual relation network (3D-ARRN) is proposed for PolSAR image. Firstly, a multilayer CNN with residual structure is used to extract depth polarimetric features. Secondly, to extract more important feature information and improve the classification results, a spatial weighted attention network (SWANet) is introduced to concentrate the feature information, which is more favorable for a classification task. Then, the features of training and test samples are integrated and CNN is utilized to compute the score of similarity between training and test samples. Finally, the similarity score is used to determine the category of test samples. Studies on four different PolSAR datasets illustrate that the proposed 3D-ARRN model can achieve higher classification results than other comparison methods with few labeled data.
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Chen, Xiaojun. "Synthetic Network and Search Filter Algorithm in English Oral Duplicate Correction Map." Complexity 2021 (April 13, 2021): 1–12. http://dx.doi.org/10.1155/2021/9960101.
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Combining the communicative language competence model and the perspective of multimodal research, this research proposes a research framework for oral communicative competence under the multimodal perspective. This not only truly reflects the language communicative competence but also fully embodies the various contents required for assessment in the basic attributes of spoken language. Aiming at the feature sparseness of the user evaluation matrix, this paper proposes a feature weight assignment algorithm based on the English spoken category keyword dictionary and user search records. The algorithm is mainly based on the self-built English oral category classification dictionary and converts the user’s query vector into a user-English-speaking type vector. Through the calculation rules proposed in this paper, the target user’s preference score for a specific type of spoken English is obtained, and this score is assigned to the unrated item of the original user’s feature matrix as the initial starting score. At the same time, in order to solve the problem of insufficient user similarity calculation accuracy, a user similarity calculation algorithm based on “Synonyms Cilin Extended Edition” and search records is proposed. The algorithm introduces “Synonyms Cilin” to calculate the correlation between the semantic items, vocabulary, and query vector in the user query record to obtain the similarity between users and finally gives a user similarity calculation that integrates user ratings and query vectors method. For the task of Chinese grammar error correction, this article uses two methods of predicting the relationship between words in the corpus, Word2Vec and GloVe, to train the word vectors of different dimensions and use the word vectors to represent the text features of the experimental samples, avoiding sentences brought by word segmentation. On the basis of word vectors, the advantages and disadvantages of CNN, LSTM, and SVM models in this shared task are analyzed through experimental data. The comparative experiment shows that the method in this paper has achieved relatively good results.
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Lei, Xiujuan, Zengqiang Fang, Luonan Chen, and Fang-Xiang Wu. "PWCDA: Path Weighted Method for Predicting circRNA-Disease Associations." International Journal of Molecular Sciences 19, no. 11 (October 31, 2018): 3410. http://dx.doi.org/10.3390/ijms19113410.
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CircRNAs have particular biological structure and have proven to play important roles in diseases. It is time-consuming and costly to identify circRNA-disease associations by biological experiments. Therefore, it is appealing to develop computational methods for predicting circRNA-disease associations. In this study, we propose a new computational path weighted method for predicting circRNA-disease associations. Firstly, we calculate the functional similarity scores of diseases based on disease-related gene annotations and the semantic similarity scores of circRNAs based on circRNA-related gene ontology, respectively. To address missing similarity scores of diseases and circRNAs, we calculate the Gaussian Interaction Profile (GIP) kernel similarity scores for diseases and circRNAs, respectively, based on the circRNA-disease associations downloaded from circR2Disease database (http://bioinfo.snnu.edu.cn/CircR2Disease/). Then, we integrate disease functional similarity scores and circRNA semantic similarity scores with their related GIP kernel similarity scores to construct a heterogeneous network made up of three sub-networks: disease similarity network, circRNA similarity network and circRNA-disease association network. Finally, we compute an association score for each circRNA-disease pair based on paths connecting them in the heterogeneous network to determine whether this circRNA-disease pair is associated. We adopt leave one out cross validation (LOOCV) and five-fold cross validations to evaluate the performance of our proposed method. In addition, three common diseases, Breast Cancer, Gastric Cancer and Colorectal Cancer, are used for case studies. Experimental results illustrate the reliability and usefulness of our computational method in terms of different validation measures, which indicates PWCDA can effectively predict potential circRNA-disease associations.
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Hussain, Syed Shah, Muhammad Arif, Osama Bin Inayat, and Haji Gul. "Link Prediction in Human Complex Network Based on Random Walk with Global Topological Features." IETI Transactions on Data Analysis and Forecasting (iTDAF) 1, no. 2 (July 6, 2023): 30–43. http://dx.doi.org/10.3991/itdaf.v1i2.39675.
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Link Prediction in Human Complex Networks aims to predict the missing, deleted, or future link formations. These complex networks are represented graphically, consisting of nodes and links, also referred to as vertices and edges, respectively. We employ Link Prediction techniques on four different human-related networks to determine the most effective methods in the Human Complex domain. The techniques utilized are similarity-based, primarily focused on determining the similarity score of each network. We select four algorithms that demonstrated superior results in other complex networks and implement them on human-related networks. Our goal is to predict links that have been removed from the network in order to evaluate the prediction accuracy of the applied techniques. To accomplish this, we convert the datasets into adjacency matrices and divide them into training and probe sets. The training session is then conducted, followed by the testing of the data. The selected techniques are implemented to calculate the similarity score, and the accuracy is subsequently measured for each dataset. This approach facilitates a comprehensive comparative analysis of the various predicting techniques to determine the most effective one.
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Lawal Abba, Hadiza, Abubakar Roko, Aminu B. Muhammad, Abdulgafar Usman, and Abba Almu. "Enhanced Semantic Similarity Detection of Program Code Using Siamese Neural Network." International Journal of Advanced Networking and Applications 14, no. 02 (2022): 5353–60. http://dx.doi.org/10.35444/ijana.2022.14205.
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Even though there are various source code plagiarism detection approaches, most of them are only concerned with lexical similarities attack with an assumption that plagiarism is only conducted by students who are not proficient in programming. However, plagiarism is often conducted not only due to student incapability but also because of bad time management. Thus, semantic similarity attacks should be detected and evaluated. This research proposes a source code semantic similarity detection approach that can detect most source code similarities by representing the source code into an Abstract Syntax Tree (AST) and evaluating similarity using a Siamese neural network. Since AST is a language-dependent feature, the SOCO dataset is selected which consists of C++ program codes. Based on the evaluation, it can be concluded that our approach is more effective than most of the existing systems for detecting source code plagiarism. The proposed strategy was implemented and an experimental study based on the AI-SOCO dataset revealed that the proposed similarity measure achieved better performance for the recommendation system in terms of precision, recall, and f1 score by 15%, 10%, and 22% respectively in the 100,000 datasets. In the future, it is suggested that the system can be improved by detecting inter-language source code similarity.
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Thangasamy, Vasantha. "Efficacious Hyperlink Based Similarity Measure Using Heterogeneous Propagation of PageRank Scores." International Journal of Information Retrieval Research 9, no. 4 (October 2019): 36–49. http://dx.doi.org/10.4018/ijirr.2019100104.
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Information available on the internet is wide, diverse, and dynamic. Since an enormous amount of information is available online, finding similarity between webpages using efficient hyperlink analysis is a challenging task. In this article, the researcher proposes an improved PageSim algorithm which measurse the importance of a webpage based on the PageRank values of connected webpage. Therefore, the proposed algorithm uses heterogeneous propagation of the PageRank score, based on the prestige measure of each webpage. The existing and the improved PageSim algorithms are implemented with a sample web graph. Real time Citation Networks, namely the ZEWAIL Citation Network and the DBLP Citation Network are used to test and compare the existing and improved PageSim algorithms. By using this proposed algorithm, it has been found that a similarity score between two different webpages significantly increases based on common information features and significantly decreases based on distinct factors.
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PEROV, ROMAN A., OLEG S. LAUTA, ALEXANDER M. KRIBEL, and YURI V. FEDULOV. "A COMPREHENSIVE TECHNIQUE FOR DETECTING CYBER AT]TACKS BASED ON THE INTEGRATION OF FRACTAL ANALYSIS AND STATISTICAL METHODS." H&ES Research 14, no. 2 (2022): 44–51. http://dx.doi.org/10.36724/2409-5419-2022-14-2-44-51.
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The article discusses a method for detecting cyber-attacks on computer networks based on detecting anomalies in network traffic by assessing its self-similarity and determining the impact of cyber-attacks using statistical methods. The proposed methodology provides for three stages, within which the analysis of the self-similarity property for reference traffic is performed (using the Dickey-Fuller test, R/S analysis and the DFA method), the analysis of the self-similarity property for real traffic (by the same methods) and additional processing of time series by statistical methods (moving average, Z-Score and CUSUM). The issues of software implementation of the proposed approach and the formation of a data set containing network packets are considered. The results of the experiments demonstrated the presence of self-similarity of network traffic and confirmed the high efficiency of the proposed method, which allows detecting cyber-attacks in real or near real time. Introduction: The use of information and communication technologies for information collection in modern computer networks makes it possible for an attacker to influence the network infrastructure by implementing cyber-attacks. Cyberattacks can achieve their goals due to the massive use of outdated operating systems, ineffective protection mechanisms and the presence of multiple vulnerabilities in unsecured network protocols. Such vulnerabilities give a potential attacker the ability to change the settings of network devices, listen and redirect traffic, block network interaction and gain unauthorized access to internal components of computer networks. The purpose of the work is to develop a methodology for detecting anomalies in network traffic by determining the degree of self-similarity of traffic using fractal analysis and statistical methods. Methods used: software implementation of the proposed methodology and the formation of a data set containing network packets. The results of the experiments demonstrated the presence of self-similarity of network traffic and confirmed the high efficiency of the proposed technique, which allows detecting cyber-attacks in real or near real time. The scientific novelty lies in the fact that the proposed methodology provides for three stages, within which the analysis of the self-similarity property for reference traffic is performed (using the Dickey-Fuller test, R/S analysis and the DFA method), the analysis of the self-similarity property for real traffic (by the same methods) and additional processing of time series by statistical methods (methods moving Average (MA), Z-Score and CUSUM). Result: the presented methodology allows detecting the impact of cyberattacks in real and close to real time, and the use of statistical methods increases the accuracy of determining cyberattacks. Practical significance: the presented methodology is universal and can be applied in the information exchange systems of public administration bodies performing the tasks of ensuring the security of the country.
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Zhang, Zhan, An Guo, and Bang Li. "Internal Similarity Network for Rejoining Oracle Bone Fragment Images." Symmetry 14, no. 7 (July 18, 2022): 1464. http://dx.doi.org/10.3390/sym14071464.
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Rejoining oracle bone fragments plays an import role in studying the history and culture of the Shang dynasty by its characters. However, current computer vision technology has a low accuracy in judging whether the texture of oracle bone fragment image pairs can be put back together. When rejoining fragment images, the coordinate sequence and texture features of edge pixels from original and target fragment images form a continuous symmetrical structure, so we put forward an internal similarity network (ISN) to rejoin the fragment image automatically. Firstly, an edge equidistant matching (EEM) algorithm was given to search similar coordinate sequences of edge segment pairs on the fragment image contours and to locally match the edge coordinate sequence of an oracle bone fragment image. Then, a target mask-based method was designed in order to put two images into a whole and to cut a local region image by the local matching edge. Next, we calculated a convolution feature gradient map (CFGM) of the local region image texture, and an internal similarity pooling (ISP) layer was proposed to compute the internal similarity of the convolution feature gradient map. Finally, ISN was contributed in order to evaluate a similarity score of a local region image texture and to determine whether two fragment images are a coherent whole. The experiments show that the correct judgement probability of ISN is higher than 90% in actual rejoining work and that our method searched 37 pairs of correctly rejoined oracle bone fragment images that have not been discovered by archaeologists.
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Nie, Chun-Xiao. "Nonlinear Correlation Analysis of Time Series Based on Complex Network Similarity." International Journal of Bifurcation and Chaos 30, no. 15 (December 9, 2020): 2050225. http://dx.doi.org/10.1142/s0218127420502259.
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Characterizing the relationship between time series is an important issue in many fields, in particular, in many cases there is a nonlinear correlation between series. This paper provides a new method to study the relationship between time series using the perspective of complex networks. This method converts a time series into a distance matrix and constructs a sequence of nearest neighbor networks, so that the nonlinear relationship between time series is expressed as similarity between networks. In addition, based on the surrogate series, we applied [Formula: see text]-score to characterize the level of significance and analyzed some benchmark models. We not only use the artificial dataset and the real dataset to verify the effectiveness of the proposed method, but also analyze its robustness, which provides an alternative method for detecting nonlinear relationships.
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Cüvitoğlu, Ali, and Zerrin Isik. "Network neighborhood operates as a drug repositioning method for cancer treatment." PeerJ 11 (July 10, 2023): e15624. http://dx.doi.org/10.7717/peerj.15624.
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Computational drug repositioning approaches are important, as they cost less compared to the traditional drug development processes. This study proposes a novel network-based drug repositioning approach, which computes similarities between disease-causing genes and drug-affected genes in a network topology to suggest candidate drugs with highest similarity scores. This new method aims to identify better treatment options by integrating systems biology approaches. It uses a protein-protein interaction network that is the main topology to compute a similarity score between candidate drugs and disease-causing genes. The disease-causing genes were mapped on this network structure. Transcriptome profiles of drug candidates were taken from the LINCS project and mapped individually on the network structure. The similarity of these two networks was calculated by different network neighborhood metrics, including Adamic-Adar, PageRank and neighborhood scoring. The proposed approach identifies the best candidates by choosing the drugs with significant similarity scores. The method was experimented on melanoma, colorectal, and prostate cancers. Several candidate drugs were predicted by applying AUC values of 0.6 or higher. Some of the predictions were approved by clinical phase trials or other in-vivo studies found in literature. The proposed drug repositioning approach would suggest better treatment options with integration of functional information between genes and transcriptome level effects of drug perturbations and diseases.
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Chowdhury, Archana, Pratyusha Rakshit, and Amit Konar. "Prediction of protein–protein interaction network using a multi-objective optimization approach." Journal of Bioinformatics and Computational Biology 14, no. 03 (June 2016): 1650008. http://dx.doi.org/10.1142/s0219720016500086.
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Protein–Protein Interactions (PPIs) are very important as they coordinate almost all cellular processes. This paper attempts to formulate PPI prediction problem in a multi-objective optimization framework. The scoring functions for the trial solution deal with simultaneous maximization of functional similarity, strength of the domain interaction profiles, and the number of common neighbors of the proteins predicted to be interacting. The above optimization problem is solved using the proposed Firefly Algorithm with Nondominated Sorting. Experiments undertaken reveal that the proposed PPI prediction technique outperforms existing methods, including gene ontology-based Relative Specific Similarity, multi-domain-based Domain Cohesion Coupling method, domain-based Random Decision Forest method, Bagging with REP Tree, and evolutionary/swarm algorithm-based approaches, with respect to sensitivity, specificity, and F1 score.
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Xu, Ningshan, Dongao Ma, Guoqiang Ren, and Yongmei Huang. "BM-IQE: An Image Quality Evaluator with Block-Matching for Both Real-Life Scenes and Remote Sensing Scenes." Sensors 20, no. 12 (June 19, 2020): 3472. http://dx.doi.org/10.3390/s20123472.
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Like natural images, remote sensing scene images; of which the quality represents the imaging performance of the remote sensor, also suffer from the degradation caused by imaging system. However, current methods measuring the imaging performance in engineering applications require for particular image patterns and lack generality. Therefore, a more universal approach is demanded to assess the imaging performance of remote sensor without constraints of land cover. Due to the fact that existing general-purpose blind image quality assessment (BIQA) methods cannot obtain satisfying results on remote sensing scene images; in this work, we propose a BIQA model of improved performance for natural images as well as remote sensing scene images namely BM-IQE. We employ a novel block-matching strategy called Structural Similarity Block-Matching (SSIM-BM) to match and group similar image patches. In this way, the potential local information among different patches can get expressed; thus, the validity of natural scene statistics (NSS) feature modeling is enhanced. At the same time, we introduce several features to better characterize and express remote sensing images. The NSS features are extracted from each group and the feature vectors are then fitted to a multivariate Gaussian (MVG) model. This MVG model is therefore used against a reference MVG model learned from a corpus of high-quality natural images to produce a basic quality estimation of each patch (centroid of each group). The further quality estimation of each patch is obtained by weighting averaging of its similar patches’ basic quality estimations. The overall quality score of the test image is then computed through average pooling of the patch estimations. Extensive experiments demonstrate that the proposed BM-IQE method can not only outperforms other BIQA methods on remote sensing scene image datasets but also achieve competitive performance on general-purpose natural image datasets as compared to existing state-of-the-art FR/NR-IQA methods.
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Dodsworth, Robin, and Richard A. Benton. "Social network cohesion and the retreat from Southern vowels in Raleigh." Language in Society 46, no. 3 (May 3, 2017): 371–405. http://dx.doi.org/10.1017/s0047404517000185.
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AbstractNetwork research in sociolinguistics suggests that integration in a local community network promotes speakers' retention of local linguistic variants in the context of pressure from external or standard dialects. In most sociolinguistic network research, a speaker is assigned a single score along an index representing the aggregate of several network and other social features. We propose that contemporary network methods in adjacent disciplines can profitably apply to sociolinguistics, thereby facilitating not only more generalizable quantitative analysis but also new questions about therelationalnature of linguistic variables. Two network analysis methods—cohesive blocking and Quadratic Assignment Procedure regression—are used to evaluate the social network factors shaping the retreat from the Southern Vowel Shift (SVS) in Raleigh, North Carolina. The data come from a 160-speaker subset of a conversational corpus. Significant network effects indicate that network proximity to Raleigh's urban core promotes retention of SVS features, and that network similarity between speakers corresponds to linguistic similarity. Contemporary social-network methods can contribute to linguistic analysis by providing a holistic picture of the community's structure. (Networks, sociophonetics, Southern Vowel Shift, dialect contact)*
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Wei, Fu. "A Method of Recommending Physical Education Network Course Resources Based on Machine Learning Algorithms." Security and Communication Networks 2021 (October 31, 2021): 1–9. http://dx.doi.org/10.1155/2021/4925605.
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Aiming at the problem of difficult selection of physical education online course resources, a method of recommending online course resources based on machine learning algorithms is proposed. The information recommendation model is established through the expression of a collaborative filtering algorithm and resource feedback matrix. According to the feedback score of any user on the same data resource in the project set, the interest matching degree is established by comparative analysis, and the matching degree is substituted into the cosine similarity function to calculate the similarity threshold between each item and so on, calculate the similarity threshold number of all items, select the project resource that best matches the user according to the threshold number, and complete the recommendation. The experimental results show that the recommended method of physical education network curriculum resources based on machine learning algorithm is relatively excellent in recommendation accuracy and efficiency; this method can realize the innovation of higher physical education network curriculum teaching mode.
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Dmitrienko, V. D., A. Yu Zakovorotnyi, and S. Yu Leonov. "The Neural Network Art which uses the Hamming Distance to Measure an Image Similarity Score." Journal of Engineering and Applied Sciences 14, no. 21 (October 31, 2019): 8121–27. http://dx.doi.org/10.36478/jeasci.2019.8121.8127.
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Chen, Zhihua, Xinke Wang, Peng Gao, Hongju Liu, and Bosheng Song. "Predicting Disease Related microRNA Based on Similarity and Topology." Cells 8, no. 11 (November 7, 2019): 1405. http://dx.doi.org/10.3390/cells8111405.
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It is known that many diseases are caused by mutations or abnormalities in microRNA (miRNA). The usual method to predict miRNA disease relationships is to build a high-quality similarity network of diseases and miRNAs. All unobserved associations are ranked by their similarity scores, such that a higher score indicates a greater probability of a potential connection. However, this approach does not utilize information within the network. Therefore, in this study, we propose a machine learning method, called STIM, which uses network topology information to predict disease–miRNA associations. In contrast to the conventional approach, STIM constructs features according to information on similarity and topology in networks and then uses a machine learning model to predict potential associations. To verify the reliability and accuracy of our method, we compared STIM to other classical algorithms. The results of fivefold cross validation demonstrated that STIM outperforms many existing methods, particularly in terms of the area under the curve. In addition, the top 30 candidate miRNAs recommended by STIM in a case study of lung neoplasm have been confirmed in previous experiments, which proved the validity of the method.
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Karatzas, Evangelos, Juan Eiros Zamora, Emmanouil Athanasiadis, Dimitris Dellis, Zoe Cournia, and George M. Spyrou. "ChemBioServer 2.0: an advanced web server for filtering, clustering and networking of chemical compounds facilitating both drug discovery and repurposing." Bioinformatics 36, no. 8 (January 8, 2020): 2602–4. http://dx.doi.org/10.1093/bioinformatics/btz976.
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Abstract Summary ChemBioServer 2.0 is the advanced sequel of a web server for filtering, clustering and networking of chemical compound libraries facilitating both drug discovery and repurposing. It provides researchers the ability to (i) browse and visualize compounds along with their physicochemical and toxicity properties, (ii) perform property-based filtering of compounds, (iii) explore compound libraries for lead optimization based on perfect match substructure search, (iv) re-rank virtual screening results to achieve selectivity for a protein of interest against different protein members of the same family, selecting only those compounds that score high for the protein of interest, (v) perform clustering among the compounds based on their physicochemical properties providing representative compounds for each cluster, (vi) construct and visualize a structural similarity network of compounds providing a set of network analysis metrics, (vii) combine a given set of compounds with a reference set of compounds into a single structural similarity network providing the opportunity to infer drug repurposing due to transitivity, (viii) remove compounds from a network based on their similarity with unwanted substances (e.g. failed drugs) and (ix) build custom compound mining pipelines. Availability and implementation http://chembioserver.vi-seem.eu.
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Ghawi, Raji, and Jurgen Pfeffer. "A Collaborative Filtering based Approach to Classify Movie Genres using User Ratings." Journal of Data Intelligence 1, no. 4 (December 2020): 442–67. http://dx.doi.org/10.26421/jdi1.4-3.
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In this paper, we present an approach for classifying movie genres based on user-ratings. Our approach is based on collaborative filtering (CF), a common technique used in recommendation systems, where the similarity between movies based on user-ratings, is used to predict the genres of movies. The results of conducted experiments show that our genres classification approach outperforms many existing approaches, by achieving an F1-score of 0.70, and a hit-rate of 94\%. We also construct a multilayer network of movies, with genres as layers. We apply agglomerative clustering on the layers of this network to obtain a comprehensible taxonomy of genres which groups together similar genres using the similarity of their movies in terms of user preferences.
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Qin, Shimei, Wan Li, Hongzheng Yu, Manyi Xu, Chao Li, Lei Fu, Shibin Sun, et al. "Guiding Drug Repositioning for Cancers Based on Drug Similarity Networks." International Journal of Molecular Sciences 24, no. 3 (January 23, 2023): 2244. http://dx.doi.org/10.3390/ijms24032244.
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Drug repositioning aims to discover novel clinical benefits of existing drugs, is an effective way to develop drugs for complex diseases such as cancer and may facilitate the process of traditional drug development. Meanwhile, network-based computational biology approaches, which allow the integration of information from different aspects to understand the relationships between biomolecules, has been successfully applied to drug repurposing. In this work, we developed a new strategy for network-based drug repositioning against cancer. Combining the mechanism of action and clinical efficacy of the drugs, a cancer-related drug similarity network was constructed, and the correlation score of each drug with a specific cancer was quantified. The top 5% of scoring drugs were reviewed for stability and druggable potential to identify potential repositionable drugs. Of the 11 potentially repurposable drugs for non-small cell lung cancer (NSCLC), 10 were confirmed by clinical trial articles and databases. The targets of these drugs were significantly enriched in cancer-related pathways and significantly associated with the prognosis of NSCLC. In light of the successful application of our approach to colorectal cancer as well, it provides an effective clue and valuable perspective for drug repurposing in cancer.
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Guiling, Song, Zhang Jingyi, Xue Feng, Lu Ru, Yang Qin, and Ming Anlong. "Visual tracking with multilevel feature, similarity attention, color constraint, and global redetection." International Journal of Advanced Robotic Systems 18, no. 5 (September 1, 2021): 172988142110518. http://dx.doi.org/10.1177/17298814211051863.
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Visual tracking is fundamental in computer vision tasks. The Siamese-based trackers have shown surprising effectiveness in recent years. However, two points have been neglected: firstly, few of them focus on fusing the image level and semantic level features in neural networks, which usually resulting in tracking failure when differentiating the target from other distractors of the same class. Secondly, the robustness of the previous redetection scheme is limited by simply expanding the search region. To address these two issues, we propose a novel multilevel feature-weighted Siamese region proposal network tracker, which employs a feature fusion module to construct discriminative feature embedding and a similarity-based attention module to suppress the distractors in the search region. Furthermore, a color-based constraint module is presented to further suppress the distractors with the same class to the target. Finally, a well-designed global redetection scheme is built to handle long-term tracking tasks. The proposed tracker achieves state-of-art performance on a series of popular benchmarks, including object tracking benchmark 2013 (0.699 in success score), object tracking benchmark 2015 (0.700 in success score), visual object tracking 2017 (0.470 in expected average overlap score), and visual object tracking (0.485 in expected average overlap score).
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Zhang, Yujian, and Daifu Liu. "Toward Vulnerability Detection for Ethereum Smart Contracts Using Graph-Matching Network." Future Internet 14, no. 11 (November 11, 2022): 326. http://dx.doi.org/10.3390/fi14110326.
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With the blooming of blockchain-based smart contracts in decentralized applications, the security problem of smart contracts has become a critical issue, as vulnerable contracts have resulted in severe financial losses. Existing research works have explored vulnerability detection methods based on fuzzing, symbolic execution, formal verification, and static analysis. In this paper, we propose two static analysis approaches called ASGVulDetector and BASGVulDetector for detecting vulnerabilities in Ethereum smart contacts from source-code and bytecode perspectives, respectively. First, we design a novel intermediate representation called abstract semantic graph (ASG) to capture both syntactic and semantic features from the program. ASG is based on syntax information but enriched by code structures, such as control flow and data flow. Then, we apply two different training models, i.e., graph neural network (GNN) and graph matching network (GMN), to learn the embedding of ASG and measure the similarity of the contract pairs. In this way, vulnerable smart contracts can be identified by calculating the similarity to labeled ones. We conduct extensive experiments to evaluate the superiority of our approaches to state-of-the-art competitors. Specifically, ASGVulDetector improves the best of three source-code-only static analysis tools (i.e., SmartCheck, Slither, and DR-GCN) regarding the F1 score by 12.6% on average, while BASGVulDetector improves that of the three detection tools supporting bytecode (i.e., ContractFuzzer, Oyente, and Securify) regarding the F1 score by 25.6% on average. We also investigate the effectiveness and advantages of the GMN model for detecting vulnerabilities in smart contracts.
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Chakravarty, Aniv, and Jagadish S. Kallimani. "Unsupervised Multi-Document Abstractive Summarization Using Recursive Neural Network with Attention Mechanism." Journal of Computational and Theoretical Nanoscience 17, no. 9 (July 1, 2020): 3867–72. http://dx.doi.org/10.1166/jctn.2020.8976.
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Text summarization is an active field of research with a goal to provide short and meaningful gists from large amount of text documents. Extractive text summarization methods have been extensively studied where text is extracted from the documents to build summaries. There are various type of multi document ranging from different formats to domains and topics. With the recent advancement in technology and use of neural networks for text generation, interest for research in abstractive text summarization has increased significantly. The use of graph based methods which handle semantic information has shown significant results. When given a set of documents of English text files, we make use of abstractive method and predicate argument structures to retrieve necessary text information and pass it through a neural network for text generation. Recurrent neural networks are a subtype of recursive neural networks which try to predict the next sequence based on the current state and considering the information from previous states. The use of neural networks allows generation of summaries for long text sentences as well. This paper implements a semantic based filtering approach using a similarity matrix while keeping all stop-words. The similarity is calculated using semantic concepts and Jiang–Conrath similarity and making use of a recurrent neural network with an attention mechanism to generate summary. ROUGE score is used for measuring accuracy, precision and recall scores.
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Wu, Xianxian, and Yan Zhang. "English Speech Scoring System Based on Computer Neural Network." International Journal of Education and Humanities 5, no. 2 (October 27, 2022): 213–16. http://dx.doi.org/10.54097/ijeh.v5i2.2143.
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In English phonetics teaching, in order to improve students' English phonetics quality, a computer neural network based English phonetics scoring method is proposed. First, the frequency domain spectrogram is used as the data input to construct a convolutional neural network model at the word and phoneme levels to detect speech similarity. Then the original sound time domain waveform is used as the data input, which is converted into text through neural network to detect the text difference. Finally, we combine the two with the assigned weight to give a relatively objective comprehensive pronunciation score. The simulation results show that the method is accurate and practical, and can promote the standardization of students' English pronunciation.
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GUO, QIANG, and JIAN-GUO LIU. "CLUSTERING EFFECT OF USER-OBJECT BIPARTITE NETWORK ON PERSONALIZED RECOMMENDATION." International Journal of Modern Physics C 21, no. 07 (July 2010): 891–901. http://dx.doi.org/10.1142/s0129183110015543.
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In this paper, the statistical property of the bipartite network, namely clustering coefficient C4 is taken into account and be embedded into the collaborative filtering (CF) algorithm to improve the algorithmic accuracy and diversity. In the improved CF algorithm, the user similarity is defined by the mass diffusion process, and we argue that the object clustering C4 of the bipartite network should be considered to improve the user similarity measurement. The statistical result shows that the clustering coefficient of the MovieLens data approximately has Poisson distribution. By considering the clustering effects of object nodes, the numerical simulation on a benchmark data set shows that the accuracy of the improved algorithm, measured by the average ranking score and precision, could be improved 15.3 and 13.0%, respectively, in the optimal case. In addition, numerical results show that the improved algorithm can provide more diverse recommendation results, for example, when the recommendation list contains 20 objects, the diversity, measured by the hamming distance, is improved by 28.7%. Since all of the real recommendation data are evolving with time, this work may shed some light on the adaptive recommendation algorithm according to the statistical properties of the user-object bipartite network.
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Mirzal, Andri. "Search Engine-inspired Ranking Algorithm for Trading Networks." Indonesian Journal of Electrical Engineering and Computer Science 9, no. 3 (March 1, 2018): 812. http://dx.doi.org/10.11591/ijeecs.v9.i3.pp812-818.
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<p>Ranking algorithms based on link structure of the network are well-known methods in web search engines to improve the quality of the searches. The most famous ones are PageRank and HITS. PageRank uses probability of random surfers to visit a page as the score of that page, and HITS instead of produces one score, proposes using two scores, authority and hub scores, where the authority scores describe the degree of popularity of pages and hub scores describe the quality of hyperlinks on pages. In this paper, we show the differences between WWW network and trading network, and use these differences to create a ranking algorithm for trading networks. We test our proposed method with international trading data from United Nations. The similarity measures between vectors of proposed algorithm and vector of standard measure give promising results.</p>
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Hoon Jeong, Seong, Hee-Yeon Jung, In Won Chung, and Yong Sik Kim. "M171. THE GENE-SHARING RELATIONSHIP OF SCHIZOPHRENIA WITH OTHER MENTAL OR SYSTEMIC DISORDERS: A DISEASE-SIMILARITY NETWORK ANALYSIS FOCUSED ON EGOCENTRIC NETWORK." Schizophrenia Bulletin 46, Supplement_1 (April 2020): S201—S202. http://dx.doi.org/10.1093/schbul/sbaa030.483.
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Abstract Background Schizophrenia is an archetypal example that a psychiatric illness may not merely be a mental or a brain disorder but rather a systemic illness. It can be glimpsed from a wide range of biomarkers that span all the imaginable body systems, and from higher co-morbidity with other systemic illnesses. However, quantitative analysis of schizophrenia’s relationship with other diseases are not yet satisfactory. Genome-wide association studies have identified more than hundreds of genetic loci associated with schizophrenia. In turn, these loci are associated with a wide variety of other diseases. From this gene-disease relationship, a bipartite network can be built which, after appropriate projection, could help to map a complex disease-similarity network. In case of schizophrenia, it would reveal the position of schizophrenia among the broader categories of systemic illnesses. Methods DisGeNET is a discovery platform which contains one of the largest collections of gene-disease association data. The major source of the integrated data is the automatized curation from MEDLINE abstract. Therefore, it contains the timestamp of reported gene-disease association. Gene-disease-timestamp (year of publication) triplet was fed into a Neo4J graph database platform. From this, disease-disease relationships with shared gene count and Jaccard similarity score was extracted. The network structure of level 1.5 egocentric network centered upon schizophrenia was inspected. Louvain community detection algorithm was applied to expose underlying group structure among the 1st order alters. For comparison, similar ego-networks centered upon several major psychiatric illnesses were also inspected. Finally, the yearly variation of Jaccard score which reflected the accumulation of research data were monitored. Results The diseases which showed the highest Jaccard score (j) were bipolar disorder (j=0.203) and depressive disorder (j=0.190) as expected. Other diseases with meaningful similarity could be grouped into three communities: 1) psychiatric illness including bipolar/depressive disorder, 2) a variety of malignancies including neuroblastoma (j=0.083), stomach cancer (j=0.070) and pancreatic cancer (j=0.065) 3) other systemic illnesses including multiple sclerosis (j=0.088), metabolic syndrome (j=0.076), myocardial infarction (j=0.073), rheumatoid arthritis (j=0.070), lupus erythematosus (0.056). The gene-sharing relationship with systemic illnesses (malignancies and other) began to be revealed after 2005. Since then, more and more evidences were accumulated to solidify the schizophrenia’s link with systemic illnesses. Discussion Recently, a couple of large-scale epidemiological studies verified the significant correlation between prevalence of schizophrenia and cancer/autoimmune disorders. The present study results may augment these epidemiological data and thus strongly support the concept of schizophrenia as a systemic illness. Gene-sharing and its reflection in prevalence data would indicate deeper link at the level of pathogenesis with systemic illnesses. Recently, many authors contemplated the possible link between schizophrenia and cancer in terms of cell cycle regulation and control of apoptosis. Likewise, others suspected immunological disturbance as the fundamental mechanism of schizophrenia. In this vein, the need for extending the concept of mental disorders as a focused manifestation of systemic illness seems gaining impetus.
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Tao, Xiaomiao, Kaijun Wu, Yongshun Wang, Panfeng Li, Tao Huang, and Chenshuai Bai. "Antiocclusion Visual Tracking Algorithm Combining Fully Convolutional Siamese Network and Correlation Filtering." Computational Intelligence and Neuroscience 2022 (August 9, 2022): 1–9. http://dx.doi.org/10.1155/2022/8051876.
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Machine learning only uses single-channel grayscale features to model the target, and the filter solution process is relatively simple. When the target has a large change relative to the initial frame, the tracking effect is poor. When there is the same kind of target interference in the target search area, the tracking results will be poor. The tracking algorithm based on the fully convolutional Siamese network can solve these problems. By learning the similarity measurement function, the similarity between the template and the target search area is evaluated, and the target area is found according to the similarity. It adopts offline pre-training and does not update online for tracking, which has a faster tracking speed. According to this study, (1) considering the accuracy and speed, the target tracking algorithm based on correlation filtering performs well. A sample adaptive update model is introduced to eliminate unreliable samples, which effectively enhances the reliability of training samples. With simultaneous changes in illumination and scale, fast motion and in-plane rotation IPR can still be maintained. (2) Determined by calculating the Hessian matrix, in the Struck function, Bike3 parameter adjustment can achieve fast tracking, and Boat5 ensures that the system stability is maintained in the presence of interference factors. The position of the highest scoring point in the fine similarity score map of the same size as the search image is obtained by bicubic interpolation as the target position. (3) The parallax discontinuity caused by the object boundary cannot be directly processed as a smooth continuous parallax. The MeanShift vector obtained by calculating the target template feature and the feature to be searched can increase the accuracy by 53.1%, reduce the robustness by 31.8%, and reduce the error by 28.6% in the SiamVGG algorithm.
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Chin, Chiun-Li, Hsien-Chun Tseng, Yu-Hsiang Shao, Chin-Luen Hsu, Hsin-Yu Lin, Hsi-Chang Chang, and Ya-Ju Hsieh. "RTGAN: ORGAN CONTOURS IN RADIATION THERAPY WITH GENERATIVE ADVERSARIAL NETWORK." Biomedical Engineering: Applications, Basis and Communications 33, no. 02 (March 3, 2021): 2150014. http://dx.doi.org/10.4015/s1016237221500149.
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Generally, radiation oncology applies evaluation and prediction in medical imaging and diagnosis, specifically for contouring organs, which results in the production of the clinical target volume (CTV) that corresponds to disease risk and organ exclusion. Medical physicists contour organs and combine computed tomography (CT) scans to digital imaging and communications in medicine (DICOM) radiation therapy (RT) to assist physicians for diagnosing tumors and calculating the dosages in treatments including radiation and chemotherapy. Thus, to generate RT images with high accuracy, this paper proposes a new Generator Adversarial Network (GAN) for RT images called radiation therapy GAN (RTGAN). We combine multiple loss functions with synthetic similarity DICOM-RT images and compare the results with Pinnacle, a radiation oncology treatment planning system. Further, we evaluate the method to get a score of 0.984 in structured similarity (SSIM) and 31.26 in peak signal-to-noise ratio (PSNR) and find that it costs 0.058 s to finish contouring one CT image. The proposed method is applied and tested in the department of radiation oncology at the Chung Shan Medical University Hospital, and the results are similar to the ground truth images. Thus, it not only effectively reduces the false-positive rate but also makes a breakthrough in medicine.
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Huang, Shichen, Chunfu Shao, Juan Li, Xiong Yang, Xiaoyu Zhang, Jianpei Qian, and Shengyou Wang. "Feature Extraction and Representation of Urban Road Networks Based on Travel Routes." Sustainability 12, no. 22 (November 18, 2020): 9621. http://dx.doi.org/10.3390/su12229621.
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Extraction of traffic features constitutes a key research direction in traffic safety planning. In previous traffic tasks, road network features are extracted manually. In contrast, Network Representation Learning aims to automatically learn low-dimensional node representations. Enlightened by feature learning in Natural Language Processing, representation learning of urban nodes is studied as a supervised task in this paper. Following this line of thinking, a deep learning framework, called StreetNode2VEC, is proposed for learning feature representations for nodes in the road network based on travel routes, and then model parameter calibration is performed. We explain the effectiveness of features from visualization, similarity analysis, and link prediction. In visualization, the features of nodes naturally present a clustered pattern, and different clusters correspond to different regions in the road network. Meanwhile, the features of nodes still retain their spatial information in similarity analysis. The proposed method StreetNode2VEC obtains a AUC score of 0.813 in link prediction, which is greater than that obtained from Graph Convolutional Network (GCN) and Node2vec. This suggests that the features of nodes can be used to effectively and credibly predict whether a link should be established between two nodes. Overall, our work provides a new way of representing road nodes in the road network, which have potential in the traffic safety planning field.
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He, Hao, Dongfang Yang, Shicheng Wang, Shuyang Wang, and Yongfei Li. "Road Extraction by Using Atrous Spatial Pyramid Pooling Integrated Encoder-Decoder Network and Structural Similarity Loss." Remote Sensing 11, no. 9 (April 29, 2019): 1015. http://dx.doi.org/10.3390/rs11091015.
Full textAbstract:
The technology used for road extraction from remote sensing images plays an important role in urban planning, traffic management, navigation, and other geographic applications. Although deep learning methods have greatly enhanced the development of road extractions in recent years, this technology is still in its infancy. Because the characteristics of road targets are complex, the accuracy of road extractions is still limited. In addition, the ambiguous prediction of semantic segmentation methods also makes the road extraction result blurry. In this study, we improved the performance of the road extraction network by integrating atrous spatial pyramid pooling (ASPP) with an Encoder-Decoder network. The proposed approach takes advantage of ASPP’s ability to extract multiscale features and the Encoder-Decoder network’s ability to extract detailed features. Therefore, it can achieve accurate and detailed road extraction results. For the first time, we utilized the structural similarity (SSIM) as a loss function for road extraction. Therefore, the ambiguous predictions in the extraction results can be removed, and the image quality of the extracted roads can be improved. The experimental results using the Massachusetts Road dataset show that our method achieves an F1-score of 83.5% and an SSIM of 0.893. Compared with the normal U-net, our method improves the F1-score by 2.6% and the SSIM by 0.18. Therefore, it is demonstrated that the proposed approach can extract roads from remote sensing images more effectively and clearly than the other compared methods.
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Gao, Lianli, Daiyuan Chen, Jingkuan Song, Xing Xu, Dongxiang Zhang, and Heng Tao Shen. "Perceptual Pyramid Adversarial Networks for Text-to-Image Synthesis." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 8312–19. http://dx.doi.org/10.1609/aaai.v33i01.33018312.
Full textAbstract:
Generating photo-realistic images conditioned on semantic text descriptions is a challenging task in computer vision field. Due to the nature of hierarchical representations learned in CNN, it is intuitive to utilize richer convolutional features to improve text-to-image synthesis. In this paper, we propose Perceptual Pyramid Adversarial Network (PPAN) to directly synthesize multi-scale images conditioned on texts in an adversarial way. Specifically, we design one pyramid generator and three independent discriminators to synthesize and regularize multi-scale photo-realistic images in one feed-forward process. At each pyramid level, our method takes coarse-resolution features as input, synthesizes highresolution images, and uses convolutions for up-sampling to finer level. Furthermore, the generator adopts the perceptual loss to enforce semantic similarity between the synthesized image and the ground truth, while a multi-purpose discriminator encourages semantic consistency, image fidelity and class invariance. Experimental results show that our PPAN sets new records for text-to-image synthesis on two benchmark datasets: CUB (i.e., 4.38 Inception Score and .290 Visual-semantic Similarity) and Oxford-102 (i.e., 3.52 Inception Score and .297 Visual-semantic Similarity).
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Piwowarski, Paweł, and Włodzimierz Kasprzak. "Evaluation of Multi-Stream Fusion for Multi-View Image Set Comparison." Applied Sciences 11, no. 13 (June 24, 2021): 5863. http://dx.doi.org/10.3390/app11135863.
Full textAbstract:
We consider the problem of image set comparison, i.e., to determine whether two image sets show the same unique object (approximately) from the same viewpoints. Our proposition is to solve it by a multi-stream fusion of several image recognition paths. Immediate applications of this method can be found in fraud detection, deduplication procedure, or visual searching. The contribution of this paper is a novel distance measure for similarity of image sets and the experimental evaluation of several streams for the considered problem of same-car image set recognition. To determine a similarity score of image sets (this score expresses the certainty level that both sets represent the same object visible from the same set of views), we adapted a measure commonly applied in blind signal separation (BSS) evaluation. This measure is independent of the number of images in a set and the order of views in it. Separate streams for object classification (where a class represents either a car type or a car model-and-view) and object-to-object similarity evaluation (based on object features obtained alternatively by the convolutional neural network (CNN) or image keypoint descriptors) were designed. A late fusion by a fully-connected neural network (NN) completes the solution. The implementation is of modular structure—for semantic segmentation we use a Mask-RCNN (Mask regions with CNN features) with ResNet 101 as a backbone network; image feature extraction is either based on the DeepRanking neural network or classic keypoint descriptors (e.g., scale-invariant feature transform (SIFT)) and object classification is performed by two Inception V3 deep networks trained for car type-and-view and car model-and-view classification (4 views, 9 car types, and 197 car models are considered). Experiments conducted on the Stanford Cars dataset led to selection of the best system configuration that overperforms a base approach, allowing for a 67.7% GAR (genuine acceptance rate) at 3% FAR (false acceptance rate).
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Song, Guanghui, and Hai Wang. "Artificial Intelligence-Assisted Fresco Restoration with Multiscale Line Drawing Generation." Complexity 2021 (March 11, 2021): 1–12. http://dx.doi.org/10.1155/2021/5567966.
Full textAbstract:
In this article, we study the mural restoration work based on artificial intelligence-assisted multiscale trace generation. Firstly, we convert the fresco images to colour space to obtain the luminance and chromaticity component images; then we process each component image to enhance the edges of the exfoliated region using high and low hat operations; then we construct a multistructure morphological filter to smooth the noise of the image. Finally, the fused mask image is fused with the original mural to obtain the final calibration result. The fresco is converted to HSV colour space, and chromaticity, saturation, and luminance features are introduced; then the confidence term and data term are used to determine the priority of shedding boundary points; then a new block matching criterion is defined, and the best matching block is obtained to replace the block to be repaired based on the structural similarity between the block to be repaired and the matching block by global search; finally, the restoration result is converted to RGB colour space to obtain the final restoration result. An improved generative adversarial network structure is proposed to address the shortcomings of the existing network structure in mural defect restoration, and the effectiveness of the improved modules of the network is verified. Compared with the existing mural restoration algorithms on the test data experimentally verified, the peak signal-to-noise ratio (PSNR) score is improved by 4% and the structural similarity (SSIM) score is improved by 2%.
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Zhu, Ruojin, Dawen Yu, Shunping Ji, and Meng Lu. "Matching RGB and Infrared Remote Sensing Images with Densely-Connected Convolutional Neural Networks." Remote Sensing 11, no. 23 (November 29, 2019): 2836. http://dx.doi.org/10.3390/rs11232836.
Full textAbstract:
We develop a deep learning-based matching method between an RGB (red, green and blue) and an infrared image that were captured from satellite sensors. The method includes a convolutional neural network (CNN) that compares the RGB and infrared image pair and a template searching strategy that searches the correspondent point within a search window in the target image to a given point in the reference image. A densely-connected CNN is developed to extract common features from different spectral bands. The network consists of a series of densely-connected convolutions to make full use of low-level features and an augmented cross entropy loss to avoid model overfitting. The network takes band-wise concatenated RGB and infrared images as the input and outputs a similarity score of the RGB and infrared image pair. For a given reference point, the similarity scores within the search window are calculated pixel-by-pixel, and the pixel with the highest score becomes the matching candidate. Experiments on a satellite RGB and infrared image dataset demonstrated that our method obtained more than 75% improvement on matching rate (the ratio of the successfully matched points to all the reference points) over conventional methods such as SURF, RIFT, and PSO-SIFT, and more than 10% improvement compared to other most recent CNN-based structures. Our experiments also demonstrated high performance and generalization ability of our method applying to multitemporal remote sensing images and close-range images.
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Hu, Shengze, Zhen Tan, Weixin Zeng, Bin Ge, and Weidong Xiao. "Entity Linking via Symmetrical Attention-Based Neural Network and Entity Structural Features." Symmetry 11, no. 4 (April 1, 2019): 453. http://dx.doi.org/10.3390/sym11040453.
Full textAbstract:
In the process of knowledge graph construction, entity linking is a pivotal step, which maps mentions in text to a knowledge base. Existing models only utilize individual information to represent their latent features and ignore the correlation between entities and their mentions. Besides, in the process of entity feature extraction, only partial latent features, i.e., context features, are leveraged to extract latent features, and the pivotal entity structural features are ignored. In this paper, we propose SA-ESF, which leverages the symmetrical Bi-LSTM neural network with the double attention mechanism to calculate the correlation between mentions and entities in two aspects: (1) entity embeddings and mention context features; (2) mention embeddings and entity description features; furthermore, the context features, structural features, and entity ID feature are integrated to represent entity embeddings jointly. Finally, we leverage (1) the similarity score between each mention and its candidate entities and (2) the prior probability to calculate the final ranking results. The experimental results on nine benchmark dataset validate the performance of SA-ESF where the average F1 score is up to 0.866.