Electroencephalography and functional near-infrared spectroscopy have potentially complementary characteristics that reflect the electrical and hemodynamic characteristics of neural responses, so EEG-fNIRS-based hybrid brain-computer interface is the research hotspots in recent years. However, current studies lack a comprehensive systematic approach to properly fuse EEG and fNIRS data and exploit their complementary potential, which is critical for improving BCI performance. To address this issue, this study proposes a novel multimodal fusion framework based on multi-level progressive learning with multi-domain features. The framework (...) consists of a multi-domain feature extraction process for EEG and fNIRS, a feature selection process based on atomic search optimization, and a multi-domain feature fusion process based on multi-level progressive machine learning. The proposed method was validated on EEG-fNIRS-based motor imagery and mental arithmetic tasks involving 29 subjects, and the experimental results show that multi-domain features provide better classification performance than single-domain features, and multi-modality provides better classification performance than single-modality. Furthermore, the experimental results and comparison with other methods demonstrated the effectiveness and superiority of the proposed method in EEG and fNIRS information fusion, it can achieve an average classification accuracy of 96.74% in the MI task and 98.42% in the MA task. Our proposed method may provide a general framework for future fusion processing of multimodal brain signals based on EEG-fNIRS. (shrink)

In this article, epoch-based dynamic features such as sequence of epoch interval values and epoch strength values are explored to classify infant cries. Epoch is the instant of significant excitation of the vocal tract system during the production of speech. For voiced speech, the most significant excitation takes place around the instant of glottal closure. The different types of infant cries considered in this work are hunger, pain, and wet diaper. In this work, epoch strength and epoch interval (...) class='Hi'>features are used to represent infant cry-specific information from the acoustic signal. In this study, the proposed features such as epoch interval and epoch strength values are determined using zero-frequency filter-based method. Gaussian mixture models are used to classify the above-mentioned cries from the features proposed in this work. GMMs are developed separately for each of the cries using the proposed features. The infant cry database collected under a telemedicine project at the Indian Institute of Technology Kharagpur has been used for this study. In the first step, infant cry recognition accuracy is investigated separately using epoch interval and epoch strength features. To enhance recognition performance, GMMs developed using various features are combined through score level fusion techniques. The recognition performance using a combination of evidence is found to be superior over individual systems. (shrink)

In order to solve the problem of the influence of feature word position in lyrics on music emotion classification, this paper designs a music classification and detection model in complex noise environment. Firstly, an intelligent detection algorithm for electronic music signals under complex noise scenes is proposed, which can solve the limitations existing in the current electronic music signal detection process. At the same time, denoising technology is introduced to eliminate the noise and extract the features from (...) the signal. Secondly, from the perspective of audio and lyrics of song sentiment analysis and the unique characteristics of lyrics text, a lyric sentiment analysis method based on text title and position weight is proposed. Finally, considering the influence of the weight of feature words in different positions on the classification of lyrics, the analytic hierarchy process is used to calculate the weight of feature words in different positions of text title and lyrics before, in, and after the text. The results show that in the complex noise environment, the accuracy of music classification and detection of the proposed model is more than 90%, which is far beyond the control range of the actual application of music processing. The effect of music classification and detection is better than that of the contrast model, which has a certain practical application value. (shrink)

With the rapid emergence of the technology of deep learning, it was successfully used in different fields such as the aquatic product. New opportunities in addition to challenges can be created according to this change for helping data processing in the smart fish farm. This study focuses on deep learning applications and how to support different activities in aquatic like identification of the fish, species classification, feeding decision, behavior analysis, estimation size, and prediction of water quality. Power and performance (...) of computing with the analyzed given data are applied in the proposed DL method within fish farming. Results of the proposed method show the significance of contributions in deep learning and how automatic features are extracted. Still, there is a big challenge of using deep learning in an era of artificial intelligence. Training of the proposed method used a large number of labeled images got from the Fish4Knowledge dataset. The proposed method based on suitable feature extracted from the fish achieved good results in terms of recognition rate and accuracy. (shrink)

Transfer learning can improve the robustness of deep learning in the case of small samples. However, when the semantic difference between the source domain data and the target domain data is large, transfer learning easily introduces redundant features and leads to negative transfer. According the mechanism of the human brain focusing on effective features while ignoring redundant features in recognition tasks, a brain-like classification method based on adaptive feature matching dual-source domain heterogeneous transfer learning is proposed (...) for the preoperative aided diagnosis of lung granuloma and lung adenocarcinoma for patients with solitary pulmonary solid nodule in the case of small samples. The method includes two parts: (1) feature extraction and (2) feature classification. In the feature extraction part, first, By simulating the feature selection mechanism of the human brain in the process of drawing inferences about other cases from one instance, an adaptive selected-based dual-source domain feature matching network is proposed to determine the matching weight of each pair of feature maps and each pair of convolution layers between the two source networks and the target network, respectively. These two weights can, respectively, adaptive select the features in the source network that are conducive to the learning of the target task, and the destination of feature transfer to improve the robustness of the target network. Meanwhile, a target network based on diverse branch block is proposed, which made the target network have different receptive fields and complex paths to further improve the feature expression ability of the target network. Second, the convolution kernel of the target network is used as the feature extractor to extract features. In the feature classification part, an ensemble classifier based on sparse Bayesian extreme learning machine is proposed that can automatically decide how to combine the output of base classifiers to improve the classification performance. Finally, the experimental results (the AUCs were 0.9542 and 0.9356, respectively) on the data of two center data show that this method can provide a better diagnostic reference for doctors. (shrink)

The classification of unvoiced stops in consonant–vowel syllables, segmented from continuous speech, is investigated by features related to speech production. As burst and vocalic transitions contribute to identification of stops in the CV context, features are computed from both regions. Although formants are the truly discriminating articulatory features, their estimation from the speech signal is a challenge especially in unvoiced regions like the release burst of stops. This may be compensated partially by sub-band energy-based features. (...) In this work, formant features from the vocalic region are combined with features from the burst region comprising sub-band energies, as well as features from a formant tracking method developed for unvoiced regions. The overall combination of features at the classifier level obtains an accuracy of 84.4%, which is significantly better than that obtained with solely sub-band features on unvoiced stops in CV syllables of TIMIT. (shrink)

The article deals with Russian scientific classifications (GRNTI, VAK) of computer science in comparison with Western scien­tific classifications Fields of Science and Technology (FOS) and Universal Decimal Classification (UDS). The author analyzes the basics and principles of these classifications, identifies their strong and weak points as well as their influence on the devel­opment of computer sciences. She also provides some recom­mendations on adjustments of Russian scientific classifications aiming to make them more flexible and adaptive to the faster scientific and (...) technological development and interdisciplinary cooperation. (shrink)

Text document classification and clustering is an important learning task which fits to both data mining and machine learning areas. The learning task throws several challenges when it is required to process high dimensional text documents. Word distribution in text documents plays a very key role in learning process. Research related to high dimensional text document classification and clustering is usually limited to application of traditional distance functions and most of the research contributions in the existing literature did (...) not consider the word distribution in documents. In this research, we propose a novel similarity function for feature pattern clustering and high dimensional text classification. The similarity function proposed is used to carry supervised learning based dimensionality reduction. The important feature of this work is that the word distribution before and after dimensionality reduction is the same. Experiment results prove the proposed approach achieves dimensionality reduction, retains the word distribution and obtained better classification accuracies compared to other measures. (shrink)

Brain tumor classification plays a niche role in medical prognosis and effective treatment process. We have proposed a combined feature and image-based classifier for brain tumor image classification in this study. Carious deep neural network and deep convolutional neural networks -based architectures are proposed for image classification, namely, actual image feature-based classifier, segmented image feature-based classifier, actual and segmented image feature-based classifier, actual image-based classifier, segmented image-based classifier, actual and segmented image-based classifier, and finally, CFIC. The Kaggle (...) Brain Tumor Detection 2020 dataset has been used to train and test the proposed classifiers. Among the various classifiers proposed, the CFIC performs better than all other proposed methods. The proposed CFIC method gives significantly better results in terms of sensitivity, specificity, and accuracy with 98.86, 97.14, and 98.97%, respectively, compared with the existing classification methods. (shrink)

As one of the most widely used methods in deep learning technology, convolutional neural networks have powerful feature extraction capabilities and nonlinear data fitting capabilities. However, the convolutional neural network method still has disadvantages such as complex network model, too long training time and excessive consumption of computing resources, slow convergence speed, network overfitting, and classification accuracy that needs to be improved. Therefore, this article proposes a dense convolutional neural network classification algorithm based on texture features for (...) images in virtual reality videos. First, the texture feature of the image is introduced as a priori information to reflect the spatial relationship between pixels and the unique characteristics of different types of ground features. Second, the grey level cooccurrence matrix is used to extract the grey level correlation features of the image in space. Then, Gauss Markov Random Field is used to establish the statistical correlation characteristics between neighbouring pixels, and the extracted GLCM-GMRF texture feature and image intensity vector are combined. Finally, based on DenseNet, an improved shallow layer dense convolutional neural network is proposed, which can compress network parameters and improve the feature extraction ability of the network. The experimental results show that compared with the current classification method, this method can effectively suppress the influence of coherent speckle noise and obtain better classification results. (shrink)

Millions of memes are created and shared every day on social media platforms. Memes are a great tool to spread humour. However, some people use it to target an individual or a group generating offensive content in a polite and sarcastic way. Lack of moderation of such memes spreads hatred and can lead to depression like psychological conditions. Many successful studies related to analysis of language such as sentiment analysis and analysis of images such as image classification have been (...) performed. However, most of these studies rely only upon either one of these components. As classifying meme is one problem which cannot be solved by relying upon only any one of these aspects, the present work identifies, addresses, and ensembles both the aspects for analyzing such data. In this research, we propose a solution to the problems in which the classification depends on more than one model. This paper proposes two different approaches to solve the problem of identifying hate memes. The first approach uses sentiment analysis based on image captioning and text written on the meme. The second approach is to combine features from different modalities. These approaches utilize a combination of glove, encoder-decoder, and OCR with Adamax optimizer deep learning algorithms. Facebook Challenge Hateful Meme Dataset is utilized which contains approximately 8500 meme images. Both the approaches are implemented on the live challenge competition by Facebook and predicted quite acceptable results. Both approaches are tested on the validation dataset, and results are found to be promising for both models. (shrink)

High-dimensional data analysis has become the most challenging task nowadays. Dimensionality reduction plays an important role here. It focuses on data features, which have proved their impact on accuracy, execution time, and space requirement. In this study, a dimensionality reduction method is proposed based on the convolution of input features. The experiments are carried out on minimal preprocessed nine benchmark datasets. Results show that the proposed method gives an average 38% feature reduction in the original dimensions. The algorithm (...) accuracy is tested using the decision tree, support vector machine, and K-nearest neighbor classifiers and evaluated with the existing principal component analysis algorithm. The average increase in accuracy is 8.06 for DT, 5.80 for SVM, and 18.80 for the KNN algorithm. The most significant characteristic feature of the proposed model is that it reduces attributes, leading to less computation time without loss in classifier accuracy. (shrink)

A classification of the global catastrophic risks of AI is presented, along with a comprehensive list of previously identified risks. This classification allows the identification of several new risks. We show that at each level of AI’s intelligence power, separate types of possible catastrophes dominate. Our classification demonstrates that the field of AI risks is diverse, and includes many scenarios beyond the commonly discussed cases of a paperclip maximizer or robot-caused unemployment. Global catastrophic failure could happen at (...) various levels of AI development, namely, before it starts self-improvement, during its takeoff, when it uses various instruments to escape its initial confinement, or after it successfully takes over the world and starts to implement its goal system, which could be plainly unaligned, or feature-flawed friendliness. AI could also halt at later stages of its development either due to technical glitches or ontological problems. Overall, we identified around several dozen scenarios of AI-driven global catastrophe. The extent of this list illustrates that there is no one simple solution to the problem of AI safety, and that AI safety theory is complex and must be customized for each AI development level. (shrink)

Aiming to realize rapid and efficient three-dimensional identification of substation equipment, this article proposes a new method in which the 3D identification of substation equipment is based on K-nearest neighbor classification of subspace feature vector. First of all, the article uses octree encoding to reduce and denoise the point cloud data obtained by a 3D laser scanner. Secondly, position calibration and size standardization are used for the point cloud after pretreatment. Then, the normalized point cloud is divided into a (...) number of cubes with same size. The cosine of the angle between the positive direction of z axis and a vector from the global centroid of the point cloud to the centroid of each subspace is regarded as the feature of the subspace. All cosines of subspaces constitute the feature of the point cloud. Finally, we classify the subspace feature vector by using the KNN algorithm and improve classification accuracy by using the particle swarm optimization algorithm. The simulation results show that the identification accuracy of the proposed method for unknown substation equipment is about 90% and the proposed method is applicable to low-degree losses. Apparently, this method can accurately identify 3D substation equipment. At the same time, increasing the number of subspaces will improve the accuracy; however, it will increase the recognition time. (shrink)

Electroencephalogram has been widely utilized in emotion recognition. Psychologists have found that emotions can be divided into conscious emotion and unconscious emotion. In this article, we explore to classify subliminal emotions with EEG signals elicited by subliminal face stimulation, that is to select appropriate features to classify subliminal emotions. First, multi-scale sample entropy, wavelet packet energy, and wavelet packet entropy of EEG signals are extracted. Then, these features are fed into the decision tree and improved random forest, respectively. (...) The classification accuracy with Ei and WpEn is higher than MSpEn, which shows that Ei and WpEn can be used as effective features to classify subliminal emotions. We compared the classification results of different features combined with the decision tree algorithm and the improved random forest algorithm. The experimental results indicate that the improved random forest algorithm attains the best classification accuracy for subliminal emotions. Finally, subliminal emotions and physiological proof of subliminal affective priming effect are discussed. (shrink)

Handwritten text recognition is considered as the most challenging task for the research community due to slight change in different characters’ shape in handwritten documents. The unavailability of a standard dataset makes it vaguer in nature for the researchers to work on. To address these problems, this paper presents an optical character recognition system for the recognition of offline Pashto characters. The problem of the unavailability of a standard handwritten Pashto characters database is addressed by developing a medium-sized database of (...) offline Pashto characters. This database consists of 11352 character images. Enriched feature extraction techniques of histogram of oriented gradients and zoning-based density features are used for feature extraction of carved Pashto characters. K-nearest neighbors is considered as a classification tool for the proposed algorithm based on the proposed feature sets. A resultant accuracy of 80.34% is calculated for the histogram of oriented gradients, while for zoning-based density features, 76.42% is achieved using 10-fold cross validation. (shrink)

We show a number of improvements in the use of Hidden Conditional Random Fields for phone classification on the TIMIT and Switchboard corpora. We first show that the use of regularization effectively prevents overfitting, improving over other methods such as early stopping. We then show that HCRFs are able to make use of non-independent features in phone classification, at least with small numbers of mixture components, while HMMs degrade due to their strong independence assumptions. Finally, we successfully (...) apply Maximum a Posteriori adaptation to HCRFs, decreasing the phone classification error rate in the Switchboard corpus by around 1% – 5% given only small amounts of adaptation data. (shrink)

In psychiatry some disorders of cognition are distinguished from instances of normal cognitive functioning and from other disorders in virtue of their surface features rather than in virtue of the underlying mechanisms responsible for their occurrence. Aetiological considerations often cannot play a significant classificatory and diagnostic role, because there is no sufficient knowledge or consensus about the causal history of many psychiatric disorders. Moreover, it is not always possible to uniquely identify a pathological behaviour as the symptom of a (...) certain disorder, as disorders that are likely to differ both in their causal histories and in their overall manifestations may give rise to very similar patterns of behaviour. -/- Consider delusions as an example. It wouldn’t be correct to define delusions as those beliefs people form as a result of a neurobiological deficit and a hypothesis-evaluation deficit (as some versions of the two-factor theory of delusions suggest), because for some delusions no neurobiological deficit may be found, and reasoning biases and motivational factors may be contributors to the formation of the delusion (e.g. McKay et al., 2005). Moreover, it would be a mistake to define delusions as symptoms of schizophrenia alone, because they occur also in other disorders, including dementia, amnesia, and delusional disorders. Thus, aetiological considerations may appear in the description and analysis of delusions, but do not feature prominently in their definition. -/- In this paper I argue that the surface features used as criteria for the classification and diagnosis of disorders of cognition are often epistemic in character. I shall offer two examples: confabulations and delusions are defined as beliefs or narratives that fail to meet standards of accuracy and justification. Although classifications and diagnoses based on features of people’s observable behaviour are necessary at these early stages of neuropsychiatric research, given the variety of conditions in which certain phenomena appear, I shall attempt to show that current epistemic accounts of confabulations and delusions have limitations. Epistemic criteria can guide both research and clinical practice, but fail to provide sufficient conditions for the identification of delusions and confabulations, and fail to demarcate pathological from non-pathological narratives or beliefs. -/- Another limitation of current epistemic accounts – which I shall not address here – is the excessive focus on epistemic faults of confabulations and delusions at the expense of their epistemically neutral or advantageous features (see Bortolotti and Cox, 2009). This may lead to a misconception of delusions and confabulations, and to an oversimplification in the assessment of the needs of people who require clinical treatment for their psychotic symptoms. (shrink)

Recently, big data analytics have gained significant attention in healthcare industry due to generation of massive quantities of data in various forms such as electronic health records, sensors, medical imaging, and pharmaceutical details. However, the data gathered from various sources are intrinsically uncertain owing to noise, incompleteness, and inconsistency. The analysis of such huge data necessitates advanced analytical techniques using machine learning and computational intelligence for effective decision making. To handle data uncertainty in healthcare sector, this article presents a novel (...) metaheuristic rough set-based feature selection with rule-based medical data classification technique on MapReduce framework. The proposed MRSFS-RMDC technique designs a butterfly optimization algorithm for minimal rough set selection. In addition, Hadoop MapReduce is applied to process massive quantity of data. Moreover, a rule-based classification approach named Repeated Incremental Pruning for Error Reduction is used with the inclusion of a set of conditional rules. The RIPPER will scale in a linear way with the number of training records utilized and is suitable to build models with data uncertainty. The proposed MRSFS-RMDC technique is validated using benchmark dataset and the results are inspected under varying aspects. The experimental results highlighted the supremacy of the MRSFS-RMDC technique over the recent state of art methods in terms of different performance measures. The proposed methodology has achieved a higher F-score of 96.49%. (shrink)

A numerical classification was performed on 69 elements with 54 chemicaland physicochemical properties. The elements fell into clusters in closeaccord with the electron shell s-, p- andd-blocks. The f-block elements were not included forlack of sufficiently complete data. The successive periods ofs- and p-block elements appeared in an ovalconfiguration, with d-block elements lying to one side. Morethan three axes were required to give good representation of thevariation, although the interpretation of the higher axes is difficult.Only 15 properties were scorable (...) for the noble gases, but despite thepaucity of properties reflecting chemical reactivity, analysis of the 69elements on these properties still showed the major features seen fromthe full set. (shrink)

We investigate an approach to improving statistical text classification by combining machine learners with an ontology-based identification of domain-specific topic categories. We apply this approach to ad hoc disclosures by public companies. This form of obligatory publicity concerns all information that might affect the stock price; relevant topic categories are governed by stringent regulations. Our goal is to classify disclosures according to their effect on stock prices (negative, neutral, positive). In the study reported here, we combine natural language parsing (...) with a formal background ontology to recognize disclosures concerning particular topics from a prescribed list. The semantic analysis identifies some of these topics with reasonable accuracy. We then demonstrate that machine learners benefit from the additional ontology-based information when predicting the cumulative abnormal return attributed to the disclosure at hand. (shrink)

The ethnic or “racial” classification of Maori and non-Maori is a pivotal feature of New Zealand’s health system and affects government policy and professional practice within the context of Treaty of Waitangi “partnership” politics. Although intended to empower Maori, ethnic categorization can have unintended and negative consequences by ignoring the causality of material forces in social phenomena. The authors begin by showing how the use of ethnic categories in health policy is justified by the Treaty of Waitangi partnership policies. (...) This provides the context for the argument made in the manuscript that an understanding of the social experience of ethnicity within the complex interaction of sociocultural factors such as socioeconomic location and lifestyle is more useful than using the political construct of ethnic categories in explaining the persistence of low health status for a section of the Maori population. (shrink)

It has long been a standard practice for the natural sciences to classify things. Thus, it is no wonder that, for two and a half millennia, philosophers have been reflecting on classifications, from Plato and Aristotle to contemporary philosophy of science. Some of the results of these reflections will be presented in this chapter. I will start by discussing a parody of a classification, namely: the purportedly ancient Chinese classification of animals described by Jorge Luis Borges. I will (...) show that many of the mistakes that account for the comic features of this parody appear in real-life scientific databases as well. As examples of the latter, I will discuss the terminology database of the National Cancer Institute (NCI) of the United States, the NCI Thesaurus. (shrink)

In this study, the classification of Zaydī fuqahā’ that emerged in the mutaaḫḫirūn period of Zaydī fiqh and related terms are examined. The book named Bulūgh al-arab wa-kunūz al-dhahab fī-maʿrifat al-madhhab, which has great importance among the studies aiming to present the Zaydī fiqh accumulation as a uniform doctrinal structure was taken as a basis in the processing of the subject. After an introduction in which Zaydī fiqh studies are evaluated in their relationship with the subject, the issue is (...) handled under three main headings. In the first title, the author and his work are introduced, and in the second and third titles, the methods and terms used in the classification of the Zaydī fukahâ are examined. The author of the work is ʿAlī b. ʿAbd Allāh al-Shaharī (d. 1190/1776) who lived during the period of the Qāsimī dynasty (al-dawla al-Qāsimiyya) in Yemen. The most important feature of the work, which was prepared using many sources that were attributed importance among the Zaydī literature, is that it was the most comprehensive of all that had written on madhhab doctrine until then. The first part of the work explains that the right madhhab to be followed is Zaydiyya. In the second part, the classes of the Zaydī fuqahā’, which is the subject of our study, are explained. The fuqahā’ classes are primarily divided into two parts: al-salaf al-ṣāliḥīn and al-ḫalaf al-ṣāliḥīn. The first dates back to the beginning of the 3rd/10th century. This class is divided into two parts, as al-sābiqūn and al-mutaaḫḫirūn. The sābiqūn starts with ʿAlī b. Abī Ṭālib, his two sons al-Ḥasan and Ḥusayn and ends with the sons of twelve imams who are their descendants. Zayd b. ʿAlī (d. 122/740) is one of the twelve imams. The most important of the faqīhs in the part of mutaaḫḫirūn, which started with al-Qāsim b. Ibrāhīm al-Rassī (d. 246/860), are the six imams called ahl-al-nuṣūṣ or aʾimma al-nuṣūṣ. The reason why they are named as such is that their words were taken as a basis in determining the views of the sect on the field of uṣūl and furūʿ l-fiqh. al-Qāsim al-Rassī is at a point where all the knowledge of the Prophet's family and the principles agreed upon by sābiqūn, reach him through many channels. al-Hādī Yaḥyā b. al-Ḥusayn (d. 298/911), grandson of al-Qāsim, is the most important imam in terms of his position in the madhhab. The ḫalaf al-ṣāliḥīn is also divided into two parts as ahl l-taḥṣīl and ahl l-naẓar. The first part also referred to as muḥaṣṣilūn or aʾimma al-taḥṣīl, has no subcategories. The second part is divided into two sub-sections as ahl l-taqrīr and mudhākirūn. The muḥaṣṣilūn class starts with those who catch up with the last class of ahl al-nuṣūṣ and ends with ʿAbd Allāh b. Ḥamza (d. 614/1217), one of the three important imams. Two other important imams of muḥaṣṣilūn are Aḥmad b. al-Huṣayn (d. 411/1020) and his brother Yahyā b. al-Husayn (d. 424/1032). It is the fiqh activity of the jurists of this class, called taḥṣīl l-madhhab, that brought Zaydī fiqh, which was fiqh of imams (madhāhib l-aʾimma) in its first period, into a single madhhab and to its classical form in the last period. Mudhākirūn jurists lived at the same time as the ahl-t-taqrīr. This period is about 700 years from the beginning of the 6th/12th century, which is the last century of the muḥaṣṣilūn class, to the end of the 12th/18th century, when Shaharī lived. The roles of the ahl al-taqrīr in the madhhab mostly consist of reviewing the works of the ahl-l tahṣīl, approving the correct ones, and correcting the wrong ones. That's why they are so named. The author of Kitāb l-Azhār, Aḥmad b. Yaḥyā l-Murtaḍā (d. 840/1437), is one of the prominent jurists among the ahl l-taqrīr. The majority of Zaydi fuqahā’ belong to the muẕākirūn class. These are the lowest class of the ahl-al-naẓar. As an important result reached in the study, it is possible to express the following: There is a difference between some of the knowledge and opinions in modern Zaydī studies, which include the same subject as Shaharī's work, in terms of the classification of Zaydī fuqahā’ and the terms used. Therefore, the findings and conclusions based on this work should be confirmed by new studies on other Zaydī classics, and existing opinions on Zaydī jurisprudence should be reviewed. (shrink)

Conditional constructions (if–then) enable us to express our thoughts about possible states of the world, and they form an important ingredient for our reasoning and argumentative capabilities. Different types and argumentative uses have been distinguished in the literature, but their applicability to actual language use is rarely evaluated. This paper focuses on the reliability of applying classifications of connections between antecedents and consequents of conditionals to discourse, and three issues are identified. First, different accounts produce incompatible results when applied to (...) language data. Second, a discrepancy between theory and data was observed in previous studies, which sometimes discard existing classifications for being detached from actual language use. Finally, language users construct various cognitive relations between clauses of conditionals without being able to rely on overt linguistic features, which poses problems for the annotation of conditionals in argumentation and discourse. This paper addresses these issues by means of comparing theoretical types and actual uses of conditionals, by inspecting the dispersion of types in natural-language corpora, and by conducting an experiment in which the inter-rater reliability of classifications was assessed. The results show that the reliability of classifications of conditionals when applied to language data is low. With respect to the aforementioned issues, different classifications produced incompatible results, a discrepancy between theory and data was indeed observed, and low reliability scores indicated a largely interpretative nature of types of conditionals. Given these results, suggestions for the enhancement of reliability in corpus studies of conditionals and beyond are provided to enhance future classification design. (shrink)

Background and objectivesThe study of brain functional connectivity alterations in children with Attention-Deficit/Hyperactivity Disorder has been the subject of considerable investigation, but the biological mechanisms underlying these changes remain poorly understood. Here, we aim to investigate the brain alterations in patients with ADHD and Typical Development children and accurately classify ADHD children from TD controls using the graph-theoretical measures obtained from resting-state fMRI.Materials and methodsWe investigated the performances of rs-fMRI data for classifying drug-naive children with ADHD from TD controls. Fifty (...) six drug-naive ADHD children and 56 age matched TD controls were included in this study. The graph measures extracted from rs-fMRI functional connectivity were used as features. Extracted network-based features were fed to the RFE feature selection algorithm to select the most discriminating subset of features. We trained and tested Support Vector Machine, Random Forest, and Gradient Boosting using Peking center data from ADHD-200 database to classify ADHD and TD children using discriminative features. In addition to the machine learning approach, the statistical analysis was conducted on graph measures to discover the differences in the brain network of patients with ADHD.ResultsAn accuracy of 78.2% was achieved for classifying drug-naive children with ADHD from TD controls employing the optimal features and the GB classifier. We also performed a hub node analysis and found that the number of hubs in TD controls and ADHD children were 8 and 5, respectively, indicating that children with ADHD have disturbance of critical communication regions in their brain network. The findings of this study provide insight into the neurophysiological mechanisms underlying ADHD.ConclusionPattern recognition and graph measures of the brain networks, based on the rs-fMRI data, can efficiently assist in the classification of ADHD children from TD controls. (shrink)

This paper presents a new method to recognize human activities based on weighted classification for the features extracted by human body. Towards this end, new features depend on weight taken from image or video used in proposed descriptor. Human pose plays an important role in extracted features; then these features are used as the weight input with classifier. We use machine learning during two steps of training and testing images of standard dataset that can be (...) used during benchmarking the system. Unlike previous methods that need size or length of shapes mainly to represent the cues when machine learning is used to recognize human activities, accurate experimental results coming from appropriate segments of the human body proved the worthiness of proposed method. Twelve activities are used in challenging of availability comparison with dataset to demonstrate our method. The results show that we achieved 87.3% in training set, while in testing set, we achieved 94% in terms of precision. (shrink)

Preoperative diagnosis of gastric cancer and primary gastric lymphoma is challenging and has important clinical significance. Inspired by the inductive reasoning learning of the human brain, transfer learning can improve diagnosis performance of target task by utilizing the knowledge learned from the other domains (source domain). However, most studies focus on single-source transfer learning and may lead to model performance degradation when a large domain shift exists between the single-source domain and target domain. By simulating the multi-modal information learning and (...) transfer mechanism of human brain, this study designed a multisource transfer learning feature extraction and classification framework, which can enhance the prediction performance of the target model by using multisource medical data (domain). First, this manuscript designs a feature extraction network that takes the maximum mean difference based on the Wasserstein distance as an adaptive measure of probability distribution and extracts the domain-specific invariant representations between source and target domain data. Then, aiming at the random generation of parameters bringing uncertainties to prediction accuracy and generalization ability of extreme learning machine network, the 1-norm regularization is used to implement sparse constraints of the output weight matrix and improve the robustness of the model. Finally, some experiments are carried out on the data of two medical centers. The experimental results show that the area under curves (AUCs) of the method are 0.958 and 0.929 in the two validation cohorts, respectively. The method in this manuscript can provide doctors with a better diagnostic reference, which has certain practical significance. (shrink)

Accurate prediction of battery quality using early-cycle data is critical for battery, especially lithium battery in microgrid networks. To effectively predict the lifetime of lithium-ion batteries, a time series classification method is proposed that classifies batteries into high-lifetime and low-lifetime groups using features extracted from early-cycle charge-discharge data. The proposed method is based on a smooth localized complex exponential model that can extract battery features from time-frequency maps and self-adaptively select the time-frequency resolution to maximize the discrepancy (...) of data from the two groups. A smooth localized complex exponential periodogram is then calculated to obtain the time-frequency decomposition of the whole time series data for further classification. The experimental results show that, by using battery features extracted from the first 128 charge-discharge processes, the proposed method can accurately classify batteries into high-lifetime and low-lifetime groups, with classification accuracy and specificity as high as 95.12% and 92.5%, respectively. (shrink)

Aristotle, in the Historia Animalium, follows the explanation‐involving approach to classification that he developed in the pattern of the Posterior Analytics. Thus, he draws in his theory of animal classification on his explanatory account of soul functions developed in De Anima. However, his project encounters a severe problem: he failed to uncover in his study of biological phenomena the unified, causally basic essences that his theory of definition required. I consider whether Aristotle can resolve this crisis while remaining (...) true to the basic features of his Analytics paradigm. (shrink)

This paper aims at proposing a criterion to analyze the concept of subjectivity by surveying and classifying the theories of some major figures in the history of the western philosophy: Plato, Aristotle, Descartes, Hobbes, Bentham, Kant, and Hegel. As proceeding in this work, I reveal two approaches which confront each other, self-centered viewpoint and system-centered viewpoint, and arrange Descartes, Hobbes, and Bentham into the former, and Aristotle and Kant into the latter. Also, I assign Plato and Hegel to an alternative, (...) say, unified viewpoint. In this project, the subjectivity is defined as the fundamental status of a man. When dealing with it, two kinds of viewpoint present and distinguish themselves from each other by two factors as follows: one is whether or not it allows mediation with others to be involved in the configuration of subjectivity, and the other is whether it is determined by its formal features or by its contents. The self-centered viewpoint gets married with the no allowance of mediation and the contents-determining, and the system-centered one does with the allowance and theformality-determining. And the unified viewpoint is suggested as an overcoming account of those two. The significance of this attempt would be these three: one is that it can provide a clue to create new spectrum through which theories in the history of the philosophy are looked out over. Secondly, it can be groundwork to seek for a source of normative ethics by a way of re-determining personal identity. Finally, criticizing the self-centered viewpoint, a dominant account in present moral discourse, it can propose a footstone for an alternative theory of it. (shrink)

In this paper, based on computer reading and processing of music frequency, amplitude, timbre, image pixel, color filling, and so forth, a method of image style transfer guided by music feature data is implemented in real-time playback, using existing music files and image files, processing and trying to reconstruct the fluent relationship between the two in terms of auditory and visual, generating dynamic, musical sound visualization with real-time changes in the visualization. Although recommendation systems have been well developed in real (...) applications, the limitations of CF algorithms are slowly coming to light as the number of people increases day by day, such as the data sparsity problem caused by the scarcity of rated items, the cold start problem caused by new items and new users. The work is dynamic, with real-time changes in music and sound. Taking portraits as an experimental case, but allowing users to customize the input of both music and image files, this new visualization can provide users with a personalized service of mass customization and generate personalized portraits according to personal preferences. At the same time, we take advantage of the BP neural network’s ability to handle complex nonlinear problems and construct a rating prediction model between the user and item attribute features, referred to as the PSO-BP rating prediction model, by combining the features of global optimization of particle swarm optimization algorithm, and make further improvements based on the traditional collaborative filtering algorithm. (shrink)

. NormalBoost is a new boosting algorithm which is capable of classifying a multi-dimensional binary class dataset. It adaptively combines several weak classifiers to form a strong classifier. Unlike many boosting algorithms which have high computation and memory complexities, NormalBoost is capable of classification with low complexity. The purpose of this paper is to present NormalBoost as a framework which establishes a platform to solve classification problems. The approach was tested with a dataset which was extracted automatically from (...) real-world traffic sign images. The dataset contains both images of traffic sign borders and speed limit pictograms. This framework involves the computation of Haar-like features of these images and then employs the NormalBoost classifier to classify these traffic signs. The total number of images which were classified was 6500 binary images. A -fold validation was invoked to check the validity of the classification which resulted in a classification rate of 98.4% and 98.9% being achieved for these two databases. This framework is distinguished by its invariance to in-plane rotation of the images under consideration. Experiments show that the classification rate remains almost constant when traffic sign images with different angles of rotations were tested. (shrink)

In this article, I begin by giving a brief history of melanoma causation. I then discuss the current manner in which malignant melanoma is classified. In general, these systems of classification do not take account of the manner of tumour causation. Instead, they are based on phenomenological features of the tumour, such as size, spread, and morphology. I go on to suggest that misclassification of melanoma is a major problem in clinical practice. I therefore outline an alternative means (...) of classifying these tumours based on causal factors. By analogy with similar systems that have recently emerged for other cancers, I suggest that this causal classification is likely to be both workable and helpful, even in the absence of a full causal-mechanistic understanding of the aetiology of the tumour. (shrink)

The purpose of this study is to determine the features of creating integrated courses using a foreign language for university students. Achieving this goal requires solving the problems of developing a conceptual framework for the use of a foreign language in integrated courses in the educational process at the university. The article presents and analyzes: the definition, classification, typology, main characteristics and advantages of the work of a teacher with students in the university within the framework of an (...) integrated course. The conclusions of the study allowed us to present some of the most important features of the educational subjects working in the framework of an integrated course at the university. (shrink)

that can serve as a foundation for more refined ontologies in the field of proteomics. Standard data sources classify proteins in terms of just one or two specific aspects. Thus SCOP (Structural Classification of Proteins) is described as classifying proteins on the basis of structural features; SWISSPROT annotates proteins on the basis of their structure and of parameters like post-translational modifications. Such data sources are connected to each other by pairwise term-to-term mappings. However, there are obstacles which stand (...) in the way of combining them together to form a robust meta-classification of the needed sort. We discuss some formal ontological principles which should be taken into account within the existing datasources in order to make such a metaclassification possible, taking into account also the Gene Ontology (GO) and its application to the annotation of proteins. (shrink)

Classification and gradation system adopts different security protection schemes for different types of data by implementing classification and gradation management of data, which is an important pretechnical means for data security protection and prevention of data leakage. This paper introduces artificial intelligence classification, machine learning, and other means to learn and train enterprise documents according to the characteristics of enterprise sensitive data. The generated training model can intelligently identify and classify file streams, improving work efficiency and accuracy (...) of classification and gradation. At the same time, the differences, advantages, and disadvantages of K-NN, DT, and LinearSVC algorithms are compared. The experimental data shows that LinearSVC algorithm is applicable to high-dimensional data, with discrete, sparse data features and large number of features, which is more suitable for classification of sensitive data of enterprises. (shrink)

In this paper we apply social epistemology to mathematical proofs and their role in mathematical knowledge. The most famous modern collaborative mathematical proof effort is the Classification of Finite Simple Groups. The history and sociology of this proof have been well-documented by Alma Steingart (2012), who highlights a number of surprising and unusual features of this collaborative endeavour that set it apart from smaller-scale pieces of mathematics. These features raise a number of interesting philosophical issues, but have (...) received very little attention. In this paper, we will consider the philosophical tensions that Steingart uncovers, and use them to argue that the best account of the epistemic status of the Classification Theorem will be essentially and ineliminably social. This forms part of the broader argument that in order to understand mathematical proofs, we must appreciate their social aspects. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Hume's Classification of the Passions and Its Precursors James Fieser Hume's theory ofthe passions appears in book 2 ofhis Treatise (1739), and, in shorter form, in his "Dissertation on the Passions" originally from Four Dissertations (1757).1 When the "Dissertation" first appeared, two reviews criticized Hume's theory for being unoriginal. The first appearing review, which was in the Literary Magazine, says of the "Dissertation" that "we do not perceive (...) any thing new. This we should notmentionifwe were not talking ofan authorfond ofnovelty."2 The Critical Review opens its account noting similarly that "in our opinion [the "Dissertation"] contains nothing new or entertaining on the occasion." After presenting excerpts, the reviewer concludes: "This whole dissertation, to say the truth, appears to us very trite and superficial; and unworthy of so eminent a writer. But no authors are always equal to themselves."3 Hume, by contrast, believed that his account of the indirect passions, which dominates both book 2 and the "Dissertation," was unique.4 William Rose, in his review of the "Dissertation" in the Monthly Review, agrees with Hume, noting that "[h]is theory depends entirely on the double relations of sentiments and ideas, and the mutual assistance which these relations lend to each other. What he says upon the subject, is extremely ingenious, and deserves the philosophical reader's attentive perusal."5 Few today would dispute the uniqueness ofHume's account ofthe indirect passions. Why, though, did two reviewers consider Hume's theory unoriginal? Part of the answer rests in the degree to which Hume's general classification of the passions follows traditional classifications. By understanding how Hume was in line with tradition, his uniqueness may be better appreciated. I will first sketch the traditional classifications of the passions. Second, I will draw comparisons between aspects of Hume's classification and other Enlightenment accounts. Finally, I will criticize the interpretations of Hume's classification offered by Kemp Smith, Ardal, and Loeb, and suggestwhatIbelieve is the most textually soundclassification. In each of these sections, my focus is on taxonomy. However, since many divisions of the passions derive from the psychological mechanisms which produce various passions, some account will be given of these operations. Volume XVIII Number 1 JAMES FIESER Traditional Classifications ofthe Passions The passions have been traditionally understood as disturbances ofthe mind or soul, the key ones beinglove, hate,joy, grief, hope, fear, desire, and aversion.6At therisk ofover-simplification, afairlyunified account ofthe passions can be found beginning with Plato and lasting until the mid-nineteenth century. One feature of this tradition is that the passions fall into positive and negative classes. This is first noted by Aristotle who argues that the passions are "feelings that are accompanied by pleasure or pain."7 Following Aristotle, most accounts divide the passions in reference to good or bad objects and experiences. Good experiences, such as dining, are pleasing, whereas bad experiences, such as illness, are displeasing. Consequently, passions such as joy and griefare mirror images according to the good (pleasing) or bad (displeasing) nature of the experience. A second feature of most theories is that there is a small foundational class of passions which gives rise to others through variation. The most popular foundational set includes four:joy, sorrow, hope, and fear. Although the earliest reference to these four is by Plato (Laches 191C), Zeno of Citium and the Stoics are attributed with developing a theory of the passions based on these. The fullest surviving account ofthe Stoic theory is given by Cicero in his Tusculan Disputations.8 In Cicero's discussion, the four most primitive passions are joy (laetitia), grief (aegritudo), desire (cupiditas or libido), and fear (metus). Joy and desire are the result of good circumstances, whereas grief and fear are the result of evil. Further, joy and griefresult when a circumstance or object is actually present, in contrast to desire and fear which arise when one anticipates a future circumstance. The division is as follows: Present Anticipated Good Object joy desire Evil Object grief fear In Roman and medieval philosophy, the Stoic division of the passions was widely endorsed by writers such as Virgil, Augustine, and Boethius.9With Thomas Aquinas, though, an alternative classification arose. Thomasrecognized... (shrink)

Feature selection is a widely studied technique whose goal is to reduce the dimensionality of the problem by removing irrelevant features. It has multiple benefits, such as improved efficacy, efficiency and interpretability of almost any type of machine learning model. Feature selection techniques may be divided into three main categories, depending on the process used to remove the features known as Filter, Wrapper and Embedded. Embedded methods are usually the preferred feature selection method that efficiently obtains a selection (...) of the most relevant features of the model. However, not all models support an embedded feature selection that forces the use of a different method, reducing the efficiency and reliability of the selection. Neural networks are an example of a model that does not support embedded feature selection. As neural networks have shown to provide remarkable results in multiple scenarios such as classification and regression, sometimes in an ensemble with a model that includes an embedded feature selection, we attempt to embed a feature selection process with a general-purpose methodology. In this work, we propose a novel general-purpose layer for neural networks that removes the influence of irrelevant features. The Feature-Aware Drop Layer is included at the top of the neural network and trained during the backpropagation process without any additional parameters. Our methodology is tested with 17 datasets for classification and regression tasks, including data from different fields such as Health, Economic and Environment, among others. The results show remarkable improvements compared to three different feature selection approaches, with reliable, efficient and effective results. (shrink)