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نتیجه جستجو - طبقه بندی گروه

تعداد مقالات یافته شده: 7
ردیف عنوان نوع
1 Applying artificial intelligence to explore sexual cyberbullying behaviour
استفاده از هوش مصنوعی برای کشف رفتار مزاحمت اینترنتی-2020
Sexual cyberbullying is becoming a serious problem in todays society. In the workplace, this issue is more complex because of the power imbalance between potential perpetrators and victims. Preventing sexual cyber- bullying in organizations is very important for a safety and respectful workplace. Occupational Safety and Health (OSH) standards establish certain policies to be considered to create an organizational culture based on zero tolerance to sexual cyberbullying. The research aims to broaden knowledge about personality and sexual cyberbullying. Therefore, this paper proposes a crucial tool to explore potential sexual cyberbullying behavior. This study analyzed how personality traits, particularly those related to the Dark Triad (psychopathy, Machia-vellianism and narcissism), might influence this behavior. Participants (N ¼ 374) were Spanish young adults, using the convenience sampling to recruit them. The methodology focused on the use of structural equation modelling and ensemble classification tree. First, we tested the proposed hypotheses with structural equation method based on covariance using the Lavaan R-package. Second, for the ensemble of classification trees, we applied the package random Forest and Adabag (bagging and boosting) in R. Results proposed high levels of psychopathy and Machiavellianism are more likely to be related to sexual cyberbullying behaviors. Organizations could use the tool proposed in this research to develop internal policies and procedures for detection and deterrence of potential cyberbullying behaviors. By raising awareness about cyberbullying behaviour including its conceptualization and measurement in training courses, organizations might build an organizational culture based on a respectful workplace without sexual cyberbullying behaviours.
Keywords: Cyberbullying | Dark triad | Machiavellianism | Narcissism | Psychopathy | Structural equation modelling | Ensemble classification tree | Artificial intelligence | Machine learning | Business | Human resource management
مقاله انگلیسی
2 Techniques Tanimoto correlated feature selection system and hybridization of clustering and boosting ensemble classification of remote sensed big data for weather forecasting
تکنیک های مربوط به سیستم انتخاب ویژگی Tanimoto و ترکیبی از خوشه بندی و افزایش طبقه بندی گروه از داده های بزرگ از راه دور برای پیش بینی آب و هوا-2020
Weather forecasting has been done using various techniques but still not efficient for handling the big remote sensed data since the data comprises the more features. Hence the techniques degrade the forecasting accuracy and take more prediction time. To enhance the prediction accuracy (PA) with minimal time, Tanimoto Correlation based Combinatorial MAP Expected Clustering and Linear Program Boosting Classification (TCCMECLPBC) Technique is proposed. At first, the data and features are gathered from big weather database. After that, relevant features are selected through finding the similarity between the features. Tanimoto Correlation Coefficient is used to find the similarity between the features for selecting the relevant features with higher feature selection accuracy. After selecting the relevant features, MAP expected clustering process is carried out to group the weather data for cluster formation. In this process, a number of cluster and cluster centroids are initialized. In this clustering process, it includes two steps namely expectation (E) and maximization (M) to discover maximum probability for grouping data into the cluster. After that, the clustering result is given to Linear Program boosting classifier to improve the prediction performance. In this classification, the weak classifier results are boosted to create strong classifier. The results evident that the TC-CMECLPBC technique enhance the PA with lesser time and false positive rate (FPR) than the conventional methods.
Keywords: Big data | Tanimoto correlation | MAP expected | Boosting classification | Expectation | Maximization | Similarity | Clustering | Cluster centroids | Strong classifier | Weak classifier
مقاله انگلیسی
3 An ensemble learning approach to lip-based biometric verification, with a dynamic selection of classifiers
یک رویکرد یادگیری گروه برای تأیید بیومتریک مبتنی بر لب ، با انتخاب پویای طبقه بندی کننده ها-2019
Machine learning approaches are largely focused on pattern or object classification, where a combination of several classifier systems can be integrated to help generate an optimal or suboptimal classification decision. Multiple classification systems have been extensively developed because a committee of clas- sifiers, also known as an ensemble, can outperform the ensemble’s individual members. In this paper, a classification method based on an ensemble of binary classifiers is proposed. Our strategy consists of two phases: (1) the competence of the base heterogeneous classifiers in a pool is determined, and (2) an ensemble is formed by combining those base classifiers with the greatest competences for the given input data. We have shown that the competence of the base classifiers can be successfully calculated even if the number of their learning examples was limited. Such a situation is particularly observed with biomet- ric data. In this paper, we propose a new biometric data structure, the Sim coefficients, along with an efficient data processing technique involving a pool of competent classifiers chosen by dynamic selection.
Keywords: Lip-based biometrics | Dynamic classifiers selection | Pattern recognition | Ensemble classification | Person verification
مقاله انگلیسی
4 An evolutionary framework for machine learning applied to medical data
یک چارچوب تکاملی برای یادگیری ماشین که برای داده های پزشکی کاربرد دارد-2019
Supervised learning problems can be faced by using a wide variety of approaches supported in machine learning. In recent years there has been an increasing interest in using the evolutionary computation paradigm as a search method for classifiers, helping the applied machine learning technique. In this context, the knowledge representation in the form of logical rules has been one of the most accepted machine learning approaches, because of its level of expressiveness. This paper proposes an evolutionary framework for rule-based classifier induction. Our proposal introduces genetic programming to build a search method for classification-rules (IF/THEN). From this approach, we deal with problems such as, maximum rule length and rule intersection. The experiments have been carried out on our domain of interest, medical data. The achieved results define a methodology to follow in the learning method evaluation for knowledge discovery from medical data. Moreover, the results compared to other methods have shown that our proposal can be very useful in data analysis and classification coming from the medical domain.
Keywords: Machine learning | Logical rule induction | Data mining | Supervised learning | Evolutionary computation | Genetic programming | Ensemble classifier | Medical data
مقاله انگلیسی
5 The Gradual Resampling Ensemble for mining imbalanced data streams with concept drift
اثر کلی مجموعه تلفیقی گسسته برای کاوش معادلات ناپایدار جریان با مفهوم رانش-2018
Knowledge extraction from data streams has received increasing interest in recent years. However, most of the existing studies assume that the class distribution of data streams is relatively balanced. The reac tion of concept drifts is more difficult if a data stream is class imbalanced. Current oversampling methods generally selectively absorb the previously received minority examples into the current minority set by evaluating similarities of past minority examples and the current minority set. However, the similarity evaluation is easily affected by data difficulty factors. Meanwhile, these oversampling techniques have ignored the majority class distribution, thus risking class overlapping. To overcome these issues, we propose an ensemble classifier called Gradual Resampling Ensemble (GRE). GRE could handle data streams which exhibit concept drifts and class imbalance. On the one hand, a selectively resampling method, where drifting data can be avoidable, is applied to select a part of pre vious minority examples for amplifying the current minority set. The disjuncts can be discovered by the DBSCAN clustering, and thus the influences of small disjuncts and outliers on the similarity evaluation can be avoidable. Only those minority examples with low probability of overlapping with the current majority set can be selected for resampling the current minority set. On the other hand, previous com ponent classifiers are updated using latest instances. Thus, the ensemble could quickly adapt to a new condition, regardless types of concept drifts. Through the gradual oversampling of previous chunks us ing the current minority events, the class distribution of past chunks can be balanced. Favorable results in comparison to other algorithms suggest that GRE can maintain good performance on minority class, without sacrificing majority class performance.
Keywords: Concept drift ، Data stream mining ، Ensemble classifier ، Class imbalance
مقاله انگلیسی
6 Disease prediction with different types of neural network classifiers
پیش بینی بیماری با انواع مختلف از طبقه بندی شبکه های عصبی-2016
Disease prediction has long been regarded as a critical topic. Artificial intelligence and machine learning techniques have already been developed to solve this type of medical care problem. Recently, neural network ensembles have been successfully utilized in a variety of applications including to assist in medical diagnosis. Neural network ensembles can significantly improve the generalization ability of learning systems through training a finite number of neural networks and then combining their results. However, the perfor- mance of multiple classifiers in disease prediction is not fully understood. The major pur- pose of this study is to investigate the performance of different classifiers, including individual classifiers involved in an ensemble classifier and solo classifiers. In addition, we use various evaluation criteria to examine the performance of these classifiers with real-life datasets. Finally, we also use statistical testing to evaluate the significance of the difference in performance among the three classifiers. The statistical testing results indicate that an ensemble classifier performs better than an individual classifier within an ensemble. However, the solo classifier does not perform worse than the ensemble clas- sifier built with the same size training dataset.© 2015 Elsevier Ltd. All rights reserved.
Disease prediction | Artificial neural network | Ensemble classifier | Statistical testing
مقاله انگلیسی
7 شناسایی خودکار سن و جنس گوینده با استفاده ازسطح ترکیب اطلاعات صوتی و عروضی
سال انتشار: 2012 - تعداد صفحات فایل pdf انگلیسی: 19 - تعداد صفحات فایل doc فارسی: 31
هدف این مقاله شناسایی رویکرد سنی و جنسی خودکار که ترکیبی ازهفت روش مختلف در هر دو سطح صوتی وعروضی (زبر زنجیری) به منظور بهبود پایه ی عملکرد است , میباشد.سه زیر سیستم پایه عبارت اند از: (1) مدل مخلوط گوسی (GMM) میتنی بر ویژگی های فرکانس ضریب شریک (MFCC) ، (2) دستگاه پشتیبانی بردار (SVM) مبتنی بر بردار میانهGMM (3) SVM مبتنی بر450 بعد ویژگی های سطح گفته ها که شامل جمله های صوتی، عروضی و اطلاعات کیفی صدا ها میباشد.علاوه بر این، ما چهار زیر سیستم دیگر را پیشنهاد میکنیم:(1) svm مبتنی بر ,UBM که ازاحتمال مرکزی Bhattacharyya استفاده میکند (2)بازنمایی پراکنده مبتنی بر UBM (3) SVM مبتنی براحتمال حداکثر رگرسیون خطی GMM و ماتریس (MLLR) (4) SVM بر اساس ضرایب بسط چند جمله ای و ویژگی های عروضی سطح هجا که در بخش مربوط به سخنرانی ابراز شده اند. به منظور تجزیه و تحلیل زیر سیستم :حد فاصل زیر و بمی صدا، انرژی حوزه زمان،دامنه فرکانس انرژی ساختارو سازه هارمونیک برای هر هجا (تقسیم با استفاده ازاطلاعات انرژی در بخش سخنرانی صدا) در نظر گرفته شده است.این چهار زیر سیستم به منظور دستیابی به نتایج رقابتی در طبقه بندی گروه های سنی و جنسی متفاوت پیشنهاد گردیده است.برای بهبود کلی عملکرد طبقه بندی، تلفیقی از این هفت زیر سیستم در سطح امتیاز نشان داده شده است.در مقایسه با پایه ی سیستم SVM(3)، "سیستم پایه ی پیشنهاد شده توسط کمیته چالش"، منجر به 5.6٪ بهبود مطلق دقت , در مسائل مربوط به سن و42٪ در مسائل مربوط به جنسیتی در مجموعه توسعه گردید.. ما در مجموعه ی آزمون نهایی،به 3.1٪ و 3.8٪ بهبود مطلق دست یافتیم.
واژه های کلیدی: تشخیص سن | تشخیص جنسیت | ویژگی های زبرزنجیری | درجه و اوج| ساختارهای هارمونیک | سازه | گسترش چند جمله ای | حداکثراحتمال رگرسیون(بازگشت) خطی | احتمال ابر بردار پیشین وزنی UBM | GMM| SVM| بازنمایی پراکنده | همجوشی و ادغام سطح امتیاز
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